MSK seeks computational oncologist to map gastric cancer immune evolution
Memorial Sloan Kettering Cancer Center is recruiting a postdoctoral fellow to drive translational work at the intersection of computational oncology, tumor immunology, and gastric cancer biology. The position centers on single-cell and spatial profiling of tumor evolution, with an explicit focus on how gastric cancers interact with the immune system across primary disease, lymph nodes, metastases, and the bloodstream.
The role sits inside a research program built around longitudinal sampling. Tumor tissue, normal mucosa, draining lymph nodes, metastatic lesions, and peripheral blood are collected from patients with gastrointestinal cancers and premalignant lesions, including in the neoadjuvant setting. In practical terms, that kind of serial tissue access lets investigators follow a cancer’s immune ecosystem as therapy begins to reshape it, rather than relying on a single static biopsy that captures only one moment in time.
A single tumor biopsy can hide the biology that drives resistance.
Multi-omic profiling is becoming the language of gastric cancer research
The platform already includes a multimodal immune profiling dataset from patients treated with immunotherapy. The dataset brings together flow cytometry, single-cell RNA sequencing, spatial transcriptomics, and co-registered multiplex immunofluorescence with H&E whole-slide images, all linked to detailed clinical annotation. For a computational biologist, that combination is unusually rich: it creates the possibility of aligning cell states, tissue architecture, and patient outcomes in one analytical framework.
This matters because gastric cancer is rarely a uniform disease. Immune infiltration, stromal organization, metastatic spread, and treatment response can differ sharply not only between patients, but within different regions of the same tumor. Single-cell methods can resolve the heterogeneity of immune and malignant populations, while spatial assays preserve the physical context that often determines whether immune cells are excluded, suppressed, or actively engaged with tumor nests.
The scientific ambition is not limited to cataloging cell types. The postdoctoral fellow will be expected to integrate these datasets in ways that address basic questions about gastric cancer immunology and evolution, and to connect those findings to the urgent clinical need that still surrounds this disease. Gastric cancer remains a setting where durable benefit from immunotherapy is difficult to predict, making mechanistic dissection of immune resistance especially relevant.
The program pairs human tissue with mechanistic mouse models
Human datasets alone rarely answer the full question of causality. To move beyond correlation, the research group has also developed mechanistic mouse models designed to establish immune and tumor co-evolutionary networks in gastric tumors, tumor-draining lymph nodes, and metastases. Those models offer a way to test whether a cell state or spatial relationship observed in patients actually contributes to progression, immune escape, or dissemination.
For computational work, that creates a powerful two-way street. Human samples can generate hypotheses from clinically relevant disease, while preclinical models can probe which signals are upstream drivers and which are downstream consequences of the tumor microenvironment. In a disease defined by complex immune suppression, this paired approach can be far more informative than either system alone.
The position is supported by a newly awarded 5-year R01, giving the project a stable runway for longitudinal analysis rather than a short-term exploratory sprint. The successful candidate would work closely with senior investigators in computational oncology, immunology, pathology, gastrointestinal oncology, and surgery, while also having room to develop methodological tools of their own.
What the role demands from the next fellow
The ideal candidate holds a Ph.D. in biostatistics, statistics, computer science, computational biology, or a related quantitative discipline. A first-author publication from doctoral or postdoctoral work is required, along with PhD-level experience in computational biology and single-cell omics applied to cancer. High-dimensional data analysis is part of the core skill set, and prior experience with tumor biology or immunology is preferred.
This is a role for someone who can move comfortably between data structure and disease mechanism. The work will likely demand attention to batch effects, multimodal integration, cellular annotation, and the statistical challenges that come with patient-linked tissue datasets. At the same time, it demands biological intuition about how immune cells behave in gastric cancer, how metastases may diverge from primary tumors, and how neoadjuvant therapy can alter the tissue ecosystem before surgery.
The salary range is listed at $75,000 to $80,000, based on PGY level, and the appointment is based in New York City. Applications are being directed to Cristina Radu.
For cancer centers trying to understand why some gastric tumors respond to immune-based treatment while others do not, this kind of postdoctoral project is more than a hiring notice. It is an invitation to build the analytical framework that connects spatial organization, immune state, and disease progression in a cancer type where those links remain incomplete.