Non-small cell lung cancer (NSCLC)

Early detection of cancer has long been one of the grand challenges of medicine. It is widely acknowledged that better methods for detection of small, asymptomatic tumors are likely to translate to substantial improvements in cancer survival rates. This is an especially important priority for lung cancer because of its high incidence, high rate of late-stage diagnosis, and high mortality. Over the past decade, liquid biopsy approaches based on detection of cancer-specific mutations or epigenetic changes in cell-free DNA (cfDNA) have made significant inroads towards this goal. However, detection of early-stage lung cancer has been particularly challenging because of the minute amounts of tumor DNA shed into blood. Methylation of cfDNA has emerged as a biomarker of choice for many early detection efforts, but existing technologies are designed to probe for cancer-specific methylation patterns either at pre-specified target sites or across broad genomic regions. The former approach prioritizes a limited subset of cancer-relevant signals, whereas the latter approach yields sparse cancer signals from extensive sequence data. Our group has developed a liquid biopsy technology that comprehensively profiles hypermethylated promoter sequences in cfDNA arising from anywhere in the genome. Using a high-stringency capture strategy based on methylation density rather than sequence, our method is able to globally profile hypermethylated promoters without pre-specifying targets. Gene silencing via promoter hypermethylation is a fundamental mechanism of carcinogenesis, and this aberrant signal can be detected at very low levels in plasma because background methylation patterns in healthy plasma are remarkably consistent. To optimize sensitivity for detection of early-stage lung cancer, we will develop a scoring scheme based on probabilistic machine learning to predict the likelihood of lung cancer by integrating hypermethylation signals across thousands of cell-free DNA fragments. Unlike most current liquid biopsy-based early detection efforts which are focused on identifying individuals with cancer based on a single time-point measurement, here we propose to develop a longitudinal early detection algorithm based on measurement of serial increases in cancer-specific epigenetic signals over time due to tumor growth and accumulating changes in the epigenome.

Two RET-specific kinase inhibitors (BLU-667 and LOXO-292) have been FDA approved for treating RET fusion positive cancers. This has led to a tremendous advance in lung cancer therapy and objective response rates in patients naïve to RET inhibitors or who have received other RET inhibitors such as cabozantinib or vandetanib. It is also apparent that RET therapy-driven resistance is common and new alternatives are needed to drug this pathway. Our hypothesis is that targeting RET with novel, first-in-class RET degraders will result in cell death that is more durable than existing inhibitors of RET kinase activity. We will test hypothesis via the following specific aims: Aim 1. Development and characterization of RET degraders for treating RET positive cancers and Aim 2. Evaluate efficacy of RET degraders in in vitro and in vivo NEPC models. In aim 1, we propose to develop RET degraders based on a new RET inhibitor, vepafestinib, and assess RET degrader specificity and activity using a panel of prostate and lung cancer cell line models that overexpress RET or contain RET fusions. We will then evaluate the efficacy of our RET degrader in in vitro and in vivo models of lung and prostate cancer. Once these novel RET protein degraders are developed and tested, we plan to perform pre-clinical optimization studies of compounds and work to identify a suitable pharma partner to develop for clinical implementation in late stage prostate, lung, and other cancers that rely on RET signaling for survival.

RET fusions result from abnormal rearrangements of the kinase domain of RET with other non-essential genes and drive tumorigenesis. These oncogenic chimeric tyrosine kinases are found in approximately  2% of non-small cell lung cancer (NSCLC).Two FDA-approved selective RET inhibitors (selpercatinib and pralsetinib) have shown great response rates in lung cancer patients. However, resistance to RET inhibitors inevitably occurs, limiting therapeutic benefit. Multiple mechanisms of resistance to RET inhibitors have been described, including acquired RET solvent front mutations (G810R/S/C/V), and RET-independent mechanisms of resistance due to amplifications of other receptor tyrosine kinases (RTK) including MET, FGFR1 and ERBB2, and alterations in the RAS-MAPK pathway. Some second-generation RET inhibitors that target secondary RET mutations have been recently developed including vepafestinib (TAS0953/HM06) which is currently being tested in phase I/II clinical trials in the US and Japan for RET fusion positive lung cancer. There is a clinical need to identify mechanisms of resistance to vepafestinib and develop strategies to overcome them.
 

