Using Targeted Transcriptome and Machine Learning of Pre- and Post-Transplant Bone Marrow Samples to Predict Acute Graft-versus-Host Disease and Overall Survival after Allogeneic Stem Cell Transplantation
Acute graft-versus-host disease (aGvHD) remains a major cause of morbidity and mortality after allogeneic hematopoietic stem cell transplantation (HSCT). We performed RNA analysis of 1408 candidate genes in bone marrow samples obtained from 167 patients undergoing HSCT. RNA expression data were used...
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MDPI AG
2024-03-01
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| Online Access: | https://www.mdpi.com/2072-6694/16/7/1357 |
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| author | Scott D. Rowley Thomas S. Gunning Michael Pelliccia Alexandra Della Pia Albert Lee James Behrmann Ayrton Bangolo Parul Jandir Hong Zhang Sukhdeep Kaur Hyung C. Suh Michele Donato Maher Albitar Andrew Ip |
| author_facet | Scott D. Rowley Thomas S. Gunning Michael Pelliccia Alexandra Della Pia Albert Lee James Behrmann Ayrton Bangolo Parul Jandir Hong Zhang Sukhdeep Kaur Hyung C. Suh Michele Donato Maher Albitar Andrew Ip |
| author_sort | Scott D. Rowley |
| collection | DOAJ |
| description | Acute graft-versus-host disease (aGvHD) remains a major cause of morbidity and mortality after allogeneic hematopoietic stem cell transplantation (HSCT). We performed RNA analysis of 1408 candidate genes in bone marrow samples obtained from 167 patients undergoing HSCT. RNA expression data were used in a machine learning algorithm to predict the presence or absence of aGvHD using either random forest or extreme gradient boosting algorithms. Patients were randomly divided into training (2/3 of patients) and validation (1/3 of patients) sets. Using post-HSCT RNA data, the machine learning algorithm selected 92 genes for predicting aGvHD that appear to play a role in PI3/AKT, MAPK, and FOXO signaling, as well as microRNA. The algorithm selected 20 genes for predicting survival included genes involved in MAPK and chemokine signaling. Using pre-HSCT RNA data, the machine learning algorithm selected 400 genes and 700 genes predicting aGvHD and overall survival, but candidate signaling pathways could not be specified in this analysis. These data show that NGS analyses of RNA expression using machine learning algorithms may be useful biomarkers of aGvHD and overall survival for patients undergoing HSCT, allowing for the identification of major signaling pathways associated with HSCT outcomes and helping to dissect the complex steps involved in the development of aGvHD. The analysis of pre-HSCT bone marrow samples may lead to pre-HSCT interventions including choice of remission induction regimens and modifications in patient health before HSCT. |
| first_indexed | 2024-04-24T10:47:24Z |
| format | Article |
| id | doaj.art-190ebf798ba9462cacb93ff7e2ea7eb5 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-04-24T10:47:24Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-190ebf798ba9462cacb93ff7e2ea7eb52024-04-12T13:16:08ZengMDPI AGCancers2072-66942024-03-01167135710.3390/cancers16071357Using Targeted Transcriptome and Machine Learning of Pre- and Post-Transplant Bone Marrow Samples to Predict Acute Graft-versus-Host Disease and Overall Survival after Allogeneic Stem Cell TransplantationScott D. Rowley0Thomas S. Gunning1Michael Pelliccia2Alexandra Della Pia3Albert Lee4James Behrmann5Ayrton Bangolo6Parul Jandir7Hong Zhang8Sukhdeep Kaur9Hyung C. Suh10Michele Donato11Maher Albitar12Andrew Ip13Georgetown University School of Medicine, Washington, DC 20007, USAHackensack Meridian School of Medicine, Nutley, NJ 07110, USAHackensack Meridian School of Medicine, Nutley, NJ 07110, USAJohn Theurer Cancer Center, Hackensack, NJ 07601, USAHackensack Meridian School of Medicine, Nutley, NJ 07110, USAHackensack Meridian School of Medicine, Nutley, NJ 07110, USAHackensack Meridian School of Medicine, Nutley, NJ 07110, USAHackensack Meridian School of Medicine, Nutley, NJ 07110, USAGenomic Testing Cooperative, Irvine, CA 