FIELD OF THE INVENTION:

The invention is in the kidney field. More particularly, the invention relates to a method for estimating the probability of maladaptive glomerular impairment in kidney diseases in a subject.

BACKGROUND OF THE INVENTION:

Glomerular hyperfiltration (GH) is a hallmark of renal dysfunction in diabetes and obesity. Glomerular hyperfiltration is associated with glomerular and tubular hypertrophy. Hyperfiltration is mainly due to an increase in glomerular capillary pressure, which increases tensile stress applied to the capillary wall structures. GH-related mechanical stress leads to both adaptive and maladaptive glomerular changes. Focal segmental glomerulosclerosis (FSGS) is both a glomerular lesion and renal disease. It refers to different diseases with very different pathophysiology and treatments ¹ . On the one hand, primary FSGSs is considered to be an immunological disease requiring immunosuppressive agents. On the other hand, maladaptive FSGS refers to the consequence of glomeruli hyperfiltration (especially in the context of nephron number reduction) requiring non-specific nephroprotective care. Pragmatically, the decision to treat with immunosuppressive agents (e.g., corticosteroid) is based on the probability that the FSGS is primary. However, it still involves a great deal of medical intuition. It mainly relies on unspecific and frequently unavailable information at the time of initial assessment: the speed of installation of the disease, the presence of a nephrotic syndrome (NS) with hypoalbuminemia < 30g/L, the percentage of podocyte foot process effacement assessed by electron microscopy (EM) ⁴ .

Thus, there is a need to find new tools to distinguish primary FSGS from maladaptive FSGS and avoid immunosuppressive treatment for the patients who have not to be treated with said treatment.

SUMMARY OF THE INVENTION:

The invention relates to a method for estimating the probability of maladaptive glomerular impairment in a subject comprising the following steps: i) obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA) value and iii) concluding that the subject has a high probability of maladaptive glomerular impairment when the MIA value is lower than the reference value; or concluding that the subject is not likely to have maladaptive glomerular impairment when the MIA value is higher than the reference value.

In particular, the invention is defined by the claims.

DETAILED DESCRIPTION OF THE INVENTION:

Inventors hypothesize that the different pathophysiological mechanisms involved in immunosuppression-sensitive and maladaptive FSGS translate into different morphometric patterns that can be identified by optical microscopy, taking advantage of the recent availability of clinical-grade scanners allowing the large-scale digitalization of renal biopsies into high-definition whole slide images (WSI). An observational cohort with 40 patients (20 patients with primary FSGS and maladaptive FSGS, respectively) was carried out to identify renal morphometric parameters of interest. In addition, a validation cohort with 40 patients (20 patients with primary FSGS and maladaptive FSGS, respectively) was established to confirm the results matching age, estimated glomerular filtration rate (eGFR), and level of proteinuria.

In the observational cohort, they found that the mean interglomerular area (MIA), a marker of glomerular scarcity described in the Table 2) was significantly lower in patients with primary FSGS compared to maladaptive FSGS 90 [76-100] vs. 198 [165-299] um2, p< 0.0001. This finding was confirmed in the validation cohort 133 [109-159] vs. 204 [170-339] um2 (p=0.0017).

MIA predictive performance measured by area under ROC (AUROC) curve was 0.96 (95%CI 0.89 - 1) and 0.83 (95%CI 0.66 - 1) for observation and validation cohort, respectively. Inventors determined an MIA cut-off value of l 60, l 34um2 to discriminate a primary FSGS from a maladaptive FSGS with a sensibility of 84% (95%CI 67 - 93) and a specificity of 85% (95%CI 68 - 94).

Accordingly, in a first aspect, the invention relates to a method for estimating the probability of maladaptive glomerular impairment in a subject comprising the following steps: i) obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA) and iii) concluding that the subject has a high probability of maladaptive glomerular impairment when MIA is lower than the reference value; or concluding that the subject is not likely to have maladaptive glomerular impairment when MIA is higher than the reference value.

More particularly, the method according to the invention comprises further a step of associated MIA value with estimated glomerular filtration rate (eGFR), and/or level of proteinuria, albuminuria or albuminemia.

Typically, the invention relates to a method for estimating the probability (p) of maladaptive glomerular impairment in a subject by comprising the following steps: i) calculating the following formula: p = 1-1/(1+ Exp(-5.69662497682848 + 0.00706798450932702 x MIA - 0.0345231327288216 x eGFR + 0.135805524527782 x Albuminemia)); and ii) concluding that the subject has a high probability of maladaptive glomerular impairment when the probability is higher than the reference value; or concluding that the subject not likely to have maladaptive glomerular impairment when the probability is lower than the reference value.

In a particular embodiment, the invention relates to a method for estimating the probability (p) of maladaptive glomerular impairment in a subject by comprising the following steps: i) obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA) value; ii) associating MIA value determined at step ii) with estimated glomerular filtration rate (eGFR), level of albuminuria and albuminemia; iii) calculating the probability of maladaptive glomerular impairment (p) by the following formula: p =1- 1/(1+ Exp(-5.69662497682848 + 0.00706798450932702 x MIA - 0.0345231327288216 x eGFR + 0.135805524527782 x Albuminemia)); and iv) concluding that the subject has a high probability of maladaptive glomerular impairment when the probability is higher than the reference value; or concluding that the subject not likely to have maladaptive glomerular impairment when the probability is lower than the reference value. More particularly, the method according to the invention comprises further a step of associated MIA value with age, glomerular filtration rate (eGFR), and/or level of proteinuria or albuminuria and albuminemia.

Accordingly, the invention relates to a method for estimating the probability (p) of maladaptive glomerular impairment in a subject comprising following steps: i) obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA) value; iii) associating MIA value determined at step ii) with age, estimated glomerular filtration rate (eGFR), level of albuminuria and albuminemia; iv) calculating the probability of maladaptive glomerular impairment (p) by the following formula: p =1- 1 / (1 + Exp( (-5.69662497682848) + (0.00625296091662126 x MIA + 0.0243941810080423 x Age - 0.0360256134329782 x eGFR + 0.161931298668144 x Albuminemia + 0.000255295983138449 x

Albuminuria)) and v) concluding that the subject has a high probability of maladaptive glomerular impairment when the probability is higher than the reference value; or concluding that the subject not likely to have maladaptive glomerular impairment when the probability is lower than the reference value.

