Cell Signaling-Based Classifier Predicts Response to Induction Therapy in Elderly Patients with Acute Myeloid Leukemia
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{"title"=>"Cell signaling-based classifier predicts response to induction therapy in elderly patients with acute myeloid leukemia", "type"=>"journal", "authors"=>[{"first_name"=>"Alessandra", "last_name"=>"Cesano", "scopus_author_id"=>"7003304252"}, {"first_name"=>"Cheryl L.", "last_name"=>"Willman", "scopus_author_id"=>"7004537186"}, {"first_name"=>"Kenneth J.", "last_name"=>"Kopecky", "scopus_author_id"=>"7101989674"}, {"first_name"=>"Urte", "last_name"=>"Gayko", "scopus_author_id"=>"6508218797"}, {"first_name"=>"Santosh", "last_name"=>"Putta", "scopus_author_id"=>"6603879985"}, {"first_name"=>"Brent", "last_name"=>"Louie", "scopus_author_id"=>"54979778500"}, {"first_name"=>"Matt", "last_name"=>"Westfall", "scopus_author_id"=>"57198331458"}, {"first_name"=>"Norman", "last_name"=>"Purvis", "scopus_author_id"=>"6603217925"}, {"first_name"=>"David C.", "last_name"=>"Spellmeyer", "scopus_author_id"=>"36773412300"}, {"first_name"=>"Carol", "last_name"=>"Marimpietri", "scopus_author_id"=>"55546862000"}, {"first_name"=>"Aileen C.", "last_name"=>"Cohen", "scopus_author_id"=>"36243086800"}, {"first_name"=>"James", "last_name"=>"Hackett", "scopus_author_id"=>"57196572118"}, {"first_name"=>"Jing", "last_name"=>"Shi", "scopus_author_id"=>"56647174900"}, {"first_name"=>"Michael G.", "last_name"=>"Walker", "scopus_author_id"=>"55364528100"}, {"first_name"=>"Zhuoxin", "last_name"=>"Sun", "scopus_author_id"=>"23104385400"}, {"first_name"=>"Elisabeth", "last_name"=>"Paietta", "scopus_author_id"=>"7004993241"}, {"first_name"=>"Martin S.", "last_name"=>"Tallman", "scopus_author_id"=>"7006159643"}, {"first_name"=>"Larry D.", "last_name"=>"Cripe", "scopus_author_id"=>"7004124115"}, {"first_name"=>"Susan", "last_name"=>"Atwater", "scopus_author_id"=>"6603893621"}, {"first_name"=>"Frederick R.", "last_name"=>"Appelbaum", "scopus_author_id"=>"35376245800"}, {"first_name"=>"Jerald P.", "last_name"=>"Radich", "scopus_author_id"=>"7006165074"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84929471466", "pui"=>"604107170", "doi"=>"10.1371/journal.pone.0118485", "sgr"=>"84929471466", "pmid"=>"25884949"}, "id"=>"b97489d8-664a-33ad-9545-683b24646061", "abstract"=>"Single-cell network profiling (SCNP) data generated from multi-parametric flow cytometry analysis of bone marrow (BM) and peripheral blood (PB) samples collected from patients >55 years old with non-M3 AML were used to train and validate a diagnostic classifier (DXSCNP) for predicting response to standard induction chemotherapy (complete response [CR] or CR with incomplete hematologic recovery [CRi] versus resistant disease [RD]). SCNP-evaluable patients from four SWOG AML trials were randomized between Training (N = 74 patients with CR, CRi or RD; BM set = 43; PB set = 57) and Validation Analysis Sets (N = 71; BM set = 42, PB set = 53). Cell survival, differentiation, and apoptosis pathway signaling were used as potential inputs for DXSCNP. Five DXSCNP classifiers were developed on the SWOG Training set and tested for prediction accuracy in an independent BM verification sample set (N = 24) from ECOG AML trials to select the final classifier, which was a significant predictor of CR/CRi (area under the receiver operating characteristic curve AUROC = 0.76, p = 0.01). The selected classifier was then validated in the SWOG BM Validation Set (AUROC = 0.72, p = 0.02). Importantly, a classifier developed using only clinical and molecular inputs from the same sample set (DXCLINICAL2) lacked prediction accuracy: AUROC = 0.61 (p = 0.18) in the BM Verification Set and 0.53 (p = 0.38) in the BM Validation Set. Notably, the DXSCNP