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Published on: February 8, 2018
Pan-cancer evaluation of regulated cell death to predict overall survival and immune checkpoint inhibitor response
Wei Zhang1,2, Yongwei Zhu1,2, Hongyi Liu1,2
1Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Abstract:
Regulated cell death (RCD) plays a pivotal role in various biological processes, including development, tissue homeostasis, and immune response. However, a comprehensive assessment of RCD status and its associated features at the pan-cancer level remains unexplored. Furthermore, despite significant advancements in immune checkpoint inhibitors (ICI), only a fraction of cancer patients currently benefit from treatments. Given the emerging evidence linking RCD and ICI efficacy, we hypothesize that the RCD status could serve as a promising biomarker for predicting the ICI response and overall survival (OS) in patients with malignant tumors. We defined the RCD levels as the RCD score, allowing us to delineate the RCD landscape across 30 cancer types, 29 normal tissues in bulk, and 2,573,921 cells from 82 scRNA-Seq datasets. By leveraging large-scale datasets, we aimed to establish the positive association of RCD with immunity and identify the RCD signature. Utilizing 7 machine-learning algorithms and 18 ICI cohorts, we developed an RCD signature (RCD.Sig) for predicting ICI response. Additionally, we employed 101 combinations of 10 machine-learning algorithms to construct a novel RCD survival-related signature (RCD.Sur.Sig) for predicting OS. Furthermore, we obtained CRISPR data to identify potential therapeutic targets. Our study presents an integrative framework for assessing RCD status and reveals a strong connection between RCD status and ICI effectiveness. Moreover, we establish two clinically applicable signatures and identify promising potential therapeutic targets for patients with tumors.
Insights
Regulated cell death (RCD) impacts cancer immunity and treatment response. This study defines an RCD score, develops predictive signatures for immune checkpoint inhibitor (ICI) therapy and overall survival (OS), and identifies therapeutic targets.
Area of Science:
- Oncology
- Immunology
- Computational Biology
Background:
- Regulated cell death (RCD) is crucial for biological processes but its pan-cancer landscape and therapeutic implications are unclear.
- Immune checkpoint inhibitors (ICI) benefit only a subset of cancer patients, highlighting the need for predictive biomarkers.
- Emerging evidence suggests a link between RCD and ICI efficacy.
Purpose of the Study:
- To comprehensively assess the RCD landscape across various cancer types and normal tissues.
- To investigate the association between RCD status, immunity, and patient outcomes.
- To develop predictive signatures for ICI response and overall survival (OS).
Main Methods:
- Calculated RCD scores across 30 cancer types and 2,573,921 cells from 82 scRNA-Seq datasets.
- Employed 7 machine-learning algorithms on 18 ICI cohorts to develop an RCD signature (RCD.Sig) for predicting ICI response.
- Utilized 10 machine-learning algorithms to create an RCD survival-related signature (RCD.Sur.Sig) for predicting OS.
- Analyzed CRISPR data to identify potential therapeutic targets.
Main Results:
- Delineated the RCD landscape across diverse cancers and single cells, revealing a positive association with immunity.
- Developed RCD.Sig, a signature demonstrating predictive power for ICI response.
- Constructed RCD.Sur.Sig, a novel signature for predicting patient overall survival.
- Identified potential therapeutic targets through CRISPR data analysis.
Conclusions:
- This study provides an integrative framework for RCD assessment in cancer.
- RCD status is strongly linked to ICI effectiveness and patient survival.
- Two clinically applicable signatures and potential therapeutic targets were identified for improved cancer treatment.
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