In a particular embodiment, the the invention relates to a method for estimating the probability (p) of maladaptive glomerular impairment in a subject comprising following steps: i) obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA) value; iii) associating MIA value determined at step ii) with age, estimated glomerular filtration rate (eGFR), level of serum protein and proteinuria; iv) calculating the probability of maladaptive glomerular impairment (p) by the following formula: p = 1- 1 / (1 + Exp( (-5.69662497682848) + (0.00625296091662126 x MIA + 0.0243941810080423 x Age - 0.0360256134329782 x eGFR + 0.161931298668144 x serum protein + 0.000255295983138449 x proteinuria)) and v) concluding that the subject has a high probability of maladaptive glomerular impairment when the probability is higher than the reference value; or concluding that the subject not likely to have maladaptive glomerular impairment when the probability is lower than the reference value.

As used herein, the term “maladaptive glomerular impairment” refers to many diseases in kidney associated to the glomerular hyperfiltration (GH). Hyperfiltration is mainly due to an increase in glomerular capillary pressure, which increases tensile stress applied to the capillary wall structures. GH-related mechanical stress leads to both adaptive and maladaptive glomerular. Glomerular hyperfiltration is a characteristic functional abnormality in insulin-dependent diabetes mellitus and occurs in the large majority of young Type 1 diabetic patients. Hyperfiltration is hypothesized to be a precursor of intraglomerular hypertension leading to albuminuria. Glomerular filtration rate (GFR) then falls progressively in parallel with a further rise in albuminuria which may lead, in the long run, to end-stage renal failure. Hyperfiltration occurs in case of nephron number reduction, which can be caused by any kidney disease, and lead to maladaptive FSGS.

As used herein, the term “subject” refers to any mammals, such as a rodent, a feline, a canine, and a primate. Particularly, in the present invention, the subject is a human. In a particular embodiment, the subject is susceptible to suffer or is suffering from maladaptive glomerular impairment.

In a particular embodiment, the subject is susceptible to suffer or is suffering from diabetes mellitus.

As used herein, the term “diabetes mellitus” also known as diabetes, is a group of metabolic disorders characterized by a high blood sugar level over a prolonged period of time.

In a particular embodiment, the diabetes mellitus is type 1 diabetes winch results from failure of the pancreas to produce enough insulin due to loss of beta cells. This form is also called as "insulin-dependent diabetes mellitus".

In a particular embodiment, the diabetes mellitus is type II diabetes mellitus (non- insulin-dependent diabetes mellitus or NIDDM). It is a metabolic disorder involving dysregulation of glucose metabolism and insulin resistance, and long-term complications involving the eyes, kidneys, nerves, and blood vessels. Type II diabetes mellitus usually develops in adulthood (middle life or later) and is described as the body's inability to make either sufficient insulin (abnormal insulin secretion) or its inability to effectively use insulin (resistance to insulin action in target organs and tissues) In a further embodiment, the subject is susceptible to suffer or is suffering from a diabetic glomerulopathy.

As used herein, the term “diabetic glomerulopathy” refers to structural changes including glomerulosclerosis attributed to diabetes (in the context of uncontrolled diabetes and compatible associated features including but not restricted to nodular glomerulosclerosis, thickening of the basement membranes).

As used herein, the term “biological sample” refers to any sample obtained from a subject, such as a serum sample, a plasma sample, a urine sample, a blood sample, a lymph sample, or a biopsy. In a particular embodiment, the biological samples is a renal biopsy. Typically, biopsy sections were scanned (Pathscan Combi, Excilone), allowing to obtain high- definition whole slide images (WSI).

As used herein, the term “mean interglomerular area (MIA)” refers to a marker of glomerular scarcity. The MIA is calculated by dividing the minimum cortical area (minimum area between glomeruli, except GSG) by the number of glomeruli (except GSG) minus one.

In a particular embodiment, the method according to the invention wherein the mean interglomerular area (MIA) is calculated with the following formula = (minimal cortical area/total number of glomeruli except GSG - 1)).

In the context of the invention, the total number of glomeruli (except GSG) is the total number of glomeruli found on the biological sample and must be >2.

In a particular embodiment, the MIA value as determined above is incorporated in a software, a machine, R software (R Development Core Team, version 1.0.44) or GraphPad PRISM® Software (GraphPad Software, San Diego, USA, version 5.02, or ImageJ, Fiji, QuPath, TRIBVN Healthcare).

As used herein, the term “globally sclerotic glomeruli” (GSG) refers to terminally damaged glomeruli with no permeable capillary.

As used herein, the term “estimated glomerular filtration rate (eGFR)” also described as x3 refers to a measure of the renal function. This test measures the level of creatinine in the blood and uses the result in a formula to calculate a number that reflects how well the kidneys are functioning, said number is called eGFR. In the context of the invention, eGFR is calculated using the CKD-EPI formula with 4 categories, derived from the CKD classification (Chronic Kidney Disease): i) category eGFR >60 mL/min/ 1.73m ² is taken as a reference (condition with the lowest risk of the event) ii) 3 other categories: 30-59 mL/min/1.73m ² , 15-29 mL/min/1.73m ² , <15 mL/min/ 1.73m ²

As used herein, the term “proteinuria” refers to the level of proteins in the urine. It is defined as a 24 h protein excretion or a protein/creatinine ratio. Typically, the level of proteinuria is determined in the urine sample by methods known in the art (in a biology lab)

As used herein, the term “albuminemia” refers to the presence of albumin in the blood.

