Abstract 6438: Decoding the archetypes and ecotypes of triple-negative breast cancer in responses to chemotherapy

Abstract Triple-negative Breast Cancer (TNBC) is an aggressive subtype of breast cancer. The pillar of treatment is chemotherapy, but only half of the patients have a complete response and good survival. To resolve inter- and intra-tumoral heterogeneity and determine their clinical associations, we performed single-cell RNA-sequencing and spatial transcriptomics methods including Visium and Xenium on treatment-naïve samples of TNBC patients in the ARTEMIS clinical trial. We find that TNBC was classified into 4 major archetypes at patient level: luminal secretory-associated, basal-associated, immunoreactive, and luminal androgen receptor. At cell level, cancer cells exhibited intratumoral heterogeneity in 13 gene expression metaprograms. The TNBC tumor microenvironment (TME) consisted of 49 distinct immune and stromal cell states, many of which were reprogrammed relative to normal breast tissues from disease-free women. We further identified 8 ecotypes of cancer cells and TME cell states that co-occurred among patients and were associated with specific archetypes and chemotherapy response groups. In contrast to previous work on T-cells, our data showed the importance of macrophage cell states and cancer cell metaprograms for interferon signaling, HLA expression and cell cycle activity that were associated with chemotherapy response. To facilitate a clinical application, we developed a 13-gene-based model to predict response. Collectively, this study provides new insights into the natural biology of untreated TNBC tumors and their association with chemotherapy response. Citation Format: Yun Yan, Yiyun Lin, Tapsi Kumar, Shanshan Bai, Aatish Thennavan, Jianzhuo Li, Tuan Tran, Min Hu, Mitchell Rao, Anna Casasent, Elizabeth Ravenberg, Gaiane Margishvili Rauch, Alyson Clayborn, Debu Tripathy, Alastair Thompson, Bora Lim, Lei Huo, Stacy Moulder, Clinton Yam, Nicholas Navin. Decoding the archetypes and ecotypes of triple-negative breast cancer in responses to chemotherapy [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 6438.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2025-04-21
DOI
https://doi.org/10.1158/1538-7445.am2025-6438
Primary Topic
Estrogen and related hormone effects
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 6438: Decoding the archetypes and ecotypes of triple-negative breast cancer in responses to chemotherapy

Yun Yan, Lei Huo, Gaiane M. Rauch, Aatish Thennavan et al.
1 links
Cancer Research
Estrogen and related hormone effects
article

Abstract 6438: Decoding the archetypes and ecotypes of triple-negative breast cancer in responses to chemotherapy

Yun Yan, Lei Huo, Gaiane M. Rauch, Aatish Thennavan, Shanshan Bai, Alastair Mark Thompson, Tapsi Kumar, Stacy L. Moulder, Jianzhuo Li, Clinton Yam, Bora Lim, Alyson R. Clayborn, Yiyun Lin, Nicholas E. Navin, Tuan M. Tran, Tod D. Casasent, Elizabeth E. Ravenberg, Min Hu, Mitchell Rao, Debu Tripathy
article en

Abstract

Abstract Triple-negative Breast Cancer (TNBC) is an aggressive subtype of breast cancer. The pillar of treatment is chemotherapy, but only half of the patients have a complete response and good survival. To resolve inter- and intra-tumoral heterogeneity and determine their clinical associations, we performed single-cell RNA-sequencing and spatial transcriptomics methods including Visium and Xenium on treatment-naïve samples of TNBC patients in the ARTEMIS clinical trial. We find that TNBC was classified into 4 major archetypes at patient level: luminal secretory-associated, basal-associated, immunoreactive, and luminal androgen receptor. At cell level, cancer cells exhibited intratumoral heterogeneity in 13 gene expression metaprograms. The TNBC tumor microenvironment (TME) consisted of 49 distinct immune and stromal cell states, many of which were reprogrammed relative to normal breast tissues from disease-free women. We further identified 8 ecotypes of cancer cells and TME cell states that co-occurred among patients and were associated with specific archetypes and chemotherapy response groups. In contrast to previous work on T-cells, our data showed the importance of macrophage cell states and cancer cell metaprograms for interferon signaling, HLA expression and cell cycle activity that were associated with chemotherapy response. To facilitate a clinical application, we developed a 13-gene-based model to predict response. Collectively, this study provides new insights into the natural biology of untreated TNBC tumors and their association with chemotherapy response. Citation Format: Yun Yan, Yiyun Lin, Tapsi Kumar, Shanshan Bai, Aatish Thennavan, Jianzhuo Li, Tuan Tran, Min Hu, Mitchell Rao, Anna Casasent, Elizabeth Ravenberg, Gaiane Margishvili Rauch, Alyson Clayborn, Debu Tripathy, Alastair Thompson, Bora Lim, Lei Huo, Stacy Moulder, Clinton Yam, Nicholas Navin. Decoding the archetypes and ecotypes of triple-negative breast cancer in responses to chemotherapy [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 6438.

Cancer ResearchVol. 85(8_Supplement_1)
The University of Texas MD Anderson Cancer Center (US), Baylor College of Medicine (US)
Openalex Percentile: Top 90%
Estrogen and related hormone effects
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.