Survival Analysis

ImportanceDisadvantaged neighborhood-level and individual-level socioeconomic status (SES) have each been associated with suboptimal cancer care and inferior outcomes. However, independent or synergistic associations between neighborhood and individual socioeconomic disadvantage have not been fully examined, and prior studies using simplistic neighborhood SES measures may not comprehensively assess multiple aspects of neighborhood SES.ObjectiveTo investigate the associations of neighborhood SES (using a validated comprehensive composite measure) and individual SES with survival among patients with nonmetastatic common cancers.Design, setting, and participantsThis prospective, population-based cohort study was derived from the Surveillance, Epidemiology, and End Results-Medicare database from January 1, 2008, through December 31, 2011, with follow-up ending on December 31, 2017. Participants included older patients (≥65 years) with breast, prostate, lung, or colorectal cancer.ExposuresNeighborhood SES was measured using the area deprivation index (ADI; quintiles), a validated comprehensive composite measure of neighborhood SES. Individual SES was assessed by Medicare-Medicaid dual eligibility (yes vs no), a reliable indicator for patient-level low income.Main outcomes and measuresThe primary outcome was overall mortality, and the secondary outcome was cancer-specific mortality. Hazard ratios (HRs) for the associations of ADI and dual eligibility with overall and cancer-specific mortality were estimated via Cox proportional hazards regression. Statistical analyses were conducted from January 23 to April 15, 2021.ResultsA total of 96 978 patients were analyzed, including 25 968 with breast, 35 150 with prostate, 16 684 with lung, and 19 176 with colorectal cancer. Median age at diagnosis was 76 years (IQR, 71-81 years) for breast cancer, 73 years (IQR, 70-77 years) for prostate cancer, 76 years (IQR, 71-81 years) for lung cancer, and 78 years (IQR, 72-84 years) for colorectal cancer. Among lung and colorectal cancer patients, 8412 (50.4%) and 10 486 (54.7%), respectively, were female. The proportion of non-Hispanic White individuals among breast cancer patients was 83.7% (n = 21 725); prostate cancer, 76.8% (n = 27 001); lung cancer, 83.5% (n = 13 926); and colorectal cancer, 81.1% (n = 15 557). Neighborhood-level and individual-level SES were independently associated with overall mortality, and no interactions were detected. Compared with the most affluent neighborhoods (ADI quintile 1), living in the most disadvantaged neighborhoods (ADI quintile 5) was associated with higher risk of overall mortality (breast: HR, 1.34; 95% CI, 1.26-1.43; prostate: HR, 1.51; 95% CI, 1.42-1.62; lung: HR, 1.21; 95% CI, 1.14-1.28; and colorectal: HR, 1.24; 95% CI, 1.17-1.32). Individual socioeconomic disadvantage (dual eligibility) was associated with higher risk of overall mortality (breast: HR, 1.22; 95% CI, 1.15-1.29; prostate: HR, 1.29; 95% CI, 1.21-1.38; lung: HR, 1.14; 95% CI, 1.09-1.20; and colorectal: HR, 1.23; 95% CI, 1.17-1.29). A similar pattern was observed for cancer-specific mortality.Conclusions and relevanceIn this cohort study, neighborhood-level deprivation was associated with worse survival among patients with nonmetastatic breast, prostate, lung, and colorectal cancer, even after accounting for individual SES. These findings suggest that, in order to improve cancer outcomes and reduce health disparities, policies for ongoing investments in low-resource neighborhoods and low-income households are needed.

BackgroundProgression-free survival (PFS) is a surrogate endpoint widely used for overall survival (OS) in oncology. Validation of PFS as a surrogate must be done for each indication and each intervention. We aimed to identify all studies evaluating the validity of PFS as a surrogate for OS in oncology, and to describe their methodological characteristics.MethodsWe conducted a systematic review by searching MEDLINE via PubMed and the Cochrane Library with no limitation on time, selected relevant studies and extracted data in duplicate on how surrogacy was evaluated (meta-analytic approach, assessment of correlation and level of evaluation).ResultsWe identified 91 studies evaluating the validity of PFS as a surrogate for OS in 24 cancer localisations. Although a meta-analytic approach was used in 83 (91%) studies, the methods used to validate PFS as a surrogate of OS were heterogeneous across studies. Of the 47 studies concluding that PFS is a good surrogate for OS, for 15 (32%), there was no quantitative argument for surrogacy.ConclusionsAlthough most studies used a meta-analytic approach as recommended, our methodological review highlights heterogeneity in methods and reporting, which stresses the importance of developing and applying clear recommendations in this area.

The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. A large number of packages and tools have been developed to correlate gene expression/mutations to the clinical outcome but lack the ability to perform such analysis based on pathways, gene sets, and gene ratios. Furthermore, in this single-cell omics era, the cluster markers from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates the associations between the enrichment of canonical cell types and survival across cancers. Here we have developed Survival Genie, a web tool to perform survival analysis on single-cell RNA-seq (scRNA-seq) data and a variety of other molecular inputs such as gene sets, genes ratio, tumor-infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden. For a comprehensive analysis, Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs related to adult and pediatric cancers. Users can upload scRNA-seq data or gene sets and select a gene expression partitioning method (i.e., mean, median, quartile, cutp) to determine the effect of expression levels on survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan-Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile. The analytical options and comprehensive collection of cancer datasets make Survival Genie a unique resource to correlate gene sets, pathways, cellular enrichment, and single-cell signatures to clinical outcomes to assist in developing next-generation prognostic and therapeutic biomarkers. Survival Genie is open-source and available online at https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/ .

In this work, we discuss what we refer to as reduction techniques for survival analysis, that is, techniques that "reduce" a survival task to a more common regression or classification task, without ignoring the specifics of survival data. Such techniques particularly facilitate machine learning-based survival analysis, as they allow for applying standard tools from machine and deep learning to many survival tasks without requiring custom learners. We provide an overview of different reduction techniques and discuss their respective strengths and weaknesses. We also provide a principled implementation of some of these reductions, such that they are directly available within standard machine learning workflows. We illustrate each reduction using dedicated examples and perform a benchmark analysis that compares their predictive performance to established machine learning methods for survival analysis.