Lori J. Pierce

PURPOSE: Healthy lifestyle behaviors, including regular exercise and balanced nutrition, affect quality of life in cancer survivors. However, barriers to adopting these behaviors across the cancer continuum are poorly understood. We evaluated barriers to adopting healthy lifestyle behaviors in a national cohort of patients and survivors of cancer. METHODS: ASCO distributed an online survey to adult patients with cancer in the United States from March to June 2020. In this secondary analysis, we evaluated the perceived barriers to incorporating healthy lifestyle behaviors after cancer diagnosis and how they differ by disease stage and treatment status. Descriptive statistics summarized barriers to lifestyle changes. Comparisons across disease stage and treatment status were assessed using Rao-Scott chi-square tests, and weighted multivariable logistic regression modeling identified associated factors. RESULTS: Of 2,419 survey respondents, 1,987 were included in this analysis and grouped by disease stage: early stage on-treatment (n = 461), early stage post-treatment (n = 916), and metastatic on-treatment (n = 610). The most common barriers to healthy behaviors included lack of energy (58.7%) and physical limitations (52.0%), with significant differences ( P < .05) in barriers related to time, logistics, cancer effects, and physical limitations across disease stages and treatment groups. Among those not meeting lifestyle guidelines, lack of motivation (51.3%-62.0%) and lack of energy (59.5%-77.0%) were major barriers, with barriers related to time, cancer effects, and physical limitations varying significantly by disease stage and treatment status. CONCLUSION: Patients with cancer face multiple barriers to adopting healthy lifestyle behaviors after diagnosis. Targeted interventions based on disease stage and treatment status may help address these challenges.

People with disabilities (PWD) face numerous challenges throughout the continuum of cancer care. Many of these are due to personal and structural biases that have historically made it more difficult for PWD to access oncologic care and which can be dehumanizing. We provide a narrative review of these challenges, starting with a review of disability statistics and methodologic considerations when reviewing the epidemiology of disability. We then discuss historic legal frameworks designed to support PWD, and barriers to oncology care faced by PWD, organized around the following primary topics: (1) biases faced by PWD from health care professionals, (2) screening, (3) diagnosis, (4) treatment, (5) survivorship, and (6) historical exclusion from oncology research. We conclude by identifying key areas that must be addressed to provide high-quality oncologic care for PWD. A variety of suggestions for addressing these issues are presented, including additional education of oncology providers, rethinking performance status, engaging stakeholders in the disability community, improving enrollment in clinical trials for PWD, and collecting consistent disability-related data.

Gene sets from the molecular signatures database were used to create a model measuring immunological activity and immunomodulatory tumor-intrinsic factors. These were merged to create a model considering the interaction between the antitumoral immune response and tumor-intrinsic immunomodulatory factors.

The tumor-intrinsic model, Proliferative Index, could predict the prognostic effect from the immunological model, Immunescore. With high Proliferative Index values, Immunescore was strongly prognostic. However, with low Proliferative Index values, trends toward the opposite were observed.

The interaction between the Integrated model and RT was significant in an unadjusted analysis (p=0.004) and an analysis adjusted for histological grade, age, ER status, and tumor size (p=0.008). The figure shows the predicted 10-year cumulative incidence of IBTR with 95% confidence intervals based on Integrated model score stratified by RT, based on Royston-Parmar models with estimates from Table S10. The stpm2 macro for Stata was used for the calculations. The dotted vertical lines represent percentiles of the Integrated score.

Supplementary Table S1 Demographics of the training cohortsSupplementary Table S2 Immunscore modelSupplementary Table S3 Proliferative Index ModelSupplementary Table S4 Immunescore:Proliferative Index

Supplementary MethodsTable S5. Clinical characteristics of the Sjöström and Servant cohortsTable S6. Comparison of included versus excluded patientsTable S7. 10-year follow-up for local recurrence (IBTR). Flexible parametric survival analysis with Royston-Parmar (RP) regression models used in Figures 2 and 3Table S8. 10-year follow-up for local recurrence (IBTR). Flexible parametric survival analysis with Royston-Parmar (RP) regression models with adjustment for other covariatesTable S9. Absolute number of events within different tertiles of Immunescore and Proliferative Index and depending on RT treatmentTable S10. Unadjusted and adjusted analysis of the interaction between the Integrated model and RT in the SweBCG91RT cohortTable S11. Distribution of subtypes in the tertiles of the Integrated score among high-risk patients

Protocol for original SweBCG91RT trial

Immunescore (A), developed in this manuscript, showed a stronger correlation with tumor-infiltrating lymphocytes than other developed methods (B and C) to quantify the degree of immune infiltration.

Ki67 was evaluated by a board-certified pathologist on TMAs.