As used herein, the term “albuminuria” refers to the level of albumin in the urine. It is defined as a 24 h albumin excretion or an albumin/creatinine ratio. Typically, the level of albuminuria is determined in the urine sample by methods known in the art (in a biology lab)

In a second aspect, the invention relates to a method for discriminating primary Focal segmental glomerulosclerosis (FSGS) from maladaptive FSGS in a subject comprising the following steps: i) obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA) value and iii) concluding that the subject is susceptible to suffer or is suffering from primary FSGS when MIA is lower than the reference value; or concluding that the subject is not susceptible to suffer or is not suffering from primary FSGS when MIA is higher than the reference value.

In a particular embodiment, the invention relates to a method for discriminating primary Focal segmental glomerulosclerosis (FSGS) from maladaptive FSGS in a subject comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1/(1+ Exp(-5.69662497682848 + 0.00706798450932702 x MIA - 0.0345231327288216 x eGFR + 0.135805524527782 x Albuminemia)); and ii) concluding that the subject is susceptible to suffer or is suffering from primary FSGS when the combined is lower than the reference value; or concluding that the subject is not susceptible to suffer or is not suffering from primary FSGS when the combined is higher than the reference value.

In a particular embodiment, the invention relates to a method for discriminating primary Focal segmental glomerulosclerosis (FSGS) from maladaptive FSGS in a subject comprising the following steps: i) obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA) value; iii) associating MIA value with estimated glomerular filtration rate (eGFR), level of albuminuria by calculating a combined value (p) with the following formula: p = 1- 1/(1+ Exp(-5.69662497682848 + 0.00706798450932702 x MIA - 0.0345231327288216 x eGFR + 0.135805524527782 x Albuminemia)); iii) concluding that the subject is susceptible to suffer or is suffering from primary FSGS when the combined value is lower than the reference value; or concluding that the subject is not susceptible to suffer or is not suffering from primary FSGS when the combined value is higher than the reference value.

In a further embodiment, the invention relates to a method for discriminating primary Focal segmental glomerulosclerosis (FSGS) from maladaptive FSGS in a subject comprising the following steps: i) obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA) value; iii) associating MIA value with age, estimated glomerular filtration rate (eGFR), and level of albuminuria by calculating a combined value (p) with the following formula: p = 1 - 1 / (1 + Exp( (-5.69662497682848) + (0.00625296091662126 x MIA + 0.0243941810080423 x Age - 0.0360256134329782 x eGFR + 0.161931298668144 x Albuminemia + 0.000255295983138449 x Albuminuria)) iv) concluding that the subject is susceptible to suffer or is suffering from primary FSGS when the combined value with age, estimated glomerular filtration rate (eGFR), level of proteinuria and albuminuria is lower than the reference value; or concluding that the subject is not susceptible to suffer or is not suffering from primary FSGS when the combined value is higher than the reference value.

In a particular embodiment, the method of the invention further comprises proteinuria parameter.

Typically, the invention relates to a method for discriminating primary Focal segmental glomerulosclerosis (FSGS) from maladaptive FSGS in a subject comprising the following steps: i) obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA) value; iii) associating MIA value with age, estimated glomerular filtration rate (eGFR), and level of proteinuria by calculating a combined value (p) with the following formula: p = 1 - 1 / (1 + Exp( (-5.69662497682848) + (0.00625296091662126 x MIA + 0.0243941810080423 x Age - 0.0360256134329782 x eGFR + 0.161931298668144 x serum protein + 0.000255295983138449 x proteinuria)) iv) concluding that the subject is susceptible to suffer or is suffering from primary FSGS when the combined value with age, estimated glomerular filtration rate (eGFR), level of proteinuria and albuminuria is lower than the reference value; or concluding that the subject is not susceptible to suffer or is not suffering from primary FSGS when the combined value is higher than the reference value.

As used herein, the term “discriminating” refers to identify, observe a difference or distinguish two groups. Typically, the method according to the invention is suitable to identify or distinguish a subject who is susceptible to have primary or maladaptive FSGS.

As used herein, the term “subject” refers to any mammals, such as a rodent, a feline, a canine, and a primate. Particularly, in the present invention, the subject is a human. In a particular embodiment, the subject is susceptible to suffer or is suffering from primary FSGS. In a particular embodiment, the subject is susceptible to suffer or is suffering from maladaptive FSGS. As used herein, the term “Focal segmental glomerulosclerosis (FSGS)” refers to a disease in which scar tissue develops on the parts of the kidneys that filter waste from the blood (glomeruli). FSGS is a serious condition that can lead to end-stage kidney disease, for which the only treatment options are dialysis or kidney transplant. Treatment options for FSGS depend on the type you have. There aredifferent types of FSGS: i) primary FSGS: many people diagnosed with FSGS have no known cause for their condition. This is called primary (idiopathic) FSGS; ii) maladaptive FSGS, also known as secondary FSGS refers to the consequence of glomeruli hyperfiltration requiring non-specific nephroprotective care. It is caused by various factors such as -but not limited to- infection, drug toxicity, diseases such as diabetes or sickle cell disease, obesity, small birth weight, congenital or acquired nephron number reduction and any other kidney disease, iii) Genetic (also called familial) FSGS: this rare form of FSGS is caused by genetic mutations. It's suspected when several members of a family show signs of FSGS. Familial FSGS can also occur when neither parent has the disease, but each carries one copy of an abnormal gene that can be passed on to the next generation.

