scholarly journals PROMISE: a real-world clinical-genomic database to address knowledge gaps in prostate cancer

2021 ◽  
Author(s):  
Vadim S. Koshkin ◽  
Vaibhav G. Patel ◽  
Alicia Ali ◽  
Mehmet A. Bilen ◽  
Deepak Ravindranathan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Real World ◽  
Data Entry ◽  
Precision Oncology ◽  
Castration Resistant Prostate Cancer ◽  
Genomic Database ◽  
Address Data ◽  
Genomic Platform ◽  
Clinical Questions ◽  
Clinical Genomic

Abstract Purpose Prostate cancer is a heterogeneous disease with variable clinical outcomes. Despite numerous recent approvals of novel therapies, castration-resistant prostate cancer remains lethal. A “real-world” clinical-genomic database is urgently needed to enhance our characterization of advanced prostate cancer and further enable precision oncology. Methods The Prostate Cancer Precision Medicine Multi-Institutional Collaborative Effort (PROMISE) is a consortium whose aims are to establish a repository of de-identified clinical and genomic patient data that are linked to patient outcomes. The consortium structure includes a (1) bio-informatics committee to standardize genomic data and provide quality control, (2) biostatistics committee to independently perform statistical analyses, (3) executive committee to review and select proposals of relevant questions for the consortium to address, (4) diversity/inclusion committee to address important clinical questions pertaining to racial disparities, and (5) patient advocacy committee to understand patient perspectives to improve patients’ quality of care. Results The PROMISE consortium was formed by 16 academic institutions in early 2020 and a secure RedCap database was created. The first patient record was entered into the database in April 2020 and over 1000 records have been entered as of early 2021. Data entry is proceeding as planned with the goal to have over 2500 patient records by the end of 2021. Conclusions The PROMISE consortium provides a powerful clinical-genomic platform to interrogate and address data gaps that have arisen with increased genomic testing in the clinical management of prostate cancer. The dataset incorporates data from patient populations that are often underrepresented in clinical trials, generates new hypotheses to direct further research, and addresses important clinical questions that are otherwise difficult to investigate in prospective studies.

scholarly journals Best Paper Selection

2018 ◽  
Vol 27 (01) ◽  
pp. 226-226
Keyword(s):  
Prostate Cancer ◽  
Knowledge Base ◽  
Prediction Models ◽  
Precision Oncology ◽  
Castration Resistant Prostate Cancer ◽  
Short Term ◽  
Castration Resistant

Chakravarty D, Gao J, Phillips SM, Kundra R, Zhang H, Wang J, Rudolph JE, Yaeger R, Soumerai T, Nissan MH, Chang MT, Chandarlapaty S, Traina TA, Paik PK, Ho AL, Hantash FM, Grupe A, Baxi SS, Callahan MK, Snyder A, Chi P, Danila D, Gounder M, Harding JJ, Hellmann MD, Iyer G, Janjigian Y, Kaley T, Levine DA, Lowery M, Omuro A, Postow MA, Rathkopf D, Shoushtari AN, Shukla N, Voss M, Paraiso E, Zehir A, Berger MF, Taylor BS, Saltz LB, Riely GJ, Ladanyi M, Hyman DM, Baselga J, Sabbatini P, Solit DB, Schultz N. OncoKB: a precision oncology knowledge base. JCO Precis Oncol 2017 Jul;2017 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28890946/ Newton Y, Novak AM, Swatloski T, McColl DC, Chopra S, Graim K, Weinstein AS, Baertsch R, Salama SR, Ellrott K, Chopra M, Goldstein TC, Haussler D, Morozova O, Stuart JM. TumorMap: exploring the molecular similarities of cancer samples in an interactive portal. Cancer Res 2017 Nov 1;77(21):e111-e114 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/29092953/ Seyednasrollah F, Koestler DC, Wang T, Piccolo SR, Vega R, Greiner R, Fuchs C, Gofer E, Kumar L, Wolfinger RD, Winner KK, Bare C, Neto EC, Yu T, Shen L, Abdallah K, Norman T, Stolovitzky G, Soule HR, Sweeney CJ, Ryan CJ, Scher HI, Sartor O, Elo LL, Zhou FL, Guinney J, Costello JC, and Prostate Cancer DREAM Challenge Community. A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer. JCO Clin Cancer Inform 2017 Aug 4;(1):1-15 http://ascopubs.org/doi/abs/10.1200/CCI.17.00018

Retrospective cohort analysis of real-world clinical outcomes in nonmetastatic (M0) castration-resistant prostate cancer (CRPC) treated with novel androgen receptor pathway inhibitors (ARPI).

