Biomarker or biomarker testing

Early detection

Liquid biopsy

Squamous cell lung cancer

Risk profile

Screening

Stage I

Stage II

Read more about Epigenetic Alterations in Blood as Markers for Early Lung Cancer Detection

Building Reliable Oncology Navigation to Ensure Adjuvant Management: BRONx-TEAM Project

2024

Career Development Award

Tamar Nobel, MD, MPH

Montefiore Medical Center

Bronx

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.

Research Summary: 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.

Technical Abstract: 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.

Key words

Adjuvant or neoadjuvant

Immune checkpoint inhibitors

Immunotherapy

Read more about Building Reliable Oncology Navigation to Ensure Adjuvant Management: BRONx-TEAM Project

Next-generation pathologic response assessment in patients with lung cancer

2024

Career Development Award

Julie Deutsch, MD

Johns Hopkins School of Medicine

Baltimore

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.

Research Summary: 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.

Technical Abstract: 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.

Key words

Adjuvant or neoadjuvant

Immune checkpoint inhibitors

Immunotherapy

Read more about Next-generation pathologic response assessment in patients with lung cancer

Integration of Liquid Biopsy Assays for the Early Detection of Lung Cancer

2023

Early Detection Research Award

Maximilian Diehn, MD, PhD

Stanford University

Stanford

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.

Research Summary

Lung cancer is the number one cause of cancer-related deaths in the U.S. and can affect both smokers and non-smokers. Lung cancer is often deadly because it is usually found after it has spread to other organs like the brain, liver, or bones, at which point it is no longer possible to cure the disease in most patients. But if caught early when the cancer cells are still confined to the lung, it can often be cured with surgery or radiation. Screening for lung cancer in current or former smokers using CT scans has been proven to reduce the number of patients who die from lung cancer by catching the disease early. Although insurance covers it in the U.S., only about 15% of those who qualify actually get screened. Some reasons for this low rate of screening include worries about false positives from CT scans and difficulty accessing radiology facilities. Our goal is to create a new way to screen for lung cancer using a blood sample that can find early stage cancers when they can still be cured. Our method looks for tiny amounts of DNA in the blood that comes from lung cancer cells. We have completed initial tests that show our idea could work. Now, we plan to further improve our test and to apply it to several hundred lung cancer patients and healthy controls. If our experiments are successful, our blood test could eventually be used alongside CT scans to cut down on unnecessary follow-up tests or even replace CT scans as the main way to screen for lung cancer. If so, this work could contribute to saving lives of patients with lung cancer.

Technical Abstract

Lung cancer is the leading cause of cancer in the U.S., with over 238,000 new cases and more than 125,000 deaths expected in 2023. Early detection is crucial for improving prognosis, and low-dose computed tomography (LDCT) scans are recommended for high-risk populations. However, LDCT screening has limitations, including a significant false positive rate and low compliance. New approaches are therefore needed to enhance early detection in high-risk individuals. Liquid biopsy assays that can detect circulating tumor DNA (ctDNA) represent a promising alternative early detection approach. Our group has developed three state-of-the-art liquid biopsy techniques that each have the potential to detect lung cancer noninvasively, including a somatic mutation approach (Lung-CLiP), a methylation-based approach (meCAPP-Seq), and a fragmentomic-based approach (EPIC-Seq). We hypothesize that combining mutation-, methylation-, and fragmentomic-based ctDNA analysis will maximize sensitivity for detecting early stage lung cancers. Our proposal includes three specific aims. In the first aim, we will compare the clinical sensitivity and specificity of meCAPP- Seq, EPIC-Seq, and Lung-CLiP for early lung cancer detection. In the second aim, we will determine the analytical limits of detection for the three methods, which is important for future regulatory considerations. In the third aim, we will develop an integrated liquid biopsy assay that combines meCAPP-Seq, EPIC-Seq, and/or Lung-CLiP. We will test if combining two or all three assays can improve performance over the best- performing single assay. The deliverable of this project will be the identification of the combination of liquid biopsy assays that achieves the best performance for early lung cancer detection. If successful, our work has the potential to significantly improve early lung cancer detection which would lead to improved outcomes for lung cancer patients.