classifier was still significant in predicting response in the BM Validation Analysis Set after controlling for DXCLINICAL2 (p = 0.03), showing that DXSCNP provides information that is independent from that provided by currently used prognostic markers. Taken together, these data show that the proteomic classifier may provide prognostic information relevant to treatment planning beyond genetic mutations and traditional prognostic factors in elderly AML.; ", "link"=>"http://www.mendeley.com/research/cell-signalingbased-classifier-predicts-response-induction-therapy-elderly-patients-acute-myeloid-le", "reader_count"=>8, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>1, "Student > Bachelor"=>2, "Researcher"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>1, "Student > Bachelor"=>2, "Researcher"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Mathematics"=>1, "Medicine and Dentistry"=>1, "Agricultural and Biological Sciences"=>1, "Psychology"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Computer Science"=>1, "Engineering"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2026221"], "description"=>"<p>A flow diagram showing all patients enrolled onto the parent ECOG AML trials and rationale for exclusion of patients from the final analysis sets. Text boxes describe the characteristics of patients carried forward.</p>", "links"=>[], "tags"=>["Acute myeloid leukemia", "BM Validation Analysis", "cr", "Validation Analysis Sets", "rd", "auroc", "SWOG AML trials", "classifier", "pb", "ECOG AML trials", "DXSCNP", "prediction accuracy", "DXCLINICAL", "SWOG BM Validation", "scnp", "BM verification sample", "prognostic", "response"], "article_id"=>1383693, "categories"=>["Biological Sciences"], "users"=>["Alessandra Cesano", "Cheryl L. Willman", "Kenneth J. Kopecky", "Urte Gayko", "Santosh Putta", "Brent Louie", "Matt Westfall", "Norman Purvis", "David C. Spellmeyer", "Carol Marimpietri", "Aileen C. Cohen", "James Hackett", "Jing Shi", "Michael G. Walker", "Zhuoxin Sun", "Elisabeth Paietta", "Martin S. Tallman", "Larry D. Cripe", "Susan Atwater", "Frederick R. Appelbaum", "Jerald P. Radich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118485.g003", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ECOG_Patient_Disposition_/1383693", "title"=>"ECOG Patient Disposition.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 04:06:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026220"], "description"=>"<p>A flow diagram showing all patients enrolled onto the SWOG parent AML trials and rationale for exclusion of patients from the final analysis sets. Text boxes describe the characteristics of patients carried forward.</p>", "links"=>[], "tags"=>["Acute myeloid leukemia", "BM Validation Analysis", "cr", "Validation Analysis Sets", "rd", "auroc", "SWOG AML trials", "classifier", "pb", "ECOG AML trials", "DXSCNP", "prediction accuracy", "DXCLINICAL", "SWOG BM Validation", "scnp", "BM verification sample", "prognostic", "response"], "article_id"=>1383692, "categories"=>["Biological Sciences"], "users"=>["Alessandra Cesano", "Cheryl L. Willman", "Kenneth J. Kopecky", "Urte Gayko", "Santosh Putta", "Brent Louie", "Matt Westfall", "Norman Purvis", "David C. Spellmeyer", "Carol Marimpietri", "Aileen C. Cohen", "James Hackett", "Jing Shi", "Michael G. Walker", "Zhuoxin Sun", "Elisabeth Paietta", "Martin S. Tallman", "Larry D. Cripe", "Susan Atwater", "Frederick R. Appelbaum", "Jerald P. Radich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118485.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_SWOG_Patient_Disposition_/1383692", "title"=>"SWOG Patient Disposition.