Frederick S. Varn

Immunoregulatory functions of VISTA

Utilization of negative checkpoint regulators (NCRs) for cancer immunotherapy has garnered significant interest with the completion of clinical trials demonstrating efficacy. While the results of monotherapy treatments are compelling, there is increasing emphasis on combination treatments in an effort to increase response rates to treatment. One of the most recently discovered NCRs is VISTA (V‐domain Ig‐containing Suppressor of T cell Activation). In this review, we describe the functions of this molecule in the context of cancer immunotherapy. We also discuss factors that may influence the use of anti‐VISTA antibody in combination therapy and how genomic analysis may assist in providing indications for treatment.

Integrative analysis of breast cancer reveals prognostic haematopoietic activity and patient-specific immune response profiles

Transcriptional programmes active in haematopoietic cells enable a variety of functions including dedifferentiation, innate immunity and adaptive immunity. Understanding how these programmes function in the context of cancer can provide valuable insights into host immune response, cancer severity and potential therapy response. Here we present a method that uses the transcriptomes of over 200 murine haematopoietic cells, to infer the lineage-specific haematopoietic activity present in human breast tumours. Correlating this activity with patient survival and tumour purity reveals that the transcriptional programmes of many cell types influence patient prognosis and are found in environments of high lymphocytic infiltration. Collectively, these results allow for a detailed and personalized assessment of the patient immune response to a tumour. When combined with routinely collected patient biopsy genomic data, this method can enable a richer understanding of the complex interplay between the host immune system and cancer.

Integrative analysis of survival-associated gene sets in breast cancer

BackgroundPatient gene expression information has recently become a clinical feature used to evaluate breast cancer prognosis. The emergence of prognostic gene sets that take advantage of these data has led to a rich library of information that can be used to characterize the molecular nature of a patient’s cancer. Identifying robust gene sets that are consistently predictive of a patient’s clinical outcome has become one of the main challenges in the field.MethodsWe inputted our previously established BASE algorithm with patient gene expression data and gene sets from MSigDB to develop the gene set activity score (GSAS), a metric that quantitatively assesses a gene set’s activity level in a given patient. We utilized this metric, along with patient time-to-event data, to perform survival analyses to identify the gene sets that were significantly correlated with patient survival. We then performed cross-dataset analyses to identify robust prognostic gene sets and to classify patients by metastasis status. Additionally, we created a gene set network based on component gene overlap to explore the relationship between gene sets derived from MSigDB. We developed a novel gene set based on this network’s topology and applied the GSAS metric to characterize its role in patient survival.ResultsUsing the GSAS metric, we identified 120 gene sets that were significantly associated with patient survival in all datasets tested. The gene overlap network analysis yielded a novel gene set enriched in genes shared by the robustly predictive gene sets. This gene set was highly correlated to patient survival when used alone. Most interestingly, removal of the genes in this gene set from the gene pool on MSigDB resulted in a large reduction in the number of predictive gene sets, suggesting a prominent role for these genes in breast cancer progression.ConclusionsThe GSAS metric provided a useful medium by which we systematically investigated how gene sets from MSigDB relate to breast cancer patient survival. We used this metric to identify predictive gene sets and to construct a novel gene set containing genes heavily involved in cancer progression.

Systematic Pan-Cancer Analysis Reveals Immune Cell Interactions in the Tumor Microenvironment

With the recent advent of immunotherapy, there is a critical need to understand immune cell interactions in the tumor microenvironment in both pan-cancer and tissue-specific contexts. Multidimensional datasets have enabled systematic approaches to dissect these interactions in large numbers of patients, furthering our understanding of the patient immune response to solid tumors. Using an integrated approach, we inferred the infiltration levels of distinct immune cell subsets in 23 tumor types from The Cancer Genome Atlas. From these quantities, we constructed a coinfiltration network, revealing interactions between cytolytic cells and myeloid cells in the tumor microenvironment. By integrating patient mutation data, we found that while mutation burden was associated with immune infiltration differences between distinct tumor types, additional factors likely explained differences between tumors originating from the same tissue. We concluded this analysis by examining the prognostic value of individual immune cell subsets as well as how coinfiltration of functionally discordant cell types associated with patient survival. In multiple tumor types, we found that the protective effect of CD8+ T cell infiltration was heavily modulated by coinfiltration of macrophages and other myeloid cell types, suggesting the involvement of myeloid-derived suppressor cells in tumor development. Our findings illustrate complex interactions between different immune cell types in the tumor microenvironment and indicate these interactions play meaningful roles in patient survival. These results demonstrate the importance of personalized immune response profiles when studying the factors underlying tumor immunogenicity and immunotherapy response. Cancer Res; 77(6); 1271-82. ©2017 AACR.

