# Advanced in Vivo Cancer Models

> **NIH NIH P30** · BAYLOR COLLEGE OF MEDICINE · 2022 · $232,806

## Abstract

PROJECT SUMMARY: Advanced In Vivo Cancer Models (AICM) Shared Resource
The Advanced In Vivo Cancer Models (AICM) Shared Resource of the Dan L Duncan Comprehensive Cancer
Center (DLDCCC) is constructed to meet the needs of Cancer Center investigators wishing to use
experimentally demanding, state-of-the-art, in vivo cancer models that require specialized techniques, and a
high degree of experimental ingenuity. These models include genetically engineered mice (“knockout,”
“knockin,” and transgenics), and patient-derived tumor xenografts (PDX) grown either in immunocompromised
mice, or in alternative platforms such as the chicken egg chorioallantoic membrane (CAM). Both mouse and
human-derived models require substantial validation work to “credential” that they are reflective of the biology
of the disease or process under study. Since patient-derived tissues used to generate the models have
considerable associated clinical and “omic” information, computational infrastructure is also required to
abstract, store, organize, retrieve, and display such information so that investigators can make rational choices
as to which model(s) to use, and to help interpret their results. The AICM Shared Resource will coordinate
these activities for the Cancer Center to enhance in vivo model access, use, and utility in a cost-effective
manner.

## Key facts

- **NIH application ID:** 10439809
- **Project number:** 5P30CA125123-16
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Michael T. Lewis
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $232,806
- **Award type:** 5
- **Project period:** 2007-07-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10439809

## Citation

> US National Institutes of Health, RePORTER application 10439809, Advanced in Vivo Cancer Models (5P30CA125123-16). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10439809. Licensed CC0.

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