# PREVENT PRECLINICAL DRUG DEVELOPMENT PROGRAM: PRECLINICAL EFFICACY AND INTERMEDIATE BIOMARKERS; TASK ORDER: PREVENTION OF COLORECTAL CANCER WITH IPSC-

> **NIH NIH N01** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2021 · $635,712

## Abstract

Recent approaches to the development of cancer vaccines for non-viral cancers have centered on targeting known oncogenic proteins (neoantigens) or tumor-associated antigens (TAA) overexpressed in pre-cancerous and cancerous lesions. In the prevention setting, these cancer vaccines are intended to elicit antitumor immunity that prevents or intercepts tumorigenic process and eliminates precancerous cells before they progress to invasive cancer. Recent advances in immune-checkpoint inhibitor-based immunotherapies for various cancers have clearly shown that the immune system can mount effective antitumor immune responses if tumor-associated immunosuppression is abrogated by immune checkpoint blockade. It is highly plausible that effective antitumor immunity can be more efficiently elicited by active immunization against tumor antigens (neoantigens and TAA) in the prevention setting, as tumor-derived immunosuppressive mechanisms play a lesser role in tumor precursor microenvironment. If long-term immunological memory can be established, such cancer vaccines can serve as a safer and more effective approach to preventing cancer including colorectal cancer. 
     One of the most important steps toward developing effective cancer preventive vaccines is the selection of vaccine antigens. The majority of immunopreventive cancer vaccines studied to date have focused on targeting common tumor-specific antigens that are expected to be widely immunogenic in a given target cohort and thus can be easily streamlined for further development. Interestingly, antitumor immune responses unleashed by immune checkpoint blockade have been shown to target a large repertoire of tumor antigens that are unique to individual patients. Individualized (personalized) immunopreventive cancer vaccines have been considered impractical because of the technical and logistical challenges expected with the development of such vaccines in the prevention setting. 
     Since the discovery of induced pluripotent stem cells (iPSCs) in 2006, much knowledge and experience have been gained with iPSC technology and its potential utility in various biomedical fields. Wu, Levy and others (Cell 2015, 161:240; Cell Stem Cell 2018, 22:501; Cell Stem Cell 2021, 28:10) have previously shown that human and murine iPSCs harbor the host’s germline mutations, the imprinted gene network dysregulation, and cancer-related mutations, and express tumor specific antigens on the cell surface. Wu et al. further demonstrated that vaccination with irradiated iPSCs with CpG adjuvant elicited robust antitumor immune responses that were associated with significant tumor growth regression in murine syngenetic tumor transplant models in vivo. While these data suggested the potential benefit of iPSCs based-immunopreventive cancer vaccines that are personalized for each host, especially for those affected with heritable cancer syndromes, logistical challenges of developing autologous “personalized” iPSCs vaccines are enor...

## Key facts

- **NIH application ID:** 10412368
- **Project number:** 75N91019D00021-0-759102100001-1
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** POWEL BROWN
- **Activity code:** N01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $635,712
- **Award type:** —
- **Project period:** 2021-05-12 → 2022-11-11

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10412368

## Citation

> US National Institutes of Health, RePORTER application 10412368, PREVENT PRECLINICAL DRUG DEVELOPMENT PROGRAM: PRECLINICAL EFFICACY AND INTERMEDIATE BIOMARKERS; TASK ORDER: PREVENTION OF COLORECTAL CANCER WITH IPSC- (75N91019D00021-0-759102100001-1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10412368. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*
