Transcriptome Analysis of Tumor-Infiltrating Lymphocytes Identifies NK Cell Gene Signatures Associated With Lymphocyte Infiltration and Survival in Soft Tissue Sarcomas.

Purpose: Clinical successes using current T-cell based immunotherapies have been limited in soft tissue sarcomas (STS), while pre-clinical studies have shown evidence of natural killer (NK) cell activity. Since tumor immune infiltration, especially tumor-infiltrating lymphocytes, is associated with improved survival in most solid tumors, we sought to evaluate the gene expression profile of tumor and blood NK and T cells, as well as tumor cells, with the goal of identifying potential novel immune targets in STS. Experimental Design: Using fluorescence-activated cell sorting, we isolated blood and tumor-infiltrating CD3-CD56+ NK and CD3+ T cells and CD45- viable tumor cells from STS patients undergoing surgery. We then evaluated differential gene expression (DGE) of these purified populations with RNA sequencing analysis. To evaluate survival differences and validate primary DGE results, we also queried The Cancer Genome Atlas (TCGA) database to compare outcomes stratified by bulk gene expression. Results: Sorted intra-tumoral CD3+ T cells showed significant upregulation of established activating (CD137) and inhibitory genes (TIM-3) compared to circulating T cells. In contrast, intra-tumoral NK cells did not exhibit upregulation of canonical cytotoxic genes (IFNG, GZMB), but rather significant DGE in mitogen signaling (DUSP4) and metabolic function (SMPD3, SLC7A5). Tumors with higher NK and T cell infiltration exhibited significantly increased expression of the pro-inflammatory receptor TLR4 in sorted CD45- tumor cells. TCGA analysis revealed that tumors with high TLR4 expression (P = 0.03) and low expression of STMN1 involved in microtubule polymerization (P < 0.001) were associated with significantly improved survival. Conclusions: Unlike T cells, which demonstrate significant DGE consistent with upregulation of both activating and inhibiting receptors in tumor-infiltrating subsets, NK cells appear to have more stable gene expression between blood and tumor subsets, with alterations restricted primarily to metabolic pathways. Increased immune cell infiltration and improved survival were positively correlated with TLR4 expression and inversely correlated with STMN1 expression within tumors, suggesting possible novel therapeutic targets for immunotherapy in STS.

Eliminating hypoxic tumor cells improves response to PARP inhibitors in homologous recombination-deficient cancer models.

Hypoxia, a hallmark feature of the tumor microenvironment, causes resistance to conventional chemotherapy, but was recently reported to synergize with poly(ADP-ribose) polymerase inhibitors (PARPis) in homologous recombination-proficient (HR-proficient) cells through suppression of HR. While this synergistic killing occurs under severe hypoxia (<0.5% oxygen), our study shows that moderate hypoxia (2% oxygen) instead promotes PARPi resistance in both HR-proficient and -deficient cancer cells. Mechanistically, we identify reduced ROS-induced DNA damage as the cause for the observed resistance. To determine the contribution of hypoxia to PARPi resistance in tumors, we used the hypoxic cytotoxin tirapazamine to selectively kill hypoxic tumor cells. We found that the selective elimination of hypoxic tumor cells led to a substantial antitumor response when used with PARPi compared with that in tumors treated with PARPi alone, without enhancing normal tissue toxicity. Since human breast cancers with BRAC1/2 mutations have an increased hypoxia signature and hypoxia reduces the efficacy of PARPi, then eliminating hypoxic tumor cells should enhance the efficacy of PARPi therapy.

Validated limited gene predictor for cervical cancer lymph node metastases.

PURPOSE: Recognizing the prognostic significance of lymph node (LN) involvement for cervical cancer, we aimed to identify genes that are differentially expressed in LN+ versus LN- cervical cancer and to potentially create a validated predictive gene signature for LN involvement. MATERIALS AND METHODS: Primary tumor biopsies were collected from 74 cervical cancer patients. RNA was extracted and RNA sequencing was performed. The samples were divided by institution into a training set (n = 57) and a testing set (n = 17). Differentially expressed genes were identified among the training cohort and used to train a Random Forest classifier. RESULTS: 22 genes showed > 1.5 fold difference in expression between the LN+ and LN- groups. Using forward selection 5 genes were identified and, based on the clinical knowledge of these genes and testing of the different combinations, a 2-gene Random Forest model of BIRC3 and CD300LG was developed. The classification accuracy of lymph node (LN) status on the test set was 88.2%, with an Area under the Receiver Operating Characteristic curve (ROC-AUC) of 98.6%. CONCLUSIONS: We identified a 2 gene Random Forest model of BIRC3 and CD300LG that predicted lymph node involvement in a validation cohort. This validated model, following testing in additional cohorts, could be used to create a reverse transcription-quantitative polymerase chain reaction (RT-qPCR) tool that would be useful for helping to identify patients with LN involvement in resource-limited settings.