As used herein, the “reference value” refers to a threshold value or a cut-off value. Typically, a "threshold value" or "cut-off value" can be determined experimentally, empirically, or theoretically. A threshold value can also be arbitrarily selected based upon the existing experimental and/or clinical conditions, as would be recognized by a person of ordinary skilled in the art. For example, retrospective measurement in properly banked historical subject samples may be used in establishing the predetermined reference value. The threshold value has to be determined in order to obtain the optimal sensitivity and specificity according to the function of the test and the benefit/risk balance (clinical consequences of false positive and false negative). Typically, the optimal sensitivity and specificity (and so the threshold value) can be determined using a Receiver Operating Characteristic (ROC) curve based on experimental data. For example, after determining the expression level of the selected peptide in a group of reference, one can use algorithmic analysis for the statistic treatment of the expression levels determined in samples to be tested, and thus obtain a classification standard having significance for sample classification. The full name of ROC curve is receiver operator characteristic curve, which is also known as receiver operation characteristic curve. It is mainly used for clinical biochemical diagnostic tests. ROC curve is a comprehensive indicator that reflects the continuous variables of true positive rate (sensitivity) and false positive rate (1 -specificity). It reveals the relationship between sensitivity and specificity with the image composition method. A series of different cut-off values (thresholds or critical values, boundary values between normal and abnormal results of diagnostic test) are set as continuous variables to calculate a series of sensitivity and specificity values. Then sensitivity is used as the vertical coordinate and specificity is used as the horizontal coordinate to draw a curve. The higher the area under the curve (AUC), the higher the accuracy of diagnosis. On the ROC curve, the point closest to the far upper left of the coordinate diagram is a critical point having both high sensitivity and high specificity values. The AUC value of the ROC curve is between 1.0 and 0.5. When AUC>0.5, the diagnostic result gets better and better as AUC approaches 1. When AUC is between 0.5 and 0.7, the accuracy is low. When AUC is between 0.7 and 0.9, the accuracy is moderate. When AUC is higher than 0.9, the accuracy is high. This algorithmic method is preferably implemented by a computer executing code instructions stored on a memory. Existing software or systems in the art may be used for the drawing of the ROC curve, such as: MedCalc 9.2.0.1 medical statistical software, SPSS 9.0, ROCPOWER.SAS, DESIGNROC.FOR, MULTIREADER POWER.SAS, CREATE-ROC.SAS, GB STAT VIO.O (Dynamic Microsystems, Inc. Silver Spring, Md., USA), etc.

In the context of the invention, the AUC value is 0.96 for observational cohort. In a particular embodiment, the AUC value is 0.83 for validation cohort. In a particular embodiment, the AUC value is 0.88 for both cohorts. In a particular embodiment, the AUC value is 0.91 for both cohorts, using a combination of MIA value minus age, estimated glomerular filtration rate (eGFR), level of albuminuria and albuminemia.

In the context of the invention, the analyses were performed with R software (R Development Core Team, version 1.0.44) and GraphPad PRISM® Software (GraphPad Software, San Diego, USA, version 5.02).

The distinction between primary and maladaptive FSGS is not always obvious, resulting in several patients with maladaptive FSGS undergoing unnecessary and potential harmful immunosuppressive therapy. Therefore, inventors propose to use MIA, an easily assessable morphometric parameter at the time of renal biopsy, to predict corticosteroid sensitivity.

Accordingly, in a third aspect, the invention relates to a method for predicting whether a subject suffering from maladaptive glomerular impairment in kidneys will achieve a response to an immunosuppressive treatment comprising the following steps: i) Obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA) and iii) concluding that the subject will achieve a response to an immunosuppressive treatment when MIA is lower than the reference value; or concluding that the subject will not achieve a response to an immunosuppressive treatment when MIA is higher than the reference value.

More particularly, the method according to the invention comprises further a step of associated MIA value with glomerular filtration rate (eGFR), and/or level of proteinuria or albuminuria and albuminemia.

In a particular embodiment, the invention relates to a method for predicting whether a subject suffering from maladaptive glomerular impairment in kidneys will achieve a response to an immunosuppressive treatment comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1/(1+ Exp(-5.69662497682848 + 0.00706798450932702 x MIA - 0.0345231327288216 x eGFR + 0.135805524527782 x Albuminemia)); and ii) concluding that the subject will achieve a response to an immunosuppressive treatment when the combined value is lower than the reference value; or concluding that the subject will not achieve a response to an immunosuppressive treatment when the combined value is higher than the reference value.

In a further embodiment, the method according to the invention comprises further a step of associated MIA value with age.

In a particular embodiment, the invention relates to a method for predicting whether a subject suffering from maladaptive glomerular impairment in kidneys will achieve a response to an immunosuppressive treatment comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1 / (1 + Exp( (-5.69662497682848) + (0.00625296091662126 *MIA + 0.0243941810080423 x Age - 0.0360256134329782 x eGFR +

0.161931298668144 x Albuminemia + 0.000255295983138449 x Albuminuria)), and ii) concluding that the subject will achieve a response to an immunosuppressive treatment when the combined value is lower than the reference value; or concluding that the subject will not achieve a response to an immunosuppressive treatment when the combined value is higher than the reference value.

In a further embodiment, the method according to the invention comprises further a step of associated MIA value with proteinuria.

In a particular embodiment, the invention relates to a method for predicting whether a subject suffering from maladaptive glomerular impairment in kidneys will achieve a response to an immunosuppressive treatment comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1 / (1 + Exp( (-5.69662497682848) + (0.00625296091662126 x MIA + 0.0243941810080423 x Age - 0.0360256134329782 x eGFR +

0.161931298668144 x serum protein + 0.000255295983138449 x proteinuria)), and ii) concluding that the subject will achieve a response to an immunosuppressive treatment when the combined value is lower than the reference value; or concluding that the subject will not achieve a response to an immunosuppressive treatment when the combined value is higher than the reference value.

In a particular embodiment, the invention relates to a method for predicting whether a subject suffering from primary or maladaptive FSGS will achieve a response to an immunosuppressive treatment comprising the following steps: i) Obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA) and iii) concluding that the subject will achieve a response to an immunosuppressive treatment when MIA is lower than the reference value; or concluding that the subject will not achieve a response to an immunosuppressive treatment when MIA is higher than the reference value.

In a particular embodiment, the invention relates to a method for predicting whether a subject suffering from primary or maladaptive FSGS will achieve a response to an immunosuppressive treatment comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1/(1+ Exp(-5.69662497682848 + 0.00706798450932702 x MIA - 0.0345231327288216 x eGFR + 0.135805524527782 x Albuminemia)); and ii) concluding that the subject will achieve a response to an immunosuppressive treatment when the combined value is lower than the reference value; or concluding that the subject will achieve a response to an immunosuppressive treatment when the combined value is higher than the reference value.

