A prognostic blood-based sphingolipid panel for men with localized prostate cancer followed on active surveillance

Abstract

Provided herein are methods for determining the disease progression of a subject having a prostate cancer.

Description

A PROGNOSTIC BLOOD-BASED SPHINGOLIPID PANEL FOR MEN WITH LOCALIZED PROSTATE CANCER FOLLOWED ON ACTIVE SURVEILLANCE

[0001] This application claims the benefit of priority of United States provisional application no. 63 / 619,515, filed January 10, 2024, the contents of which are incorporated by reference as if written herein in their entirety.

[0002] This invention was made with government support under CAI 40388 awarded by the National Institutes of Health. The government has certain rights in the invention.

[0003] Active surveillance (AS) is the preferred management option for men diagnosed with low risk prostate cancer and is an option for select men with intermediate risk disease. While clinically safe, a very small proportion of men are at risk of future metastases on AS. There is concern regarding disease progression and future metastasis form the basis of current AS protocols, all of which rely upon invasive biopsies as a gold standard in monitoring disease. Gleason grade group (GG) biopsy upgrading remains the most common reason for initiation of radical treatments, such as prostatectomy or radiotherapy, for patients on AS, which places them at risk of treatment-related changes to quality of life and affects an estimated 30-40% of men initiated on surveillance.

[0004] Clinical factors and risk calculators have been developed to aid in determining men at increased risk for biopsy upgrading and, conversely, those who may be candidates for de-escalation of surveillance intensity. However, they lack discriminatory power and have not resulted in guideline-based de-escalation strategies. Further, studies that evaluate the use of blood-based and tissue-based biomarkers to augment clinical factors in predicting biopsy upgrading on AS have yielded only incremental improvements.

[0005] Accordingly, a need exists for new strategies in the management of low- and intermediate-risk prostate cancer. A newly developed sphingolipid panel has been found to provide risk prediction for GG biopsy upgrade for men on AS.SUMMARY

[0006] Provided herein is a method of determining the disease progression of a subject having a prostate cancer, comprising: calculating a model score from the subject’s measured levels of one or more lactosylceramides, one or more sphingomyelins, and one or more sulfatides in a biological sample obtained from the subject; andidentifying the subject as being at risk for biopsy upgrade or not being at risk for biopsy upgrade by comparing the model score to a pre-defined parameter.

[0007] Also provided is a method of identifying and treating a subject having a prostate cancer, comprising: calculating a model score from the subject’s measured levels of one or more lactosylceramides, one or more sphingomyelins, and one or more sulfatides in a biological sample obtained from the subject; identifying the subject as being at risk for biopsy upgrade or not being at risk for biopsy upgrade by comparing the model score to a pre-defined parameter; administering an imaging study and / or biopsy to the subject identified as being at risk for biopsy upgrade; and treating the prostate cancer by surgical removal, radiation therapy, biological therapy, cryotherapy, chemotherapy, and / or hormone therapy.

[0008] Also provided is a method comprising: calculating a model score from the subject’s measured levels of one or more lactosylceramides, one or more sphingomyelins, and one or more sulfatides in a biological sample obtained from the subject; identifying the subject as being at risk for biopsy upgrade or not being at risk for biopsy upgrade by comparing the model score to a pre-defined parameter; and administering an imaging study to the subject identified as being at risk for biopsy upgrade.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] FIG. 1 depicts a schematic workflow of the model development.

[0010] FIG. 2 depicts a correlation heatmap showing the association between sphingolipid panel score, PSA density, and positive core biopsy rates.[Oi l] FIG. 3 depicts cumulative incidence curves for biopsy upgrade based on the combined model at high-, intermediate-, and low-risk strata in the PASS cohort.

[0012] FIG. 4 depicts cumulative incidence curves for biopsy upgrade based on the combined model at high-, intermediate-, and low-risk strata in the Testing Set #1 (Validation) cohort.

[0013] FIG. 5 depicts cumulative incidence curves for biopsy upgrade based on combined model at high-, intermediate-, and low-risk strata in the Testing Set #2 (Discovery) cohort.DETAILED DESCRIPTION

[0014] Provided herein is a method of determining the disease progression of a subject having a prostate cancer, comprising: calculating a model score from the subject’s measured levels of one or more lactosylceramides, one or more sphingomyelins, and one or more sulfatides in a biological sample obtained from the subject; and identifying the subject as being at risk for biopsy upgrade or not being at risk for biopsy upgrade by comparing the model score to a pre-defined parameter.

