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Abstract Background Naturally occurring colorectal cancers (CRC) in rhesus macaques share many features with their human counterparts and are useful models for cancer immunotherapy; but mechanistic data are lacking regarding the comparative molecular pathogenesis of these cancers. Methods We conducted state-of-the-art imaging including CT and PET, clinical assessments, and pathological review of 24 rhesus macaques with naturally occurring CRC. Additionally, we molecularly characterized these tumors utilizing immunohistochemistry (IHC), microsatellite instability assays, DNAseq, transcriptomics, and developed a DNA methylation-specific qPCR assay for MLH1, CACNA1G, CDKN2A, CRABP1, and NEUROG1, human markers for CpG island methylator phenotype (CIMP). We furthermore employed Monte-Carlo simulations to in-silico model alterations in DNA topology in transcription-factor binding site-rich promoter regions upon experimentally demonstrated DNA methylation. Results Similar cancer histology, progression patterns, and co-morbidities could be observed in rhesus as reported for human CRC patients. IHC identified loss of MLH1 and PMS2 in all cases, with functional microsatellite instability. DNA sequencing revealed the close genetic relatedness to human CRCs, including a similar mutational signature, chromosomal instability, and functionally-relevant mutations affecting KRAS (G12D), TP53 (R175H, R273*), APC, AMER1, ALK, and ARID1A. Interestingly, MLH1 mutations were rarely identified on a somatic or germline level. Transcriptomics not only corroborated the similarities of rhesus and human CRCs, but also demonstrated the significant downregulation of MLH1 but not MSH2, MSH6, or PMS2 in rhesus CRCs. Methylation-specific qPCR suggested CIMP-positivity in 9/16 rhesus CRCs, but all 16/16 exhibited significant MLH1 promoter hypermethylation. DNA hypermethylation was modelled to affect DNA topology, particularly propeller twist and roll profiles. Modelling the DNA topology of a transcription factor binding motif (TFAP2A) in the MLH1 promoter that overlapped with a methylation-specific probe, we observed significant differences in DNA topology upon experimentally shown DNA methylation. This suggests a role of transcription factor binding interference in epigenetic silencing of MLH1 in rhesus CRCs. Conclusions These data indicate that epigenetic silencing suppresses MLH1 transcription, induces the loss of MLH1 protein, abrogates mismatch repair, and drives genomic instability in naturally occurring CRC in rhesus macaques. We consider this spontaneous, uninduced CRC in immunocompetent, treatment-naïve rhesus macaques to be a uniquely informative model for human CRC. Graphical abstract

Abstract Background Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti–PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. Methods Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. Results A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression–based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. Conclusions This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. Trial registration: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Abstract Brain metastases is the most common intracranial tumor and account for approximately 20% of all systematic cancer cases. It is a leading cause of death in advanced-stage cancer, resulting in a five-year overall survival rate below 10%. Therefore, there is a critical need to identify effective biomarkers that can support frequent surveillance and promote efficient drug guidance in brain metastasis. Recently, the remarkable breakthroughs in single-cell RNA-sequencing (scRNA-seq) technology have advanced our insights into the tumor microenvironment (TME) at single-cell resolution, which offers the potential to unravel the metastasis-related cellular crosstalk and provides the potential for improving therapeutic effects mediated by multifaceted cellular interactions within TME. In this study, we have applied scRNA-seq and profiled 10,896 cells collected from five brain tumor tissue samples originating from breast and lung cancers. Our analysis reveals the presence of various intratumoral components, including tumor cells, fibroblasts, myeloid cells, stromal cells expressing neural stem cell markers, as well as minor populations of oligodendrocytes and T cells. Interestingly, distinct cellular compositions are observed across different samples, indicating the influence of diverse cellular interactions on the infiltration patterns within the TME. Importantly, we identify tumor-associated fibroblasts in both our in-house dataset and external scRNA-seq datasets. These fibroblasts exhibit high expression of type I collagen genes, dominate cell-cell interactions within the TME via the type I collagen signaling axis, and facilitate the remodeling of the TME to a collagen-I-rich extracellular matrix similar to the original TME at primary sites. Additionally, we observe M1 activation in native microglial cells and infiltrated macrophages, which may contribute to a proinflammatory TME and the upregulation of collagen type I expression in fibroblasts. Furthermore, tumor cell-specific receptors exhibit a significant association with patient survival in both brain metastasis and native glioblastoma cases. Taken together, our comprehensive analyses identify type I collagen-secreting tumor-associated fibroblasts as key mediators in metastatic brain tumors and uncover tumor receptors that are potentially associated with patient survival. These discoveries provide potential biomarkers for effective therapeutic targets and intervention strategies.

