Statistics and measurable residual disease (MRD) testing: uses and abuses in hematopoietic cell transplantation
Series Editors’ Note The decision whether to recommend a transplant to someone with acute leukemia in first remission is complex and challenging. Diverse, often confounded co-variates interact to influence one’s recommendation. Briefly, the decision metric can be viewed in three spheres: (1) subject...
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| Published in | Bone marrow transplantation (Basingstoke) Vol. 55; no. 5; pp. 843 - 850 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
01.05.2020
Nature Publishing Group |
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| Online Access | Get full text |
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| Abstract | Series Editors’ Note
The decision whether to recommend a transplant to someone with acute leukemia in first remission is complex and challenging. Diverse, often confounded co-variates interact to influence one’s recommendation. Briefly, the decision metric can be viewed in three spheres: (1) subject-; (2) transplant-; and (3) disease-related co-variates. Subject-related co-variates include items such as age and comorbidities. Transplant-related co-variates include items such as donor-types, graft source, proposed conditioning and pre- and post-transplant immune suppression.
But what of disease-related variables? Previously haematologists relied on co-variates such as WBC at diagnosis, chemotherapy cycles to achieve first remission, cytogenetics and most recently, mutation topography. However, these co-variates have largely been replaced by results of measurable residual disease (MRD)-testing. Many chemotherapy-only and transplant studies report strong correlations between results of MRD-testing on therapy outcomes such as cumulative incidence of relapse (CIR), leukemia-free survival (LFS) or survival. (CIR makes biological sense in a transplant context whereas LFS and survival do not give competing causes of death such as transplant-related mortality (TRM), graft-versus-host disease and interstitial pneumonia unrelated to relapse probability).
This raises the question of how useful results are of MRD-testing in predicting CIR after transplants. Elsewhere we discussed accuracy and precision of MRD-testing in predicting outcomes of therapy of acute myeloid leukemia (Estey E, Gale RP. Leukemia 31:1255−1258, 2017; Hourigan CS, Gale RP, Gormley NJ, Ossenkoppele GJ, Walter RB. Leukemia 31:1482−1490, 2017). Briefly put, not terribly good. Although results of MRD-testing are often the most powerful predictor of CIR in multivariable analyses, the C-statistic (a measure of prediction accuracy) is often only about 0.75. This is much better than flipping a
fair coin
but far from ideal.
In the typescript which follows, Othus and colleagues discuss statistical issues underlying MRD-testing in the context of haematopoietic cell transplants. We hope readers, especially haematologists who often need to make transplant recommendations to people with acute leukemia in first remission, will read it carefully and critically. The bottom line is MRD-test data are useful but considerable uncertainty is unavoidable with substantial false-positive and -negative rates. We need to acknowledge this uncertainty to ourselves and to the people we counsel. The authors quote Voltaire who said:
Doubt is not a pleasant condition, but certainty is an absurd one
. Sadly so, but we do the best we can.
Robert Peter Gale, Imperial College London, and Mei-Jie Zhang, Medical College of Wisconsin and CIBMTR. |
|---|---|
| AbstractList | Series Editors' Note The decision whether to recommend a transplant to someone with acute leukemia in first remission is complex and challenging. Diverse, often confounded co-variates interact to influence one's recommendation. Briefly, the decision metric can be viewed in three spheres: (1) subject-; (2) transplant-; and (3) disease-related co-variates. Subject-related co-variates include items such as age and comorbidities. Transplant-related co-variates include items such as donor-types, graft source, proposed conditioning and pre- and post-transplant immune suppression.But what of disease-related variables? Previously haematologists relied on co-variates such as WBC at diagnosis, chemotherapy cycles to achieve first remission, cytogenetics and most recently, mutation topography. However, these co-variates have largely been replaced by results of measurable residual disease (MRD)-testing. Many chemotherapy-only and transplant studies report strong correlations between results of MRD-testing on therapy outcomes such as cumulative incidence of relapse (CIR), leukemia-free survival (LFS) or survival. (CIR makes biological sense in a transplant context whereas LFS and survival do not give competing causes of death such as transplant-related mortality (TRM), graft-versus-host