Mots-Clés

tumor microenvironment immunity single cell multiplexed imaging machine learning

Description

Job Description:

Analysis of NGS data and development of machine learning and cloud-based visualization tools for precision immunology project in cancer (lung, bladder, head and neck). The project involves exploration of large datasets generated by state-of-the-art technologies (single-cell RNA-sequencing, spatial transcriptomics, multiplex imaging), and using machine learning algorithms to unbiasely characterize cell-types acting in cancer, in close collaboration with wet-lab researchers and clinicians. Datasets are already available. Statistical analysis of the association between immune-stroma-tumor cellular interactions and variability between patients and medical records will allow the study of the clinical implications of these interactions. Such analysis will offer valuable knowledge of tumor-driven stromal and immune changes, providing a powerful tool for the rational design of therapies aimed at modulating these cell compartments in humans.

Context:

Hélène Salmon is the head of a junior group established at Institut Curie, Paris, and holds an Assistant Professor position at Icahn School of Medicine at Mount Sinai, New York. Both institutes are composed of a cancer hospital and a research center, which allows for a unique continuum between fundamental research and innovations for patients, and provides direct access to tissue samples from cancer patients. The Salmon Lab, which is composed of 2 PhD students, 1 bioinformatician post-doc, 3 engineers, and 1 clinician, is part of the “Immunity and Cancer” Department (U932), gathering experts in cell biology, immunology, clinical immunotherapy, as well as a large working group of bioinformaticians. The team combines experimental and computational approaches to understand the contribution of stromal cells, especially cancer-associated fibroblasts, to shaping immune responses against cancer.

Ephraim Kenigsberg, Assistant Professor in Icahn Institute for Data Science and Genomic Technology is a computational biologist applying computer science, statistical, data science methods to uncover the regulatory programs controlling activation of immune and stromal cell states during pathogenic processes, and their association with clinical outcomes.

Requirements:

- Master or PhD in computer sciences, mathematics, or bioinformatics

- Experience with R, Python and bash scripting

- Good knowledge of statistics

- Experience in manipulation, analysis, and visualization of Next Generation Sequencing (NGS) data or other big datasets would be an asset

- Ability to communicate in an interdisciplinary research group

 Applicants should send their detailed CV along with a cover letter and contact details for two references to helene.salmon@curie.fr. Applications will be studied immediately.

Starting date: ASAP. The selected candidate will be appointed for one year with possibility of extension.

References:

• Martin JC, et al. Single-cell analysis of Crohn's disease lesions identifies a pathogenic cellular module associated with resistance to anti TNF therapy. Cell 2019. 178(6):1493-1508.e20.

• Salmon H, et al. Expansion and Activation of CD103(+) Dendritic Cell Progenitors at the Tumor Site Enhances Tumor Responses to Therapeutic PD-L1 and BRAF Inhibition. Immunity 2016. doi: 10.1016/j.immuni.2016.03.012.

• Jaitin DA*, Kenigsberg E*, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 2014. doi: 10.1126/science.1247651