{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T08:32:05Z","timestamp":1786609925298,"version":"build-2736575974"},"reference-count":48,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: Lung cancer remains the leading cause of cancer-related mortality in the United States, characterized by poor overall survival rates (OS), particularly for advanced-stage disease. While smoking is a primary risk factor, other contributors include environmental exposures, genetics, comorbidities, and socioeconomic factors. Methods: We included 232 patients with lung cancer (96% NSCLC). Patients were categorized into DNA damage response (DDR)-mutant (DDRmt, n = 67, 29%) and DDR-wild-type (DDRwt, n = 165, 71%) groups. We evaluated the correlations between individual and socioeconomic factors between DDRmt and DDRwt lung cancer patients. Results:Nineteen DDR genes were identified, with ARID1A(31.3%), CHEK2 (19.4%), ATM (14.9%), BRCA2 (14.9%), ATRX (11.9%), MUTYH (11.9%), ATR (8.9%), and MSH6 (5.9%) being the most common. The DDRmt group had a significantly higher median tumor mutational burden (TMB) (12 vs. 9; p = 0.006) and a higher prevalence of adenocarcinoma (85.1% vs. 64.8%, p = 0.003). Logistic regression identified adenocarcinoma histology (OR = 10.03, p = 0.002) and lower area deprivation index (OR = 0.98, p = 0.04) as predictors of DDR mutation status. Having advanced stage at diagnosis was associated with, age (p = 0.03), lack of lung cancer screening (p &lt; 0.001), and adenocarcinoma histology (p &lt; 0.001). While median OS was 61 months for the DDRmt group vs. 44 months for the DDRwt group (p = 0.654), patients with ATR mutations had significantly shorter survival (13 vs. 67 months; p = 0.0008). Population-level factors, including food insecurity (HR = 2.33, p = 0.002), lack of insurance (HR = 2.39, p = 0.008), and proximity to potential chemical accidents (HR = 1.73, p = 0.037), were significant predictors of OS. Conclusions: These findings suggest that DDRmt lung cancers represent a biologically distinct subgroup characterized by higher tumor mutational burden and enrichment for adenocarcinoma histology; however, DDR mutation status alone was not associated with overall survival. Instead, outcomes appeared to vary by individual DDR gene, with ATR alterations identifying a subgroup with particularly poor survival. In parallel, food insecurity, lack of insurance, and proximity to potential environmental hazards were associated with outcomes, highlighting the need to integrate genomic biomarkers with social and environmental determinants of health. These data suggest the need for future prospective studies incorporating treatment-response data, longitudinal social determinants of health (SDOH) assessment, and environmental exposure measures to define how DDR alterations can guide precision oncology strategies while addressing modifiable barriers to equitable cancer care.<\/jats:p>","DOI":"10.3390\/cancers18162606","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T08:06:16Z","timestamp":1786608376000},"page":"2606","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Association of Social and Biological Factors with Clinical Outcomes in Patients with Lung Cancer and DNA Damage Repair Pathway Mutations: A Single-Institution Experience"],"prefix":"10.3390","volume":"18","author":[{"given":"Ahmad","family":"Nanaa","sequence":"first","affiliation":[{"name":"Department of Medicine, University of Illinois Chicago, Chicago, IL 60607, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremy","family":"Kao","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Illinois Chicago, Chicago, IL 60607, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5528-4536","authenticated-orcid":false,"given":"Martin","family":"Davis","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Illinois Chicago, Chicago, IL 60607, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary","family":"Pasquinelli","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Illinois Chicago, Chicago, IL 60607, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4410-4157","authenticated-orcid":false,"given":"Margaret Wright","family":"Geise","sequence":"additional","affiliation":[{"name":"Data Integration Shared Resource, University of Illinois Cancer Center, Chicago, IL 60612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Illinois Chicago, Chicago, IL 60607, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan Huu-Tuan","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Illinois Chicago, Chicago, IL 60607, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0094-3725","authenticated-orcid":false,"given":"Frank","family":"Weinberg","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Illinois Chicago, Chicago, IL 60607, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107338","DOI":"10.1016\/j.compbiomed.2023.107338","article-title":"Predicting lung cancer survival based on clinical data using machine learning: A review","volume":"165","author":"Altuhaifa","year":"2023","journal-title":"Comput. 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