# Data Science Core

> **NIH NIH U54** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $82,813

## Abstract

Project Summary/Abstract. The goal of this BAATAAR-UP NCI ARTNet U54 application is to characterize and
therapeutically counteract mechanisms of acquired resistance to molecularly-targeted therapies against mutant
EGFR and KRAS in non-small cell lung cancer (NSCLC) by delineating the tumor-tumor microenvironment
(TME) ecosystem and its plasticity during treatment. To achieve this goal, multi-omics data from annotated
clinical specimens and several complimentary model systems will be generated. Bioinformatics, computational
biology and biostatistics play an important role in this ARTNet Research Center. The major objective of the Data
Science Core is to build and manage centralized multi-omics database and provide a full set of bioinformatical,
computational and statistical support and integrate all 3 Project. This will include basic and translational science
in systems such as clinical biopsies, PDX, PDO and cell line models, and integration of transcriptomics, spatial,
genomics, proteomics and functional biology studies. We will contribute by providing computational and
statistical support and applying and developing optimal bioinformatic, and statistical algorithms, tools and
pipelines. The Core is staffed by expert faculty and computational scientists from the Bioinformatics and
Computational Biology and Biostatistics Departments at MD Anderson and from the Bioengineering and
Therapeutic Sciences Department at UCSF. This program’s PI and Co-investigators have previously worked
closely and synergistically with Data Science Core’s investigators in other projects and grant applications. The
Data Science Core will work closely with Projects 1–3 and the Administrative Core to manage and analyze
the data resources utilizing the existing, robust IT structure in place at MD Anderson and UCSF. The Data
Science Core has built various pipelines and algorithms for “-omics” and functional biology data processing and
analyses. The Core will apply these pipelines and algorithms to all types of data generated. The Core will utilize
standard design principles, bioinformatical, computational and statistical algorithms, and will develop new
methods as needed to analyze all data collected in these projects, including spatial transcriptomics, cell-cell
interaction analysis, and CRISPR- and proteomic profiling. Parametric and nonparametric methods will be used
for parameter estimation and hypothesis testing. Linear models and generalized additive models will be used to
find the best models to fit complex data structures. The Core will facilitate hypothesis testing across projects by
integrating datasets from multiple laboratories using various algorithms, including Bayesian network-based
models and Modular Analysis of Genomic NET works In Cancer (MAGNETIC). All data analyses will be
performed using R and Bioconductor packages. The Core will document all analyses and produce HTML or PDF
reports (using R packages: Sweave, knitR, markdown) for documentation and reprod...

## Key facts

- **NIH application ID:** 10705116
- **Project number:** 5U54CA224081-06
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Jing Wang
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $82,813
- **Award type:** 5
- **Project period:** 2017-09-30 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10705116

## Citation

> US National Institutes of Health, RePORTER application 10705116, Data Science Core (5U54CA224081-06). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10705116. Licensed CC0.

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