Edwin Roger Parra Cuentas

Epithelial-Mesenchymal Transition Is Associated with a Distinct Tumor Microenvironment Including Elevation of Inflammatory Signals and Multiple Immune Checkpoints in Lung Adenocarcinoma.

Purpose: Promising results in the treatment of non–small cell lung cancer (NSCLC) have been seen with agents targeting immune checkpoints, such as programmed cell death 1 (PD-1) or programmed death ligand-1 (PD-L1). However, only a select group of patients respond to these interventions. The identification of biomarkers that predict clinical benefit to immune checkpoint blockade is critical to successful clinical translation of these agents. Methods: We conducted an integrated analysis of three independent large datasets, including The Cancer Genome Atlas of lung adenocarcinoma and two datasets from MD Anderson Cancer Center (Houston, TX), Profiling of Resistance Patterns and Oncogenic Signaling Pathways in Evaluation of Cancers of the Thorax (named PROSPECT) and Biomarker-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination (named BATTLE-1). Comprehensive analysis of mRNA gene expression, reverse-phase protein array, IHC, and correlation with clinical data were performed. Results: Epithelial–mesenchymal transition (EMT) is highly associated with an inflammatory tumor microenvironment in lung adenocarcinoma, independent of tumor mutational burden. We found immune activation coexistent with elevation of multiple targetable immune checkpoint molecules, including PD-L1, PD-L2, PD-1, TIM-3, B7-H3, BTLA, and CTLA-4, along with increases in tumor infiltration by CD4+Foxp3+ regulatory T cells in lung adenocarcinomas that displayed an EMT phenotype. Furthermore, we identify B7-H3 as a prognostic marker for NSCLC. Conclusions: The strong association between EMT status and an inflammatory tumor microenvironment with elevation of multiple targetable immune checkpoint molecules warrants further investigation of using EMT as a predictive biomarker for immune checkpoint blockade agents and other immunotherapies in NSCLC and possibly a broad range of other cancers. Clin Cancer Res; 22(14); 3630–42. ©2016 AACR. See related commentary by Datar and Schalper, p. 3422

Cancer-Associated Fibroblasts Induce a Collagen Cross-link Switch in Tumor Stroma

Intratumoral collagen cross-links heighten stromal stiffness and stimulate tumor cell invasion, but it is unclear how collagen cross-linking is regulated in epithelial tumors. To address this question, we used KrasLA1 mice, which develop lung adenocarcinomas from somatic activation of a KrasG12D allele. The lung tumors in KrasLA1 mice were highly fibrotic and contained cancer-associated fibroblasts (CAF) that produced collagen and generated stiffness in collagen gels. In xenograft tumors generated by injection of wild-type mice with lung adenocarcinoma cells alone or in combination with CAFs, the total concentration of collagen cross-links was the same in tumors generated with or without CAFs, but coinjected tumors had higher hydroxylysine aldehyde–derived collagen cross-links (HLCC) and lower lysine-aldehyde–derived collagen cross-links (LCCs). Therefore, we postulated that an LCC-to-HLCC switch induced by CAFs promotes the migratory and invasive properties of lung adenocarcinoma cells. To test this hypothesis, we created coculture models in which CAFs are positioned interstitially or peripherally in tumor cell aggregates, mimicking distinct spatial orientations of CAFs in human lung cancer. In both contexts, CAFs enhanced the invasive properties of tumor cells in three-dimensional (3D) collagen gels. Tumor cell aggregates that attached to CAF networks on a Matrigel surface dissociated and migrated on the networks. Lysyl hydroxylase 2 (PLOD2/LH2), which drives HLCC formation, was expressed in CAFs, and LH2 depletion abrogated the ability of CAFs to promote tumor cell invasion and migration. Implications: CAFs induce a collagen cross-link switch in tumor stroma to influence the invasive properties of tumor cells. Mol Cancer Res; 14(3); 287–95. ©2015 AACR.

Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer

With increasing use of immunotherapy agents, pretreatment strategies for identifying responders and non-responders is useful for appropriate treatment assignment. We hypothesize that the local immune micro-environment of NSCLC is associated with patient outcomes and that these local immune features exhibit distinct radiologic characteristics discernible by quantitative imaging metrics. We assembled two cohorts of NSCLC patients treated with definitive surgical resection and extracted quantitative parameters from pretreatment CT imaging. The excised primary tumors were then quantified for percent tumor PDL1 expression and density of tumor-infiltrating lymphocyte (via CD3 count) utilizing immunohistochemistry and automated cell counting. Associating these pretreatment radiomics parameters with tumor immune parameters, we developed an immune pathology-informed model (IPIM) that separated patients into 4 clusters (designated A-D) utilizing 4 radiomics features. The IPIM designation was significantly associated with overall survival in both training (5 year OS: 61%, 41%, 50%, and 91%, for clusters A-D, respectively, P = 0.04) and validation (5 year OS: 55%, 72%, 75%, and 86%, for clusters A-D, respectively, P = 0.002) cohorts and immune pathology (all P < 0.05). Specifically, we identified a favorable outcome group characterized by low CT intensity and high heterogeneity that exhibited low PDL1 and high CD3 infiltration, suggestive of a favorable immune activated state. We have developed a NSCLC radiomics signature based on the immune micro-environment and patient outcomes. This manuscript demonstrates model creation and validation in independent cohorts.

