Science faces a reproducibility crisis, and public trust in science declines when large clinical trials, which had been qualified by promising preclinical studies, fail. While some clinical trial designs may have been inadequate, preclinical assessments of disease interventions might have lacked key elements of rigor such as treatment concealment, randomization, blinded outcomes, prespecified and adequate sample sizes, and models including comorbidities. Here, to demonstrate feasibility and practicality of enhanced rigor in preclinical assessment, we designed a six-laboratory network that implemented rigorous study elements, using acute ischemic stroke for demonstration. This network enrolled 2,615 rodents in 5 different models and implemented a multistage, multiarm statistical design that sequentially eliminated candidate interventions during interim analyses. The methods included centralized intervention packaging, randomization, data quality assessment and data archiving. Blinded analysis of 9,274 video-recorded behavioral tasks and 3,652 magnetic resonance images were evaluated. All tools and protocols are presented and could be adapted to preclinical assessment in other disease areas.

Supplementary Methods S1 provides a comprehensive statistical description of the two applied predictive survival models (PC Cox model and PC Cox BLUP model).

Supplementary Figure S6 (a) shows the actual eight PROs of patient 1 at two time points, baseline, and 6 months (shown by the black dotted line). This patient is also overweight at baseline (25 < BMI < 30). In the clinical data set, this patient discontinued treatment at 14 months, but this information was not used in the model. Supplementary Figure S6 (b) shows the estimated probability of treatment discontinuation for patient 1 anytime after 6 months but before 18 months. Supplementary Figure S6 (c) shows similar information for patient 2 who is also overweight. However, patient 2 completed treatment by 60 months. Supplementary Figure S6 (d) shows the estimated probability of treatment discontinuation for patient 2 anytime after 6 months but before 18 months.

Supplementary Figure S7 presents a screenshot of the web tool; the supplementary data provides instructions for utilizing the web-based treatment discontinuation predictive tool developed based on the predictive survival models.

Supplementary Table S3 provides information about the number of patients with missing adverse events at each timepoint for tamoxifen treatment.

Supplementary Figure S5 illustrates the estimated time-dependent ROC curves using the PCCox and PCCox BLUP models in the validation cohort for tamoxifen treatment. The area under the ROC curve (AUC) and its corresponding 95% confidence interval for the four sets of s and tau are embedded within the plots.

BACKGROUND:The PRO-CTCAE Measurement System was designed to enhance the quality of the standard toxicity evaluation in clinical trials. We developed a substudy within NRG-BR004, a phase III clinical trial in patients with newly documented HER2-positive metastatic breast cancer (MBC), to examine the added value and feasibility of frequent PRO-CTCAE data collection. METHODS:Patients were asked to complete 23 PRO-CTCAE items assessing 12 symptoms. Electronic PRO (ePRO) reporting was preferred; however, paper administration was allowed. The data on items assessed before treatment initiation, then weekly during Cycles 1-2 (12 weeks), are presented herein. Feasibility of frequent assessment with ePRO reporting was assessed using these data and was predefined as ≥25% of patients being compliant (submitted ≥75% of scheduled assessments). We also examined PRO-CTCAE and clinician-reported CTCAE data for key symptoms using maximum toxicity grade and the toxicity index (TI). RESULTS:Overall, 80% of patients (82 of 103) were compliant with expected weekly assessments (90% CI = 0.72 to 0.86). For all symptoms, the median maximum grade (TI value) of clinician-reported CTCAE was lower than the median maximum score (TI value) of patient-reported PRO-CTCAE. The differences in the data trend for weekly vs less frequent assessment were more apparent when data were evaluated using the TI vs the maximum score. CONCLUSIONS:Weekly assessments within the first two chemotherapy cycles were feasible in this trial of MBC patients. As expected, patients reported greater severity of symptoms than clinicians. Demonstrating the feasibility of frequent assessment could have implications for future research and clinical practice. CLINICALTRIALS.GOV:NCT03199885 (https://clinicaltrials.gov/study/NCT03199885).

