METHOD TO PREDICT SYSTEMIC PROGRESSION IN PROSTATE CANCER PATIENTS

Related Patent Applications

This patent application claims priority benefit of U.S. Provisional Patent Application serial number 60/743,018 filed December 6, 2005 and entitled METHOD TO PREDICT SYSTEMIC PROGRESSION IN PROSTATE CANCER PATIENTS (attorney reference number 1833.020PRV) by inventors Michael Kattan and Kevin M. Slawin.

Background

Prostate cancer is the most commonly diagnosed cancer and the second leading cause of cancer death for men in the United States. In 1999, an estimated 179.300 men were diagnosed with prostate cancer and 37,000 died of this disease. Despite the identification of several new potential biomarkers for prostate cancer (e.g., p53, p21, p27, and E-cadherin), prostate specific antigen (PSA) and the histologic Gleason score have remained the most commonly used predictors of prostate cancer biology. In fact, the widespread use of PSA-based screening has dramatically increased the number of men diagnosed and treated for clinically localized prostate cancer over the past decade. Concomitantly, the incidence of clinical metastatic disease at the time of initial diagnosis has dropped considerably, in concert with an overall decrease in prostate cancer mortality (Merill et al., 2000).

Radical prostatectomy (RP) for clinically localized prostate cancer provides excellent cancer control and was recently shown to improve disease- specific survival when compared with surveillance (Holmberg et al., 2002; Hull et al., 2002; Han et al., 2001). Nevertheless, biochemical progression (BCR), manifested by increasing prostate-specific antigen (PSA), occurs in 15% to 40% of patients within 10 years after RP (Dillioglugil et al., 1997; Han et al., 2003; Roberts et al., 2001). Distant clinical progression after RP almost never develops without increasing PSA levels (Pound et al., 1999). The optimal means of evaluating patients with increasing serum PSA after RP has not been

determined. However, it is clear that imaging should not be used for patients without elevated PSA levels after RP (Ferguson et al., 1995; Nudell et al., 2000).

For patients with biochemical failure after RP, the ability to differentiate between local and distant recurrence is critical for choosing appropriate treatment. Predictors for metastases may include preoperative variables, radical prostatectomy pathology variables, and characteristics of PSA failure. For instance, Pound et al. (1999) disclose that risk factors for systemic progression following RP include pathology Gleason score, the interval from surgery to biochemical failure, and PSADT, while D'Amico et al. (2002; 2004) disclose that PSA doubling time is a surrogate end point for prostate cancer specific mortality after RP or radiation therapy.

Nevertheless, current risk factors for systemic progression following RP use limited variables, are based on categorical variables, lack internal and external validation, and lack the ability to predict systemic progression at a prolonged interval from biochemical failure. Moreover, none of the current predictive methods evaluate the impact of a second treatment.

Given that there are 220,000 new cases of prostate cancer, that 65 to 75% of those are treated with local therapy, and of those treated cases there are 50,000 to 60,000 new cases of patients with biochemical failure annually, there is a need for methods to predict outcomes in those patients.

Summary of the Invention

The invention provides methods, apparatus and nomograms to predict the probability of freedom from metastases progression in patients with rising PSA after RP. Based on data available shortly after biochemical failure, the significant predictors for metastases progression among patients with rising PSA after RP were determined and a nomogram was prepared that predicts the probability of metastases progression among patients biochemical recurrence (BCR) after RP.

Thus, the invention provides a nomogram for the graphic representation of a quantitative probability of freedom from metastases progression in a prostate cancer patient with BCR after RP. The nomogram includes a plurality of scales and a solid support, the plurality of scales being disposed on the

