| 1. |
- Liu, Anbu, et al.
(författare)
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DDR1/2 enhance KIT activation and imatinib resistance of primary and secondary KIT mutants in gastrointestinal stromal tumors
- 2024
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Ingår i: Molecular Carcinogenesis. - 0899-1987. ; 63:1, s. 75-93
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Tidskriftsartikel (refereegranskat)abstract
- Gastrointestinal stromal tumors (GISTs) are predominantly initiated by KIT mutations. In this study, we observed that discoidin domain receptors 1 and 2 (DDR1 and DDR2) exhibited high expression in GISTs, were associated with KIT, and enhanced the activation of both wild-type KIT and primary KIT mutants. Inhibition of DDR1/2 led to a reduction in the activation of KIT and its downstream signaling molecules, ultimately impairing GIST cell survival and proliferation in vitro. Consequently, treatment of mice carrying germline KIT/V558A mutation with DDR1/2 inhibitor significantly impeded tumor growth, and the combined use of DDR1/2 inhibitor and imatinib, the first-line targeted therapeutic agent for GISTs, markedly enhanced tumor growth suppression. In addition, DDR1/2 inhibition resulted in decreased KIT expression, while KIT inhibition led to upregulation of DDR1/2 expression in GISTs. The presence of DDR1/2 also decreased the sensitivity of wild-type KIT or primary KIT mutants to imatinib, indicating a possible role for DDR1/2 in promoting GIST survival during KIT-targeted therapy. The development of drug-resistant secondary KIT mutations is a primary factor contributing to GIST recurrence following targeted therapy. Similar to primary KIT mutants, DDR1/2 can associate with and enhance the activation of secondary KIT mutants, further diminishing their sensitivity to imatinib. In summary, our data demonstrate that DDR1/2 contribute to KIT activation in GISTs and strengthen resistance to imatinib for both primary and secondary KIT mutants, providing a rationale for further exploration of DDR1/2 targeting in GIST treatment.
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| 2. |
- Nasimian, Ahmad, et al.
(författare)
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A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer
- 2023
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Ingår i: Computational and Structural Biotechnology Journal. - : Elsevier BV. - 2001-0370. ; 21, s. 956-964
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Tidskriftsartikel (refereegranskat)abstract
- Cisplatin, a platinum-based chemotherapeutic agent, is widely used as a front-line treatment for several malignancies. However, treatment outcomes vary widely due to intrinsic and acquired resistance. In this study, cisplatin-perturbed gene expression and pathway enrichment were used to define a gene signature, which was further utilized to develop a cisplatin sensitivity prediction model using the TabNet algorithm. The TabNet model performed better (>80 % accuracy) than all other machine learning models when compared to a wide range of machine learning algorithms. Moreover, by using feature importance and comparing predicted ovarian cancer patient samples, BCL2L1 was identified as an important gene contributing to cisplatin resistance. Furthermore, the pharmacological inhibition of BCL2L1 was found to synergistically increase cisplatin efficacy. Collectively, this study developed a tool to predict cisplatin sensitivity using cisplatin-perturbed gene expression and pathway enrichment knowledge and identified BCL2L1 as an important gene in this setting.
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| 3. |
- Nasimian, Ahmad, et al.
(författare)
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A Receptor Tyrosine Kinase Inhibitor Sensitivity Prediction Model Identifies AXL Dependency in Leukemia
- 2023
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Ingår i: International Journal of Molecular Sciences. - : MDPI AG. - 1422-0067. ; 24:4
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Tidskriftsartikel (refereegranskat)abstract
- Despite incredible progress in cancer treatment, therapy resistance remains the leading limiting factor for long-term survival. During drug treatment, several genes are transcriptionally upregulated to mediate drug tolerance. Using highly variable genes and pharmacogenomic data for acute myeloid leukemia (AML), we developed a drug sensitivity prediction model for the receptor tyrosine kinase inhibitor sorafenib and achieved more than 80% prediction accuracy. Furthermore, by using Shapley additive explanations for determining leading features, we identified AXL as an important feature for drug resistance. Drug-resistant patient samples displayed enrichment of protein kinase C (PKC) signaling, which was also identified in sorafenib-treated FLT3-ITD-dependent AML cell lines by a peptide-based kinase profiling assay. Finally, we show that pharmacological inhibition of tyrosine kinase activity enhances AXL expression, phosphorylation of the PKC-substrate cyclic AMP response element binding (CREB) protein, and displays synergy with AXL and PKC inhibitors. Collectively, our data suggest an involvement of AXL in tyrosine kinase inhibitor resistance and link PKC activation as a possible signaling mediator.
