As many as 30% of the patients with non–small cell lung cancer harbor oncogenic KRAS mutations, which leads to extensive remodeling of the tumor immune microenvironment. Although co-mutations in several genes have prognostic relevance in KRAS-mutated patients, their effect on tumor immunogenicity are poorly understood. In the present study, a total of 189 patients with non–small cell lung cancer underwent a standardized analysis including IHC, whole-exome DNA sequencing, and whole-transcriptome RNA sequencing. Patients with activating KRAS mutations demonstrated a significant increase in PDL1 expression and CD8+ T-cell infiltration. Both were increased in the presence of a co-occurring TP53 mutation and lost with STK11 co-mutation. Subsequent genomic analysis demonstrated that KRAS/TP53 co-mutated tumors had a significant decrease in the expression of glycolysis-associated genes and an increase in several genes involved in lipid metabolism, notably lipoprotein lipase, low-density lipoprotein receptor, and LDLRAD4. Conversely, in the immune-excluded KRAS/STK11 co-mutated group, we observed diminished lipid metabolism and no change in anaerobic glycolysis. Interestingly, in patients with low expression of lipoprotein lipase, low-density lipoprotein receptor, or LDLRAD4, KRAS mutations had no effect on tumor immunogenicity. However, in patients with robust expression of these genes, KRAS mutations were associated with increased immunogenicity and associated with improved overall survival. Our data further suggest that the loss of STK11 may function as a metabolic switch, suppressing lipid metabolism in favor of glycolysis, thereby negating KRAS-induced immunogenicity. Hence, this concept warrants continued exploration, both as a predictive biomarker and potential target for therapy in patients receiving ICI-based immunotherapy. Significance: In patients with lung cancer, we demonstrate that KRAS mutations increase tumor immunogenicity; however, KRAS/STK11 co-mutated patients display an immune-excluded phenotype. KRAS/STK11 co-mutated patients also demonstrated significant downregulation of several key lipid metabolism genes, many of which were associated with increased immunogenicity and improved overall survival in KRAS-mutated patients. Hence, alteration to lipid metabolism warrants further study as a potential biomarker and target for therapy in patients with KRAS-mutated lung cancer.

PURPOSE: Precision oncology in non–small cell lung cancer (NSCLC) relies on biomarker testing for clinical decision making. Despite its importance, challenges like the lack of genomic oncology training, nonstandardized biomarker reporting, and a rapidly evolving treatment landscape hinder its practice. Generative artificial intelligence (AI), such as ChatGPT, offers promise for enhancing clinical decision support. Effective performance metrics are crucial to evaluate these models' accuracy and their propensity for producing incorrect or hallucinated information. We assessed various ChatGPT versions' ability to generate accurate next-generation sequencing reports and treatment recommendations for NSCLC, using a novel Generative AI Performance Score (G-PS), which considers accuracy, relevancy, and hallucinations. METHODS: We queried ChatGPT versions for first-line NSCLC treatment recommendations with an Food and Drug Administration–approved targeted therapy, using a zero-shot prompt approach for eight oncogenes. Responses were assessed against National Comprehensive Cancer Network (NCCN) guidelines for accuracy, relevance, and hallucinations, with G-PS calculating scores from –1 (all hallucinations) to 1 (fully NCCN-compliant recommendations). G-PS was designed as a composite measure with a base score for correct recommendations (weighted for preferred treatments) and a penalty for hallucinations. RESULTS: Analyzing 160 responses, generative pre-trained transformer (GPT)-4 outperformed GPT-3.5, showing higher base score (90% v 60%; P < .01) and fewer hallucinations (34% v 53%; P < .01). GPT-4's overall G-PS was significantly higher (0.34 v –0.15; P < .01), indicating superior performance. CONCLUSION: This study highlights the rapid improvement of generative AI in matching treatment recommendations with biomarkers in precision oncology. Although the rate of hallucinations improved in the GPT-4 model, future generative AI use in clinical care requires high levels of accuracy with minimal to no room for hallucinations. The GP-S represents a novel metric quantifying generative AI utility in health care compared with national guidelines, with potential adaptation beyond precision oncology.

