Clinical Evidence Generation During a Pandemic: Lessons Learned for Sustaining Progress.

ABSTRACT: Because of significant adaptations forced by the COVID-19 pandemic, resultant changes within health care delivery and clinical research introduced the potential for evaluation of novel evidence generation approaches in oncology. On July 26 and 27, 2021, the National Academies of Science, Engineering, and Medicine, National Cancer Policy Forum hosted a virtual workshop entitled "Cancer Care and Cancer Research in the Context of the COVID-19 Pandemic: A Workshop on Lessons Learned." This workshop examined changes in cancer care and cancer research that occurred in response to the COVID-19 pandemic and considered lessons learned from that experience. The goal was to identify what changes could improve the delivery of high-quality cancer care and the conduct of cancer clinical trials in the postpandemic era, with an emphasis on health equity. How can we sustain the valuable lessons learned that might accelerate progress and enhance clinical evidence generation for patients and clinicians? In this overview, we discuss ways in which the COVID-19 experience has catalyzed research efficiencies as well as fostered a broader array of trial design and research methods that may facilitate improved cancer drug development during the pandemic and beyond.

Assessment of a prognostic model, PSA metrics and toxicities in metastatic castrate resistant prostate cancer using data from Project Data Sphere (PDS).

BACKGROUND: Prognostic models in metastatic castrate resistant prostate cancer (mCRPC) may have clinical utility. Using data from PDS, we aimed to 1) validate a contemporary prognostic model (Templeton et al., 2014) 2) evaluate prognostic impact of concomitant medications and PSA decrease 3) evaluate factors associated with docetaxel toxicity. METHODS: We accessed data on 2,449 mCRPC patients in PDS. The existing model was validated with a continuous risk score, time-dependent receiver operating characteristic (ROC) curves, and corresponding time-dependent Area under the Curve (tAUC). The prognostic effects of concomitant medications and PSA response were assessed by Cox proportional hazards models. One year tAUC was calculated for multivariable prognostic model optimized to our data. Conditional logistic regression models were used to assess associations with grade 3/4 adverse events (G3/4 AE) at baseline and after cycle 1 of treatment. RESULTS: Despite limitations of the PDS data set, the existing model was validated; one year AUC, was 0.68 (95% CI 95% CI, .66 to .71) to 0.78 (95%CI, .74 to .81) depending on the subset of datasets used. A new model was constructed with an AUC of .74 (.72 to .77). Concomitant medications low molecular weight heparin and warfarin were associated with poorer survival, Metformin and Cox2 inhibitors were associated with better outcome. PSA response was associated with survival, the effect of which was greatest early in follow-up. Age was associated with baseline risk of G3/4 AE. The odds of experiencing G3/4 AE later on in treatment were significantly greater for subjects who experienced a G3/4 AE in their first cycle (OR 3.53, 95% CI 2.53-4.91, p < .0001). CONCLUSION: Despite heterogeneous data collection protocols, PDS provides access to large datasets for novel outcomes analysis. In this paper, we demonstrate its utility for validating existing models and novel model generation including the utility of concomitant medications in outcome analyses, as well as the effect of PSA response on survival and toxicity prediction.

A DREAM Challenge to Build Prediction Models for Short-Term Discontinuation of Docetaxel in Metastatic Castration-Resistant Prostate Cancer.

PURPOSE: Docetaxel has a demonstrated survival benefit for patients with metastatic castration-resistant prostate cancer (mCRPC); however, 10% to 20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and the management of risk factors for toxicity remains a challenge. PATIENTS AND METHODS: The comparator arms of four phase III clinical trials in first-line mCRPC were collected, annotated, and compiled, with a total of 2,070 patients. Early discontinuation was defined as treatment stoppage within 3 months as a result of adverse treatment effects; 10% of patients discontinued treatment. We designed an open-data, crowd-sourced DREAM Challenge for developing models with which to predict early discontinuation of docetaxel treatment. Clinical features for all four trials and outcomes for three of the four trials were made publicly available, with the outcomes of the fourth trial held back for unbiased model evaluation. Challenge participants from around the world trained models and submitted their predictions. Area under the precision-recall curve was the primary metric used for performance assessment. RESULTS: In total, 34 separate teams submitted predictions. Seven models with statistically similar area under precision-recall curves (Bayes factor ≤ 3) outperformed all other models. A postchallenge analysis of risk prediction using these seven models revealed three patient subgroups: high risk, low risk, or discordant risk. Early discontinuation events were two times higher in the high-risk subgroup compared with the low-risk subgroup. Simulation studies demonstrated that use of patient discontinuation prediction models could reduce patient enrollment in clinical trials without the loss of statistical power. CONCLUSION: This work represents a successful collaboration between 34 international teams that leveraged open clinical trial data. Our results demonstrate that routinely collected clinical features can be used to identify patients with mCRPC who are likely to discontinue treatment because of adverse events and establishes a robust benchmark with implications for clinical trial design.

Estimation of tumour regression and growth rates during treatment in patients with advanced prostate cancer: a retrospective analysis.

