NY

Anti–PD-1 therapy has transformed the management of NSCLC, but only a subset of patients respond to anti–PD-1 therapy, and responses are rarely curative. Understanding the effects of anti–PD-1 therapy on the composition and functional state of tumor-associated immune subsets can identify mechanisms of response and resistance to anti–PD-1 therapy and provide insights into rational combination strategies to improve upon response rates. Although the effects of anti–PD-1 therapy within the tumor immune microenvironment in preclinical and human subjects have been previously reported, we propose to broadly characterize the effects of anti–PD-1 therapy on the tumor draining lymph nodes (TDLN). Studying the TDLN is particularly valuable because it is the primary sites at which the tumor antigen exposure and the main site of immune T-cell differentiation. To determine the effect of ICI response in the lymph node, we have created an immunocompetent murine model that recapitulates early-stage treatment and outcomes in patients. Additionally, we have characterized TDLN T cells from early-stage cancer patients and have been able to recapitulate our preclinical findings of unique memory T-cell subsets. Our preliminary results of the immune cell type composition, checkpoint expression, and cytokine expression within TDLNs suggest that there are major remodeling events within the TDLN mediated by anti–PD-1 therapy that are critical for conferring effective antitumor long-term immune responses. We demonstrate that unique memory T cell subsets undergo significant activation, expansion, and maturation in response to anti–PD-1 therapy, to shape antitumor programming in TDLNs. Importantly, our proposed study will provide a methodological demonstration in both a murine model and human TDLN that simultaneous uses Flowcytometry, functional immune assays and single cell sequencing that will enable deep interrogation of the immune profile to PD-1 inhibition resistant and responsive patients.

Rearrangement of the BRAF gene results in a potent oncogene that is a driver in 2.8% of NSCLC with BRAF alterations. To date there is no approve targeted therapy for any cancer type with BRAF fusions. Studies by our group and others have shown that mutant-specific BRAF kinase inhibitors do no inhibit BRAF fusion kinases. Instead, pan-RAF inhibitors seem more effective as therapy for cancer cells harboring these fusions. We therefore hypothesize that targeting BRAF fusions with pan-RAF inhibitors are likely to be effective therapy for this molecularly defined subset of NSCLC. Our goal in this proposal is to generate the preclinical data necessary to propel a phase 1 clinical trial of a pan-RAF inhibitor specific to patients with NSCLC harboring a BRAF fusion. We have generated seven preclinical disease models with BRAF rearrangement, including two NSLC patient-derived xenograft models with matching cell lines that will allow this project to go forward immediately. In Aim 1 we propose to generate additional NSCLC PDX, organoid and cell line models that will expand the platforms available to examine the efficacy of these BRAF inhibitors. We will test the efficacy of three pan-RAF inhibitors (LY3009120, belvarafenib, KIN-2787) in vitro and in vivo in Aim 2. We believe that combination therapy may eventually be necessary to extend the duration of response to BRAF targeted therapy. To find candidates that can be exploited for combination therapy, in Aim 3 we will map the signaling pathways that are activated by BRAF fusions in isogenic cell lines and inhibited specifically by BRAF inhibitors in cells with BRAF fusions. Given that resistance to therapy is inevitable, we will seek to generate resistance to the three pan-RAF inhibitors in vitro and in vivo and then identify and validate potential mechanisms of resistance. To be able to bring the goals of this proposal to fruition, I have assembled a team with clinical, translational and basic research expertise to help guide me. As a thoracic oncologist with clinical trial expertise, I am ideally situated to translate the findings of this study to the clinic.