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 04:06:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026229"], "description"=>"<p>Prediction accuracy of DX<sub>SCNP</sub> in BM and PB Validation Analysis subsets defined by AML Type (De Novo vs. Secondary) and availability of samples for both tissue types.</p>", "links"=>[], "tags"=>["Acute myeloid leukemia", "BM Validation Analysis", "cr", "Validation Analysis Sets", "rd", "auroc", "SWOG AML trials", "classifier", "pb", "ECOG AML trials", "DXSCNP", "prediction accuracy", "DXCLINICAL", "SWOG BM Validation", "scnp", "BM verification sample", "prognostic", "response"], "article_id"=>1383701, "categories"=>["Biological Sciences"], "users"=>["Alessandra Cesano", "Cheryl L. Willman", "Kenneth J. Kopecky", "Urte Gayko", "Santosh Putta", "Brent Louie", "Matt Westfall", "Norman Purvis", "David C. Spellmeyer", "Carol Marimpietri", "Aileen C. Cohen", "James Hackett", "Jing Shi", "Michael G. Walker", "Zhuoxin Sun", "Elisabeth Paietta", "Martin S. Tallman", "Larry D. Cripe", "Susan Atwater", "Frederick R. Appelbaum", "Jerald P. Radich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118485.t007", "stats"=>{"downloads"=>0, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_accuracy_of_DX_SCNP_in_BM_and_PB_Validation_Analysis_subsets_defined_by_AML_Type_De_Novo_vs_Secondary_and_availability_of_samples_for_both_tissue_types_/1383701", "title"=>"Prediction accuracy of DX<sub>SCNP</sub> in BM and PB Validation Analysis subsets defined by AML Type (De Novo vs. Secondary) and availability of samples for both tissue types.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-17 04:06:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026228"], "description"=>"<p><i>Score =</i></p><p></p><p></p><p></p><p></p><p></p><p><mi>e</mi></p><p></p><p><mi>χ</mi><mo>'</mo></p><p><mi>β</mi><mo stretchy=\"true\">^</mo></p><p></p><p></p><p></p><p><mn>1</mn><mo>+</mo></p><p><mi>e</mi></p><p></p><p><mi>χ</mi><mo>'</mo></p><p><mi>β</mi><mo stretchy=\"true\">^</mo></p><p></p><p></p><p></p><p></p><p></p><p></p><p></p><i>where</i><p></p><p></p><p></p><p><mi>χ</mi><mo>'</mo></p><p></p><p></p><p></p><i>is the vector of node-metric values and</i><p></p><p></p><p></p><p><mi>β</mi><mo stretchy=\"true\">^</mo></p><p></p><p></p><p></p><i>is the vector of regression coefficients</i><p></p><p>Locked DX<sub>SCNP</sub> Classifier Inputs.</p>", "links"=>[], "tags"=>["Acute myeloid leukemia", "BM Validation Analysis", "cr", "Validation Analysis Sets", "rd", "auroc", "SWOG AML trials", "classifier", "pb", "ECOG AML trials", "DXSCNP", "prediction accuracy", "DXCLINICAL", "SWOG BM Validation", "scnp", "BM verification sample", "prognostic", "response"], "article_id"=>1383700, "categories"=>["Biological Sciences"], "users"=>["Alessandra Cesano", "Cheryl L. Willman", "Kenneth J. Kopecky", "Urte Gayko", "Santosh Putta", "Brent Louie", "Matt Westfall", "Norman Purvis", "David C. Spellmeyer", "Carol Marimpietri", "Aileen C. Cohen", "James Hackett", "Jing Shi", "Michael G. Walker", "Zhuoxin Sun", "Elisabeth Paietta", "Martin S. Tallman", "Larry D. Cripe", "Susan Atwater", "Frederick R. Appelbaum", "Jerald P. Radich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118485.t002", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Locked_DX_SCNP_Classifier_Inputs_/1383700", "title"=>"Locked DX<sub>SCNP</sub> Classifier Inputs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-17 04:06:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026225"], "description"=>"<p>Prediction accuracy of DX<sub>SCNP</sub> in various subgroups in the BM Validation Analysis Set. For age and WBC, the subgroups were defined by thresholding at the median value. For all samples cytogenetic risk was determined using NCCN 2013 guideline criteria. Similarly to what is done in clinical practice, patients with unknown cytogenetics were imputed as intermediate risk cytogenetics. The point estimate of accuracy measured by AUROC and confidence intervals (Delong method) are also shown.