PURPOSE:177Lu-DOTA(0)-Tyr(3)-octreotate (177Lu-DOTATATE) is a somatostatin receptor (SSTR)-targeting radiopharmaceutical that shows promise for treating metastatic pheochromocytomas/paragangliomas (PPGLs), a rare SSTR-expressing tumor. METHODS:In the first stage of this two-stage Simon phase II trial, 36 PPGL patients with RECIST 1.1 progression within 12 months were prospectively recruited into two genetic cohorts (succinate dehydrogenase [SDHx]-mutated v apparent sporadic, 18 per cohort) and treated with four cycles of 177Lu-DOTATATE. The primary end point was progression-free survival (PFS) rate at 6 months (from initiation of treatment). Secondary end points included safety, overall survival (OS), response rate, imaging/serum biomarkers, and antihypertensive medication reduction. Computed tomography/magnetic resonance imaging (CT/MRIs) and positron emission tomography (PET)-CTs (68Ga-DOTATATE and 18F-labeled fluorodeoxyglucose) were obtained after two and four cycles, then every 3 (CT/MRIs) to 6 months (PET/CTs). Patients with systolic blood pressure (SBP) > 200 mmHg despite medical management were treated in the intensive care unit (ICU). RESULTS:Six-month PFS rate for all patients was 0.861 (95% CI, 0.755 to 0.982), which was significantly lower (P = .009) for SDHx at 0.72 (95% CI, 0.542 to 0.962) versus sporadic at 1.00 (95% CI, 1.0 to 1.0). Median PFS was 19.9 months (12.9 months SDHx v 24.3 months sporadic) and median OS was 51.7 months (31.2 months SDHx v not reached in sporadic). Best response was achieved on average 11.0 months after completing 177Lu-DOTATATE. A 17% incidence of grade 3+ catecholamine release syndrome (CRS) was noted, which may benefit from preemptive ICU admission. Plasma chromogranin A and normetanephrine were the best tumor-marker surrogates and correlated well with changes in RECIST sum and total tumor lesion uptake on serial 68Ga-DOTATATE PET-CT scans. CONCLUSION:177Lu-DOTATATE demonstrated effectiveness and acceptable safety profile for progressive, metastatic PPGL. CRS may occur but can be mitigated through pretreatment with antihypertensives, and, when appropriate, intensified monitoring in the ICU with intravenous antihypertensives.

BACKGROUND. Variability in prostate biparametric MRI (bpMRI) interpretation limits diagnostic reliability for prostate cancer (PCa). Artificial intelligence (AI) has the potential to reduce this variability and improve diagnostic accuracy. OBJECTIVE. The objective of this study was to evaluate the impact of a deep learning AI model on lesion- and patient-level rates of detection of PCa and clinically significant PCa (csPCa) and interreader agreement for bpMRI interpretations. METHODS. This retrospective, multireader, multicenter study used a balanced incomplete block design for MRI randomization. Six radiologists of varying experience interpreted bpMRI scans with and without AI assistance in alternating sessions. The reference standard for lesion-level detection for cases was whole-mount pathology after radical prostatectomy; for control patients, it was negative 12-core systematic biopsies. In all, 180 patients (120 in the case group and 60 in the control group) who underwent mpMRI and prostate biopsy or radical prostatectomy between January 2013 and December 2022 were included. Lesion-level sensitivity, PPV, and patient-level AUC for csPCa and PCa detection and interreader agreement for lesion-level PI-RADS scores and size measurements were assessed. RESULTS. AI assistance improved lesion-level PPV (PI-RADS ≥ 3: 77.2% [95% CI, 71.0-83.1%] vs 67.2% [95% CI, 61.1-72.2%] for csPCa; 80.9% [75.2-85.7%] vs 69.4% [95% CI, 63.4-74.1%] for PCa; both p < .001), reduced lesion-level sensitivity (PI-RADS ≥ 3: 44.4% [95% CI, 38.6-50.5%] vs 48.0% [95% CI, 42.0-54.2%] for csPCa; p = .01; 41.7% [95% CI, 37.0-47.4%] vs 44.9% [95% CI, 40.5-50.2%] for PCa; p = .01), and no difference in patient-level AUC (0.822 [95% CI, 0.768-0.866] vs 0.832 [95% CI, 0.787-0.868] for csPCa; p = .61; 0.833 [0.782-0.874] vs 0.835 [95% CI, 0.792-0.871] for PCa; p = .91). AI assistance improved interreader agreement for lesion-level PI-RADS scores (κ = 0.748 [95% CI, 0.701-0.796] vs 0.336 [95% CI, 0.288-0.381]; p < .001), lesion size measurements (coverage probability of 0.397 [95% CI, 0.376-0.419] vs 0.367 [95% CI, 0.349-0.383]; p < .001), and patient-level PI-RADS scores (κ = 0.704 [95% CI, 0.627-0.767] vs 0.507 [95% CI, 0.421-0.584]; p < .001). CONCLUSION. AI improved lesion-level PPV and interreader agreement with slightly lower lesion-level sensitivity. CLINICAL IMPACT. AI may enhance consistency and reduce false-positives in bpMRI interpretations. Further optimization is required to improve sensitivity without compromising specificity.