Abstract Different types of therapy are currently being used to treat non-small cell lung cancer (NSCLC) depending on the stage of tumor and the presence of potentially druggable mutations. However, few biomarkers are available to guide clinicians in selecting the most effective therapy for all patients with various genetic backgrounds. To examine whether patients’ mutation profiles are associated with the response to a specific treatment, we collected comprehensive clinical characteristics and sequencing data from 524 patients with stage III and IV NSCLC treated at Atrium Health Wake Forest Baptist. Overall survival based Cox-proportional hazard regression models were applied to identify mutations that were “beneficial” (HR 1) for patients treated with chemotherapy (chemo), immune checkpoint inhibitor (ICI) and chemo+ICI combination therapy (Chemo+ICI) followed by the generation of mutation composite scores (MCS) for each treatment. We also found that MCS is highly treatment specific that MCS derived from one treatment group failed to predict the response in others. Receiver operating characteristics (ROC) analyses showed a superior predictive power of MCS compared to TMB and PD-L1 status for immune therapy-treated patients. Mutation interaction analysis also identified novel co-occurring and mutually exclusive mutations in each treatment group. Our work highlights how patients’ sequencing data facilitates the clinical selection of optimized treatment strategies.

Combining cytarabine and mitoxantrone with the tricarboxylic acid cycle inhibitor devimistat has been reported in a phase I clinical trial with relapsed or refractory acute myeloid leukaemia (AML). Here, the authors report the outcomes of a phase II study, analyse samples from both phases and perform preclinical analyses that show mitochondrial fission or autophagy inhibition sensitizes AML cells to devimistat.

In gene expression profiling studies, including single-cell RNA sequencing (scRNA-seq) analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in scRNA-seq data presents certain challenges. We show that commonly used methods for single-cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present single-cell Latent-variable Model (scLM), a gene co-clustering algorithm tailored to single-cell data that performs well at detecting gene clusters with significant biologic context. Importantly, scLM can simultaneously cluster multiple single-cell datasets, i.e., consensus clustering, enabling users to leverage single-cell data from multiple sources for novel comparative analysis. scLM takes raw count data as input and preserves biological variation without being influenced by batch effects from multiple datasets. Results from both simulation data and experimental data demonstrate that scLM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of scLM, we apply it to our in-house and public experimental scRNA-seq datasets. scLM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. A user-friendly R package with all the key features of the scLM method is available at https://github.com/QSong-github/scLM.

KRAS is a key oncogenic driver in lung adenocarcinoma (LUAD). Chromatin‐remodeling gene SMARCA4 is comutated with KRAS in LUAD; however, the impact of SMARCA4 mutations on clinical outcome has not been adequately established. This study sought to shed light on the clinical significance of SMARCA4 mutations in LUAD. The association of SMARCA4 mutations with survival outcomes was interrogated in four independent cohorts totaling 564 patients: KRAS‐mutant patients with LUAD who received nonimmunotherapy treatment from (a) The Cancer Genome Atlas (TCGA) and (b) the MSK‐IMPACT Clinical Sequencing (MSK‐CT) cohorts; and KRAS‐mutant patients with LUAD who received immune checkpoint inhibitor‐based immunotherapy treatment from (c) the MSK‐IMPACT (MSK‐IO) and (d) the Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) immunotherapy cohorts. Of the patients receiving nonimmunotherapy treatment, in the TCGA cohort (n = 155), KRAS‐mutant patients harboring SMARCA4 mutations (KS) showed poorer clinical outcome [P = 6e‐04 for disease‐free survival (DFS) and 0.031 for overall survival (OS), respectively], compared to KRAS‐TP53 comutant (KP) and KRAS‐only mutant (K) patients; in the MSK‐CT cohort (n = 314), KS patients also exhibited shorter OS than KP (P = 0.03) or K (P = 0.022) patients. Of patients receiving immunotherapy, KS patients consistently exhibited the shortest progression‐free survival (PFS; P = 0.0091) in the MSK‐IO (n = 77), and the shortest PFS (P = 0.0026) and OS (P = 0.0014) in the WFBCCC (n = 18) cohorts, respectively. Therefore, mutations of SMARCA4 represent a genetic factor leading to adverse clinical outcome in lung adenocarcinoma treated by either nonimmunotherapy or immunotherapy.

