Comprehensive mutation profiling and mRNA expression analysis in atypical chronic myeloid leukemia in comparison with chronic myelomonocytic leukemia
Abstract Atypical chronic myeloid leukemia (aCML) and chronic myelomonocytic leukemia (CMML) represent two histologically and clinically overlapping myelodysplastic/myeloproliferative neoplasms. Also the mutational landscapes of both entities show congruencies. We analyzed and compared an aCML cohor...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-02-01
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| Series: | Cancer Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/cam4.1946 |
| _version_ | 1797781067237687296 |
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| author | Muhammad Faisal Helge Stark Guntram Büsche Jerome Schlue Kristin Teiken Hans H. Kreipe Ulrich Lehmann Stephan Bartels |
| author_facet | Muhammad Faisal Helge Stark Guntram Büsche Jerome Schlue Kristin Teiken Hans H. Kreipe Ulrich Lehmann Stephan Bartels |
| author_sort | Muhammad Faisal |
| collection | DOAJ |
| description | Abstract Atypical chronic myeloid leukemia (aCML) and chronic myelomonocytic leukemia (CMML) represent two histologically and clinically overlapping myelodysplastic/myeloproliferative neoplasms. Also the mutational landscapes of both entities show congruencies. We analyzed and compared an aCML cohort (n = 26) and a CMML cohort (n = 59) by next‐generation sequencing of 25 genes and by an nCounter approach for differential expression in 107 genes. Significant differences were found with regard to the mutation frequency of TET2, SETBP1, and CSF3R. Blast content of the bone marrow revealed an inverse correlation with the mutation status of SETBP1 in aCML and TET2 in CMML, respectively. By linear discriminant analysis, a mutation‐based machine learning algorithm was generated which placed 19/26 aCML cases (73%) and 54/59 (92%) CMML cases into the correct category. After multiple correction, differential mRNA expression could be detected between both cohorts in a subset of genes (FLT3, CSF3R, and SETBP1 showed the strongest correlation). However, due to high variances in the mRNA expression, the potential utility for the clinic is limited. We conclude that a medium‐sized NGS panel provides a valuable assistance for the correct classification of aCML and CMML. |
| first_indexed | 2024-03-12T23:52:33Z |
| format | Article |
| id | doaj.art-bb068ec494524e49bb8551bef0ef8ed2 |
| institution | Directory Open Access Journal |
| issn | 2045-7634 |
| language | English |
| last_indexed | 2024-03-12T23:52:33Z |
| publishDate | 2019-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj.art-bb068ec494524e49bb8551bef0ef8ed22023-07-13T12:30:56ZengWileyCancer Medicine2045-76342019-02-018274275010.1002/cam4.1946Comprehensive mutation profiling and mRNA expression analysis in atypical chronic myeloid leukemia in comparison with chronic myelomonocytic leukemiaMuhammad Faisal0Helge Stark1Guntram Büsche2Jerome Schlue3Kristin Teiken4Hans H. Kreipe5Ulrich Lehmann6Stephan Bartels7Institute of Pathology Hannover Medical School Hannover GermanyInstitute of Pathology Hannover Medical School Hannover GermanyInstitute of Pathology Hannover Medical School Hannover GermanyInstitute of Pathology Hannover Medical School Hannover GermanyInstitute of Pathology Hannover Medical School Hannover GermanyInstitute of Pathology Hannover Medical School Hannover GermanyInstitute of Pathology Hannover Medical School Hannover GermanyInstitute of Pathology Hannover Medical School Hannover GermanyAbstract Atypical chronic myeloid leukemia (aCML) and chronic myelomonocytic leukemia (CMML) represent two histologically and clinically overlapping myelodysplastic/myeloproliferative neoplasms. Also the mutational landscapes of both entities show congruencies. We analyzed and compared an aCML cohort (n = 26) and a CMML cohort (n = 59) by next‐generation sequencing of 25 genes and by an nCounter approach for differential expression in 107 genes. Significant differences were found with regard to the mutation frequency of TET2, SETBP1, and CSF3R. Blast content of the bone marrow revealed an inverse correlation with the mutation status of SETBP1 in aCML and TET2 in CMML, respectively. By linear discriminant analysis, a mutation‐based machine learning algorithm was generated which placed 19/26 aCML cases (73%) and 54/59 (92%) CMML cases into the correct category. After multiple correction, differential mRNA expression could be detected between both cohorts in a subset of genes (FLT3, CSF3R, and SETBP1 showed the strongest correlation). However, due to high variances in the mRNA expression, the potential utility for the clinic is limited. We conclude that a medium‐sized NGS panel provides a valuable assistance for the correct classification of aCML and CMML.https://doi.org/10.1002/cam4.1946aCMLCMMLmachine learning algorithmnCounterNGS |
| spellingShingle | Muhammad Faisal Helge Stark Guntram Büsche Jerome Schlue Kristin Teiken Hans H. Kreipe Ulrich Lehmann Stephan Bartels Comprehensive mutation profiling and mRNA expression analysis in atypical chronic myeloid leukemia in comparison with chronic myelomonocytic leukemia Cancer Medicine aCML CMML machine learning algorithm nCounter NGS |
| title | Comprehensive mutation profiling and mRNA expression analysis in atypical chronic myeloid leukemia in comparison with chronic myelomonocytic leukemia |
| title_full | Comprehensive mutation profiling and mRNA expression analysis in atypical chronic myeloid leukemia in comparison with chronic myelomonocytic leukemia |
| title_fullStr | Comprehensive mutation profiling and mRNA expression analysis in atypical chronic myeloid leukemia in comparison with chronic myelomonocytic leukemia |
| title_full_unstemmed | Comprehensive mutation profiling and mRNA expression analysis in atypical chronic myeloid leukemia in comparison with chronic myelomonocytic leukemia |
| title_short | Comprehensive mutation profiling and mRNA expression analysis in atypical chronic myeloid leukemia in comparison with chronic myelomonocytic leukemia |
| title_sort | comprehensive mutation profiling and mrna expression analysis in atypical chronic myeloid leukemia in comparison with chronic myelomonocytic leukemia |
| topic | aCML CMML machine learning algorithm nCounter NGS |
| url | https://doi.org/10.1002/cam4.1946 |
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