BACKGROUND:Hereditary leiomyomatosis and renal-cell cancer (HLRCC) is an inherited disorder characterized by germline pathogenic variants in the gene encoding fumarate hydratase and an increased risk of papillary renal-cell carcinoma. No effective therapy is known for patients with advanced HLRCC-associated papillary renal-cell carcinoma, and most patients die from progressive disease. METHODS:In this open-label, phase 2 study, we evaluated the efficacy of bevacizumab (10 mg per kilogram of body weight every 2 weeks) and erlotinib (150 mg once daily) in patients with advanced HLRCC-associated or sporadic papillary renal-cell carcinoma. The primary end point was overall response; secondary end points included progression-free and overall survival. RESULTS:A total of 43 patients with HLRCC-associated papillary renal-cell carcinoma and 40 patients with sporadic papillary renal-cell carcinoma were enrolled. A confirmed response occurred in 31 patients (72%; 95% confidence interval [CI], 57 to 83) with HLRCC-associated papillary renal-cell carcinoma; the median progression-free survival was 21.1 months (95% CI, 15.6 to 26.6), and the median overall survival was 44.6 months (95% CI, 32.7 to could not be estimated). A confirmed response occurred in 14 patients (35%; 95% CI, 22 to 51) with sporadic papillary renal-cell carcinoma, with a median progression-free survival of 8.9 months (95% CI, 5.5 to 18.3) and a median overall survival of 18.2 months (95% CI, 12.6 to 29.3). The most common treatment-related adverse events were acneiform rash (93%), diarrhea (89%), and proteinuria (78%). The most common treatment-related adverse events of grade 3 or higher were hypertension (34%) and proteinuria (17%). CONCLUSIONS:The combination of bevacizumab and erlotinib showed antitumor activity in patients with HLRCC-associated or sporadic papillary renal-cell carcinoma. Toxic effects were those known to be associated with this combination. (Funded by the National Cancer Institute and others; ClinicalTrials.gov number, NCT01130519.).

PURPOSE:We postulated that PANVAC™, a recombinant poxviral vector vaccine, could enhance the immunologic and clinical response to an additional induction course of bacillus Calmette-Guérin (BCG) in patients with recurrent high-grade non-muscle-invasive bladder cancer (NMIBC). METHODS:This was a randomized, open-label, prospective, phase II study in subjects with high-grade NMIBC who had failed at least 1 induction course of intravesical BCG. Patients were randomized to either BCG alone or BCG+PANVAC. All subjects received intravesical BCG for 6 weeks. Patients in the combination arm also received priming and booster doses of PANVAC. The primary endpoint was recurrence-free survival. Secondary endpoints included progression-free survival and radical cystectomy-free survival. We also evaluated exploratory secondary immunological response endpoints. RESULTS:Our study concluded based on preplanned futility analysis. Overall, 32 patients were enrolled; 2 withdrew. Thirty patients (15/arm) were analyzed; 5 (33.3%) in the BCG-alone arm and 5 (33.3%) in the BCG+PANVAC arm met criteria for BCG- unresponsive disease. 12-month recurrence-free survival was 53.3% for the BCG-alone arm and 40% for the BCG+PANVAC arm. Overall recurrence rate at any time point was 73.3% at a median of 10.8 months, for an overall recurrence rate of 66.7% in the BCG-alone arm and 80% in the BCG+PANVAC arm. There was no difference in median recurrence-free survival or progression-free survival. CONCLUSIONS:This phase II study demonstrated no improvement in recurrence-free survival with BCG+PANVAC compared to BCG alone in patients with NMIBC who failed to respond to intravesical BCG.

