We fund translational research to move knowledge as quickly as possible from basic discovery to treatment of patients.
Since 2002, LUNGevity has invested in 193 research projects at 69 institutions in 24 states and the District of Columbia focusing on early detection as well as more effective treatments of lung cancer.
Dr. Deutsch’s proposal centers around finding better pathologic predictors of response to neoadjuvant IO in early stage NSCLC. She will utilize machine learning/artificial intelligence to test an algorithm that she and her team have developed that assesses percent residual viable tumor (%RVT), which is the amount of tumor left at the time of surgery. Dr. Deutsch will also characterize tissue specimens using a novel immunofluorescence platform to identify cell types and spatial relationships that are associated with patient benefit to immunotherapy+chemotherapy. This approach can help inform which patients should receive a given therapy, how they will respond, and additional possible targets for the development of new therapies.
Immunotherapy revolutionized the treatment of lung cancer, and is now being extended so patients can receive therapy before surgery. This was supported by a large clinical trial, CheckMate 816 (CM816), where patients with lung cancer showed improved survival when treated with immunotherapy+chemotherapy before surgery, compared to chemotherapy alone followed by surgery. However, there is an unmet need to identify who is most likely to benefit from such an approach. To address this gap, we will apply novel, next-generation pathology biomarkers utilizing machine learning/artificial intelligence and multispectral imaging. Specifically, we have shown that the amount of tumor left at the time of surgery, termed percent residual viable tumor (%RVT), predicts survival. To date, %RVT assessment is primarily performed visually on glass slides using a light microscope. We developed a machine learning-based algorithm for assessing %RVT on digitized glass slides using a small cohort of patients at Johns Hopkins to improve standardization and throughput in preparation for broad usage. Here, we will test the algorithm’s performance in a larger cohort of patients (the CM816 patients). Additionally, we will characterize tissue specimens using the novel multiplex immunofluorescence AstroPath platform, which uses algorithms first developed in astronomy, to identify cell types and spatial relationships that are associated with patient benefit to immunotherapy+chemotherapy. Our goal is to use cutting-edge technologies to improve the care of lung cancer patients by informing which patients should receive a given therapy, how well patients will do after receiving therapy, and possible additional targets for the development of new therapies.
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.
The introduction of targeted therapies and immunotherapy for early-stage lung cancer is associated with improved survival, but patients can only benefit if they partake in adjuvant and neoadjuvant therapies. Data has shown that inequalities exist for patients with lower socioeconomic status as well as non-White patients when it comes to being referred for and receiving treatment after surgery. These inequalities are likely to increase as new drugs are developed in clinical trials comprised of predominantly white patients. In this project, Dr. Nobel will study the impact of disparities on uptake of adjuvant therapy for NSCLC in a largely minority patient population at Montefiore Medical Center in Bronx, NY. She will provide social support and health literacy to engage patients in their care and collect genetic data about their tumors, which will contribute to future clinical trials that are more inclusive.
Systemic therapy after surgery to remove lung cancer has been demonstrated to improve survival. However, data has shown that there are inequalities in which patients are referred for and receive treatment after surgery, specifically for lower socioeconomic status and non-White patients. As new treatments have been developed, these inequalities are likely to increase as these drugs have been developed in clinical trials predominantly composed of White patients and the benefits in other populations are not known. We have previously demonstrated that using nursing and peer navigators to help guide patients in their cancer care improves treatment adherence in our predominantly Black and Hispanic low socioeconomic status population in the Bronx. The BRONx-TEAM project aims to improve patient outcomes by using a navigation pathway focused on increasing patient adherence to systemic therapy after surgery for non-small cell lung cancer resection. We believe that by providing social support and improving health literacy we can get patients to be more informed and engaged in their cancer care. Furthermore, we will gather genetic data about the patients tumors. Given our patient population, we have a unique opportunity to contribute to the literature to understand the relationships between tumor genetics, treatment types and outcomes in non-White patients. Furthermore, we will investigate the use of a commercial genetic panel to assess risk for recurrence. Given the lack of this type of data in low income non-White patients, we believe that this exploratory portion of our study will serve as an important foundation for future clinical trials that are more inclusive than the currently available literature.