</p>", "links"=>[], "tags"=>["Acute myeloid leukemia", "BM Validation Analysis", "cr", "Validation Analysis Sets", "rd", "auroc", "SWOG AML trials", "classifier", "pb", "ECOG AML trials", "DXSCNP", "prediction accuracy", "DXCLINICAL", "SWOG BM Validation", "scnp", "BM verification sample", "prognostic", "response"], "article_id"=>1383697, "categories"=>["Biological Sciences"], "users"=>["Alessandra Cesano", "Cheryl L. Willman", "Kenneth J. Kopecky", "Urte Gayko", "Santosh Putta", "Brent Louie", "Matt Westfall", "Norman Purvis", "David C. Spellmeyer", "Carol Marimpietri", "Aileen C. Cohen", "James Hackett", "Jing Shi", "Michael G. Walker", "Zhuoxin Sun", "Elisabeth Paietta", "Martin S. Tallman", "Larry D. Cripe", "Susan Atwater", "Frederick R. Appelbaum", "Jerald P. Radich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118485.g007", "stats"=>{"downloads"=>0, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_Classifier_in_Subgroups_BM_/1383697", "title"=>"Performance of Classifier in Subgroups (BM).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 04:06:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026223"], "description"=>"<p>Schematic of the cell signaling pathways probed in the Training set. An SCNP node consists of the combination of a modulator and the corresponding intracellular readout. Modulators are shown outside the cell initiating signaling pathways that produce an intracellular proteomic response (readouts shown below the curve indicating cell membrane).</p>", "links"=>[], "tags"=>["Acute myeloid leukemia", "BM Validation Analysis", "cr", "Validation Analysis Sets", "rd", "auroc", "SWOG AML trials", "classifier", "pb", "ECOG AML trials", "DXSCNP", "prediction accuracy", "DXCLINICAL", "SWOG BM Validation", "scnp", "BM verification sample", "prognostic", "response"], "article_id"=>1383695, "categories"=>["Biological Sciences"], "users"=>["Alessandra Cesano", "Cheryl L. Willman", "Kenneth J. Kopecky", "Urte Gayko", "Santosh Putta", "Brent Louie", "Matt Westfall", "Norman Purvis", "David C. Spellmeyer", "Carol Marimpietri", "Aileen C. Cohen", "James Hackett", "Jing Shi", "Michael G. Walker", "Zhuoxin Sun", "Elisabeth Paietta", "Martin S. Tallman", "Larry D. Cripe", "Susan Atwater", "Frederick R. Appelbaum", "Jerald P. Radich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118485.g005", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pathways_investigated_using_SCNP_in_the_Training_study_/1383695", "title"=>"Pathways investigated using SCNP in the Training study.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 04:06:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026224"], "description"=>"<p>Illustration of gating to identify blast cell population and cPARP negative blast cells. Intact cells were identified using scatter. Amine Aqua was then used to identify viable cells. CD45 was then used to identify Blast Cells. In wells where short term signaling was assayed, the blast cells were gated using cPARP expression to identify healthy leukemic cells.</p>", "links"=>[], "tags"=>["Acute myeloid leukemia", "BM Validation Analysis", "cr", "Validation Analysis Sets", "rd", "auroc", "SWOG AML trials", "classifier", "pb", "ECOG AML trials", "DXSCNP", "prediction accuracy", "DXCLINICAL", "SWOG BM Validation", "scnp", "BM verification sample", "prognostic", "response"], "article_id"=>1383696, "categories"=>["Biological Sciences"], "users"=>["Alessandra Cesano", "Cheryl L. Willman", "Kenneth J. Kopecky", "Urte Gayko", "Santosh Putta", "Brent Louie", "Matt Westfall", "Norman Purvis", "David C. Spellmeyer", "Carol Marimpietri", "Aileen C. Cohen", "James Hackett", "Jing Shi", "Michael G. Walker", "Zhuoxin Sun", "Elisabeth Paietta", "Martin S. Tallman", "Larry D. Cripe", "Susan Atwater", "Frederick R. Appelbaum", "Jerald P. Radich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118485.g006", "stats"=>{"downloads"=>2, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gating_Identification_of_Blast_Cell_Population_/1383696", "title"=>"Gating: Identification of Blast Cell Population.