Smoking and the Risk of Second Primary Lung Cancer Among Breast Cancer Survivors from the Population-Based UK Biobank Study.

OBJECTIVE: Long-term breast cancer (BC) survivors are known to develop second malignancies, with second primary lung cancer (SPLC) one common type. Smoking was identified as a main risk factor for SPLC among BC survivors. These findings were limited to the U.S. and focused on smoking status, not incorporating cumulative smoking exposures (eg, pack-years). We examine SPLC incidence and evaluate the associations between SPLC risk and cumulative cigarette smoking exposures and other potential factors among BC survivors in a prospective European cohort. METHODS: Of 502,505 participants enrolled in the UK Biobank in 2006 to 2010, we identified 8429 patients diagnosed with BC between 2006 and 2016 and followed for second malignancies through 2016. Smoking information was collected at enrollment, and treatment data were collected using electronic health records. Multivariable cause-specific Cox regression (CSC) evaluated the association between each factor and SPLC risk. RESULTS: Of 8429 BC patients, 40 (0.47%) developed SPLC over 45,376 person-years. The 10-year cumulative SPLC incidence was 0.48% (95% CI = 0.33%-0.62%). The CSC analysis confirmed the association between SPLC and ever-smoking status (adjusted hazard-ratio (aHR) = 3.46 (P < .001). The analysis showed a 24% increment in SPLC risk per 10 smoking pack-years among BC survivors (aHR = 1.24 per-10 pack-years, P = .01). The associations between SPLC and other variables remained statistically insignificant. We applied the USPSTF lung cancer screening eligibility criteria and found that 80% of the 40 BC survivors who developed SPLC would have been ineligible for lung cancer screening. CONCLUSION: In a large, European cohort, cumulative smoking exposure is significantly associated with SPLC risk among BC survivors.

Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy.

Characterization of the diverse malignant and stromal cell states that make up soft tissue sarcomas and their correlation with patient outcomes has proven difficult using fixed clinical specimens. Here, we employed EcoTyper, a machine-learning framework, to identify the fundamental cell states and cellular ecosystems that make up sarcomas on a large scale using bulk transcriptomes with clinical annotations. We identified and validated 23 sarcoma-specific, transcriptionally defined cell states, many of which were highly prognostic of patient outcomes across independent datasets. We discovered three conserved cellular communities or ecotypes associated with underlying genomic alterations and distinct clinical outcomes. We show that one ecotype defined by tumor-associated macrophages and epithelial-like malignant cells predicts response to immune-checkpoint inhibition but not chemotherapy and validate our findings in an independent cohort. Our results may enable identification of patients with soft tissue sarcomas who could benefit from immunotherapy and help develop new therapeutic strategies.