RET with solvent front mutations, amplification of MET/FGFR1/ERBB2 and RAS-MAPK pathway mutations account for >30% of all resistance mechanisms to first-generation RET drugs, and importantly, all of these alterations are expected to activate the RASMAPK pathway. Therefore, a therapeutic strategy that tackles RAS-MAPK pathway activation is expected to benefit >30% of patients who acquire resistance to first-generation RET drugs. Moreover, given that RET fusions, like all tumors arising from activated RTKs engage the RAS-MPAK pathway for oncogenesis, we believe that many treatment-naïve patients may also benefit from a therapeutic strategy that targets RET and the RAS-MAPK pathway.  

Our first goal in this proposal is to simultaneously address resistance due to RAS-MAPK pathway alterations and extending the benefit of first-generation RET drugs by developing a combination therapy strategy involving RET and pan-RAS, MEK1/2 or ERK1/2 inhibitors. Our second goal is to decipher mechanisms by which the transcription factor capicua (CIC) regulate RET-driven tumorigenesis and resistance to RET inhibitors. We will perform transcriptomic, epigenic and proteomic profiling to gain insights into RET-ERK-CIC interaction. Our third goal is to identify and target resistance mechanisms to vepafestinib, so that a therapeutic strategy will be in place for when patients being treated with this drug develop resistance.

Our team includes leaders in the field of lung cancer clinical and translation research who have been at the forefront of lung cancer genomics and therapy, developing state of the art therapeutic strategies. We are well positioned to translate the findings from this study to the clinic within two years. These studies have the potential to benefit more than 30% of lung cancer patients with RET fusions.

RET-rearranged non-small cell lung cancer (NSCLC) presents challenges in management following progression on selective tyrosine kinase inhibitors (TKIs). Platinum-based chemotherapy and docetaxel are available options but are without durable benefit. Real world data with single-agent and combination chemo-immunotherapy suggests modest benefit and possible efficacy if immunotherapy-based approaches are appropriately optimized. For the past decade, our group has meticulously curated hundreds of NSCLC biospecimens including matched tissue and blood from multiple timepoints, including 7 RET-rearranged NSCLC cases that have not previously been analyzed. We have extensive expertise in neoepitope prediction and personalized immunotherapy through dendritic cell (DC)-based vaccination. Our most recent collaboration enabled functional assessments of T-cells through nanovial-based affinity repertoires, further enhancing our ability to predict and translate immunogenic peptides through a personalized vaccine-based program. We propose to use our RET-rearranged NSCLC biospecimens to systematically study T-cell-specific responses to identify optimal immunogenic peptide targets, an approach that could be translated in our currently open and approved DC vaccination trial (NCT03546361) through single patient exemptions.

The prognosis for patients with metastatic non-small cell lung cancer (NSCLC) remains poor despite recent progress in immune checkpoint blockade (ICB) therapy. Thus, there is an urgent need to understand mechanisms for lung cancer immune evasion within the tumor microenvironment (TME) in order to develop more effective and durable strategies for treating lung cancer. Tumor associated macrophages (TAMs), a major component of the tumor stromal mass, generally display an anti-inflammatory phenotype and can facilitate tumor growth by promoting angiogenesis, invasion, and metastasis, as well as immune evasion. However, it remains largely undefined exactly how these TAMs regulate anti-tumor immune responses within the TME. Will test the hypothesis that hedgehog signaling in TAMs interferes with recruitment of CD8+ T cells to the TME through the following specific aims: 1) Investigate the role of Hh inhibition with anti-PD-L1 therapy in non-small cell lung cancer; 2) Study the impact of Hh inhibition on TAMs and changes within the TME. The objective of this project is to understand signals required for functional polarization of TAMs within the TME and its contributions to immune cell dysregulation and cancer progression, and whether combined Hh inhibition and ICB can overcome resistance to immunotherapy.