Programmed Death Cell Ligand 1 (PD-L1) Is Associated With Survival in Stage I Non–Small Cell Lung Cancer

Programmed cell death ligand (PD-L1) has been studied as a predictive immunotherapy biomarker. We investigated PD-L1 expression in the whole tumor and in tumor-infiltrating macrophages (TIMs) as a prognostic biomarker in surgically resected pathologic stage I non-small cell lung cancer. Pathologic specimen from 113 patients with stage I lung cancer (pT1-2a, N0, M0, tumor size 1-5 cm, 79 adenocarcinoma, 34 squamous cell carcinoma) were analyzed for PD-L1 expression in the tumor and in the TIMs using immunohistochemistry and image analysis. Statistics included recursive partitioning, univariable, multivariable, and Kaplan-Meier analyses. Patients whose tumors expressed <4.7% PD-L1 (N = 87) experienced significantly better overall survival (OS) (P = 0.001) than patients with PD-L1 >4.7% (N = 26). Patients with PD-L1 expression in macrophages <6.3% (N = 24) also experienced significantly better (P = 0.005) OS than patients with >6.3% (N = 89). The best outcomes were observed in patients with low PD-L1 expression in both tumor and macrophages with 5-year OS of 94% (N = 17). Contrarily, patients with high PD-L1 expression in both tumor and macrophages experienced 5-year OS of 20% (N = 19). Low PD-L1 expression in the tumor and in the TIMs was independently associated with survival in multivariable analysis (P = 0.000 and P = 0.030, respectively). Lower PD-L1 % expression in the tumor and in the TIMs seems to be associated with significantly better OS in surgically resected stage I lung cancer. Additional studies are needed to validate PD-L1 as a prognostic biomarker in lung cancer and to study the mechanisms of intratumoral immune response.

Development and Validation of a Predictive Radiomics Model for Clinical Outcomes in Stage I Non-Small Cell Lung Cancer

PURPOSE To develop and validate a radiomics signature that can predict the clinical outcomes for patients with stage I non-small cell lung cancer (NSCLC). METHODS AND MATERIALS We retrospectively analyzed contrast-enhanced computed tomography images of patients from a training cohort (n = 147) treated with surgery and an independent validation cohort (n = 295) treated with stereotactic ablative radiation therapy. Twelve radiomics features with established strategies for filtering and preprocessing were extracted. The random survival forests (RSF) method was used to build models from subsets of the 12 candidate features based on their survival relevance and generate a mortality risk index for each observation in the training set. An optimal model was selected, and its ability to predict clinical outcomes was evaluated in the validation set using predicted mortality risk indexes. RESULTS The optimal RSF model, consisting of 2 predictive features, kurtosis and the gray level co-occurrence matrix feature homogeneity2, allowed for significant risk stratification (log-rank P < .0001) and remained an independent predictor of overall survival after adjusting for age, tumor volume and histologic type, and Karnofsky performance status (hazard ratio [HR] 1.27; P < 2e-16) in the training set. The resultant mortality risk indexes were significantly associated with overall survival in the validation set (log-rank P = .0173; HR 1.02, P = .0438). They were also significant for distant metastasis (log-rank P < .05; HR 1.04, P = .0407) and were borderline significant for regional recurrence on univariate analysis (log-rank P < .05; HR 1.04, P = .0617). CONCLUSIONS Our radiomics model accurately predicted several clinical outcomes and allowed pretreatment risk stratification in stage I NSCLC, allowing the choice of treatment to be tailored to each patients individual risk profile.