[0015] Also provided is a method of identifying and treating a subject having a prostate cancer, comprising: calculating a model score from the subject’s measured levels of one or more lactosylceramides, one or more sphingomyelins, and one or more sulfatides in a biological sample obtained from the subject; identifying the subject as being at risk for biopsy upgrade or not being at risk for biopsy upgrade by comparing the model score to a pre-defined parameter; administering an imaging study and / or biopsy to the subject identified as being at risk for biopsy upgrade; and treating the prostate cancer by surgical removal, radiation therapy, biological therapy, cryotherapy, chemotherapy, and / or hormone therapy.

[0016] Also provided is a method comprising: calculating a model score from the subject’s measured levels of one or more lactosylceramides, one or more sphingomyelins, and one or more sulfatides in a biological sample obtained from the subject; identifying the subject as being at risk for biopsy upgrade or not being at risk for biopsy upgrade by comparing the model score to a pre-defined parameter; and administering an imaging study to the subject identified as being at risk for biopsy upgrade.

[0017] In some embodiments, the imaging study is chosen from computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET).

[0018] In some embodiments, the model score is calculated using a machine learning model.

[0019] In some embodiments, the machine learning model is a deep learning model.

[0020] In some embodiments, the deep learning model comprises an artificial neural network which has at least one hidden layer and at least one node in each layer.

[0021] In some embodiments, the deep learning model comprises an artificial neural network with three hidden layers and six nodes in each layer.

[0022] In some embodiments, the parameter is about 0. 17.

[0023] In some embodiments, the parameter corresponds to a high risk for biopsy upgrade.

[0024] In some embodiments, the parameter is about 0.16.

[0025] In some embodiments, the parameter corresponds to an intermediate risk for biopsy upgrade.

[0026] In some embodiments, the model score further comprises PSA density and the rate of biopsy core positivity.

[0027] In some embodiments, the parameter is about 3.21.

[0028] In some embodiments, the parameter corresponds to a high risk for biopsy upgrade.

[0029] In some embodiments, the parameter is about 2.90.

[0030] In some embodiments, the parameter corresponds to an intermediate risk for biopsy upgrade.

[0031] In some embodiments, at least one lactosylceramide is chosen from lactosylceramide(18:l / 16:0) and lactosylceramide(32:l).

[0032] In some embodiments, the lactosylceramides are lactosylceramide(18:l / 16:0) and lactosylceramide(32: 1).

[0033] In some embodiments, at least one sphingomyelin is chosen from sphingomyelin(40: l), sphingomyelin(33:2), sphingomyelin(34:2), sphingomyelin(39: l), sphingomyelin(42:2), sphingomyelin(33:l), sphingomyelin(40:3), sphingomyelin(41: l), sphingomyelin(33:l), sphingomyelin(36:2), sphingomyelin(39:2), sphingomyelin(34:0), sphingomyelin(32:l), sphingomyelin(40:2), and sphingomyelin(32:2).

[0034] In some embodiments, the sphingomyelins are sphingomyelin(40:l), sphingomyelin(33:2), sphingomyelin(34:2), sphingomyelin(39:l), sphingomyelin(42:2), sphingomyelin(33: l), sphingomyelin(40:3), sphingomyelin(41 : l), sphingomyelin(33: l), sphingomyelin(36:2), sphingomyelin(39:2), sphingomyelin(34:0), sphingomyelin(32: l), sphingomyelin(40:2), and sphingomyelin(32:2).

[0035] In some embodiments, at least one sull'atide is chosen from C 16 sulfatide, C22 sulfatide, C16(OH) sulfatide, and C22(OH) sulfatide.

[0036] In some embodiments, the sulfatides are C 16 sulfatide, C22 sulfatide, C16(OH) sulfatide, and C22(OH) sulfatide.

[0037] In some embodiments, a model score greater than the pre-defined parameter is considered a positive test.

[0038] In some embodiments, a model score less than the pre-defined parameter is considered a negative test.

[0039] In some embodiments, the levels of the one or more lactosylceramides, one or more sphingomyelins, and one or more sulfatides in a biological sample obtained from the subject generate a detectable signal.

[0040] In some embodiments, the detectable signals are detectable by a spectrometric method.

[0041] In some embodiments, the spectrometric method is chosen from UV-visible spectroscopy, mass spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, proton NMR spectroscopy, nuclear magnetic resonance (NMR) spectrometry, gas chromatography, mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), correlation spectroscopy (COSY), nuclear Overhauser effect spectroscopy (NOESY), rotating-frame nuclear Overhauser effect spectroscopy (ROESY), time-of-flight LC-MS (LC- TOF-MS), liquid chromatography-tandem mass spectrometry (LC-MS / MS), and capillary electrophoresis-mass spectrometry.

[0042] In some embodiments, the spectrometric method is mass spectrometry.

[0043] In some embodiments, the mass spectrometry is LC-TOF-MS.

[0044] In some embodiments, the prostate cancer is low-risk or intermediate-risk.