A key principle of oncolytic viral therapy is that many cancers develop defects in their antiviral responses, making them more susceptible to virus infection. However, some cancers display resistance to viral infection. Many of these resistant cancers constitutively express interferon-stimulated genes (ISGs). The goal of these experiments was to determine the role of two tumor suppressor genes, MAP3K7 and CHD1, in viral resistance and ISG expression in PC3 prostate cancer cells resistant to oncolytic vesicular stomatitis virus (VSV). MAP3K7 and CHD1 are often co-deleted in aggressive prostate cancers. Silencing expression of MAP3K7 and CHD1 in PC3 cells increased susceptibility to the matrix (M) gene mutant M51R-VSV, as shown by increased expression of viral genes, increased yield of progeny virus, and reduction of tumor growth in nude mice. Silencing MAP3K7 alone had a greater effect on virus susceptibility than did silencing CHD1. Silencing MAP3K7 and CHD1 decreased constitutive expression of ISG mRNAs and proteins, whereas silencing MAP3K7 alone decreased expression of ISG proteins, but actually increased expression of ISG mRNAs. These results suggest a role for the protein product of MAP3K7, transforming growth factor β-activated kinase 1 (TAK1), in regulating translation of ISG mRNAs and a role of CHD1 in maintaining the transcription of ISGs.

BackgroundTriggering receptor expressed on myeloid cells (TREM)-1 is a key mediator of innate immunity previously associated with the severity of inflammatory disorders, and more recently, the inferior survival of lung and liver cancer patients. Here, we investigated the prognostic impact and immunological correlates of TREM1 expression in breast tumors.MethodsBreast tumor microarray and RNAseq expression profiles (n=4,364 tumors) were analyzed for associations between gene expression, tumor immune subtypes, distant metastasis-free survival (DMFS) and clinical response to neoadjuvant chemotherapy (NAC). Single-cell (sc)RNAseq was performed using the 10X Genomics platform. Statistical associations were assessed by logistic regression, Cox regression, Kaplan-Meier analysis, Spearman correlation, Student’s t-test and Chi-square test.ResultsIn pre-treatment biopsies, TREM1 and known TREM-1 inducible cytokines (IL1B, IL8) were discovered by a statistical ranking procedure as top genes for which high expression was associated with reduced response to NAC, but only in the context of immunologically “hot” tumors otherwise associated with a high NAC response rate. In surgical specimens, TREM1 expression varied among tumor molecular subtypes, with highest expression in the more aggressive subtypes (Basal-like, HER2-E). High TREM1 significantly and reproducibly associated with inferior distant metastasis-free survival (DMFS), independent of conventional prognostic markers. Notably, the association between high TREM1 and inferior DMFS was most prominent in the subset of immunogenic tumors that exhibited the immunologically hot phenotype and otherwise associated with superior DMFS. Further observations from bulk and single-cell RNAseq analyses indicated that TREM1 expression was significantly enriched in polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) and M2-like macrophages, and correlated with downstream transcriptional targets of TREM-1 (IL8, IL-1B, IL6, MCP-1, SPP1, IL1RN, INHBA) which have been previously associated with pro-tumorigenic and immunosuppressive functions.ConclusionsTogether, these findings indicate that increased TREM1 expression is prognostic of inferior breast cancer outcomes and may contribute to myeloid-mediated breast cancer progression and immune suppression.