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.
In patients with EGFR-mutant NSCLC, tyrosine kinase inhibitors (TKIs) have been an effective treatment, but over time these patients develop resistance to TKIs, leading to tumor relapse. Dr. Yang’s project focuses on cancer cells called drug-tolerant persisters (DTPs), which are implicated in TKI resistance. A gene called HER3 is expressed in DTPs, and Dr. Yang will use specially engineered immune cells, called CAR-T cells, to target both HER3 and EGFR simultaneously. If successful, this approach would result in a bi-specific CAR-T cell that can be further evaluated in clinical trials.
In lung cancer patients with a specific genetic mutation in EGFR, certain drugs can be helpful, but over time, the cancer can learn to resist these drugs, making them less effective. We're focusing on a small group of tough cancer cells, called DTPCs, that survive the initial treatment because they might be the key to curing the cancer for good. We've noticed that a gene called HER3 is expressed more in these cells. We also have promising results from CAR-T cell therapy. It is a treatment using T cells, a type of immune cells, engineered with the chimeric antigen receptor (CAR) so they can find and destroy cancer cells. We've found that CAR-T cells targeting EGFR can kill DTPCs. We believe if we use these T cells to attack both HER3 and EGFR at the same time, we might have a better chance of killing off these stubborn cancer cells. To make sure this idea works, we'll first check if HER3 is indeed more expressed in these DTPCs in the lab and samples from patients. Then, we'll test our HER3-targeting CAR-T cells to see if they can kill these DTPCs. If that looks promising, we'll tweak the T cells to target both HER3 and EGFR and see if they work even better. If all goes well, we'll be able to make one of the first effective CAR-T cells to target both HER3 and EGFR and try them out in clinical trials.
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.
This grant was co-funded by Rising Tide Foundation for Clinical Cancer Research
The objective of this project is to develop a blood test that can improve upon current limitations in lung cancer screening. Dr. Patel and his team have developed a method to accurately measure alterations in DNA that are cancer-specific by looking at levels of methylation of circulating tumor DNA (ctDNA) in the bloodstream. Using this method, Dr. Patel will develop a predictive model to identify patients with lung cancer based on these DNA alterations at a single time point, as well as an algorithm that can track these changes in a patient’s DNA over time. If successful, this could help detect lung cancer earlier in its development, thereby leading to better outcomes for patients.
Lung cancer is by far the most deadly cancer in the U.S., with total lung cancer deaths exceeding those of the next three major cancers combined. Such dismal statistics are largely attributable to the insidious nature of the disease; by the time symptoms appear, the cancer has often spread to an extent that makes cure unlikely or impossible. In contrast, patients who are diagnosed at earlier stages have much better outcomes, as their tumors can be entirely removed or eradicated prior to distant spread. Thus, annual chest CT scans for lung cancer screening have proven to be effective at reducing lung cancer deaths, and are currently recommended for patients with a heavy smoking history. However, CT-based screening programs have been practically challenging to implement, and uptake has been slow. An alternative screening approach that has been garnering much enthusiasm is based on development of a simple blood test that detects DNA fragments shed from tumor cells into the bloodstream. Several commercial and academic groups have been racing to develop blood tests for cancer screening based on this concept, and the field has made impressive progress. However, detection of early-stage lung cancers has remained particularly challenging, with sensitivities reaching only ~20-40% for Stage I disease. A key limitation for detection of small, early-stage tumors has been the extremely low abundance of DNA fragments bearing cancer-specific features (such as mutations) in the circulation. To overcome this limitation, our group has developed a technology that can accurately measure cancer-specific alterations in DNA which are more highly abundant (known as “hypermethylation”). In the current project, we propose to develop a predictive model to identify patients with lung cancer based on probabilities inferred from measurement of these DNA alterations. We will then further improve the sensitivity for detecting the earliest stages of lung cancer by developing an algorithm that tracks longitudinal changes in a patient’s DNA signal over time rather than relying on just a single time-point.
