No abstract

Immune checkpoint inhibitors (ICIs) are part of standard of care for patients with many advanced solid tumors. Patients with poor performance status or organ dysfunction are traditionally ineligible to partake in pivotal randomized clinical trials of ICIs.OBJECTIVE To assess ICI use and survival outcomes among patients with advanced cancers who are traditionally trial ineligible based on poor performance status or organ dysfunction. DESIGN, SETTING, AND PARTICIPANTSThis retrospective cohort study was conducted in 280 predominantly community oncology practices in the US and included 34 131 patients (9318 [27.3%] trial ineligible) who initiated first-line systemic therapy from January 2014 through December 2019 for newly diagnosed metastatic or recurrent nontargetable non-small cell lung, urothelial cell, renal cell, or hepatocellular carcinoma. Data analysis was performed from December 1, 2019, to June 1, 2021.EXPOSURES Trial ineligibility (Eastern Cooperative Oncology Group performance status Ն2 or the presence of kidney or liver dysfunction); first-line systemic therapy. MAIN OUTCOMES AND MEASURESThe association between trial ineligibility and ICI monotherapy uptake was assessed using inverse probability-weighted (IPW) logistic regressions. The comparative survival outcomes following ICI and non-ICI therapy among trial-ineligible patients were assessed using treatment IPW survival analyses. Because we observed nonproportional hazards, we reported 12-month and 36-month restricted mean survival times (RMSTs) and time-varying hazard ratios (HRs) of less than 6 months and 6 months or greater. RESULTS Among the overall cohort (n = 34 131), the median (IQR) age was 70 (62-77) years; 23 586 (69%) were White individuals, and 14 478 (42%) were women. Over the study period, the proportion of patients receiving ICI monotherapy increased from 0% to 30.2% among trial-ineligible patients and 0.1% to 19.4% among trial-eligible patients. Trial ineligibility was associated with increased ICI monotherapy use (IPW-adjusted odds ratio compared with non-ICI therapy, 1.8; 95% CI, 1.7-1.9). Among trial-ineligible patients, there were no overall survival differences between ICI monotherapy, ICI combination therapy, and non-ICI therapy at 12 months (RMST, 7.8 vs 7.7 vs 8.1 months) or 36 months (RMST, 15.0 vs 13.9 vs 14.4 months). Compared with non-ICI therapy, ICI monotherapy showed evidence of early harm (IPW-adjusted HR within 6 months, 1.2; 95% CI, 1.1-1.2) but late benefit (adjusted HR among patients who survived 6 months, 0.8; 95% CI, 0.7-0.8). CONCLUSIONS AND RELEVANCEIn this cohort study, compared with trial-eligible patients, trial-ineligible patients with advanced cancers preferentially received first-line ICI therapy. A survival difference was not detected between ICI and non-ICI therapies among trial-ineligible patients. Positive results for ICI in phase 3 trials may not translate to this vulnerable population.

Classification methods that leverage the strengths of data from multiple sources (multiview data) simultaneously have enormous potential to yield more powerful findings than two-step methods: association followed by classification. We propose two methods, sparse integrative discriminant analysis (SIDA), and SIDA with incorporation of network information (SIDANet), for joint association and classification studies. The methods consider the overall association between multiview data, and the separation within each view in choosing discriminant vectors that are associated and optimally separate subjects into different classes. SIDANet is among the first methods to incorporate prior structural information in joint association and classification studies. It uses the normalized Laplacian of a graph to smooth coefficients of predictor variables, thus encouraging selection of predictors that are connected. We demonstrate the effectiveness of our methods on a set of synthetic datasets and explore their use in identifying potential nontraditional risk factors that discriminate healthy patients at low versus high risk for developing atherosclerosis cardiovascular disease in 10 years. Our findings underscore the benefit of joint association and classification methods if the goal is to correlate multiview data and to perform classification.

Supplementary materials are available at Bioinformatics online.

Background: Multiple co-inertia analysis (mCIA) is a multivariate analysis method that can assess relationships and trends in multiple datasets. Recently it has been used for integrative analysis of multiple high-dimensional -omics datasets. However, its estimated loading vectors are non-sparse, which presents challenges for identifying important features and interpreting analysis results. We propose two new mCIA methods: 1) a sparse mCIA method that produces sparse loading estimates and 2) a structured sparse mCIA method that further enables incorporation of structural information among variables such as those from functional genomics. Results: Our extensive simulation studies demonstrate the superior performance of the sparse mCIA and structured sparse mCIA methods compared to the existing mCIA in terms of feature selection and estimation accuracy. Application to the integrative analysis of transcriptomics data and proteomics data from a cancer study identified biomarkers that are suggested in the literature related with cancer disease. Conclusion: Proposed sparse mCIA achieves simultaneous model estimation and feature selection and yields analysis results that are more interpretable than the existing mCIA. Furthermore, proposed structured sparse mCIA can effectively incorporate prior network information among genes, resulting in improved feature selection and enhanced interpretability.