Background: Administration of amplitude modulated 27·12 MHz radiofrequency electromagnetic fields (AM RF EMF) by means of a spoon-shaped applicator placed on the patient's tongue is a newly approved treatment for advanced hepatocellular carcinoma (HCC). The mechanism of action of tumour-specific AM RF EMF is largely unknown. Methods: Whole body and organ-specific human dosimetry analyses were performed. Mice carrying human HCC xenografts were exposed to AM RF EMF using a small animal AM RF EMF exposure system replicating human dosimetry and exposure time. We performed histological analysis of tumours following exposure to AM RF EMF. Using an agnostic genomic approach, we characterized the mechanism of action of AM RF EMF. Findings: Intrabuccal administration results in systemic delivery of athermal AM RF EMF from head to toe at levels lower than those generated by cell phones held close to the body. Tumour shrinkage results from differentiation of HCC cells into quiescent cells with spindle morphology. AM RF EMF targeted antiproliferative effects and cancer stem cell inhibiting effects are mediated by Ca2+ influx through Cav3·2 T-type voltage-gated calcium channels (CACNA1H) resulting in increased intracellular calcium concentration within HCC cells only. Interpretation: Intrabuccally-administered AM RF EMF is a systemic therapy that selectively block the growth of HCC cells. AM RF EMF pronounced inhibitory effects on cancer stem cells may explain the exceptionally long responses observed in several patients with advanced HCC. Fund: Research reported in this publication was supported by the National Cancer Institute's Cancer Centre Support Grant award number P30CA012197 issued to the Wake Forest Baptist Comprehensive Cancer Centre (BP) and by funds from the Charles L. Spurr Professorship Fund (BP). DWG is supported by R01 AA016852 and P50 AA026117. Keywords: Advanced hepatocellular carcinoma, T-type voltage gated calcium channels, Calcium influx, Cav 3·2, CACNA1H, Amplitude-modulated, Radiofrequency, Electromagnetic fields, AM RF EMF

Abstract Tumor‐infiltrating myeloid cells are the most abundant leukocyte population within tumors. Molecular cues from the tumor microenvironment promote the differentiation of immature myeloid cells toward an immunosuppressive phenotype. However, the in situ dynamics of the transcriptional reprogramming underlying this process are poorly understood. Therefore, we applied single cell RNA‐seq (scRNA‐seq) to computationally investigate the cellular composition and transcriptional dynamics of tumor and adjacent normal tissues from 4 early‐stage non‐small cell lung cancer (NSCLC) patients. Our scRNA‐seq analyses identified 11 485 cells that varied in identity and gene expression traits between normal and tumor tissues. Among these, myeloid cell populations exhibited the most diverse changes between tumor and normal tissues, consistent with tumor‐mediated reprogramming. Through trajectory analysis, we identified a differentiation path from CD14+ monocytes to M2 macrophages (monocyte‐to‐M2). This differentiation path was reproducible across patients, accompanied by increased expression of genes (eg, MRC1/CD206, MSR1/CD204, PPARG, TREM2) with significantly enriched functions (Oxidative phosphorylation and P53 pathway) and decreased expression of genes (eg, CXCL2, IL1B) with significantly enriched functions (TNF‐α signaling via NF‐κB and inflammatory response). Our analysis further identified a co‐regulatory network implicating upstream transcription factors (JUN, NFKBIA) in monocyte‐to‐M2 differentiation, and activated ligand‐receptor interactions (eg, SFTPA1‐TLR2, ICAM1‐ITGAM) suggesting intratumoral mechanisms whereby epithelial cells stimulate monocyte‐to‐M2 differentiation. Overall, our study identified the prevalent monocyte‐to‐M2 differentiation in NSCLC, accompanied by an intricate transcriptional reprogramming mediated by specific transcriptional activators and intercellular crosstalk involving ligand‐receptor interactions.