[0045] In some embodiments, the individual is subsequently designated for further prostate cancer monitoring or treatment.

[0046] In some embodiments, the monitoring is chosen from endoscopic ultrasound, magnetic resonance imaging (MRI), computed tomography (CT) scans, PSA blood tests, and serial blood or urine tests.

[0047] In some embodiments, the monitoring is performed annually.

[0048] In some embodiments, the monitoring is performed semi-annually.

[0049] In some embodiments, the monitoring is chosen from surgery, chemotherapy, immunotherapy, radiation therapy, targeted therapy, or a combination thereof.Definitions

[0050] As used herein, the terms below have the meanings indicated.

[0051] When ranges of values are disclosed, and the notation “from m ... to m” or “between ni . . . and 2” is used, where ni and n 2 are the numbers, then unless otherwise specified, this notation is intended to include the numbers themselves and the range between them. This range may be integral or continuous between and including the end values. By way of example, the range “from 2 to 6 carbons” is intended to include two, three, four, five, and six carbons, since carbons come in integer units. Compare, by way of example, the range “from 1 to 3 pM (micromolar),” which is intended to include 1 pM, 3 pM, and everything in between to any number of significant figures (e.g., 1.255 pM, 2.1 pM, 2.9999 pM, etc.).

[0052] The term “about,” as used herein, is intended to qualify the numerical values which it modifies, denoting such a value as variable within a range. When no particular range, such as a margin of error or a standard deviation to a mean value given in a chart or table of data, is recited, the term “about” should be understood to mean the greater of the range which would encompass the recited value and the range which would be included by rounding up or down to that figure as well, taking into account significant figures, and the range which would encompass the recited value plus or minus 20%.|053 ] As used herein, the term “prostate cancer” refers to a malignant neoplasm of the prostate characterized by the abnormal proliferation of cells, the growth of which cells exceeds and is uncoordinated with that of the normal tissues around it.

[0054] As used herein, the term “low-risk prostate cancer” refers to a prostate cancer with a tumor confined to the prostate, a PSA of <10 ng / mL, and a Gleason grade group of 6.

[0055] As used herein, the term “intermediate-risk prostate cancer” refers to a prostate cancer with a tumor confined to the prostate, a PSA of 10-20 ng / mL, and a Gleason grade group of 7.

[0056] As used herein, the term “high-risk prostate cancer” refers to a prostate cancer with a tumor extended outside the prostate, a PSA of >20 ng / mL, and a Gleason grade group of 8-10.

[0057] As used herein, the term “PSA” means prostate-specific antigen. In some embodiments, PSA density is the serum PSA level of a subject divided by the volume of the prostate gland of the same subject.

[0058] As used herein, the terms “subject” or “patient” refer to a mammal, preferably a human.

[0059] As used herein, “treating,” “treatment,” and the like means the administration of therapy to an individual who already manifests at least one symptom of a disease or condition or who has previously manifested at least one symptom of a disease or condition. For example, “treating” can include alleviating, abating, or ameliorating a disease or condition symptoms, preventing additional symptoms, ameliorating the underlying metabolic causes of symptoms, inhibiting the disease or condition, e.g., arresting the development of the disease or condition, relieving the disease or condition, causing regression of the disease or condition, relieving a condition caused by the disease or condition, or stopping the symptoms of the disease or condition. For example, the term “treating” in reference to a disorder means a reduction in severity of one or more symptoms associated with that particular disorder. Therefore, treating a disorder does not necessarily mean a reduction in severity of all symptoms associated with a disorder and does not necessarily mean a complete reduction in the severity of one or more symptoms associated with a disorder. As related to the present disclosure, the term may also mean the administration of pharmacological substances or formulations, or the performance of non-pharmacological methods including, but not limited to, radiation therapy and surgery. Pharmacological substances as used herein may include, but are not limited to, chemotherapeutics that are established in the art, such as abiraterone acetate (Zytiga), apalutamide (Erleada®), bicalutamide (Casodex®), cabazitaxel (Jevtana®), darolutamide (Nubeqa®), degarelix (Firmagon®), docetaxel (Taxotere®), leuprolide acetate (Eligard®), enzalutamide (Xtandi®), flutamide, goserelin acetate (Zoladex®), leuprolide acetate (Lupron® or Lupron Depot®), olaparib (Lynparza®), mitoxantrone hydrochloride, nilutamide (Nilandron®), sipuleucel-T (Provenge®), radium 223 dichloride (Xofigo®), and rucaparib camsylate (Rubraca®). The terms “pharmacological substance” and “anticancer therapy” may also include substances used in immunotherapy, such as checkpoint inhibitors. Treatment may include a multiplicity of pharmacological substances, or a multiplicity of treatment methods, including, but not limited to, surgery and chemotherapy.