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 51-51
Author(s):  
Richard Gagnon ◽  
Nimira S. Alimohamed ◽  
Alexander Watson ◽  
Eugene Batuyong ◽  
Alyssa Chow ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Clinical Outcomes ◽  
Real World ◽  
Retrospective Cohort ◽  
Cohort Analysis ◽  
Progression Free Survival ◽  
Rank Test ◽  
Castration Resistant Prostate Cancer ◽  
Survival Times ◽  
Free Survival

51 Background: The landscape of M0 CRPC has changed with the recent demonstration of metastasis-free survival (MFS) and overall survival (OS) improvements with the use of ARPIs in clinical trial settings. However, the extrapolation of this data to clinical practice is limited by strict exclusion criteria in these trials, including prior or concurrent malignancy, cardiovascular disease, or hypertension. The purpose of this study was to assess real-world outcomes in patients with M0 CRPC treated with ARPIs compared to historical controls. Methods: We designed a retrospective cohort study with the inclusion of patients in Alberta, Canada diagnosed with M0 CRPC between 2001-2020. Via chart review, we identified baseline characteristics, potential confounders, treatment details, and clinical outcomes. The primary outcome of interest was MFS. Secondary outcomes included: second progression-free survival (PFS2) and OS. Median survival times were measured using the Kaplan-Meier method and the log-rank test was used for comparison of outcomes based on ARPI exposure. Cox proportional hazard regression models were used to calculate hazard ratios (HR) accounting for impact of PSA doubling time (PSADT), use of osteoclast inhibiting agents, and presence of pelvic lymphadenopathy. Results: We identified 211 patients across multiple centres in Alberta with M0 CRPC, with 54 having received apalutamide (40/54), enzalutamide (7/54), or darolutamide (7/54). Median age at M0 CRPC diagnosis was 74 years; median PSADT was 4.4 months; and 19% of patients (40/211) had pelvic lymphadenopathy at diagnosis. Median MFS in patients treated with ARPIs was 47.5 months compared to 20.6 months in those not treated with ARPIs (HR, 0.23; 95% confidence interval [CI], 0.11-0.49; p < 0.001). Median PFS2 in ARPI treated patients was 66.3 months compared with 35.6 months (HR, 0.40; 95% CI, 0.18-0.87; p = 0.022). Median OS for patients treated with ARPI was not reached. Conclusions: Given the older age of men with advanced prostate cancer, real-world outcomes that include patients with comorbidities are important adjuncts to the interpretation of clinical trials exploring the benefit of novel therapeutics. Here, we demonstrate that in a real-world, unselected population of men with M0 CRPC, apalutamide, enzalutamide, and darolutamide seem to confer similar MFS and PFS2 benefits to those demonstrated in the SPARTAN, PROSPER, and ARAMIS studies. Real-world OS data remain immature and will be an important addition to these findings.

Genomic landscape of advanced prostate cancer in racial minority populations: Real-world experience in a safety-net hospital oncology clinic.

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 14-14
Author(s):  
Tamer Khashab ◽  
Alexander D Le ◽  
Samantha Cohen ◽  
Salma Kaochar ◽  
Heidi Dowst ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Real World ◽  
Safety Net ◽  
Access To Healthcare ◽  
Minority Populations ◽  
Precision Oncology ◽  
Genomic Landscape ◽  
Safety Net Hospital ◽  
Ngs Data ◽  
Racial Ethnic

14 Background: The largest US cancer health disparity exists in prostate cancer (PC), with African American (AA) men having: ~1.6-1.8-fold higher risk of developing PC; younger age and more advanced stage at diagnosis; increased risk of recurrence after radical prostatectomy; and up to 2.5-fold higher mortality rate relative to men of other ancestries. Access to healthcare and other socioeconomic and environmental factors contribute to the disparity in clinical outcomes. However, genetic factors may also be involved, and their role and prevalence need to be better defined, especially in real-world clinical settings, as the high cost of next-generation sequencing (NGS) may have resulted in underrepresentation of uninsured and minority patients in prior studies. Methods: We retrospectively analyzed NGS data obtained via Tempus|xT tissue assay (DNA sequencing of 648 genes in tumor and matched normal samples at 500x depth) and/or Tempus|xF liquid biopsy assay (ctDNA sequencing of 105 genes in peripheral blood samples at 5,000x depth) for germline and/or somatic mutations detected in 100 patients (53 AA) receiving androgen deprivation therapy for locally advanced, biochemically recurrent or metastatic PC at Ben Taub Hospital (BTH), a safety net hospital in Harris County/Houston serving a patient population of which 91% are racial/ethnic minorities. For confirmation, we analyzed de-identified NGS data from a nationwide cohort of 1,211 metastatic PC patients (213 AA) previously sequenced with xT and/or xF by Tempus Labs (Chicago, IL). Results: We found higher frequencies of AR (18.9%), TP53 (41.5%), SPOP (20.7%) and homologous recombination repair (HRR) pathway gene mutations, in particular BRCA2 (17%), in our AA BTH cohort, as compared to PC patients of other races/ethnicities. The latter finding was confirmed in the nationwide Tempus Labs cohort, with 91/213 (42.7%) AA patients exhibiting mutation in at least one of 14 HRR pathway genes associated with PC sensitivity to PARP inhibitors, compared to 347/998 (34.7%) non-AA patients (P < 0.05). This difference was mainly driven by higher frequency of BRCA2 (16.9%), CDK12 (8%) and PALB2 (5.2%) mutations in AA patients. In both cohorts, TMPRSS2 fusions were much less common in AA PC patients. Conclusions: The observed high frequency of mutations in key PC drivers in AA patients may reflect differences in disease biology between racial/ethnic groups or the more advanced disease presentation of AA patients due to socioeconomic factors delaying access to healthcare. Our study provides a real-world snapshot of the genomic landscape of advanced PC in a safety net hospital serving large racial/ethnic minority populations and highlights the role that NGS testing can play to improve their access to treatment with novel targeted therapies and to biomarker-based Precision Oncology clinical trials.

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