RELATED APPLICATION DATA

This application claims priority from Australian Patent Application No 2021902314 filed on 28 July 2021 and entitled “Methods of detecting and/or diagnosing pancreatic cancer”. The entire contents of that application are hereby incorporated by reference.

SEQUENCE LISTING

The present application is filed together with a Sequence Listing in electronic form. The entire contents of the Sequence Listing are hereby incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to methods of detecting and/or diagnosing pancreatic cancer in a subject. The present invention also provides methods of resolving an inconclusive cytological assessment in a subject.

BACKGROUND OF THE INVENTION

Pancreatic ductal adenocarcinoma (PDAC) is the 7 ^th leading cause of cancer related death worldwide. Most patients present with locally advanced or metastatic disease, with fewer than 20% presenting with lesions amenable to potentially curative surgery. The mainstay of treatment for patients with locally advanced and metastatic disease is chemotherapy, which may prolong survival for several months. However, despite incremental improvements in recent years the prognosis of PDAC remains dire, with a 5-year survival rate of just 9%.

Multiple factors contribute to the dismal prognosis associated with PDAC. Many patients present with non-specific symptoms, and current imaging modalities and biomarkers such as carbohydrate antigen 19.9 (CA19.9) may not detect early stage disease or clearly differentiate PDAC from other causes of solid pancreatic masses, which may include other malignant conditions such as pancreatic neuroendocrine tumours (pNETs), lymphomas and metastases, as well as benign conditions such as autoimmune pancreatitis and pseudo-tumoural lesions. Other factors contributing to poor outcomes in PDAC include the propensity for early distant metastasis, a complex tumour microenvironment characterized by dense stromal desmoplasia and immune dysregulation, and inherent resistance to standard treatments such as chemotherapy. Screening programs have shown some benefit in applying early imaging or targeted molecular screening in high risk populations, although observed benefits remain limited largely to those with high familial risk, who make up only a small minority of all PD AC patients. There have been conflicting reports on the benefits of reducing lead time to PDAC diagnosis, with some studies showing poorer prognosis and higher risk of unanticipated metastasis with an increasing interval from symptom onset to diagnosis, while others demonstrate no significant prognostic impact from diagnostic delay.

Endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) is considered the gold standard diagnostic method for the biopsy of suspicious pancreatic masses, and is a widely available procedure with low procedural morbidity and mortality. A recent meta-analysis reported pooled sensitivity for the diagnosis of PDAC of 85%, with high specificity of 98% (Hewitt et al., 2012). However, approximately 15% of patients fail to achieve a tissue diagnosis with their first attempt at biopsy and may require further diagnostic procedures. EUS-FNA sensitivity has also been reported to be lower in the setting of chronic pancreatitis, an established risk factor for PDAC.

SUMMARY OF THE INVENTION

The present inventors have identified a gene expression signature associated with solid pancreatic cancer. In particular, the inventors have identified a gene expression signature that provides a prompt and accurate clinical diagnosis of solid pancreatic cancer using EUS-FNA biopsy, where cellularity and tissue yield are highly variable between patients. In particular, the gene expression signature provides diagnostic accuracy when a cytological assessment of cell morphology is inconclusive. The inventors also found that KRAS mutant allele detection further improves diagnostic accuracy, whilst also provides a novel surrogate marker of cellular adequacy in frozen EUS-FNA biopsies.

Accordingly, in one example, the present invention provides a method of detecting and/or diagnosing solid pancreatic cancer in a subject, the method comprising determining a level of expression of at least five or more or all of the genes selected from the group consisting of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, S100A2, COL17A1, MSLN, SI OOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5 in the subject.

In one example, the method comprises determining the level of expression of at least seven genes selected from the group consisting of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, S100A2, COL17A1, MSLN, SI OOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5 in the subject. In one example, the method comprises determining the level of expression of at least ten genes selected from the group consisting of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, S100A2, COL17A1, MSLN, SI OOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5 in the subject.

In one example, the method comprises determining the level of expression of at least fifteen genes selected from the group consisting of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, S100A2, COL17A1, MSLN, SI OOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5 in the subject.

In one example, the method comprises determining the level of expression of all of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB 3, S100A2, COL17A1, MSLN, SIOOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5 in the subject.

In one example, at least one or more of the genes is selected from the group consisting of LAMC2, IQGAP3, SIM2, PTK6, LAMB3, COL17A1, GJB4 and PADI1.

In one example, at least one gene is LAMC2.

In one example, at least one gene is IQGAP3.

In one example, at least one gene is SIM2.

In one example, at least one gene is PTK6.

In one example, at least one gene is LAMB3.

In one example, at least one gene is COL17A1.

In one example, at least one gene is GJB4.

In one example, at least one gene is PADI1

In one example, the method comprises: a) at least one or more of the genes selected from the group consisting of GJB3, b) at least one or more of the genes selected from the group consisting of LAMC2, IQGAP3, SIM2, PTK6, LAMB3, COL17A1, GJB4 and PADI1.

In one example, the method is performed on an endoscopic ultrasound fine needle aspiration (EUS-FNA) biopsy obtained from a subject.

In one example, the method further comprises performing genomic sequencing on the EUS-FNA biopsy if solid pancreatic cancer cells are present.

In one example, the method comprises normalizing the level of expression of the gene to a standard to obtain a normalized level of the gene.

In one example, the standard is one or more or all control genes selected from the group consisting of RPS11, RPL11, RPL28, RPSW, GDI2, RPL37A, PARK7, CNBP, CSNK1A1, RPS4X, MAZ, SF3B1, HSD17B4, DAP3, SET, MTIF3, Clorf43, CNOT2, GSTK1, DCTD, FNDC3B, AKIRIN1, ANXA7, SUPT5H, ZMYM2, DDX3X, HNRNPDL, ECD, MAEA, ADAR and ARCN1.

In one example, the one or more or all control genes comprise or consist of a sequence set forth in any one of SEQ ID NOs: 35 to 65.

The present disclosure also provides one or more control genes selected from the group consisting of RPS11, RPL11, RPL28, RPS16, GDI2, RPL37A, PARK7, CNBP, CSNK1A1, RPS4X, MAZ, SF3B1, HSD17B4, DAP3, SET, MTIF3, Clorf43, CNOT2, GSTK1, DCTD, FNDC3B, AKIRIN1, ANXA7, SUPT5H, ZMYM2, DDX3X, HNRNPDL, ECD, MAEA, ADAR and ARCN1. For example, the disclosure provides one or more control genes comprising or consisting of a sequence set forth in any one of SEQ ID NOs: 35 to 65.

In one example, the method comprises comparing the normalized level of expression of the gene in the subject to at least one reference level.

In one example, the reference value is a predetermined level of the gene and/or a predetermined score.

In one example, the method comprises a higher level of expression of the gene in the subject compared to the reference level is indicative of solid pancreatic cancer in the subject.

In one example, the methods as described herein can detect pancreatic cancer in a subject with a greater specificity and sensitivity (assessed by ROC analysis as area under the curve; AUC) than standard cytological analysis.

In one example, the area under the curve (AUC) of the gene is between about 0.70 and about 0.95. For example, the AUC of the gene is at least 0.70, or at least 0.70, or at least 0.75, or at least 0.85, or at least 0.90, or at least 0.95.

In one example, the method comprises determining the level of LAMC2 and the AUC is at least 0.90. For example, the AUC is 0.91.

In one example, the method comprises determining the level of TFAP2A and the AUC is at least 0.70. For example, the AUC is 0.74.

In one example, the method comprises determining the level of IQGAP3 and the AUC is at least 0.80. For example, the AUC is 0.84.

In one example, the method comprises determining the level of SIM2 and the AUC is at least 0.80. For example, the AUC is 0.80.

In one example, the method comprises determining the level of PTK6 and the AUC is at least 0.75. For example, the AUC is 0.79. In one example, the method comprises determining the level of TMPRSS4 and the AUC is at least 0.90. For example, the AUC is 0.91.

In one example, the method comprises determining the level of SERPINB5 and the AUC is at least 0.85. For example, the AUC is 0.89.

In one example, the method comprises determining the level of LAMB3 and the AUC is at least 0.85. For example, the AUC is 0.86.

In one example, the method comprises determining the level of S100A2 and the AUC is at least 0.85. For example, the AUC is 0.86.

In one example, the method comprises determining the level of COL17A1 and the AUC is at least 0.80. For example, the AUC is 0.82.

In one example, the method comprises determining the level of MSLN and the AUC is at least 0.90. For example, the AUC is 0.94.

In one example, the method comprises determining the level of SIOOP and the AUC is at least 0.75. For example, the AUC is 0.79.

In one example, the method comprises determining the level of PLEKHN1 and the AUC is at least 0.75. For example, the AUC is 0.76.

In one example, the method comprises determining the level of GJB3 and the AUC is at least 0.85. For example, the AUC is 0.86.

In one example, the method comprises determining the level of GJB4 and the AUC is at least 0.85. For example, the AUC is 0.89.

In one example, the method comprises determining the level of PADI1 and the AUC is at least 0.75. For example, the AUC is 0.77.

In one example, the method comprises determining the level of CEACAM5 and the AUC is at least 0.85. For example, the AUC is 0.89.

In one example, the combined area under the curve (AUC) of the at least 5 or more or all genes is at least 0.85, or at least 0.90, or at least 0.95. For example, the combined AUC of the at least 5 or more or all genes is 0.90. In another example, the combined AUC of the at least 5 or more or all genes is 0.97.

In one example, the methods as described herein can detect pancreatic cancer in a subject with greater accuracy than standard cytology analysis.

In one example, the method comprises diagnosing solid pancreatic cancer in a subject with at least 75% accuracy. For example, the accuracy is at least about 75%, at least about 80%, at least about 85%, at least about 90%, or at least about 95%. For example, the accuracy is about 75%. In another example, the accuracy is about 80%. In a further example, the accuracy is about 90%. In one example, the method comprises performing real-time reverse transcription polymerase chain reaction (RT-PCR), droplet digital PCR (ddPCR), RNA sequencing and/or a microarray assay.

In one example, the method comprises performing RT-PCR.

In one example, the method comprises performing ddPCR.

In one example, the method comprises performing RNA sequencing.

In one example, the method comprises performing a microarray assay.

In one example, the level of expression is detected by using one or more probes or primers specific for each gene. For example, the level of expression is detected by using one, or two, or three, or four probes or primers specific for each gene. For example, the level of expression is detected by using one probe or primer specific for each gene. For example, the level of expression is detected by using two probes or primers specific for each gene. For example, the level of expression is detected by using three probes or primers specific for each gene. For example, the level of expression is detected by using four probes or primers specific for each gene.

In one example, the level of expression of the five or more or all genes and/or the level of expression of one or more or all control genes is detected by using one or more probes or primers specific for each gene.

In one example, the one or more probes or primers for the five or more or all genes bind to a sequence set forth in any one of SEQ ID NO: 18 to 34.

In one example, the one or more probes or primers bind to LAMC2. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 18.

In one example, the one or more probes or primers bind to TFAP2A. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 19.

In one example, the one or more probes or primers bind to IQGAP3. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 20.

In one example, the one or more probes or primers bind to SIM2. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 21.

In one example, the one or more probes or primers bind to PTK6. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 22.

In one example, the one or more probes or primers bind to TMPRSS4. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO:

23.

In one example, the one or more probes or primers bind to SERPINB5. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO:

24. In one example, the one or more probes or primers bind to LAMB3. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 25.

In one example, the one or more probes or primers bind to S100A2. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 26.

In one example, the one or more probes or primers bind to COL17A1. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 27.

In one example, the one or more probes or primers bind to MSLN. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 28.

In one example, the one or more probes or primers bind to SI OOP. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 29.

In one example, the one or more probes or primers bind to PLEKHN1. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 30.

In one example, the one or more probes or primers bind to GJB3. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 31.

In one example, the one or more probes or primers bind to GJB4. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 32.

In one example, the one or more probes or primers bind to PADI1. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 33.

In one example, the one or more probes or primers bind to CEACAM5. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 34.

In one example, the one or more probes or primers for the one or more or all control genes bind to a sequence set forth in any one of SEQ ID NO: 66 to 96.

In one example, the one or more probes or primers bind to RPS11. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 66.

In one example, the one or more probes or primers bind to RPL11. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 67.

In one example, the one or more probes or primers bind to RPL28. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 68.

In one example, the one or more probes or primers bind to RPS16. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 69.

In one example, the one or more probes or primers bind to GD12. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 70. In one example, the one or more probes or primers bind to RPL37A. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 71.

In one example, the one or more probes or primers bind to PARK7. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 72.

In one example, the one or more probes or primers bind to CNBP. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 73.

In one example, the one or more probes or primers bind to CSNK1A1. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 74.

In one example, the one or more probes or primers bind to RPS4X. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 75.

In one example, the one or more probes or primers bind to MAZ. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 76.

In one example, the one or more probes or primers bind to SF3B1. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 77.

In one example, the one or more probes or primers bind to HSD17B4. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 78.