92618, USAJohn Theurer Cancer Center, Hackensack, NJ 07601, USAJohn Theurer Cancer Center, Hackensack, NJ 07601, USAJohn Theurer Cancer Center, Hackensack, NJ 07601, USAGenomic Testing Cooperative, Irvine, CA 92618, USAJohn Theurer Cancer Center, Hackensack, NJ 07601, USAAcute graft-versus-host disease (aGvHD) remains a major cause of morbidity and mortality after allogeneic hematopoietic stem cell transplantation (HSCT). We performed RNA analysis of 1408 candidate genes in bone marrow samples obtained from 167 patients undergoing HSCT. RNA expression data were used in a machine learning algorithm to predict the presence or absence of aGvHD using either random forest or extreme gradient boosting algorithms. Patients were randomly divided into training (2/3 of patients) and validation (1/3 of patients) sets. Using post-HSCT RNA data, the machine learning algorithm selected 92 genes for predicting aGvHD that appear to play a role in PI3/AKT, MAPK, and FOXO signaling, as well as microRNA. The algorithm selected 20 genes for predicting survival included genes involved in MAPK and chemokine signaling. Using pre-HSCT RNA data, the machine learning algorithm selected 400 genes and 700 genes predicting aGvHD and overall survival, but candidate signaling pathways could not be specified in this analysis. These data show that NGS analyses of RNA expression using machine learning algorithms may be useful biomarkers of aGvHD and overall survival for patients undergoing HSCT, allowing for the identification of major signaling pathways associated with HSCT outcomes and helping to dissect the complex steps involved in the development of aGvHD. The analysis of pre-HSCT bone marrow samples may lead to pre-HSCT interventions including choice of remission induction regimens and modifications in patient health before HSCT.https://www.mdpi.com/2072-6694/16/7/1357allogeneic transplantationacute GvHDpost-transplant survivaltranscriptomicsmachine learningsignaling pathways |
| spellingShingle | Scott D. Rowley Thomas S. Gunning Michael Pelliccia Alexandra Della Pia Albert Lee James Behrmann Ayrton Bangolo Parul Jandir Hong Zhang Sukhdeep Kaur Hyung C. Suh Michele Donato Maher Albitar Andrew Ip Using Targeted Transcriptome and Machine Learning of Pre- and Post-Transplant Bone Marrow Samples to Predict Acute Graft-versus-Host Disease and Overall Survival after Allogeneic Stem Cell Transplantation Cancers allogeneic transplantation acute GvHD post-transplant survival transcriptomics machine learning signaling pathways |
| title | Using Targeted Transcriptome and Machine Learning of Pre- and Post-Transplant Bone Marrow Samples to Predict Acute Graft-versus-Host Disease and Overall Survival after Allogeneic Stem Cell Transplantation |
| title_full | Using Targeted Transcriptome and Machine Learning of Pre- and Post-Transplant Bone Marrow Samples to Predict Acute Graft-versus-Host Disease and Overall Survival after Allogeneic Stem Cell Transplantation |
| title_fullStr | Using Targeted Transcriptome and Machine Learning of Pre- and Post-Transplant Bone Marrow Samples to Predict Acute Graft-versus-Host Disease and Overall Survival after Allogeneic Stem Cell Transplantation |
| title_full_unstemmed | Using Targeted Transcriptome and Machine Learning of Pre- and Post-Transplant Bone Marrow Samples to Predict Acute Graft-versus-Host Disease and Overall Survival after Allogeneic Stem Cell Transplantation |
| title_short | Using Targeted Transcriptome and Machine Learning of Pre- and Post-Transplant Bone Marrow Samples to Predict Acute Graft-versus-Host Disease and Overall Survival after Allogeneic Stem Cell Transplantation |
| title_sort | using targeted transcriptome and machine learning of pre and post transplant bone marrow samples to predict acute graft versus host disease and overall survival after allogeneic stem cell transplantation |
| topic | allogeneic transplantation acute GvHD post-transplant survival transcriptomics machine learning signaling pathways |
| url | https://www.mdpi.com/2072-6694/16/7/1357 |
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