Start Date: Summer 2022

Work Hours: 40 hours per week

Length of Assignment: 6 months

Education Level: Master's, PhD (pursuing, not completed).

Preferred Majors/Disciplines: Biostatistics, computational biology, bioinformatics, cancer biology, genomics.

Given the current uncertainty of the global pandemic and work from home situation for 2021, this internship is currently planned to be virtual with the option for interns to work remotely from within U.S. Borders. We will keep candidates informed if this changes.

The Oncology Biomarker Development (OBD) group translates its understanding of cancer biology to develop personalized therapies and diagnostics to transform clinical practice. Our mission is to translate our knowledge of cancer biology into robust biomarkers to inform decision-making about our candidate medicines in early development and guide the selection of patients into our clinical trials to maximize benefit, competitive differentiation and sustainable development.

Job Description

Tumor-intrinsic and -extrinsic mechanisms drive response and resistance to immune checkpoint blockade in cancer. One emerging feature of response/resistance to CPI is the spatial organization of tumors in relation to tumor and immune features. Our group is generating spatial transcriptomic data from 25 samples from urothelial carcinoma (UC, n = 10) and non-small cell lung cancer (NSCLC, n = 15). This samples set also has significant orthogonal data already available (MHC, B2m, and PD-L1 IHC, bulk RNA-seq, targeted DNA-seq). This project aims to understand the spatial relationship of histologically distinct cancer cells to the TME that may impact CPI responsiveness.

The intern will:

• Compare transcriptional programs in histologically distinct regions of interest in specific cell populations to support/disprove existing hypotheses.
• Assess capacity of spatial transcriptomics (ST) to predict MHC loss compared to bulk RNA-seq, using MHC IHC as gold standard.
• Demonstrate that ST detects cell interaction patterns specific to histologically distinct tumors that cannot be resolved in bulk RNA-seq.
• Compare transcriptional signatures of tumor cell-rich regions of interest between NSCLC and UC.
• The intern will be responsible for communicating work in progress to a broad audience with diverse backgrounds.

Requirements/Qualifications/Education:

This project will require R programming skills to efficiently apply these approaches across several datasets, as well as a basic background in immunology and/or cancer biology.

• R programming.

• Biostatistics.

• Basic background in immunology/cancer biology.