disease and interstitial pneumonia unrelated to relapse probability).This raises the question of how useful results are of MRD-testing in predicting CIR after transplants. Elsewhere we discussed accuracy and precision of MRD-testing in predicting outcomes of therapy of acute myeloid leukemia (Estey E, Gale RP. Leukemia 31:1255-1258, 2017; Hourigan CS, Gale RP, Gormley NJ, Ossenkoppele GJ, Walter RB. Leukemia 31:1482-1490, 2017). Briefly put, not terribly good. Although results of MRD-testing are often the most powerful predictor of CIR in multivariable analyses, the C-statistic (a measure of prediction accuracy) is often only about 0.75. This is much better than flipping a fair coin but far from ideal.In the typescript which follows, Othus and colleagues discuss statistical issues underlying MRD-testing in the context of haematopoietic cell transplants. We hope readers, especially haematologists who often need to make transplant recommendations to people with acute leukemia in first remission, will read it carefully and critically. The bottom line is MRD-test data are useful but considerable uncertainty is unavoidable with substantial false-positive and -negative rates. We need to acknowledge this uncertainty to ourselves and to the people we counsel. The authors quote Voltaire who said: Doubt is not a pleasant condition, but certainty is an absurd one. Sadly so, but we do the best we can. Robert Peter Gale, Imperial College London, and Mei-Jie Zhang, Medical College of Wisconsin and CIBMTR.SERIES EDITORS' NOTEThe decision whether to recommend a transplant to someone with acute leukemia in first remission is complex and challenging. Diverse, often confounded co-variates interact to influence one's recommendation. Briefly, the decision metric can be viewed in three spheres: (1) subject-; (2) transplant-; and (3) disease-related co-variates. Subject-related co-variates include items such as age and comorbidities. Transplant-related co-variates include items such as donor-types, graft source, proposed conditioning and pre- and post-transplant immune suppression.But what of disease-related variables? Previously haematologists relied on co-variates such as WBC at diagnosis, chemotherapy cycles to achieve first remission, cytogenetics and most recently, mutation topography. However, these co-variates have largely been replaced by results of measurable residual disease (MRD)-testing. Many chemotherapy-only and transplant studies report strong correlations between results of MRD-testing on therapy outcomes such as cumulative incidence of relapse (CIR), leukemia-free survival (LFS) or survival. (CIR makes biological sense in a transplant context whereas LFS and survival do not give competing causes of death such as transplant-related mortality (TRM), graft-versus-host disease and interstitial pneumonia unrelated to relapse probability).This raises the question of how useful results are of MRD-testing in predicting CIR after transplants. Elsewhere we discussed accuracy and precision of MRD-testing in predicting outcomes of therapy of acute myeloid leukemia (Estey E, Gale RP. Leukemia 31:1255-1258, 2017; Hourigan CS, Gale RP, Gormley NJ, Ossenkoppele GJ, Walter RB. Leukemia 31:1482-1490, 2017). Briefly put, not terribly good. Although results of MRD-testing are often the most powerful predictor of CIR in multivariable analyses, the C-statistic (a measure of prediction accuracy) is often only about 0.75. This is much better than flipping a fair coin but far from ideal.In the typescript which follows, Othus and colleagues discuss statistical issues underlying MRD-testing in the context of haematopoietic cell transplants. We hope readers, especially haematologists who often need to make transplant recommendations to people with acute leukemia in first remission, will read it carefully and critically. The bottom line is MRD-test data are useful but considerable uncertainty is unavoidable with substantial false-positive and -negative rates. We need to acknowledge this uncertainty to ourselves and to the people we counsel. The authors quote Voltaire who said: Doubt is not a pleasant condition, but certainty is an absurd one. Sadly so, but we do the best we can. Robert Peter Gale, Imperial College London, and Mei-Jie Zhang, Medical College of Wisconsin and CIBMTR. Series Editors' Note The decision whether to recommend a transplant to someone with acute leukemia in first remission is complex and challenging. Diverse, often confounded co-variates interact to influence one's recommendation. Briefly, the decision metric can be viewed in three spheres: (1) subject-; (2) transplant-; and (3) disease-related co-variates. Subject-related co-variates include items such as age and comorbidities. Transplant-related co-variates include items such as donor-types, graft source, proposed conditioning and pre- and post-transplant immune suppression. But what of disease-related