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Progress towards accurate prediction of overall survival in men with metastatic castration-resistant prostate cancer

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Prostate cancer (PC) is the most frequently diagnosed male malignancy and the 2 nd or 3 rd leading cause of cancer deaths in men in the developed countries. The disease progresses from locally invasive carcinoma to metastatic prostate cancer (mPC). While PC metastasizes to the liver and lung, bone is the most frequent site of PC metastasis. Distant metastasis likely marks the point of no return progress towards the worst prognosis. Owning to the landmark discovery that metastatic PC requires androgen receptor (AR) signaling by Charles Huggins in 1941, androgen deprivation therapy (ADT) remains the standard of care for mPC patients. Although the treatment provides initial benefits in the majority of patients with mPC, metastatic castration-resistant prostate cancer (mCRPC) inevitably arises. Prior to 2011, docetaxel was the only second line-therapy (1), and prolonged median overall survival (OS) in patients with mCRPC by 3 months. Since then, the second generation anti-androgens (abiraterone and enzalutamide), radium-223, cabazitaxel, and Sipuleucel-T have become available in the clinic. Although these therapies are not curative, they extend OS in patients with mCRPC (2). As these drugs have different mechanisms of action, they could be used in a variety of combinations either sequentially or simultaneously to maximize benefits to patients with mPC or mCRPC. For example, ADT plus docetaxel is superior to either alone (3) and is becoming the new standard of care for patients with mPC with good performance status. Clearly, improving our knowledge on the course of mCRPC will contribute to the development of rational treatment plans with the currently available medicines and thereby improves patient management. In this regard, identifying parameters to accurately predict survival of patients with mCRPC is an area of active research; there are 113 and 40 published studies related to the topic of mCRPC and prognostic biomarkers or prognostic models in PubMed (https://www.ncbi.nlm.nih.gov/pubmed/advanced) up to Dec 3, 2016. In order to yield a robust predictive model, it will be essential for a team with combined expertise in clinic and machine learning to analyze comprehensive sets of clinical data.
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Prostate cancer (PC) is the most frequently diagnosed male
malignancy and the 2nd or 3rd leading cause of cancer deaths
in men in the developed countries. The disease progresses
from locally invasive carcinoma to metastatic prostate
cancer (mPC). While PC metastasizes to the liver and lung,
bone is the most frequent site of PC metastasis. Distant
metastasis likely marks the point of no return progress
towards the worst prognosis. Owning to the landmark
discovery that metastatic PC requires androgen receptor
(AR) signaling by Charles Huggins in 1941, androgen
deprivation therapy (ADT) remains the standard of care
for mPC patients. Although the treatment provides initial
benefits in the majority of patients with mPC, metastatic
castration-resistant prostate cancer (mCRPC) inevitably
arises. Prior to 2011, docetaxel was the only second line-
therapy (1), and prolonged median overall survival (OS) in
patients with mCRPC by 3 months. Since then, the second
generation anti-androgens (abiraterone and enzalutamide),
radium-223, cabazitaxel, and Sipuleucel-T have become
available in the clinic. Although these therapies are not
curative, they extend OS in patients with mCRPC (2). As
these drugs have different mechanisms of action, they could
be used in a variety of combinations either sequentially or
simultaneously to maximize benets to patients with mPC
or mCRPC. For example, ADT plus docetaxel is superior
to either alone (3) and is becoming the new standard of
care for patients with mPC with good performance status.
Clearly, improving our knowledge on the course of mCRPC
will contribute to the development of rational treatment
plans with the currently available medicines and thereby
improves patient management. In this regard, identifying
parameters to accurately predict survival of patients with
mCRPC is an area of active research; there are 113 and
40 published studies related to the topic of mCRPC and
prognostic biomarkers or prognostic models in PubMed
(https://www.ncbi.nlm.nih.gov/pubmed/advanced) up to
Dec 3, 2016. In order to yield a robust predictive model, it
will be essential for a team with combined expertise in clinic
and machine learning to analyze comprehensive sets of
clinical data.