[0060] As used herein, “amount” or “level” refers to a typically quantifiable measurement for a biomarker described herein, wherein the measurement enables comparison of the marker between samples and / or to control samples. In some embodiments, an amountor level is quantifiable and refers to the levels of a particular marker in a biological sample (e.g., blood, serum, urine, etc.), as determined by laboratory methods or tests such as an immunoassay, (e.g., antibodies), mass spectrometry, or liquid chromatography. In some embodiments, a marker may be present in the sample in an increased amount, or in a decreased amount. Marker comparisons may be based on direct measurement of the levels of a biomarker described herein, (e.g., through protein quantification or gene expression analysis) or may be based on measurement of e.g., reporter molecules, biomarker-receptor complexes, biomarker-relay-receptor complexes, or the like.

[0061] As used herein, the term “biopsy upgrade” refers to an increase in the Gleason score (“grade group”) of a subject’s prostate cancer. In some embodiments, the biopsy upgrade is an increase from initial needle biopsy to pathological assessment of the entire surgical specimen.

[0062] As used herein, the term “positive core biopsy rate” refers to the percentage of a subject’s biopsy cores that tested positive for prostate cancer at the time of diagnosis.

[0063] As used herein, the term “elevated” refers to a biomarker level or model score in a given subject that is greater relative to the same biomarker level or model score in a given set of healthy patients or subjects. In some embodiments, an elevated PLCOm2oi2 model score is 0.00948 or greater. In some embodiments, an elevated PLCOm2oi2 model score is 0.016082 or greater.

[0064] As used herein, the term “hazard ratio” refers to a measure of how often a particular event happens in one group compared to how often it happens in another group, over time. Hazard ratios are often used in clinical trials to measure survival at any point in time in a group of patients who have been given a specific treatment compared to a control group given another treatment or a placebo. Hazard is defined as the slope of the survival curve — a measure of how rapidly subjects are dying. A hazard ratio of one means that there is no difference in survival between the two groups. A hazard ratio of greater than one or less than one means that survival was better in one of the groups. If the hazard ratio is 2.0, then the rate of deaths in one treatment group is twice the rate in the other group.

[0065] As used herein, the term “model score” refers to a numerical score calculated from a given set of biomarkers or biomarker panel measured in a sample from a subject. The model score is calculated by normalizing or weighting the measured levels using fixed coefficients as prescribed by the statistical method for a given biomarker panel. Individual biomarker levels are used as components in calculating a model score for the subject.

[0066] As used herein, the “use” of markers for evaluating prostate cancer refers to quantification of the levels or amounts in a biological sample of one or more markers described herein. Quantification may be done using any known methods or techniques in the art or described herein. In some embodiments, markers may be used or combined together as a panel for statistical comparison to other samples.

[0067] As used herein, a “sample” refers to a test substance to be tested for the presence of, and levels or concentrations thereof, of a biomarker as described herein. A sample may he any substance appropriate in accordance with the present disclosure, including, but not limited to, blood, blood serum, blood plasma, or any part thereof.

[0068] As used herein, the term “p-value” or “p” refers to the probability that the distributions of biomarker scores for a subject are identical in the context of a Wilcoxon rank sum test. Generally, a p-value close to zero indicates that a particular statistical method will have high predictive power in classifying a subject.

[0069] As used herein, the term “CI” refers to a confidence interval, i.e., an interval in which a certain value can be predicted to lie with a certain level of confidence. As used herein, the term “95% CI” refers to an interval in which a certain value can be predicted to lie with a 95% level of confidence.

[0070] As used herein, the term “disease progression” or “early disease progression” is defined as upgrading of Gleason score and / or increased tumor volume on surveillance biopsy within 18 months after start of active surveillance.

[0071] The phrase "therapeutically effective" is intended to qualify the amount of active ingredients used in the treatment of a disease or disorder or on the effecting of a clinical endpoint.EXAMPLES

[0072] The following examples are included to demonstrate embodiments of the disclosure. The following examples are presented only by way of illustration and to assist one of ordinary skill in using the disclosure. The examples are not intended in any way to otherwise limit the scope of the disclosure. Those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the disclosure.EXAMPLE 1The PASS Specimen Set

[0073] The Canary Prostate Active Surveillance Study (PASS) is a prospective (ClinicalTrials.gov identifier: NCT00756665) study enrolling men diagnosed with localized prostate cancer who have opted for AS. In the PASS cohort, PSA was measured every 3 months, clinic visits occurred every six months, and ultrasound guided biopsy (at least 10 cores) were performed 6-12 months and 24 months after diagnosis, then every 2 years. Patients were followed until biopsy reclassification, prostate cancer treatment, voluntary withdrawal or until 2 years following their prior biopsy.

