Veterans Affairs Research Scholar Award

Despite recent advances, locally advanced non-small cell lung cancer (LA-NSCLC) continues to have a high degree of morbidity and mortality related to both disease progression and sequelae from treatment. The purpose of this study is to develop and evaluate a more personalized radiation therapy (RT) approach with the potential to reduce time and financial burden to patients and increase tumor control. Patients undergoing fractionated RT with concurrent systemic therapy for LA-NSCLC will be treated to a total dose of 60 Gy with the number of fractions determined by ability to meet key dose constraints resulting in a shorter treatment course and higher biologic effective dose (BED) among those treated with shorter fractionation. To reduce the iterative time to determine fractionation schedule and standardize the"‘isotoxic hypofractionation" approach, we will employ a previously developed automated RT planning technique after delineation of the tumor and organs at risk. Fifty patients will be enrolled in a prospective non-randomized 2-stage trial to test the primary objective of whether isotoxic hypofractionation results in an acceptable rate of grade 2 or higher toxicity. Secondary endpoints will include G3+ toxicity, local control, progression free survival and overall survival. Finally, to better inform future "isotoxic" dosing strategies we will evaluate additional predictors of radiation toxicity using established retrospective cohorts through the Stanford Cancer Institute and the Veteran’s Affairs Information and Computing Infrastructure (VINCI).

Though the introduction of immune checkpoint inhibitors (ICI) has improved outcomes for patients with stage III non-small-cell lung cancer, most patients do not have robust tumor responses to ICI and real-world patients discontinue ICI due to toxicity at a higher rate than clinical trial populations. There is an unmet need to develop predictive models to identify patients at risk of poor ICI response, high risk of ICI toxicity, and high risk of non-cancer mortality, as these patients are unlikely to benefit from ICI and may require treatment intensification or de-intensification, selection of alternative therapies, or enrollment on clinical trials. In this proposal we seek to leverage the richness and national scale of Veterans Affairs (VA) clinical data to develop statistical models to predict ICI efficacy, toxicity, and non-cancer mortality. We will evaluate specific hypotheses generated from preclinical research and use machine-learning approaches to identify new predictors from high-dimensional VA healthcare data. These models will be integrated into a unified multistate model, allowing prediction of the individualized overall survival benefit of ICI for each patient. Clinical decision-making based on model predictions will be compared to other decision making strategies using decision-curve analysis. This work will identify novel biomarkers of ICI efficacy and toxicity, validate putative ICI effect modifiers from the preclinical literature, and develop the first unified model for ICI clinical benefit. If successful, this tool can be used for patient counseling, physician decision-making, and identification of patients likely to benefit from alternative therapies, treatment de-escalation, or clinical trial enrollment.

Veterans have significantly greater rates of lung cancer and mortality as compared to civilians. In our prior work, these disparities are partly due to lack of receipt of evidence-based cancer care especially among patients with complex comorbidities, psychosocial concerns, and limited social support, characteristics common among Veteran populations. The overall goal of this research is to overcome structural barriers to ensure delivery of evidence-based lung cancer care among Veterans. In our prior work we identified workforce shortages along with complex psychosocial factors and medical conditions that prevent Veterans from fully engaging in their care. We used community-based participatory research and expert panel methods to develop Engagement of Patients with Advanced Cancer (EPAC) – an intervention that integrates community health workers into care.1 In our randomized trial, EPAC improved evidence-based end-of-life care, Veteran satisfaction and reduced acute care use and total healthcare costs. In this study, with our established Advisory Board comprised of Veterans, caregivers, oncology clinicians, and VA leaders, we will refine EPAC to incorporate evidence-based lung cancer care (Aim 1). We will randomize 60 Veterans newly diagnosed with advanced lung cancer to establish feasibility of the refined EPAC intervention (primary outcome) and secondarily, the effects on evidence-based lung cancer care and Veteran activation (Aim 2). The results will inform a multisite randomized study in the VA.

Background: Provider engagement is key for successful implementation of evidence-based practices. Our prior work reported that providers’ guideline knowledge is strongly correlated to screening practices but other behavioral factors have not yet been measured in lung cancer screening.