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 04:06:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026222"], "description"=>"<p>Flowchart of the study design with descriptive schematics of the patient sets in the Training, Verification and Validation analysis sets. SWOG samples were randomized into a Training and a Validation Analysis set and were sorted by tissue type (PB or BM). An initial subset of classifiers was trained separately in PB and BM samples in the Training Analysis sets and then PB classifiers were applied to BM and BM classifiers were applied to PB. From this training process 5 candidate classifiers were selected and applied to the ECOG Verification Analysis set. The final SCNP classifier was further refined and applied to 1) ECOG Verification Analysis set, 2) SWOG BM Validation Analysis set and 3) SWOG PB Validation Analysis set.</p>", "links"=>[], "tags"=>["Acute myeloid leukemia", "BM Validation Analysis", "cr", "Validation Analysis Sets", "rd", "auroc", "SWOG AML trials", "classifier", "pb", "ECOG AML trials", "DXSCNP", "prediction accuracy", "DXCLINICAL", "SWOG BM Validation", "scnp", "BM verification sample", "prognostic", "response"], "article_id"=>1383694, "categories"=>["Biological Sciences"], "users"=>["Alessandra Cesano", "Cheryl L. Willman", "Kenneth J. Kopecky", "Urte Gayko", "Santosh Putta", "Brent Louie", "Matt Westfall", "Norman Purvis", "David C. Spellmeyer", "Carol Marimpietri", "Aileen C. Cohen", "James Hackett", "Jing Shi", "Michael G. Walker", "Zhuoxin Sun", "Elisabeth Paietta", "Martin S. Tallman", "Larry D. Cripe", "Susan Atwater", "Frederick R. Appelbaum", "Jerald P. Radich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118485.g004", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Study_Design_Diagram_/1383694", "title"=>"Study Design Diagram.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 04:06:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026219"], "description"=>"<p>Bone marrow or blood cells (1) are modulated, fixed and permeabilized (2), then stained with an antibody cocktail containing antibodies directed against both cell surface markers as well as post-translational modifications of intra-cellular proteins(3). Cells are acquired using multiparametric flow cytometry (4) thus allowing quantification of intracellular pathway activity in cell subsets identified by gating on lineage surface markers (5). Various metrics to quantify basal and induced signaling and to assess association with biologic and clinical outcomes are applied.</p>", "links"=>[], "tags"=>["Acute myeloid leukemia", "BM Validation Analysis", "cr", "Validation Analysis Sets", "rd", "auroc", "SWOG AML trials", "classifier", "pb", "ECOG AML trials", "DXSCNP", "prediction accuracy", "DXCLINICAL", "SWOG BM Validation", "scnp", "BM verification sample", "prognostic", "response"], "article_id"=>1383691, "categories"=>["Biological Sciences"], "users"=>["Alessandra Cesano", "Cheryl L. Willman", "Kenneth J. Kopecky", "Urte Gayko", "Santosh Putta", "Brent Louie", "Matt Westfall", "Norman Purvis", "David C. Spellmeyer", "Carol Marimpietri", "Aileen C. Cohen", "James Hackett", "Jing Shi", "Michael G. Walker", "Zhuoxin Sun", "Elisabeth Paietta", "Martin S. Tallman", "Larry D. Cripe", "Susan Atwater", "Frederick R. Appelbaum", "Jerald P. Radich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118485.