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BackgroundAdjuvant sunitinib significantly improved disease-free survival (DFS) versus placebo in patients with locoregional renal cell carcinoma (RCC) at high risk of recurrence after nephrectomy (hazard ratio [HR] 0.76, 95% confidence interval [CI] 0.59-0.98; p=0.03).ObjectiveTo report the relationship between baseline factors and DFS, pattern of recurrence, and updated overall survival (OS).Design, setting, and participantsData for 615 patients randomized to sunitinib (n=309) or placebo (n=306) in the S-TRAC trial.Outcome measurements and statistical analysisSubgroup DFS analyses by baseline risk factors were conducted using a Cox proportional hazards model. Baseline risk factors included: modified University of California Los Angeles integrated staging system criteria, age, gender, Eastern Cooperative Oncology Group performance status (ECOG PS), weight, neutrophil-to-lymphocyte ratio (NLR), and Fuhrman grade.Results and limitationsOf 615 patients, 97 and 122 in the sunitinib and placebo arms developed metastatic disease, with the most common sites of distant recurrence being lung (40 and 49), lymph node (21 and 26), and liver (11 and 14), respectively. A benefit of adjuvant sunitinib over placebo was observed across subgroups, including: higher risk (T3, no or undetermined nodal involvement, Fuhrman grade ≥2, ECOG PS ≥1, T4 and/or nodal involvement; hazard ratio [HR] 0.74, 95% confidence interval [CI] 0.55-0.99; p=0.04), NLR ≤3 (HR 0.72, 95% CI 0.54-0.95; p=0.02), and Fuhrman grade 3/4 (HR 0.73, 95% CI 0.55-0.98; p=0.04). All subgroup analyses were exploratory, and no adjustments for multiplicity were made. Median OS was not reached in either arm (HR 0.92, 95% CI 0.66-1.28; p=0.6); 67 and 74 patients died in the sunitinib and placebo arms, respectively.ConclusionsA benefit of adjuvant sunitinib over placebo was observed across subgroups. The results are consistent with the primary analysis, which showed a benefit for adjuvant sunitinib in patients at high risk of recurrent RCC after nephrectomy.Patient summaryMost subgroups of patients at high risk of recurrent renal cell carcinoma after nephrectomy experienced a clinical benefit with adjuvant sunitinib.Trial registrationClinicalTrials.gov NCT00375674.

Elevated expression of members of the BCL-2 pro-survival family of proteins can confer resistance to apoptosis in cancer cells. Small molecule obatoclax (GX15-070), which is predicted to occupy a hydrophobic pocket within the BH3 binding groove of BCL-2, antagonizes these members and induces apoptosis, dependent on BAX and BAK. Reconstitution in yeast confirmed that obatoclax acts on the pathway and overcomes BCL-2-, BCL-XL-, BCL-w-, and MCL-1-mediated resistance to BAX or BAK. The compound potently interfered with the direct interaction between MCL-1 and BAK in intact mitochondrial outer membrane and inhibited the association between MCL-1 and BAK in intact cells. MCL-1 has been shown to confer resistance to the BCL-2/BCL-XL/BCL-w-selective antagonist ABT-737 and to the proteasome inhibitor bortezomib. In both cases, this resistance was overcome by obatoclax. These findings support a rational clinical development opportunity for the compound in cancer indications or treatments where MCL-1 contributes to resistance to cell killing. Bcl-2 | melanoma | Bax | Bak | caspase

Introduction: No consensus exists on performance standards for evaluation of generative artificial intelligence (AI) to generate medical responses. The purpose of this study was the assessment of Chat Generative Pre-trained Transformer (ChatGPT) to address medical questions in prostate cancer., Methods: A global online survey was conducted from April to June 2023 among > 700 medical oncologists or urologists who treat patients with prostate cancer. Participants were unaware this was a survey evaluating AI. In component 1, responses to 9 questions were written independently by medical writers (MWs; from medical websites) and ChatGPT-4.0 (AI generated from publicly available information). Respondents were randomly exposed and blinded to both AI-generated and MW-curated responses; evaluation criteria and overall preference were recorded. Exploratory component 2 evaluated AI-generated responses to 5 complex questions with nuanced answers in the medical literature. Responses were evaluated on a 5-point Likert scale. Statistical significance was denoted by P < .05., Results: In component 1, respondents (N = 602) consistently preferred the clarity of AI-generated responses over MW-curated responses in 7 of 9 questions ( P < .05). Despite favoring AI-generated responses when blinded to questions/answers, respondents considered medical websites a more credible source (52%-67%) than ChatGPT (14%). Respondents in component 2 (N = 98) also considered medical websites more credible than ChatGPT, but rated AI-generated responses highly for all evaluation criteria, despite nuanced answers in the medical literature., Conclusions: These findings provide insight into how clinicians rate AI-generated and MW-curated responses with evaluation criteria that can be used in future AI validation studies.