[0074] The MD Anderson Cancer Center active surveillance cohort included men who were enrolled on a prospective AS protocol from 2006-2014 at single institution and who had over 1 year of follow-up (following confirmatory biopsy) and baseline plasma sample availability. Men were followed with biannual digital rectal exam, laboratory testing (serum PSA, testosterone), and 11 core systematic biopsies every 1-2 years. Plasma specimens collected from patients enrolled in the respective cohorts during study follow-up, typically before or at time of the first on-study (confirmatory) biopsy were used for metabolomics assays.Lipidomic Analyses

[0075] Assaying of plasma sphingolipids, including sphingomyelin, ceramides, associated glycosphingolipid, and sulfatides, was conducted using a Waters Acquity™ UPLC system coupled to a Xevo G2-XS quadrupole time-of-flight (qTOF) mass spectrometer. Chromatographic separation was performed using a Cl 8 (Acquity ™ UPLC HSS T3, 100 A, 1.8 pm, 2.1x100mm, Water Corporation, Milford, U.S.A) column at 55°C. The mobile phases were (A) water, (B) Acetonitrile, (C) 2-propanol and (D) 500mM ammonium formate, pH 3. A starting elution gradient of 20% A, 30% B, 49% C and 1% D was increased linearly to 10% B, 89% C and 1 % D for 5.5 min, followed by isocratic elution at 10% B, 89%C and 1%D for 1.5 min and column equilibration with initial conditions for Imin.

[0076] Mass spectrometry data were acquired using ‘sensitivity’ mode in positive and negative electrospray ionization mode within 100-2000 Da. For the electrospray acquisition, the capillary voltage was set at 1.5 kV (positive), 3.0kV (negative), sample cone voltage 30V, source temperature at 120° C, cone gas flow 50 L / h and desolvation gas flow rate of 800 L / h with scan time of 0.5 sec in continuum mode. Leucine Enkephalin; 556.2771 Da (positive)and 554.2615 Da (negative) was used for lockspray correction and scans were performed at 0.5 min. The injection volume for each sample was 3 L. The acquisition was carried out with instrument auto gain control to optimize instrument sensitivity over the samples acquisition time.Data Processing

[0077] LC-MS and LC-MSe data were processed using Progenesis QI (Nonlinear, Waters). Peak picking and retention time alignment of LC-MS and MSe data were performed using Progenesis QI software (Nonlinear, Waters). Data processing and peak annotations were performed using an in-house automated pipeline. Annotations were determined by matching accurate mass and retention times using updated customized libraries created from authentic standards and by matching experimental tandem mass spectrometry data against the NIST MSMS, LipidBlast or HMDB v3 theoretical fragmentations. To correct for injection order drift, each feature was normalized using data from repeat injections of quality control samples collected every 10 injections throughout the run sequence. Measurement data were smoothed by Locally Weighted Scatterplot Smoothing (LOESS) signal correction (QC- RLSC).Statistical Analyses

[0078] Univariable and multivariable Cox Proportional hazard models were used to evaluate associations between individual sphingolipids or derived models with disease progression (defined here as Gleason GG biopsy upgrade) and to establish the combination rule that considered the lipid panel as well as clinical risk factors.

[0079] Model building was performed using metabolic profiles generated using plasma samples from the PASS cohort (FIG. 1). Eight different models, including deep learning model (from h2o package), Conditional Non-Parametric Survival Estimator (from akritas package in R), Cox-Time Survival Neural Network (from coxtime package in R), Cox melding with top 20 features, Survival Neural Network (from DeepHit package in R), Deep Survival Neural Network (from Deepsurv package in R), Logistic-Hazard Survival Neural Network, and PC-Hazard Survival Neural Network were used to identify patients on AS at increased risk of GG biopsy upgrade (Table 1). Individual model performance was evaluated by introducing various model perturbations.Table 1. Performance estimates of different deep-learning models evaluating association of sphingolipid panel with biopsy Gleason GG upgrade on AS.HR: hazard ratio per unit increase

[0080] A deep learning model (DLM) with 3 hidden layers and 6 nodes in each layer was selected for modeling the 21 -marker metabolite panel based on AUC and Hazard Ratio (Table 2). Grid-search was performed across hyperparameters to tune and find the best performing combination. The method used to assign importance score for variables included in the model removes irrelevant or noisy signal by analyzing for the relative weight of each variable within the overall data matrix. An importance score is calculated by dividing the absolute value of the weight of an input connecting to an output by the total absolute value of all weights from that input. When applied in the deep learning model, this approach is recursively extended backwards through layers by taking the effect of a neuron on a connected node, then multiplying the derived weight by the effect of the given node on the target output and summing all connecting nodes.