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.
This project will investigate the role of cells called macrophages, key components of the immune system that have multiple functions, including immune surveillance within a unique communication pathway called hedgehog (Hh). The hedgehog signaling pathway is involved in cell growth and differentiation, as well as maintenance of stem cells and tissue repair. Disruption or inhibition of Hh can create an environment that is less favorable for survival of cancer cells, allowing a patient’s immune system to combat it more effectively. This research has the potential to benefit patients who have been diagnosed with NSCLC, who have not responded to current treatments including immunotherapy by boosting the body’s own defense mechanisms.
Lung cancer remains one of the most lethal types of cancer worldwide, with non-small cell lung cancer (NSCLC) accounting for a majority of cases. The goal of our research is to better understand the relationship between certain immune cells called macrophages and NSCLC, and how this interaction contributes to the cancer's survival and resistance to treatment. The scientific premise of our project lies in investigating a unique communication pathway known as hedgehog signaling within these macrophages and determining how it impacts the immune system's ability to fight lung cancer. If successful, our research has the potential to benefit patients who have been diagnosed with NSCLC, particularly those who have not responded to current treatments including treatment with immune therapies. By disrupting the hedgehog signaling pathway in macrophages, we hope to create a tumor immune environment that is less favorable for cancer cell survival, allowing patients' immune systems to effectively combat the disease. This research can pave the way for innovative therapeutic approaches that boost the body's own defense mechanisms.
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.
This project will investigate novel protein degraders (called PROTACs) as a treatment for RET-positive cancers, and will evaluate their efficacy in vitro and in vivo in prostate and lung cancer. PROTACs are highly specific molecules that degrade unwanted or harmful proteins in cells (in this case, RET tyrosine kinase). This research aims to provide a novel therapeutic approach targeting RET signaling, which could overcome resistance to existing RET inhibitors. If successful, it would be a first-in-class compound for further clinical development.
RET receptor tyrosine kinase is a proto-oncogene that requires a co-receptor and secreted ligand for activation. Activating RET mutations are oncogenic targets in non-small cell lung cancer, medullary thyroid cancer and neuroendocrine type cancers. 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 these novel degraders on several models of prostate and lung cancer to assess on target RET degradation and efficacy in cell line and mouse models. Once novel RET protein degraders are developed and tested in prostate and lung cancer models in vitro and in vivo, we plan to perform pre-clinical optimization studies of compounds and work towards clinical implementation in late stage prostate, lung, and other cancers that rely on RET signaling for survival.
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.
This project will explore the use of neoantigens to evaluate immunogenic priming of dendritic cells (DC) in RET+ NSCLC. Neoantigens are short protein fragments present only in cancer cells that bind to genetically encoded proteins known as human leukocyte antigens (HLA). Dr. Cummings will use features of HLA to predict which cancer-specific protein fragments best match an individual’s immune system, utilizing a biobank of RET-rearranged NSCLC biospecimens. This approach could help identify optimal immunogenic targets, that could be translated into a pathway for clinical use of personalized DC vaccines.
RET-rearranged non-small cell lung cancer (NSCLC) is a rare subtype of lung cancer that is driven by growth signals triggered by RET activation. RET-specific inhibitors are effective initially, but most benefit from this treatment for only 1-2 years before additional treatment is needed. Chemotherapy is a widely-available option but typically provides less than six months of benefit, and it is unclear whether immunotherapy alone or in combination with chemotherapy is a better option. Findings from other gene-rearranged NSCLC studies, particularly those on ALK-rearranged NSCLC, suggest that immunotherapy works better when the immune system is better exposed to abnormalities created by the gene-rearrangement. These are neoantigens, or short protein fragments present only in cancer cells that bind to human leukocyte antigen (HLA), a scaffold that displays these protein fragments to the immune system. One issue with this approach is that these fragments have to be specifically matched to the immune system of an individual, and even the most common forms of HLA are only found in 20% of people. This means that these types of approaches would be applicable to at most 1 out of 5 people with RET-rearranged NSCLC. Our techniques broaden this approach by using features of HLA to predict which cancer-specific protein fragments best match an individual’s immune system (motif neoepitopes), including neoantigens from RET rearrangements and those predicted from the individual’s tumor. We propose to use our biobank of RET-rearranged NSCLC biospecimens, which have not been previously analyzed, to determine whether we can detect and elicit enhanced immune responses with motif neoepitopes, neoantigens related to RET-rearrangements, or other predicted neoantigens. We can then offer this approach in a currently open clinical trial investigating immune system optimization through an application to the FDA.