Purpose: Despite successful clinical management of castration-sensitive prostate cancer (CSPC), the 5-year survival rate for men with castration-resistant prostate cancer is only 32%. Combination treatment strategies to prevent disease recurrence are increasing, albeit in biomarker-unselected patients. Identifying a biomarker in CSPC to stratify patients who will progress on standard-of-care therapy could guide therapeutic strategies. Experimental Design: Targeted deep sequencing was performed for the University of Illinois (UI) cohort (n = 30), and immunostaining was performed on a patient tissue microarray (n = 149). Bioinformatic analyses identified pathways associated with biomarker overexpression (OE) in the UI cohort, consolidated RNA sequencing samples accessed from Database of Genotypes and Phenotypes (n = 664), and GSE209954 (n = 68). Neutralizing antibody patritumab and ectopic HER3 OE were utilized for functional mechanistic experiments. Results: We identified ERBB3 OE in diverse patient populations with CSPC, where it was associated with advanced disease at diagnosis. Bioinformatic analyses showed a positive correlation between ERBB3 expression and the androgen response pathway despite low dihydrotestosterone and stable expression of androgen receptor (AR) transcript in Black/African American men. At the protein level, HER3 expression was negatively correlated with intraprostatic androgen in Black/African American men. Mechanistically, HER3 promoted enzalutamide resistance in prostate cancer cell line models and HER3-targeted therapy resensitized therapy-resistant prostate cancer cell lines to enzalutamide. Conclusions: In diverse patient populations with CSPC, ERBB3 OE was associated with high AR signaling despite low intraprostatic androgen. Mechanistic studies demonstrated a direct link between HER3 and enzalutamide resistance. ERBB3 OE as a biomarker could thus stratify patients for intensification of therapy in castration-sensitive disease, including targeting HER3 directly to improve sensitivity to AR-targeted therapies.

Smoke-free ordinances (SFO) have been shown to be effective public health interventions, but there is limited data on the impact SFO on lung cancer outcomes. We explored the effect of county-level SFO strength with smoking prevalence and lung cancer incidence in Indiana. We obtained county-level lung cancer incidence from the Indiana State Cancer Registry and county-level characteristics from the Indiana Tobacco Prevention and Cessation Commission’s policy database between 1995 and 2016. Using generalized estimating equations, we performed multivariable analyses of smoking prevalence and age-adjusted lung cancer rates with respect to the strength of smoke-free ordinances at the county level over time. Of Indiana’s 92 counties, 24 had a SFO by 2011. In 2012, Indiana enacted a state-wide SFO enforcing at least moderate level SFO protection. Mean age-adjusted lung cancer incidence per year was 76.8 per 100,000 population and mean smoking prevalence per year was 25% during the study period. Counties with comprehensive or moderate SFO had a smoking prevalence 1.2% (95% CI [-1.88, -0.52]) lower compared with counties with weak or no SFO. Counties that had comprehensive or moderate SFO also had an 8.4 (95% CI [-11.5, -5.3]) decrease in new lung cancer diagnosis per 100,000 population per year compared with counties that had weak or no SFO. Counties with stronger smoke-free air ordinances were associated with decreased smoking prevalence and fewer new lung cancer cases per year. Strengthening SFO is paramount to decreasing lung cancer incidence.

Community-engaged research (CEnR) is a potent tool for addressing health inequities and fostering equitable relationships among communities, researchers, and institutions. CEnR involves collaboration throughout the research process, demonstrating improvements in study recruitment and retention, intervention efficacy, program sustainability, capacity building among partners, and enhanced cultural relevance. Despite the increasing demand for CEnR, institutional policies, particularly human participation protection training (HPP), lag behind, creating institutional barriers to community partnerships. Here, we highlight challenges encountered in our ongoing study, Fostering Opportunities in Research through Messaging and Education (FOR ME), focused on promoting shared decision-making around clinical trial participation among Black women diagnosed with breast cancer. Grounded in CEnR methods, FOR ME has a partnership with a community-based organization (CBO) that addresses the needs of Black women with breast cancer. Our CBO partner attempted to obtain HPP training, which was administratively burdensome and time-consuming. As CEnR becomes more prevalent, academic and research institutions, along with researchers, are faced with a call to action to become more responsive to community partner needs. Accordingly, we present a guide to HPP training for community partners, addressing institutional barriers to community partner participation in research. This guide outlines multiple HPP training pathways for community partners, aiming to minimize institutional barriers and enhance their engagement in research with academic partners.

We report the case of a 73-year-old male with primary myeloid sarcoma (MS) of the prostate. He underwent remission-induction chemotherapy followed by conventional consolidation for acute myeloid leukemia (AML). One year after initial diagnosis, he was without evidence of AML, the longest reported period of time in the literature for a case of primary MS of the prostate. From 1985 to 2017, fifteen other cases of MS of the prostate have been reported and are reviewed here. Five cases occurred as primary MS, without evidence of AML on bone marrow examination or prior history of hematologic disorders, and progressed to AML within a range of three weeks to seven months. None of these cases were started on conventional chemotherapy for AML prior to progression. Due to its rarity, primary MS of the prostate is often diagnosed incidentally, but prompt AML-targeted treatment is crucial to delaying the progression to AML.