In one example, the one or more probes or primers bind to DAP 3. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 79.

In one example, the one or more probes or primers bind to SET. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 80.

In one example, the one or more probes or primers bind to MTIF3. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 81.

In one example, the one or more probes or primers bind to Clorf43. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 82.

In one example, the one or more probes or primers bind to CNOT2. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 83.

In one example, the one or more probes or primers bind to GSTK1. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 84.

In one example, the one or more probes or primers bind to DCTD. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 85.

In one example, the one or more probes or primers bind to FNDC3B. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 86.

In one example, the one or more probes or primers bind to AKIRIN1. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 87. In one example, the one or more probes or primers bind to ANXA7. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 88.

In one example, the one or more probes or primers bind to SUPT5H. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 89.

In one example, the one or more probes or primers bind to ZMYM2. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 90.

In one example, the one or more probes or primers bind to DDX3X. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 91.

In one example, the one or more probes or primers bind to HNRNPDL. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 92.

In one example, the one or more probes or primers bind to ECD. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 93.

In one example, the one or more probes or primers bind to MAEA. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 94.

In one example, the one or more probes or primers bind to ADAR. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 95.

In one example, the one or more probes or primers bind to ARCN1. For example, the one or more probes or primers bind to a sequence set forth in SEQ ID NO: 96.

In one example, the solid pancreatic cancer is selected from the group consisting of ductal adenocarcinoma, osteoclast-like giant cell tumour, acinar cell carcinoma, a neuroendocrine tumour and pancreatoblastoma.

In one example, the solid pancreatic cancer is ductal adenocarcinoma.

In one example, the solid pancreatic cancer is osteoclast-like giant cell tumour.

In one example, the solid pancreatic cancer is acinar cell carcinoma.

In one example, the solid pancreatic cancer is pancreatoblastoma.

In one example, the solid pancreatic cancer is a neuroendocrine tumour.

In one example, the method comprises detecting and/or diagnosing a solid pancreatic cancer from normal tissue. For example, the method comprises distinguishing or differentiating a solid pancreatic cancer from normal tissue.

In one example, the method comprises detecting and/or diagnosing a solid pancreatic cancer from pancreatitis. For example, the method comprises distinguishing or differentiating a solid pancreatic cancer from pancreatitis. In one example, the pancreatitis is autoimmune pancreatitis.

In one example, the method comprises detecting and/or diagnosing a pancreatic ductal adenocarcinoma (PD AC) from a non- PD AC. For example, the method comprises distinguishing or differentiating a PDAC from a non-PDAC. In one example, the non- PDAC is a pancreatic neuroendocrine tumour (pNET). For example, the method comprises distinguishing or differentiating a PDAC from a pNET.

In one example, the method further comprises determining the KRAS mutation status of the subject. For example, the KRAS mutation is a codon 12, 13 and/or 61 mutation. In one example, the KRAS mutation is a codon 12 mutation. For example, the KRAS mutation is a G12D (c.35G>A), G12V (c.35G>T), G12R (c.35G>C), G12C (c.34G>T) and/or a G12A (c.35G>C) mutation. In one example, the KRAS mutation is a G12D (c.35G>A) mutation. In another example, the KRAS mutation is a G12V (c.35G>T) mutation. In a further example, the KRAS mutation is a G12R (c.35G>C) mutation. In another example, the KRAS mutation is a G12A (c.35G>C) mutation. In another example, the KRAS mutation is a G12C (c.34G>T) mutation. In another example, the KRAS mutation is a codon 13 mutation. For example, the KRAS mutation is a G13C (c.37G>T) mutation. In a further example, the KRAS mutation is a codon 61 mutation. For example, the KRAS mutation is a Q61H (c. 183A>C), Q61R (C.182A>G), and/or Q61F (C.182A>T) mutation. In one example, the KRAS mutation is a Q61H (c. 183A>C) mutation. In one example, the KRAS mutation is a Q61R (C.182A>G) mutation. In a further example, the KRAS mutation is a Q61F (C.182A>T) mutation.

In one example, determining the KRAS mutation status of the subject comprises determining the KRAS mutation allele fraction (MAF). For example, determining the KRAS MAF comprises determining the ratio of mutant KRAS and wild-type KRAS alleles at the mutation site.

The present invention also provides a method of resolving an inconclusive cytological assessment of clinically relevant cells in a sample obtained from a subject, the method comprising determining a level of expression of at least five or more or all of the genes selected from the group consisting of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, S100A2, COL17A1, MSLN, SI OOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5 in the sample, wherein the expression of at least five or more or all of the genes in the clinically relevant cells indicates the presence of malignant pancreatic cells.

The present invention further provides a method of determining whether a subject has solid pancreatic cancer when a cytological assessment of cell morphology is inconclusive for the cancer, the method comprising determining a level of expression of at least five or more or all of the genes selected from the group consisting of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, S100A2, COL17A1, MSLN, SIOOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5 in the sample, wherein the expression of at least five or more or all of the genes in the clinically relevant cells indicates that the subject has solid pancreatic cancer.

The present invention also provides a method of determining whether a subject has solid pancreatic cancer, the method comprising: i) performing a cytological assessment of cell morphology on a sample obtained from the subject to determine the morphology of one or more clinically relevant cells; and ii) determining a level of expression of at least five or more or all of the genes selected from the group consisting of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, S100A2, COL17A1, MSLN, SI OOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5 in the sample, wherein when the cytological assessment of cell morphology is inconclusive for the cancer, the expression of at least five or more or all of the genes in the clinically relevant cells indicates that the subject has solid pancreatic cancer.

The present invention further provides a method of treating solid pancreatic cancer in a subject, the method comprising detecting and/or diagnosing pancreatic cancer in the subject according to a method disclosed herein, and administering a treatment to the subject. In one example, treatment comprises surgery, ablative or embolization therapy, chemotherapy, radiation therapy, targeted drug therapy, immunotherapy or a combination thereof.

In one example, the treatment comprises surgery. For example, the surgery is debulking surgery.

In one example, the treatment comprises ablative or embolization therapy. For example, the ablative therapy is selected from the group consisting of radio frequency ablation (RFA), microwave thermotherapy, ethanol (alcohol) ablation and cryosurgery. In one example, the embolization therapy is selected from the group consisting of arterial embolization, chemoembolization and radioembolization.

In another example, the treatment comprises chemotherapy. For example, the chemotherapy is selected from the group consisting of gemcitabine, 5-fluoro uracil, oxaliplatin, albumin-bound paclitaxel, capecitabine, docetaxel, cisplatin, irinotecan.

In one example, the treatment comprises radiation therapy. For example, the radiation therapy is selected from the group consisting of external beam therapy (EBT), stereotactic body radiation (SBRT), or proton beam radiation therapy.

In one example, the treatment comprises targeted drug therapy. For example, the targeted drug therapy is selected from the group consisting of erlotinib (e.g., Tarceva®), olaparib (e.g., Lynparza®), larotrectinib (e.g., Vitrakvi®) and entrectinib (e.g., Rozlytrek®).

In one example, the treatment comprises immunotherapy. For example, the immunotherapy is pembrolizumab (e.g., Keytruda®).

The present invention also provides a kit or panel for detecting and/or diagnosing solid pancreatic cancer in an EUS-FNA biopsy obtained from a subject, the kit or panel comprising five or more probes or primers for detecting at least five or more of all of the genes selected from the group consisting of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, S100A2, COL17A1, MSLN, SI OOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5.

In one example, the method further comprising one or more probes or primers for detecting one or more control genes selected from the group consisting of RPS11, RPL11, RPL28, RPS16, GDI2, RPL37A, PARK7, CNBP, CSNK1A1, RPS4X, MAZ, SF3B1, HSD17B4, DAP3, SET, MTIF3, Clorf43, CNOT2, GSTK1, DCTD, FNDC3B, AKIRIN1, ANXA7, SUPT5H, ZMYM2, DDX3X, HNRNPDL, ECD, MAEA, ADAR and ARCNL

Any embodiment herein shall be taken to apply mutatis mutandis to any other embodiment unless specifically stated otherwise. For instance, as the skilled person would understand, examples outlined above for one example of the invention equally apply to other examples the invention.

The present invention is not to be limited in scope by the specific embodiments described herein, which are intended for the purpose of exemplification only. Functionally-equivalent products, compositions and methods are clearly within the scope of the invention, as described herein.

Throughout this specification, unless specifically stated otherwise or the context requires otherwise, reference to a single step, composition of matter, group of steps or group of compositions of matter shall be taken to encompass one and a plurality (i.e. one or more) of those steps, compositions of matter, groups of steps or group of compositions of matter.

BRIEF DESCRIPTION OF FIGURES

Figure 1. Retrospective review of diagnostic accuracy of EUS-FNA biopsies in VPCB cohort. Clinico-patho logical information was assessed for 416 consecutive biopsies stored in the VBCP between Jan 2016 and Dec 2019, to identify 308 pancreatic EUS-FNA biopsies meeting the criteria indicated. The final clinical and cytological diagnosis for these biopsies is indicated in the flow chart. Figure 2. Selection of candidate genes from RNAseq cohort. Two-dimensional principle component analysis plot of gene expression of each sample in the VPCB RNA seq cohort (A). Genes fulfilling the abundance criteria were selected and ranked according to their ability to discriminate between PD AC and non- PD AC samples (B). Discriminative performance of each gene was measured by calculating the area under the curve of a receiver operating characteristic curve and the top 20 genes were selected (C). The expression of the 20 genes were summarised into a single score for each sample using the ssGSEA method (D). Boxplot (E) and receiver operating characteristic plot (F) of the 20-gene signature demonstrate good discrimination between PD AC and non-PDAC samples in RNA-seq cohort.

Figure 3. Validation of diagnostic gene signature in five external cohorts. Heat maps and ROC curves were generated to assess the 20-gene signature in five external cohorts containing PD AC and non-PDAC samples. AUC results under ROC curves demonstrate excellent discriminating power for the gene signature in E-MEXP-1121/E-MEXP-950, GSE101462, GSE15471, GSE28735 and GSE101448. Cohorts E-MEXP1121/ E- MEXP-950, GSE15471 and GSE28735 did not contain microarray probes for ENSG00000105388.

Figure 4. Performance of 17-gene signature in validation cohort. The performance of each of the genes in the 20-gene signature were calculated in the Nanostring validation cohort by constructing a receiver operating characteristic curve and measuring the area under the curve (A). Three poorly performing genes were removed from the signature, and the expression of the remaining 17 genes were summarised into a single gene score for each sample (B). Boxplot (C) and receiver operating characteristic curve (D) indicate that the gene score performs well at discriminating between PDAC and non-PDAC samples in the validation cohort.

Figure 5. Improving the accuracy of the diagnostic signature in PDAC biopsies using KRAS mutation analysis. Within the 41 PDAC cases in our NanoString cohort, DNA was available for ddPCR analysis in 32 cases (A). To evaluate the possibility that low tumour cellularity may be the cause of false negative results using the 17-gene signature, differences in tumour purity between false negative samples and true positive samples were measured using KRAS mutant allele frequency, revealing that false negative samples have a significantly lower KRAS mutant allele frequency (B). A positive trend between the 17-gene signature score and KRAS mutant allele frequency was also established (C).