The addition of systematic prostate biopsy enhances the detection of clinically significant prostate cancer compared to MRI-targeted biopsy alone. However, there is growing interest in using only MRI-targeted biopsy. We sought to evaluate PSA density as an adjunctive predictor of clinically significant prostate cancer detection in men undergoing combined biopsy and as a potential metric to stratify which patients may reasonably avoid systematic biopsies in favor of MRI-targeted biopsy only. Men with elevated PSA and/or abnormal digital rectal exam found to have an MRI-visible prostate lesion underwent MRI-targeted and systematic prostate biopsy. Primary outcomes were clinically significant cancer detection rates by MRI-targeted, systematic biopsy, and combined biopsy across four discrete PSA density intervals (<0.1, >0.1 and <0.15, >0.15 and <0.2, and >0.2 ng/ml/cm3). Secondary outcomes were the added value of systematic biopsy relative to MRI-targeted biopsy alone. Among men with PI-RADS >2 lesions, as PSA density surpassed each interval, the rate of grade group >3 cancer detection approximately doubled (<0.1:9.3%, >0.1 and <0.15:18.2%, >0.15 and <0.2:36%, and >0.2:61.2%). For PSA density >0.2, added detection of clinically significant cancer with systematic biopsy was low(2%, 95%CI:0.4%-5.9%). Given an approximate doubling in grade group >3 cancer detection on combined biopsy with rising PSA density intervals, clinicians may consider PSA density in risk stratifying men at high risk of clinically significant prostate cancer. Clinicians may consider omitting systematic biopsy for targeted biopsy of MRI-visible lesions in patients with PSA density >0.2ng/ml/cm3, as systematic biopsy results in low rates of additional clinically significant cancer detection.

PURPOSE:The addition of systematic prostate biopsy enhances the detection of clinically significant prostate cancer compared with MRI-targeted biopsy alone. However, there is growing interest in using only MRI-targeted biopsy. We sought to evaluate PSA density (PSAD) as an adjunctive predictor of clinically significant prostate cancer detection in men undergoing combined biopsy and as a potential metric to stratify which patients may reasonably avoid systematic biopsies in favor of MRI-targeted biopsy only. MATERIALS AND METHODS:Men with elevated PSA and/or abnormal digital rectal examination found to have an MRI-visible prostate lesion underwent MRI-targeted and systematic prostate biopsy. Primary outcomes were clinically significant cancer detection rates by MRI-targeted, systematic biopsy, and combined biopsy across 4 discrete PSAD intervals (<0.1, ≥0.1 and <0.15, ≥0.15 and <0.2, and ≥0.2 ng/mL/cm3). Secondary outcomes were the added value of systematic biopsy relative to MRI-targeted biopsy alone. RESULTS:Among men with Prostate Imaging Reporting and Data System ≥ 2 lesions, as PSAD surpassed each interval, the rate of grade group ≥ 3 cancer detection approximately doubled (<0.1: 9.3%, ≥0.1 and <0.15: 18.2%, ≥0.15 and <0.2: 36%, and ≥0.2: 61.2%). For PSAD ≥ 0.2, added detection of clinically significant cancer with systematic biopsy was low (2%, 95% CI: 0.4%-5.9%). CONCLUSIONS:Given an approximate doubling in grade group ≥ 3 cancer detection on combined biopsy with rising PSAD intervals, clinicians may consider PSAD in risk stratifying men at high risk of clinically significant prostate cancer. Clinicians may consider omitting systematic biopsy for targeted biopsy of MRI-visible lesions in patients with PSAD ≥ 0.2 ng/mL/cm3, as systematic biopsy results in low rates of additional clinically significant cancer detection.