In a particular embodiment, the invention relates to a method for predicting whether a subject suffering from primary or maladaptive FSGS will achieve a response to an immunosuppressive treatment comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1 / (1 + Exp ((-5.69662497682848) + (0.00625296091662126 x MIA + 0.0243941810080423 x Age - 0.0360256134329782 x eGFR +

0.161931298668144 x Albuminemia + 0.000255295983138449 x Albuminuria)) ii) concluding that the subject will achieve a response to an immunosuppressive treatment when the combined value is lower than the reference value; or concluding that the subject will achieve a response to an immunosuppressive treatment when the combined value is higher than the reference value.

In a particular embodiment, the invention relates to a method for predicting whether a subject suffering from primary or maladaptive FSGS will achieve a response to an immunosuppressive treatment comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1 / (1 + Exp( (-5.69662497682848) + (0.00625296091662126 x MIA + 0.0243941810080423 x Age - 0.0360256134329782 x eGFR +

0.161931298668144 x serum protein + 0.000255295983138449 x proteinuria)), and ii) concluding that the subject will achieve a response to an immunosuppressive treatment when the combined value is lower than the reference value; or concluding that the subject will achieve a response to an immunosuppressive treatment when the combined value is higher than the reference value.

In a particular embodiment, the invention relates to a method for predicting the probability of response to an immunosuppressive treatment in a subject suffering from maladaptive glomerular impairment in kidneys comprising the following steps: i) Obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA) and iii) concluding that the probability that the subject will respond to an immunosuppressive treatment when MIA is lower than the reference value; or concluding that the subject will likely not respond to an immunosuppressive treatment when MIA is higher than the reference value.

In a particular embodiment, the invention relates to a method for predicting the probability of response to an immunosuppressive treatment in a subject suffering from maladaptive glomerular impairment in kidneys comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1/(1+ Exp(-5.69662497682848 + 0.00706798450932702 x MIA - 0.0345231327288216 x eGFR + 0.135805524527782 x Albuminemia)); and ii) concluding that the probability that the subject will respond to an immunosuppressive treatment when the combined value is lower than the reference value; or concluding that the subject will likely not respond to an immunosuppressive treatment when the combined value is higher than the reference value.

In a particular embodiment, the invention relates to a method for predicting the probability of response to an immunosuppressive treatment in a subject suffering from maladaptive glomerular impairment in kidneys comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1 / (1 + Exp( (-5.69662497682848) + (0.00625296091662126 x MIA + 0.0243941810080423 x Age - 0.0360256134329782 x eGFR +

0.161931298668144 x Albuminemia + 0.000255295983138449 x Albuminuria)) ii) concluding that the probability that the subject will respond to an immunosuppressive treatment when the combined value is lower than the reference value; or concluding that the subject will likely not respond to an immunosuppressive treatment when the combined value is higher than the reference value.

In a particular embodiment, the invention relates to a method for predicting the probability of response to an immunosuppressive treatment in a subject suffering from maladaptive glomerular impairment in kidneys comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1 / (1 + Exp( (-5.69662497682848) + (0.00625296091662126 x MIA + 0.0243941810080423 x Age - 0.0360256134329782 x eGFR + 0.161931298668144 x serum protein + 0.000255295983138449 x proteinuria)), and ii) concluding that the probability that the subject will respond to an immunosuppressive treatment when the combined value is lower than the reference value; or concluding that the subject will likely not respond to an immunosuppressive treatment when the combined value is higher than the reference value.

In a particular embodiment, the invention relates to a method for predicting the probability of response to an immunosuppressive treatment in a subject suffering from primary or maladaptive FSGS comprising the following steps: i) Obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA) and iii) concluding that the probability that the subject will respond to an immunosuppressive treatment when MIA is lower than the reference value; or concluding that the subject will likely not respond to an immunosuppressive treatment when MIA is higher than the reference value.

In a particular embodiment, the invention relates to a method for predicting the probability of response to an immunosuppressive treatment in a subject suffering from primary or maladaptive FSGS comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1/(1+ Exp(-5.69662497682848 + 0.00706798450932702 x MIA - 0.0345231327288216 x eGFR + 0.135805524527782 x Albuminemia)); and ii) concluding that the probability that the subject will respond to an immunosuppressive treatment when the combined value is lower than the reference value; or concluding that the subject will likely not respond to an immunosuppressive treatment when the combined value with is higher than the reference value. In a particular embodiment, the invention relates to a method for predicting the probability of response to an immunosuppressive treatment in a subject suffering from primary or maladaptive FSGS comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1 / (1 + Exp( (-5.69662497682848) + (0.00625296091662126 x MIA + 0.0243941810080423 x Age - 0.0360256134329782 x eGFR +

0.161931298668144 x Albuminemia + 0.000255295983138449 x Albuminuria)) and ii) concluding that the probability that the subject will respond to an immunosuppressive treatment when the combined value is lower than the reference value; or concluding that the subject will likely not respond to an immunosuppressive treatment when the combined value with is higher than the reference value.

In a particular embodiment, the invention relates to a method for predicting the probability of response to an immunosuppressive treatment in a subject suffering from primary or maladaptive FSGS comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1 / (1 + Exp( (-5.69662497682848) + (0.00625296091662126 x MIA + 0.0243941810080423 x Age - 0.0360256134329782 x eGFR +

0.161931298668144 x serum protein + 0.000255295983138449 x proteinuria)), ii) concluding that the probability that the subject will respond to an immunosuppressive treatment when the combined value is lower than the reference value; or concluding that the subject will likely not respond to an immunosuppressive treatment when the combined value with is higher than the reference value.

As used herein, the term “predicting” means that the subject to be analyzed by the method of the invention is allocated either into the group of subjects who will relapse, or into a group of subjects who will not relapse after a treatment.

