Summer Intern - Early Development Translational Sciences

Title: Summer Intern - Early Development Translational Sciences

Contract: 2.5 Months

Location: Cambridge, MA - Hybrid 50% of the time dependent on candidate's schedule

Max PR: Bachelors: $24.97 / Masters: $29.23

Job Summary

Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous disease with tumors broadly classified into classical and basal-like molecular subtypes, which differ in biology, prognosis, and therapeutic response. Currently, molecular subtyping relies on tumor tissue RNA profiling, but obtaining sufficient tumor biopsies in PDAC is often challenging.
Emerging technologies such as liquid biopsy–based transcriptomic profiling and artificial intelligence (AI) driven histopathology analysis offer the potential to infer tumor biology using minimally invasive or routinely collected samples. This project will explore whether plasma gene expression signatures and AI-derived features from H&E tumor images can recapitulate PDAC molecular subtypes defined by tumor gene expression.

Job Responsibilities

Objectives: The intern will investigate whether non-invasive and digital pathology approaches can identify PDAC molecular subtypes by:

• Evaluating plasma gene expression signatures associated with PDAC molecular subtypes.
• Assessing concordance between plasma transcriptomic profiles and tumor gene expression data.
• Exploring AI-based models trained on H&E pathology images to classify PDAC subtypes.
• Examining whether integrating plasma transcriptomics and AI-derived histopathology features improves subtype prediction.

Methods: The intern will work with existing datasets or generate the data to include tumor gene expression data, matched plasma transcriptomic profiles, and digitized H&E pathology images. Key activities will include:

• Processing and analyzing tumor and plasma gene expression data to identify subtype-associated signatures.
• Comparing plasma-derived signatures with tumor RNA expression profiles to determine concordance.
• Evaluating performance of AI-based histopathology models (already developed internally) in predicting PDAC subtypes.
• Integrating multi-modal data (plasma transcriptomics and digital pathology) to assess predictive accuracy.

Expected Outcomes: By the end of the internship, the intern will:

• Have developed experience in working with different technologies
• Identify plasma-based gene expression patterns associated with PDAC molecular subtypes.
• Assess the alignment between plasma and tumor gene expression signatures.
• Evaluate the potential of AI-driven H&E analysis for subtype classification.
• Present findings in a summary report and internal presentation, highlighting opportunities for further translational research.

Impact: This project aims to advance non-invasive strategies for PDAC molecular subtyping, which could enable improved patient stratification, translational biomarker development, and clinical trial enrichment strategies. Integrating liquid biopsy and AI-based pathology approaches may provide scalable tools for precision oncology research.

Learning Experience: The intern will gain experience in translational oncology, biomarker discovery, bioinformatics analysis, and AI applications in pathology, while contributing to ongoing efforts to improve molecular characterization of PDAC.

Education & Qualifications 

• Pursuing a bachelor’s or master’s degree in life sciences with a minimum GPA of 3.3   
• Must have an interest in pursuing a career in Life Sciences/Biotech/Pharmaceuticals 
• Ability to manage workload effectively including planning, organizing, prioritizing, and meeting deadlines