Proteomic and genomic integration identifies kinase and differentiation determinants of kinase inhibitor sensitivity in leukemia cells.
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ABSTRACT:
SUBMITTER: Casado P
PROVIDER: S-EPMC5949212 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:To elucidate whether tyrosine kinase inhibitor (TKI) resistance in chronic myeloid leukemia is associated with characteristic genomic alterations, we analyzed DNA samples from 45 TKI-resistant chronic myeloid leukemia patients with 250K single nucleotide polymorphism arrays. From 20 patients, matched serial samples of pretreatment and TKI resistance time points were available. Eleven of the 45 TKI-resistant patients had mutations of BCR-ABL1, including 2 T315I mutations. Besides known TKI resistance-associated genomic lesions, such as duplication of the BCR-ABL1 gene (n = 8) and trisomy 8 (n = 3), recurrent submicroscopic alterations, including acquired uniparental disomy, were detectable on chromosomes 1, 8, 9, 17, 19, and 22. On chromosome 22, newly acquired and recurrent deletions of the IGLC1 locus were detected in 3 patients, who had previously presented with lymphoid or myeloid blast crisis. This may support a hypothesis of TKI-induced selection of subclones differentiating into immature B-cell progenitors as a mechanism of disease progression and evasion of TKI sensitivity.
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Project description:Using ex vivo drug screening of primary patient specimens, we identified the combination of the p38 MAPK inhibitor doramapimod (DORA) with the BCL2 inhibitor venetoclax (VEN) as demonstrating broad, enhanced efficacy compared with each single agent across 335 acute myeloid leukemia (AML) patient samples while sparing primary stromal cells. Single-agent DORA and VEN sensitivity was associated with distinct, nonoverlapping tumor cell differentiation states. In particular, increased monocytes, M4/M5 French-American-British classification, and CD14+ immunophenotype tracked with sensitivity to DORA and resistance to VEN but were mitigated with the combination. Increased expression of MAPK14 and BCL2, the respective primary targets of DORA and VEN, were observed in monocytic and undifferentiated leukemias, respectively. Enrichment for DORA and VEN sensitivities was observed in AML with monocyte-like and progenitor-like transcriptomic signatures, respectively, and these associations diminished with the combination. The mechanism underlying the combination's enhanced efficacy may result from inhibition of p38 MAPK-mediated phosphorylation of BCL2, which in turn enhances sensitivity to VEN. These findings suggest exploiting complementary drug sensitivity profiles with respect to leukemic differentiation state, such as dual targeting of p38 MAPK and BCL2, offers opportunity for broad, enhanced efficacy across the clinically challenging heterogeneous landscape of AML.
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Project description:Integrative analyses that summarize and link molecular data to treatment sensitivity are crucial to capture the biological complexity which is essential to further precision medicine. We introduce Weighted Orthogonal Nonnegative parallel factor analysis (WON-PARAFAC), a data integration method that identifies sparse and interpretable factors. WON-PARAFAC summarizes the GDSC1000 cell line compendium in 130 factors. We interpret the factors based on their association with recurrent molecular alterations, pathway enrichment, cancer type, and drug-response. Crucially, the cell line derived factors capture the majority of the relevant biological variation in Patient-Derived Xenograft (PDX) models, strongly suggesting our factors capture invariant and generalizable aspects of cancer biology. Furthermore, drug response in cell lines is better and more consistently translated to PDXs using factor-based predictors as compared to raw feature-based predictors. WON-PARAFAC efficiently summarizes and integrates multiway high-dimensional genomic data and enhances translatability of drug response prediction from cell lines to patient-derived xenografts.
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Project description:BackgroundInappropriate activation of AKT signaling is a relatively common occurrence in human tumors, and can be caused by activation of components of, or by loss or decreased activity of inhibitors of, this signaling pathway. A novel, pan AKT kinase inhibitor, GSK690693, was developed in order to interfere with the inappropriate AKT signaling seen in these human malignancies. Causal network modeling is a systematic computational analysis that identifies upstream changes in gene regulation that can serve as explanations for observed changes in gene expression. In this study, causal network modeling is employed to elucidate mechanisms of action of GSK690693 that contribute to its observed biological effects. The mechanism of action of GSK690693 was evaluated in multiple human tumor cell lines from different tissues in 2-D cultures and xenografts using RNA expression and phosphoproteomics data. Understanding the molecular mechanism of action of novel targeted agents can enhance our understanding of various biological processes regulated by the intended target and facilitate their clinical development.ResultsCausal network modeling on transcriptomic and proteomic data identified molecular networks that are comprised of activated or inhibited mechanisms that could explain observed changes in the sensitive cell lines treated with GSK690693. Four networks common to all cell lines and xenografts tested were identified linking GSK690693 inhibition of AKT kinase activity to decreased proliferation. These networks included increased RB1 activity, decreased MYC activity, decreased TFRC activity, and increased FOXO1/FOXO3 activity.ConclusionAKT is involved in regulating both cell proliferation and apoptotic pathways; however, the primary effect with GSK690693 appears to be anti-proliferative in the cell lines and xenografts evaluated. Furthermore, these results indicate that anti-proliferative responses to GSK690693 in either 2-D culture or xenograft models may share common mechanisms within and across sensitive cell lines.
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Project description:The biology, clinical phenotype and progression rate of chronic myelomonocytic leukemia (CMML) are highly variable due to diverse initiating and secondary clonal genetic events. To determine the effects of molecular features including clonal hierarchy in CMML, we studied whole-exome and targeted next-generation sequencing data from 150 patients with robust clinical and molecular annotation assessed cross-sectionally and at serial time points of disease evolution. To identify molecular lesions unique to CMML, we compared it to the related myeloid neoplasms (N=586), including juvenile myelomonocytic leukemia, myelodysplastic syndromes (MDS) and primary monocytic acute myeloid leukemia and discerned distinct molecular profiles despite similar pathomorphological features. Within CMML, mutations in certain pathways correlated with clinical classification, for example, proliferative vs dysplastic features. While most CMML patients (59%) had ancestral (dominant/co-dominant) mutations involving TET2, SRSF2 or ASXL1 genes, secondary subclonal hierarchy correlated with clinical phenotypes or outcomes. For example, progression was associated with acquisition of new expanding clones carrying biallelic TET2 mutations or RAS family, or spliceosomal gene mutations. In contrast, dysplastic features correlated with mutations usually encountered in MDS (for example, SF3B1 and U2AF1). Classification of CMML based on hierarchies of ancestral and subclonal mutational events may correlate strongly with clinical features and prognosis.
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