Abstract 142: Mutation and immune profiles in early-stage lung squamous cell carcinoma

PURPOSE: Lung squamous cell carcinoma (LUSC) accounts for 20-30% of non-small cell lung cancers (NSCLCs). There are limited treatment strategies for LUSC in part due to our inadequate understanding of the molecular underpinnings of the disease. We sought to perform whole-exome sequencing (WES), comprehensive immune profiling and clinicopathological analysis of early-stage LUSCs to increase our understanding of the pathobiology of this malignancy. METHODS: Matched pairs of surgically resected stage I-III LUSCs and normal lung tissues (n = 108) were analyzed by WES. Immunohistochemistry and image analysis-based profiling of 10 immune markers was done on a subset of LUSCs (n = 91). Associations among mutations, immune markers and clinicopathological variables were statistically examined using ANOVA and Fisher tests. Cox proportional hazards regression models were used for statistical analysis of clinical outcome. RESULTS: This early-stage LUSC cohort displayed an average of 209 exonic mutations per tumor. Fourteen genes exhibited significant enrichment for mutation: TP53, MLL2, PIK3CA, NFE2L2, CDH8, KEAP1, PTEN, ADCY8, PTPRT, CALCR, GRM8, FBXW7, RB1 and CDKN2A. Among mutated genes associated with poor recurrence-free survival, MLL2 mutations predicted poor prognosis in both TP53 mutant and wild type LUSCs. We also found that in treated patients, FBXW7 and KEAP1 mutations were associated with poor response to adjuvant therapy, particularly in TP53-mutant tumors. Analysis of mutations with immune markers revealed that ADCY8 and PIK3CA mutations were associated with markedly decreased tumoral PD-L1 expression, LUSCs with PIK3CA mutations exhibited elevated CD45ro levels and CDKN2A-mutant tumors displayed an up-regulated immune response. CONCLUSION: Our findings pinpoint mutated genes that may impact clinical outcome as well as personalized strategies for targeted immunotherapies in early-stage LUSC. Citation Format: Murim Choi, Humam Kadara, Jiexin Zhang, Edwin Parra Cuentas, Jaime Rodriguez Canales, Stephen G. Gaffney, Zi-Ming Zhao, Carmen Behrens, Junya Fujimoto, Chi-Wan Chow, Neda Kalhor, Cesar Moran, David Rimm, Stephen Swisher, Don L. Gibbons, John V. Heymach, Edward Kaftan, Jeffrey Townsend, Thomas J. Lynch, Joseph Schlessinger, J. Jack Lee, Richard Lifton, Roy S. Herbst, Ignacio I. Wistuba. Mutation and immune profiles in early-stage lung squamous cell carcinoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 142.

Abstract 89: Whole-exome sequencing and immune profiling of early-stage lung adenocarcinoma

PURPOSE: Lung adenocarcinomas (LUADs) lead to the preponderance of deaths attributable to lung cancer. We performed whole-exome sequencing (WES), comprehensive immune profiling and clinicopathological analysis of LUADs to better understand the molecular pathogenesis of this disease and identify clinically relevant molecular markers. METHODS: We performed WES of 108 paired surgically resected stage I-III LUADs and normal lung tissues using the Illumina HiSeq 2000 platform. Additionally, ten immune related markers (PD-L1, PD-1, CD3, CD4, CD8, CD45ro, CD57, CD68, FOXP3 and Granzyme B) were profiled by imaging-based immunohistochemistry in a subset of LUADs (n = 92). Associations among mutations, immune markers and clinicopathological variables were analyzed using ANOVA and Fishers Exact tests. Cox proportional hazards regression models were employed for multivariate analysis of clinical outcome. RESULTS: LUADs in this cohort exhibited an average of 243 coding mutations per tumor. We identified 28 genes with significant enrichment for mutation. SETD2-mutant LUADS exhibited relatively poor recurrence-free survival (RFS) and mutations in STK11 and ATM were associated with poor RFS in KRAS-mutant tumors. EGFR, KEAP1 and PIK3CA mutations were predictive of poor response to adjuvant therapy. Immune marker analysis demonstrated that PD-L1 expression was increased in smoker compared to non-smoker LUADs and, along with other immune markers, was positively correlated with somatic mutation burden. Moreover, immune marker levels including PD-L1 were elevated in TP53-mutant LUADs. In contrast, STK11 and U2AF1 mutant tumors exhibited a suppressed immune response and LUADs with PIK3CA mutations exhibited markedly decreased tumoral PD-L1 expression. CONCLUSION: Our study highlights mutations that may impact clinical outcome and personalized strategies for immune-based therapy of early-stage LUAD patients. Citation Format: Humam Kadara, Murim Choi, Jiexin Zhang, Edwin Parra Cuentas, Jaime Rodriguez Canales, Stephen Gaffney, Zi-Ming Zhao, Carmen Behrens, Junya Fujimoto, Chi-Wan Chow, Neda Kalhor, Cesar Moran, David Rimm, Stephen G. Swisher, Don L. Gibbons, John V. Heymach, Edward Kaftan, Jeffrey Townsend, Thomas J. Lynch, Joseph Schlessinger, J. Jack Lee, Richard Lifton, Ignacio I. Wistuba, Roy S. Herbst. Whole-exome sequencing and immune profiling of early-stage lung adenocarcinoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 89.