variables? Previously haematologists relied on co-variates such as WBC at diagnosis, chemotherapy cycles to achieve first remission, cytogenetics and most recently, mutation topography. However, these co-variates have largely been replaced by results of measurable residual disease (MRD)-testing. Many chemotherapy-only and transplant studies report strong correlations between results of MRD-testing on therapy outcomes such as cumulative incidence of relapse (CIR), leukemia-free survival (LFS) or survival. (CIR makes biological sense in a transplant context whereas LFS and survival do not give competing causes of death such as transplant-related mortality (TRM), graft-versus-host disease and interstitial pneumonia unrelated to relapse probability). This raises the question of how useful results are of MRD-testing in predicting CIR after transplants. Elsewhere we discussed accuracy and precision of MRD-testing in predicting outcomes of therapy of acute myeloid leukemia (Estey E, Gale RP. Leukemia 31:1255-1258, 2017; Hourigan CS, Gale RP, Gormley NJ, Ossenkoppele GJ, Walter RB. Leukemia 31:1482-1490, 2017). Briefly put, not terribly good. Although results of MRD-testing are often the most powerful predictor of CIR in multivariable analyses, the C-statistic (a measure of prediction accuracy) is often only about 0.75. This is much better than flipping a fair coin but far from ideal. In the typescript which follows, Othus and colleagues discuss statistical issues underlying MRD-testing in the context of haematopoietic cell transplants. We hope readers, especially haematologists who often need to make transplant recommendations to people with acute leukemia in first remission, will read it carefully and critically. The bottom line is MRD-test data are useful but considerable uncertainty is unavoidable with substantial false-positive and -negative rates. We need to acknowledge this uncertainty to ourselves and to the people we counsel. The authors quote Voltaire who said: Doubt is not a pleasant condition, but certainty is an absurd one. Sadly so, but we do the best we can. Robert Peter Gale, Imperial College London, and Mei-Jie Zhang, Medical College of Wisconsin and CIBMTR. Series Editors’ Note The decision whether to recommend a transplant to someone with acute leukemia in first remission is complex and challenging. Diverse, often confounded co-variates interact to influence one’s recommendation. Briefly, the decision metric can be viewed in three spheres: (1) subject-; (2) transplant-; and (3) disease-related co-variates. Subject-related co-variates include items such as age and comorbidities. Transplant-related co-variates include items such as donor-types, graft source, proposed conditioning and pre- and post-transplant immune suppression. But what of disease-related variables? Previously haematologists relied on co-variates such as WBC at diagnosis, chemotherapy cycles to achieve first remission, cytogenetics and most recently, mutation topography. However, these co-variates have largely been replaced by results of measurable residual disease (MRD)-testing. Many chemotherapy-only and transplant studies report strong correlations between results of MRD-testing on therapy outcomes such as cumulative incidence of relapse (CIR), leukemia-free survival (LFS) or survival. (CIR makes biological sense in a transplant context whereas LFS and survival do not give competing causes of death such as transplant-related mortality (TRM), graft-versus-host disease and interstitial pneumonia unrelated to relapse probability). This raises the question of how useful results are of MRD-testing in predicting CIR after transplants. Elsewhere we discussed accuracy and precision of MRD-testing in predicting outcomes of therapy of acute myeloid leukemia (Estey E, Gale RP. Leukemia 31:1255−1258, 2017; Hourigan CS, Gale RP, Gormley NJ, Ossenkoppele GJ, Walter RB. Leukemia 31:1482−1490, 2017). Briefly put, not terribly good. Although results of MRD-testing are often the most powerful predictor of CIR in multivariable analyses, the C-statistic (a measure of prediction accuracy) is often only about 0.75. This is much better than flipping a fair coin but far from ideal. In the typescript which follows, Othus and colleagues discuss statistical issues underlying MRD-testing in the context of haematopoietic cell transplants. We hope readers, especially haematologists who often need to make transplant recommendations to people with acute leukemia in first remission, will read it carefully and critically. The bottom line is MRD-test data are useful but considerable uncertainty is unavoidable with substantial false-positive and -negative rates. We need to acknowledge this uncertainty to ourselves and to the people we counsel. The authors quote Voltaire who said: Doubt is not a pleasant condition, but certainty is an absurd one . Sadly so, but we do the best we can. Robert Peter