Gene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy

Akram Yazdani; Heinz-Josef Lenz; Gianluigi Pillonetto; Raul Mendez-Giraldez; Azam Yazdani; Hanna Sanoff; Reza Hadi; Esmat Samiei; Alan P Venook; Mark J Ratain; Naim Rashid; Benjamin G Vincent; Xueping Qu; Yujia Wen; Michael Kosorok; William F Symmans; John Paul Y C Shen; Michael S Lee; Scott Kopetz; Andrew B Nixon; Monica M Bertagnolli; Charles M Perou; Federico Innocenti

doi:10.1038/s43856-024-00728-z

Gene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy

Commun Med (Lond). 2025 Jan 8;5(1):9. doi: 10.1038/s43856-024-00728-z.

Authors

Akram Yazdani^{1

2}, Heinz-Josef Lenz³, Gianluigi Pillonetto⁴, Raul Mendez-Giraldez⁵, Azam Yazdani⁶, Hanna Sanoff^{7

8}, Reza Hadi⁹, Esmat Samiei¹⁰, Alan P Venook¹¹, Mark J Ratain¹², Naim Rashid¹³, Benjamin G Vincent¹⁴, Xueping Qu¹⁵, Yujia Wen¹⁶, Michael Kosorok¹³, William F Symmans¹⁷, John Paul Y C Shen¹⁸, Michael S Lee¹⁸, Scott Kopetz^{18

19}, Andrew B Nixon²⁰, Monica M Bertagnolli²¹, Charles M Perou^{8

22}, Federico Innocenti²³

Affiliations

¹ Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. [email protected].
² University of Texas Health Science Center at Houston, Texas, TX, USA. [email protected].
³ USC Norris Comprehensive Cancer Center, Los Angeles, CA, USA.
⁴ Department of Information Engineering, University of Padova, Padova, Italy.
⁵ Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA.
⁶ Center of Perioperative Genetics and Genomics, Perioperative and Pain Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁷ Division of Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁸ Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁹ School of Mathematics, University of Science and Technology of Iran, Tehran, Iran.
¹⁰ Gamelectronic, Tehran, Iran.
¹¹ University of California at San Francisco, San Francisco, CA, USA.
¹² Division of the Biological Sciences, University of Chicago, Chicago, IL, USA.
¹³ Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, US.
¹⁴ Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, USA.
¹⁵ Genentech, South San Francisco, San Francisco, CA, USA.
¹⁶ Alliance for Clinical Trials in Oncology, Chicago, IL, USA.
¹⁷ Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
¹⁸ Departments of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
¹⁹ Departments of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
²⁰ Duke Center for Cancer Immunotherapy, Duke University, Durham, NC, USA.
²¹ Dana-Farber/ Partners Cancer Care, Harvard Medical School, Boston, MA, USA.
²² Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
²³ Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

PMID: 39779996
DOI: 10.1038/s43856-024-00728-z

Abstract

Background: Gene signatures derived from transcriptomic-causal networks offer potential for tailoring clinical care in cancer treatment by identifying predictive and prognostic biomarkers. This study aimed to uncover such signatures in metastatic colorectal cancer (CRC) patients to aid treatment decisions.

Methods: We constructed transcriptomic-causal networks and integrated gene interconnectivity into overall survival (OS) analysis to control for confounding genes. This integrative approach involved germline genotype and tumor RNA-seq data from 1165 metastatic CRC patients. The patients were enrolled in a randomized clinical trial receiving either cetuximab or bevacizumab in combination with chemotherapy. An external cohort of paired CRC normal and tumor samples, along with protein-protein interaction databases, was used for replication.

Results: We identify promising predictive and prognostic gene signatures from pre-treatment gene expression profiles. Our study discerns sets of genes, each forming a signature that collectively contribute to define patient subgroups with different prognosis and response to the therapies. Using an external cohort, we show that the genes influencing OS within the signatures, such as FANCI and PRC1, are upregulated in CRC tumor vs. normal tissue. These signatures are highly associated with immune features, including macrophages, cytotoxicity, and wound healing. Furthermore, the corresponding proteins encoded by the genes within the signatures interact with each other and are functionally related.

Conclusions: This study underscores the utility of gene signatures derived from transcriptomic-causal networks in patient stratification for effective therapies. The interpretability of the findings, supported by replication, highlights the potential of these signatures to identify patients likely to benefit from cetuximab or bevacizumab.

Plain language summary

Response to cancer treatment varies greatly among patients. To improve outcomes, it is crucial to identify patients who are more likely to respond well to particular treatments. Changes in gene expression within cancer cells lead to alterations in cellular behavior, which can influence the response to treatment. In this study, we investigated how these changes affect patient outcomes. We identified specific gene expression patterns in patients who benefited from certain treatments. These findings could help guide treatment decisions, enabling personalized therapies that are more likely to be effective and improve patient outcomes.

Abstract

Plain language summary

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