Abstract IA24: Molecular correlates of T cell-inflamed and non-T cell-inflamed tumors
Authors: Bao Riyue , Luke Jason J. 
Published: September 2020

Abstract
Analysis of the tumor microenvironment from patients with melanoma suggests a paradigm of two broad immunologic phenotypes characterized by the presence or absence of CD8 effector tumor-infiltrating lymphocytes (TIL) and other mediators of an adaptive immune response. Those with activated TIL are sometimes referred T cell-inflamed tumors and appear to be those associated with clinical benefit to cancer immunotherapy. T cell-inflamed tumors demonstrate high levels of gene transcripts associated with type II interferons as well as resistance mechanisms linked to interferon (IFN)-γ. This is in contrast with non-T cell-inflamed tumors, which have low TIL count and inflammatory signature. Further, the transcripts associated with CD8 and IFN-γ are correlated with the presence of type I IFN, suggesting that the major deficit in non-T cell-inflamed tumors may be lack of early signals of initial innate immune. The interaction of tumor and immune system is now considered a hallmark of cancer, given the growing importance of immune-checkpoint blocking immunotherapy. These treatments, mostly related to the programmed-death receptor 1 (PD1), are now standard of care in many tumor types. Multiple biomarkers may predict the activity of checkpoint immunotherapy but a biologic predict to effective cancer immunotherapy is an IFN-γ driven response and thus the T cell-inflamed tumor microenvironment. As such, this phenotype may be considered as an approximate surrogate of clinical benefit to study factors promoting or limiting the immune response against cancer.
One aspect impacting efficacy of cancer immunotherapy may be tumor microenvironment-intrinsic molecular biology. The first oncogene to be linked in tumor-intrinsic fashion to immune exclusion was β-catenin in melanoma. Activation of β-catenin eliminated chemokine gradients that attract dendritic cells, leading to resistance to checkpoint and adoptive cellular immunotherapy. This impact of β-catenin has further been confirmed across multiple tumors. Other molecular changes beyond β-catenin have additionally been identified to mediate immune evasion. For example, PTEN loss, STK11 mutation, or MYC activation appears to have this effect. It is not clear whether the immune suppression associated with these pathways comes from an individual molecular event or whether the phenotype is multifactorial and due to complex interactions via multiple pathways. Recognizing the T cell-inflamed phenotype as an approximation of clinical immunotherapy response, we investigated patterns on the molecular level that associate or are anti-correlated with the presence of this phenotype, assuming that they nominate therapeutic opportunities.
To investigate correlates of the T cell-inflamed or non-T cell-inflamed tumor microenvironments we downloaded gene expression, somatic mutation, and clinical data for 31 solid tumors from The Cancer Genome Atlas. A total of 9,555 tumor samples and 742 normal samples were initially downloaded. After excluding the three tumor types, 9,244 tumors and 683 matched normal were included in the analysis. Using a defined T-cell inflamed gene expression signature consisting of 160 genes (“concordant gene list,” described previously), the tumor samples were categorized into three groups: non-T-cell inflamed, T-cell inflamed, and intermediate following previous protocols. In brief, a quantitative scoring system was developed to categorize tumors into three groups: non-T cell-inflamed, T cell-inflamed, and intermediate, based on the expression profile of the T cell-inflamed gene expression signature. First, gene expression values were converted to a score Si = μi ± βiσi (i = 1,2, … n), where μ and σ represent the mean and standard deviation (sd) of the i th gene’s expression across all samples, respectively. n is the total number of genes. β represents the distance between the i th gene’s expression in a sample and its mean in the unit of sd (equivalent to a z-score). Within each individual cancer, the non-T cell-inflamed group was contrasted against the T cell-inflamed tumor groups and differentially expressed genes (DEGs) were identified by Linear Models for Microarray and RNA-Seq Data (limma) voom algorithm with precision weights (v3.38.3). Pathways significantly altered by the DEGs were detected by IPA® (QIAGEN Inc., Germany) with the curated Ingenuity Knowledge Base (accessed November 2015). Upstream transcriptional regulators and their change of direction were predicted by the cumulative effect of target molecules (encoded by DEGs) up or downregulated under condition implemented in IPA® causal network analysis. Somatic mutations were annotated by Oncotator and Annovar (release date April 16, 2018) and silent mutations were removed. The total tumor mutational burden (TMB) was calculated as the total number of predicted protein-changing mutations (referred to as NSSMs, non-synonymous somatic mutations), including non-synonymous/stoploss/stopgain small nucleotide variants (SNVs), frameshift/non-frameshift small insertions and deletions (indels), and variants that affect the splicing site. The relative enrichment of 64 stroma and immune cell types in tumor microenvironment was estimated by xCell (v1.1.0) from the bulk-tissue RNAseq data. Level 3 Clinical Proteomic Tumor Analysis Consortium (CPTAC) high-throughput mass spec antibody-level protein abundance data (release date January 28, 2016) produced by Georgetown University were downloaded from CPTAC Data Portal (https://cptac-data-portal.georgetown.edu/cptacPublic) (accessed December 08, 2018). Protein level was estimated using median-centered normalized values from the data files with gene annotation.
We observed multiple correlations between somatic mutation profiles and gene expression patterns (manifest via pathway analysis) that associated with either the T cell-inflamed or non-T cell-inflamed tumor types. We note that candidate cancer oncogenes or tumor suppressors are sporadically enriched in select tumor types. Additionally, somatic mutations in oncogenic signaling pathways are enriched in non-T cell-inflamed tumors of individual cancer types. Further, somatic mutations in oncogenic signaling pathways can be shared in non-T cell-inflamed tumors across cancer types. We went on to observe that activation of transcriptional programs correlates with the non-T cell-inflamed tumor microenvironment across cancer types and that integration of somatic mutations and transcriptional programs forms an interweaving network of resistance mechanisms. Finally, using available drug databases we note that gene-drug interaction analysis nominates drugs specific to molecular targets in non-T cell-inflamed and T cell-inflamed tumors.
In summary, we observe that molecular correlates of T cell-inflamed versus non-inflamed tumors can nominate and therapeutic targets of high priority for clinical investigation and next-generation clinical trials across cancer types.
Citation Format: Riyue Bao, Jason J. Luke. Molecular correlates of T cell-inflamed and non-T cell-inflamed tumors [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; 2019 Apr 29-30; Austin, TX. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_2):Abstract nr IA24.