As used herein, the term "risk" in the context of the present invention, relates to the probability that an event will occur over a specific time period, as in the conversion to relapse, and can mean a subject's "absolute" risk or "relative" risk. Absolute risk can be measured with reference to either actual observation post-measurement for the relevant time cohort, or with reference to index values developed from statistically valid historical cohorts that have been followed for the relevant time period. Relative risk refers to the ratio of absolute risks of a subject compared either to the absolute risks of low risk cohorts or an average population risk, which can vary by how clinical risk factors are assessed. Odds ratios, the proportion of positive events to negative events for a given test result, are also commonly used (odds are according to the formula p/(l-p) where p is the probability of event and (1- p) is the probability of no event) to no- conversion. "Risk evaluation," or "evaluation of risk" in the context of the present invention encompasses making a prediction of the probability, odds, or likelihood that an event or disease state may occur, the rate of occurrence of the event or conversion from one disease state to another, i.e., from a normal condition to relapse or to one at risk of developing relapse. Risk evaluation can also comprise prediction of future clinical parameters, traditional laboratory risk factor values, or other indices of relapse, either in absolute or relative terms in reference to a previously measured population. The methods of the present invention may be used to make continuous or categorical measurements of the risk of conversion to relapse, thus diagnosing and defining the risk spectrum of a category of subjects defined as being at risk of having relapse. In the categorical scenario, the invention can be used to discriminate between normal and other subject cohorts at higher risk of having relapse. In some embodiments, the present invention may be used so as to discriminate those at risk of having relapse from normal, or those having relapse disease from normal.

As used herein, the terms "will achieve a response" or "respond" refer to the response to a treatment of the subject suffering from primary or maladaptive FSGS. Typically such treatment induces, ameliorates or otherwise causes an improvement in the pathological symptoms, disease progression or physiological conditions associated with or resistance to succumbing to a maladaptive FSGS. In particular, in the context of the invention, the term "respond" refers to the ability of corticosteroid treatment to an improvement of the pathological symptoms, thus, the subject presents a clinical improvement compared to the subject who does not receive the treatment. The said subject is considered as a "responder" to the treatment. The term "not respond" refers to a subject who does not present any clinical improvement to the treatment with a corticosteroid treatment. This subject is considered as a "non-responder" to the treatment. Accordingly, the subject as considered "non-responder" has a particular monitoring in the therapeutic regimen. In a particular embodiment, the response to a treatment is determined by Response evaluation criteria in solid tumors (RECIST) criteria. This criteria refers to a set of published rules that define when the disease in the subjects improve ("respond"), stay the same ("stabilize"), or worsen ("progress") during treatment. In the context of the invention, when the subject is identified as responder, it means that said subject improves overall and progression- free survival (OS/PFS). More particularly, the MIA value is a tool to determine the overall survival (OS) of the subject at the time of renal biopsy to an immunosuppressive treatment.

As used herein, the term "relapse" refers to the return of signs and symptoms of a disease after a subject has enjoyed a remission after a treatment. Thus, if initially the target disease is alleviated or healed, or progression of the disease was halted or slowed down, and subsequently the disease or one or more characteristics of the disease return, the subject is referred to as being "relapsed."

In a particular embodiment, the biological sample as described above is a renal biopsy. Typically, biopsy sections were scanned (Pathscan Combi, Excilone), allowing to obtain high-definition whole slide images (WSI).

In a particular embodiment, the mean interglomerular area (MIA) as described above is calculated with the following formula = (minimal cortical area/total number of glomeruli except GSG - 1)).

In the context of the invention, the total number of glomeruli (except GSG) = 14. In the context of the invention, the minimal cortical area is 1052838.56pm ² .

In a particular embodiment, the MIA value as determined above is incorporated in a soft ware, a machine, R software (R Development Core Team, version 1.0.44) or GraphPad PRISM® Software (GraphPad Software, San Diego, USA, version 5.02, ImageJ, Fiji, QuPath, TRIBVN Healthcare).

In a particular embodiment, the method according to the invention is performed at the time of renal biopsy.

As used herein, the term “immunosuppressive treatment” refers to a treatment that suppresses some immune function. Immunosuppressive treatment means that the subject is administered with one or more immunosuppressive drugs. Immunosuppressive drugs or other drugs that are currently known in the art or that will be identified in the future. In a particular embodiment, the immunosuppressive drugs include but not limited to azathioprine, tacrolimus, rapamycin derivative (sirolimus and everolimus), mycophenolic acid (mycophenolate mofetil and enteric-coated mycophenolate sodium), corticosteroids, and cyclosporin, anti CD20 antibodies (rituximab, obinutuzumab), cyclophosphamide, chlorambucil. These drugs may be used in monotherapy or in combination therapies.

In a particular embodiment, the immunosuppressive treatment is corticosteroid treatment.

As used, the term “corticosteroid treatment” has its general meaning in the art and refers to a treatment with a class of active ingredients having a hydrogenated cyclopentoperhydrophenanthrene ring system endowed with an anti-inflammatory activity. Corticosteroid drugs typically include cortisone, cortisol, hydrocortisone (1 ip,17-dihydroxy, 21-(phosphonooxy)-pregn-4-ene, 3,20-dione disodium), dihydroxycortisone, dexamethasone (21 -(acetyloxy)-9-fluoro- 1 P, 17 -dihydroxy- 16a-m-ethylpregna- 1 ,4-diene-3 ,20-dione), and highly derivatized steroid drugs such as beconase (beclomethasone dipropionate, which is 9- chloro-11-P, 17,21, trihydroxy- 16P-methylpregna- 1,4 diene-3, 20-dione 17,21 -dipropionate). Other examples of corticosteroids include flunisolide, prednisone, prednisolone, methylprednisolone, triamcinolone, deflazacort and betamethasone, corticosteroids, for example, cortisone, hydrocortisone, methylprednisolone, prednisone, prednisolone, betamethesone, beclomethasone dipropionate, budesonide, dexamethasone sodium phosphate, flunisolide, fluticasone propionate, triamcinolone acetonide, betamethasone, fluocinolone, fluocinonide, betamethasone dipropionate, betamethasone valerate, desonide, desoximetasone, fluocinolone, triamcinolone, triamcinolone acetonide, clobetasol propionate, and dexamethasone, and endogenous glucocorticoid stimulating agents (adrenocorticotropic hormone).

The methods as described above are implemented by a computer executing code instructions stored on a memory.