Gale, Imperial College London, and Mei-Jie Zhang, Medical College of Wisconsin and CIBMTR. The decision whether to recommend a transplant to someone with acute leukemia in first remission is complex and challenging. Diverse, often confounded co-variates interact to influence one's recommendation. Briefly, the decision metric can be viewed in three spheres: (1) subject-; (2) transplant-; and (3) disease-related co-variates. Subject-related co-variates include items such as age and comorbidities. Transplant-related co-variates include items such as donor-types, graft source, proposed conditioning and pre- and post-transplant immune suppression.But what of disease-related variables? Previously haematologists relied on co-variates such as WBC at diagnosis, chemotherapy cycles to achieve first remission, cytogenetics and most recently, mutation topography. However, these co-variates have largely been replaced by results of measurable residual disease (MRD)-testing. Many chemotherapy-only and transplant studies report strong correlations between results of MRD-testing on therapy outcomes such as cumulative incidence of relapse (CIR), leukemia-free survival (LFS) or survival. (CIR makes biological sense in a transplant context whereas LFS and survival do not give competing causes of death such as transplant-related mortality (TRM), graft-versus-host disease and interstitial pneumonia unrelated to relapse probability).This raises the question of how useful results are of MRD-testing in predicting CIR after transplants. Elsewhere we discussed accuracy and precision of MRD-testing in predicting outcomes of therapy of acute myeloid leukemia (Estey E, Gale RP. Leukemia 31:1255-1258, 2017; Hourigan CS, Gale RP, Gormley NJ, Ossenkoppele GJ, Walter RB. Leukemia 31:1482-1490, 2017). Briefly put, not terribly good. Although results of MRD-testing are often the most powerful predictor of CIR in multivariable analyses, the C-statistic (a measure of prediction accuracy) is often only about 0.75. This is much better than flipping a fair coin but far from ideal.In the typescript which follows, Othus and colleagues discuss statistical issues underlying MRD-testing in the context of haematopoietic cell transplants. We hope readers, especially haematologists who often need to make transplant recommendations to people with acute leukemia in first remission, will read it carefully and critically. The bottom line is MRD-test data are useful but considerable uncertainty is unavoidable with substantial false-positive and -negative rates. We need to acknowledge this uncertainty to ourselves and to the people we counsel. The authors quote Voltaire who said: Doubt is not a pleasant condition, but certainty is an absurd one. Sadly so, but we do the best we can. Robert Peter Gale, Imperial College London, and Mei-Jie Zhang, Medical College of Wisconsin and CIBMTR. Series Editors’ NoteThe decision whether to recommend a transplant to someone with acute leukemia in first remission is complex and challenging. Diverse, often confounded co-variates interact to influence one’s recommendation. Briefly, the decision metric can be viewed in three spheres: (1) subject-; (2) transplant-; and (3) disease-related co-variates. Subject-related co-variates include items such as age and comorbidities. Transplant-related co-variates include items such as donor-types, graft source, proposed conditioning and pre- and post-transplant immune suppression.But what of disease-related variables? Previously haematologists relied on co-variates such as WBC at diagnosis, chemotherapy cycles to achieve first remission, cytogenetics and most recently, mutation topography. However, these co-variates have largely been replaced by results of measurable residual disease (MRD)-testing. Many chemotherapy-only and transplant studies report strong correlations between results of MRD-testing on therapy outcomes such as cumulative incidence of relapse (CIR), leukemia-free survival (LFS) or survival. (CIR makes biological sense in a transplant context whereas LFS and survival do not give competing causes of death such as transplant-related mortality (TRM), graft-versus-host disease and interstitial pneumonia unrelated to relapse probability).This raises the question of how useful results are of MRD-testing in predicting CIR after transplants. Elsewhere we discussed accuracy and precision of MRD-testing in predicting outcomes of therapy of acute myeloid leukemia (Estey E, Gale RP. Leukemia 31:1255−1258, 2017; Hourigan CS, Gale RP, Gormley NJ, Ossenkoppele GJ, Walter RB. Leukemia 31:1482−1490, 2017). Briefly put, not terribly good. Although results of MRD-testing are often the most powerful predictor of CIR in multivariable analyses, the C-statistic (a measure of prediction accuracy) is often only about 0.75. This is much better than flipping a fair coin but far from ideal.In the typescript which follows, Othus and colleagues discuss