This effort was recently reported (4). Using the Dialogue
for Reverse Engineering Assessments and Methods
(DREAM) challenge platform through Project Data
Sphere, Guinney and colleagues analyzed the impact of
more than 150 clinical baseline variables on OS of 2,336
mCRPC patients with an array of state-of-the-art machine
learning tools including an ensemble of penalized Cox
regression (ePCR) model, and formulated a powerful
model for predicting OS of mCRPC patients (4). More
importantly, the study was a part of an even much larger
Progress towards accurate prediction of overall survival in men
with metastatic castration-resistant prostate cancer
Wenjuan Mei1,2,3,4, Anil Kapoor2,5, Pierre Major6, Bobby Shayegan2,5, Damu Tang1,2,3
1Division of Nephrology, Department of Medicine, McMaster University, Hamilton, Ontario, Canada; 2Father Sean O’Sullivan Research
Institute, Hamilton, Ontario, Canada; 3The Hamilton Center for Kidney Research, St. Joseph’s Hospital, Hamilton, Ontario, Canada; 4Division
of Nephrology, The First Afliated Hospital of Nanchang University, Nanchang 330006, China; 5Department of Surgery, McMaster University,
Hamilton, Ontario, Canada; 6Division of Medical Oncology, Juravinski Cancer Center, Hamilton, Ontario, Canada
Correspondence to: Damu Tang. T3310, St. Joseph’s Hospital, 50 Charlton Ave East, Hamilton, Ontario L8N 4A6, Canada. Email: damut@mcmaster.ca.
Provenance: This is a Guest Commentary commissioned by Section Editor Longfei Liu, MD, PhD (Department of Urology, Xiangya Hospital,
Central South University, Changsha, China) and Assistant Editor Yongbao Wei, MD (Department of Urology, Fujian Provincial Hospital, the
teaching hospital of Fujian Medical University, Fuzhou, China).
Comment on: Guinney J, Wang T, Laajala TD, et al. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer:
development of a prognostic model through a crowdsourced challenge with open clinical trial data. Lancet Oncol 2017;18:132-42.
Received: 13 December 2016; Accepted: 11 January 2017; Published: 13 February 2017.
doi: 10.21037/jxym.2017.02.10
View this article at: http://dx.doi.org/10.21037/jxym.2017.02.10
Commentary
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and comprehensive effort: sharing and analyzing the
clinical data by 163 experts consisting of 50 independent
teams worldwide (4). The clinical data used were compiled
from the control arms of five large randomized phase III
clinical trials through the effort of Project Data Sphere
(n=2,336, Table 1). Except ENTHUSE M1, patients in the
comparator arms from the rest of clinical trials were treated
with docetaxel (Table 1). More than 150 clinical variables
provided in individual clinical trials were centrally curated
by the organizers of the DREAM challenge to yield a core
table; of which data from three clinical trials (n=1,600) were
distributed to 50 teams for training analysis, and data from
ENTHUSE 33 and ENTHUSE M1 were used to score a
winning ePCR model and validate the model (Figure 1) (4).
The model performances well for stratifying low and
high risk patients in both ENTHUSE 33 (n=313) and
ENTHUSE M1 (n=226) cohorts (Figure 1) with predictive
accuracy determined by iAUC (integrated time-dependent
area under the curve) of respectively 0.791 and 0.768 (4).
Both risk groups have a significant difference in OS as
analyzed by the Kaplan-Meier method: hazard ratio (HR)
3.32, 95% confidence intervals (CI): 2.39–4.62, P<0.0001
for ENTHUSE 33 and HR 2.86, 95% CI: 2–4.12,
P<0.0001 for the control arm of ENTHUSE M1 (4).
Unlike patients in the control arms of other four clinical
trials, patients from the comparator arm of ENTHUSE M1
were receiving only placebo (Table 1) (4). Validation of the
ePCR model in the latter cohort confirmed its predictive
value as disease- rather than treatment-dependent.
The ePCR model not only won the challenge amount
50 competitive teams but also outperformed a prognostic
model recently published by Halabi and colleagues (5) in
stratifying low and high risk patients in both ENTHUSE
33 and ENTHUSE M1 (4). The Halabi model holds
superiority to similar models published prior to their
research (5); ePCR is thus likely the best prognostic
platform currently available in the public domain to predict
Table 1 Patientsa used by the DREAM challenge platform
CohortbNumber Treatment received Application LivereKidneye
ASCENT2 476 Doc + predncTraining Normal Normal
MAINSAIL 526 Doc + predn + placebo Training AdequatefAdequateg
VENICE 598 Doc + predn + placebo Training Adequate Adequate
ENTHUSE 33 470 Doc + placebo Test and scoredAdequatehAdequatei
ENTHUSE M1 266 Placebo Validation Adequate Adequate
a, mCRPC patients; b, all cohorts were from the control arms of the indicated phase III clinical trials; c, Doc/docetaxel and predn/prednisone
(prednisone or prednisolone for VENICE); d, a subgroup (n=157) was used for testing individual models and a second subgroup (n=313)
was for predicting OS (see Figure 1 for details); e, liver and kidney function at the time of patient recruitment; f, total bilirubin <1xULN (up
limit of normal), AST and/or ALT (alanine aminotransferase) <1.5×ULN; g, creatinine clearance >50 mL/minute; h, patients with ALT or AST
>2.5×ULN in the presence of liver metastasis were excluded; i, patients with 24 hours creatinine clearance <50 mL/min were excluded.