BackgroundThe incidence of brain metastasis continues to increase as therapeutic strategies have improved for a number of solid tumors. The presence of brain metastasis is associated with worse prognosis but it is unclear if distinctive biomarkers can separate patients at risk for CNS related death.MethodsWe executed a single institution retrospective collection of brain metastasis from patients who were diagnosed with lung, breast, and other primary tumors. The brain metastatic samples were sent for RNA sequencing, proteomic and metabolomic analysis of brain metastasis. The primary outcome was distant brain failure after definitive therapies that included craniotomy resection and radiation to surgical bed. Novel prognostic subtypes were discovered using transcriptomic data and sparse non-negative matrix factorization.ResultsWe discovered two molecular subtypes showing statistically significant differential prognosis irrespective of tumor subtype. The median survival time of the good and the poor prognostic subtypes were 7.89 and 42.27 months, respectively. Further integrated characterization and analysis of these two distinctive prognostic subtypes using transcriptomic, proteomic, and metabolomic molecular profiles of patients identified key pathways and metabolites. The analysis suggested that immune microenvironment landscape as well as proliferation and migration signaling pathways may be responsible to the observed survival difference.ConclusionA multi-omics approach to characterization of brain metastasis provides an opportunity to identify clinically impactful biomarkers and associated prognostic subtypes and generate provocative integrative understanding of disease.

The Comprehensive, Computable NanoString Diagnostic gene panel (C2Dx) is a promising solution to address the need for a molecular pathological research and diagnostic tool for precision oncology utilizing small volume tumor specimens. We translate subtyping-related gene expression patterns of Non-Small Cell Lung Cancer (NSCLC) derived from public transcriptomic data which establish a highly robust and accurate subtyping system. The C2Dx demonstrates supreme performance on the NanoString platform using microgram-level FNA samples and has excellent portability to frozen tissues and RNA-Seq transcriptomic data. This workflow shows great potential for research and the clinical practice of cancer molecular diagnosis.

Background: Understanding how tumors subvert immune destruction is essential to the development of cancer immunotherapies. New evidence suggests that tumors limit anti-tumor immunity by exploiting transcriptional programs that regulate intratumoral trafficking and accumulation of effector cells. Here, we investigated the gene expression profiles that distinguish immunologically “cold” and “hot” tumors across diverse tumor types.Methods: RNAseq profiles of tumors (n = 8,920) representing 23 solid tumor types were analyzed using immune gene signatures that quantify CD8+ T cell abundance. Genes and pathways associated with a low CD8+ T cell infiltration profile (CD8-Low) were identified by correlation, differential expression, and statistical ranking methods. Gene subsets were evaluated in immunotherapy treatment cohorts and functionally characterized in cell lines and mouse tumor models.Results: Among different cancer types, we observed highly significant overlap of genes enriched in CD8-Low tumors, which included known immunomodulatory genes (e.g., BMP7, CMTM4, KDM5B, RCOR2) and exhibited significant associations with Wnt signaling, neurogenesis, cell-cell junctions, lipid biosynthesis, epidermal development, and cancer-testis antigens. Analysis of mutually exclusive gene clusters demonstrated that different transcriptional programs may converge on the T cell-cold phenotype as well as predict for response and survival of patients to Nivo treatment. Furthermore, we confirmed that a top-ranking candidate belonging to the TGF-β superfamily, BMP7, negatively regulates CD8+ T cell abundance in immunocompetent murine tumor models, with and without anti-PD-L1 treatment.Conclusions: This study presents the first evidence that solid tumors of diverse anatomical origin acquire conserved transcriptional alterations that may be operative in the T cell-cold state. Our findings demonstrate the potential clinical utility of CD8-Low tumor-associated genes for predicting patient immunotherapy outcomes and point to novel mechanisms with potential for broad therapeutic exploitation.