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| 4. |
- Nasimian, Ahmad, et al.
(författare)
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AlphaML : A clear, legible, explainable, transparent, and elucidative binary classification platform for tabular data
- 2024
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Ingår i: Patterns. - 2666-3899. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- Leveraging the potential of machine learning and recognizing the broad applications of binary classification, it becomes essential to develop platforms that are not only powerful but also transparent, interpretable, and user friendly. We introduce alphaML, a user-friendly platform that provides clear, legible, explainable, transparent, and elucidative (CLETE) binary classification models with comprehensive customization options. AlphaML offers feature selection, hyperparameter search, sampling, and normalization methods, along with 15 machine learning algorithms with global and local interpretation. We have integrated a custom metric for hyperparameter search that considers both training and validation scores, safeguarding against under- or overfitting. Additionally, we employ the NegLog2RMSL scoring method, which uses both training and test scores for a thorough model evaluation. The platform has been tested using datasets from multiple domains and offers a graphical interface, removing the need for programming expertise. Consequently, alphaML exhibits versatility, demonstrating promising applicability across a broad spectrum of tabular data configurations.
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| 5. |
- Shah, Kinjal, et al.
(författare)
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PLK1 as a cooperating partner for BCL2-mediated antiapoptotic program in leukemia
- 2023
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Ingår i: Blood Cancer Journal. - 2044-5385. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- The deregulation of BCL2 family proteins plays a crucial role in leukemia development. Therefore, pharmacological inhibition of this family of proteins is becoming a prevalent treatment method. However, due to the emergence of primary and acquired resistance, efficacy is compromised in clinical or preclinical settings. We developed a drug sensitivity prediction model utilizing a deep tabular learning algorithm for the assessment of venetoclax sensitivity in T-cell acute lymphoblastic leukemia (T-ALL) patient samples. Through analysis of predicted venetoclax-sensitive and resistant samples, PLK1 was identified as a cooperating partner for the BCL2-mediated antiapoptotic program. This finding was substantiated by additional data obtained through phosphoproteomics and high-throughput kinase screening. Concurrent treatment using venetoclax with PLK1-specific inhibitors and PLK1 knockdown demonstrated a greater therapeutic effect on T-ALL cell lines, patient-derived xenografts, and engrafted mice compared with using each treatment separately. Mechanistically, the attenuation of PLK1 enhanced BCL2 inhibitor sensitivity through upregulation of BCL2L13 and PMAIP1 expression. Collectively, these findings underscore the dependency of T-ALL on PLK1 and postulate a plausible regulatory mechanism. [Figure not available: see fulltext.]
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| 6. |
- Shah, Kinjal, et al.
(författare)
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Venetoclax-Resistant T-ALL Cells Display Distinct Cancer Stem Cell Signatures and Enrichment of Cytokine Signaling
- 2023
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Ingår i: International Journal of Molecular Sciences. - : MDPI AG. - 1661-6596 .- 1422-0067. ; 24:5
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Tidskriftsartikel (refereegranskat)abstract
- Therapy resistance remains one of the major challenges for cancer treatment that largely limits treatment benefits and patient survival. The underlying mechanisms that lead to therapy resistance are highly complicated because of the specificity to the cancer subtype and therapy. The expression of the anti-apoptotic protein BCL2 has been shown to be deregulated in T-cell acute lymphoblastic leukemia (T-ALL), where different T-ALL cells display a differential response to the BCL2-specific inhibitor venetoclax. In this study, we observed that the expression of anti-apoptotic BCL2 family genes, such as BCL2, BCL2L1, and MCL1, is highly varied in T-ALL patients, and inhibitors targeting proteins coded by these genes display differential responses in T-ALL cell lines. Three T-ALL cell lines (ALL-SIL, MOLT-16, and LOUCY) were highly sensitive to BCL2 inhibition within a panel of cell lines tested. These cell lines displayed differential BCL2 and BCL2L1 expression. Prolonged exposure to venetoclax led to the development of resistance to it in all three sensitive cell lines. To understand how cells developed venetoclax resistance, we monitored the expression of BCL2, BCL2L1, and MCL1 over the treatment period and compared gene expression between resistant cells and parental sensitive cells. We observed a different trend of regulation in terms of BCL2 family gene expression and global gene expression profile including genes reported to be expressed in cancer stem cells. Gene set enrichment analysis (GSEA) showed enrichment of cytokine signaling in all three cell lines which was supported by the phospho-kinase array where STAT5 phosphorylation was found to be elevated in resistant cells. Collectively, our data suggest that venetoclax resistance can be mediated through the enrichment of distinct gene signatures and cytokine signaling pathways.
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