Combined population dynamics and entropy modelling supports patient stratification in chronic myeloid leukemia

Modelling the parameters of multistep carcinogenesis is key for a better understanding of cancer progression, biomarker identification and the design of individualized therapies. Using chronic myeloid leukemia (CML) as a paradigm for hierarchical disease evolution we show that combined population dynamic modelling and CML patient biopsy genomic analysis enables patient stratification at unprecedented resolution. Linking CD34+ similarity as a disease progression marker to patientderived gene expression entropy separated established CML progression stages and uncovered additional heterogeneity within disease stages. Importantly, our patient data informed model enables quantitative approximation of individual patients’ disease history within chronic phase (CP) and significantly separates “early” from “late” CP. Our findings provide a novel rationale for personalized and genome-informed disease progression risk assessment that is independent and complementary to conventional measures of CML disease burden and prognosis

Rapid initial decline in BCR-ABL1 is associated with superior responses to second-line nilotinib in patients with chronic-phase chronic myeloid leukemia

BACKGROUND: We evaluated BCR-ABL1 kinetics in patients treated with nilotinib and analyzed whether a dynamic model of changes in BCR-ABL1 levels over time could be used to predict long-term responses. METHODS: Patients from the nilotinib registration trial (CAMN107A2101; registered at http://www.clinicaltrials.gov as NCT00109707) who had imatinib-resistant or -intolerant Philadelphia chromosome–positive (Ph+) chronic myeloid leukemia (CML) in chronic phase (CP) or accelerated phase with BCR-ABL1 > 10% (on the international scale [IS]) at baseline and, in the first 6 months, had at least three BCR-ABL1 transcript measurements and an average daily dose of at least 720 mg were included in this analysis (N = 123). RESULTS: More than half of patients (65/123; 53%) had a slow monophasic response and the remainder (58/123; 47%) had a biphasic response, in which patients had a rapid initial decrease in BCR-ABL1 transcripts followed by a more gradual response. The biphasic response type strongly correlated with improved event-free survival (EFS). Data in the first 6 months of follow-up were sufficient to predict EFS at 24 months. CONCLUSIONS: Unlike newly diagnosed patients with Ph+ CML-CP—in whom the majority had a biphasic response—approximately half of patients with imatinib-resistant or -intolerant CML had a slower, monophasic response. Second-line patients who did have a biphasic response had an EFS outlook similar to that of newly diagnosed patients treated with imatinib. Our model was comparable to using BCR-ABL1 (IS) ≤ 10% at 6 months as a threshold for predicting EFS

Association of immunophenotype with expression of topoisomerase II α and β in adult acute myeloid leukemia.

Anthracyclines used in the treatment of acute myelogenous leukemia (AML) inhibit the activity of the mammalian topoisomerase II (topo II) isoforms, topo II α and topo IIβ. In 230 patients with non-M3 AML who received frontline ara-C/daunorubicin we determined expression of topo IIα and topo IIβ by RT-PCR and its relationship to immunophenotype (IP) and outcomes. Treatment outcomes were analyzed by logistic or Cox regression. In 211 patients, available for analysis, topo IIα expression was significantly lower than topo IIβ (P \u3c 0.0001). In contrast to topo IIα, topo IIβ was significantly associated with blast percentage in marrow or blood (P = 0.0001), CD7 (P = 0.01), CD14 (P \u3c 0.0001) and CD54 (P \u3c 0.0001). Event free survival was worse for CD56-negative compared to CD56-high (HR = 1.9, 95% CI [1.0-3.5], p = 0.04), and overall survival was worse for CD-15 low as compared to CD15-high (HR = 2.2, 95% CI [1.1-4.2], p = 0.02). Ingenuity pathway analysis indicated topo IIβ and immunophenotype markers in a network associated with cell-to-cell signaling, hematological system development/function and inflammatory response. Topo IIβ expression reflects disease biology of highly proliferative disease and distinct IP but does not appear to be an independent variable influencing outcome in adult AML patients treated with anthracycline-based therapy

AML risk stratification models utilizing ELN-2017 guidelines and additional prognostic factors: a SWOG report.

Background: The recently updated European LeukemiaNet risk stratification guidelines combine cytogenetic abnormalities and genetic mutations to provide the means to triage patients with acute myeloid leukemia for optimal therapies. Despite the identification of many prognostic factors, relatively few have made their way into clinical practice. Methods: In order to assess and improve the performance of the European LeukemiaNet guidelines, we developed novel prognostic models using the biomarkers from the guidelines, age, performance status and select transcript biomarkers. The models were developed separately for mononuclear cells and viable leukemic blasts from previously untreated acute myeloid leukemia patients (discovery cohort, Results: Models using European LeukemiaNet guidelines were significantly associated with clinical outcomes and, therefore, utilized as a baseline for comparisons. Models incorporating age and expression of select transcripts with biomarkers from European LeukemiaNet guidelines demonstrated higher area under the curve and C-statistics but did not show a substantial improvement in performance in the validation cohort. Subset analyses demonstrated that models using only the European LeukemiaNet guidelines were a better fit for younger patients (age \u3c 55) than for older patients. Models integrating age and European LeukemiaNet guidelines visually showed more separation between risk groups in older patients. Models excluding results for Conclusions: While European LeukemiaNet guidelines remain a critical tool for triaging patients with acute myeloid leukemia, the findings illustrate the need for additional prognostic factors, including age, to improve risk stratification

In Support of a Patient-Driven Initiative and Petition to Lower the High Price of Cancer Drugs

Comment in Lowering the High Cost of Cancer Drugs--III. [Mayo Clin Proc. 2016] Lowering the High Cost of Cancer Drugs--I. [Mayo Clin Proc. 2016] Lowering the High Cost of Cancer Drugs--IV. [Mayo Clin Proc. 2016] In Reply--Lowering the High Cost of Cancer Drugs. [Mayo Clin Proc. 2016] US oncologists call for government regulation to curb drug price rises. [BMJ. 2015