KEY TO SEQUENCE LISTING

SEQ ID NO: 1 is a nucleotide sequence of human LAMC2 SEQ ID NO: 2 is a nucleotide sequence of human TFAP2A SEQ ID NO: 3 is a nucleotide sequence of human IQGAP3 SEQ ID NO: 4 is a nucleotide sequence of human SIM2 SEQ ID NO: 5 is a nucleotide sequence of human PTK6 SEQ ID NO: 6 is a nucleotide sequence of human TMPRSS4 SEQ ID NO: 7 is a nucleotide sequence of human SERPINB5 SEQ ID NO: 8 is a nucleotide sequence of human LAMB3 SEQ ID NO: 9 is a nucleotide sequence of human S100A2 SEQ ID NO: 10 is a nucleotide sequence of human COL17A1 SEQ ID NO: 11 is a nucleotide sequence of human MSLN SEQ ID NO: 12 is a nucleotide sequence of human SIOOP SEQ ID NO: 13 is a nucleotide sequence of human PLEKHN1 SEQ ID NO: 14 is a nucleotide sequence of human GJB3 SEQ ID NO: 15 is a nucleotide sequence of human GJB4 SEQ ID NO: 16 is a nucleotide sequence of human PADI1 SEQ ID NO: 17 is a nucleotide sequence of human CEACAM5 SEQ ID NO: 18 is a target nucleotide sequence of human LAMC2 SEQ ID NO: 19 is a target nucleotide sequence of human TFAP2A SEQ ID NO: 20 is a target nucleotide sequence of human IQGAP3 SEQ ID NO: 21 is a target nucleotide sequence of human SIM2 SEQ ID NO: 22 is a target nucleotide sequence of human PTK6 SEQ ID NO: 23 is a target nucleotide sequence of human TMPRSS4 SEQ ID NO: 24 is a target nucleotide sequence of human SERPINB5 SEQ ID NO: 25 is a target nucleotide sequence of human LAMB 3 SEQ ID NO: 26 is a target nucleotide sequence of human SI 00 A2 SEQ ID NO: 27 is a target nucleotide sequence of human COL17A1 SEQ ID NO: 28 is a target nucleotide sequence of human MSLN SEQ ID NO: 29 is a target nucleotide sequence of human SI OOP SEQ ID NO: 30 is a target nucleotide sequence of human PLEKHN1 SEQ ID NO: 31 is a target nucleotide sequence of human GJB3 SEQ ID NO: 32 is a target nucleotide sequence of human GJB4 SEQ ID NO: 33 is a target nucleotide sequence of human PADI1 SEQ ID NO: 34 is a target nucleotide sequence of human CEACAM5 SEQ ID NO: 35 is a nucleotide sequence of human RPS11 SEQ ID NO: 36 is a nucleotide sequence of human RPL11 SEQ ID NO: 37 is a nucleotide sequence of human RPL28 SEQ ID NO: 38 is a nucleotide sequence of human RPS16 SEQ ID NO: 39 is a nucleotide sequence of human GDI2 SEQ ID NO: 40 is a nucleotide sequence of human RPL37A SEQ ID NO: 41 is a nucleotide sequence of human PARK7 SEQ ID NO: 42 is a nucleotide sequence of human CNBP SEQ ID NO: 43 is a nucleotide sequence of human CSNK1A1 SEQ ID NO: 44 is a nucleotide sequence of human RPS4X SEQ ID NO: 45 is a nucleotide sequence of human MAZ SEQ ID NO: 46 is a nucleotide sequence of human SF3B1 SEQ ID NO: 47 is a nucleotide sequence of human HSD17B4 SEQ ID NO: 48 is a nucleotide sequence of human DAP3 SEQ ID NO: 49 is a nucleotide sequence of human SET SEQ ID NO: 50 is a nucleotide sequence of human MTIF3 SEQ ID NO: 51 is a nucleotide sequence of human Clorf43 SEQ ID NO: 52 is a nucleotide sequence of human CNOT2 SEQ ID NO: 53 is a nucleotide sequence of human GSTK1 SEQ ID NO: 54 is a nucleotide sequence of human DCTD SEQ ID NO: 55 is a nucleotide sequence of human FNDC3B SEQ ID NO: 56 is a nucleotide sequence of human A KIRIN I SEQ ID NO: 57 is a nucleotide sequence of human ANXA7 SEQ ID NO: 58 is a nucleotide sequence of human SUPT5H SEQ ID NO: 59 is a nucleotide sequence of human ZMYM2 SEQ ID NO: 60 is a nucleotide sequence of human DDX3X SEQ ID NO: 61 is a nucleotide sequence of human HNRNPDL SEQ ID NO: 62 is a nucleotide sequence of human ECD SEQ ID NO: 63 is a nucleotide sequence of human MAEA SEQ ID NO: 64 is a nucleotide sequence of human ADAR SEQ ID NO: 65 is a nucleotide sequence of human ARCN1 SEQ ID NO: 66 is a target nucleotide sequence of human RPS11 SEQ ID NO: 67 is a target nucleotide sequence of human RPL11 SEQ ID NO: 68 is a target nucleotide sequence of human RPL28 SEQ ID NO: 69 is a target nucleotide sequence of human RPS16 SEQ ID NO: 70 is a target nucleotide sequence of human GDI2 SEQ ID NO: 71 is a target nucleotide sequence of human RPL37A SEQ ID NO: 72 is a target nucleotide sequence of human PARK7 SEQ ID NO: 73 is a target nucleotide sequence of human CNBP SEQ ID NO: 74 is a target nucleotide sequence of human CSNK1A1 SEQ ID NO: 75 is a target nucleotide sequence of human RPS4X SEQ ID NO: 76 is a target nucleotide sequence of human MAZ SEQ ID NO: 77 is a target nucleotide sequence of human SF3B1 SEQ ID NO: 78 is a target nucleotide sequence of human HSD17B4 SEQ ID NO: 79 is a target nucleotide sequence of human DAP 3 SEQ ID NO: 80 is a target nucleotide sequence of human SET SEQ ID NO: 81 is a target nucleotide sequence of human MTIF3 SEQ ID NO: 82 is a target nucleotide sequence of human Clorf43 SEQ ID NO: 83 is a target nucleotide sequence of human CNOT2 SEQ ID NO: 84 is a target nucleotide sequence of human GSTK1 SEQ ID NO: 85 is a target nucleotide sequence of human DCTD SEQ ID NO: 86 is a target nucleotide sequence of human FNDC3B SEQ ID NO: 87 is a target nucleotide sequence of human AKIRIN1 SEQ ID NO: 88 is a target nucleotide sequence of human ANXA7 SEQ ID NO: 89 is a target nucleotide sequence of human SUPT5H SEQ ID NO: 90 is a target nucleotide sequence of human ZMYM2 SEQ ID NO: 91 is a target nucleotide sequence of human DDX3X SEQ ID NO: 92 is a target nucleotide sequence of human HNRNPDL SEQ ID NO: 93 is a target nucleotide sequence of human ECD SEQ ID NO: 94 is a target nucleotide sequence of human MAEA SEQ ID NO: 95 is a target nucleotide sequence of human ADAR SEQ ID NO: 96 is a target nucleotide sequence of human ARCN1

DETAILED DESCRIPTION OF THE INVENTION

General Techniques and Definitions

Unless specifically defined otherwise, all technical and scientific terms used herein shall be taken to have the same meaning as commonly understood by one of ordinary skill in the art (e.g., molecular biology, cancer diagnostics, RNA or DNA detection, pharmacology, protein chemistry, and biochemistry). Unless otherwise indicated, the techniques utilized in the present invention are standard procedures, well known to those skilled in the art. Such techniques are described and explained throughout the literature in sources such as, J. Perbal, A Practical Guide to Molecular Cloning, John Wiley and Sons (1984), J. Sambrook et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbour Laboratory Press (1989), T.A. Brown (editor), Essential Molecular Biology: A Practical Approach, Volumes 1 and 2, IRL Press (1991), D.M. Glover and B.D. Hames (editors), DNA Cloning: A Practical Approach, Volumes 1-4, IRL Press (1995 and 1996), and F.M. Ausubel et al., (editors), Current Protocols in Molecular Biology, Greene Pub. Associates and Wiley-Interscience (1988, including all updates until present).

As used herein, the term about, unless stated to the contrary, refers to +/- 10%, more preferably +/- 5%, more preferably +/- 1%, of the designated value.

Throughout this specification the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.

As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. Further, at least one of A and B and/or the like generally means A or B or both A and B. In addition, the articles “a” and “an” as used in this application and the appended claims may generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form.

The particular use of terms “nucleic acid,” “oligonucleotide,” and “polynucleotide” should in no way be considered limiting and may be used interchangeably herein. “Oligonucleotide” is used when the relevant nucleic acid molecules typically comprise less than about 100 bases. “Polynucleotide” is used when the relevant nucleic acid molecules typically comprise more than about 100 bases. Both terms are used to denote DNA, RNA, modified or synthetic DNA or RNA (including, but not limited to nucleic acids comprising synthetic and naturally-occurring base analogs, dideoxy or other sugars, thiols or other non-natural or natural polymer backbones), or other nucleobase containing polymers capable of hybridizing to DNA and/or RNA. Accordingly, the terms should not be construed to define or limit the length of the nucleic acids referred to and used herein, nor should the terms be used to limit the nature of the polymer backbone to which the nucleobases are attached.

The term “nucleic acid sequence” or “polynucleotide sequence” refers to a contiguous string of nucleotide bases and in particular contexts also refers to the particular placement of nucleotide bases in relation to each other as they appear in a polynucleotide.

As used herein, the term “subject” shall be taken to mean any animal including humans, for example a mammal. Exemplary subjects include but are not limited to humans, non-human primates, canines and felines. For example, the subject is a human. In another example, the subject is a canine.

As used herein, the term "detecting" refers to the identification of the presence or existence of solid pancreatic cancer in a subject at any stage of its development.

As used herein, the term "diagnosis" refers to the identification of the specific disease or condition in the subject. For example, “diagnosis” occurs following the manifestation of symptoms but prior to a clinical diagnosis. In one example, "diagnosis" allows a confirmation of pancreatic cancer in a subject suspected of having pancreatic cancer.

Solid pancreatic cancer

As used herein, the term "solid pancreatic cancer" refers to the presence of cells in the pancreas possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features known in the art.

In one example, the "solid pancreatic cancer" includes pre-malignant as well as malignant cancers. For example, the pancreatic cancer is a malignant cancer.

It will be apparent to the skilled person that the methods described herein are applicable for detecting and/or diagnosing all types of solid pancreatic cancer. For example, the solid pancreatic cancer is selected from the group consisting of ductal adenocarcinoma, osteoclast-like giant cell tumour, acinar cell carcinoma, a neuroendocrine tumour and pancreatoblastoma.

In one example, the solid pancreatic cancer is ductal adenocarcinoma.

In one example, the solid pancreatic cancer is osteoclast-like giant cell tumour.

In one example, the solid pancreatic cancer is acinar cell carcinoma.

In one example, the solid pancreatic cancer is pancreatoblastoma.

In one example, the solid pancreatic cancer is a neuroendocrine tumour. The skilled person will understand that solid pancreatic cancer can be classified based on the grade of the cancer.

In one example, the solid pancreatic cancer is a grade I tumour.

In one example, the solid pancreatic cancer is a grade II tumour. In one example, the solid pancreatic cancer is a grade III tumour.

In one example, the solid pancreatic cancer is a grade IV tumour.

The methods of the present disclosure can be readily applied to any form of solid pancreatic cancer. For example, the present disclosure provides a method of detecting and/or diagnosing solid pancreatic cancer in a subject irrespective of the grade of the cancer.

In one example, the subject suffers from solid pancreatic cancer. For example, a subject suffering from solid pancreatic cancer has a clinically accepted diagnosis of solid pancreatic cancer.

In one example, the subject suffers from one or more symptoms of solid pancreatic cancer. For example, the method of the present disclosure is performed after the onset of one or more symptoms, i.e., the method is performed on a subject in need thereof.

In one example, the subject does not suffer from one or more symptoms of solid pancreatic cancer. For example, the method of the present disclosure is performed before the onset of one or more symptoms. In one example, the subject is asymptomatic.

Symptoms of solid pancreatic cancer will be apparent to the skilled person and include, for example:

• Jaundice;

• Belly or back pain;

• Weight loss or poor appetite;

• Unexplained nausea or vomiting;

• Gallbladder or liver enlargement;

• Blood clots; and

• Diabetes.

The present inventors have also found that the methods of the present disclosure may be combined with other diagnostic tests. For example, methods further comprise determining the KRAS mutation status of the subject. In another example, methods of the disclosure further comprise performing a tumour biopsy. Gene Signature

The present disclosure provides a method of detecting and/or diagnosing solid pancreatic cancer in a subject, the method comprising determining a level of expression of at least five or more or all of the genes selected from the group consisting of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, S100A2, COL17A1, MSLN, SIOOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5 in the subject.

LAMC2 encodes a protein Laminin subunit gamma 2 which is part of the family of extracellular matrix glycoproteins, a major noncollagenous constituent of basement membranes. For example, LAMC2 as set forth in Ensembl Gene ID ENSG00000058085. For the purposes of nomenclature only and not limitation an exemplary sequence of human LAMC2 is set out in NCBI reference sequence NM_005562.2 and SEQ ID NO: 1. An exemplary target sequence of LAMC2 is set forth in SEQ ID NO: 18.

TFAP2A encodes the Transcription Factor AP-2 Alpha protein which acts as a sequence- specific DNA-binding transcription factor recognising and binding to the specific DNA sequence and recruiting transcription machinery. For example, TFAP2A as set forth in Ensembl Gene ID ENSG00000137203. For the purposes of nomenclature only and not limitation an exemplary sequence of human TFAP2A is set out in NCBI reference sequence NM_003220.2 and SEQ ID NO: 2. An exemplary target sequence of TFAP2A is set forth in SEQ ID NO: 19. IQGAP3 encodes Ras GTPase-activating-lIke protein IQGAP3, also known as pl95, which is a ubiquitously expressed protein involved in regulating various cellular processes ranging from organization of the actin cytoskeleton, transcription, and cellular adhesion to regulating the cell cycle. For example, IQGAP3 as set forth in Ensembl Gene ID ENSG00000183856. For the purposes of nomenclature only and not limitation an exemplary sequence of human IQGAP3 is set out in NCBI reference sequence NM_178229.4 and SEQ ID NO: 3. An exemplary target sequence of IQGAP3 is set forth in SEQ ID NO: 20.