DREAM, Dialogue for Reverse Engineering Assessments and Methods.
ASCENT2
MAINSAIL
VENICE
n=1600
ENTHUSE 33
n=470
standardization of
clinical variables
ASCENT2
MAINSAIL
VENICE
*
training by
50 teams
1 ... ... 50
ENTHUSE 33 *
n=470
n=126
n=126
n=126
total n=157
round 1
round 2
round 3
3 rounds of submission and test
1 ... ... 50
n=333
prediction of survival
scoring the best model
split
ePCR
ENTHUSE M1
n=266
curation of
clinical
virables
ENTHUSE M1*
validation
Figure 1 Illustration of the research flow reported by Guinney
and colleagues. The control arms of the indicated clinical trials are
shown. *, indicates the sets of clinical variables generated through
central standardization. 1 … … 50 are for teams 1–50. A sub-group
(n=157) from ENTHUSE 33* was randomly (100 random split)
divided into three overlap subgroups (n=126) for teams to test their
models in the three rounds of submission and test; three (n=126)
subgroups collectively cover all 157 patients.
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OS of mCRPC patients. However, this comes with no
surprise, as the DREAM challenge also outmatched the
Halabi group in terms of patient resources, team size (50
teams and 163 individuals) and collective expertise, as well as
state-of-the-art machine learning and statistical modelling
methods (4). While the Halabi model incorporates 22
clinical variables using the adaptive least absolute shrinkage
and selection operator (LASSO) penalized Cox regression,
ePCR analyzed 150 clinical variables using an advanced
Cox regression model: an ePCR (4,5). Nonetheless, the
Halabi model was able to stratify very similar groups of
low or high risk patients from ENTHUSE 33 compared
to the ePCR model (4), and is a much simpler model than
ePCR. Importantly, the Halabi model is available on line
as a reference tool for physicians to evaluate their mCRPC
patients (https://www.cancer.duke.edu/Nomogram/
firstlinechemotherapy.html.); the model thus has its
applications. Likewise, Guinney and colleagues will likely
make their model available in the same manner in the near
future. Nonetheless, it is clear that the ePCR model is more
comprehensive compared to the Halabi model. The study
thus has set a new standard of data sharing and analyzing in
model-building. This is particularly beneficial for clinical
trials not only because of the massive effort that has been
spent on these trials but also due to immediate benets that
it can bring to patients.
In addition to producing a robust prognostic model,
the data sharing effort also provides novel knowledge
in the prediction of OS for mCRPC patients. Aspartate
aminotransferase (AST) was indicated as an important
prognostic biomarker (4). However, most patients were
recruited to these clinical trials with normal or adequate
liver function (Table 1). The prognostic potential of
AST might be resulted from liver injury caused by liver
metastasis, as 1–14% of patients in all ve clinical trials used
in this study had liver metastasis (4). Hepatic metastasis is a
well-known cause for poor outcome. It will be interesting to
examine AST’s prognostic values in patients with only bone
metastasis or lack of liver metastasis. The same concern
also applies to kidney function. Patients were recruited also
with adequate renal function (Table 1); whether the renal
functional measurements (creatinine, creatinine clearance,
and calculated creatinine clearance) display prognostic
values should be further investigated. Will it be possible that
the prognostic values of these kidney criteria reflect their
interactions with other hematologic markers? As Guinney
and colleagues acknowledged that these interactions did not
reach a significant level (4), the network contributions to
the evaluation of OS in patients with mCRPC, as implied in
this report, should be explored in future.
Nonetheless, the concept of network of interaction
in predicting OS for mCRPC patients is intriguing. The
network involves biomarkers derived from the immune,
liver, and renal systems (4). While the prognostic
contributions of immunologic biomarkers are not surprising,
how renal functions impairment contributes to mCRPC
progression remains unclear. Will insufficient filtration
of some compounds contribute to mCRPC progression?