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Single_Cell_Network_Profiling_SCNP_Technology_/1383691", "title"=>"Single Cell Network Profiling (SCNP) Technology.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 04:06:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026230", "https://ndownloader.figshare.com/files/2026231", "https://ndownloader.figshare.com/files/2026232", "https://ndownloader.figshare.com/files/2026233", "https://ndownloader.figshare.com/files/2026234", "https://ndownloader.figshare.com/files/2026235", "https://ndownloader.figshare.com/files/2026236", "https://ndownloader.figshare.com/files/2026237", "https://ndownloader.figshare.com/files/2026238", "https://ndownloader.figshare.com/files/2026239", "https://ndownloader.figshare.com/files/2026240"], "description"=>"<div><p>Single-cell network profiling (SCNP) data generated from multi-parametric flow cytometry analysis of bone marrow (BM) and peripheral blood (PB) samples collected from patients >55 years old with non-M3 AML were used to train and validate a diagnostic classifier (DX<sub>SCNP</sub>) for predicting response to standard induction chemotherapy (complete response [CR] or CR with incomplete hematologic recovery [CRi] versus resistant disease [RD]). SCNP-evaluable patients from four SWOG AML trials were randomized between Training (N = 74 patients with CR, CRi or RD; BM set = 43; PB set = 57) and Validation Analysis Sets (N = 71; BM set = 42, PB set = 53). Cell survival, differentiation, and apoptosis pathway signaling were used as potential inputs for DX<sub>SCNP</sub>. Five DX<sub>SCNP</sub> classifiers were developed on the SWOG Training set and tested for prediction accuracy in an independent BM verification sample set (N = 24) from ECOG AML trials to select the final classifier, which was a significant predictor of CR/CRi (area under the receiver operating characteristic curve AUROC = 0.76, p = 0.01). The selected classifier was then validated in the SWOG BM Validation Set (AUROC = 0.72, p = 0.02). Importantly, a classifier developed using only clinical and molecular inputs from the same sample set (DX<sub>CLINICAL2</sub>) lacked prediction accuracy: AUROC = 0.61 (p = 0.18) in the BM Verification Set and 0.53 (p = 0.38) in the BM Validation Set. Notably, the DX<sub>SCNP</sub> classifier was still significant in predicting response in the BM Validation Analysis Set after controlling for DX<sub>CLINICAL2</sub> (p = 0.03), showing that DX<sub>SCNP</sub> provides information that is independent from that provided by currently used prognostic markers. Taken together, these data show that the proteomic classifier may provide prognostic information relevant to treatment planning beyond genetic mutations and traditional prognostic factors in elderly AML.</p></div>", "links"=>[], "tags"=>["Acute myeloid leukemia", "BM Validation Analysis", "cr", "Validation Analysis Sets", "rd", "auroc", "SWOG AML trials", "classifier", "pb", "ECOG AML trials", "DXSCNP", "prediction accuracy", "DXCLINICAL", "SWOG BM Validation", "scnp", "BM verification sample", "prognostic", "response"], "article_id"=>1383702, "categories"=>["Biological Sciences"], "users"=>["Alessandra Cesano", "Cheryl L. Willman", "Kenneth J. Kopecky", "Urte Gayko", "Santosh Putta", "Brent Louie", "Matt Westfall", "Norman Purvis", "David C. Spellmeyer", "Carol Marimpietri", "Aileen C. Cohen", "James Hackett", "Jing Shi", "Michael G. Walker", "Zhuoxin Sun", "Elisabeth Paietta", "Martin S. Tallman", "Larry D. Cripe", "Susan Atwater", "Frederick R. Appelbaum", "Jerald