Background: Poor patients often reside in neighborhoods of lower socioeconomic status (SES) with high levels of airborne pollutants. They also have higher mortality from non–small cell lung cancer (NSCLC) than those living in wealthier communities. We investigated whether living in polluted neighborhoods is associated with somatic mutations linked with lower survival rates, i.e., TP53 mutations. Methods: In a retrospective cohort of 478 patients with NSCLC treated at a comprehensive cancer center between 2015 and 2018, we used logistic regression to assess associations between individual demographic and clinical characteristics, including somatic TP53 mutation status and environmental risk factors of annual average particulate matter (PM2.5) levels, and neighborhood SES. Results: 277 patients (58%) had somatic TP53 mutations. Of those, 45% lived in neighborhoods with “moderate” Environmental Protection Agency–defined PM2.5 exposure, compared with 39% of patients without TP53 mutations. We found significant associations between living in neighborhoods with “moderate” versus “good” PM2.5 concentrations and minority population percentage [OR, 1.06; 95% confidence interval (CI), 1.04–1.08]. There was a significant association between presence of TP53 mutations and PM2.5 exposure (moderate versus good: OR, 1.66; 95% CI, 1.02–2.72) after adjusting for patient characteristics, other environmental factors, and neighborhood-level SES. Conclusions: When controlling for individual- and neighborhood-level confounders, we find that the odds of having a TP53-mutated NSCLC are increased in areas with higher PM2.5 exposure. Impact: The link between pollution and aggressive biology may contribute to the increased burden of adverse NSCLC outcomes in individuals living in lower SES neighborhoods.

PURPOSE Although BRCA1/ 2 testing in ovarian cancer improves outcomes, it is vastly underutilized. Scalable approaches are urgently needed to improve genomically guided care. METHODS We developed a Natural Language Processing (NLP) pipeline to extract electronic medical record information to identify recipients of BRCA testing. We applied the NLP pipeline to assess testing status in 308 patients with ovarian cancer receiving care at a National Cancer Institute Comprehensive Cancer Center (main campus [MC] and five affiliated clinical network sites [CNS]) from 2017 to 2019. We compared characteristics between (1) patients who had/had not received testing and (2) testing utilization by site. RESULTS We found high uptake of BRCA testing (approximately 78%) from 2017 to 2019 with no significant differences between the MC and CNS. We observed an increase in testing over time (67%-85%), higher uptake of testing among younger patients (mean age tested = 61 years v untested = 65 years, P = .01), and higher testing among Hispanic (84%) compared with White, Non-Hispanic (78%), and Asian (75%) patients ( P = .006). Documentation of referral for an internal genetics consultation for BRCA pathogenic variant carriers was higher at the MC compared with the CNS (94% v 31%). CONCLUSION We were able to successfully use a novel NLP pipeline to assess use of BRCA testing among patients with ovarian cancer. Despite relatively high levels of BRCA testing at our institution, 22% of patients had no documentation of genetic testing and documentation of referral to genetics among BRCA carriers in the CNS was low. Given success of the NLP pipeline, such an informatics-based approach holds promise as a scalable solution to identify gaps in genetic testing to ensure optimal treatment interventions in a timely manner.

10600 Background: Germline testing (GT) use is on the rise given testing implications for identifying cancer susceptibility and therapeutically actionable alterations. Scalable models of care that emphasize post-test, as opposed to pre-test, genetic counseling are needed to meet demand. However, little is known about the psychological impact (PI) of test result disclosure in such models. Methods: The enterprise-wide City of Hope INSPIRE study offers all consented patients GT for cancer susceptibility (155 genes) and actionable disorders (59 genes). In 2022, we surveyed a sub-set of English-speaking participants ~1 month following test result disclosure. We evaluated PI using the Feelings about Genomic Testing Results (FACToR) measure (distress/ uncertainty subscales 0-12; positive subscale 0-16) and explored associations between patient characteristics, GT results and PI. Results: Of 1000 patients surveyed, 615 completed at least one of the FACToR questions. Participants were mostly white (n=463, 75%) or Asian (n=12.52, 13%) and female (n=419, 68%) with a mean age of 62 yrs. 357 (61%) had a cancer diagnosis and most opted for both GT panels (97%). Eighteen percent had a pathogenic/likely pathogenic variant (PV/LPV) in an autosomal dominant condition, 8% were carriers for an autosomal recessive condition, 53% had at least one variant of unknown significance (VUS) and 21% had negative results. Most patients (n=583, 95%) had low levels of distress with a mean score of 1.67 (SD 2.31). Out of the 30 (5%) patients with higher levels of distress (score >7), results were similar for patients with and without cancer (5% and 4% respectively). Of the 30 patients with higher distress, 67% had a P/LPV variant (13 with and 7 without cancer). Patients also had low levels of uncertainty with a mean score of 2.22 (SD 2.61) and 93% (n=569) scored <7. For those who scored higher on uncertainty (n=44, 7%), most people had cancer and a VUS (n=16, 36%), followed by cancer and LPV/PV (n=10, 23%) followed by patient without cancer with a VUS (n=6, 14%). Finally, 56% (n=44) had high positivity scores (9-16); mean 9.18 (SD 4.31). Fifty-six percent of patients felt a “good/great deal” happy about their GT and 54% were a good/great deal relieved about their results. Conclusions: After implementing an enterprise-wide germline testing program with an emphasis on robust post-test genetic counseling, we found very little evidence of post-disclosure distress or uncertainty. Similar to prior studies, we found that a small proportion of patients may be more vulnerable to negative PI. More work is needed to prospectively identify at risk patients to provide support to this population as we continue to develop safe, effective, and scalable models of care.