Estimating scenarios for survival time in patients with advanced melanoma receiving immunotherapy and targeted therapy.

BACKGROUND: We aim to provide survival scenario estimates for patients with advanced melanoma starting targeted therapies and immunotherapies. MATERIALS AND METHODS: We sought randomized trials of targeted therapies and immunotherapies for advanced melanoma and recorded the following percentiles (represented survival scenario) from each overall survival (OS) curve: 90th (worst-case), 75th (lower-typical), 50th (median), 25th (upper-typical), and 10th (best-case). We tested whether these scenarios can be estimated for each OS curve by multiplying its median by 4 multiples: 0.25 (worst-case), 0.5 (lower-typical), 2 (upper-typical), and 3 (best-case). RESULTS: We identified 15 trials with 8025 patients. For first-line combination targeted therapy treatment groups, the median (interquartile range, IQR) in months for each percentile was: 90th, 6.2 (6.0-6.5); 75th, 11.3 (11.3-11.4); and median, 24.4 (23.5-25.3). For the first-line combination immunotherapy treatment group, the percentiles in months were: 90th, 3.9 (2.8-4.5); 75th, 13.4 (10.1-15.4), median 73 (not applicable). In targeted therapy groups, simple multiples of the median OS were accurate for estimating the 90th percentile in 80%; 75th percentile in 40%; 25th percentile in 100%. In immunotherapy groups, these multiples were accurate at 0% for the 90th percentile, and 43% for the 75th percentile. The 90th percentile (worst-case scenario) was better estimated as 1/6× median OS, and the 75th percentile (lower-typical) as 1/3× median OS. CONCLUSIONS: Simple multiples of the median OS are a useful framework to estimate scenarios for survival for patients receiving targeted therapies, not immunotherapy. Longer follow-up is required to estimate upper-typical and best-case scenarios.

Accuracy of oncologists' estimates of expected survival time in advanced cancer.

BACKGROUND: To evaluate the claim that oncologists overestimate expected survival time (EST) in advanced cancer. METHODS: We pooled 7 prospective studies in which observed survival time (OST) was compared with EST (median survival in a group of similar patients estimated at baseline by the treating oncologist). We hypothesized that EST would be well calibrated (approximately 50% of EST longer than OST) and imprecise (<30% of EST within 0.67 to 1.33 of OST), and that multiples of EST would provide well-calibrated scenarios for survival time: worst-case (approximately 10% of OST <1/4 of EST), typical (approximately 50% of OST within half to double EST), and best-case (approximately 10% of OST >3 times EST). Associations between baseline characteristics and calibration of EST were assessed. RESULTS: Characteristics of 1,211 patients: median age 66 years, male 61%, primary site lung (40%) and upper gastrointestinal (16%). The median OST was 8 months, and EST was 9 months. Oncologists' estimates of EST were well calibrated (50% longer than OST) and imprecise (28% within 0.67 to 1.33 of OST). Scenarios for survival time based on simple multiples of EST were well calibrated: 8% of patients had an OST less than 1/4 their EST (worst-case), 56% had an OST within half to double their EST (typical), and 11% had an OST greater than 3 times their EST (best-case). Calibration was independent of age, sex, and cancer type. CONCLUSIONS: Oncologists were no more likely to overestimate survival time than to underestimate it. Simple multiples of EST provide well-calibrated estimates of worst-case, typical, and best-case scenarios for survival.

Motivations and barriers to pursue cancer genomic testing: A systematic review.

OBJECTIVES: Single-gene testing is associated with psycho-social challenges for cancer patients. Genomic testing may amplify these. The aim of this study was to understand patients' motivations and barriers to pursue cancer genomic testing, to enable healthcare providers to support their patients throughout the testing process and interpretation of test results. METHODS: Five databases were searched for original peer reviewed research articles published between January 2001 and September 2018 addressing motivation for genomic cancer testing. QualSyst was used to assess quality. RESULTS: 182 studies were identified and 17 were included for review. Studies were heterogenous. Both somatic and germline testing were included, and 14 studies used hypothetical scenarios. 3249 participants were analyzed, aged 18 to 94. Most were female and white. The most common diagnoses were breast, ovarian, lung and colorectal cancer. Interest in testing was high. Motivations included ability to predict cancer risk, inform disease management, benefit families, and understand cancer. Barriers included concerns about cost, privacy/confidentiality, clinical utility, and psychological harm. CONCLUSIONS: Despite concerns, consumers are interested in cancer genomic testing if it can provide actionable results for themselves and their families. PRACTICE IMPLICATIONS: Providers must manage understanding and expectations of testing and translate genetic information into health-promoting behaviours.