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Cell signaling-based classifier predicts response to induction therapy in elderly patients with acute myeloid leukemia.

Cesano A, Willman CL, Kopecky KJ, Gayko U, Putta S, Louie B, Westfall M, Purvis N, Spellmeyer DC, Marimpietri C, Cohen AC, Hackett J, Shi J, Walker MG, Sun Z, Paietta E, Tallman MS, Cripe LD, Atwater S, Appelbaum FR, Radich JP - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Nodality, Inc., South San Francisco, California, United States of America.

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.

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Performance of Classifier in Subgroups (BM).Prediction accuracy of DXSCNP 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.
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pone.0118485.g007: Performance of Classifier in Subgroups (BM).Prediction accuracy of DXSCNP 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.

Mentions: The DXSCNP classifier was validated as a predictor of CR/CRi in the BM Validation Analysis Set, with AUROC of 0.72, p = 0.02, 95% CI = (0.51, 0.87). In contrast, the DXCLINICAL2 classifier did not show a significant association with response to induction therapy in either the BM Verification Analysis Set (AUROC = 0.61, p = 0.18) or the BM Validation Analysis Set (AUROC = 0.53, p = 0.38). Furthermore, analysis was conducted to assess if DXSCNP provided information for prediction of response that is independent of the DXCLINICAL2. The predictions from DXCLINICAL2 and DXSCNP were both included (i.e., controlling for each other) in a combined logistic regression model for response. If predictions from DXSCNP are redundant to those from DXCLINICAL2, a non-significant p-value is expected for the coefficient of DXSCNP in the combined model. However, the SCNP classifier was still significant in predicting response in the BM Validation Analysis Set (p-value for DXSCNP when controlling for DXCLINICAL2 = 0.03) from this analysis, showing that DXSCNP may provide information that is independent from that provided by currently used prognostic markers. While the small sample sizes do not permit definitive comparisons of classifier accuracy between clinical subsets, the accuracy of predictions from DXSCNP in BM sample subsets defined by several clinical characteristics are shown in Fig 7.


Cell signaling-based classifier predicts response to induction therapy in elderly patients with acute myeloid leukemia.

Cesano A, Willman CL, Kopecky KJ, Gayko U, Putta S, Louie B, Westfall M, Purvis N, Spellmeyer DC, Marimpietri C, Cohen AC, Hackett J, Shi J, Walker MG, Sun Z, Paietta E, Tallman MS, Cripe LD, Atwater S, Appelbaum FR, Radich JP - PLoS ONE (2015)

Performance of Classifier in Subgroups (BM).Prediction accuracy of DXSCNP 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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401549&req=5

pone.0118485.g007: Performance of Classifier in Subgroups (BM).Prediction accuracy of DXSCNP 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.
Mentions: The DXSCNP classifier was validated as a predictor of CR/CRi in the BM Validation Analysis Set, with AUROC of 0.72, p = 0.02, 95% CI = (0.51, 0.87). In contrast, the DXCLINICAL2 classifier did not show a significant association with response to induction therapy in either the BM Verification Analysis Set (AUROC = 0.61, p = 0.18) or the BM Validation Analysis Set (AUROC = 0.53, p = 0.38). Furthermore, analysis was conducted to assess if DXSCNP provided information for prediction of response that is independent of the DXCLINICAL2. The predictions from DXCLINICAL2 and DXSCNP were both included (i.e., controlling for each other) in a combined logistic regression model for response. If predictions from DXSCNP are redundant to those from DXCLINICAL2, a non-significant p-value is expected for the coefficient of DXSCNP in the combined model. However, the SCNP classifier was still significant in predicting response in the BM Validation Analysis Set (p-value for DXSCNP when controlling for DXCLINICAL2 = 0.03) from this analysis, showing that DXSCNP may provide information that is independent from that provided by currently used prognostic markers. While the small sample sizes do not permit definitive comparisons of classifier accuracy between clinical subsets, the accuracy of predictions from DXSCNP in BM sample subsets defined by several clinical characteristics are shown in Fig 7.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Nodality, Inc., South San Francisco, California, United States of America.

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.

Show MeSH