The goal of this Project is to develop personalized therapeutic approaches for NSCLC patients based on tumor molecular profiles taken prior to treatment. Currently, molecularly-guided therapy for NSCLC patients is limited to the minority of patients with targetable oncogenic drivers (e.g. EGFR mutations) but the majority of patients to not have such alterations with matching drugs, and these markers to not inform the selection of chemotherapy or other treatments. This project will create a broadly useful classification for NSCLC patients using two approaches. Expression Clades (ECs), based on mRNA expression profiles, and Mutational Clades (MCs), based on DNA mutational alterations. We hypothesize that: 1) tumor ECs and MCs alone, or when combined together (""""""""Genomic Clades"""""""", GCs), reveal underlying biologically distinct lung cancer subgroups with different therapeutic responses to targeted agents, chemotherapy and acquired vulnerabilities (""""""""synthetic lethalities"""""""");2) clades may be used to predict treatment response and facilitate developing novel targeting strategies. Our preliminary data support these hypotheses as well as the feasibility of applying the clades to the clinic. The translational goals are to develop these clades as CLIA-certified """"""""enrollment biomarkers"""""""" for such personalization and to study them in preclinical models, patient tumor specimens, and in a """"""""window of opportunity"""""""" neoadjuvant trial. We have developed the following specific aims to bring this project to fruition.
Aim 1 : We will develop and refine our classification of ECs, MCs, and GCs using clinical and molecularly annotated data sets (e.g. TCGA Lung datasets, which we have profiled for a panel of protein markers), and apply the clades to our existing preclinical models including cell lines and xenograft models.
Aim 2. We will test and validate the association between clades and drug response for selected targeted agents (nintedanib, sorafenib) and chemotherapy regimens in preclincial in vitro and in vivo models, and identify novel clade-based targeting strategies and molecular vulnerabilities. Using this approach we have recently identified novel monogenic vulnerabilities for specific clades.
Aim 3. We will translate clades into the clinic by testing their value in predicting prognosis and benefit from adjuvant, chemotherapy, as well as treatment response to targeted agents (nintedanib and sorafenib) and nintedanib in combination with chemotherapy in the neoadjuvant trial. To do this we will leverage our existing SPORE Pathology Core resources, those from the completed BATTLE study, and those from this project's prospective """"""""window of opportunity"""""""" neoadjuvant therapy trial. We have assembled a multidisciplinary team of laboratory and clinical investigators including collaborations with all ofthe SPORE Cores.

Public Health Relevance

Success in this project would have a major impact in overcoming the barriers to biomarker-driven selection of therapies for individual NSCLC patients, by creating a new functional molecular classification of NSCLC directly tied to preclinical models, chemotherapy and targeted agent response patterns, and molecular vulnerabilities. This will lead to improved therapies, mechanistic insights into the differences between NSCLC subgroups, and will accelerate the integration of biomarker-selected therapies into clinical trials.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Specialized Center (P50)
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Special Emphasis Panel (ZCA1-RPRB-C (M1))
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University of Texas Sw Medical Center Dallas
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Ma, Junsheng; Hobbs, Brian P; Stingo, Francesco C (2018) Integrating genomic signatures for treatment selection with Bayesian predictive failure time models. Stat Methods Med Res 27:2093-2113
Yi, Faliu; Yang, Lin; Wang, Shidan et al. (2018) Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks. BMC Bioinformatics 19:64
Song, Kai; Bi, Jia-Hao; Qiu, Zhe-Wei et al. (2018) A quantitative method for assessing smoke associated molecular damage in lung cancers. Transl Lung Cancer Res 7:439-449
Ji, Xuemei; Bossé, Yohan; Landi, Maria Teresa et al. (2018) Identification of susceptibility pathways for the role of chromosome 15q25.1 in modifying lung cancer risk. Nat Commun 9:3221
He, Min; Liu, Shanshan; Gallolu Kankanamalage, Sachith et al. (2018) The Epithelial Sodium Channel (?ENaC) Is a Downstream Therapeutic Target of ASCL1 in Pulmonary Neuroendocrine Tumors. Transl Oncol 11:292-299
Parra, Edwin R; Villalobos, Pamela; Behrens, Carmen et al. (2018) Effect of neoadjuvant chemotherapy on the immune microenvironment in non-small cell lung carcinomas as determined by multiplex immunofluorescence and image analysis approaches. J Immunother Cancer 6:48
Guo, Hou-Fu; Tsai, Chi-Lin; Terajima, Masahiko et al. (2018) Pro-metastatic collagen lysyl hydroxylase dimer assemblies stabilized by Fe2+-binding. Nat Commun 9:512
Meraz, Ismail M; Majidi, Mourad; Cao, Xiaobo et al. (2018) TUSC2 Immunogene Therapy Synergizes with Anti-PD-1 through Enhanced Proliferation and Infiltration of Natural Killer Cells in Syngeneic Kras-Mutant Mouse Lung Cancer Models. Cancer Immunol Res 6:163-177
Zhang, Liren; Lin, Jing; Ye, Yuanqing et al. (2018) Serum MicroRNA-150 Predicts Prognosis for Early-Stage Non-Small Cell Lung Cancer and Promotes Tumor Cell Proliferation by Targeting Tumor Suppressor Gene SRCIN1. Clin Pharmacol Ther 103:1061-1073
Bayo, Juan; Tran, Tram Anh; Wang, Lei et al. (2018) Jumonji Inhibitors Overcome Radioresistance in Cancer through Changes in H3K4 Methylation at Double-Strand Breaks. Cell Rep 25:1040-1050.e5

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