MD

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.

Immunometabolic T cell profiling as a prognostic liquid biopsy in NSCLC

Career Development Award
Kellie Smith, PhD
Johns Hopkins School of Medicine
Baltimore
MD

Checkpoint inhibitors, a type of immunotherapy, are now available in the first-line and second-line settings for certain subsets of NSCLC patients. Furthermore, the U.S. Food and Drug Administration recently approved an immunotherapy-combination treatment regimen for the treatment of a subset of advanced-stage NSCLC patients. While we are making progress in combining and sequencing immunotherapy with other conventional treatments, it is still unclear which patients will respond to these combinations. Dr. Kellie Smith’s laboratory is studying immune cells in blood samples from patients who have received the recently approved combination therapy. She postulates that immune cells from patients receiving the combination behave very differently from immune cells from patients who have received single-agent immunotherapy. Dr. Smith’s team will identify and exploit these differences to develop a blood test that will help predict which patients may benefit from combination therapies, thereby sparing patients the exposure to ineffective treatments.

Dynamics of neoantigen landscape during immunotherapy in lung cancer

Career Development Award
This grant was funded in part by the Schmidt Legacy Foundation
Valsamo Anagnostou, MD, PhD
Johns Hopkins University
Baltimore
MD

The lung cancer treatment landscape is rapidly evolving with the advent of immunotherapy. Checkpoint inhibitors, a class of immune-targeted agents, are now available in both the first-line and second-line settings for certain subsets of lung cancer patients. However, the fraction of patients achieving a durable response remains low and, even among patients who respond, the majority develop resistance. Dr. Valsamo Anagnostou is using a comprehensive approach employing genome-wide and functional immune analyses to identify mechanisms of resistance to immune checkpoint blockade. In addition, she is developing a blood-based molecular assay utilizing serial blood samples of lung cancer patients to more accurately predict response and resistance to these therapies.

Survivorship: Improving the recognition and treatment of psychosocial distress in lung cancer patients

Targeted Therapeutics Research Award
LUNGevity Foundation/The Cancer Institute at St. Joseph Medical Center Research Grant
Mark Jonathan Krasna, MD
The Cancer Institute, St. Joseph Medical Center
Towson
MD

Patients often face anxiety and distress following a lung cancer diagnosis. Dr. Krasna is studying how we can improve the recognition and treatment of psychosocial distress in lung cancer patients.

Key words

A Probabilistic Approach to High-Dose Lung IGRT

Targeted Therapeutics Research Award
LUNGevity Foundation/Partnership for Cures Research Grant
Erik J. Tryggestad, PhD
Johns Hopkins University School of Medicine
Baltimore
MD

Dr. Tryggestad is developing magnetic resonance imaging (MRI)-based methods to characterize breathing motion. This information can then be used for radiotherapy planning, delivery, and optimization for the treatment of lung cancer patients.

Key words

Examining LKB1 status as a biomarker for response of lung cancer to metformin

Targeted Therapeutics Research Award
Edward Gabrielson, MD
Johns Hopkins University School of Medicine
Baltimore
MD

Metformin is an FDA-approved drug for the treatment of diabetes. Dr. Edward Gabrielson and his colleagues have found that a gene called LKB1 is altered in 40% of lung cancer patients. He is studying whether lung cancer cells with mutations in LKB1 are sensitive to metformin. His ultimate goal is to use an already-approved drug for the treatment of LKB1-positive lung cancers.

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.

Sputum biomarkers for the early detection of lung cancer

Early Detection Research Award
This grant was funded in part by Upstage Lung Cancer.
Feng Jiang, MD, PhD
University of Maryland
Baltimore
MD
Sanford Stass, MD
University of Maryland
Baltimore
MD
Dr. Jiang is identifying sputum biomarkers that could improve the process of detecting early-stage lung cancer by contributing to development of a non-invasive test that complements low-dose computed tomography (CT) scans and improves the accuracy of diagnosis.