Supplementary Table S1 outlines all predictors employed to predict treatment discontinuation, including the predictor variable name, variable description, variable type, and whether the data was collected over time or only at baseline.

BACKGROUND:Advancements in cancer therapies and improvements in survival have led to an increasing need to understand the impact of treatment on both severe and low-grade persisting symptoms and treatment-related toxicities. The National Cancer Institute launched the Cancer Moonshot Tolerability Consortium in 2018 to develop methods to analyze clinical and patient-reported tolerability data. METHODS:Through this initiative, the toxicity index (TI) was applied in the analysis of clinical trial tolerability data, and its performance was compared to other existing scoring methods. The TI is a tool developed to quantify the cumulative burden of all levels of clinician-graded toxicity data using Common Terminology Criteria for Adverse Events and patient-reported toxicity. This review describes the statistical properties of the TI and highlights key findings on the utility of the TI in summarizing tolerability data and its applications to existing clinical trial data. RESULTS:Compared to other commonly used summary measures, the TI was shown to be more efficient and increased the statistical power to detect treatment differences. The TI was also applied to existing toxicity and patient-reported symptom data from selected trials, where it was used to predict dose-limiting toxicities and treatment discontinuation. Visualization methods were explored and incorporated into interactive web-based research and patient-facing tools to enhance the interpretability and clinical utility of the TI. CONCLUSION:In conclusion, the TI is a useful summary measure that can be applied to a range of adverse event and patient-reported data to maximize the information collected and provide a more accurate and complete account of the tolerability experience. Future research will focus on extending applications of the TI and exploring its use for the design of clinical trials.

Supplementary Methods S2 provides a statistical description of the prediction error used to assess the model performance.

Supplementary Table S4 presents the optimal risk thresholds (c) and estimates of diagnostic measures such as sensitivity, specificity, and accuracy, along with their corresponding 95% confidence intervals. These values are derived from the PCCox and PCCox BLUP models based on treatment, as applied in the validation cohort.

Supplementary Figure S3 shows the estimated discontinuation curves by training dataset and validation cohort for the tamoxifen-treated patients.

PURPOSE Endocrine treatments for patients with hormone-sensitive breast cancer are associated with significant side effects that can negatively affect health-related quality of life and result in treatment discontinuation. The objective of this qualitative study was to obtain feedback from stakeholder clinicians and patients about an online interactive tool that was designed to provide information and visualizations of breast cancer symptoms. METHODS The online Breast Cancer Symptom Explorer tool was developed to allow patients to visualize trajectories for common symptoms associated with tamoxifen and anastrozole using symptom data from the NSABP B35 breast cancer clinical trial. To refine the tool, virtual focus groups were conducted among oncology clinicians and women with a history of breast cancer who had received treatment with an aromatase inhibitor or tamoxifen, seeking feedback on the tool and its potential usefulness. Discussions took place using a secure web-conferencing platform following a semi-structured interview guide. Focus groups were audio-recorded, transcribed, and analyzed using reflexive thematic analysis. RESULTS Nine focus groups were conducted (n = 21 participants: eight clinicians and 13 patients). Key benefits and barriers to tool use emerged from the discussions. Both patients and oncologists valued the ability to engage with the tool and visualize symptoms over time. They indicated that ideal settings for its use would be at home before treatment initiation. Combinations of graphical representations with text were perceived to be most effective in communicating symptoms. Key barriers identified included concerns about accessibility to the tool and digital literacy, with recommendations to simplify the text and provide health literacy support to enhance its clinical utility in the future. CONCLUSION Clinician and patient involvement was critical for refinement of the breast cancer symptom explorer and provided insights into its future use and evaluation of the tool in clinical decision making.