SIM2 encodes the protein single-minded homolog 2 that plays a major role in the development of the central nervous system midline a well as the construction of the face and head. For example, SIM2 as set forth in Ensembl Gene ID ENSG00000159263. For the purposes of nomenclature only and not limitation an exemplary sequence of human SIM2 is set out in NCBI reference sequence NM_005069.3 and SEQ ID NO: 4. An exemplary target sequence of SIM2 is set forth in SEQ ID NO: 21.

PTK6 encodes a cytoplasmic non-receptor protein kinase, tyrosine-protein kinase 6, which functions as an intracellular signal transduce in epithelial tissues. The encoded protein has also been shown to undergo autophosphorylation. For example, PTK6 as set forth in Ensembl Gene ID ENSG00000101213. For the purposes of nomenclature only and not limitation an exemplary sequence of human PTK6 is set out in NCBI reference sequence NM_001256358.1 and SEQ ID NO: 5. An exemplary target sequence of PTK6 is set forth in SEQ ID NO: 22.

TMPRSS4 encodes transmembrane protease serine 4 which is a member of the serine protease family known to be involved in a variety of biological processes. The encoded protein is membrane bound with a N-terminal anchor sequence and a glycosylated extracellular region containing the serine protease domain. For example, TMPRSS4 as set forth in Ensembl Gene ID ENSG00000137648. For the purposes of nomenclature only and not limitation an exemplary sequence of human TMPRSS4 is set out in NCBI reference sequence NM_019894.3 and SEQ ID NO: 6. An exemplary target sequence of TMPRSS4 is set forth in SEQ ID NO: 23.

SERPINB5 encodes the mammary serine protein inhibitor protein maspin. Maspin is a non-inhibitory and obligate intracellular member of the serpin superfamily. For example, SERPINB5 as set forth in Ensembl Gene ID ENSG00000206075. For the purposes of nomenclature only and not limitation an exemplary sequence of human SERPINB5 is set out in NCBI reference sequence NM_002639.4 and SEQ ID NO: 7. An exemplary target sequence of SERPINB5 is set forth in SEQ ID NO: 24.

LAMB 3 encodes the beta 3 subunit of laminin, a basement membrane protein. For example, LAMB3 as set forth in Ensembl Gene ID ENSG00000196878. For the purposes of nomenclature only and not limitation an exemplary sequence of human EAMB3 is set out in NCBI reference sequence NM_000228.2 and SEQ ID NO: 8. An exemplary target sequence of LAMB3 is set forth in SEQ ID NO: 25.

S100A2 encodes the S100 calcium-binding protein A2 which is important in cyto skeletal organisation, whilst also playing a role in differentiation and regeneration of tissues. For example, S100A2 as set forth in Ensembl Gene ID ENSG00000196754. For the purposes of nomenclature only and not limitation an exemplary sequence of human S100A2 is set out in NCBI reference sequence NM_005978.3 and SEQ ID NO: 9. An exemplary target sequence of S100A2 is set forth in SEQ ID NO: 26.

COL17A1 encodes the alpha chain of type XVII collagen. Collagen XVII is a structural component of hemidesmosomes, multiprotein complexes at the dermal- epidermal basement membrane zone that mediate adhesion of keratinocytes to the underlying membrane. For example, COL17A1 as set forth in Ensembl Gene ID ENSG00000065618. For the purposes of nomenclature only and not limitation an exemplary sequence of human COL17A1 is set out in NCBI reference sequence NM_000494.3 and SEQ ID NO: 10. An exemplary target sequence of COL17A1 is set forth in SEQ ID NO: 27.

MSLN encodes mesothelin a 40kDa protein that is expressed in mesothelial cells. For example, MSLN as set forth in Ensembl Gene ID ENSG00000102854. For the purposes of nomenclature only and not limitation an exemplary sequence of human MSLN is set out in NCBI reference sequence NM_013404.3 and SEQ ID NO: 11. An exemplary target sequence of MSLN is set forth in SEQ ID NO: 28.

SIOOP encodes S100 calcium-binding protein P expressed in various normal tissues. S100P is involved in diverse biological functions but the exact role or mechanism of its action is still largely unknown. For example, SWOP as set forth in Ensembl Gene ID ENSG00000163993. For the purposes of nomenclature only and not limitation an exemplary sequence of human SWOP is set out in NCBI reference sequence NM_005980.2 and SEQ ID NO: 12. An exemplary target sequence of SWOP is set forth in SEQ ID NO: 29.

PLEKHN1 encodes pleckstrin homology domain containing, family N member 1 which is involved in intracellular signaling or as a constituent of the cytoskeleton. For example, PLEKHN1 as set forth in Ensembl Gene ID ENSG00000187583. For the purposes of nomenclature only and not limitation an exemplary sequence of human PEEKHN1 is set out in NCBI reference sequence NM_032129.2 and SEQ ID NO: 13. An exemplary target sequence of PLEKHN1 is set forth in SEQ ID NO: 30.

GJB3 encodes the protein Gap junction beta-3, also known as connexin 31. The encoded protein is a component of gap junctions, which are composed of arrays of intercellular channels that provide a route for the diffusion of low molecular weight materials from cell to cell. For example, GJB3 as set forth in Ensembl Gene ID ENSG00000188910. For the purposes of nomenclature only and not limitation an exemplary sequence of human GJB3 is set out in NCBI reference sequence NM_001005752.1 and SEQ ID NO: 14. An exemplary target sequence of GJB3 is set forth in SEQ ID NO: 31.

GJB4 encodes the protein Gap junction beta-4 protein (GJB4), also known as connexin 30.3 (Cx30.3). Connexin 30.3 is part of the group of proteins that form gap junctions on the surface of cells and is also thought to play a role in the growth and maturation of epidermal cells. For example, GJB4 as set forth in Ensembl Gene ID ENSG00000189433. For the purposes of nomenclature only and not limitation an exemplary sequence of human GJB4 is set out in NCBI reference sequence NM_153212.1 and SEQ ID NO: 15. An exemplary target sequence of GJB4 is set forth in SEQ ID NO: 32. PADI1 encodes the protein Peptidyl arginine deiminase, type I Peptidyl arginine deiminase, type I, which is a member of the peptidyl arginine deiminase family of enzymes which catalyze the post-translational deimination of proteins by converting arginine residues into citrullines in the presence of calcium ions. For example, PADI1 as set forth in Ensembl Gene ID ENSG00000142623. For the purposes of nomenclature only and not limitation an exemplary sequence of human PADI1 is set out in NCBI reference sequence NM_013358.2 and SEQ ID NO: 16. An exemplary target sequence of PADI1 is set forth in SEQ ID NO: 33.

CEACAM5 encodes the cell surface glycoprotein carcinoembryonic antigen- related cell adhesion molecule 5. The encoded protein is thought to play a role in regulating differentiation, apoptosis and cell polarity. For example, CEACAM5 as set forth in Ensembl Gene ID ENSG00000105388. For the purposes of nomenclature only and not limitation an exemplary sequence of human CEACAM5 is set out in NCBI reference sequence NM_004363.2 and SEQ ID NO: 17. An exemplary target sequence of CEACAM5 is set forth in SEQ ID NO: 34.

The methods of any disclosure described herein comprise determining a level of expression of at least five or more or all of the genes recited in the previous 17 paragraphs.

The skilled person will appreciate that the genes of the present disclosure includes genes with partly modified and/or substituted nucleotides. For example, the genes include all transcripts and/or variants of the genes disclosed herein. Accordingly, in any of the methods as described herein, the genes do not have 100% sequence identity with the sequences (e.g., the target sequence) of the genes as listed above. Thus, in one example, the measured gene has at least 75%, or at least 80%, or at least 85%, or at least 90%, or at least 95%, or at least 97.5%, or at least 98%, or at least 99%, or at least 99.9% sequence identity to the target gene sequence recited above. In one example, the gene sequence has one, two, three or four nucleotide substitutions.

Methods of Determining the Level of Expression of a Gene

Methods of determining the level of expression of a gene of the disclosure will be apparent to the skilled person and/or are described herein.

As used herein, the term “level” or “level of expression” in reference to a gene shall be understood to refer to a measure of the gene transcript or number of copies of the gene.

In one example, methods of the present disclosure involve extracting or isolating DNA or RNA fractions from the biological sample. In another example, the methods involve isolating only the RNA fraction from the biological sample. In a further example, the methods involve isolating only the DNA fraction from the biological sample. In one example, the methods involve extracting cell-free DNA or RNA from the biological sample. For example, the methods involve isolating cell-free DNA from the biological sample. In another example, the methods involve isolating cell-free RNA from the biological sample.

Methods for the extraction of DNA or RNA fractions from the biological samples will be apparent to the skilled person and/or are described herein and include, for example, phenol-based techniques, combined phenol and column-based techniques. A commercial kit may be used for RNA and/or DNA extraction including for example, isolation with the AllPrep DNA/RNA Universal Kit (Qiagen).

In one example, the quality and/or quantity of the extracted RNA and/or DNA may also be determined by any method known to a person skilled in the art e.g. spectrophotometrically at 260, 280 and 230 nm, agarose gel electrophoreses, or Bioanalyzer analysis (Agilent). For example, quantity of DNA and RNA may be assessed using the Nanodrop spectrophotometer (ThermoScientific) and Qubit Fluorometer (Life Technologies). For example, quality of DNA and RNA may be assessed using Bioanalyser and TapeStation systems (Agilent).

Normalisation and controls

In one example of the methods described herein the level of expression of the gene may be normalized.

The term “normalizing” or “normalized” as used herein with regard to RNA transcript, refers to the level of the RNA transcript, relative to the mean levels of a set or control set of reference RNA transcripts. The reference RNA transcripts are based on their minimal variation across patients, tissues, or treatments. Alternatively, the RNA transcript may be normalized to the totality of tested RNA transcripts, or a subset of such tested RNA transcripts.

In one example of the methods described herein comprises normalizing the level of expression of the gene to a standard to obtain a normalized level of the gene.

In one example, the standard is an endogenous control. For example, the endogenous control is a gene that shows minimal variation across patients, tissues, or treatments. For example, the endogenous control gene is a housekeeping gene. In one example, the endogenous control is one or more genes selected from the group consisting of RPS11, RPL11, RPL28, RPS16, GDI2, RPL37A, PARK 7, CNBP, CSNK1A1, RPS4X, MAZ, SF3B1, HSD17B4, DAP3, SET, MTIF3, Clorf43, CNOT2, GSTK1, DCTD, FNDC3B, AKIRIN1, ANXA7, SUPT5H, ZMYM2, DDX3X, HNRNPDL, ECD, MAEA, ADAR and ARCNL

In one example, the endogenous control is ribosomal protein Sll ( RPS11 ). For example, RPS11 as set forth in Ensembl Gene ID ENSG00000142534. For the purposes of nomenclature only and not limitation an exemplary sequence of human RPS11 is set out in NCBI reference sequence NM_001015.3 and SEQ ID NO: 35. An exemplary target sequence of RPS11 is set forth in SEQ ID NO: 66.

In one example, the endogenous control is ribosomal protein Lll ( RPL11 ). For example, RPL11 as set forth in Ensembl Gene ID ENSG00000142676. For the purposes of nomenclature only and not limitation an exemplary sequence of human RPL11 is set out in NCBI reference sequence NM_000975.2 and SEQ ID NO: 36. An exemplary target sequence of RPL11 is set forth in SEQ ID NO: 67.

In one example, the endogenous control is ribosomal protein L28 ( RPL28 ). For example, RPL28 as set forth in Ensembl Gene ID ENSG00000108107. For the purposes of nomenclature only and not limitation an exemplary sequence of human RPL28 is set out in NCBI reference sequence NM_000991.3 and SEQ ID NO: 37. An exemplary target sequence of RPL28 is set forth in SEQ ID NO: 68.

In one example, the endogenous control is ribosomal protein S16 ( RPS16 ). For example, RPS16 as set forth in Ensembl Gene ID ENSG00000105193. For the purposes of nomenclature only and not limitation an exemplary sequence of human RPS16 is set out in NCBI reference sequence NM_001020.4 and SEQ ID NO: 38. An exemplary target sequence of RPS16 is set forth in SEQ ID NO: 69.

In one example, the endogenous control is GDP dissociation inhibitor 2 ( GDI2 ). For example, GDI2 as set forth in Ensembl Gene ID ENSG00000057608. For the purposes of nomenclature only and not limitation an exemplary sequence of human GDI2 is set out in NCBI reference sequence NM_001494.3 and SEQ ID NO: 39. An exemplary target sequence of CDI2 is set forth in SEQ ID NO: 70.

In one example, the endogenous control is ribosomal protein L37a ( RPL37A ). For example, RPL37A as set forth in Ensembl Gene ID ENSG00000197756. For the purposes of nomenclature only and not limitation an exemplary sequence of human RPL37A is set out in NCBI reference sequence NM_000998.4 and SEQ ID NO: 40. An exemplary target sequence of RPL37A is set forth in SEQ ID NO: 71.