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.
This project aims to develop new therapeutic approaches for RET-positive cancers, focusing on overcoming resistance to currently available RET inhibitors. Dr. Somwar and colleagues will investigate ways to block the growth of lung cancers with altered RET in a pathway called MAPK (mitogen activated kinase), which is involved in many biological processes involving cell growth and survival. MAPK is implicated in developing resistance to RET inhibitors and finding strategies to target this pathway in combination with RET could benefit many patients who have no approved therapy options after tumor reoccurence.
Lung cancers are one of the leading causes of death in the US. Significant progress has been made over the past three decades to understand the biology of lung cancers and to stratify these diseases into subsets of patients who will get the maximum benefit of a given form of therapy. New technologies now allow for each patient to have their tumor DNA sequenced to find genetic causes of their cancer. Many genes that regulate cell growth are altered by mutations that cause the unrestricted growth that lead to cancer. Scientists have developed strategies to take advantage of these aberrant genes by finding chemicals or biological agents that will antagonize the protein products of these genes. One gene that is altered in 2% of lung cancers is called RET and there are two drugs that block the tumorigenic function of this cancer-causing gene (oncogene). Although patients respond very well to these two anti-RET drugs at first, they soon become resistant to the therapeutic effects. Additional genetic changes in RET or other genes in the cancer cells that regulate growth are responsible for the drug resistance. Our goal in this grant proposal is to find ways to block the growth of lung cancers with altered RET that stopped responding to anti-RET inhibitors. The strategy that we will test involves the simultaneous inhibition of RET and other proteins in another growth promoting pathway called the MAPK (mitogen activated kinase) pathway. We believe that this therapeutic strategy can benefit more than 30% of patients who stop responding to current drugs that target lung cancers with RET genetic alterations.
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.
Small cell lung cancer (SCLC) is difficult to treat, and most patients diagnosed have a poor prognosis. Most patients with SCLC treated with first line chemoimmunotherapy progress within months of immune checkpoint inhibitor (ICI) maintenance therapy. Previous studies in mice have revealed that SCLC treated with iadademstat and maintenance ICI shows enhanced tumor response compared to ICI alone. Dr. Choudhury will conduct a phase II randomized trial investigating this combination in patients with SCLC versus standard of care ICI alone to evaluate progression free survival.
This grant was funded in part by Lung Cancer Initiative
Although the average age at diagnosis is 70, thousands of new patients under 45 are diagnosed with lung cancer every year, most of whom have never smoked. Dr. LoPiccolo hypothesizes that these patients may share inherited genetic changes that predispose them to developing lung cancer at a younger age. In a preliminary analysis of young-onset lung cancer patients, Dr. LoPiccolo has found that approximately 30% of these patients carry rare mutations in known cancer-associated genes. In this study, Dr. LoPiccolo will investigate whether these mutations affect response to targeted or immune-based therapies. This insight is likely to identify risk factors among young lung cancer patients, which could lead to improved screening and treatment options for this population.
Checkpoint immunotherapy has advanced treatment of NSCLC, but the majority of patients do not experience long-term disease control and are at risk for autoimmune-related side effects. In this study, Dr. Tseng will examine specialized cells called CD8+ T that express receptors (KIR+) that suppress autoimmunity to understand how these cells regulate the immune system’s cancer-fighting ability during checkpoint immunotherapy treatment. Insights gained from this study could result in better strategies for improving efficacy while decreasing immune-related side effects.
