Biomedical Data Science

The Dvir Aran Lab focuses on the discovery of predictive biomarkers, improving existing treatments, and developing novel therapeutic strategies. The lab will develop novel computational and experimental cancer immunology methodologies for characterizing the interactions of the tumor and its microenvironment, and specifically focus on the mechanisms enabling immune resistance. By incorporating publicly available datasets with self-generated data, from bulk and single-cell profiling of multiple ‘omics’ types, mass spectrometry cytometry, immune repertoires, and other cutting-edge technologies, we will adequately portray the cellular dynamics of cancer initiation, progression, and response to treatment. The lab branches to both basic and clinical research, and collaborates with computer scientists, bioengineers, basic experimental researchers and clinicians.

Cancer Immunology
The recent breakthroughs in cancer immunology and the development of a newer generation of cancer immunotherapies have opened a brand-new chapter in the war on cancer. The tumor microenvironment composition has a major effect on the response to immunotherapy agents. Thus, an improved understanding of the interactions between the tumor, its microenvironment, and its proximal surroundings is crucial for improving existing treatments and the design of novel immunotherapy strategies.

Cancer Metastasis
Metastasis is the primary cause of mortality in most cancers, yet today, metastatic spread is still poorly understood. In recent years, there is increasing evidence that metastatic colonization depends not just on the inherent properties of cancer cells, but also on properties of the microenvironment in distant sites. Understanding how disseminated cells evade and corrupt the immune system during the metastatic colonization will be pivotal in developing new therapeutic methods to combat metastasis.

Clinical Informatics
Clinicians have been using Electronic Health Records (EHR) for over a decade for organizing and preserving patient information. However, only recently, these rich and valuable datasets became widely accessible for research. EHR analysis is expected to drive future precision medicine efforts and improve healthcare quality. Our lab is collaborating with leading hospitals and research institutions and will enjoy unique access to one of the largest health care datasets in the world.

Single-cell RNA-seq
Until recently, genomic analyses have been performed on heterogeneous populations of cells and thus observed signals represented a combination of the unique characteristics of each individual cell. In the last few years, single-cell techniques combined with high-throughput technologies have revolutionized the field, allowing for high-dimensional analysis of isolated subpopulations of individual cells and enabling an unprecedented level of granularity in characterizing gene expression changes in disease models. Researchers can now address core challenges that bar advancement in the field of onco-immunology, enabling the mapping of the variable spectrum of immune, stromal, and other cell states and ascertaining which of these features predict or explain clinical responses to anticancer agents.

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