Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer
Abstract Recent advances in machine learning research, combined with the reduced sequencing costs enabled by modern next-generation sequencing, paved the way to the implementation of precision medicine through routine multi-omics molecular profiling of tumours. Thus, there is an emerging need of rel...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-33954-x |
| _version_ | 1827948169188605952 |
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| author | L. Manganaro S. Bianco P. Bironzo F. Cipollini D. Colombi D. Corà G. Corti G. Doronzo L. Errico P. Falco L. Gandolfi F. Guerrera V. Monica S. Novello M. Papotti S. Parab A. Pittaro L. Primo L. Righi G. Sabbatini A. Sandri S. Vattakunnel F. Bussolino G.V. Scagliotti |
| author_facet | L. Manganaro S. Bianco P. Bironzo F. Cipollini D. Colombi D. Corà G. Corti G. Doronzo L. Errico P. Falco L. Gandolfi F. Guerrera V. Monica S. Novello M. Papotti S. Parab A. Pittaro L. Primo L. Righi G. Sabbatini A. Sandri S. Vattakunnel F. Bussolino G.V. Scagliotti |
| author_sort | L. Manganaro |
| collection | DOAJ |
| description | Abstract Recent advances in machine learning research, combined with the reduced sequencing costs enabled by modern next-generation sequencing, paved the way to the implementation of precision medicine through routine multi-omics molecular profiling of tumours. Thus, there is an emerging need of reliable models exploiting such data to retrieve clinically useful information. Here, we introduce an original consensus clustering approach, overcoming the intrinsic instability of common clustering methods based on molecular data. This approach is applied to the case of non-small cell lung cancer (NSCLC), integrating data of an ongoing clinical study (PROMOLE) with those made available by The Cancer Genome Atlas, to define a molecular-based stratification of the patients beyond, but still preserving, histological subtyping. The resulting subgroups are biologically characterized by well-defined mutational and gene-expression profiles and are significantly related to disease-free survival (DFS). Interestingly, it was observed that (1) cluster B, characterized by a short DFS, is enriched in KEAP1 and SKP2 mutations, that makes it an ideal candidate for further studies with inhibitors, and (2) over- and under-representation of inflammation and immune systems pathways in squamous-cell carcinomas subgroups could be potentially exploited to stratify patients treated with immunotherapy. |
| first_indexed | 2024-04-09T12:50:07Z |
| format | Article |
| id | doaj.art-083f2e128a964ce08ee1ec244724b731 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T12:50:07Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-083f2e128a964ce08ee1ec244724b7312023-05-14T11:13:38ZengNature PortfolioScientific Reports2045-23222023-05-0113111410.1038/s41598-023-33954-xConsensus clustering methodology to improve molecular stratification of non-small cell lung cancerL. Manganaro0S. Bianco1P. Bironzo2F. Cipollini3D. Colombi4D. Corà5G. Corti6G. Doronzo7L. Errico8P. Falco9L. Gandolfi10F. Guerrera11V. Monica12S. Novello13M. Papotti14S. Parab15A. Pittaro16L. Primo17L. Righi18G. Sabbatini19A. Sandri20S. Vattakunnel21F. Bussolino22G.V. Scagliotti23aizoOn Technology Consulting S.R.LaizoOn Technology Consulting S.R.LMedical Oncology Division at San Luigi Hospital, Department of Oncology, University of TorinoaizoOn Technology Consulting S.R.LaizoOn Technology Consulting S.R.LDepartment of Translational Medicine, Piemonte Orientale UniversityDepartment of Oncology, University of TorinoDepartment of Oncology, University of TorinoDivision of Thoracic Surgery at AOU San Luigi, Department of Oncology, University of TorinoaizoOn Technology Consulting S.R.LDepartment of Oncology, University of TorinoDivision of Thoracic Surgery at AOU Città della Salute e della Scienza, Department of Surgical Sciences, University of TorinoDepartment of Oncology, University of TorinoMedical Oncology Division at San Luigi Hospital, Department of Oncology, University of TorinoPathology Division at AOU Città della Salute e della Scienza, Department of Oncology, University of TorinoDepartment of Oncology, University of TorinoPathology Division at AOU Città della Salute e della Scienza, Department of Oncology, University of TorinoDepartment of Oncology, University of TorinoPathology Division at AOU San Luigi, Department of Oncology, University of TorinoaizoOn Technology Consulting S.R.LDivision of Thoracic Surgery at AOU San Luigi, Department of Oncology, University of TorinoaizoOn Technology Consulting S.R.LDepartment of Oncology, University of TorinoMedical Oncology Division at San Luigi Hospital, Department of Oncology, University of TorinoAbstract Recent advances in machine learning research, combined with the reduced sequencing costs enabled by modern next-generation sequencing, paved the way to the implementation of precision medicine through routine multi-omics molecular profiling of tumours. Thus, there is an emerging need of reliable models exploiting such data to retrieve clinically useful information. Here, we introduce an original consensus clustering approach, overcoming the intrinsic instability of common clustering methods based on molecular data. This approach is applied to the case of non-small cell lung cancer (NSCLC), integrating data of an ongoing clinical study (PROMOLE) with those made available by The Cancer Genome Atlas, to define a molecular-based stratification of the patients beyond, but still preserving, histological subtyping. The resulting subgroups are biologically characterized by well-defined mutational and gene-expression profiles and are significantly related to disease-free survival (DFS). Interestingly, it was observed that (1) cluster B, characterized by a short DFS, is enriched in KEAP1 and SKP2 mutations, that makes it an ideal candidate for further studies with inhibitors, and (2) over- and under-representation of inflammation and immune systems pathways in squamous-cell carcinomas subgroups could be potentially exploited to stratify patients treated with immunotherapy.https://doi.org/10.1038/s41598-023-33954-x |
| spellingShingle | L. Manganaro S. Bianco P. Bironzo F. Cipollini D. Colombi D. Corà G. Corti G. Doronzo L. Errico P. Falco L. Gandolfi F. Guerrera V. Monica S. Novello M. Papotti S. Parab A. Pittaro L. Primo L. Righi G. Sabbatini A. Sandri S. Vattakunnel F. Bussolino G.V. Scagliotti Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer Scientific Reports |
| title | Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer |
| title_full | Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer |
| title_fullStr | Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer |
| title_full_unstemmed | Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer |
| title_short | Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer |
| title_sort | consensus clustering methodology to improve molecular stratification of non small cell lung cancer |
| url | https://doi.org/10.1038/s41598-023-33954-x |
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