support and comprising a scale for one or more, e.g., two, three, four, five or more patient factors, a points scale, a total points scale and a predictor scale. The factors may include one or more of prostate specific antigen (PSA) doubling time (PSADT) ₅ PSA level at BCR (PSABCR), Gleason score (GS), the presence or absence of seminal vesicle invasion (SVI), preRP PSA level, and/or the absence or extent of extracapular extension (ECE). The scales for PSADT, PSABCR, GS, SVI, preRP PSA level, and/or ECE, each has values on the scales, wherein the scales for PSADT, PSABCR, GS, SVI, preRP PSA level, and/or ECE, are disposed on the solid support with respect to the points scale so that each of the values for PSADT, PSABCR, GS, SVI, preRP PSA level, and/or ECE, can be correlated with values on the points scale. The total points scale has values on the total points scale and is disposed on the solid support with respect to the predictor scale so that the values on the total points scale may be correlated with values on the predictor scale, such that the values on the points scale correlating with the patient's PSADT, PSABCR, GS, presence or absence of SVI, preRP PSA level, and/or the absence or extent of ECE, can be added together to yield a total points value. The total points value can then be correlated with the predictor scale to predict the quantitative probability of freedom from metastases progression in a prostate cancer patient with BCR after RP. A method of using the nomogram is also provided.

Further provided is a nomogram for the graphic representation of a quantitative probability of metastases progression in a prostate cancer patient with BCR after RP. The nomogram includes a plurality of scales, a solid support, a points scale, a total points scale and a predictor scale. The plurality of scales are disposed on the support and include a scale for at least one, two, three, four, five or more patient factors, factors including PSADT, PSABCR, GS, the presence or absence of SVI, preRP PSA level, and/or the absence or extent of ECE. The scales for PSADT, PSABCR, GS, SVI, preRP PSA level, and/or ECE each has values on the scales, and the scales for PSADT, PSABCR, GS, SVI, preRP PSA level, and/or ECE, are disposed on the solid support with respect to the points scale so that each of the values for PSADT, PSABCR, GS, SVI, preRP PSA level, and/or ECE, can be correlated with values on the points scale. The total points scale has values on the total points scale and is disposed on the

solid support with respect to the predictor scale so that the values on the total points scale may be correlated with values on the predictor scale. The values on the points scale correlating with the patient's PSADT, PSABCR, GS, presence or absence of SVI, preRP PSA level, and/or the absence or extent of ECE ₃ can be added together to yield a total points value, and the total points value can be correlated with the predictor scale to predict the quantitative probability of metastases progression in a prostate cancer patient with BCR after RP. A method of using the nomogram is also provided.

Also provided is a method to predict the probability of freedom from metastases progression in a prostate cancer patient with BCR after RP. The method includes providing a value for one, two or more patient factors, including PSADT, PSABR, GS, presence or absence of SVI, preRP PSA level, and/or absence or extent of ECE. The values for the one or more factors are correlated with the probability of freedom from metastases progression in the patient.

An apparatus is also provided. The apparatus includes: a data input means, for input of information of one, two or more prostate cancer patient factors, factors including PSADT ₅ PSABCR, GS, preRP PSA level, presence or absence of SVI, and/or absence or extent of ECE; a processor, executing a software for analysis of the information; wherein the software analyzes the information and provides the probability of freedom from metastases progression in the patient.

In another embodiment, a method to predict the probability of freedom from metastases progression in a prostrate cancer patient with BCR after RP is provided. The method includes inputting information from a prostate cancer patient with BCR after RP to a data input means, where the information includes at least one, two, three, four, five or more patient factors, including at least one of PSADT, PSABCR, GS, preRP PSA level, the presence or absence of SVI, and/or the absence or extent of ECE; executing a software for analysis of the information; and analyzing the information so as to provide the probability of freedom from metastases progression in the patient.

The invention also provides an apparatus for predicting the probability of freedom from metastases progression in a prostate cancer patient with BCR after

RP. The apparatus includes a correlation of a set of factors for each of a plurality of persons with prostate cancer having BCR after RP, with the probability of freedom from metastases progression for each person of the plurality of persons. The set of factors comprises one or more factors including PSADT, PSABCR, GS, the presence or absence of SVI, preRP PSA level, and/or the absence or extent of ECE. The apparatus includes a means for comparing an identical set of factors determined from a patient having BCR after RP to the correlation to predict the quantitative probability of freedom from metastases progression in the patient.