In one example, the endogenous control is Parkinsonism associated deglycase ( PARK7 ). For example, PARK7 as set forth in Ensembl Gene ID ENSG00000116288. For the purposes of nomenclature only and not limitation an exemplary sequence of human PARK7 is set out in NCBI reference sequence NM_001123377.1 and SEQ ID NO: 41. An exemplary target sequence of PARK7 is set forth in SEQ ID NO: 72.

In one example, the endogenous control is CCHC-type zinc finger nucleic acid binding protein ( CNBP ). For example, CNBP as set forth in Ensembl Gene ID ENSG00000169714. For the purposes of nomenclature only and not limitation an exemplary sequence of human CNBP is set out in NCBI reference sequence NM_003418.4 and SEQ ID NO: 42. An exemplary target sequence of CNBP is set forth in SEQ ID NO: 73.

In one example, the endogenous control is casein kinase 1 alpha 1 ( CSNK1A1 ). For example, CSNK1A1 as set forth in Ensembl Gene ID ENSG00000113712. For the purposes of nomenclature only and not limitation an exemplary sequence of human CSNK1A1 is set out in NCBI reference sequence NM_001892.4 and SEQ ID NO: 43. An exemplary target sequence of CSNK1A1 is set forth in SEQ ID NO: 74.

In one example, the endogenous control is ribosomal protein S4 X-linked ( RPS4X ). For example, RPS4X as set forth in Ensembl Gene ID ENSG00000198034. For the purposes of nomenclature only and not limitation an exemplary sequence of human RPS4X is set out in NCBI reference sequence NM_001007.4 and SEQ ID NO: 44. An exemplary target sequence of RPS4X is set forth in SEQ ID NO: 75.

In one example, the endogenous control is MYC associated zinc finger protein {MAZ). For example, MAZ as set forth in Ensembl Gene ID ENSG00000103495. For the purposes of nomenclature only and not limitation an exemplary sequence of human MAZ is set out in NCBI reference sequence NM_002383.2 and SEQ ID NO: 45. An exemplary target sequence of MAZ is set forth in SEQ ID NO: 76.

In one example, the endogenous control is splicing factor 3b subunit 1 ( SF3B1 ). For example, SF3B1 as set forth in Ensembl Gene ID ENSG00000115524. For the purposes of nomenclature only and not limitation an exemplary sequence of human SF3B1 is set out in NCBI reference sequence NM_001005526.1 and SEQ ID NO: 46. An exemplary target sequence of SF3B1 is set forth in SEQ ID NO: 77.

In one example, the endogenous control is hydroxysteroid 17-beta dehydrogenase 4 (HSD17B4). For example, FISD17B4 as set forth in Ensembl Gene ID ENSG00000133835. For the purposes of nomenclature only and not limitation an exemplary sequence of human HSD17B4 is set out in NCBI reference sequence NM_000414.2 and SEQ ID NO: 47. An exemplary target sequence of FISD17B4 is set forth in SEQ ID NO: 78.

In one example, the endogenous control is death associated protein 3 ( DAP3 ). For example, DAP3 as set forth in Ensembl Gene ID ENSG00000132676. For the purposes of nomenclature only and not limitation an exemplary sequence of human DAP3 is set out in NCBI reference sequence NM_004632.3 and SEQ ID NO: 48. An exemplary target sequence of DAP 3 is set forth in SEQ ID NO: 79.

In one example, the endogenous control is SET nuclear proto-oncogene (SET). For example, SET as set forth in Ensembl Gene ID ENSG00000119335. For the purposes of nomenclature only and not limitation an exemplary sequence of human SET is set out in NCBI reference sequence NM_001122821.1 and SEQ ID NO: 49. An exemplary target sequence of SET is set forth in SEQ ID NO: 80.

In one example, the endogenous control is mitochondrial translational initiation factor 3 ( MTIF3 ). For example, MTIF3 as set forth in Ensembl Gene ID ENSG00000122033. For the purposes of nomenclature only and not limitation an exemplary sequence of human MTIF3 is set out in NCBI reference sequence NM_152912.3 and SEQ ID NO: 50. An exemplary target sequence of MTIF3 is set forth in SEQ ID NO: 81.

In one example, the endogenous control is chromosome 1 open reading frame 43 ( Clorf43 ). For example, Clorf43 as set forth in Ensembl Gene ID ENSG00000143612. For the purposes of nomenclature only and not limitation an exemplary sequence of human Clorf43 is set out in NCBI reference sequence NM_015449.2 and SEQ ID NO: 51. An exemplary target sequence of Clorf43 is set forth in SEQ ID NO: 82.

In one example, the endogenous control is CCR4-NOT transcription complex subunit 2 ( CNOT2 ). For example, CNOT2 as set forth in Ensembl Gene ID ENSG00000111596. For the purposes of nomenclature only and not limitation an exemplary sequence of human CNOT2 is set out in NCBI reference sequence NM_015449.2 and SEQ ID NO: 52. An exemplary target sequence of CNOT2 is set forth in SEQ ID NO: 83.

In one example, the endogenous control is glutathione S-transferase kappa 1 ( GSTK1 ). For example, GSTK1 as set forth in Ensembl Gene ID ENSG00000197448. For the purposes of nomenclature only and not limitation an exemplary sequence of human GSTK1 is set out in NCBI reference sequence NM_015917.2 and SEQ ID NO: 53. An exemplary target sequence of GSTK1 is set forth in SEQ ID NO: 84.

In one example, the endogenous control is dCMP deaminase (DCTD). For example, DCTD as set forth in Ensembl Gene ID ENSG00000129187. For the purposes of nomenclature only and not limitation an exemplary sequence of human DCTD is set out in NCBI reference sequence NM_001012732.1 and SEQ ID NO: 54. An exemplary target sequence of DCTD is set forth in SEQ ID NO: 85. In one example, the endogenous control is fibronectin type III domain containing 3B ( FNDC3B ). For example, FNDC3B as set forth in Ensembl Gene ID ENSG00000075420. For the purposes of nomenclature only and not limitation an exemplary sequence of human FNDC3B is set out in NCBI reference sequence NM_022763.3 and SEQ ID NO: 55. An exemplary target sequence of FNDC3B is set forth in SEQ ID NO: 86.

In one example, the endogenous control is akirin 1 ( AKIRIN1 ). For example, AKIRIN1 as set forth in Ensembl Gene ID ENSG00000174574. For the purposes of nomenclature only and not limitation an exemplary sequence of human AKIRIN1 is set out in NCBI reference sequence NM_001136275.1 and SEQ ID NO: 56. An exemplary target sequence of AKIRIN1 is set forth in SEQ ID NO: 87.

In one example, the endogenous control is annexin A7 ( ANXA7 ). For example, ANXA7 as set forth in Ensembl Gene ID ENSG00000138279. For the purposes of nomenclature only and not limitation an exemplary sequence of human ANXA7 is set out in NCBI reference sequence NM_001156.3 and SEQ ID NO: 57. An exemplary target sequence of ANXA7 is set forth in SEQ ID NO: 88.

In one example, the endogenous control is SPT5 homolog, DSIF elongation factor subunit ( SUPT5FT ). For example, SUPT5FI as set forth in Ensembl Gene ID ENSG00000196235. For the purposes of nomenclature only and not limitation an exemplary sequence of human SUPT5H is set out in NCBI reference sequence NM_003169.3 and SEQ ID NO: 58. An exemplary target sequence of SUPT5FI is set forth in SEQ ID NO: 89.

In one example, the endogenous control is zinc finger MYM-type containing 2 ( ZMYM2 ). For example, ZMYM2 as set forth in Ensembl Gene ID ENSG00000121741. For the purposes of nomenclature only and not limitation an exemplary sequence of human ZMYM2 is set out in NCBI reference sequence NM_003169.3 and SEQ ID NO:

59. An exemplary target sequence of ZMYM2 is set forth in SEQ ID NO: 90.

In one example, the endogenous control is DEAD-box helicase 3 X-linked ( DDX3X ). For example, DDX3X as set forth in Ensembl Gene ID ENSG00000215301. For the purposes of nomenclature only and not limitation an exemplary sequence of human DDX3X is set out in NCBI reference sequence NM_001356.3 and SEQ ID NO:

60. An exemplary target sequence of DDX3X is set forth in SEQ ID NO: 91.

In one example, the endogenous control is heterogeneous nuclear ribonucleoprotein D like (. HNRNPDL ). For example, HNRNPDL as set forth in Ensembl Gene ID ENSG00000152795. For the purposes of nomenclature only and not limitation an exemplary sequence of human HNRNPDL is set out in NCBI reference sequence NR_003249.1 and SEQ ID NO: 61. An exemplary target sequence of HNRNPDL is set forth in SEQ ID NO: 92.

In one example, the endogenous control is ecdysoneless cell cycle regulator ( ECD ). For example, ECD as set forth in Ensembl Gene ID ENSG00000122882. For the purposes of nomenclature only and not limitation an exemplary sequence of human ECD is set out in NCBI reference sequence NM_001135752.1 and SEQ ID NO: 62. An exemplary target sequence of ECD is set forth in SEQ ID NO: 93.

In one example, the endogenous control is macrophage erythroblast attacher, E3 ubiquitin ligase ( MAEA ). For example, MAE A as set forth in Ensembl Gene ID ENSG00000090316. For the purposes of nomenclature only and not limitation an exemplary sequence of human MAEA is set out in NCBI reference sequence NM_001017405.2 and SEQ ID NO: 63. An exemplary target sequence of MAEA is set forth in SEQ ID NO: 94.

In one example, the endogenous control is adenosine deaminase RNA specific ( ADAR ). For example, ADAR as set forth in Ensembl Gene ID ENSG00000160710. For the purposes of nomenclature only and not limitation an exemplary sequence of human ADAR is set out in NCBI reference sequence NM_001111.3 and SEQ ID NO: 64. An exemplary target sequence of ADAR is set forth in SEQ ID NO: 95.

In one example, the endogenous control is archain 1 ( ARCN1 ). For example, ARCN1 as set forth in Ensembl Gene ID ENSG00000095139. For the purposes of nomenclature only and not limitation an exemplary sequence of human ARCN1 is set out in NCBI reference sequence NM_001655.4 and SEQ ID NO: 65. An exemplary target sequence of ARCN1 is set forth in SEQ ID NO: 96.

The skilled person will appreciate that the control genes of the present disclosure includes genes with partly modified and/or substituted nucleotides. For example, the genes include all transcripts and/or variants of the genes disclosed herein. Accordingly, in any of the methods as described herein, the genes do not have 100% sequence identity with the sequences (e.g., the target sequence) of the genes as listed above. Thus, in one example, the measured gene has at least 75%, or at least 80%, or at least 85%, or at least 90%, or at least 95%, or at least 97.5%, or at least 98%, or at least 99%, or at least 99.9% sequence identity to the target gene sequence recited above. In one example, the gene sequence has one, two, three or four nucleotide substitutions.

In one example, the control is an exogenous control, for example an exogenous RNA added to the biological sample before RNA extraction (e.g., a spike-in control). Spike-in controls may be added to a sample before RNA is recovered, the amount of the spike-in control recovered after RNA is directly correlated with the amount of total RNA recovered.

In one example, the exogenous RNA is isolated from a host source or is synthetic.

It will be apparent to the skilled person that synthetic spike-in controls are available from a number of commercial manufactures including for example, Qiagen and Norgen Biotek Corporation and Life Technologies.

In one example, normalizing the level of the gene is global mean normalisation. As used herein, “global mean normalization” refers to normalization of expression of a gene to a set of reference genes. For example, the method may comprise measuring the expression of at least three endogenous control genes and taking the geometric mean to provide a normalization factor. In one example, the at least three endogenous control genes are expressed in all samples being analysed.

Reference samples

In one example of any method described herein, the method comprises comparing the level of expression of the RNA in the subject to a level of expression of the RNA in at least one reference.

Suitable reference samples for use in the methods of the present disclosure will be apparent to the skilled person and/or described herein. For example, the reference may be an internal reference (i.e., from the same subject), from a normal individual or an established data set (e.g., matched by age, gender, ethnicity, sample type and/or stage of disease).

In one example, the reference is an internal reference or sample. For example, the reference is an autologous reference. In one example, the internal reference is obtained from the subject at an earlier time point as the sample under analysis.

As used herein, the term “normal individual” shall be taken to mean that the subject is selected on the basis that they do not have a solid pancreatic cancer (e.g., healthy control) or other malignant and/or benign condition, or that they are not suspected of having such condition.

In one example, the reference is an established data set. Established data sets suitable for use in the present disclosure will be apparent to the skilled person and include, for example:

• A data set from a normal subject or a population of normal subjects matched by age and sample type;

• A data set from another subject or a population of subjects matched by age, sample type and/or stage of disease; • A data set comprising cells in vitro, wherein the cells have been treated to induce

RNA expression; and

• A data set comprising in vitro, wherein the cells have been treated to inhibit RNA expression.

In one example, the method comprises determining:

(a) if the level of expression of the gene in the subject is higher than the level of expression of the gene in the reference; or

(b) if the level of expression of the gene in the subject is lower than the level of expression of the gene in the reference.

