# Research Program: Biostatistics & Computational Biology

> **NIH NIH P30** · FRED HUTCHINSON CANCER RESEARCH CENTER · 2020 · $81,433

## Abstract

PROJECT SUMMARY: BIOSTATISTICS & COMPUTATIONAL BIOLOGY (BCB)
Quantitative and data sciences have penetrated nearly all aspects of biomedical research. With that comes
challenges and opportunities to develop the methods needed to make valid and efficient use of these data for
inference on human health and medicine. The Biostatistics & Computational Biology (BCB) Program provides
the intellectual environment for advancing these efforts.
Our research program portfolio spans a broad range of activities from statistical methods development to
biological research that uses experimental studies in conjunction with computational methods. Our statistical
research emphasizes analytic approaches to genome-scale data sets, molecular diagnostics, development and
applications of objective measures of lifestyle and environmental exposures, and methods for clinical trials.
Highlights include breakthroughs in prostate and colorectal cancer screening analysis, new methods for design
and analysis of therapeutic trials, and the development of new statistical approaches for precision medicine
and biomarker discovery. Biological research is concentrated on cancer-relevant aspects of quantitative
immune profiling, infectious disease/microbiome, and basic molecular biology. BCB members have identified
new therapeutic avenues for treatment of leukemias and novel predictive markers of immunotherapy response.
Our research is characterized by a productive interplay between applied work and methods development.
Our specific aims are to develop rigorous statistical and mathematical methods relevant to predictive and
personalized medicine; to develop and use experimental, technological, and companion computational or
mathematical methods to gain understanding of the natural history of cancer, and to develop and disseminate
statistical and computational methods in cancer research.
A substantial portion of our research is in areas of emphasis such as high-dimensional data analysis, immune
profiling, mobile device data, and machine learning that were not a major focus 5 years ago. The ongoing
growth and development of high-throughput technologies for acquiring biological data provides great
opportunities and challenges for statisticians and computational researchers to make impactful contributions in
cancer research. BCB members are well-positioned to capitalize on these exciting opportunities: we have a
wide range of quantitative methodological training augmented by cancer-relevant domain knowledge; we have
outstanding collaborations; we are strongly committed to translating our methods research into new diagnostic
tools and therapies; and we are attentive to emerging opportunities in biomedical data science.

## Key facts

- **NIH application ID:** 9853666
- **Project number:** 2P30CA015704-45
- **Recipient organization:** FRED HUTCHINSON CANCER RESEARCH CENTER
- **Principal Investigator:** Charles L Kooperberg
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $81,433
- **Award type:** 2
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9853666

## Citation

> US National Institutes of Health, RePORTER application 9853666, Research Program: Biostatistics & Computational Biology (2P30CA015704-45). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9853666. Licensed CC0.

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