Accordingly, in a fourth aspect, the invention relates to a computer-implemented method for performing methods as described above.

Typically, the invention relates to a computer-implemented method for estimating the probability of maladaptive glomerular impairment, discriminating primary Focal segmental glomerulosclerosis (FSGS) from maladaptive FSGS and/or for predicting the probability of response to an immunosuppressive treatment in a subject. In a particular embodiment, the invention relates to a computer-implemented method for estimating the probability of maladaptive glomerular impairment in a subject comprising the following steps: i) obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA); iii) incorporating the MIA value determined at step ii) in a software; and iv) concluding that the subject has a high probability of maladaptive glomerular impairment when MIA is lower than the reference value; or concluding that the subject is not likely to have maladaptive glomerular impairment when MIA is higher than the reference value.

In a further embodiment, the invention relates to a computer-implemented method for estimating the probability of maladaptive glomerular impairment in a subject comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1/(1+ Exp(-5.69662497682848 + 0.00706798450932702 x MIA -

0.0345231327288216 x eGFR + 0.135805524527782 x Albuminemia)); ii) incorporating the combined value in a software; and iii) concluding that the subject has a high probability of maladaptive glomerular impairment when the combined value is lower than the reference value; or concluding that the subject not likely to have maladaptive glomerular impairment when the combined value is higher than the reference value.

In a further embodiment, the invention relates to a computer-implemented method for estimating the probability of maladaptive glomerular impairment in a subject comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1 / (1 + Exp( (-5.69662497682848) + (0.00625296091662126 *MIA + 0.0243941810080423 x Age - 0.0360256134329782 x eGFR +

0.161931298668144 x Albuminemia + 0.000255295983138449 x Albuminuria)) ii) incorporating the combined value in a software; and iii) concluding that the subject has a high probability of maladaptive glomerular impairment when the combined value is lower than the reference value; or concluding that the subject not likely to have maladaptive glomerular impairment when the combined value is higher than the reference value.

In a further embodiment, the invention relates to a computer-implemented method for estimating the probability of maladaptive glomerular impairment in a subject comprising the following steps: i) calculating a combined value (p) with the following formula: p = 1 - 1 / (1 + Exp( (-5.69662497682848) + (0.00625296091662126 *MIA + 0.0243941810080423 x Age - 0.0360256134329782 x eGFR + 0.161931298668144 x serum protein + 0.000255295983138449 x proteinuria)), ii) incorporating the combined value in a software; and iii) concluding that the subject has a high probability of maladaptive glomerular impairment when the combined value is lower than the reference value; or concluding that the subject not likely to have maladaptive glomerular impairment when the combined value is higher than the reference value.

In a particular embodiment, the invention relates to a computer-implemented method for discriminating primary Focal segmental glomerulosclerosis (FSGS) from maladaptive FSGS in a subject comprising the following steps: i) obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA), iii) incorporating the MIA value determined at step ii) in a software; and iv) concluding that the subject is susceptible to suffer or is suffering from primary FSGS when the MIA value is lower than the reference value; or concluding that the subject is not susceptible to suffer or is not suffering from primary FSGS when the MIA value is higher than the reference value.

In a further embodiment, the invention relates to a computer-implemented method for discriminating primary Focal segmental glomerulosclerosis (FSGS) from maladaptive FSGS in a subject comprising the following steps: i) calculating a combined value (p) with the following formula as described above with age, estimated glomerular filtration rate (eGFR), level of proteinuria or albuminuria; ii) incorporating the combined with age, estimated glomerular filtration rate (eGFR), and level of proteinuria in a software; and iii) concluding that the subject is susceptible to suffer or is suffering from primary FSGS when the combined value is lower than the reference value; or concluding that the subject is not susceptible to suffer or is not suffering from primary FSGS when the combined value is higher than the reference value.

In a further embodiment, the method of the present invention is implemented by a computer executing code instructions stored on a memory.

More particularly, the invention relates to a computer-implemented method for predicting whether a subject suffering from primary or maladaptive FSGS will achieve a response to an immunosuppressive treatment comprising the following steps: i) Obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA) iii) incorporating MIA value determined at step ii) in a software; and iv) concluding that the subject will achieve a response to an immunosuppressive treatment when MIA is lower than the reference value; or concluding that the subject will not achieve a response to an immunosuppressive treatment when MIA is higher than the reference value.

In a particular embodiment, the invention relates to a computer-implemented method for predicting whether a subject suffering from primary or maladaptive FSGS will achieve a response to an immunosuppressive treatment comprising the following steps: i) calculating a combined value (p) with the following formula as described above with age, estimated glomerular filtration rate (eGFR), level of proteinuria or albuminuria ii) incorporating the combined value iii) in a software; and iii) concluding that the subject will achieve a response to an immunosuppressive treatment when combined value is lower than the reference value; or concluding that the subject will not achieve a response to an immunosuppressive treatment when combined value is higher than the reference value. In a particular embodiment, the invention relates to a computer-implemented method for predicting the probability of response to an immunosuppressive treatment in a subject suffering from primary or maladaptive FSGS comprising the following steps: i) obtaining a biological sample from said subject; ii) determining mean interglomerular area (MIA); iii) incorporating MIA value determined at step ii) in a software; and iv) concluding that the probability that the subject will respond to an immunosuppressive treatment when MIA is lower than the reference value; or concluding that the subject will likely not respond to an immunosuppressive treatment when MIA is higher than the reference value.

In a particular embodiment, the invention relates to a computer-implemented method for predicting the probability of response to an immunosuppressive treatment in a subject suffering from primary or maladaptive FSGS comprising the following steps: i) calculating a combined value (p) with the following formula as described above with age, estimated glomerular filtration rate (eGFR), level of proteinuria or albuminuria ii) incorporating the combined value in a software; and iii) concluding that the probability that the subject will respond to an immunosuppressive treatment when the combined value is lower than the reference value; or concluding that the subject will likely not respond to an immunosuppressive treatment when combined value is higher than the reference value.

