https://spiral.imperial.ac.uk/bitstream/10044/1/71213/5/1-s2.0-S1074761318301213-main.pdf

The Immune Landscape of Cancer

Recent clinical successes of cancer immunotherapy necessitate the investigation of the interaction between malignant cells and the host immune system. However, elucidation of complex tumor-immune interactions presents major computational and experimental challenges. Here, we present Tumor Immune Estimation Resource (TIMER; cistrome.shinyapps.io/timer) to comprehensively investigate molecular characterization of tumor-immune interactions. Levels of six tumor-infiltrating immune subsets are precalculated for 10,897 tumors from 32 cancer types. TIMER provides 6 major analytic modules that allow users to interactively explore the associations between immune infiltrates and a wide spectrum of factors, including gene expression, clinical outcomes, somatic mutations, and somatic copy number alterations. TIMER provides a user-friendly web interface for dynamic analysis and visualization of these associations, which will be of broad utilities to cancer researchers. Cancer Res; 77(21); e108-10. ©2017 AACR.

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TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells

The clinical successes in immunotherapy have been both astounding and at the same time unsatisfactory. Countless patients with varied tumor types have seen pronounced clinical response with immunotherapeutic intervention; however, many more patients have experienced minimal or no clinical benefit when provided the same treatment. As technology has advanced, so has the understanding of the complexity and diversity of the immune context of the tumor microenvironment and its influence on response to therapy. It has been possible to identify different subclasses of immune environment that have an influence on tumor initiation and response and therapy; by parsing the unique classes and subclasses of tumor immune microenvironment (TIME) that exist within a patient's tumor, the ability to predict and guide immunotherapeutic responsiveness will improve, and new therapeutic targets will be revealed.

Understanding the tumor immune microenvironment (TIME) for effective therapy.

Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade

Molecular profiles of tumors and tumor-associated cells hold great promise as biomarkers of clinical outcomes. However, existing data sets are fragmented and difficult to analyze systematically. Here we present a pan-cancer resource and meta-analysis of expression signatures from ∼18,000 human tumors with overall survival outcomes across 39 malignancies. By using this resource, we identified a forkhead box MI (FOXM1) regulatory network as a major predictor of adverse outcomes, and we found that expression of favorably prognostic genes, including KLRB1 (encoding CD161), largely reflect tumor-associated leukocytes. By applying CIBERSORT, a computational approach for inferring leukocyte representation in bulk tumor transcriptomes, we identified complex associations between 22 distinct leukocyte subsets and cancer survival. For example, tumor-associated neutrophil and plasma cell signatures emerged as significant but opposite predictors of survival for diverse solid tumors, including breast and lung adenocarcinomas. This resource and associated analytical tools (http://precog.stanford.edu) may help delineate prognostic genes and leukocyte subsets within and across cancers, shed light on the impact of tumor heterogeneity on cancer outcomes, and facilitate the discovery of biomarkers and therapeutic targets.

The prognostic landscape of genes and infiltrating immune cells across human cancers

We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersortstanfordedu/)

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Robust enumeration of cell subsets from tissue expression profiles

Methods for creating a selector of mutated genomic regions and for using the selector set to analyze genetic alterations in a cell-free nucleic acid sample are provided. The methods can be used to measure tumor-derived nucleic acids in a blood sample from a subject and thus to monitor the progression of disease in the subject. The methods can also be used for cancer screening, cancer diagnosis, cancer prognosis, and cancer therapy designation.

Identification and Use of Circulating Nucleic Acid Tumor Markers

Methods of enhancing the efficacy of antibody-directed cellular cytotoxicity (ADCC) for therapy directed to killing of tumor cells are disclosed. Cancer specific cell surface antigens are bound by monoclonal antibodies, thereby stimulating a cytotoxic T cell response characterized by an upregulation of cell surface expression of costimulatory molecules on the T cell. The ADCC response is augmented by the subsequent administration of a second antibody that is an agonist of the costimulatory molecule.

Methods for enhancing anti-tumor antibody therapy

Methods are provided for treatment of hematologic cancers, particularly lymphomas and leukemias, including without limitation myelogenous and lymphocytic leukemias. A combination of antibodies specific for CD47; and specific for a cancer associated cell surface marker are administered to the patient, and provide for a synergistic decrease in cancer cell burden. The combination of antibodies may comprise a plurality of monospecific antibodies, or a bispecific or multispecific antibody. Markers of interest include without limitation, CD20, CD22, CD52, CD33; CD96; CD44; CD123; CD97; CD99; PTHR2; and HAVCR2.

Synergistic anti-CD47 therapy for hematologic cancers

Disclosed herein are polynucleotide adaptors and methods of use thereof for identifying and analyzing nucleic acids, including cell-free nucleic acids from a patient sample. Also disclosed herein are methods of using the adaptors to detect, diagnose, or determine prognosis of cancers.

Arash Ash Alizadeh

Papers

Robust enumeration of cell subsets from tissue expression profiles

Identification and Use of Circulating Nucleic Acid Tumor Markers

Methods for enhancing anti-tumor antibody therapy

Synergistic anti-CD47 therapy for hematologic cancers

Identification and use of circulating nucleic acids