Use of crowdsourced research to develop a prognostic model for first-line metastatic castrate resistant prostate cancer (mCRPC).

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 180-180
Author(s):  
Fang Liz Zhou ◽  
Justin Guinney ◽  
Tao Wang ◽  
J. Christopher Bare ◽  
Thea C Norman ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Clinical Data ◽  
Prognostic Model ◽  
Trial Data ◽  
Machine Learning Techniques ◽  
Phase Iii ◽  
Prognostic Models ◽  
Data Sets ◽  
First Line ◽  
Validation Data

180 Background: Project Data Sphere, LLC (PDS) and Sage Bionetworks/DREAM have completed the “Prostate Cancer DREAM Challenge” (Challenge), a crowdsourced competition, using historical prostate cancer clinical trial data from PDS. The Challenge aimed to improve prognostic models for overall survival (OS) and to explore predictive models for treatment toxicity in mCRPC patients. Methods: Control arms of 4 randomized phase III trials (total 2,070 patients) were used as training and validation data sets for the Challenge: ASCENT2, MAINSAIL, VENICE and ENTHUSE33. All subjects were first line mCRPC patients receiving docetaxel treatment. Curated baseline clinical covariates (demographics, comorbidity, prior treatment, laboratory, lesion and vital signs) were modeled along with raw clinical data tables. The primary purpose of the Challenge was to develop a prognostic model for OS (SubChallenge 1). The models were scored using concordance index and integrated area under receiver operator curve (iAUC) from 6-30 months. The published mCRPC OS model of Halabi, et al., JCO, 2014, was used as the benchmark. Results: The Challenge attracted over 160 active participants who formed 50 teams that submitted final models for SubChallenge 1. Median iAUC was 0.76 (0.67-0.78) with a maximum score of 0.792. Over half (n = 35) of these models exceeded the published benchmark (0.743 iAUC). Teams explored new methodologies such as model-based imputation and machine learning techniques to develop the best performing models. Many leveraged raw clinical data sets to create their own covariates and expanded beyond existing prognostic models. Conclusions: The Challenge externally validated Halabi’s first line prognostic model. New prognostic models were proposed and validated with significant improvements over the benchmark. Further analyses are needed to examine the winning models for new prognostic factors and to validate them using additional trial data from PDS. The Challenge drove interest from cross-disciplinary teams of global experts to explore and enhance their technical abilities using real clinical data whilst serving as a vehicle to accelerate medical innovation.

scholarly journals Updated Prognostic Model for Predicting Overall Survival in First-Line Chemotherapy for Patients With Metastatic Castration-Resistant Prostate Cancer

2014 ◽  
Vol 32 (7) ◽  
pp. 671-677 ◽  
Author(s):  
Susan Halabi ◽  
Chen-Yen Lin ◽  
W. Kevin Kelly ◽  
Karim S. Fizazi ◽  
Judd W. Moul ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Model ◽  
Risk Groups ◽  
Phase Iii ◽  
Castration Resistant Prostate Cancer ◽  
First Line ◽  
Phase Iii Trial ◽  
Castration Resistant ◽  
Validation Set ◽  
Line Chemotherapy

Purpose Prognostic models for overall survival (OS) for patients with metastatic castration-resistant prostate cancer (mCRPC) are dated and do not reflect significant advances in treatment options available for these patients. This work developed and validated an updated prognostic model to predict OS in patients receiving first-line chemotherapy. Methods Data from a phase III trial of 1,050 patients with mCRPC were used (Cancer and Leukemia Group B CALGB-90401 [Alliance]). The data were randomly split into training and testing sets. A separate phase III trial served as an independent validation set. Adaptive least absolute shrinkage and selection operator selected eight factors prognostic for OS. A predictive score was computed from the regression coefficients and used to classify patients into low- and high-risk groups. The model was assessed for its predictive accuracy using the time-dependent area under the curve (tAUC). Results The model included Eastern Cooperative Oncology Group performance status, disease site, lactate dehydrogenase, opioid analgesic use, albumin, hemoglobin, prostate-specific antigen, and alkaline phosphatase. Median OS values in the high- and low-risk groups, respectively, in the testing set were 17 and 30 months (hazard ratio [HR], 2.2; P < .001); in the validation set they were 14 and 26 months (HR, 2.9; P < .001). The tAUCs were 0.73 (95% CI, 0.70 to 0.73) and 0.76 (95% CI, 0.72 to 0.76) in the testing and validation sets, respectively. Conclusion An updated prognostic model for OS in patients with mCRPC receiving first-line chemotherapy was developed and validated on an external set. This model can be used to predict OS, as well as to better select patients to participate in trials on the basis of their prognosis.