More attention has been paid to search for tumor-derived
prognostic factors. Guinney and colleagues raise an
interesting issue about the impact of the patient overall
health condition on the deadly progression of mCRPC.
Furthermore, work on the impact of the heterogeneities
in individual tumors versus individual host conditions in
mCRPC progression may reveal novel insights.
Answering the above questions will certainly requires
extensive and open data sharing and collaborative research
efforts. Furthermore, this type of big-data analyses needs to
incorporate molecular events. For example, prostate cancer
stem cells (PCSCs) are the driving force in PC evolution;
potential PCSC biomarkers should be considered. Will
the tumor samples in the clinical trials used in the study
by Guinney et al. be available for proling gene expression
changes using RNA sequencing and genomic alterations
through whole genome sequencing? If not, future studies
should be coordinated at the levels of RNA and DNA
alterations.
Acknowledgements
D Tang is supported by grants from Teresa Cascioli
Charitable Foundation Research Award in Women’s
Health, Canadian Breast Cancer Foundation, and Cancer
Research Society.
Footnote
Conicts of Interest: The authors have no conicts of interest
to declare.
References
1. Mukherji D, Omlin A, Pezaro C, et al. Metastatic
castration-resistant prostate cancer (CRPC): preclinical
and clinical evidence for the sequential use of novel
therapeutics. Cancer Metastasis Rev 2014;33:555-66.
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2. Agarwal N, Di Lorenzo G, Sonpavde G, et al. New agents
for prostate cancer. Ann Oncol 2014;25:1700-9.
3. Sweeney CJ, Chen YH, Carducci M, et al. Chemohormonal
Therapy in Metastatic Hormone-Sensitive Prostate Cancer.
N Engl J Med 2015;373:737-46.
4. Guinney J, Wang T, Laajala TD, et al. Prediction of
overall survival for patients with metastatic castration-
resistant prostate cancer: development of a prognostic
model through a crowdsourced challenge with open
clinical trial data. Lancet Oncol 2017;18:132-142.
5. Halabi S, Lin CY, Kelly WK, et al. Updated prognostic
model for predicting overall survival in rst-line
chemotherapy for patients with metastatic castration-
resistant prostate cancer. J Clin Oncol 2014;32:671-7.
doi: 10.21037/jxym.2017.02.10
Cite this article as: Mei W, Kapoor A, Major P, Shayegan B,
Tang D. Progress towards accurate prediction of overall survival
in men with metastatic castration-resistant prostate cancer. J
Xiangya Med 2017;2:17.
... Machine learning has been rising as a powerful tool in classification and regression modeling of high dimensional variables in cancer recurrence and OS. For insistence, 150 clinical baseline variables have been modeled for prediction (21,22) and more than 600 differentially expressed genes have been selected to stratify BCR (18). The majority of these machine learning efforts were based on the Cox PH model. ...
... The feature (variable) importance derived from random forest modeling should provide an indication on this issue should this data be reported. Furthermore, lactate dehydrogenase (LDH), albumin, hemoglobin, alkaline phosphatase (ALP) (23) along with a set of clinical factors related to kidney function, haematology, and others (21,22) display predictive value toward OS in patients with mCRPC. Should these factors be relevant to the author's models? ...
... This model was generated by a massive collaborative effort through the Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge platform and involved five phase III clinical trials (n=2,336). In addition to the parameters described above, this model also include many other factors related to kidney function, haematology, and others (5,6). ...
