# Data Processing, Analysis and Modeling Unit

> **NIH NIH U2C** · SLOAN-KETTERING INST CAN RESEARCH · 2020 · $554,259

## Abstract

PROJECT SUMMARY (Data Analysis Unit)
Metastatic tumors are the leading cause of cancer deaths and are difficult to treat. The biology
underlying cell state plasticity and the distinct molecular programs that govern adaptation to
foreign microenvironments will require a much deeper understanding of the complex environment
of tumor growth. We aim to address this significant knowledge gap by building a spatial-temporal
atlas of the metastatic transition of three exceptionally lethal sets of malignancies – lung cancer,
pancreatic cancer, and CNS metastases- by combining single-cell genomics with multi-
dimensional spatial mapping in deeply annotated patient-derived primary and metastatic clinical
samples. Towards this goal, our first aim will be to develop experimental design methods to select
the patients, samples, and experimental parameters for creation of these three atlases. This aim
is based on the rationale that atlas construction poses novel statistical challenges in experimental
design that must be developed to maximally utilize resources towards atlas construction. In our
second aim we will construct and implement the infrastructure of the data analysis unit at scale.
The rationale for this aim is that quality control, and scalable quantification and annotation of the
data at large scale will guide the use of the atlas for searching, comparing and interpreting
samples. Finally, we will develop novel computational methods for data integration and
interpretation towards a spatial-temporal atlas. In this instance we posit that data from different
technologies and platforms often include redundant, biologically informative information that can
be extracted for producing an annotated atlas. We will illustrate the impact of our atlas in the use
case of predicting those early stage lung adenocarcinomas that are likely to metastasize to the
brain. Ultimately the constructed atlases will provide insight on convergent events and bottlenecks
in the metastatic transition, suggesting potential therapeutic targets and opportunities for
intervention.

## Key facts

- **NIH application ID:** 10001477
- **Project number:** 5U2CCA233284-03
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Dana Pe'er
- **Activity code:** U2C (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $554,259
- **Award type:** 5
- **Project period:** 2018-09-30 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10001477

## Citation

> US National Institutes of Health, RePORTER application 10001477, Data Processing, Analysis and Modeling Unit (5U2CCA233284-03). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10001477. Licensed CC0.

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