In one example, a higher level of expression of the gene in the subject compared to the reference level is indicative of solid pancreatic cancer in the subject.

The term “higher” in reference to the level of expression of a gene means that the amount of the RNA nucleic acid molecules or copies of RNA in the subject is greater, increased or up-regulated, compared to a control or reference level. It will be apparent from the foregoing that the level of expression of the gene needs only be increased by a statistically significant amount, for example, by at least about 10%, or about 20%, or about 30%, or about 40%, or about 50%, or about 60%, or about 70%, or about 80%, or about 90%, or about 95%.

The term “lower” in reference to the level of expression of a gene means that the amount of RNA nucleic acid molecules or copies of RNA in the subject is reduced, decreased or down-regulated, compared to a control or reference level. It will be apparent from the foregoing that the level of expression of the gene need only be decreased by a statistically significant amount, for example, by at least about 10%, or about 20%, or about 30%, or about 40%, or about 50%, or about 60%, or about 70%, or about 80%, or about 90%, or about 95%.

The term “same” or “similar” in reference to the level of expression of the gene means that the amount of the RNA nucleic acid molecules or copies of RNA in the subject is within about +/- 5% of the control or reference level.

In one example, the reference level is a predetermined threshold level of the gene assessed. In one example, the reference level is a standard curve of the gene assessed. In one example, there is a reference level for each of the genes assessed. Thus, in some examples of the present disclosure, the reference level may comprise a predetermined threshold or standard curve of one, two, three, four, five, six, seven or more genes. In one example, the method, kit or panel described herein comprises a reference level for the gene LAMC2. In one example, the method, kit or panel described herein comprises a reference level for the gene TFAP2A. In one example, the method, kit or panel described herein comprises a reference level for the gene IQGAP3. In one example, the method, kit or panel described herein comprises a reference level for the gene SIM2. In one example, the method, kit or panel described herein comprises a reference level for the gene PTK6. In one example, the method, kit or panel described herein comprises a reference level for the gene TMPRSS4. In one example, the method, kit or panel described herein comprises a reference level for the gene SERPINB5. In one example, the method, kit or panel described herein comprises a reference level for the gene LAMB3. In one example, the method, kit or panel described herein comprises a reference level for the gene S100A2. In one example, the method, kit or panel described herein comprises a reference level for the gene COL17A1. In one example, the method, kit or panel described herein comprises a reference level for the gene MSLN. In one example, the method, kit or panel described herein comprises a reference level for the gene SIOOP. In one example, the method, kit or panel described herein comprises a reference level for the gene PLEKHN1. In one example, the method, kit or panel described herein comprises a reference level for the gene GJB3. In one example, the method, kit or panel described herein comprises a reference level for the gene GJB4. In one example, the method, kit or panel described herein comprises a reference level for the gene PADI1. In one example, the method, kit or panel described herein comprises a reference level for the gene CEACAM5.

In one example, a reference is not included in an assay. Instead, a suitable reference is derived from an established data set previously generated. Data derived from processing, analyzing and/or assaying a test sample is then compared to data obtained for the sample.

Detection and analysis

A person skilled in the art will appreciate that the level of gene expression can be detected with any method known to a person skilled in the art including, for example, the methods described or adapted from Git et al. (2010), Hunt et al. (2015), Tackett et al. (2017) and Hu et al. (2017). This includes, for example, next generation sequencing, single-molecule real-time sequencing, mass spectrometry, digital color-coded barcode technology analysis, microarray expression profiling, quantitative PCR, reverse transcriptase PCR, reverse transcriptase real-time PCR, quantitative real-time PCR, end point PCR, multiplex end-point PCR, cold PCR, ice-cold PCR, droplet digital PCR, in situ hybridization, Northern hybridization, hybridization protection assay (HPA), branched DNA (bDNA) assay, rolling circle amplification (RCA), single molecule hybridization detection, invader assay, Bridge Litigation Assay, nucleic acid sequence- base amplification (NASBA), ligase chain reaction, multiplex ligatable probe amplification, invader technology (Third Wave), in vitro transcription (IVT), strand displacement amplification, transcription- mediated amplification (TMA) and/or RNA (Eberwine) amplification.

Detection may include methods comprising direct labelling of a RNA (e.g. with a modified nucleotide, labelled nucleotide or tag incorporated into the RNA) or binding of the RNA with a binding molecule which binds a RNA or a truncated version thereof forming a RNA-binding molecule complex.

In one example, the binding molecule is selected from: i) a polynucleotide, ii) an aptamer, iii) an antibody. In one example, the polynucleotide is complementary to the RNA or a truncated version thereof or detects a tag attached to the RNA. In one example, the polynucleotide is a primer.

In one example, the binding molecule is detectably labelled or capable of binding a detectable label. In one example, the binding molecule is linked to an enzyme, enzyme substrate, a fluorescent or fluorescent substrate, chemiluminescent molecule, chemiluminescent substrate, purification tag and/or a solid support. In one example, the mRNA-binding complex is directly or indirectly detected.

Sensitivity and Specificity

In one example, the methods as described herein can detect and/or diagnose solid pancreatic cancer in a subject with high specificity and sensitivity.

In one example of any method described herein, the sensitivity achieved by the presently claimed method for determining whether a subject has cancer is at least about at least about 60%, at least about 70%, at least about 71%, at least about 72%, at least about 73%, at least about 74%, at least about 75%, at least about 76%, at least about 77%, at least about 78%, at least about 79%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%.

In one example, the sensitivity achieved by the presently claimed method for determining whether a subject has cancer is between about 65% and 90%. For example, the sensitivity is about 69%. In another example, the sensitivity is about 80%. In a further example, the sensitivity is about 86%.

In one example of any method described herein, the specificity achieved by the presently claimed method for determining whether a subject has cancer is at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, or about 100%. In one example, the specificity achieved by the presently claimed method for determining whether a subject has cancer is 100%.

In one example, the specificity and sensitivity is assessed by receiver operative characteristic (ROC) analysis as area under the curve (AUC).

In one example, the AUC of the level of the at least five genes detected is between about 0.8 and about 1.0. In one example, the AUC of the level of the at least five genes detected is between about 0.80 and about 0.90, for example at least 0.80, or at least 0.85, or at least 0.90. In another example, the AUC of the level of the at least one miRNA detected is between about 0.90 and about 1.0, for example at least 0.90, or at least 0.95, or 1.0. In one example, the AUC of the level of the at least five genes detected is 0.90. In another example, the AUC of the level of the at least five genes detected is 0.95. In another example, the AUC of the level of the at least five genes detected is 1.0.

In one example, the AUC of the level of each gene detected is at least 0.80, or at least 0.85, or at least 0.90, or at least 0.95, or at least 0.96, or at least 0.97, or at least 0.98, or at least 0.98.

In one example, the method comprises detecting the level of expression of at least five or more or all of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, S100A2, COL17A1, MSLN, SI OOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5 and the combined AUC is between about 0.8 and about 1.0. For example, the combined AUC of the at least five or more or all genes is 0.90.

In one example, the method comprises detecting the level of expression of all of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB 3, S100A2, COL17A1, MSLN, SIOOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5 and the combined AUC is at least 0.90.

Determining KRAS mutation status

The inventors of the present disclosure found that KRAS mutant allele detection further improves diagnostic accuracy of the method described herein, whilst also provides a novel surrogate marker of cellular adequacy in frozen EUS-FNA biopsies.

In one example of any method described herein, the method comprises determining the KRAS mutation status of the subject.

Methods of determining KRAS mutation status will be apparent to the skilled person and/or are described herein. For example, KRAS mutation status is determined using a commercially available kit (e.g., KRAS XL StripAssay™; ViennaLab Diagnostics GmBH). In one example, the KRAS mutation is a codon 12, 13 and/or 61 mutation. In one example, the KRAS mutation is a codon 12 mutation. For example, the KRAS mutation is a G12D (c.35G>A), G12V (c.35G>T), G12R (c.35G>C), G12C (c.34G>T) and/or a G12A (c.35G>C) mutation. In one example, the KRAS mutation is a G12D (c.35G>A) mutation. In another example, the KRAS mutation is a G12V (c.35G>T) mutation. In a further example, the KRAS mutation is a G12R (c.35G>C) mutation. In another example, the KRAS mutation is a G12A (c.35G>C) mutation. In another example, the KRAS mutation is a G12C (c.34G>T) mutation. In another example, the KRAS mutation is a codon 13 mutation. For example, the KRAS mutation is a G13C (c.37G>T) mutation. In a further example, the KRAS mutation is a codon 61 mutation. For example, the KRAS mutation is a Q61H (c. 183A>C), Q61R (C.182A>G), and/or Q61L(c.l82A>T) mutation. In one example, the KRAS mutation is a Q61H (c. 183A>C) mutation. In one example, the KRAS mutation is a Q61R (c.l82A>G) mutation. In a further example, the KRAS mutation is a Q61L (C.182A>T) mutation.

In one example, further determining the KRAS mutation status increases the accuracy of the method described herein by at least about 10%. For example, the accuracy of the method described herein increases by 10%, or by 12%, or by 14%, or by 16%, or by 18%, or by 18%, or by 20%.

In one example, further determining the KRAS mutation status increases the accuracy of the method described herein to at least 90% accuracy. For example, the accuracy is increased to about 90% accuracy, or about 91% accuracy, or about 92% accuracy, or about 93% accuracy, or about 94% accuracy, or about 95% accuracy, or about 96% accuracy, or about 97% accuracy.

In one example, determining the KRAS mutation status of the subject comprises determining the KRAS mutation allele fraction (MAF). For example, determining the KRAS MAF comprises determining the ratio of mutant KRAS and wild-type KRAS alleles at the mutation site.

Methods of determining KRAS MAF are known in the art and/or are described herein and include, for example, digital droplet PCT (ddPCR). In one example, the KRAS MAF is determined using ddPCR.

In one example, the KRAS MAF is >1% as measured by ddPCR. It will be apparent to the skilled person from the disclosure herein that a MAF of >1% as measured by ddPCR is a good marker of cellular adequacy in EUS-FNA biopsy samples.

In one example, the cellular adequacy of an EUS-FNA biopsy samples is determined by determining the KRAS MAF. Methods of Resolving an Inconclusive Cyto logical Assessment

The present disclosure also provides a method of resolving an inconclusive cytological assessment of clinically relevant cells in a sample. The present disclosure also provides a method of determining whether a subject has solid pancreatic cancer when a cytological assessment of cell morphology is inconclusive for the cancer.

As will be appreciated by one of skill in the art, cytological assessment involves the assessment of individual cells. "Cytological assessment" of cell morphology seeks to identify malignant cells based on morphologic characteristics. Cytological assessment of cell morphology is a procedure that is part of the standard of care and used alongside, or as a reflex to, further investigation for the detection of recurrence or the diagnosis of cancer. It is not a test per se but a pathology consultation based on a particular sample or sample set. The assessment procedure is complex and requires expertise and care in sample collection to provide a correct assessment.

In performing a cytological assessment of cell morphology, a cell sample is typically fixed to a slide and viewed under a microscope to visually assess the morphology and cellular features. Before, visually assessing the slide, the sample may be stained to assist in visualising morphological changes to cells and cellular components. These stains can include a haematoxylin and eosin stain or Papanicolaou stain (Pap stain). Morphological changes that may be associated with cancer include enlarged nuclei with irregular size and shape, prominent nucleoli, scarce cytoplasm which may be intense or pale in colour.

The phrase “clinically relevant cells” refers to those cells that the cytologist or cytopathologist is examining to determine the cancer status of the patient. For example, in EUS-FNA biopsies from patients being examined for pancreatic cancer, the clinically relevant cells are epithelial cells from the pancreas. Excluded cells are considered not clinically relevant to determining whether a subject has cancer. The excluded cells will depend on the cancer being detected. More specifically, the skilled person will be aware of cell types in a sample related to a particular cancer. Examples of excluded cells include, but are not necessarily limited to, one or more or all of T-cells, B-cells, neutrophils, macrophages, granulocytes, dendritic cells, mast cells, memory-cells, plasma cells, eosinophils and squamous cells. For example, the cells listed above will be excluded when assessing pancreatic cancer using the methods of the invention.

Historically, the performance of cytology was described as extremely good with high-grade cancer. On the other hand, the majority of studies to date are in general agreement regarding the low sensitivity of cytology in low grade cancer. Accordingly, cytology assessment can often be inconclusive and not achieve its intended goal to aid in the diagnosis of cancer. Further, given the low sensitivity of cytology assessment, a negative or inconclusive cytology result does not preclude the presence of cancer (especially low grade cancer).

In the context of the present invention, an inconclusive cytological assessment of cell morphology refers to an assessment that does not allow the presence of cancer to be determined. Often a cytological assessment of cell morphology is inconclusive as the assessment identifies cells that have lost their normal appearance but have not reached the level of abnormality of malignant cells. These cells are commonly referred to in the art as atypical cells in light of their atypical morphology.

