Molecular profile or molecular testing

Laboratory tests that help decide course of treatment 

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

Career Development Award
Tamar Nobel, MD, MPH
Montefiore Medical Center
Bronx
NY

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.

Next-generation pathologic response assessment in patients with lung cancer

Career Development Award
Julie Deutsch, MD
Johns Hopkins School of Medicine
Baltimore
MD

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.

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

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
OH

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.

Molecular Characterization of Lineage Plasticity

Partner Awards
Grant title (if any)
EGFR Resisters/LUNGevity Lung Cancer Research Award
Helena Yu, MD
Memorial Sloan Kettering Cancer Center
New York
NY

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.

Overcoming ALK resistance with covalent cysteine-reactive inhibitors

Partner Awards
A. John Iafrate, MD. PhD
Massachusetts General Hospital
Boston
MA
Liron Bar-Peled, PhD
Massachusetts General Hospital and Harvard Medical School
Boston
MA

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

Partner Awards
Ibiayi Dagogo-Jack, MD
Massachusetts General Hospital
Boston
MA

Predictive biomarkers of radio-immunotherapeutic response in NSCLC

Career Development Award
Sean Pitroda, MD
The University of Chicago
Chicago
IL

Dr. Pitroda and his team will develop a biomarker signature that can predict which patients are the most likely to benefit from an immunotherapy-radiation therapy combination. The ultimate goal is to determine which patients are likely to benefit from this combination treatment.

Lung cancer detection by CRISPR-based detection of circulating tumor DNA

Career Development Award
This grant was funded in part by Schmidt Legacy Foundation and Upstage Lung Cancer
Edwin Yau, MD, PhD
Roswell Park Cancer Institute
Buffalo
NY

Currently,  computed tomography (CT) is available as a tool for the early detection of lung cancer in high-risk individuals. Unfortunately, it has a high false-positive rate: less than 5% of people with nodules found through CT actually have lung cancer. Apart from the distress associated with false positives, individuals may have to undergo invasive procedures, such as a biopsy, to rule out lung cancer.

Circulating tumor DNA (ctDNA) is DNA released from dying cancer cells into the bloodstream. Individuals with early-stage lung cancer may have ctDNA in their blood, even when the cancer is localized. CRISPR-Cas technology is a novel DNA modifying tool that can be used to develop sensitive, specific, and economic ctDNA assays. Dr. Edwin Yau will develop a CRISPR-Cas-based blood test to detect ctDNA in the blood of individuals suspected of having lung cancer. While the immediate goal of the project is to evaluate this blood test in individuals who have already undergone a CT scan, the ultimate goal of the project is to develop a blood test for screening all individuals.

Genome Alterations Associated With Airway Premalignant Lesion Progression

Career Development Award
Joshua Campbell, PhD
Boston University
Boston
MA

One of the challenges for early detection and prevention of squamous cell lung cancer, a type of non-small cell lung cancer (NSCLC), is the lack of understanding of how premalignant lesions develop and progress to lung cancer. Dr. Campbell is studying how normal lung cells acquire changes in their DNA to form premalignant lesions. His ultimate goal is to develop a biomarker to predict development of squamous cell lung cancer.

Detecting early stage lung cancer with circulating tumor cells

Career Development Award
Rajan Kulkarni, MD, PhD
Oregon Health and Science University (formerly at UCLA Medical Center)
Portland
OR

Dr. Kulkarni is studying how circulating tumor cells (cancer cells that are released into the blood stream) can be used to develop a blood test for lung cancer early detection and treatment. Funding from LUNGevity will help him use a novel technology called the Vortex Chip to test two things: first, if lung cancer be detected early by identifying circulating tumor cells in the blood and second, if there are biomarkers in circulating tumor cells that can differentiate patients who will respond to immunotherapy or chemotherapy.