Rationale and Objectives: To analyze variables that can predict the positivity of 18 F-DCFPyL- positron emission tomography/computed tomography (PET/CT) and extent of disease in patients with biochemically recurrent (BCR) prostate cancer after primary local therapy with either radical prostatectomy or radiation therapy. Materials and Methods: This is a retrospective analysis of a prospective single institutional review board -approved study. We included 199 patients with biochemical recurrence and negative conventional imaging after primary local therapies (radical prostatectomy n = 127, radiation therapy n = 72). All patients underwent 18 F-DCFPyL-PET/CT. Univariate and multivariate logistic regression analyses were used to determine predictors of a positive scan for both cohort of patients. Regression -based coefficients were used to develop nomograms predicting scan positivity and extra -pelvic disease. Decision curve analysis (DCA) was implemented to quantify nomogram's clinical benefit. Results: Of the 127 (63%) post -radical prostatectomy patients, 91 patients had positive scans - 61 of those with intrapelvic lesions and 30 with extra -pelvic lesions (i.e., retroperitoneal or distant nodes and/or bone/organ lesions). Of the 72 post -radiation therapy patients, 65 patients had positive scans - 39 of them had intrapelvic lesions and 26 extra -pelvic lesions. In the radical prostatectomy cohort, multivariate regression analysis revealed original International Society of Urological Pathology category, prostate -specific antigen (PSA), prostate -specific antigen doubling time (PSAdt), and time from BCR (mo) to scan were predictors for scan positivity and presence of extra -pelvic disease, with an area under the curve of 80% and 78%, respectively. Positive versus negative tumor margin after radical prostatectomy was not related to scan positivity or to the presence of positive extra -pelvic foci. In the radiation therapy cohort, multivariate regression analysis revealed that PSA, PSAdt, and time to BCR (mo) were predictors of extra -pelvic disease, with area under the curve of 82%. Because only seven patients in the radiation therapy cohort had negative scans, a prediction model for scan positivity could not be analyzed and only the presence of extra -pelvic disease was evaluated. Conclusion: PSA and PSAdt are consistently significant predictors of 18 F-DCFPyL PET/CT positivity and extra -pelvic disease in BCR prostate cancer patients. Stratifying the patient population into primary local treatment group enables the use of other variables as predictors, such as time since BCR. This nomogram may guide selection of the most suitable candidates for 18 F-DCFPyL-PET/CT imaging.

We present timing metrics and technical solutions used to monitor performance of the science calibration pipeline for JWST data. Software tools for managing and facilitating the daily operations of the pipeline are discussed, while the first two years of pipeline processing and reprocessing of JWST data are assessed against technical requirements.

