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Background: Phase III trials with prospective biomarker validation are essential to drug development in the era of personalized oncology. However, concerns have emerged regarding the design and reporting of phase III trials with prospective biomarker validation.Methods: We searched MEDLINE for phase III oncology trials with prospective biomarker validation published in high-impact medical journals from 2011 to 2020. Information regarding trial design and reporting were extracted. Descriptive methods were used to summarize the results.Results: We identified 45 phase III trials with prospective biomarker validation. There was a trend for increasing use of biomarker validation phase III trials (from 1 trial in 2011 to 12 trials in 2020). For 39 (86.7%) trials, results in biomarker-negative population were either listed as an exploratory subgroup analysis (62.2%) or not mentioned in the methods (24.4%). Twenty-one (46.7%) trials were originally designed without biomarker validation but were then apparently modified to incorporate prospective biomarker validation after trial commencement, albeit only 15 (33.3%) trials reported this change. Treatment effect and primary outcome values in biomarker-negative patients were not reported in 24.4% and 40.0% trials, respectively. For 18 trials with statistically significant results in the overall population, only 7 trials reported a hazard ratio less than 0.8 in the biomarker-negative population.Conclusion: Although biomarker validation in phase III trials have been increasingly used in the past decade, issues regarding changes in trial design after commencement without disclosure, under-reporting of results in biomarker-negative groups and recommending treatment in biomarker negative groups despite modest effects, require substantial improvement. [ABSTRACT FROM AUTHOR]

Background: Previous studies have indicated coronavirus disease 2019 (COVID-19) patients with cancer have a high fatality rate.Methods: We conducted a systematic review of studies that reported fatalities in COVID-19 patients with cancer. A comprehensive meta-analysis that assessed the overall case fatality rate and associated risk factors was performed. Using individual patient data, univariate and multivariable logistic regression analyses were used to estimate odds ratios (OR) for each variable with outcomes.Results: We included 15 studies with 3019 patients, of which 1628 were men; 41.0% were from the United Kingdom and Europe, followed by the United States and Canada (35.7%), and Asia (China, 23.3%). The overall case fatality rate of COVID-19 patients with cancer measured 22.4% (95% confidence interval [CI] = 17.3% to 28.0%). Univariate analysis revealed age (OR = 3.57, 95% CI = 1.80 to 7.06), male sex (OR = 2.10, 95% CI = 1.07 to 4.13), and comorbidity (OR = 2.00, 95% CI = 1.04 to 3.85) were associated with increased risk of severe events (defined as the individuals being admitted to the intensive care unit, or requiring invasive ventilation, or death). In multivariable analysis, only age greater than 65 years (OR = 3.16, 95% CI = 1.45 to 6.88) and being male (OR = 2.29, 95% CI = 1.07 to 4.87) were associated with increased risk of severe events.Conclusions: Our analysis demonstrated that COVID-19 patients with cancer have a higher fatality rate compared with that of COVID-19 patients without cancer. Age and sex appear to be risk factors associated with a poorer prognosis. [ABSTRACT FROM AUTHOR]

Background: We examined the prognostic significance of circulating tumor cell (CTC) dynamics during treatment in metastatic breast cancer (MBC) patients receiving first-line chemotherapy.Methods: Serial CTC data from 469 patients (2202 samples) were used to build a novel latent mixture model to identify groups with similar CTC trajectory (tCTC) patterns during the course of treatment. Cox regression was used to estimate hazard ratios for progression-free survival (PFS) and overall survival (OS) in groups based on baseline CTCs, combined CTC status at baseline to the end of cycle 1, and tCTC. Akaike information criterion was used to select the model that best predicted PFS and OS.Results: Latent mixture modeling revealed 4 distinct tCTC patterns: undetectable CTCs (56.9% ), low (23.7%), intermediate (14.5%), or high (4.9%). Patients with low, intermediate, and high tCTC patterns had statistically significant inferior PFS and OS compared with those with undetectable CTCs (P < .001). Akaike Information Criterion indicated that the tCTC model best predicted PFS and OS compared with baseline CTCs and combined CTC status at baseline to the end of cycle 1 models. Validation studies in an independent cohort of 1856 MBC patients confirmed these findings. Further validation using only a single pretreatment CTC measurement confirmed prognostic performance of the tCTC model.Conclusions: We identified 4 novel prognostic groups in MBC based on similarities in tCTC patterns during chemotherapy. Prognostic groups included patients with very poor outcome (intermediate + high CTCs, 19.4%) who could benefit from more effective treatment. Our novel prognostic classification approach may be used for fine-tuning of CTC-based risk stratification strategies to guide future prospective clinical trials in MBC. [ABSTRACT FROM AUTHOR]