Lung cancer is the number one cause of cancer-related deaths in the US because it is often found only after it has spread to other organs in the body, decreasing the likelihood of surviving at least 5 years after diagnosis. Only 21% of patients are diagnosed then their lung cancer is early stage, when it is most treatable. The goal of this project is to create a new way to screen for lung cancer using a blood sample that can find early stage disease when patients can still be treated and/or cured. In preliminary work, Dr. Diehn has developed a blood test that can identify tiny amounts of DNA from lung cancer cells and in this study he will improve this test and apply it to patients and healthy controls. If successful, Dr. Diehn’s work has the potential to significantly improve early detection of lung cancer and improve outcomes for patients.
Funded by the American Society for Radiation Oncology
Radiation therapy remains a cornerstone treatment for patients with locally advanced lung cancer, however knowing which patients will respond and which will not respond is still poorly understood. The goal of this project is to analyze genomic and radiomic data from patients with NSCLC to understand how tumors change during therapy and create models to predict therapeutic response that will assist with clinical decision making.
Radiation therapy is the single most utilized anti-cancer agent; nearly 70% of all cancer patients will receive radiation at some point in their cancer journey. Radiation plays a crucial role in almost half of all cancer cures. The sequencing of the human genome, completed nearly 20 years ago, followed by the large scale cancer sequencing effort in The Cancer Genome Atlas (TCGA) have provided an unprecedented understanding of cancers in the primary and metastatic setting. In those same years, medical oncology has undergone three major phase transitions: targeted therapies have changed the way we think many diseases with specific actionable mutations; immunotherapy has revolutionized the treatment of many of those without; and antibody-drug conjugates have increased the specificity of our cytotoxics. Radiation treatment decision making, however, has not seen these same changes from biological influences, instead having relied on advances in medical physics and computer science to drive our advances. While the number of trials has ballooned in radiation oncology of late, spurred on by encouragement, and funding, from pharmaceutical companies interested in the synergy between novel (and profitable) compounds in the form of immune checkpoint inhibitors and antibody-drug-conjugates, with radiation, our understanding of the relative benefits and best choices for individual patients has not seen the same increases. In fact, we have struggled to parse out the differences between these novel combinations and standard chemoradiotherapy in phase II trials, largely because of the combinatorial nature of our trials, and the sheer number of open questions. In this project, we seek to make headway toward personalizing radiation therapy treatment choices. Leveraging our experience in using gene signatures to predict individual patient radiation benefit, together with expertise in radiomics and genomics, we will track the progress and outcomes of patients with non-small cell lung cancer treated with standard of care chemoradiation using high resolution genomic and radiomic measures. The dynamics of these genomics through time will be correlated to the high temporal density radiomics features to allow for translation and generalization to all patients treated with modern technique. Through these complimentary -omic modalities, we aim to leverage our experience in creating signatures of therapeutic response to admit personalized treatment choice in the upfront setting, and opportunities to change course using real- time information gleaned from daily imaging. This pilot project will enable the development of novel multimodal data integration methodologies to interrogate radiation treatment response in a comprehensive manner.
Tyrosine kinase inhibitors (TKI) are a class of drugs that are used to treat EGFR NSCLC. These drugs eventually stop working and some cancer cells called drug-tolerant persisters (DTPs) are implicated in this resistance. Dr. Kobayashi and his team have found that a protein called CD74 plays a role in developing a resistance to osimertinib. In this project, he will investigate whether CD74-expressing cells allow for the development of DTPs and if inhibition of CD74 by combining an antibody-drug conjugate (CD74-MMAE) with osimertinib, prevents resistance. If successful, this has the potential to significantly impact the survival of EGFR patients by allowing them to stay on osimertinib for a longer duration.
This grant was co-funded by Stand Up to Cancer, LUNGevity, and the American Lung Association
Lung cancer is the leading cause of cancer death globally, primarily due to challenges in early detection. With funding from Stand Up to Cancer, LUNGevity Foundation, and the American Lung Association, a multidisciplinary team called the Lung Cancer Interception Dream Team was formed in 2017 to tackle this challenge, uniting expertise from various fields to enhance lung cancer interception and prevention.