[0081] Here, Pjk represents the average contribution of a node j in a layer to a node k in the next layer, w is the weight on the connection and nh is the number of nodes in the next layer.

[0082] The contribution of an input neuron to an output is:

[0083] The derived sphingolipid panel was then applied to an independent external testing set, and its performance was verified in a second, external testing set used in prior analyses (FIG. 1). For the combined panel including sphingolipid panel, biopsy core positivity rate, PSA density (PS AD) Cox proportional hazards models were used as noted above. Cumulative incidence curves were generated using the ‘Survminer’ package in the R statistical software (https: / / www.r-project.org / ).Table 2. Sphingolipids selected for inclusion in the deep learning model.Application of artificial intelligence to circulating sphingolipid profiles to establish an improved risk prediction model in the PASS Cohort

[0084] A cohort of plasma samples collected from 547 men on AS from the CanaryPASS study was assembled to confirm the association between circulating sphingolipids andbiopsy upgrade among men on AS. Table 3 lists baseline demographic, oncologic and follow-up data. Of the 544 patients, median age was 67 years (IQR 58-70) and 467 (85.8%) had Gleason GG 1 disease, at most, on diagnostic or on-study confirmatory biopsy. During a median follow-up of 2.1 years (IQR 1.4-4.5 years), 98 (18.0%) had a biopsy Gleason GG upgrading.Table 3. Patient characteristics of the Canary PASS cohort and Validation Cohort

[0085] Assaying of sphingolipids was performed using ultra high-pressure liquid chromatography mass spectrometry (UHPLC-MS) and a total of 87 uniquely annotated sphingolipids were quantified. Individual sphingolipids tended to be associated with Gleason GG biopsy upgrade on AS. Among the eight different machine learning algorithms used to establish a sphingolipid panel-based models associated with GG upgrading, a neural network with 3 layers and 32 nodes in each layer based on 21 sphingolipids (hereon referred to as the“sphingolipid panel”) achieved the highest performance in the PASS cohort, yielding a hazard ratio (HR) of 1.36 (95% CI: 1.07-1.70) per unit standard deviation (StDev) increase based on univariate Cox proportional hazards analysis. Multivariable Cox proportional hazards models that accounted for relevant clinical (ex. age, BMI) and oncologic (ex. biopsy characteristics) factors demonstrated that the sphingolipid panel was independently associated with biopsy GG upgrade, with an HR of 1.33 (95% CI: 1.05-1.70) per StDev increase (Table 4). It was also noted that PSAD (HR 1.36, 95% CI 1.16-1.61) and diagnostic biopsy core positivity rate (HR 1 .27, 95% CI 1 .00-1 .61) were independently associated with biopsy GG upgrading. Assumptions of Cox proportional hazard were met in the model.Table 4. Cox proportional hazard models evaluating association of clinicopathologic characteristics and the sphingolipid panel with biopsy Gleason GG upgrading in the Canary PASS cohort.f per unit standard deviation increase; J per unit increase Global p (constant HR): 0.25

[0086] To better inform clinically actionable cut points, the contributions of the three independent factors most strongly associated with biopsy progression — the sphingolipid panel, PSAD and positive core biopsy rate (FIG. 2) — for identifying men on AS at increased risk of GG biopsy upgrade were assessed. A combined model of the sphingolipid panel + PSAD + positive core biopsy rate had an HR of 1.63 (95% CI: 1.33-2.00) per unit StDev increase for PFS (Table 5). To further assess potential cut points, men on AS were stratified into high-, intermediate-, or low-risk strata based on combined model score tertiles (Table 6). Stratification into high- or intermediate -risk groups was significantly associated with risk of biopsy GG upgrade compared to the low-risk group (HR 3.17, 95% CI 1.84-5.46 and HR 2.05, 95% CI 1.18-3.58, respectively), and this association was more pronounced than strata based on individual factors alone (Table 7). Cumulative incidence curves demonstrated that patients in the high-risk strata had a higher incidence of biopsy upgrade (23.2%), especially in comparison to those in the low risk strata (10.5%) (FIGS. 3 and 4). Table 5. Univariable Cox proportional hazard models evaluating the association of the sphingolipid panel, PSA density, and percent positive core biopsies for Gleason GG upgrading in the Canary PASS and Testing Set #1 (Validation) cohorts.Abbreviations: HR-hazard ratio; Cl- confidence interval; PCB- % positive core biopsy rate f per unit standard deviation increaseTable 6. Cut points for high-, intermediate-, and low-risk strata.Table 7. Performance estimates of the sphingolipid panel, PSA density, % positive core biopsy and the model that combines all three at different risk strata for predicting biopsy upgrade for men on AS in the Canary PASS and Testing Set #1 (Validation)Cohorts.Performance of the sphingolipid panel, the combined model, and corresponding risk thresholds for biopsy upgrade in the testing sets.

