Abstract 7161: HMGA2 predicts treatment outcome in pancreatic cancer

Abstract The recognition of distinct transcriptional subtypes in pancreatic ductal adenocarcinoma (PDAC) has defined a group of poorly differentiated tumors with worse prognosis, but these findings have yet to reach clinical application. These tumors, termed “basal, ” are unique for their loss of epithelial identity and relative chemoresistance compared to “classical” tumors. To develop a prognostic biomarker for the basal subtype, we have identified that the chromatin architectural protein HMGA2 is highly expressed in this subset of cancers. Using a tumor microarray of 580 primary biopsies from a diverse set of PDAC patients undergoing surgical resection, we performed multiplex immunohistochemistry for HMGA2, previously published markers of basal and classical disease, and immune subsets. We then associated patient outcome and known clinical data from 491 of these samples to staining patterns. We found that expression of HMGA2, but not published basal markers CK5 or CK17, predicted overall survival in our cohort. Combination of HMGA2 status with GATA6 status allowed for identification of two key study groups: an HMGA2+/GATA6- cohort with worse survival, decreased CD8+ T cells, and poorer response to gemcitabine-based chemotherapies (n=94, median survival = 11.2 months post-surgery); and an HMGA2-/GATA6+ cohort with improved survival, increased CD8+ T cell infiltrate, and improved survival with gemcitabine-based chemotherapy (n=198, median survival = 21.7 months post-surgery). Importantly, these findings were also true for Black patients, who have been underrepresented in previous subtyping studies. HMGA2 was also predictive of overall survival in RNA sequencing from metastatic tumors in an independent cohort. As a positive nuclear marker for basal disease, HMGA2 complements GATA6 as a dual-indicator test for disease subtype in PDAC. We aim to introduce this novel biomarker in a prospective multi-center clinical trial to further validate its use in selecting chemotherapy regimens and across other under-represented racial groups. Citation Format: Naomi Yamamoto, Stephanie Dobersch, Ian Loveless, Annie Samraj, Gun Ho Jang, Miki Haraguchi, Ryan Fields, David DeNardo, Faiyaz Notta, Howard Crawford, Nina Steele, Sita Kugel. HMGA2 predicts treatment outcome in pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7161.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2025-04-21
DOI
https://doi.org/10.1158/1538-7445.am2025-7161
Primary Topic
Cancer-related molecular mechanisms research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 7161: HMGA2 predicts treatment outcome in pancreatic cancer

Miki Haraguchi, Nina G. Steele, Annie N. Samraj, Sita Kugel et al.
Cancer Research
Cancer-related molecular mechanisms research
article

Abstract 7161: HMGA2 predicts treatment outcome in pancreatic cancer

Miki Haraguchi, Nina G. Steele, Annie N. Samraj, Sita Kugel, Naomi Yamamoto, Stephanie Dobersch, Howard C. Crawford, Ian M. Loveless, Faiyaz Notta, David G. DeNardo, Ryan C. Fields, Gun Ho Jang
article en

Abstract

Abstract The recognition of distinct transcriptional subtypes in pancreatic ductal adenocarcinoma (PDAC) has defined a group of poorly differentiated tumors with worse prognosis, but these findings have yet to reach clinical application. These tumors, termed “basal, ” are unique for their loss of epithelial identity and relative chemoresistance compared to “classical” tumors. To develop a prognostic biomarker for the basal subtype, we have identified that the chromatin architectural protein HMGA2 is highly expressed in this subset of cancers. Using a tumor microarray of 580 primary biopsies from a diverse set of PDAC patients undergoing surgical resection, we performed multiplex immunohistochemistry for HMGA2, previously published markers of basal and classical disease, and immune subsets. We then associated patient outcome and known clinical data from 491 of these samples to staining patterns. We found that expression of HMGA2, but not published basal markers CK5 or CK17, predicted overall survival in our cohort. Combination of HMGA2 status with GATA6 status allowed for identification of two key study groups: an HMGA2+/GATA6- cohort with worse survival, decreased CD8+ T cells, and poorer response to gemcitabine-based chemotherapies (n=94, median survival = 11.2 months post-surgery); and an HMGA2-/GATA6+ cohort with improved survival, increased CD8+ T cell infiltrate, and improved survival with gemcitabine-based chemotherapy (n=198, median survival = 21.7 months post-surgery). Importantly, these findings were also true for Black patients, who have been underrepresented in previous subtyping studies. HMGA2 was also predictive of overall survival in RNA sequencing from metastatic tumors in an independent cohort. As a positive nuclear marker for basal disease, HMGA2 complements GATA6 as a dual-indicator test for disease subtype in PDAC. We aim to introduce this novel biomarker in a prospective multi-center clinical trial to further validate its use in selecting chemotherapy regimens and across other under-represented racial groups. Citation Format: Naomi Yamamoto, Stephanie Dobersch, Ian Loveless, Annie Samraj, Gun Ho Jang, Miki Haraguchi, Ryan Fields, David DeNardo, Faiyaz Notta, Howard Crawford, Nina Steele, Sita Kugel. HMGA2 predicts treatment outcome in pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7161.

Cancer ResearchVol. 85(8_Supplement_1)
Henry Ford Health System (US), University of Washington (US), Washington University in St. Louis (US), Fred Hutch Cancer Center (US)
Openalex Percentile: Top 90%
Cancer-related molecular mechanisms research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.