Key words

Early detection

Liquid biopsy

Squamous cell lung cancer

Risk profile

Screening

Stage I

Stage II

Read more about Integration of Liquid Biopsy Assays for the Early Detection of Lung Cancer

Radiogenomic Biomarker and Multiomic Data Integration to Predict Radiation Response in Lung Cancer

2023

Partner Awards

Grant title (if any)

ASTRO-LUNGevity Residents/Fellows in Radiation Oncology Seed Grant

Funded by the American Society for Radiation Oncology

Kailin Yang, MD, PhD

Cleveland Clinic Foundation

Cleveland

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.

Research Summary

Lung cancer remains the leading cause of cancer death in the USA, and the combination of chemotherapy and radiation remains the mainstay for locally advanced disease. While radiation therapy accounts for nearly half of cancer cures, our understanding of which patients will respond well or poorly remains rudimentary. Medical oncology has seen a flurry of genomic measures and signatures to indicate treatment choice, but in radiation oncology our dosing and combination therapy choices are made empirically, with little, if any, regard to individual patient biology or on-treatment response, with the exception of some geometric changes or clinical gestalt. Our overarching goal in this project is to collect and analyze high temporal density, multi-modality data from lung cancer patients treated with standard of care chemoradiation, with an eye to marrying and correlating our understanding of changing transcriptomics over time to non-invasively observable radiomic feature changes during therapy to outcome. We propose to use modern genomic and radiomic techniques to measure the dynamic responses of non-small cell lung cancer tumors to understand how these tumors change during therapy. Such knowledge will generate broad impact on the precision care for lung cancer patients through developing radiogenomic biomarkers that predict therapeutic response and inform clinical decision making.

Technical Abstract

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.

Key words

Radiotherapy

Read more about Radiogenomic Biomarker and Multiomic Data Integration to Predict Radiation Response in Lung Cancer

Phase 2 trial of neoadjuvant KRAS G12C directed therapy in resectable NSCLC

2022

Career Development Award

Kristen Marrone, MD

Johns Hopkins School of Medicine

Baltimore

Around one in three patients with non-small cell lung cancer are diagnosed with early-stage disease, where surgery is offered as curative therapy. Unfortunately, the cancer can recur in 50%-60% of patients. The rate of recurrence is higher in patients whose tumors have certain mutations, such as mutations in the KRAS gene. Dr. Marrone and her team will be conducting a phase 2 trial to test whether treatment with a KRAS G12C blocking drug, adagrasib, given as a single drug or in combination with an immunotherapy drug, nivolumab, before a patient undergoes surgery can delay or prevent recurrence in patients whose tumors have a KRAS G12C mutation.

Research Summary

Although one in three patients diagnosed with non- small cell lung cancer (NSCLC) are found to have potentially curable earlier stage disease, for many the cancer will return. Rates of cancer returning are higher in patients whose tumors have certain mutations, such as KRAS. Immunotherapy with immune checkpoint inhibitors has improved survival for patients with later stages of NSCLC and around the time of surgery. Targeted therapies for KRAS are also improving outcomes for patients with advanced NSCLC. The goal of this study is to evaluate if immunotherapy alone or in combination with a targeted pill for a specific KRAS mutation called G12C is safe and can eliminate cancer cells when given prior to surgery for earlier stage NSCLC. Using immunotherapy and targeted therapies in early stage NSCLC may increase rates of cure compared to surgery and chemotherapy alone.

It is important to understand how and why this therapy may improve cure rates in early stage KRAS mutated NSCLC. Therefore, we will also evaluate the quantity and quality of the immune response with this clinical trial therapy. By evaluating the T cells in the tumors after these treatments, we will better understand how to create a deeper and stronger immune response in KRAS-mutated NSCLC. This is essential because we know that a patient’s own immune system can help increase cure rates and control cancer for longer than other therapies alone. Overall, our trial seeks to create new treatment opportunities for surgical lung cancer while improving outcomes across all stages of KRAS-mutated NSCLC.