We present a Bayesian adaptive design for dose finding in oncology trials with application to a first-in-human trial. The design is based on the escalation with overdose control principle and uses an intermediate grade 2 toxicity in addition to the traditional binary indicator of dose-limiting toxicity (DLT) to guide the dose escalation and de-escalation. We model the dose–toxicity relationship using the proportional odds model. This assumption satisfies an important ethical concern when a potentially toxic drug is first introduced in the clinic; if a patient experiences grade 2 toxicity at the most, then the amount of dose escalation is lower relative to that wherein if this patient experienced a maximum of grade 1 toxicity. This results in a more careful dose escalation. The performance of the design was assessed by deriving the operating characteristics under several scenarios for the true MTD and expected proportions of grade 2 toxicities. In general, the trial design is safe and achieves acceptable efficiency of the estimated MTD for a planned sample size of twenty patients. At the time of writing this manuscript, twelve patients have been enrolled to the trial.

PURPOSE:Longitudinal patient tolerability data collected as part of randomized controlled trials are often summarized in a way that loses information and does not capture the treatment experience. To address this, we developed an interactive web application to empower clinicians and researchers to explore and visualize patient tolerability data. METHODS:We used adverse event (AE) data (Common Terminology Criteria for Adverse Events) and patient-reported outcomes (PROs) from the NSABP-B35 phase III clinical trial, which compared anastrozole with tamoxifen for breast cancer-free survival, to demonstrate the tools. An interactive web application was developed using R and the Shiny web application framework that generates Sankey diagrams to visualize AEs and PROs using four tools: AE Explorer, PRO Explorer, Cohort Explorer, and Custom Explorer. RESULTS:To illustrate how users can use the interactive tool, examples for each of the four applications are presented using data from the NSABP-B35 phase III trial and the NSABP-B30 trial for the Custom Explorer. In the AE and PRO explorers, users can select AEs or PROs to visualize within specified time periods and compare across treatments. In the cohort explorer, users can select a subset of patients with a specific symptom, severity, and treatment received to visualize the trajectory over time within a specified time interval. With the custom explorer, users can upload and visualize structured longitudinal toxicity and tolerability data. CONCLUSION:We have created an interactive web application and tool for clinicians and researchers to explore and visualize clinical trial tolerability data. This adaptable tool can be extended for other clinical trial data visualization and incorporated into future patient-clinician interactions regarding treatment decisions.

Regulatory guidance suggests capturing patient-reported overall side effect impact in cancer trials. We examined whether the Functional Assessment of Cancer Therapy (FACT) GP5 item (“I am bothered by side effects of treatment”) post-neoadjuvant chemotherapy/radiotherapy differed between oxaliplatin vs. non- oxaliplatin arms in the National Surgical Adjuvant Breast and Bowel Project (NSABP) R-04 trial of stage II–III rectal cancer patients. The R-04 neoadjuvant trial compared local-regional tumor control between patients randomized to receive 5-fluorouracil or capecitabine with radiation, with or without oxaliplatin (4 treatment arms). Participants completed surveys at baseline and immediately after chemoradiotherapy. GP5 has a 5-point response scale: “Not at all” (0), “A little bit” (1), “Somewhat” (2), “Quite a bit” (3), and “Very much” (4). Logistic regression compared the odds of reporting moderate-high side effect impact (GP5 2–4) between patients receiving oxaliplatin or not after chemoradiotherapy, controlling for relevant patient characteristics. We examined associations between GP5 and other patient-reported outcomes reflecting side effects. Analyses were performed among 1132 study participants. Participants receiving oxaliplatin were 1.58 times (95

Individual histograms of perforin expression by memory CD8 T cell cluster pre- and post-vaccination

<p>CYTOF Panel</p>

<p>Concurrent HER2 therapies for each patient for whom CYTOF analysis was done</p>

A, miR-409-5p and miR-409-3p binding sties in 3`UTR of RSU1 mRNA. B, Effect miR-409-5p mimic binding on 3` UTR of RSU1 luciferase construct, both wild type and mutated construct. Effect of miR-409-5p mimic and miR-409-3p mimic on STAG2 3`UTR luciferase construct measured by luciferase assay.