# Data Sharing and Integrative Analysis Core

> **NIH NIH U54** · WEILL MEDICAL COLL OF CORNELL UNIV · 2022 · $212,053

## Abstract

This Data Sharing and Integrative Analysis (DSIA) Core will support the overall mission of the Center through
three interlocking functions: (Aim 1) to ensure effective data quality and full computability, (Aim 2) to provide
innovative integrative analyses to support the scientific goals of this ROBIN center, and (Aim 3) to ensure
seamless data sharing for inter-ROBIN network collaborations as well as to the NCI Cancer Research Data
Ecosystem. Aim 1. To ensure effective data quality and computability. Under this Aim, we will collect, harmonize,
and make accessible the digital data collected in this ROBIN Center, including the Molecular Characterization
Trial, as well as Projects 1 and 2. These diverse sets of data include: 1) clinical data (de-identified patient and
tumor characteristics, Immunoscore of the diagnostic rectal cancer specimen, etc.), imaging data (MRI / CT
images at baseline and prior to surgery), 2) radiotherapy treatment planning data (DICOM images, dose
distributions to the contoured anatomic structures), and 3) biological data resulting from the two scientific projects
associated with the MCT (e.g., spatial transcriptomics, microbiome, immune biomarkers in circulating blood,
etc.). We will curate and transfer data from the Molecular Characterization Trial (MCT) and Scientific Projects 1
and 2, and will fully link all ROBIN data within NCI Cloud Resource FireCloud workspaces and the Imaging Data
Commons, with imputation where necessary, providing fully computable subject data profiles. Aim 2. To conduct
innovative, integrative analyses to support the scientific goals of this ROBIN center. Under this Aim, we will apply
both unbiased/non-parametric and machine learning integrative (multi-datatype) analyses to identify critical
immune phenotypes and their tumor/immune molecular signatures using the full spectrum of available biological
and imaging data. To identify biological and imaging/radiomics signatures or subtypes, we will apply innovative
clustering using network optimal mass transport methods. To understand the impact of radiation on Peripheral
Blood Mononuclear Cells (PBMCs), we will conduct systematic multivariate analyses using machine learning
approaches. To understand subtypes of RT response, we will apply a novel non-linear machine-learning
integrative phenotypic mapping tool (iPhenMap), based on sparse Bayesian factor analysis modeling, that
integrates molecular and functional multimodal patterns. Aim 3. To support seamless data sharing for inter-
ROBIN network collaborations and cross-training. Under this Aim, we will document and demonstrate our tools,
data, and rerunnable analysis workflows in FireCloud and Imaging Data Commons infrastructure, to support
inter-ROBIN network collaborations, as well as inter-disciplinary cross-training.

## Key facts

- **NIH application ID:** 10517809
- **Project number:** 1U54CA274291-01
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Joseph O Deasy
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $212,053
- **Award type:** 1
- **Project period:** 2022-09-21 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10517809

## Citation

> US National Institutes of Health, RePORTER application 10517809, Data Sharing and Integrative Analysis Core (1U54CA274291-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10517809. Licensed CC0.

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