Adjuvant or neoadjuvant

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.

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

Career Development Award
Kristen Marrone, MD
Johns Hopkins School of Medicine
Baltimore
MD

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.

Intercept Lung Cancer Through Immune, Imaging & Molecular Evaluation-InTIME

SU2C-LUNGevity-ALA LC Interception Award
Grant title (if any)
SU2C-LUNGevity Foundation-American Lung Association Lung Cancer Interception Dream Team
This grant was co-funded by Stand Up to Cancer, LUNGevity, and the American Lung Association
Avrum Spira, MD, MSc
Boston University
Boston
MA
Steven Dubinett, MD
UCLA
Los Angeles
CA
Julie Brahmer, MD
Johns Hopkins Kimmel Cancer Center
Baltimore
MD
Sam Gambhir, MD, PhD
Stanford University
Palo Alto
CA
Matthew Meyerson, MD, PhD
Harvard/Dana-Farber Cancer Institute
Boston
MA
Charles Swanton, PhD
Francis Crick Institute
London, England

The SU2C-LUNGevity Foundation-American Lung Association Lung Cancer Interception Dream Team, led by LUNGevity SAB member Dr. Avrum Spira, is developing a combination of diagnostic tools, such as non-invasive nasal swabs, blood tests, and radiological imaging, to confirm whether lung abnormalities found on chest imaging are benign lung disease or lung cancer.

Neoadjuvant anti-PD-1 antibody, Nivolumab, in resectable NSCLC

Career Development Award
Patrick Forde, MD (MB, BCh)
Johns Hopkins Kimmel Cancer Center
Baltimore
MD

Dr. Forde is working to apply a kind of immunotherapy that has been successful in people with lung cancer in later stages to people with early-stage lung cancer, stimulating their immune system to attack cancer cells. This treatment, nivolumab, uses anti PD-1 antibodies to release the “brakes” on the immune system.