# Data Analysis Unit

> **NIH NIH U2C** · DANA-FARBER CANCER INST · 2020 · $530,539

## Abstract

Abstract 
Tumor drug response and resistance is driven by the tumor ecosystem, which includes an intricate combination 
of tumor cell properties and complex interactions among those cells organized in histological structures with 
surrounding immune and stromal cells. However, we lack a systematic framework of this ecosystem across 
tumor subtypes and patients upon which we can predict, study, and understand drug response in order to enable 
more precise diagnostics and better therapeutics. Tumor atlases at high spatial, cellular and genetic resolution 
provide an extraordinary opportunity to make these discoveries but require overcoming key methodological 
challenges. The Data Analysis Unit (DAU) will take advantage of this opportunity in the context of three metastatic 
cancers (melanoma, colon, and breast) and their resistance to immunotherapy or targeted therapy. The DAU 
will develop the next generation of computational methods to reconstruct these atlases from complex, massive, 
diverse and multidimensional spatial and cellular data, and ensure their immediate impact by formulating specific 
predictive models about drug effects and patient response. To do this, we will design adaptive power analyses 
for experimental design methods to drive the choice of samples, data modalities, and experimental parameters 
in a systematic way. We will develop approaches to quantify features from each data modality and across 
modalities. We will create an infrastructure to identify the scaffold of shared cellular, histological and clinical 
features across samples to build a tumor atlas, and illustrate the value of these atlases in discovering the 
mechanisms of drug resistance. Finally, we will design methods for querying, visualizing, and sharing atlas 
knowledge at scale to enable immediate access, partner with others in the Human Tumor Atlas Network (HTAN) 
and impact researchers and clinicians. Overall, the DAU will create the methodological framework to create the 
tumor atlases herein and for others developed in HTAN and the broader community.

## Key facts

- **NIH application ID:** 9994967
- **Project number:** 5U2CCA233195-03
- **Recipient organization:** DANA-FARBER CANCER INST
- **Principal Investigator:** GAD A GETZ
- **Activity code:** U2C (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $530,539
- **Award type:** 5
- **Project period:** 2018-09-24 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9994967

## Citation

> US National Institutes of Health, RePORTER application 9994967, Data Analysis Unit (5U2CCA233195-03). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/9994967. Licensed CC0.

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