Brief Description of the Figures

Figures IA-B. Disease states in prostate cancer. Figure 2. Nomogram for systemic progression. Figure 3. Calibration of the nomogram.

Detailed Description of the Invention

The present invention provides methods, apparatus and nomograms to predict the probability of freedom from metastases progression in a prostate cancer patient with BCR after RP, to aid patients and physicians. In one embodiment, the invention provides methods, apparatus and nomograms to predict the probability of metastases progression in a prostate cancer patient with BCR after RP. Because the use of a single variable in predictive models, particularly for a disease with heterogenicity such as prostate cancer, is likely suboptimal, models that include multiple variables (e.g., nomograms) were developed. These models have been shown to be more accurate, measured by concordance index, than risk group assignment. The pre- and post-operative variables were evaluated in a large cohort of patients with elevated serum PSA after RP for association with metastases progression. Those predictors were employed to construct a nomogram for predicting freedom from metastases progression or predicting metastases progression within 8 years of BCR.

One embodiment of the invention is directed to a method to predict the probability of freedom from metastases progression in a prostate cancer patient with BCR after RP, specifically in a patient not undergoing salvage RP,

chemotherapy, or nonhormoπal therapy (NHT), while in other embodiments of the invention the patient may have undergone therapy for prostate cancer, e.g., radiation therapy. The methods include detecting or determining a plurality of factors including at least two of PSADT, PSABCR, GS, the presence or absence of SVI, preRP PSA level, and/or the absence or extent of ECE, or any combination of these factors, and correlating the amount, level or score of the plurality of factors with the probability of freedom from metastases progression or probability of metastases progression in a prostate cancer patient with BCR after RP. In another embodiment, the factors include at least PSADT and one or more of the following factors: PSABCR, SVI, and GS. In one embodiment, factors are employed which include GS, PSADT, and PSABCR. In another embodiment, the factors include ECE, SVI, GS, PSADT, and PSABCR. In yet another embodiment, the factors include ECE, PSADT, PSABCR, SVI and GS.

In one embodiment, the correlating may be accomplished by computer. In one embodiment, the correlating includes accessing a memory storing the selected set of factors. In another embodiment, the correlating includes generating a functional representation and displaying the functional representation on a display. In one embodiment, the displaying includes transmitting the functional representation from a source. In one embodiment, the correlating is executed by a processor or a virtual computer program or interactive web site. In another embodiment, the method further includes transmitting the quantitative probability of freedom from metastases progression, or probability of metastases progression, in a prostate cancer patient with BCR after RP. In yet another embodiment, the method further includes inputting the identical set of factors for the patient within an input device. In another embodiment, the method further includes storing any of the set of factors to a memory or to a database.

Another embodiment of the invention is directed to an apparatus for predicting the probability of freedom from metastases progression, or probability of metastases progression, in a prostate cancer patient with BCR after RP. The apparatus includes a data input means, for input of test information including one or more of the following factors: PSADT, PSABCR, GS, the presence or absence of SVI, preRP PSA level, and/or the absence or extent of ECE, a

processor, executing a software for analysis of the amount, level or score of one or more of the factors and provides the probability of freedom from metastases progression or metastases progress in a prostate cancer patient with BCR after RP.

Another embodiment of the invention is directed to a nomogram. The nomogram may be generated with a Cox proportional hazards regression model (Cox, 1972). Alternatively, the nomogram may be generated with a neural network model (Rumelhart et al., 1986). In another embodiment, the nomogram is generated with a recursive partitioning model (Breiman et al., 1984). In yet another embodiment, the nomogram is generated with support vector machine technology (Cristianni et al., 2000). Other models known to those skilled in the art may alternatively be used. In one embodiment, the invention includes the use of software that implements Cox regression models or support vector machines to predict metastases progression, or freedom from metastases progression, in a patient with BCR after RP.