Genomic discoveries of driver-gene alterations in lung cancer have led to development of effective target therapeutics; however patients who initially respond to the therapy inevitably experience regrowth of the disease. The reversible drug-tolerant persister (DTP) stage, where cells enter a quiescence, is gaining attention as a major source of non-genetic drug resistance. In our EGFR-TKI-induced DTP model, we found a marked overload of repressive chromatin modification (H3K9me3 and H3K27me3) reminiscent of a quiescent state. In Aim1, we seek to characterize the epigenomic dynamics of DTP stage in RET-positive lung cancer under Selpercatinib treatment, and determine the roles of histone-modifying enzymes on maintaining DTP stage as potential combinatorial therapeutic targets. The process of DTP development requires terminally differentiated cells to regain plasticity and undergo re-differentiation, while the origin of DTP plasticity remains unknown. A recent scRNA-seq study from lung cancer biopsies representing DTP states showed an elevated alveolar signature. During lung regeneration, an alveolar lineage factor HOPX is required for the AT1 plasticity to regenerate AT2 cells upon injury, suggesting a differentiation potential of HOPX(+) cells. Our data showed HOPX is an early marker for treatment-induced DTP and necessary for DTP development, where our scATAC-seq data exhibited increased epigenomic heterogeneity. We hypothesize DTP contains heterogeneous subpopulations that hijack developmental pathways to sustain plasticity for subsequent differentiation. In Aim2, we will determine the role of HOPX and associated developmental pathways in RET-induced DTP model, to identify novel targets determining the DTP plasticity thereby preventing relapse from heterogeneous pathways to acquire Selpercatinib resistance.

On-/off-target resistance mechanisms to RET tyrosine kinase inhibition (TKI) are not identified in ~40% of patients with RET fusion-positive lung cancers. As such, focusing solely on genomic alterations (e.g. mutation/amplification) limits our ability to develop novel therapies for all patients. This project addresses the unmet need of identifying and characterizing alternate resistance mechanisms to selective RET TKI therapy via epigenetic mechanisms leading to altered programs of gene expression. As with other oncogene-TKI pairs, we hypothesize that epigenetic re-programming and lineage plasticity drives resistance to RET TKIs. Serial pre- and post-TKI tumor samples will be interrogated (DEG/GSEA analysis) via complementary methylome (EPIC) and transcriptomic (RNAseq) profiling. Pathologic review and immunohistochemical profiling for markers of squamous (p40, CK5/6), small cell (chromogranin, synaptophysin), and epithelial-mesenchymal (E/N-cadherin, vimentin) transformation will be performed. Candidate resistance pathways will be explored in engineered isogenic overexpression/knockout models and our institutional library of xenografts generated from patients treated with a selective RET TKI on a registrational program or standard of care. Genetic manipulation of targets that we and others have associated with licensing lineage plasticity (e.g. myrAKT, MYC, EZH2, and beta-catenin) in RET fusion-positive models to induce histologic transformation will be performed. These analyses will define strategies to constrain or abrogate therapeutic resistance.

A common source of lung cancer morbidity is presented by incidental pulmonary nodules (IPNs), which can represent missed cancers when incompletely followed. Underrepresented patients are particularly at risk of guidelines discordant care. Our institution cares for a large proportion of these minority communities. We therefore propose to develop a comprehensive program for the identification, risk stratification and management of IPNs identified through imaging in the emergency setting. We first seek to incorporate lung nodule detection and management software into our electronic medical record (EMR). To facilitate recognition of nodules, we will utilize natural language processing to screen and flag imaging. A multidisciplinary ensemble of practitioners will validate findings and participate in management. At the crux of this team will be a patient navigator to communicate with patients and assist in follow-up. We next seek to create an IPN risk prediction algorithm. Clinical and radiographic elements from the EMR will be subjected to artificial intelligence-based review to automatically leverage data towards a machine learning-derived algorithm. The final element of our proposal will investigate a blood-based gene expression assay as a marker for malignancy. Following its evaluation using a cohort of biobanked samples, we will apply this RNA-based detection assay to look for markers of malignancy to facilitate the early detection of lung cancers. We ultimately seek to improve lung cancer care within our largely underserved population through a combination of incorporating patients with IPNs into the health care system and providing appropriate and automated risk stratification to expedite care and diagnosis.