Technical Abstract

Although about a third of patients with non-small cell lung cancer (NSCLC) present with resectable disease, a majority will experience death and relapse from their cancer. Exciting recent advances in the use of immune checkpoint inhibition in this early stage space presents a promising way forward for our patients. As the most commonly mutated oncogene, activating KRAS mutations are found in 30% of lung adenocarcinomas; G12C mutations have been associated with higher risk of recurrence following resection. Adagrasib (MRTX849), a potent, highly selective novel small molecule inhibitor of KRAS G12C, has shown promise in previously treated advanced NSCLC. Our study represents a novel application of precision oncology in the resectable NSCLC disease setting with the neoadjuvant use of adagrasib monotherapy or in combination with immune checkpoint inhibition. We will identify safety and clinical efficacy signals of both of these therapeutic approaches. Leveraging our institutional expertise of using single- cell transcriptomics of tumor- infiltrating lymphocytes (TIL), we will evaluate neoantigen-specific transcriptional programs with a focus on KRAS-specific changes, and correlate with clinical outcomes. Overall, our study aims to ultimately improve cure rates of resectable KRAS G12C-mutated NSCLC.

Key words

Adjuvant or neoadjuvant

Immune checkpoint inhibitors

Immunotherapy

KRAS

Stage I

Stage II

Targeted therapy

Read more about Phase 2 trial of neoadjuvant KRAS G12C directed therapy in resectable NSCLC

Ensuring precision-medicine delivery for veterans with lung cancer

2021

Veterans Affairs Research Scholar Award

Manali Patel, MD

Stanford University Medical Center/Veterans Affairs Palo Alto Health Care System

Stanford

Research Summary

Veterans suffer from greater rates of lung cancer and lung cancer deaths than non-Veteran populations. Recent clinical advancements have significantly reduced lung cancer death rates, yet, many Veterans do not receive this care due to many factors including chronic and complex medical conditions, limited social support, and psychosocial concerns. In the VA, we developed the Engagement of Patients with Advanced Cancer (EPAC) intervention, which integrates community health workers and Veteran volunteers to assist Veterans with their cancer care at the end-of-life.1 In our randomized pilot study, EPAC improved evidence-based end-of-life care, improved Veteran and caregiver experiences, reduced hospitalizations and emergency department visits and total health care costs.1 In this study, we will refine EPAC to ensure that Veterans receive evidence-based lung cancer care. We established an Advisory Board comprised of Veterans, caregivers, oncology clinicians, and VA facility leaders who will assist in refining EPAC. We will randomly assign 60 Veterans newly diagnosed with lung cancer to either the refined EPAC intervention or to usual care and determine whether the intervention is feasible. Secondarily we will determine whether EPAC improves evidence-based lung cancer care and Veteran activation. This award will support plans to conduct a larger study across other VA facilities.

Technical Abstract

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.

Key words

Veterans Affairs

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Molecular Characterization of Lineage Plasticity

2021

Partner Awards

Grant title (if any)

EGFR Resisters/LUNGevity Lung Cancer Research Award

Helena Yu, MD

Memorial Sloan Kettering Cancer Center

New York

As a mechanism of resistance to EGFR inhibitors, cancers can change histology from adenocarcinoma to small cell or squamous cell lung cancer. Once this happens, EGFR inhibitors are no longer effective treatment; there are no strategies currently available to prevent or reverse transformation after it has occurred. Dr. Yu will use advanced molecular techniques to identify genetic changes that contribute to transformation. Understanding these genetic changes will identify biomarkers that can be utilized to develop treatments to prevent and reverse transformation.

Research Summary

Metastatic EGFR-mutant lung cancer remains an incurable disease despite significant advances in cancer therapeutics. With better EGFR inhibitors, we have identified new mechanisms of resistance that are independent of EGFR signaling, notably small cell and squamous cell histologic transformation. Targeted therapies are ineffective and survival is shortened once transformation has occurred, and no strategies are available to prevent or reverse transformation after it has occurred.

Mutations in EGFR, TP53, and RB1 are associated with transformation, but additional genetic or epigenetic changes must occur for transformation to happen. We will use advanced molecular techniques to identify the mutations and cell signaling pathways that drive transformation in order to identify biomarkers that can be utilized to develop treatments to prevent and reverse transformation. Treatments will then be validated in our unique pre-clinical models which can be rapidly translated into clinical trials, providing treatments for these aggressive cancers.