You have accessJournal of UrologySurgical Technology & Simulation: Artificial Intelligence III (PD36)1 May 2024PD36-02 EVALUATING AI ASSISTANCE IN PROSTATE BPMRI INTERPRETATION: A MULTI-READER STUDY David G. Gelikman, Enis C. Yilmaz, Stephanie A. Harmon, Julie Y. An, Sena Azamat, Yan Mee Law, Daniel J. A. Margolis, Jamie Marko, Valeria Panebianco, Sonia Gaur, Marco Bicchetti, Erich P. Huang, Sandeep Gurram, Joanna H. Shih, Peter L. Choyke, Bradford J. Wood, Peter A. Pinto, and Baris Turkbey David G. GelikmanDavid G. Gelikman , Enis C. YilmazEnis C. Yilmaz , Stephanie A. HarmonStephanie A. Harmon , Julie Y. AnJulie Y. An , Sena AzamatSena Azamat , Yan Mee LawYan Mee Law , Daniel J. A. MargolisDaniel J. A. Margolis , Jamie MarkoJamie Marko , Valeria PanebiancoValeria Panebianco , Sonia GaurSonia Gaur , Marco BicchettiMarco Bicchetti , Erich P. HuangErich P. Huang , Sandeep GurramSandeep Gurram , Joanna H. ShihJoanna H. Shih , Peter L. ChoykePeter L. Choyke , Bradford J. WoodBradford J. Wood , Peter A. PintoPeter A. Pinto , and Baris TurkbeyBaris Turkbey View All Author Informationhttps://doi.org/10.1097/01.JU.0001008916.72488.6a.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Interpretation of biparametric magnetic resonance imaging (bpMRI) for prostate cancer is subject to significant inter-reader variability. Artificial intelligence (AI) has the potential to enhance diagnostic accuracy and consistency among radiologists. This study aims to evaluate the effectiveness of a deep-learning AI model as a first reader in assisting radiologists of varied experience in interpreting prostate bpMRI. METHODS: Six radiologists (3 prostate-focused and 3 generalists) from different institutions each evaluated 120 prostate bpMRIs, of which 80 were from cases with pathologically confirmed prostate cancer and 40 were controls. In 60 of these scans, readers used AI assistance using a first-reader method in which they were only allowed to accept or reject lesions that were detected on AI prediction maps without reporting any additional lesions. The remaining 60 cases were read without AI. We conducted a patient-level analysis to compare sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for lesion detection at bpMRI with and without AI. Inter-reader agreement on lesion measurement and PI-RADS scores was also evaluated. RESULTS: The patient cohort had a median age of 63 years (IQR, 57-68) and prostate-specific antigen of 7.1 ng/mL (IQR, 5.1-10.8). AI assistance varied in effectiveness across all readers, with sensitivity ranging from 83%-93%, specificity from 16%-90%, and accuracy from 68%-87%. Without AI, the ranges were 82%-98% for sensitivity, 19%-81% for specificity, and 70%-87% for accuracy, Table 1. One prostate-focused reader improved accuracy by 10% with AI and one general radiologist showed a 5% improvement. AI-assistance resulted in a slight, non-significant reduction in the median absolute difference in largest lesion dimension measurements (1.56 mm with AI vs. 2.27 mm without; p=.373). The quadratic weighted Cohen's kappa indicated a slight improvement in PI-RADS score agreement from 0.438 to 0.459 with AI. CONCLUSIONS: AI assistance in the interpretation of bpMRI can improve accuracy for some readers. Despite a slight improvement in measurement agreement and PI-RADS scores, the varied impact of AI on different readers calls for further investigation into how AI tools can best complement radiologists in an effective and consistent manner. Source of Funding: Intramural Research Program of the NCI, NIH © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e792 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information David G. Gelikman More articles by this author Enis C. Yilmaz More articles by this author Stephanie A. Harmon More articles by this author Julie Y. An More articles by this author Sena Azamat More articles by this author Yan Mee Law More articles by this author Daniel J. A. Margolis More articles by this author Jamie Marko More articles by this author Valeria Panebianco More articles by this author Sonia Gaur More articles by this author Marco Bicchetti More articles by this author Erich P. Huang More articles by this author Sandeep Gurram More articles by this author Joanna H. Shih More articles by this author Peter L. Choyke More articles by this author Bradford J. Wood More articles by this author Peter A. Pinto More articles by this author Baris Turkbey More articles by this author Expand All Advertisement PDF downloadLoading ...

Supplementary Tables 1-4 from hnRNP A2/B1 Modulates Epithelial-Mesenchymal Transition in Lung Cancer Cell Lines

RT-PCR primers, gene sequences, parts of discussion beyond the scope of the paper (167 pages).

Supplementary Figure 4 from A Gene Signature Predicting for Survival in Suboptimally Debulked Patients with Ovarian Cancer

Supplementary Figure 1 from A Gene Signature Predicting for Survival in Suboptimally Debulked Patients with Ovarian Cancer

Supplementary Figure 2 from A Gene Signature Predicting for Survival in Suboptimally Debulked Patients with Ovarian Cancer

Disease incidence data in a national-based cohort study would ideally be obtained through a national disease registry. Unfortunately, no such registry currently exists in the United States. Instead, the results from individual state registries need to be combined to ascertain certain disease diagnoses in the United States. The National Cancer Institute has initiated a program to assemble all state registries to provide a complete assessment of all cancers in the United States. Unfortunately, not all registries have agreed to participate. In this article, we develop an imputation-based approach that uses self-reported cancer diagnosis from longitudinally collected questionnaires to impute cancer incidence not covered by the combined registry. We propose a two-step procedure, where in the first step a mover-stayer model is used to impute a participant's registry coverage status when it is only reported at the time of the questionnaires given at 10-year intervals and the time of the last-alive vital status and death. In the second step, we propose a semiparametric working model, fit using an imputed coverage area sample identified from the mover-stayer model, to impute registry-based survival outcomes for participants in areas not covered by the registry. The simulation studies show the approach performs well as compared with alternative ad hoc approaches for dealing with this problem. We illustrate the methodology with an analysis that links the United States Radiologic Technologists study cohort with the combined registry that includes 32 of the 50 states.