Adaptive immunity programmes in breast cancer

The role of the immune system in shaping cancer development and patient prognosis has recently become an area of intense focus in industry and academia. Harnessing the adaptive arm of the immune system for tumour eradication has shown great promise in a variety of tumour types. Differences between tissues, however, necessitate a greater understanding of the adaptive immunity programmes that are active within each tumour type. In breast cancer, adaptive immune programmes play diverse roles depending on the cellular infiltration found in each tumour. Cytotoxic T lymphocytes and T helper type 1 cells can induce tumour eradication, whereas regulatory T cells and T helper type 2 cells are known to be involved in tumour‐promoting immunosuppressive responses. Complicating these matters, heterogeneous expression of hormone receptors and growth factors in different tumours leads to disparate, patient‐specific adaptive immune responses. Despite this non‐conformity in adaptive immune behaviours, encouraging basic and clinical results have been observed that suggest a role for immunotherapeutic approaches in breast cancer. Here, we review the literature pertaining to the adaptive immune response in breast cancer, summarize the primary findings relating to the breast tumours biology, and discuss potential clinical immunotherapies.

Regulators Associated with Clinical Outcomes Revealed by DNA Methylation Data in Breast Cancer

The regulatory architecture of breast cancer is extraordinarily complex and gene misregulation can occur at many levels, with transcriptional malfunction being a major cause. This dysfunctional process typically involves additional regulatory modulators including DNA methylation. Thus, the interplay between transcription factor (TF) binding and DNA methylation are two components of a cancer regulatory interactome presumed to display correlated signals. As proof of concept, we performed a systematic motif-based in silico analysis to infer all potential TFs that are involved in breast cancer prognosis through an association with DNA methylation changes. Using breast cancer DNA methylation and clinical data derived from The Cancer Genome Atlas (TCGA), we carried out a systematic inference of TFs whose misregulation underlie different clinical subtypes of breast cancer. Our analysis identified TFs known to be associated with clinical outcomes of p53 and ER (estrogen receptor) subtypes of breast cancer, while also predicting new TFs that may also be involved. Furthermore, our results suggest that misregulation in breast cancer can be caused by the binding of alternative factors to the binding sites of TFs whose activity has been ablated. Overall, this study provides a comprehensive analysis that links DNA methylation to TF binding to patient prognosis.

E2F4 Program Is Predictive of Progression and Intravesical Immunotherapy Efficacy in Bladder Cancer

Bladder cancer is a common malignant disease, with non–muscle-invasive bladder cancer (NMIBC) representing the majority of tumors. This cancer subtype is typically treated by transurethral resection. In spite of treatment, up to 70% of patients show local recurrences. Intravesical BCG (Bacillus Calmette-Guerin) immunotherapy has been widely used to treat NMIBC, but it fails to suppress recurrence of bladder tumors in up to 40% of patients. Therefore, the development of prognostic markers is needed to predict the progression of bladder cancer and the efficacy of intravesical BCG treatment. This study demonstrates the effectiveness of an E2F4 signature for prognostic prediction of bladder cancer. E2F4 scores for each sample in a bladder cancer expression dataset were calculated by summarizing the relative expression levels of E2F4 target genes identified by ChIP-seq, and then the scores were used to stratify patients into good- and poor-outcome groups. The molecular signature was investigated in a single bladder cancer dataset and then its effectiveness was confirmed in two meta-bladder datasets consisting of specimens from multiple independent studies. These results were consistent in different datasets and demonstrate that the E2F4 score is predictive of clinical outcomes in bladder cancer, with patients whose tumors exhibit an E2F4 score >0 having significantly shorter survival times than those with an E2F4 score <0, in both non–muscle-invasive, and muscle-invasive bladder cancer. Furthermore, although intravesical BCG immunotherapy can significantly improve the clinical outcome of NMIBC patients with positive E2F4 scores (E2F4>0 group), it does not show significant treatment effect for those with negative scores (E2F4<0 group). Implications: The E2F4 signature can be applied to predict the progression/recurrence and the responsiveness of patients to intravesical BCG immunotherapy in bladder cancer. Mol Cancer Res; 13(9); 1316–24. ©2015 AACR.