In NSCLC patients harboring mutant EGFR, treatment with tyrosine kinase inhibitors (TKIs) has demonstrated efficacy. However, the emergence of resistance to EGFR TKIs remains a significant challenge, leading to disease progression. Targeting drug-tolerant persister cells (DTPCs), a rare subpopulation surviving initial treatment, emerges as a more effective strategy than awaiting the development of complete drug resistance, holding potential for curative cancer therapy. Based on our preliminary data on the upregulation of HER3 in DTPCs resistant to EGFR TKIs and the potent antitumor activity of EGFR-targeting chimeric antigen receptor (CAR)-T cells against DTPCs. We propose that HER3 is a promising target expressed on EGFR-TKI DTPCs and that CAR-T cells simultaneously targeting both EGFR and HER3 are an effective approach for targeting DTPCs with improved overall efficacy. In this proposal, we will evaluate HER3 as a therapeutic target for CAR-T cell therapy against EGFR-TKI DTPCs by validating its expression in DTPCs using in vitro cell models, in vivo xenograft and PDX models, and patient tissues. Additionally, we will evaluate the anti-tumor activity of HER3-targeting CAR-T cells against DTPCs. Subsequently, we will develop and characterize EGFRxHER3 bispecific CAR-T cells, evaluating their CAR expression, antigen binding affinity, and anti-tumor activity. We next will assess their anti-tumor efficacy in xenograft and PDX models to guide the selection of the most promising bispecific CAR-T cells for further development. If completed successfully, we will have EGFRxHER3 bispecific CAR-T cells ready for GMP-compliant clinical-grade CAR manufacturing, in preparation for clinical trial evaluation.

As seen in the phase III trial CheckMate 816 (CM816), neoadjuvant anti-PD-1+chemotherapy improves survival for patients with resectable non-small cell lung cancer (NSCLC), with pathologic response as a major trial endpoint. Our team led the Central Pathology Review for CM816, and we showed the first prospective evidence that the full spectrum of % residual viable tumor (%RVT) associates with event free survival. Given the data supporting pathologic response as a survival surrogate, %RVT will likely be incorporated into the next generation of clinical trials and may ultimately guide clinical decision-making. %RVT is primarily evaluated using visual assessment of routine hematoxylin and eosin-stained slides. We developed a machine learning-based approach to score %RVT, which allows for a standardized approach that can be completed rapidly for a large volume of patients, and we propose to test this algorithm in resection specimens from CM816. Additionally, we will use multiplex immunofluorescence (mIF) to quantify individual features of pathologic response, locate them within the larger tumor bed, and determine the relative contribution in predicting patient outcomes. Furthermore, we will use the novel AstroPath platform, a mIF whole-slide imaging platform that uses algorithms first developed in astronomy to generate tumor-immune maps, to identify additional pre- and on-treatment biomarkers of response. Our goal is to leverage emerging technologies (i.e, machine learning and mIF) to develop the next generation of pathology biomarkers, including pathologic response assessment, and to identify additional features that can potentially be targeted in combination with anti-PD-(L)1+chemotherapy to improve clinical benefit in patients with NSCLC.

As seen in the phase III trial CheckMate 816 (CM816), neoadjuvant anti-PD-1+chemotherapy improves survival for patients with resectable non-small cell lung cancer (NSCLC), with pathologic response as a major trial endpoint. Our team led the Central Pathology Review for CM816, and we showed the first prospective evidence that the full spectrum of % residual viable tumor (%RVT) associates with event free survival. Given the data supporting pathologic response as a survival surrogate, %RVT will likely be incorporated into the next generation of clinical trials and may ultimately guide clinical decision-making. %RVT is primarily evaluated using visual assessment of routine hematoxylin and eosin-stained slides. We developed a machine learning-based approach to score %RVT, which allows for a standardized approach that can be completed rapidly for a large volume of patients, and we propose to test this algorithm in resection specimens from CM816. Additionally, we will use multiplex immunofluorescence (mIF) to quantify individual features of pathologic response, locate them within the larger tumor bed, and determine the relative contribution in predicting patient outcomes. Furthermore, we will use the novel AstroPath platform, a mIF whole-slide imaging platform that uses algorithms first developed in astronomy to generate tumor-immune maps, to identify additional pre- and on-treatment biomarkers of response. Our goal is to leverage emerging technologies (i.e, machine learning and mIF) to develop the next generation of pathology biomarkers, including pathologic response assessment, and to identify additional features that can potentially be targeted in combination with anti-PD-(L)1+chemotherapy to improve clinical benefit in patients with NSCLC.