Supplementary Figure Legends 1-3, Table Legend, Microarray Analysis from hnRNP A2/B1 Modulates Epithelial-Mesenchymal Transition in Lung Cancer Cell Lines

Background: Glioblastoma (GBM) is the most common brain tumor with an overall survival (OS) of less than 30% at two years. Valproic acid (VPA) demonstrated survival benefits documented in retrospective and prospective trials, when used in combination with chemo-radiotherapy (CRT). Purpose: The primary goal of this study was to examine if the differential alteration in proteomic expression pre vs. post-completion of concurrent chemoirradiation (CRT) is present with the addition of VPA as compared to standard-of-care CRT. The second goal was to explore the associations between the proteomic alterations in response to VPA/RT/TMZ correlated to patient outcomes. The third goal was to use the proteomic profile to determine the mechanism of action of VPA in this setting. Materials and Methods: Serum obtained pre- and post-CRT was analyzed using an aptamer-based SOMAScan® proteomic assay. Twenty-nine patients received CRT plus VPA, and 53 patients received CRT alone. Clinical data were obtained via a database and chart review. Tests for differences in protein expression changes between radiation therapy (RT) with or without VPA were conducted for individual proteins using two-sided t-tests, considering p-values of <0.05 as significant. Adjustment for age, sex, and other clinical covariates and hierarchical clustering of significant differentially expressed proteins was carried out, and Gene Set Enrichment analyses were performed using the Hallmark gene sets. Univariate Cox proportional hazards models were used to test the individual protein expression changes for an association with survival. The lasso Cox regression method and 10-fold cross-validation were employed to test the combinations of expression changes of proteins that could predict survival. Predictiveness curves were plotted for significant proteins for VPA response (p-value < 0.005) to show the survival probability vs. the protein expression percentiles. Results: A total of 124 proteins were identified pre- vs. post-CRT that were differentially expressed between the cohorts who received CRT plus VPA and those who received CRT alone. Clinical factors did not confound the results, and distinct proteomic clustering in the VPA-treated population was identified. Time-dependent ROC curves for OS and PFS for landmark times of 20 months and 6 months, respectively, revealed AUC of 0.531, 0.756, 0.774 for OS and 0.535, 0.723, 0.806 for PFS for protein expression, clinical factors, and the combination of protein expression and clinical factors, respectively, indicating that the proteome can provide additional survival risk discrimination to that already provided by the standard clinical factors with a greater impact on PFS. Several proteins of interest were identified. Alterations in GALNT14 (increased) and CCL17 (decreased) (p = 0.003 and 0.003, respectively, FDR 0.198 for both) were associated with an improvement in both OS and PFS. The pre-CRT protein expression revealed 480 proteins predictive for OS and 212 for PFS (p < 0.05), of which 112 overlapped between OS and PFS. However, FDR-adjusted p values were high, with OS (the smallest p value of 0.586) and PFS (the smallest p value of 0.998). The protein PLCD3 had the lowest p-value (p = 0.002 and 0.0004 for OS and PFS, respectively), and its elevation prior to CRT predicted superior OS and PFS with VPA administration. Cancer hallmark genesets associated with proteomic alteration observed with the administration of VPA aligned with known signal transduction pathways of this agent in malignancy and non-malignancy settings, and GBM signaling, and included epithelial–mesenchymal transition, hedgehog signaling, Il6/JAK/STAT3, coagulation, NOTCH, apical junction, xenobiotic metabolism, and complement signaling. Conclusions: Differential alteration in proteomic expression pre- vs. post-completion of concurrent chemoirradiation (CRT) is present with the addition of VPA. Using pre- vs. post-data, prognostic proteins emerged in the analysis. Using pre-CRT data, potentially predictive proteins were identified. The protein signals and hallmark gene sets associated with the alteration in the proteome identified between patients who received VPA and those who did not, align with known biological mechanisms of action of VPA and may allow for the identification of novel biomarkers associated with outcomes that can help advance the study of VPA in future prospective trials.

Supplementary Figure 5 from A Gene Signature Predicting for Survival in Suboptimally Debulked Patients with Ovarian Cancer

Supplementary Methods from Identification of an Integrated SV40 T/t-Antigen Cancer Signature in Aggressive Human Breast, Prostate, and Lung Carcinomas with Poor Prognosis