The nomogram may be a graphic representation of a probability of freedom from metastases progression or metastases progress. The nomogram includes a set of indicia on a solid support, the indicia comprising one or more factor lines including a PSADT line, a SVI line, a preRP PSA level line, a GS line, a PSABCR level, and/or a ECE line, a points line, a total points line and a predictor line, wherein the PSADT line, SVI line, preRP PSA level line, GS line, PSABCR level line, and/or ECE line each have values on a scale which can be correlated with values on a scale on the points line. The total points line has values on a scale which may be correlated with values on a scale on the predictor line, such that the value of each of the points correlating with the patient's PSADT, SVI, preRP PSA level, GS, PSABCR level, and/or ECE can be added together to yield a total points value. The total points value can be correlated with the predictor line to predict the probability of freedom from metastases progression metastases progress. The solid support may assume any appropriate form such as, for example, a laminated card. Any other suitable representation, picture, depiction or exemplification may be used.

The nomogram may assume any form, such as a computer program, e.g., in a hand-held device, world-wide- web page, e.g., written in FLASH, or a card,

such as a laminated card. Any other suitable representation, picture, depiction or exemplification may be used. The nomogram may comprise a graphic representation and/or may be stored in a database or memory, e.g., a random access memory, read-only memory, disk, virtual memory or processor.

The invention also provides an apparatus including a nomogram. The apparatus including a nomogram may further comprise a storage mechanism, wherein the storage mechanism stores the nomogram; an input device that inputs the set of factors determined from a patient into the apparatus; and a display mechanism, wherein the display mechanism displays the quantitative probability of a positive bone scan in a prostate cancer patient with BCR after RP. The storage mechanism may be random access memory, read-only memory, a disk, virtual memory, a database, and a processor. The input device may be a keypad, a keyboard, stored data, a touch screen, a voice activated system, a downloadable program, downloadable data, a digital interface, a hand-held device, or an infra-red signal device. The display mechanism may be a computer monitor, a cathode ray tub (CRT), a digital screen, a light-emitting diode (LED), a liquid crystal display (LCD), an X-ray, a compressed digitized image, a video image, or a hand-held device. The apparatus may further comprise a display that displays the quantitative probability of freedom from metastases or probability of metastases progression in a prostate cancer patient with BCR after RP, e.g., the display is separated from the processor such that the display receives the quantitative probability of freedom from metastases progression, or probability of metastases progression, in a prostate cancer patient with BCR after RP. The apparatus may further comprise a database, wherein the database stores the correlation of factors and is accessible by the processor. The apparatus may further comprise an input device that inputs the set of factors determined from the patient with BCR after RP into the apparatus. The input device stores the set of factors in a storage mechanism that is accessible by the processor. The apparatus may further comprise a transmission medium for transmitting the selected set of factors. The transmission medium is coupled to the processor and the correlation of factors. The apparatus may further comprise a transmission medium for transmitting the set of factors determined from the patient with BCR after RP, preferably the transmission medium is coupled to the processor and the

correlation of factors. The processor may be a multi-purpose or a dedicated processor. The processor includes an object oriented program having libraries, said libraries storing said correlation of factors.

In addition to assisting the patient and physician in selecting an appropriate course of therapy, the nomograms of the present invention are also useful in clinical trials to identify patients appropriate for a trial, to quantify the expected benefit relative to baseline risk, to verify the effectiveness of randomization, to reduce the sample size requirements, and to facilitate comparisons across studies.

The invention will be further described by the following non-limiting example.

Example 1

Methods Patients

Data from 5,376 patients that underwent radical prostatectomy (RP) at Memorial Sloan-Kettering, or RP by a single surgeon at Baylor College of Medicine, were collected. Data from the following patients were excluded: patients subjected to salvage RP, prior chemotherapy, or nonhormonal therapy (NHT) (overall exclusions 923 patients, 17%). Data were prospectively collected in the SPORE prostate cancer database. Patient characteristics are shown in Table 1.

PSA progression (biochemical recurrence, BCR) was defined as: PSA ≥ 0.2 ng/ml and rising (n = 616); a single PSA > 0.4 ng/ml (n = 19); and salvage treatment (XRT, HxRx) for rising PSA (n = 40). PSA doubling time (PSADT) was calculated by using all PSA values prior to reaching BCR; 570 (85%) had at least two PSA values for the PSADT calculations. Pathology Gleason score, PSA doubling time, interval RP-BCR and PSA at time of BCR were used as continuous variables.