Technical Abstract

EGFR-mutant lung cancers (LC) serve as the prototype oncogene-driven cancer and mechanisms of resistance to EGFR therapy are well-established. Selective pressure of EGFR inhibitors can induce lineage plasticity to escape oncogene-dependence. While we have shown EGFR/TP53/RB1-mutations are necessary but insufficient to induce small cell transformation, no other genomic signatures and transcriptomic/epigenomic effectors that induce transformation have been identified.

Using parallel exome sequencing, bulk RNA sequencing, and bisulfite sequencing, we will characterize the biomarkers of transformation in EGFR/TP53/RB1-mutant LC and mixed adenocarcinoma/squamous cell tumors. We will also utilize single-cell RNA sequencing to identify the transcriptional changes associated with transformation and to identify distinct cell types within a heterogenous tumor. We will reconstruct a phenotypic landscape to identify subclones in a transitional state between histologies. The information obtained from these studies will nominate novel biomarkers of lineage plasticity that will be validated and proposed for study in clinical trials.

Key words

Acquired resistance

Epidermal growth factor receptor (EGFR)

Mutation profile

Small cell lung cancer (SCLC)

Squamous cell lung cancer

Tyrosine kinase inhibitors (TKIs)

Targeted therapy

Tumor microenvironment

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Overcoming ALK resistance with covalent cysteine-reactive inhibitors

2020

Partner Awards

A. John Iafrate, MD. PhD

Massachusetts General Hospital

Boston

Liron Bar-Peled, PhD

Massachusetts General Hospital and Harvard Medical School

Boston

Research Summary

The treatment of ALK fusion-positive lung cancer has been revolutionized by precision medicines including ALK kinase inhibitors resulting in improved survival for most patients. However, as we learned with crizotinib and with subsequent ALK inhibitors, most patients eventually relapse with treatment resistant tumors. Unfortunately, once resistance to multiple kinase inhibitors develops, there are few effective options available. Here, we propose to address this urgent need by developing novel inhibitors which target ALK-rearranged lung cancer. Using cutting-edge chemical proteomic technologies, we will develop therapeutic inhibitors that bind tightly to the ALK fusion proteins and cause the protein structure to become inactive. We will test the efficacy of these new inhibitors in reliable cell models of ALK tumors to demonstrate that they can induce tumor cell death. If successful, our program should deliver frontier inhibitors for ALK fusions, providing critical knowledge to direct the development of transformative lung cancer therapies with the hope of making ALK-fusion positive lung cancer a chronic disease.

Technical Abstract

The current standard of care for ALK rearranged non-small cell lung cancer (NSCLC) is targeted therapy with ALK tyrosine kinase inhibitors (TKIs) which bind within the kinase domain of ALK. Due to the development of ALK resistance mutations or bypass activation of parallel or downstream signal pathways, patients inevitably relapse. Even with the introduction of second or third generation TKIs, median overall survival has been estimated to be 6.8 years . Recent advances in immunotherapy have demonstrated progress in lung cancer treatment. However, a majority of ALK positive NSCLC patients have yet to realize the benefits from checkpoint inhibitors or CAR-T cell therapies due to primary resistance or toxicity. Therefore, the development of new treatments for ALK-rearranged NSCLC is an area of critical need. We believe that an effective assault on ALK-driven tumors requires the implementation of creative strategies that allow us to expand our arsenal of drugs to target ALK beyond its kinase domain. To meet this challenge, we have developed and employed cysteine druggability mapping (CDM), a method to define the landscape of inhibitor-protein interactions in a cancer cell. CDM identifies cysteines that can bind to covalent drug-like molecules which serve as early prototypes on which to base cancer drug development programs.
Cysteine-reactive covalent inhibitors have already emerged as an effective approach to targeting protein kinases. Osimertinib, a third-generation epidermal growth factor receptor (EGFR) TKI covalent inhibitor, overcomes EGFR T790M resistance mutations following treatment with EGFR TKI, demonstrating significant improvements over standard EGFR TKIs when used as first line therapy. Importantly, targeting cysteines with covalent inhibitors massively expands the number of proteins which are targetable with small molecule inhibitors, including many targets previously considered undruggable.