BACKGROUND: We applied mathematical models to clinical trial data available at Project Data Sphere LLC (Cary, NC, USA), a non-profit universal access data-sharing warehouse. Our aim was to assess the rates of cancer growth and regression using the comparator groups of eight randomised clinical trials that enrolled patients with metastatic castration-resistant prostate cancer. METHODS: In this retrospective analysis, we used data from eight randomised clinical trials with metastatic castration-resistant prostate cancer to estimate the growth (g) and regression (d) rates of disease burden over time. Rates were obtained by applying mathematical models to prostate-specific antigen levels as the representation of tumour quantity. Rates were compared between study interventions (prednisone, mitoxantrone, and docetaxel) and off-treatment data when on-study treatment had been discontinued to understand disease behaviour during treatment and after discontinuation. Growth (g) was examined for association with a traditional endpoint (overall survival) and for its potential use as an endpoint to reduce sample size in clinical trials. FINDINGS: Estimates for g, d, or both were obtained in 2353 (88%) of 2678 patients with data available for analysis; g differentiated docetaxel (a US Food and Drug Administration-approved therapy) from prednisone and mitoxantrone and was predictive of overall survival in a landmark analysis at 8 months. A simulated sample size analysis, in which g was used as the endpoint, compared docetaxel data with mitoxantrone data and showed that small sample sizes were sufficient to achieve 80% power (16, 47, and 25 patients, respectively, in the three docetaxel comparator groups). Similar results were found when the mitoxantrone data were compared with the prednisone data (41, 39, and 41 patients in the three mitoxantrone comparator groups). Finally, after discontinuation of docetaxel therapy, median tumour growth (g) increased by nearly five times. INTERPRETATION: The application of mathematical models to existing clinical data allowed estimation of rates of growth and regression that provided new insights in metastatic castration-resistant prostate cancer. The availability of clinical data through initiatives such as Project Data Sphere, when combined with innovative modelling techniques, could greatly enhance our understanding of how cancer responds to treatment, and accelerate the productivity of clinical development programmes. FUNDING: None.

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.

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.

Development of ipilimumab: contribution to a new paradigm for cancer immunotherapy.

Identification of cytotoxic T-lymphocyte antigen-4 (CTLA-4) as a key negative regulator of T-cell activity led to development of the fully human, monoclonal antibody ipilimumab to block CTLA-4 and potentiate antitumor T-cell responses. Animal studies first provided insight into the ability of an anti-CTLA-4 antibody to cause tumor regression, particularly in combination regimens. Early clinical studies defined ipilimumab pharmacokinetics and possibilities for combinability. Phase II trials of ipilimumab in advanced melanoma showed objective responses, but a greater number of patients had disease stabilization. In a phase III trial, ipilimumab was the first agent to demonstrate an improvement in overall survival in patients with previously treated, advanced melanoma. The adverse event profile associated with ipilimumab was primarily immune-related. Adverse events can be severe and life-threatening, but most were reversible using treatment guidelines. Ipilimumab monotherapy exhibits conventional and new patterns of activity in advanced melanoma, with a delayed separation of Kaplan-Meier survival curves. The observation of some new response patterns with ipilimumab, which are not captured by standard response criteria, led to novel criteria for the evaluation of immunotherapy in solid tumors. Overall, lessons from the development of ipilimumab contributed to a new clinical paradigm for cancer immunotherapy evolved by the Cancer Immunotherapy Consortium.

Chronic rejection: insights from a novel immunosuppressive-free model of kidney transplantation.

The use of immunosuppressive drugs in models of chronic rejection may limit their usefulness for mechanistic studies. We have developed a new minor histocompatibility-mismatched rat kidney transplant model without the need for immunosuppression. Kidneys from LEW (RT1(l)) donors were transplanted to congenic WF.1L (RT1(l)) recipients and compared with the reversed strain combination and isogenic controls. Urinary protein excretion was measured serially in all recipients; kidneys were harvested 90, 120, and 180 d after transplantation for morphologic analysis and cytokine gene expression. In vitro lymphocytic reactivity and cytokine analysis of mixed lymphocyte reaction (MLR) culture supernatants by ELISA was also carried out. LEW into WF.1L kidney grafts developed proteinuria starting 120 d after transplantation and were associated with morphologic changes of focal segmental glomerulosclerosis together with interstitial cell infiltrates, upregulated gene expression of IL-1beta, IL-2, and TNF-alpha/-beta, as well as IL-2, IFN-gamma, and TNF-alpha production by lymphocytes in MLR culture supernatants. WF.1L kidneys transplanted into LEW recipients did not develop chronic rejection and had upregulation of Th2 cytokines, both within the allograft and in MLR supernatant of recipient lymphocytes cultured with WF.1L cells. Furthermore, these lymphocytes produced both Th1 and Th2 cytokines when cultured with WF cells, unlike lymphocytes from the LEW isografts, which produced Th1 cytokines when challenged with WF cells. These studies show that indirect allorecognition can cause strain-dependent chronic rejection associated with Th1-like cytokine production, whereas production of Th2 cytokines is associated with protection from the development of chronic rejection.