[0087] Validation of the sphingolipid panel as well as the combined model of the sphingolipid panel + PSAD + positive core biopsy rate using fixed model coefficients was then performed in an independent set of plasmas collected from 238 patients on AS. Table 3 shows the characteristics of Testing Set #1, which was used for validation. Of the 238 patients, median age was 63 years (IQR 58-68) and 204 (90.0%) had Gleason GG 1 disease, at most, on diagnostic or on-study confirmatory biopsy. During a median follow-up of 3.5 years (IQR 1.0-5.1 years), 33 (13.9%) had a biopsy Gleason GG upgrading.

[0088] In multivariable Cox proportional hazards models, the sphingolipid panel (HR 2.51, 95% CI: 1.42-2.4 per unit StDev increase; Table 8) and the combined model that included the sphingolipid panel, PSAD and positive core biopsy rate yielded an HR of 3.07 (95% CI: 2.07-4.54; Table 5) were associated with GG upgrade. Stratification of patients into high-, intermediate-, or low-risk groups using the same cut points derived in the PASS cohort demonstrated that patients in the high- and intermediate-risk strata, when compared to low-risk patients, had increased risk of GG upgrading, with respective HRs of 9.70 (95% CI: 2.89-32.50) and 1.48 (95% CI: 0.33-6.60) per StDev increase (Table 7; FIGS. 3 and 4). Table 8. Cox proportional hazard models evaluating association of the sphingolipid panel with Gleason GG upgrading in Testing Set #1 (Validation) cohort.f per unit standard deviation increase Global p (constant HR): 0.36

[0089] Similar results were achieved in Testing Set #2, which included patient samples used in the initial discovery of the association between sphingolipids and biopsy GG upgrade on AS (Table 9 and FIG. 5).Table 9. Univariable and multivariable Cox proportional hazard models evaluating association of sphingolipid panel with biopsy Gleason GG upgrading in Testing Set #2 (Discovery Set).t per unit standard deviation increaseModel that combines the Sphingolipid panel + PSA Density + % positive core biopsies

[0090] The description of a sphingolipid-based panel that is independently associated with risk of biopsy upgrading, and clinically-based cut points for use in risk stratification, represent potential steps forward in the management of men with newly diagnosed prostate cancer that is managed on AS. When combined with clinical factors that are independently associated with Gleason GG upgrading, the panel effectively risk-stratifies men into groups at increased (and decreased) risk of biopsy upgrading. The sphingolipid panel may serve as a useful adjunct in determining patient management among men with low- and intermediaterisk prostate cancer managed on active surveillance.

[0091] All references, patents or applications, U.S. or foreign, cited in the application are hereby incorporated by reference as if written herein in their entireties. Where any inconsistencies arise, material literally disclosed herein controls.

[0092] From the foregoing description, one skilled in the art can easily ascertain the essential characteristics of this invention, and without departing from the spirit and scope thereof, can make various changes and modifications of the invention to adapt it to various usages and conditions.

Claims

CLAIMSWhat is claimed is:

1. A method of determining the disease progression of a subject having a prostate cancer, comprising: calculating a model score from the subject’s measured levels of one or more lactosylceramides, one or more sphingomyelins, and one or more sulfatides in a biological sample obtained from the subject; and identifying the subject as being at risk for biopsy upgrade or not being at risk for biopsy upgrade by comparing the model score to a pre-defined parameter.

2. A method of identifying and treating a subject having a prostate cancer, comprising: calculating a model score from the subject’s measured levels of one or more lactosylceramides, one or more sphingomyelins, and one or more sulfatides in a biological sample obtained from the subject; identifying the subject as being at risk for biopsy upgrade or not being at risk for biopsy upgrade by comparing the model score to a pre-defined parameter; administering an imaging study and / or biopsy to the subject identified as being at risk for biopsy upgrade; and treating the prostate cancer by surgical removal, radiation therapy, biological therapy, cryotherapy, chemotherapy, and / or hormone therapy.

3. A method comprising: calculating a model score from the subject’s measured levels of one or more lactosylceramides, one or more sphingomyelins, and one or more sulfatides in a biological sample obtained from the subject; identifying the subject as being at risk for biopsy upgrade or not being at risk for biopsy upgrade by comparing the model score to a pre-defined parameter; and administering an imaging study to the subject identified as being at risk for biopsy upgrade.

4. The method of any one of the preceding claims, wherein the imaging study is chosen from computed tomography, magnetic resonance imaging, and positron emission topography.

