Turning large genetic and genomic data into insights to help people with cancer
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer Research
['FUNDING_OTHER'] · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · NIH-11127757
This project builds new data and machine-learning tools to find genetic patterns across diverse people with cancer to help guide prevention and treatment.
Quick facts
| Phase | ['FUNDING_OTHER'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH (nih funded) |
| Locations | 1 site (BOSTON, UNITED STATES) |
| Trial ID | NIH-11127757 on ClinicalTrials.gov |
What this research studies
As a patient, this project combines huge genetic and health datasets from multi-ethnic biobanks and clinical studies with laboratory data such as single-cell RNA and ATAC sequencing to look for cancer-related signals. The team will create scalable, interpretable statistical and machine-learning methods that can handle whole-genome, whole-exome, and rare-variant data. They plan to test these methods across diverse populations and link genetic findings to biological experiments to understand how genes cause cancer. The aim is to produce tools researchers can share and, over time, translate into prevention and treatment strategies that work across different ethnic groups.
Who could benefit from this research
Good fit: Ideal participants are people with cancer or individuals enrolled in large biobanks or clinical studies who can share genetic data, health records, and where possible tissue or blood samples, with special interest in underrepresented ethnic groups.
Not a fit: People looking for immediate new therapies or those who do not have genetic or clinical data available are unlikely to receive direct benefits from this project in the short term.
Why it matters
Potential benefit: If successful, this work could identify genetic markers and mechanisms that lead to better cancer prevention, earlier detection, and more personalized treatments for diverse populations.
How similar studies have performed: Prior large-scale genetic and genomic analyses have produced important cancer discoveries, but combining whole-genome data with multi-omic single-cell experiments across diverse populations at this scale is relatively new.
Where this research is happening
BOSTON, UNITED STATES
- HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH — BOSTON, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: LIN, XIHONG — HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- Study coordinator: LIN, XIHONG
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.
Conditions: Advanced Cancer, Breast Cancer Genetics, Cancer Prognosis, Cancers