Abstract 7434: Metabolic dependence of prostate cancer subtypes and its association with the tumor-immune microenvironment

Abstract Due to the rising use of androgen deprivation therapy (ADT) and AR signaling inhibitors (ARSIs), metastatic castration-resistant prostate cancer is expanding and although it is known that its subtypes provide predictive utility, their individual tumor-immune microenvironments are woefully underexplored mechanistically. Careful investigation of these subtypes of mCRPC may provide insights into therapeutic resistance beyond mCRPC. Using both publicly available and in-house single-cell RNA-sequencing and spatial transcriptomics datasets, we have characterized mCRPC cells and their accompanying tumor-immune microenvironment using established marker genes and verified their identity using inferred copy-number variation status. Firstly, we have explored metabolic profiles of the various cell-types in our samples by calculating scores based on transcription of genes involved in metabolic processes. We have performed ligand-receptor pair analysis to predict which cell types are interacting and through which inflammatory and metabolic axes these interactions are occurring. Finally, we have demonstrated interaction feasibility by measuring distance in space via our spatial transcriptomics data. Our preliminary results indicate that these subtypes have significantly different metabolic profiles. Additionally, the immune cells near to these different subtypes have shown differential immunosuppressive programs and metabolic reprogramming. These findings suggest the potential role of tumor metabolic forces in the induction of an immunosuppressive tumor microenvironment and point to a promising utility of metabolic perturbations in mCRPC as a neoadjuvant to enhance response to immune checkpoint blockade (ICB). This work highlights novel lenses in which to analyze tumors in the hopes of suggesting combination therapies that may overcome treatment obstacles. Citation Format: Tonatiuh A. Gonzalez, Anisha Tehim, Inna Serganova, Roberta Zappasodi, Ekta Khurana. Metabolic dependence of prostate cancer subtypes and its association with the tumor-immune microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7434.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-7434
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 7434: Metabolic dependence of prostate cancer subtypes and its association with the tumor-immune microenvironment

Inna Serganova, Roberta Zappasodi, T. González, Anisha Tehim et al.
Cancer Research
Single-cell and spatial transcriptomics
article

Abstract 7434: Metabolic dependence of prostate cancer subtypes and its association with the tumor-immune microenvironment

Inna Serganova, Roberta Zappasodi, T. González, Anisha Tehim, Ekta Khurana
article en

Abstract

Abstract Due to the rising use of androgen deprivation therapy (ADT) and AR signaling inhibitors (ARSIs), metastatic castration-resistant prostate cancer is expanding and although it is known that its subtypes provide predictive utility, their individual tumor-immune microenvironments are woefully underexplored mechanistically. Careful investigation of these subtypes of mCRPC may provide insights into therapeutic resistance beyond mCRPC. Using both publicly available and in-house single-cell RNA-sequencing and spatial transcriptomics datasets, we have characterized mCRPC cells and their accompanying tumor-immune microenvironment using established marker genes and verified their identity using inferred copy-number variation status. Firstly, we have explored metabolic profiles of the various cell-types in our samples by calculating scores based on transcription of genes involved in metabolic processes. We have performed ligand-receptor pair analysis to predict which cell types are interacting and through which inflammatory and metabolic axes these interactions are occurring. Finally, we have demonstrated interaction feasibility by measuring distance in space via our spatial transcriptomics data. Our preliminary results indicate that these subtypes have significantly different metabolic profiles. Additionally, the immune cells near to these different subtypes have shown differential immunosuppressive programs and metabolic reprogramming. These findings suggest the potential role of tumor metabolic forces in the induction of an immunosuppressive tumor microenvironment and point to a promising utility of metabolic perturbations in mCRPC as a neoadjuvant to enhance response to immune checkpoint blockade (ICB). This work highlights novel lenses in which to analyze tumors in the hopes of suggesting combination therapies that may overcome treatment obstacles. Citation Format: Tonatiuh A. Gonzalez, Anisha Tehim, Inna Serganova, Roberta Zappasodi, Ekta Khurana. Metabolic dependence of prostate cancer subtypes and its association with the tumor-immune microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7434.

Cancer ResearchVol. 86(7_Supplement)
Cornell University (US), Weill Cornell Medicine (US), Ithaca College (US)
Openalex Percentile: Top 6%
Single-cell and spatial transcriptomics
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.