This initiative includes the development of a lung pre-cancer genome atlas (PCGA) aimed at understanding molecular changes linked to the progression of pre-cancerous lesions to lung carcinoma. With continued funding from LUNGevity Foundation and the American Lung Association, the team plans to establish a temporal atlas for premalignant lung adenocarcinoma by utilizing robot-assisted bronchoscopy to collect samples from patients with ground glass opacities (GGOs) suspected of lung cancer. This effort will not only help identify these lesions but also facilitate the targeted delivery of intervention agents.
By gaining insights into progression-associated molecular alterations and cellular interactions, the team aims to significantly advance lung cancer interception strategies - catching cancer at its earliest stages and treatment it before it grows and spreads. Ultimately, the goal is to provide personalized interception approaches for individuals at risk of developing lung cancer.
Cancer interception is catching cancer at its earliest stages and treatment it before it grows and spreads. Our current lack of effective lung cancer interception methods stems from an incomplete understanding of the early molecular events in lung cancer development, Through the 2017 Stand Up To Cancer – LUNGevity Foundation – American Lung Association grant, a multidisciplinary team called the Lung Cancer Interception Dream Team has established the Lung Pre-Cancer Genome Atlas (PCGA), identifying immune and epithelial changes linked to who normal cells become pre-malignant cancer cells. With a second round of funding from LUNGevity Foundation and the American Lung Association, the team will be building on these foundational findings and enhance their efforts by developing a temporal atlas of genomic (DNA-level changes), transcriptomic (RNA-level), and epigenetic changes in pre-malignant lung adenocarcinoma lesions through longitudinal sampling. The team hypothesizes that these lesions exhibit specific genomic, transcriptomic, and epigenetic alterations, with some evading immune detection and advancing to invasive cancer. Ultimately, the insights gained will provide valuable resources for the research community and significantly impact early-stage lung cancer interception.
We lack effective lung cancer interception approaches due to our incomplete understanding of the earliest molecular events associated with lung carcinogenesis, which leave clinicians with few tools to manage precancerous lesions that may be found on CT screening. Our multidisciplinary Lung Cancer Interception Dream Team has made significant progress in establishing a Lung Pre-Cancer Genome Atlas(PCGA) where we have begun to identify immune and epithelial alterations associated with premalignant disease progression. To extend our findings in order to refine targets for lung cancer interception trials, we are proposing to extend our on-going PCGA efforts with two important aims 1) Develop a temporal atlas of premalignant lung adenocarcinoma via establishment of a cohort of longitudinally-sampled ground glass opacities (GGOs) collected with robot-assisted bronchoscopy, representing premalignant and minimally-invasive lung adenocarcinomas and 2) based on our current findings and feedback from our previous reviewers, we will expand our profiling to include spatial and epigenetic profiling of precancerous lesions and minimally invasive carcinoma in biopsy samples collected from the GGO cohort and our Pre-Cancer Genome Atlas 2.0 cohorts. We hypothesize that premalignant lesions bear specific genomic, transcriptomic and epigenetic aberrations, and a subset of these lesions escape immune surveillance and progress to invasive cancer. Our team, will apply spatial profiling using imaging mass cytometry and spatial transcriptomics will allow us to uncover the tissue architecture of the molecular processes associated with progression which in turn will help delineate the cell-cell interactions underlying these processes. Epigenetic profiling via single cell ATAC and bulk DNA methylation sequencing will allow us to overlay information about transcriptional regulation with the other ‘omic data to better understand the regulation of processes associated with progression. Critical to the success of the proposal is the multidisciplinary expertise of the team, involvement of patient advocates and the extensive preliminary data supporting the feasibility of the proposed approaches. The insights gained from successful completion of this project and the data that will made available to the research community will serve as a foundational resource for other investigators in the field and will result in a significant and sustained impact on the interception of early-stage lung cancers.