675 patients were diagnosed according to the above criteria, and the median follow-up (FU) period was 7.8 years overall, 1.4 years for RP to BCR, and 5.2 years for BCR to the last FU.

Table 1

Results

Metastases progression was identified in 141 patients, and of those 68 patients had non castrated metastases and 73 patients had castrated metastases. 491 patients were subjected to secondary treatment for PSA progression: 191 patients had external radiation and 300 patients received hormonal therapy for BCR only.

Table 2 shows the results for different predictors in a univariate analysis for metastases progression in BCR patients, and Table 3 shows the results for different predictors in a multivariate analysis for metastases progression in BCR patients. Table 4 shows the results for different predictors in a multivariate analysis that includes secondary treatments for metastases progression in BCR patients. Using the data, a nomogram was prepared (Figure 2).

Table 2

Table 4

Calibration of the nomogram was assessed by applying a nonparametric smoothing algorithm to the jackknife-predicted probabilities and found to be accurate. The nomogram's concordance index of 0.88 (Figure 3) is significantly better as compared to categorical PSADT of 10 months (0.88 versus 0.7; P < 0.001), the Pound 1999 model (0.88 versus 0.68; P < 0.001), and the Pound 2003 model (0.88 versus 0.79; P < 0.001).

Conclusion

In patients who have not been treated with androgen deprivation therapy, multiple variables were associated with metastases progression in a multivariate analysis. These variables include extracapsular extension (ECE), seminal vesicle invasion (SVI), pathologic Gleason score, PSA doubling time, PSA at the time of BCR, and lack of salvage radiation. Those variables and others were used to construct a nomogram, which estimates the probability of patients with rising PSA for metastases progression in 8 years from BCR. As noted above, the nomogram's prediction power is significantly higher as compared to the current available prediction tools.

The present nomogram can be used to identify high risk patients for systemic progression, select candidates for salvage therapy following biochemical failure, and/or establish an accurate entry point for clinical trials,

based on the probability of the individual patient having a systemic progression within 8 years from biochemical failure.

References

Breiman et al., Classification and Regression Trees. Monterey, Calif., Wadsworth and Brooks/Cole (1984).

Cox, J. Roval Statistical Society. B34, 187-220 ( 1972)

Cristianni et al., An Introduction to Support Vector Machines, Cambridge University Press (2000).

D'Amico et al., J. Natl. Cancer Inst.. 20:4567 (2002).

D'Amico et al., J. Urol..172:542 (2004).

Dillioglugil et al., Urology, 50:93 (1997).

Ferguson et al., Urol. Clin. North Am., 21:677 (1994).

Han et al., J. Urol..169:517 (2003).

Han et al., Urol. Clin. North Am.. 28:555 (2001).

Harrell, In: Regression Modeling Strategies with Applications to Linear Models, Logistic Regression, and Survival Analysis, Springer- Verlag, NY (2001).

Hastie, The Elements of Statistical Learning, Springer (2001).

Holmberg et al., N. Engl. J. Med.. 347:781 (2002).

Hull et al., J. Urol.. 167:528 (2002).

Merrill et al., J. Urol.. 163:503 (2000).

Nudell et al., Radiol. Clin. North Am.. 38:213 (2000).

Pound et al., JAMA. 281:1591 (1999).

Roberts et al., Mayo Clin. Proc. 76:576 (2001).

Rumelhart et al., (eds), Parallel Distributed Processing: Exploration in the Microstructure of Cognition Volume 1, Foundations Cambridge, Mass, The MIT Press (1986).

All publications, patents and patent applications are incorporated herein by reference. While in the foregoing specification, this invention has been described in relation to certain preferred embodiments thereof, and many details have been set forth for purposes of illustration, it will be apparent to those skilled

in the art that the invention is susceptible to additional embodiments and that certain of the details herein may be varied considerably without departing from the basic principles of the invention.