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Background: Androgen-deprivation therapy (ADT) has been the backbone of treatment for metastatic prostate cancer since the 1940s. We assessed whether concomitant treatment with ADT plus docetaxel would result in longer overall survival than that with ADT alone. Methods: We assigned men with metastatic, hormone-sensitive prostate cancer to receive either ADT plus docetaxel (at a dose of 75 mg per square meter of body-surface area every 3 weeks for six cycles) or ADT alone. The primary objective was to test the hypothesis that the median overall survival would be 33.3% longer among patients receiving docetaxel added to ADT early during therapy than among patients receiving ADT alone. Results: A total of 790 patients (median age, 63 years) underwent randomization. After a median follow-up of 28.9 months, the median overall survival was 13.6 months longer with ADT plus docetaxel (combination therapy) than with ADT alone (57.6 months vs. 44.0 months; hazard ratio for death in the combination group, 0.61; 95% confidence interval [CI], 0.47 to 0.80; P<0.001). The median time to biochemical, symptomatic, or radiographic progression was 20.2 months in the combination group, as compared with 11.7 months in the ADT-alone group (hazard ratio, 0.61; 95% CI, 0.51 to 0.72; P<0.001). The rate of a prostate-specific antigen level of less than 0.2 ng per milliliter at 12 months was 27.7% in the combination group versus 16.8% in the ADT-alone group (P<0.001). In the combination group, the rate of grade 3 or 4 febrile neutropenia was 6.2%, the rate of grade 3 or 4 infection with neutropenia was 2.3%, and the rate of grade 3 sensory neuropathy and of grade 3 motor neuropathy was 0.5%. Conclusions: Six cycles of docetaxel at the beginning of ADT for metastatic prostate cancer resulted in significantly longer overall survival than that with ADT alone. (Funded by the National Cancer Institute and others; ClinicalTrials.gov number, NCT00309985.).
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The therapeutic landscape of metastatic castration-resistant prostate cancer (mCRPC) has been revolutionized by the arrival of multiple novel agents in the past 2 years. Immunotherapy in the form of sipuleucel-T, androgen axis inhibitors, including abiraterone acetate and enzalutamide, a chemotherapeutic agent, cabazitaxel, and a radiopharmaceutical, radium-223, have all yielded incremental extensions of survival and have been recently approved. A number of other agents appear promising in early studies, suggesting that the armamentarium against castrate-resistant prostate cancer is likely to continue to expand. Emerging androgen pathway inhibitors include androgen synthesis inhibitors (TAK700), androgen receptor inhibitors (ARN-509, ODM-201), AR DNA binding domain inhibitors (EPI-001), selective AR downregulators or SARDs (AZD-3514), and agents that inhibit both androgen synthesis and receptor binding (TOK-001/galeterone). Promising immunotherapeutic agents include poxvirus vaccines and CTLA-4 inhibitor (ipilimumab). Biologic agents targeting the molecular drivers of disease are also being investigated as single agents, including cabozantinib (Met and VEGFR2 inhibitor) and tasquinimod (angiogenesis and immune modulatory agent). Despite the disappointing results seen from studies evaluating docetaxel in combination with other agents, including GVAX, anti-angiogentic agents (bevacizumab, aflibercept, lenalinomide), a SRC kinase inhibitor (dasatinib), endothelin receptor antagonists (atrasentan, zibotentan), and high-dose calcitriol (DN-101), the results from the trial evaluating docetaxel in combination with the clusterin antagonist, custirsen, are eagerly awaited. New therapeutic hurdles consist of discovering new targets, understanding resistance mechanisms, the optimal sequencing and combinations of available agents, as well as biomarkers predictive for benefit. Novel agents targeting bone metastases are being developed following the success of zoledronic acid and denosumab. Finally, all of these modalities do not appear curative, suggesting that clinical trial enrollment and a better understanding of biology remain of paramount importance.
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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. 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). 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. 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.
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Background: Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods: Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings: 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39-4·62, p<0·0001; reference model: 2·56, 1·85-3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker. Interpretation: Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer. Funding: Sanofi US Services, Project Data Sphere.
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With five novel therapies shown to improve survival in metastatic castration-resistant prostate cancer (CRPC) in the last 3 years, patients are now living longer and experiencing better quality of life. Since docetaxel became standard of care for men with symptomatic metastatic CRPC, three artificial treatment "spaces" have emerged for prostate cancer drug development: pre-docetaxel, docetaxel combinations, and following docetaxel. Multiple therapies are currently under development in both early and late stage CRPC. Additionally, the novel agents abiraterone, radium-223, cabazitaxel, and enzalutamide have all been approved in the post-docetaxel setting. Strategies for patient selection and treatment sequencing are therefore urgently required. In this comprehensive review, we will summarize the preclinical and clinical data available with regards to sequencing of the novel treatments for CRPC.
Updated prognostic model for predicting overall survival in first-line chemotherapy for patients with metastatic castrationresistant prostate cancer
  • S Halabi
  • C Y Lin
  • W K Kelly
Halabi S, Lin CY, Kelly WK, et al. Updated prognostic model for predicting overall survival in first-line chemotherapy for patients with metastatic castrationresistant prostate cancer. J Clin Oncol 2014;32:671-7. doi: 10.21037/jxym.2017.02.10