P. Radich"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0118485.s001", "https://dx.doi.org/10.1371/journal.pone.0118485.s002", "https://dx.doi.org/10.1371/journal.pone.0118485.s003", "https://dx.doi.org/10.1371/journal.pone.0118485.s004", "https://dx.doi.org/10.1371/journal.pone.0118485.s005", "https://dx.doi.org/10.1371/journal.pone.0118485.s006", "https://dx.doi.org/10.1371/journal.pone.0118485.s007", "https://dx.doi.org/10.1371/journal.pone.0118485.s008", "https://dx.doi.org/10.1371/journal.pone.0118485.s009", "https://dx.doi.org/10.1371/journal.pone.0118485.s010", "https://dx.doi.org/10.1371/journal.pone.0118485.s011"], "stats"=>{"downloads"=>26, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cell_Signaling_Based_Classifier_Predicts_Response_to_Induction_Therapy_in_Elderly_Patients_with_Acute_Myeloid_Leukemia_/1383702", "title"=>"Cell Signaling-Based Classifier Predicts Response to Induction Therapy in Elderly Patients with Acute Myeloid Leukemia", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-04-17 04:06:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026227"], "description"=>"<p>a) The number of patients who achieved CR/CRi varied between 8 and 12 for each of the classifiers depending on the availability of node-metric data of all the predictor variables involved in a classifier. The number of RDs was 12 for all models.</p><p>Candidate Models.</p>", "links"=>[], "tags"=>["Acute myeloid leukemia", "BM Validation Analysis", "cr", "Validation Analysis Sets", "rd", "auroc", "SWOG AML trials", "classifier", "pb", "ECOG AML trials", "DXSCNP", "prediction accuracy", "DXCLINICAL", "SWOG BM Validation", "scnp", "BM verification sample", "prognostic", "response"], "article_id"=>1383699, "categories"=>["Biological Sciences"], "users"=>["Alessandra Cesano", "Cheryl L. Willman", "Kenneth J. Kopecky", "Urte Gayko", "Santosh Putta", "Brent Louie", "Matt Westfall", "Norman Purvis", "David C. Spellmeyer", "Carol Marimpietri", "Aileen C. Cohen", "James Hackett", "Jing Shi", "Michael G. Walker", "Zhuoxin Sun", "Elisabeth Paietta", "Martin S. Tallman", "Larry D. Cripe", "Susan Atwater", "Frederick R. Appelbaum", "Jerald P. Radich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118485.t001", "stats"=>{"downloads"=>3, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Candidate_Models_/1383699", "title"=>"Candidate Models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-17 04:06:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026226"], "description"=>"<p>Predicted probability of CR for BM and PB samples from donors with SCNP data with paired samples in the validation set. Denovo vs secondary AML subtypes are noted in the inset. A majority of the predictions were concordant between the tissues types of de novo. Of note, two RD donors that were discordant are secondary AML.</p>", "links"=>[], "tags"=>["Acute myeloid leukemia", "BM Validation Analysis", "cr", "Validation Analysis Sets", "rd", "auroc", "SWOG AML trials", "classifier", "pb", "ECOG AML trials", "DXSCNP", "prediction accuracy", "DXCLINICAL", "SWOG BM Validation", "scnp", "BM verification sample", "prognostic", "response"], "article_id"=>1383698, "categories"=>["Biological Sciences"], "users"=>["Alessandra Cesano", "Cheryl L. Willman", "Kenneth J. Kopecky", "Urte Gayko", "Santosh Putta", "Brent Louie", "Matt Westfall", "Norman Purvis", "David C. Spellmeyer", "Carol Marimpietri", "Aileen C. Cohen", "James Hackett", "Jing Shi", "Michael G. Walker", "Zhuoxin Sun", "Elisabeth Paietta", "Martin S. Tallman", "Larry D. Cripe", "Susan Atwater", "Frederick R. Appelbaum", "Jerald P. Radich"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118485.g008", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_predictions_between_paired_PB_and_BM_samples_/1383698", "title"=>"Comparison of predictions between paired PB and BM samples.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 04:06:13"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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