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Xu K, Yin X, Chen H, Huang Y, Zheng X, Zhou B, Cai X, Gao H, Tian M, Hu S, Zheng S, Yuan C, Nie Y, Guo T, Shao Y. Prediction of overall survival in stage II and III colon cancer through machine learning of rapidly-acquired proteomics. Cell Discov 2024; 10:85. [PMID: 39134531 PMCID: PMC11319451 DOI: 10.1038/s41421-024-00707-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 06/25/2024] [Indexed: 08/15/2024] Open
Affiliation(s)
- Kailun Xu
- Department of Breast Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang, China), Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaoyang Yin
- Department of Radiation Oncology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Hui Chen
- School of Public Health, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yuhui Huang
- School of Public Health, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xi Zheng
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Biting Zhou
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Xue Cai
- School of Medicine, Westlake University, Hangzhou, Zhejiang, China
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Huanhuan Gao
- School of Medicine, Westlake University, Hangzhou, Zhejiang, China
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - Miaomiao Tian
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Sijun Hu
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Shu Zheng
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China
- Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China
| | - Changzheng Yuan
- School of Public Health, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
| | - Yongzhan Nie
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi, China.
| | - Tiannan Guo
- School of Medicine, Westlake University, Hangzhou, Zhejiang, China.
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China.
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
| | - Yingkuan Shao
- Department of Breast Surgery and Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang, China), Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, Zhejiang, China.
- Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China.
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Mondello A, Dal Bo M, Toffoli G, Polano M. Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges. Front Pharmacol 2024; 14:1260276. [PMID: 38264526 PMCID: PMC10803549 DOI: 10.3389/fphar.2023.1260276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/26/2023] [Indexed: 01/25/2024] Open
Abstract
Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.
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Affiliation(s)
| | | | | | - Maurizio Polano
- Experimental and Clinical Pharmacology Unit, Centro di Riferimento Oncologico di Aviano (CRO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Aviano, Italy
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Robles J, Prakash A, Vizcaíno JA, Casal JI. Integrated meta-analysis of colorectal cancer public proteomic datasets for biomarker discovery and validation. PLoS Comput Biol 2024; 20:e1011828. [PMID: 38252632 PMCID: PMC10833860 DOI: 10.1371/journal.pcbi.1011828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 02/01/2024] [Accepted: 01/15/2024] [Indexed: 01/24/2024] Open
Abstract
The cancer biomarker field has been an object of thorough investigation in the last decades. Despite this, colorectal cancer (CRC) heterogeneity makes it challenging to identify and validate effective prognostic biomarkers for patient classification according to outcome and treatment response. Although a massive amount of proteomics data has been deposited in public data repositories, this rich source of information is vastly underused. Here, we attempted to reuse public proteomics datasets with two main objectives: i) to generate hypotheses (detection of biomarkers) for their posterior/downstream validation, and (ii) to validate, using an orthogonal approach, a previously described biomarker panel. Twelve CRC public proteomics datasets (mostly from the PRIDE database) were re-analysed and integrated to create a landscape of protein expression. Samples from both solid and liquid biopsies were included in the reanalysis. Integrating this data with survival annotation data, we have validated in silico a six-gene signature for CRC classification at the protein level, and identified five new blood-detectable biomarkers (CD14, PPIA, MRC2, PRDX1, and TXNDC5) associated with CRC prognosis. The prognostic value of these blood-derived proteins was confirmed using additional public datasets, supporting their potential clinical value. As a conclusion, this proof-of-the-concept study demonstrates the value of re-using public proteomics datasets as the basis to create a useful resource for biomarker discovery and validation. The protein expression data has been made available in the public resource Expression Atlas.
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Affiliation(s)
- Javier Robles
- Department of Molecular Biomedicine, Centro de Investigaciones Biológicas Margarita Salas, Consejo Superior de Investigaciones Científicas, Madrid, Spain
- Protein Alternatives SL, Tres Cantos, Madrid, Spain
| | - Ananth Prakash
- European Molecular Biology Laboratory—European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory—European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - J. Ignacio Casal
- Department of Molecular Biomedicine, Centro de Investigaciones Biológicas Margarita Salas, Consejo Superior de Investigaciones Científicas, Madrid, Spain
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Bartolomé RA, Casal JI. Proteomic profiling and network biology of colorectal cancer liver metastasis. Expert Rev Proteomics 2023; 20:357-370. [PMID: 37874121 DOI: 10.1080/14789450.2023.2275681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 10/23/2023] [Indexed: 10/25/2023]
Abstract
INTRODUCTION Tissue-based proteomic studies of colorectal cancer (CRC) metastasis have delivered fragmented results, with very few therapeutic targets and prognostic biomarkers moving beyond the discovery phase. This situation is likely due to the difficulties in obtaining and analyzing large numbers of patient-derived metastatic samples, the own heterogeneity of CRC, and technical limitations in proteomics discovery. As an alternative, metastatic CRC cell lines provide a flexible framework to investigate the underlying mechanisms and network biology of metastasis for target discovery. AREAS COVERED In this perspective, we comment on different in-depth proteomic studies of metastatic versus non-metastatic CRC cell lines. Identified metastasis-related proteins are introduced and discussed according to the spatial location in different cellular fractions, with special emphasis on membrane/adhesion proteins, secreted proteins, and nuclear factors, including miRNAs associated with liver metastasis. Moreover, we analyze the biological significance and potential therapeutic applications of the identified liver metastasis-related proteins. EXPERT OPINION The combination of protein discovery and functional analysis is the only way to accelerate the progress to clinical translation of the proteomic-derived findings in a relatively fast pace. Patient-derived organoids represent a promising alternative to patient tissues and cell lines, but further optimizations are still required for achieving solid and reproducible results.
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Affiliation(s)
- Rubén A Bartolomé
- Department of Molecular Biomedicine, Centro de Investigaciones Biológicas Margarita Salas, Madrid, Spain
| | - J Ignacio Casal
- Department of Molecular Biomedicine, Centro de Investigaciones Biológicas Margarita Salas, Madrid, Spain
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