Biological Sample

As will be apparent to the skilled person, the type and size of the biological sample will depend upon the detection means used.

As used herein, the term “sample” or “biological sample” refers to any type of suitable material obtained from the subject. The term encompasses a clinical sample or biological fluid (e.g., biopsy or aspirated fluid, whole blood, serum, plasma, cerebrospinal fluid (CSF) sample, urine and saliva), tissue sample, live cells and also includes cells in culture, cell supernatants, cell lysates derived therefrom. The sample can be used as obtained directly from the source or following at least one-step of (partial) purification. It will be apparent to the skilled person that the sample can be prepared in any medium which does not interfere with the method of the disclosure. The sample may comprise cells or tissues and/or is an aqueous solution or biological fluid comprising cells or tissues. The sample may also be a cell- free preparation. For example, the sample may be a cell-free aqueous solution or biological fluid, such as biopsy fluid following removal of the cells. The skilled person will be aware of selection and pre-treatment methods. Pre-treatment may involve, for example, diluting viscous fluids. Treatment of a sample may involve filtration, distillation, separation, concentration.

In one example, the biological sample is an endoscopic ultrasound fine needle aspiration (EUS-FNA) biopsy sample. For example, the EUS-FNA biopsy is a formalin- fixed, paraffin embedded (FFPE) biopsy, a snap frozen biopsy or a fresh biopsy. In one example, the EUS-FINA biopsy is a FFPE biopsy. In another example, the EUS-FNA biopsy is a snap frozen EUS-FNA biopsy sample. In a further example, the EUS-FNA biopsy is a fresh biopsy sample. In another example, the EUS-FNA biopsy is a cell-free fluid biopsy sample. For example, the EUS-FNA biopsy is an aspirated cell-free fluid sample. It will be apparent to the skilled person that the aspirated cell-free fluid is the supernatant that is suspended above a cell pellet that is normally discarded and contains cell-free RNA.

In one example, the biological sample has been derived previously from the subject. Accordingly, in one example, a method as described herein according to any embodiment additionally comprises providing the biological sample.

In one example, a method as described herein according to any embodiment is performed using an extract from a sample, such as, for example, nucleic acids.

Methods of Treating Solid Pancreatic Cancer

In one example, the present invention provides a method of treating solid pancreatic in a subject, the method comprising performing the method as described herein and treating the subject for pancreatic cancer.

As used herein, the terms “treating”, “treat” or “treatment” includes surgically removing all or part of the cancer or administering a therapeutically effective amount of a compound/molecule/radiation sufficient to reduce or eliminate at least one symptom of the pancreatic cancer. For example, an "effective amount" for therapeutic uses is the amount of the compound required to provide a clinically significant decrease in disease symptoms without undue adverse side effects. An appropriate "effective amount" in any individual case may be determined using techniques, such as a dose escalation study. An "effective amount" of a compound is an amount effective to achieve a desired pharmacologic effect or therapeutic improvement without undue adverse side effects. It is understood that "an effective amount" or "a therapeutically effective amount" can vary from subject to subject, due to variation in metabolism of the compound of any of age, weight, general condition of the subject, the condition being treated, the severity of the condition being treated, and the judgment of the prescribing physician.

In one example, treatment comprises surgery, ablative or embolization therapy, chemotherapy, radiation therapy, targeted drug therapy, immunotherapy or a combination thereof.

In one example, the treatment comprises surgery. For example, the surgery is debulking surgery.

In one example, the treatment comprises ablative or embolization therapy. For example, the ablative therapy is selected from the group consisting of radio frequency ablation (RFA), microwave thermotherapy, ethanol (alcohol) ablation and cryosurgery. In one example, the embolization therapy is selected from the group consisting of arterial embolization, chemoembolization and radioembolization. In another example, the treatment comprises chemotherapy. For example, the chemotherapy is selected from the group consisting of gemcitabine, 5-fluoro uracil, oxaliplatin, albumin-bound paclitaxel, capecitabine, docetaxel, cisplatin, irinotecan.

In one example, the treatment comprises radiation therapy. For example, the radiation therapy is selected from the group consisting of external beam therapy (EBT), stereotactic body radiation (SBRT), or proton beam radiation therapy.

In one example, the treatment comprises targeted drug therapy. For example, the targeted drug therapy is selected from the group consisting of erlotinib (e.g., Tarceva®), olaparib (e.g., Lynparza®), larotrectinib (e.g., Vitrakvi®) and entrectinib (e.g., Rozlytrek®).

In one example, the treatment comprises immunotherapy. For example, the immunotherapy is pembrolizumab (e.g., Keytruda®).

Panels and Kits

The present disclosure provides panels or kits for detecting and/or diagnosing cancer in a subject. The kits of the invention will preferably comprise a nucleotide array comprising RNA-specific probes and/or oligonucleotides for amplifying at least five or more or all of the genes described herein.

The present invention also provides a panel or kit comprising one or more reagents for detecting at least five or more or all of the genes selected from the group consisting of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, S100A2, COL17A1, MSLN, SIOOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5. In another example, the kit comprises reagents for detecting at least seven genes selected from the group consisting of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, SW0A2, COL17A1, MSLN, SWOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5. In a further example, the kit comprises reagents for detecting at least ten genes selected from the group consisting of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, SW0A2, COL17A1, MSLN, SWOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5 in the subject. In one example, the kit comprises reagents for detecting at least fifteen genes selected from the group consisting of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, SW0A2, COL17A1, MSLN, SWOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5. In another example, the kit comprises reagents for detecting all of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, SW0A2, COL17A1, MSLN, SWOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5. In a further example, the kit comprises reagents for detecting at least one or more of the genes is selected from the group consisting of LAMC2, IQGAP3, SIM2, PTK6, LAMB3, COL17A1, GJB4 and PADI1.

In one example, the panel or kit further comprises a control as described herein.

In one example, the panel or kit further comprises one or more reagents for detecting the level of a control.

In one example, the panel or kit comprises a reference level. In one example, the reference level comprises a standard curve of the at least five or more or all genes as selected from the group consisting of LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, S100A2, COL17A1, MSLN, SI OOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5. In one example, the reference level comprises a predetermined threshold of the at least five or more or all genes selected from the group consisting LAMC2, TFAP2A, IQGAP3, SIM2, PTK6, TMPRSS4, SERPINB5, LAMB3, S100A2, COL17A1, MSLN, SIOOP, PLEKHN1, GJB3, GJB4, PADI1 and CEACAM5.

In one example, the panel or kit as described herein is for next generation sequencing, real-time reverse transcription polymerase chain reaction (RT-PCR), droplet digital PCR (ddPCR), RNA sequencing and/or a microarray assay.

In one example, the panel or kit as described herein is for ex vivo analysis. In one example, the kit is suitable for use with tissue biopsy samples, for example EUS-FNA biopsy samples.

In one example, the panel or kit as described herein is suitable for high-throughput screening. The term “high-throughput screening” refers to screening methods that can be used to test or assess more than one sample at a time and that can reduce the time for testing multiple samples. In one example, the methods are suitable for testing or assessing at least 5 samples, at least 10, at least 20, at least 30, at least 50, at least 70, at least 90, at least 150, at least 200, at least 300 samples at a time. Such high-throughput screening methods can analyse more than one sample rapidly e.g. in at least 30 minutes, in at least 1 hour, in at least 2 hours, in at least 3 hours, in at least 4 hours, in at least 5 hours, in at least 6 hours, in at least 7 hours, in at least 8 hours, in at least 9 hours or in at least 10 hours. High-throughput screening may also involve the use of liquid handling devices. In one example, high-throughput analysis may be automated.

All publications discussed and/or referenced herein are incorporated herein in their entirety.

Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present invention. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention as it existed before the priority date of each claim of this application.

EXAMPLES

Example 1 - Materials and Methods Clinical samples and data

Pancreatic tissue samples were sourced from the Victorian Pancreatic Cancer Biobank (VPCB, HREC/15/MonH/117), which currently stores biospecimens from seven major tertiary centres in Victoria, Australia. For EUS-FNA biopsies, patients consented to an additional needle pass at the time of their standard of care biopsy, which is snap frozen and stored in the VPCB. Relevant clinical data was extracted by retrospective review of medical records, and stored in a de-identified manner.

Statistical analysis

Tests for diagnostic accuracy were assessed by constructing 2 x 2 contingency tables using GraphPad Prism v8.0, with true positive cytology defined as those with confirmed malignancy or suspicious cytology. Cytology reported as “scant atypical cells” but no definite diagnosis, “indeterminate”, and “non-diagnostic” were considered false negative results for patients with PD AC. Fisher’s exact test was used to calculate P values and statistical significance, and Wilson-Brown method was used to calculate 95% confidence intervals. For descriptive statistics of the yield of RNA and DNA, data presented is the mean+SEM.

Genomic analysis

RNA and DNA were simultaneously extracted from snap-frozen pancreatic biopsies following the manufacturer’s protocol (Qiagen AllPrep DNA/RNA Universal Kit). Quantity of gDNA and RNA was assessed using the Nanodrop spectrophotometer (ThermoScientific) and Qubit Fluorometer (Life Technologies), and quality assessed using Bioanalyser and TapeStation systems (Agilent). KRAS testing was performed on gDNA using the KRAS XL StripAssay (ViennaLab Diagnostics GmBH).

RNA sequencing

For the test cohort, RNA was sequenced on the Ion Torrent Proton Sequencer using the Ion Ampliseq Transcriptome Human Gene Expression Kit for library preparation, with amplified samples ligated, purified and quantified by qPCR before being pooled for sequencing. Sequence reads were aligned to GRCh38 using STAR aligner. Read counts were calculated using Htseq.

Selection of genes from RNAseq data

Using standard bioinformatic methods, read counts for each gene in each sample were tallied, normalised to total read counts, and log transformed. Genes with arithmetic average expression across all samples below the 90% percentile were removed. Genes were then ranked according to their ability to distinguish PD AC from normal pancreas and pancreatitis by constructing a receiver operating characteristic curve (ROC) for each gene and calculating the area under the curve (AUC) metric. Twenty genes with the highest AUC were selected. 30 housekeeping genes were selected by ranking genes based on their coefficient of variation in the RNAseq data across all samples and selecting the genes with the lowest coefficient of variation.

Nanostring validation

The diagnostic signature was assessed in the validation cohort using a custom designed Nanostring Custom CodeSet. RNA (50ng) from each sample was added to a Master Mix containing the Hybridization Buffer and Reporter CodeSet, then underwent hybridization at 65°C for 16 hours before a ramp down to 4°C. Samples were immediately made up to 35pL using RNAse free water, loaded into nCounter Sprint Cartridges and run using the SPRINT profiler (Nanostring). Gene expression normalisation was performed by dividing the expression of each signature gene by the geometric mean of the 30 housekeeping genes for each sample. Gene expression values were then z-transformed. To calculate the summarised gene expression score for each sample, z-transformed gene expression values were summarised into a single value for each sample using simple addition.

Digital droplet PCR

KRAS mutations in PDAC tissues were verified in duplicate by digital droplet PCR (ddPCR) with the inclusion of positive, negative and no-template controls, following manufacturer protocols ( KRAS G12/G13 or Q61H Screening Kit, Bio-Rad). Droplets (15000-20000 per well) generated using the Q200X droplet generator were transferred to a 96- well PCR plate, heat- sealed, and subjected to thermocycling in a C1000 touch thermal cycler (Bio-Rad) under the following cycling conditions: 95°C for 10 min, 40 cycles at 94°C for 30 s and subsequently 55°C for 1 min, then followed by an enzyme deactivation step through incubation for 10 min at 98°C. Amplified droplets were detected using a QX200 droplet reader (Bio-Rad Laboratories, Hercules, CA, USA) with two fluorescent detectors (FAM and HEX. The determination of the number of mutation copies, ratio and fractional abundance (FA) of the samples was adjusted by the Quanta- Soft software (Bio-Rad Laboratories, Hercules, CA, USA) to fit a Poisson distribution model with a 95% confidence level. A minimum of three positive droplets across the two wells was required for a positive result for detection of rare events. The ratio was calculated as the number of copies per microliter of mutant allele, divided by copies per microliter of wild-type allele. The fractional abundance of mutant allele was measures by dividing the number of copies per microliter of mutant allele by the total copies per microliter of wild-type allele plus mutant allele.

Example 2: Results

Diagnostic accuracy of EUS FNA biopsies in a large series

A retrospective chart review on 308 sequential pancreatic biopsies where tissue was donated to the VPCB between 2016 and 2019 was performed to determine the diagnostic accuracy of EUS FNA. Biopsies were included if EUS-FNA biopsy was performed for a solid pancreatic mass and/or imaging appearances suspicious for malignancy. Biopsies were excluded if there was only a surgical biopsy, predominantly cystic lesion, ampullary or bile duct pathology, or inadequate clinicopathological information for accurate classification (Figure 1A).