Long-term Career Goal: To be a leader in implementation science with expertise in translating evidence-based lung cancer prevention and early detection into clinical practice through mixed-methods research (qualitative and quantitative) and pragmatic trials in the Veterans Affairs (VA). This award will advance the following career goals: To gain experience in: a) measurement science; b) quantitative analysis methods, and to transition to research independence.

Specific Aim: To measure and validate providers’ behavioral factors that influence lung cancer screening implementation.

Methodology: We will adapt the Determinants of Implementation Behavior Questionnaire (DIBQ) for lung cancer screening and leverage ongoing research infrastructure to survey primary care providers at 10 VA medical centers (VAMCs) participating in the Veterans Affairs Partnership to increase Access to Lung Screening (VA-PALS). The adapted questionnaire will be validated using factor analysis and association with prior screening practices. We will assess which behavioral factors are associated with screening using multivariable regression.

Future Directions: The data gathered will be used to design future interventions aimed at increasing provider engagement and measure how provider engagement changes before and after interventions. This proposal will advance the field of implementation science while also leading towards my independence as an implementation researcher.

Immune checkpoint inhibitors have revolutionized the care of patients with metastatic non-small cell lung cancer (NSCLC). Unfortunately, not all patients benefit from this therapy, and rational combinatorial strategies to enhance immune checkpoint inhibitors (ICI) efficacy in therapy non-responders are needed. We and others have shown that patients with liver metastases derive limited clinical benefit from ICI across a wide variety of disease types. In preclinical models, we discovered that liver metastases cause anti-PD-L1 resistance by siphoning tumor-specific T cells from systemic circulation. Within the liver, activated antigen-specific CD8+ T cells undergo apoptosis. Consequently, liver metastases create a systemic immune desert in preclinical models. Similarly, patients with liver metastases have reduced peripheral T cell numbers and diminished tumoral T cell diversity and function. In preclinical models, liver-directed radiotherapy eliminates immunosuppressive hepatic macrophages, increases hepatic T cell survival, and reduces hepatic siphoning of T cells. The central hypothesis of this proposal is that liver SBRT can enhance CPP therapy non-responders in NSCLC patients with liver metastases. In Aim 1, we will conduct a Phase IB clinical trial establishing the feasibility of delivering multi-target liver SBRT following the first cycle of carboplatin, pembrolizumab, and pemetrexed (CPP) in metastatic NSCLC patients with liver metastases. Up to four liver metastases will be targeted. In Aim 2, we will determine whether liver SBRT combined with ipilimumab and nivolumab modulates the hepatic and systemic immune microenvironment in NSCLC patients with liver metastases. The completion of these aims will assess the feasibility of ablating liver metastases with SBRT in combination with chemoimmunotherapy and deepen our understanding of the contribution of hepatic immune tolerance mechanisms in patients. The ultimate goal of this work is to test rationally-developed combinatorial treatment approaches in hopes of improving the care of NSCLC patients.

Activating mutations in KRAS are found in 20 to 40% of lung cancers and not infrequently in combination with additional alterations in genes such as tumor suppressor TP53 or STK11. How activating KRAS mutations and/or co-mutations affect gene expression in both tumor cells and the cells in the surrounding microenvironment is not known. The impact, if any, of these mutations on intra-tumoral heterogeneity in gene expression is not known. Applying single cell RNA sequencing to resected lung adenocarcinoma samples from US Veterans, I will examine and compare the gene expression profiles of tumor cells from lung adenocarcinomas harboring either mutant and wild-type KRAS, and the expression profiles of cells in the surrounding microenvironment, including tumor infiltrating lymphocytes. In addition, I will determine whether KRAS mutational status affects the degree of intra-tumoral heterogeneity in gene expression, a known factor in survival and resistance to treatment. In sum, we hope to identify specific pathways affected by KRAS in both tumor and surrounding non-tumor cells that could be hypothesis-generating and learn use this knowledge to understand how resistance to KRAS specific therapies might be facilitated by pre-existing intra-tumoral heterogeneity.