scholarly journals A predictive model of overall survival in patients with metastatic castration-resistant prostate cancer

F1000Research ◽  
2019 ◽  
Vol 5 ◽  
pp. 2674
Author(s):  
Mehrad Mahmoudian ◽  
Fatemeh Seyednasrollah ◽  
Liisa Koivu ◽  
Outi Hirvonen ◽  
Sirkku Jyrkkiö ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Overall Survival ◽  
Clinical Features ◽  
Clinical Information ◽  
Phase Iii ◽  
Prognostic Models ◽  
Castration Resistant Prostate Cancer ◽  
Phase Iii Clinical Trials ◽  
Castration Resistant ◽  
Improved Performance

Metastatic castration resistant prostate cancer (mCRPC) is one of the most common cancers with a poor prognosis. To improve prognostic models of mCRPC, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) Consortium organized a crowdsourced competition known as the Prostate Cancer DREAM Challenge. In the competition, data from four phase III clinical trials were utilized. A total of 1600 patients’ clinical information across three of the trials was used to generate prognostic models, whereas one of the datasets (313 patients) was held out for blinded validation. The previously introduced prognostic model of overall survival of chemotherapy-naive mCRPC patients treated with docetaxel or prednisone (so called Halabi model) was used as a performance baseline. This paper presents the model developed by the team TYTDreamChallenge and its improved version to predict the prognosis of mCRPC patients within the first 30 months after starting the treatment based on available clinical features of each patient. In particular, by replacing our original larger set of eleven features with a smaller more carefully selected set of only five features the prediction performance on the independent validation cohort increased up to 5.4 percent. While the original TYTDreamChallenge model (iAUC=0.748) performed similarly as the performance-baseline model developed by Halabi et al. (iAUC=0.743), the improved post-challenge model (iAUC=0.779) showed markedly improved performance by using only PSA, ALP, AST, HB, and LESIONS as features. This highlights the importance of the selection of the clinical features when developing the predictive models.

scholarly journals Clinical Prognostic Model of Autophagy-Related LncRNA Genes in The Esophageal Adenocarcinoma (EAC) to Predicting Overall Survival (OS) of The Patients：The Evidence From Bioinformatic Analysis

2021 ◽  
Author(s):  
Liusheng Wu ◽  
Xiaoqiang Li ◽  
Jixian Liu ◽  
Da Wu ◽  
Dingwang Wu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Esophageal Adenocarcinoma ◽  
Clinical Data ◽  
Roc Curve ◽  
Prognostic Model ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Prognostic Models ◽  
Cox Regression Analysis