5. The method of any one of the preceding claims, wherein the model score is calculated using a machine learning model.

6. The method of claim 5, wherein the machine learning model is a deep learning model.

7. The method of claim 6, wherein the deep learning model comprises an artificial neural network which has at least one hidden layer and at least one node in each layer.

8. The method of claim 7, wherein the deep learning model comprises an artificial neural network with three hidden layers and six nodes in each layer.

9. The method of any one of the preceding claims, wherein the parameter is about 0.17.

10. The method of claim 9, wherein the parameter corresponds to a high risk for biopsy upgrade.1 1 . The method of any one of claims 1-7, wherein the parameter is about 0.16.

12. The method of claim 11 , wherein the parameter corresponds to an intermediate risk for biopsy upgrade.

13. The method of any one of the preceding claims, wherein the model score further comprises PSA density and positive core biopsy rate.

14. The method of claim 13, wherein the parameter is about 3.21.

15. The method of claim 14, wherein the parameter corresponds to a high risk for biopsy upgrade.

16. The method of claim 13, wherein the parameter is about 2.90.

17. The method of claim 16, wherein the parameter corresponds to an intermediate risk for biopsy upgrade.

18. The method of any one of the preceding claims, wherein at least one lactosylceramide is chosen from lactosylceramide(18:l / 16:0) and lactosylceramide(32: l).

19. The method of any one of the preceding claims, wherein the lactosylceramides are lactosylceramide(18:l / 16:0) and lactosylceramide(32:l).

20. The method of any one of the preceding claims, wherein at least one sphingomyelin is chosen from sphingomyelin(40: l), sphingomyelin(33:2), sphingomyelin(34:2), sphingomyelin(39: l), sphingomyelin(42:2), sphingomyelin(33: l), sphingomyelin(40:3), sphingomyelin(41 :l), sphingomyelin(33:l), sphingomyelin(36:2), sphingomyelin(39:2), sphingomyelin(34:0), sphingomyelin(32:l), sphingomyelin(40:2), and sphingomyelin(32:2).

21. The method of any one of the preceding claims, wherein the sphingomyelins are sphingomyelin(40: l), sphingomyelin(33:2), sphingomyelin(34:2), sphingomyelin(39: l), sphingomyelin(42:2), sphingomyelin(33:l), sphingomyelin(40:3), sphingomyelin(41: l), sphingomyelin(33:l), sphingomyelin(36:2), sphingomyelin(39:2), sphingomyelin(34:0), sphingomyelin(32:l), sphingomyelin(40:2), and sphingomyelin(32:2).

22. The method of any one of the preceding claims, wherein at least one sulfatide is chosen from Cl 6 sulfatide, C22 sulfatide, C16(OH) sulfatide, and C22(OH) sulfatide.

23. The method of any one of the preceding claims, wherein the sulfatides are C 16 sulfatide, C22 sulfatide, C16(0H) sulfatide, and C22(OH) sulfatide.

24. The method of any one of the preceding claims, wherein a model score greater than the pre-defined parameter is considered a positive test.

25. The method of any one of claims 1-23, wherein a model score less than the predefined parameter is considered a negative test.

26. The method of any one of the preceding claims, wherein the levels of the one or more lactosylceramides, one or more sphingomyelins, and one or more sulfatides in a biological sample obtained from the subject generate a detectable signal.

27. The method of claim 26, wherein the detectable signals are detectable by a spectrometric method.

28. The method of claim 27, wherein the spectrometric method is chosen from UV-visible spectroscopy, mass spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, proton NMR spectroscopy, nuclear magnetic resonance (NMR) spectrometry, gas chromatography, mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), correlation spectroscopy (COSY), nuclear Overhauser effect spectroscopy (NOESY), rotating-frame nuclear Overhauser effect spectroscopy (ROESY), time-of-flight LC-MS (LC- TOF-MS), liquid chromatography-tandem mass spectrometry (LC-MS / MS), and capillary electrophoresis-mass spectrometry.

29. The method of claim 28, wherein the spectrometric method is mass spectrometry.

30. The method of claim 29, wherein the mass spectrometry is LC-TOF-MS.

31. The method of any one of the preceding claims, wherein the prostate cancer is low- risk or intermediate-risk.

32. The method of any one of the preceding claims, wherein the individual is subsequently designated for further prostate cancer monitoring or treatment.

33. The method of claim 32, wherein the monitoring is chosen from endoscopic ultrasound, magnetic resonance imaging, computed tomography scans, PSA blood tests, and serial blood or urine tests.

34. The method of claim 33, wherein the monitoring is performed annually.

35. The method of claim 33, wherein the monitoring is performed semi-annually.

36. The method of claim 32, wherein the treatment is chosen from surgery, chemotherapy, immunotherapy, radiation therapy, targeted therapy, or a combination thereof.

Images