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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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Comparison of predictions between paired PB and BM samples.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.
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pone.0118485.g008: Comparison of predictions between paired PB and BM samples.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.

Mentions: When the DXSCNP classifier was applied to the PB Validation Analysis Set it did not accurately predict induction response (AUROC = 0.53, p = 0.39). A pre-specified subgroup analysis was performed for those with de novo AML vs. secondary AML at diagnosis since these subtypes have marked differences in clinical outcome [12], [13], [14], [15], [16] and data on a limited number of samples had previously shown that PB AML blasts in secondary AML have different signaling profiles than BM blasts [20]. Only three patients with secondary AML had paired PB and BM, precluding any useful analysis of concordance between the tissue types in this subgroup. However, in the de novo subgroup, DXSCNP was a significant predictor of induction response in both PB and BM samples (Table 7). The correlation coefficient (Pearson’s R) was 0.67 when comparing predicted probability of CR for BM and PB samples among patient patients with both tissue types (Fig 8), with the predictions being concordant for a majority of the donors. However, the two donors with secondary AML that had paired samples were discordant. Further, among patients with de novo AML having both BM and PB samples, the values of DXSCNP were correlated (Pearson’s R = 0.7) and had similar predictive value for the two sample types (AUROC = 0.71, p = 0.044, 95% CI = (0.50, 0.88) for the BM samples and AUROC = 0.79, p = 0.02, CI = (0.62–0.92) for PB samples). Given the small number of secondary AML donors that were RD in the training set, this difference in performance accuracy was not fully appreciated in the training phase.


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)

Comparison of predictions between paired PB and BM samples.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.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0118485.g008: Comparison of predictions between paired PB and BM samples.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.
Mentions: When the DXSCNP classifier was applied to the PB Validation Analysis Set it did not accurately predict induction response (AUROC = 0.53, p = 0.39). A pre-specified subgroup analysis was performed for those with de novo AML vs. secondary AML at diagnosis since these subtypes have marked differences in clinical outcome [12], [13], [14], [15], [16] and data on a limited number of samples had previously shown that PB AML blasts in secondary AML have different signaling profiles than BM blasts [20]. Only three patients with secondary AML had paired PB and BM, precluding any useful analysis of concordance between the tissue types in this subgroup. However, in the de novo subgroup, DXSCNP was a significant predictor of induction response in both PB and BM samples (Table 7). The correlation coefficient (Pearson’s R) was 0.67 when comparing predicted probability of CR for BM and PB samples among patient patients with both tissue types (Fig 8), with the predictions being concordant for a majority of the donors. However, the two donors with secondary AML that had paired samples were discordant. Further, among patients with de novo AML having both BM and PB samples, the values of DXSCNP were correlated (Pearson’s R = 0.7) and had similar predictive value for the two sample types (AUROC = 0.71, p = 0.044, 95% CI = (0.50, 0.88) for the BM samples and AUROC = 0.79, p = 0.02, CI = (0.62–0.92) for PB samples). Given the small number of secondary AML donors that were RD in the training set, this difference in performance accuracy was not fully appreciated in the training phase.

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
Related in: MedlinePlus