Glioblastoma (GBM) is the most aggressive malignant glioma. Therapeutic targeting of GBM is made more difficult due to its heterogeneity, resistance to treatment, and diffuse infiltration into the brain parenchyma. Better understanding of the tumor microenvironment should aid in finding more effective management of GBM. GBM-associated macrophages (GAM) comprise up to 30% of the GBM microenvironment. Therefore, exploration of GAM activity/function and their specific markers are important for developing new therapeutic agents. In this study, we identified and evaluated the expression of ALDH1A2 in the GBM microenvironment, and especially in M2 GAM, though it is also expressed in reactive astrocytes and multinucleated tumor cells. We demonstrated that M2 GAM highly express ALDH1A2 when compared to other ALDH1 family proteins. Additionally, GBM samples showed higher expression of ALDH1A2 when compared to low-grade gliomas (LGG), and this expression was increased upon tumor recurrence both at the gene and protein levels. We demonstrated that the enzymatic product of ALDH1A2, retinoic acid (RA), modulated the expression and activity of MMP-2 and MMP-9 in macrophages, but not in GBM tumor cells. Thus, the expression of ALDH1A2 may promote the progressive phenotype of GBM.

Abstract As cancer strikes, individuals vary not only in terms of factors that contribute to its occurrence and development, but as importantly, in their capacity to respond to treatment. While exciting new therapeutic options that mobilize the immune system against cancer have led to breakthroughs for a variety of malignancies, success is limited to a subset of patients. Pre-existing immunological features of both the host and the tumor may contribute to how patients will eventually fare with immunotherapy. A broad understanding of baseline immunity, both in the periphery and in the tumor microenvironment, is needed in order to fully realize the potential of cancer immunotherapy. Such interrogation of the tumor, blood, and host immune parameters prior to treatment is expected to identify biomarkers predictive of clinical outcome as well as to elucidate why some patients fail to respond to immunotherapy. To approach these opportunities for progress, the Society for Immunotherapy of Cancer (SITC) reconvened the Immune Biomarkers Task Force. Comprised of an international multidisciplinary panel of experts, Working Group 4 sought to make recommendations that focus on the complexity of the tumor microenvironment, with its diversity of immune genes, proteins, cells, and pathways naturally present at baseline and in circulation, and novel tools to aid in such broad analyses.

Mounting evidence supports a role for the immune system in breast cancer outcomes. The ability to distinguish highly immunogenic tumors susceptible to anti-tumor immunity from weakly immunogenic or inherently immune-resistant tumors would guide development of therapeutic strategies in breast cancer. Genomic, transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) breast cancer cohorts were used to examine statistical associations between tumor mutational burden (TMB) and the survival of patients whose tumors were assigned to previously-described prognostic immune subclasses reflecting favorable, weak or poor immune-infiltrate dispositions (FID, WID or PID, respectively). Tumor immune subclasses were associated with survival in patients with high TMB (TMB-Hi, P

The increased application of high-throughput approaches in translational research has expanded the number of publicly available data repositories. Gathering additional valuable information contained in the datasets represents a crucial opportunity in the biomedical field. To facilitate and stimulate utilization of these datasets, we have recently developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB). In this note, we describe a curated compendium of 13 public datasets on human breast cancer, representing a total of 2142 transcriptome profiles. We classified the samples according to different immune based classification systems and integrated this information into the datasets. Annotated and harmonized datasets were uploaded to GXB. Study samples were categorized in different groups based on their immunologic tumor response profiles, intrinsic molecular subtypes and multiple clinical parameters. Ranked gene lists were generated based on relevant group comparisons. In this data note, we demonstrate the utility of GXB to evaluate the expression of a gene of interest, find differential gene expression between groups and investigate potential associations between variables with a specific focus on immunologic classification in breast cancer. This interactive resource is publicly available online at: http://breastcancer.gxbsidra.org/dm3/geneBrowser/list.

Sarcoma is comprised of a heterogeneous group of tumors originating from the mesenchyme. Sarcoma is also the first tumor that responded to immunotherapeutic agents often termed as “Coley’s toxins.” However, immunotherapy is yet to establish its presence in sarcomas. Complex interactions between tumor and immune cells in the tumor microenvironment play a crucial role in response to immunotherapy. There is a dynamic equilibrium created by the immune cells infiltrating the tumor, and this forms the basis of tumor evasion. Manipulating the intratumoral microenvironment will help overcome tumor evasion.