statistical issues underlying MRD-testing in the context of haematopoietic cell transplants. We hope readers, especially haematologists who often need to make transplant recommendations to people with acute leukemia in first remission, will read it carefully and critically. The bottom line is MRD-test data are useful but considerable uncertainty is unavoidable with substantial false-positive and -negative rates. We need to acknowledge this uncertainty to ourselves and to the people we counsel. The authors quote Voltaire who said: Doubt is not a pleasant condition, but certainty is an absurd one. Sadly so, but we do the best we can.Robert Peter Gale, Imperial College London, and Mei-Jie Zhang, Medical College of Wisconsin and CIBMTR. |
| Audience | Academic |
| Author | Gale, Robert Peter Hourigan, Christopher S. Walter, Roland B. Othus, Megan |
| AuthorAffiliation | 1 Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA 4 Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA 2 Haematology Research Centre, Division of Experimental Medicine, Department of Medicine, Imperial College London, London, UK 6 Department of Pathology, University of Washington, Seattle, WA, USA 7 Department of Epidemiology, University of Washington, Seattle, WA, USA 3 Myeloid Malignancies Section, Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA 5 Department of Medicine, Division of Hematology, University of Washington, Seattle, WA, USA |
| AuthorAffiliation_xml | – name: 5 Department of Medicine, Division of Hematology, University of Washington, Seattle, WA, USA – name: 6 Department of Pathology, University of Washington, Seattle, WA, USA – name: 3 Myeloid Malignancies Section, Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA – name: 2 Haematology Research Centre, Division of Experimental Medicine, Department of Medicine, Imperial College London, London, UK – name: 7 Department of Epidemiology, University of Washington, Seattle, WA, USA – name: 1 Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA – name: 4 Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA |
| Author_xml | – sequence: 1 givenname: Megan orcidid: 0000-0001-8176-6371 surname: Othus fullname: Othus, Megan organization: Public Health Sciences Division, Fred Hutchinson Cancer Research Center – sequence: 2 givenname: Robert Peter orcidid: 0000-0002-9156-1676 surname: Gale fullname: Gale, Robert Peter organization: Haematology Research Centre, Division of Experimental Medicine, Department of Medicine, Imperial College London – sequence: 3 givenname: Christopher S. surname: Hourigan fullname: Hourigan, Christopher S. organization: Myeloid Malignancies Section, Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health – sequence: 4 givenname: Roland B. orcidid: 0000-0002-9268-3341 surname: Walter fullname: Walter, Roland B. email: rwalter@fredhutch.org organization: Clinical Research Division, Fred Hutchinson Cancer Research Center, Department of Medicine, Division of Hematology, University of Washington, Department of Pathology, University of Washington, Department of Epidemiology, University of Washington |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31666655$$D View this record in MEDLINE/PubMed |
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The decision whether to recommend a transplant to someone with acute leukemia in first remission is complex and challenging. Diverse,... The decision whether to recommend a transplant to someone with acute leukemia in first remission is complex and challenging. Diverse, often confounded... Series Editors' Note The decision whether to recommend a transplant to someone with acute leukemia in first remission is complex and challenging. Diverse,... Series Editors' Note Series Editors’ NoteThe decision whether to recommend a transplant to someone with acute leukemia in first remission is complex and challenging. Diverse, often... |
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| SubjectTerms | 692/699/1541/1990/283/1895 692/699/1541/1990/283/1896 692/699/1541/1990/283/1897 692/699/1541/1990/283/2125 692/699/1541/1990/804 Acute myeloid leukemia Analysis Cancer Cell Biology Chemotherapy Comorbidity Context Correlation analysis Cytogenetics Editorial Graft vs Host Disease Graft-versus-host reaction Hematology Hematopoietic Stem Cell Transplantation Hematopoietic stem cells Humans Internal Medicine Leukemia Leukemia, Myeloid, Acute Medical colleges Medical research Medical tests Medicine Medicine & Public Health Medicine, Experimental Mortality Mutation Myeloid leukemia Neoplasm, Residual Public Health Recurrence Remission Statistical analysis Statistics Stem cell transplantation Stem Cells Survival Transplantation Transplants Transplants & implants Uncertainty |
| Title | Statistics and measurable residual disease (MRD) testing: uses and abuses in hematopoietic cell transplantation |
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