Cargando…

Identification of Prognostic Biomarker Candidates Associated With Melanoma Using High-Dimensional Genomic Data

Survival of patients with metastatic melanoma varies widely. Melanoma is a highly proliferative, chemo-resistant disease. With the recent availability of immunotherapies such as checkpoint inhibitors, durable response rates have improved but are often still limited to 2–3 years. Response rates to tr...

Descripción completa

Detalles Bibliográficos
Autores principales: Kutt, Brody, Burdorf, Rachel, Bain, Travaughn, Cameron, Nicardo, Pearah, Alexia, Subasi, Ersoy, Carroll, David J., Moore, Lisa K., Subasi, Munevver Mine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473904/
https://www.ncbi.nlm.nih.gov/pubmed/34589115
http://dx.doi.org/10.3389/fgene.2021.707105
_version_ 1784575099590934528
author Kutt, Brody
Burdorf, Rachel
Bain, Travaughn
Cameron, Nicardo
Pearah, Alexia
Subasi, Ersoy
Carroll, David J.
Moore, Lisa K.
Subasi, Munevver Mine
author_facet Kutt, Brody
Burdorf, Rachel
Bain, Travaughn
Cameron, Nicardo
Pearah, Alexia
Subasi, Ersoy
Carroll, David J.
Moore, Lisa K.
Subasi, Munevver Mine
author_sort Kutt, Brody
collection PubMed
description Survival of patients with metastatic melanoma varies widely. Melanoma is a highly proliferative, chemo-resistant disease. With the recent availability of immunotherapies such as checkpoint inhibitors, durable response rates have improved but are often still limited to 2–3 years. Response rates to treatment range from 30 to 45% with combination therapy however no improvement in overall survival is frequently observed. Of the available therapies, many have targeted the BRAFV600E mutation that results in abnormal MAPK pathway activation which is important for regulating cell proliferation. Immune checkpoint inhibitors such as anti-PD-1 and anti-PD-L1 offer better success but response rates are still low. Identifying biomarkers to better target those who will respond and identify the right combination of treatment is the best approach. In this study, we utilize data from the Cancer Cell Line Encyclopedia (CCLE), including 62 samples, to examine features of gene expression (19K+) and copy number (20K+) in the melanoma cell lines. We perform a clustering analysis on the feature set to assess genetically similarity among the cell lines. We then discover which specific genes and combinations thereof maximize cluster density. We design a feature selection approach for high-dimensional datasets that integrates multiple disparate machine learning techniques into one cohesive pipeline. Our approach provides a small subset of genes that can accurately distinguish between the clusters of melanoma cell lines across multiple types of classifiers. In particular, we find only the 15 highest ranked genes among the original 19 K are necessary to achieve perfect or near-perfect test split classification performance. Of these 15 genes, some are known to be linked to melanoma or other cancer progressions, while others have not previously been linked to melanoma and are of interest for further examination.
format Online
Article
Text
id pubmed-8473904
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-84739042021-09-28 Identification of Prognostic Biomarker Candidates Associated With Melanoma Using High-Dimensional Genomic Data Kutt, Brody Burdorf, Rachel Bain, Travaughn Cameron, Nicardo Pearah, Alexia Subasi, Ersoy Carroll, David J. Moore, Lisa K. Subasi, Munevver Mine Front Genet Genetics Survival of patients with metastatic melanoma varies widely. Melanoma is a highly proliferative, chemo-resistant disease. With the recent availability of immunotherapies such as checkpoint inhibitors, durable response rates have improved but are often still limited to 2–3 years. Response rates to treatment range from 30 to 45% with combination therapy however no improvement in overall survival is frequently observed. Of the available therapies, many have targeted the BRAFV600E mutation that results in abnormal MAPK pathway activation which is important for regulating cell proliferation. Immune checkpoint inhibitors such as anti-PD-1 and anti-PD-L1 offer better success but response rates are still low. Identifying biomarkers to better target those who will respond and identify the right combination of treatment is the best approach. In this study, we utilize data from the Cancer Cell Line Encyclopedia (CCLE), including 62 samples, to examine features of gene expression (19K+) and copy number (20K+) in the melanoma cell lines. We perform a clustering analysis on the feature set to assess genetically similarity among the cell lines. We then discover which specific genes and combinations thereof maximize cluster density. We design a feature selection approach for high-dimensional datasets that integrates multiple disparate machine learning techniques into one cohesive pipeline. Our approach provides a small subset of genes that can accurately distinguish between the clusters of melanoma cell lines across multiple types of classifiers. In particular, we find only the 15 highest ranked genes among the original 19 K are necessary to achieve perfect or near-perfect test split classification performance. Of these 15 genes, some are known to be linked to melanoma or other cancer progressions, while others have not previously been linked to melanoma and are of interest for further examination. Frontiers Media S.A. 2021-09-13 /pmc/articles/PMC8473904/ /pubmed/34589115 http://dx.doi.org/10.3389/fgene.2021.707105 Text en Copyright © 2021 Kutt, Burdorf, Bain, Cameron, Pearah, Subasi, Carroll, Moore and Subasi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Kutt, Brody
Burdorf, Rachel
Bain, Travaughn
Cameron, Nicardo
Pearah, Alexia
Subasi, Ersoy
Carroll, David J.
Moore, Lisa K.
Subasi, Munevver Mine
Identification of Prognostic Biomarker Candidates Associated With Melanoma Using High-Dimensional Genomic Data
title Identification of Prognostic Biomarker Candidates Associated With Melanoma Using High-Dimensional Genomic Data
title_full Identification of Prognostic Biomarker Candidates Associated With Melanoma Using High-Dimensional Genomic Data
title_fullStr Identification of Prognostic Biomarker Candidates Associated With Melanoma Using High-Dimensional Genomic Data
title_full_unstemmed Identification of Prognostic Biomarker Candidates Associated With Melanoma Using High-Dimensional Genomic Data
title_short Identification of Prognostic Biomarker Candidates Associated With Melanoma Using High-Dimensional Genomic Data
title_sort identification of prognostic biomarker candidates associated with melanoma using high-dimensional genomic data
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473904/
https://www.ncbi.nlm.nih.gov/pubmed/34589115
http://dx.doi.org/10.3389/fgene.2021.707105
work_keys_str_mv AT kuttbrody identificationofprognosticbiomarkercandidatesassociatedwithmelanomausinghighdimensionalgenomicdata
AT burdorfrachel identificationofprognosticbiomarkercandidatesassociatedwithmelanomausinghighdimensionalgenomicdata
AT baintravaughn identificationofprognosticbiomarkercandidatesassociatedwithmelanomausinghighdimensionalgenomicdata
AT cameronnicardo identificationofprognosticbiomarkercandidatesassociatedwithmelanomausinghighdimensionalgenomicdata
AT pearahalexia identificationofprognosticbiomarkercandidatesassociatedwithmelanomausinghighdimensionalgenomicdata
AT subasiersoy identificationofprognosticbiomarkercandidatesassociatedwithmelanomausinghighdimensionalgenomicdata
AT carrolldavidj identificationofprognosticbiomarkercandidatesassociatedwithmelanomausinghighdimensionalgenomicdata
AT moorelisak identificationofprognosticbiomarkercandidatesassociatedwithmelanomausinghighdimensionalgenomicdata
AT subasimunevvermine identificationofprognosticbiomarkercandidatesassociatedwithmelanomausinghighdimensionalgenomicdata