Abstract Objective: Autophagy-related LncRNA genes play a vital role in the development of esophageal adenocarcinoma.Our study try to construct a prognostic model of autophagy-related LncRNA esophageal adenocarcinoma, and use this model to calculate patients with esophageal adenocarcinoma. The survival risk value of esophageal adenocarcinoma can be used to evaluate its survival prognosis. At the same time, to explore the sites of potential targeted therapy genes to provide valuable guidance for the clinical diagnosis and treatment of esophageal adenocarcinoma.Methods: Our study have downloaded 261 samples of LncRNA-related transcription and clinical data of 87 patients with esophageal adenocarcinoma from the TCGA database, and 307 autophagy-related gene data from www.autuphagy.com. We applied R software (Version 4.0.2) for data analysis, merged the transcriptome LncRNA genes, autophagy-related genes and clinical data, and screened autophagy LncRNA genes related to the prognosis of esophageal adenocarcinoma. We also performed KEGG and GO enrichment analysis and GSEA enrichment analysis in these LncRNA genes to analysis the risk characteristics and bioinformatics functions of signal transduction pathways. Univariate and multivariate Cox regression analysis were used to determine the correlation between autophagy-related LncRNA and independent risk factors. The establishment of ROC curve facilitates the evaluation of the feasibility of predicting prognostic models, and further studies the correlation between autophagy-related LncRNA and the clinical characteristics of patients with esophageal adenocarcinoma. Finally, we also used survival analysis, risk analysis and independent prognostic analysis to verify the prognosis model of esophageal adenocarcinoma.Results: We screened and identified 22 autophagic LncRNA genes that are highly correlated with the overall survival (OS) of patients with esophageal adenocarcinoma. The area under the ROC curve（AUC=0.941）and the calibration curve have a good lineup, which has statistical analysis value. In addition, univariate and multivariate Cox regression analysis showed that the autophagy LncRNA feature of this esophageal adenocarcinoma is an independent predictor of esophageal adenocarcinoma.Conclusion: These LncRNA screened and identified may participate in the regulation of cellular autophagy pathways, and at the same time affect the tumor development and prognosis of patients with esophageal adenocarcinoma. These results indicate that risk signature and nomogram are important indicators related to the prognosis of patients with esophageal adenocarcinoma.

Baseline covariates impacting overall survival (OS) in a phase III study of men with bone metastases from castration-resistant prostate cancer.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 4642-4642 ◽  
Author(s):  
Karim Fizazi ◽  
Christophe Massard ◽  
Matthew Raymond Smith ◽  
Michael E. Rader ◽  
Janet Elizabeth Brown ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Alkaline Phosphatase ◽  
Bone Metastases ◽  
Clin Oncol ◽  
Performance Status ◽  
Prognostic Significance ◽  
Phase Iii ◽  
Prognostic Models ◽  
Selection Strategies ◽  
Baseline Covariates

4642 Background: Prognostic models of OS in men with metastatic castrate-resistant prostate cancer (M+CRPC), have been limited. Here we present an analysis of baseline covariates associated with OS from an international phase 3 study that demonstrated superiority of denosumab over zoledronic acid for prevention of skeletal-related events (SRE) in this population (Fizazi et al., Lancet 2011;377:813-822). Methods: Patients had confirmed bone metastases (BM) from CRPC (a rising PSA despite castrate testosterone levels) and no prior bone anti-resorptive therapy. Proportional hazards modeling with various selection strategies was used to assess the prognostic significance of baseline covariates in multivariate analyses. Study-specified factors (previous SRE [Y vs N], PSA level [<10 vs ≥10 ng /mL]) and additional variables (Cook et al., Clin Cancer Res 2006;12:3361-3367; Halabi et al., J Clin Oncol 2003;21:1232-1237; Halabi et al., J Clin Oncol 2008;26:2544-2549) were explored. As no difference in OS was observed between treatment arms, analyses were performed using the pooled overall patient population. Results: Analyses included all randomized subjects with available baseline covariate data (n=1745). At the primary analysis date (median study duration 12.2 months), OS was 51%. Various selection strategies produced consistent results. In multivariate analysis, bone-specific alkaline phosphatase (BAP) ≥146 μg/L (p<0.0001) and corrected urinary N-telopeptide (uNTx) >50 nmol/mmol (p=0.0008) were associated with shorter OS, as were prior SRE (p=0.0002), PSA ≥10 ng /mL (p<0.0001), visceral metastases (p=0.0002), greater time from either diagnosis to first BM or first BM to randomization (p<0.0001 for both), ECOG performance status 2 vs. 0/1 (p=0.017), BPI-SF pain score >4 (p<0.0001), age (p=0.008), alkaline phosphatase >143 U/L (p<0.0001), and hemoglobin ≤128 g/L (p<0.0001). Conclusions: Besides known factors previously associated with OS in men with CRPC (Halabi et al., 2003), we show that bone-associated covariates (pain, prior SRE, BAP, and uNTx) are also important and independent prognostic factors for OS.

PROPEL: A randomized, phase III trial evaluating the efficacy and safety of olaparib combined with abiraterone as first-line therapy in patients with metastatic castration-resistant prostate cancer (mCRPC).