Cancer immunotherapy is revolutionizing the clinical management of several tumors, but has demonstrated limited activity in breast cancer. The development of more effective treatments is hindered by incomplete knowledge of the genetic determinant of immune responsiveness. To fill this gap, we mined copy number alteration, somatic mutation, and expression data from The Cancer Genome Atlas (TCGA). By using RNA-sequencing data from 1,004 breast cancers, we defined distinct immune phenotypes characterized by progressive expression of transcripts previously associated with immune-mediated rejection. The T helper 1 (Th-1) phenotype (ICR4), which also displays upregulation of immune-regulatory transcripts such as PDL1, PD1, FOXP3, IDO1, and CTLA4, was associated with prolonged patients' survival. We validated these findings in an independent meta-cohort of 1,954 breast cancer gene expression data. Chromosome segment 4q21, which includes genes encoding for the Th-1 chemokines CXCL9-11, was significantly amplified only in the immune favorable phenotype (ICR4). The mutation and neoantigen load progressively decreased from ICR4 to ICR1 but could not fully explain immune phenotypic differences. Mutations of TP53 were enriched in the immune favorable phenotype (ICR4). Conversely, the presence of MAP3K1 and MAP2K4 mutations were tightly associated with an immune-unfavorable phenotype (ICR1). Using both the TCGA and the validation dataset, the degree of MAPK deregulation segregates breast tumors according to their immune disposition. These findings suggest that mutation-driven perturbations of MAPK pathways are linked to the negative regulation of intratumoral immune response in breast cancer. Modulations of MAPK pathways could be experimentally tested to enhance breast cancer immune sensitivity.

We report herein that trefoil factor 3 (TFF3) is oncogenic and mediates anti-estrogen resistance in human mammary carcinoma. Forced expression of TFF3 in mammary carcinoma cells increased cell proliferation and survival, enhanced anchorage-independent growth, and promoted migration and invasion. Moreover, forced expression of TFF3 increased tumor size in xenograft models. Conversely, depletion of endogenous TFF3 with small interfering RNA (siRNA) decreased the oncogenicity and invasiveness of mammary carcinoma cells. Neutralization of secreted TFF3 by antibody promoted apoptosis, decreased cell growth in vitro, and arrested mammary carcinoma xenograft growth. TFF3 expression was significantly correlated to decreased survival of estrogen receptor (ER)-positive breast cancer patients treated with tamoxifen. Forced expression of TFF3 in mammary carcinoma cells increased ER transcriptional activity, promoted estrogen-independent growth, and produced resistance to tamoxifen and fulvestrant in vitro and to tamoxifen in xenograft models. siRNA-mediated depletion or antibody inhibition of TFF3 significantly enhanced the efficacy of antiestrogens. Increased TFF3 expression was observed in tamoxifen-resistant (TAMR) cells and antibody inhibition of TFF3 in TAMR cells improved tamoxifen sensitivity. Functional antagonism of TFF3 therefore warrants consideration as a novel therapeutic strategy for mammary carcinoma.

Driver genes are directly responsible for oncogenesis and identifying them is essential in order to fully understand the mechanisms of cancer. However, it is difficult to delineate them from the larger pool of genes that are deregulated in cancer (ie, passenger genes). In order to address this problem, we developed an approach called TRIAngulating Gene Expression (TRIAGE through clinico-genomic intersects). Here, we present a refinement of this approach incorporating a new scoring methodology to identify putative driver genes that are deregulated in cancer. TRIAGE triangulates - or integrates -three levels of information: gene expression, gene location, and patient survival. First, TRIAGE identifies regions of deregulated expression (ie, expression footprints) by deriving a newly established measure called the Local Singular Value Decomposition (LSVD) score for each locus. Driver genes are then distinguished from passenger genes using dual survival analyses. Incorporating measurements of gene expression and weighting them according to the LSVD weight of each tumor, these analyses are performed using the genes located in significant expression footprints. Here, we first use simulated data to characterize the newly established LSVD score. We then present the results of our application of this refined version of TRIAGE to gene expression data from five cancer types. This refined version of TRIAGE not only allowed us to identify known prominent driver genes, such as MMP1, IL8 , and COL1A2 , but it also led us to identify several novel ones. These results illustrate that TRIAGE complements existing tools, allows for the identification of genes that drive cancer and could perhaps elucidate potential future targets of novel anticancer therapeutics.