Objective and Rationale: Lung cancer is the leading cause of cancer-related deaths worldwide. In 2016, more than 2 million cases of lung cancer and 1.7 million lung cancer-related deaths were documented across the globe. In the United States alone, the estimated direct medical cost of treating lung cancer was more than $13.4 billion in 2016. Recently, immune-checkpoint blockade treatments have demonstrated promising clinical efficacy in the second-line setting for advanced non-small cell lung cancer (NSCLC). However, the response rates to these immunotherapy (IO) drugs remain modest (~20%). Unfortunately, there are currently no established, validated biomarkers for predicting benefit of IO. Given the prohibitive costs associated with IO (>$200K/year per patient), there is a critical unmet need to develop and validate predictive biomarkers to identify upfront which patients will not benefit from IO. Additionally, the current standard clinical approach to evaluating tumor response (i.e., RECIST and irRC, which evaluate change in tumor size and nodule disappearance) is sub-optimal in evaluating early clinical benefit from IO drugs. Consequently, there is a critical unmet need to develop and validate non-invasive imaging biomarkers to (1) accurately identify which NSCLC patients are most likely to benefit from IO and (2) for non-invasively monitoring response for patients already undergoing IO.

In a preliminary study using pre-treatment CT scans, the PI was able to show that computer-extracted imaging measurements of tumor-associated vasculature was associated with the likelihood of response for NSCLC patients treated with IO. Specifically, the PI showed that Quantitative Vessel Tortuosity (QVT) measurements of the lesions on pre-treatment CT scans of N=139 NSCLC patients undergoing IO were significantly different between responders (less convoluted) and non-responders (more convoluted). The PI also showed that pre-treatment QVT features were associated with overall survival for the patients. Additionally, the change in QVT features between baseline and 4-week post-IO CT scans, was shown to be a strong predictor of early response compared to the current gold standard (RECIST, irRC). In this work the PI seeks to build on these promising initial results to (a) further develop and validate QVT as a predictive biomarker of IO response, thereby enabling upward selection of NSCLC patients who will most likely respond to IO based on pretreatment CT scans, and (b) validate the use of QVT as a potential imaging biomarker for monitoring and identifying early response to IO therapy using routine CT scans. The PI will also seek to explore the associations of QVT with biological pathways that include genes implicated in soliciting an immune response. This research project addresses two areas of emphasis for LCRP, which include developing or optimizing predictive markers to assist with therapeutic decision-making and understanding mechanisms of resistance to treatment (primary and secondary).

PI's Career Goals: Dr. Alilou's long-term career goals consist of pursuing an academic career in lung cancer research, leading to a full-time faculty position with a focus on developing novel imaging biomarkers for predicting response to checkpoint inhibitors, chemotherapy, and radiation therapy. During his career, the PI aspires to develop a translational research program with the goal of deploying novel imaging biomarkers into prospective clinical trials for treatment response prediction and monitoring. In the short term, the PI will seek to (1) develop a multisite validation of diagnostic imaging tools for non-invasive characterization of lung nodules on screening CT scans; and (2) develop and validate QVT as an imaging biomarker for predicting and monitoring response to immunotherapy and chemotherapy. This project will help Dr. Alilou to start to establish and lead his own academic-clinical team for jointly attacking problems in predicting therapy response for NSCLC. The PI is fortunate enough to work with an extremely qualified and diverse mentoring team and collaborators, consisting of expert thoracic oncologists and chest radiologists. This multi-disciplinary team will provide him with multi-faceted clinical and imaging expertise to help him mature as a technical and translational scientist in lung cancer.

Ultimate Applicability: The validated QVT biomarker will help clinicians to (a) make better therapeutic decisions by accurately identifying the NSCLC patients that are most likely going to benefit from IO and (b) determine early response in the course of treatment to allow early adjustment of treatment regimens. The successful completion of this project in the course of 2 years will set the stage for initiation of clinical trials based on the machine interpretation of vessel tortuosity to select the best therapeutic strategy based on either checkpoint inhibitor therapy or its combination with anti-VEGF or chemotherapy for NSCLC patients. We anticipate at least 40% of the lung cancer population (i.e., stage III and IV NSCLC who are undergoing IO) would benefit from this research. The PI will seek to validate the QVT biomarker on the Veteran population of N=300 patients undergoing IO for NSCLC at the Louis Stokes Cleveland VA Medical Center.