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. TPS340-TPS340 ◽  
Author(s):  
Noel W. Clarke ◽  
Andrew J. Armstrong ◽  
Antoine Thiery-Vuillemin ◽  
Mototsugu Oya ◽  
Dingwei Ye ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Phase Ii ◽  
Phase Ii Trial ◽  
Parp Inhibitor ◽  
Phase Iii ◽  
Castration Resistant Prostate Cancer ◽  
First Line ◽  
Double Blind ◽  
Phase Iii Trial ◽  
Metastatic Setting

TPS340 Background: A Phase II trial showed olaparib (tablets, 300 mg bid) in combination with abiraterone (1000 mg od plus prednisone/prednisolone 5 mg bid) significantly prolonged radiologic progression-free survival (rPFS) compared with abiraterone alone (median 13.8 vs 8.2 months; hazard ratio 0.65, 95% CI 0.44–0.97, P=0.034) in patients (pts) with mCRPC in the second-line metastatic setting who received prior docetaxel (Clarke et al. Lancet Oncol 2018). Treatment benefits were achieved irrespective of homologous recombination repair (HRR) mutation status, suggesting potential synergy between the two treatments that could impact a broader patient population. PROpel (EudraCT: 2018-002011-10) is the follow-on study to this, and the first Phase III trial to assess a PARP inhibitor in combination with abiraterone as first-line treatment in a genetically unselected mCRPC pt population. Methods: PROpel is a double-blind, placebo-controlled, international, multicenter study of pts randomized (1:1), as for the Phase II trial, to abiraterone (1000 mg od plus prednisone/prednisolone 5 mg bid) plus either olaparib (tablets, 300 mg bid) or placebo. Pts must not have received prior chemotherapy, new hormonal agents or other systemic treatment at mCRPC stage (except docetaxel at metastatic hormone-sensitive prostate cancer stage [mHSPC]). Randomization is stratified according to site of metastases (bone only vs visceral vs other) and docetaxel treatment at mHSPC stage (yes, no). The primary endpoint is investigator-assessed rPFS (RECIST v1.1 [soft tissue] and Prostate Working Cancer Group 3 [PCWG-3 criteria; bone]). Secondary objectives include time to first subsequent therapy or death, time to pain progression, overall survival, and health-related quality of life. Safety and tolerability will also be described. Exploratory endpoints include HRR subgroup analyses to confirm that efficacy is independent of HRR status. Screening across ~200 sites in 20 countries is being conducted to identify a target sample of ~720 pts. Enrollment is expected to begin in October 2018. (Study 8, NCT01972217). Clinical trial information: NCT03732820.

scholarly journals Development and External Validation of a Nomogram Predicting the Probability of Significant Gleason Sum Upgrading among Japanese Patients with Localized Prostate Cancer

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Takashi Imamoto ◽  
Takanobu Utsumi ◽  
Makoto Takano ◽  
Atsushi Komaru ◽  
Satoshi Fukasawa ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Model ◽  
Japanese Patient ◽  
External Validation ◽  
Specific Antigen ◽  
Japanese Patients ◽  
Study Cohort ◽  
Validation Data ◽  
Data Set ◽  
Pathological Assessment

Objective. The aim of this study is to develop a prognostic model capable of predicting the probability of significant upgrading among Japanese patients.Methods. The study cohort comprised 508 men treated with RP, with available prostate-specific antigen levels, biopsy, and RP Gleason sum values. Clinical and pathological data from 258 patients were obtained from another Japanese institution for validation.Results. Significant Gleason sum upgrading was recorded in 92 patients (18.1%) at RP. The accuracy of the nomogram predicting the probability of significant Gleason sum upgrading between biopsy and RP specimens was 88.9%. Overall AUC was 0.872 when applied to the validation data set. Nomogram predictions of significant upgrading were within 7.5% of an ideal nomogram.Conclusions. Nearly one-fifth of Japanese patients with prostate cancer will be significantly upgraded. Our nomogram seems to provide considerably accurate predictions regardless of minor variations in pathological assessment when applied to Japanese patient populations.

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