We will use ALK fusion positive cancer cell lines, together with tumor tissues from patients who developed resistance mechanisms, to generate druggable cysteine profiles by CDM. Druggable proteins specific to ALK fusion positive samples, including EML4-ALK itself, will be prioritized in our screening for protein degraders from a library of over 2,500 advanced cysteine reactive compounds. After screening, we will assess the specificity of the compound hits and validate their anti-tumor activity using ALK fusion positive cell lines and patient derived ALK TKI-resistant cell lines. We consider the use of the novel, cutting-edge cysteine targeting technology to screen for potent therapeutic compounds as a valuable proposition to improve management of patients with ALK positive lung cancer, with the goal of transforming this cancer into a chronic condition.

Key words

Acquired resistance

Anaplastic lymphoma kinase (ALK)

Mutation profile

Tyrosine kinase inhibitors (TKIs)

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Overcoming bypass signaling to enhance clinical responses in ALK-positive lung cancer

2020

Partner Awards

Ibiayi Dagogo-Jack, MD

Massachusetts General Hospital

Boston

Research Summary

Despite initial sensitivity to treatment with ALK tyrosine kinase inhibitors (TKIs), ALK-rearranged (ALK+) lung cancers invariably develop resistance. The majority of ALK+ lung cancers will eventually acquire ALK-independent survival mechanisms that allow them to escape ALK inhibition. These ALK-independent survival signals can originate from alternative growth receptors (“bypass tracks”). We have recently identified activation of MET signaling resulting from increased copies of the MET gene in approximately 20% of ALK+ tumors that are resistant to next-generation ALK TKIs. In the laboratory, combining lorlatinib with crizotinib (an ALK/MET TKI) overcame resistance caused by MET signaling. Based on these findings, we will conduct a trial to evaluate whether this promising combination can induce durable responses in ALK+ tumors with MET amplification. As many different bypass signals can mediate ALK-independent resistance in the remaining tumors, our trial will also evaluate whether dual targeting of ALK and MEK or SHP2 (proteins that serve as common signaling hubs for many different receptors) can broadly overcome resistance. During the study, serial biopsies will be performed to assess changes in gene and protein expression induced by treatment. This unique trial will also assess feasibility of using plasma findings to select patients with sensitive ALK+ NSCLC who may benefit from an early combination approach. The overarching goal of these studies is to develop multiple promising strategies for overcoming ALK-independent resistance.

Technical Abstract

With the development of increasingly potent and selective ALK tyrosine kinase inhibitors (TKIs), resistance is commonly mediated by ALK-independent mechanisms, such as bypass pathway activation. As bypass signaling dependencies vary across ALK-rearranged (ALK+) lung cancers, a multipronged approach is needed to broadly suppress potential bypass signals. We have identified MET activation in 20% of ALK+ tumors that are resistant to next-generation ALK TKIs. In preclinical studies, dual ALK/MET blockade re-sensitized MET-amplified ALK+ tumors to lorlatinib. MEK and SHP2 have recently been identified as important effector proteins downstream of several bypass signaling pathways. In ALK+ models harboring diverse bypass signals, pairing an ALK TKI with either a MEK or SHP2 inhibitor demonstrated broad anti-proliferative activity. Based on these findings, we will conduct a clinical trial to evaluate lorlatinib in combination with a MET, MEK, or SHP2 inhibitor for patients with ALK+ lung cancers with ALK-independent resistance. Serial tumor biopsies will be analyzed to characterize molecular determinants of response to combination therapy and identify expression changes induced by treatment. This innovative multi-arm trial will also assess whether dynamics of genetic alterations in plasma can be used to guide intensification of treatment from monotherapy to early combination therapy for patients who have not yet developed resistance to ALK-directed therapy. Collectively, the studies in this grant strive to develop diverse strategies for overcoming ALK-independent resistance, a heterogeneous molecular entity that undermines efficacy of current ALK therapeutics.

Key words

Acquired resistance

Anaplastic lymphoma kinase (ALK)

Circulating tumor DNA

Mutation profile