IDEA: Integrated Drug Expression Analysis—Integration of Gene Expression and Clinical Data for the Identification of Therapeutic Candidates

Cancer drug discovery is an involved process spanning efforts from several fields of study and typically requires years of research and development. However, the advent of high‐throughput genomic technologies has allowed for the use of in silico, genomics‐based methods to screen drug libraries and accelerate drug discovery. Here we present a novel approach to computationally identify drug candidates for the treatment of breast cancer. In particular, we developed a Drug Regulatory Score similarity metric to evaluate gene expression profile similarity, in the context of drug treatment, and incorporated time‐to‐event patient survival information to develop an integrated analysis pipeline: Integrated Drug Expression Analysis (IDEA). We were able to predict drug candidates that have been known and those that have not been known in the literature to exhibit anticancer effects. Overall, our method enables quick preclinical screening of drug candidates for breast cancer and other diseases by using the most important indicator of drug efficacy: survival.

Computational immune profiling in lung adenocarcinoma reveals reproducible prognostic associations with implications for immunotherapy

ABSTRACT Non-small cell lung cancer is one of the leading causes of cancer-related death in the world. Lung adenocarcinoma, the most common type of non-small cell lung cancer, has been well characterized as having a dense lymphocytic infiltrate, suggesting that the immune system plays an active role in shaping this cancers growth and development. Despite these findings, our understanding of how this infiltrate affects patient prognosis and its association with lung adenocarcinoma-specific clinical factors remains limited. To address these questions, we inferred the infiltration level of six distinct immune cell types from a series of four lung adenocarcinoma gene expression datasets. We found that naive B cell, CD8+ T cell, and myeloid cell-derived expression signals of immune infiltration were significantly predictive of patient survival in multiple independent datasets, with B cell and CD8+ T cell infiltration associated with prolonged prognosis and myeloid cell infiltration associated with shorter survival. These associations remained significant even after accounting for additional clinical variables. Patients stratified by smoking status exhibited decreased CD8+ T cell infiltration and altered prognostic associations, suggesting potential immunosuppressive mechanisms in smokers. Survival analyses accounting for immune checkpoint gene expression and cellular immune infiltrate indicated checkpoint protein-specific modulatory effects on CD8+ T cell and B cell function that may be associated with patient sensitivity to immunotherapy. Together, these analyses identified reproducible associations that can be used to better characterize the role of immune infiltration in lung adenocarcinoma and demonstrate the utility in using computational approaches to systematically characterize tissue-specific tumor-immune interactions.

Therapeutically targeting tumor microenvironment–mediated drug resistance in estrogen receptor–positive breast cancer

Drug resistance to approved systemic therapies in estrogen receptor–positive (ER+) breast cancer remains common. We hypothesized that factors present in the human tumor microenvironment (TME) drive drug resistance. Screening of a library of recombinant secreted microenvironmental proteins revealed fibroblast growth factor 2 (FGF2) as a potent mediator of resistance to anti-estrogens, mTORC1 inhibition, and phosphatidylinositol 3-kinase inhibition in ER+ breast cancer. Phosphoproteomic analyses identified ERK1/2 as a major output of FGF2 signaling via FGF receptors (FGFRs), with consequent up-regulation of Cyclin D1 and down-regulation of Bim as mediators of drug resistance. FGF2-driven drug resistance in anti-estrogen–sensitive and –resistant models, including patient-derived xenografts, was reverted by neutralizing FGF2 or FGFRs. A transcriptomic signature of FGF2 signaling in primary tumors predicted shorter recurrence-free survival independently of age, grade, stage, and FGFR amplification status. These findings delineate FGF2 signaling as a ligand-based drug resistance mechanism and highlights an underdeveloped aspect of precision oncology: characterizing and treating patients according to their TME constitution.