In the context of the invention, the analyses were performed with R software (R Development Core Team, version 1.0.44) and GraphPad PRISM® Software (GraphPad Software, San Diego, USA, version 5.02).

In a fifth aspect, the invention relates to a kit for performing the method according to the invention, wherein said kit comprises (i) means for determining MIA with a biological sample obtained from said subject, ii) means for calculating a combined value with MIA and age, estimated glomerular filtration rate (eGFR), level of proteinuria or albuminuria and (iii) instructions use. In a further embodiment, the kit according to the invention further comprises instructions to calculate MIA value and its association with age, estimated glomerular filtration rate (eGFR), level of proteinuria and albuminuria.

Typically, the kit comprises means for measuring MIA estimated glomerular filtration rate (eGFR), level of proteinuria and/or albuminuria. Typically, the kits described above will also comprise one or more other containers, containing for example, wash reagents, and/or other reagents capable of performing the renal biopsy analysis. The kit also contains instructions suitable for incorporating MIA value and its association with age, estimated glomerular filtration rate (eGFR), level of proteinuria and albuminuria in a software.

Typically compartmentalised kit includes any kit in which reagents are contained in separate containers, and may include small glass containers, plastic containers or strips of plastic or paper. Such containers may allow the efficient transfer of reagents from one compartment to another compartment whilst avoiding cross-contamination of the samples and reagents, and the addition of agents or solutions of each container from one compartment to another in a quantitative fashion. Such kits may also include a container which will accept the renal biopsy sample, a container which contains the antibody(s) used in the assay, containers which contain wash reagents (such as phosphate buffered saline, Tris-buffers, and like), and containers which contain the detection reagent.

The invention will be further illustrated by the following figures and examples. However, these examples and figures should not be interpreted in any way as limiting the scope of the present invention.

FIGURES:

Figure 1. Mean interglomerular area (MIA) in observation and validation cohort and ROC curves, MIA threshold with sensibility and specificity. AUROC: area under receiver operating characteristic; CI: confidence interval; FSGS: focal segmental glomerulosclerosis; MIA: mean interglomerular area; ROC: receiver operating characteristic. Statistical analysis was carried out using GraphPad Prism 9.0.0 (GraphPad Software, San Diego, California, USA). Quantitative variables were compared using Mann- Whitney test. A value of double-sided p <0.05 was considered statistically significant.

EXAMPLE:

Material & Methods The Tenon hospital Renal Human Pathology Database was searched for adult patients with an FSGS diagnosis on native renal biopsy from January 2002 through January 2020. Primary FSGS was defined as a steroid-sensitive NS (remission obtained within 4 weeks after starting steroid). Maladaptive FSGS was defined by gradually increasing proteinuria, with at least one cause of nephron number reduction (hypertension, obesity, medications, sickle cell anemia). An observational cohort with 40 patients (20 patients with primary FSGS and maladaptive FSGS, respectively) was carried out to identify renal morphometric parameters of interest. In addition, a validation cohort with 40 patients (20 patients with primary FSGS and maladaptive FSGS, respectively) was established to confirm the results matching age, estimated glomerular filtration rate (eGFR), and level of proteinuria. We excluded patients with missing clinical data at the time of renal biopsy (serum albumin, proteinuria) or if renal biopsies were unavailable. The clinical data and the pathology findings (optical, immunofluorescence, and EM when available) were retrieved from the electronic medical records.

All biopsy sections were scanned (Pathscan Combi, Excilone), allowing to obtain high-definition WSI. The morphometric evaluation was performed with QuPath5, including glomeruli count, glomerular area, total biopsy area, cortical area, interglomerular area (Table 2). The renal biopsy of each patient was analyzed, blinded to the clinical data, by two nephrologists (A.O. and C.V.), separately.

Results

Twenty-eight patients in the observational cohort and 30 patients in the validation cohort were included. There were no statistically significant differences concerning age, gender, body mass index, baseline serum creatinine, eGFR, or hematuria (Table 1) between primary and maladaptive FSGS. However, patients with primary FSGS had lower serum albumin than maladaptive FSGSs in the observational cohort but not in the matched validation cohort. In addition, no significant difference was observed regarding the mean number of globally sclerotic glomeruli (GSG) per biopsy section.

In the observational cohort, we found that the mean interglomerular area (MIA, a marker of glomerular scarcity) was significantly lower in patients with primary FSGS compared to maladaptive FSGS 90 [76-100] vs. 198 [165-299] um2, p< 0.0001 (Figure 1). This finding was confirmed in the validation cohort 133 [109-159] vs. 204 [170-339] um2 (p=0.0017).

MIA predictive performance measured by area under ROC (AUROC) curve was 0.96 (95%CI 0.89 - 1) and 0.83 (95%CI 0.66 - 1) for observation and validation cohort, respectively (Figure 1). We determined an MIA cut-off value of l 60, l 34um2 to discriminate a primary FSGS from a maladaptive FSGS with a sensibility of 84% (95%CI 67 - 93) and a specificity of 85% (95%CI 68 - 94).

Table 1. Baseline demographics, clinical and biological data from the observation cohort and validation cohort

BMI: body mass index; eGFR: estimated glomerular filtration rate; FSGS: focal segmental glomerulosclerosis; GSG: globally sclerotic glomeruli; HBV: hepatitis B virus; HCV: hepatitis C virus; HIV: human immunodeficiency virus; MIA: mean interglomerular area;

Hematuria was defined as > 104 red blood cells/ml and leukocyturia as > 104 leukocytes/ml. Statistical analysis was carried out using GraphPad Prism 9.0.0 (GraphPad Software, San Diego, California, USA). Continuous variables are reported as the mean ± standard deviation or median [interquartile range] if the variable did not correspond to a normal distribution and categorical variables are reported as numbers (percentages). Quantitative variables were compared using a t-test (normal distribution) or Mann- Whitney test and qualitative variables were compared using Fisher's exact test. A value of double-sided p <0.05 was considered statistically significant.

Table 2 The morphometric evaluation was performed with QuPath5, including glomeruli count, glomerular area, total biopsy area, cortical area, interglomerular area.

REFERENCES:

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