The 308 pancreatic biopsies included 219 with a final clinical diagnosis of PD AC, 50 with benign pathology, 21 with pNETs, and 18 with other tumours, including 12 metastases from other primary sites (3 renal cell carcinomas, 2 lung cancers, 2 gastric cancers, and 1 each of melanoma, colorectal, ovarian, breast and prostate cancer), as well as 1 pancreatic sarcoma, 3 lymphomas and 2 patients with solid pseudopapillary tumours.

Cytology confirmed a diagnosis of PD AC in 137 biopsies, and was suspicious or highly suspicious in a further 29. 11 biopsies were reported as atypical without definite evidence of malignancy, and 42 were either non-diagnostic or inadequate. There was one false positive result for PDAC, with initial cytology reported as adenocarcinoma, but at later review including clinical history and extensive immunohistochemical staining, the diagnosis was changed to pNET. Therefore, the sensitivity of EUS FNA in diagnosing solid pancreatic masses in our cohort was 78.6% (95% Cl 73.2 to 83.2%) with a specificity of 98.0% (95% Cl 89.7 to 99.9%), PPV of 99.5% (95% Cl 97.3 to 100%), NPV of 47.6% (95% Cl 38.3 to 57.1%). In our series, 18.2% of patients underwent more than one procedure to establish a diagnosis (range 1-3). CA 19.9 is a widely used, well validated biomarker for PDAC, although inadequate sensitivity and specificity limit its utility in the diagnostic setting. We examined the diagnostic accuracy of CA 19.9 in our cohort when this data was available (n=166 patients). As expected, CA 19.9 displayed significant variability across patient samples (serum levels ranging from <1 to >640,000kU/L) and was a poor diagnostic biomarker for PDAC, with sensitivity of 71.2% (95% Cl 63.4 to 80%) and specificity of 63.6% (95% Cl 43.0 to 80.3%) (Figure IB).

KRAS mutation analysis improves diagnostic accuracy ofEUS FNA

DNA and RNA was simultaneously extracted from an EUS-FNA biopsy and KRAS mutation testing performed.

There was significant variability in the yield and quality of genomic material extracted from these unselected biopsies, particularly with regards to RNA. The average yield of RNA was 2839+341.6ng, and DNA 2427+289.4ng. The mean RIN was 3.4+0.15, while the quality of DNA was generally higher, with a mean DIN of 7.1+0.13. Only 1 of 175 DNA samples (0.006%) failed quality control testing for KRAS mutation analysis, due to a very low yield of genetic material from a paucicellular specimen. The KRAS XL StripAssay™ (ViennaLab Diagnostics GmBH) was selected as a commercially validated, relatively cost-effective method of testing with quick turn around time, which can detect mutations in specimens comprising 1-5% mutation positive cells.

KRAS mutation analysis was available for 174 PDAC samples and 23 benign tissues, and had diagnostic sensitivity for PDAC of 86.8% (95% Cl 80.1 to 91.0%), and specificity of 95.7% (95% Cl 79.0 to 99.8%) (Figure IB). The single positive result in a benign biopsy was an equivocal KRAS G12V mutation, present at the very lower limit of detection of the assay. On subsequent follow up, a pancreatico-duodenectomy confirmed no invasive malignancy but focal areas of low grade pancreatic intraepithelial neoplasia (PanIN-lB), malignant pre-cursor lesions of which 10%-30% may harbour pathogenic KRAS mutations.

Notably, in the group of 51 PDAC patients with only atypical features or non diagnostic cytology, a pathological KRAS mutation could be detected in 34 of 42 (81%) available samples, suggesting the presence of malignant cells within the biopsy despite negative cytology. RNA sequencing differentiates PD AC from non-PDAC

To determine the gene expression profile of EUS-FNA biopsies, we performed whole human transcriptome sequencing on 96 PD AC patients, and 38 non-PDAC controls. The clinical features of the 134 patients in this initial RNA sequencing cohort are summarized in Table 1. AmpliSeq was used which is a whole transcriptome sequencing approach which provides targeted amplification of greater than 20,000 RNA targets using a single primer pool, and allows for differential gene expression profiling using small starting RNA quantities (lOng) and requires fewer total sequencing reads when compared to other RNA sequencing approaches.

Table 1: Clinicopathological features and demographics of patient cohort used for gene signature development. _

_ PDAC (n=96) _ Non-PDAC (n=38)

Mean age

_ Years (range) _ 71 (47-92) _ 62 (18-81) _

Sex [number (%)] _

Male 48 (50) 22 (58) Female 48 (50) 16 (42)

Tumour location [number (%)]

Pathology

PDAC 96 (100) 0 (0)

Normal pancreas 0 (0) 14 (37)

Autoimmune pancreatitis 0 (0) 10 (26)

Pancreatitis (other) 0 (0) 6 (16)

Neuroendocrine tumour_ 0 (0) 8 (21)

TNM stage [number (%)]

The non-PDAC control biopsies included normal pancreatic tissues obtained at surgery (n=14), as well as EUS-FNA biopsies from patients with autoimmune pancreatitis (n=10), non-specific pancreatitis (n=6), and pNET (n=8). The control EUS- FNA biopsies were selected to include causes of suspicious pancreatic inflammation or solid masses on imaging which would generally warrant an urgent biopsy to exclude malignancy.

EUS-FNA PDAC biopsies provided a feasible source of genetic material for molecular analysis, and displayed a distinct gene expression profile when compared to non- PD AC controls. 2 patients initially diagnosed with pancreatitis (with benign cytology and no clinical diagnosis of malignancy) were found to have outlying gene expression profiles in our test set, clustering with PDAC samples (Figure 2A). Pathologic KRAS G12D mutations were detected in both apparently benign biopsies. On retrospective chart review, both patients ultimately developed progressive symptoms after observation, and were ultimately diagnosed with PDAC at a later date.

The presence of pathogenic KRAS mutations in combination with a distinct gene expression profile in non-diagnostic biopsies suggests that transcriptomic profiling of patients with a clinical suspicion of malignancy may be more sensitive than standard cytology in distinguishing PDAC from other causes of pancreatic masses.

Selection of candidate genes and validation of diagnostic signature in 5 external cohorts

Each gene was ordered in their ability to distinguish PDAC from non-PDAC controls and the top 20 genes within the top 20% of abundance were selected to create a diagnostic signature for PDAC for further analysis and validation (Figure 2B-C).

The diagnostic performance of each individual gene was assessed (Figure 2C) and combined to generate a 20-gene signature score (Figure 2D-E). A receiver operating characteristic (ROC) curve was generated to assess the diagnostic performance of the gene signature in our test cohort, and demonstrated an excellent predictive AUC of 98% (Figure 2F).

The diagnostic signature was applied to 5 publicly available cohorts of patients containing both PDAC and non-malignant controls (either benign specimens or microdissected adjacent normal tissue): E-MEXP-1121/E-MEXP-950, GSE101462, GSE15471, GSE28735 and GSE101448. Heat maps and ROC curves were generated to assess the diagnostic performance of the gene signature in each cohort (Figure 3). The predictive AUC in the respective external validation cohorts was 82%, 98%, 89%, 94% and 96%.

The selected genes were then used to create a custom NanoString CodeSet for testing in an independent patient cohort. The NanoString system utilises a simple workflow which accommodates input of relatively low RNA quality and quantity (25ng) and allows complementary capture and reporter probes for all mRNA targets of interest to be mixed with RNA in a single hybridization reaction with no need for library preparation, with subsequent digital counting of colour-tagged codes for each mRNA target. Results are available within 24 hours, an attractive feature if applied in the clinical setting as data could be used in real time alongside standard cytology to aid in the interpretation of biopsy results.

NanoString Custom CodeSet testing of diagnostic gene signature in validation cohort

Using the NanoString Custom CodeSet, the diagnostic gene signature was tested in an independent local cohort of a further 60 EUS-FNA patient biopsies. The validation cohort consisted of 24 patients with cytologically confirmed PDAC, 20 patients with indeterminate or non-diagnostic cytology, 10 patients with clinically and cyto-logically benign pancreatic disease, and six patients with cytologically confirmed pNETs (Table 2).

Table 2: Clinicopathological features and demographics of patient cohort used for validation of diagnostic gene signature (n=60).

_ Pancreatic FNA biopsies (n=60)

Mean age

Years (range) _ 69 (45-86) _

Sex [number (%)]

Male 34 (57)

Female_ 26 (43)

Cytology [number (%)]

PD AC 24 (40)

Non-diagnostic 20 (33)

Benign 10 (17) pNET_ 6 (10)

Final clinicopathological diagnosis [number (%)] PD AC 41 (68)

Benign 11 (18) pNET_ 8 (13)

TNM stage of PDAC patients [number (%)] Notably, one of the patients with a final clinical diagnosis of pNET was initially erroneously diagnosed with adenocarcinoma on cytological assessment. The diagnostic gene signature profile for this sample scored lowly, consistent with other pNETs, and lower than the PD AC biopsies. Of the 20 cytologically non-diagnostic samples, the final clinical diagnoses consisted of two further pNETs, one benign pancreatitis, and a further 17 PDACs. The mean RIN was 4.8 (range 2.3-8.7) in the validation cohort.

The performance of the diagnostic gene signature in this cohort is shown in Figure 4. Among the 20 genes, three were noted to perform poorly in the expanded cohort and the diagnostic panel was refined to a 17-gene signature (Figure 4A-B). The combined gene signature score was consistently low in non-PDAC samples, and high for the majority of samples with a final clinical diagnosis of PD AC (Figure 4C). The performance of the 17-gene diagnostic gene signature was also re-tested in the original RNAseq cohort and demonstrated an excellent predictive AUC of 0.9814.

To optimize specificity to avoid false positives, a cutoff score of -1.5 to establish a definite diagnosis of PD AC, as the minimum score which did not include non-PDAC samples. Using the gene expression score at this cutoff has a specificity of 100% and a sensitivity of 70.7% (Table 3).

Table 3: Sensitivity, specificity, and accuracy of cytology, KRAS and NanoString signature in 60 validation samples.

Using these diagnostic criteria has a specificity of 100% and a sensitivity of 87.8% which outperforms current standard cytology in test sensitivity and specificity (Table 3).

Several of the outlier PDAC samples which displayed low expression of the diagnostic signature were also KRAS wild-type. Given the high frequency of KRAS mutations in PDAC, it is likely that these outlier samples predominantly comprised of benign or inflamed pancreatic tissue with low tumour cellularity. KRAS mutant allele assessment by ddPCR is a good surrogate marker of tumour cellularity and biopsy adequacy

In order to extract genetic material of optimal quality, snap frozen EUS-FNA biopsies which are processed in their entirety were used. The concurrent specimen which is sent to pathology can be assessed for cellularity, but may not accurately reflect the cellularity of the frozen sample, thereby making it difficult to clearly differentiate true negative signature expression results from those due to sampling error.

As KRAS is commonly expressed in PDAC cells, it was hypothesized that measurement of the KRAS mutation allele fraction (MAF; mutant ATCkS'/wild type KRAS ) may provide a useful surrogate marker of tumour cellularity in our specimens, and allow assessment of whether sampling error may be responsible for the KRAS wild-type, low PDAC signature expressing specimens. From the 41 PDAC patients included in the validation cohort, ddPCR analysis was performed on a representative set of 32 specimens where adequate tumour-derived DNA was available (Figure 5A). As expected, MAF varied across the population but closely correlated with higher RNA signature expression (Figure 5B-C), suggesting higher tumour cellularity in these samples and providing a rational explanation for lower signature expression in the outlier PDAC samples.

Cyto logically non-diagnostic specimens with negative KRAS, low signature expression and low MAF could therefore be presumed to be samples with very low (or no) tumour cells and would represent the population who would require further diagnostic biopsies. In total, 5 samples were identified which failed to meet the minimum requirement of either definitive cytology, positive KRAS (by StripAssay testing or MAF >1%), or a signature score above the defined PDAC cutoff. Therefore, these samples (8.3%) would represent the patient cohort who are likely to require a further diagnostic biopsy, a significantly lower proportion than we observed in our retrospective review of real-world patients.

REFERENCES

Ausubel et al. (ed.) Current Protocols in Molecular Biology, 1988, John Wiley and Sons, Inc.

Brown (ed.) Essential Molecular Biology: A Practical Approach, 1991, IRL Press, Volumes 1 and 2.

Git et al. (2010) RNA, 16:1991-1006.

Glover et al. (ed.) DNA Cloning: A Practical Approach, 1995 and 1996, IRL Press, Volumes 1 to 4.

Hewitt et al. (2012) Gastrointest Endosc. 75:319-31.

Hu et al. (2017) Methods in Molecular Biology, 1617: 169-177.

Hunt et al. (2015) Annual Review of Analytical Chemistry, 8:217-37.

Perbal, A Practical Guide to Molecular Cloning, 1984, John Wiley and Sons. Sambrook et al. Molecular Cloning: A Laboratory Manual, 1989, Cold Spring Harbor Laboratory Press.

Tackett et al. (2017) Methods in Molecular Biology 1654:209-219.