Bosutinib for the treatment of Philadelphia chromosome-positive leukemias

Varallo-Rodriguez, Cristina; Freyer, Craig W.; Ontiveros, Evelena P.; Griffiths, Elizabeth A.; Wang, Eunice S.; Wetzler, Meir

doi:10.1517/21678707.2015.1036027

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…The most crucial path passes through the pheno-type Ph+ ALL , the disease chronic myelogenous leukemia, BCR-ABL1 positive , and the gene BCR , before connecting to Bosutinib via the drug protein relation (Figure 3F, right). Indeed, Bosutinib was originally indicated for chronic myeloid leukemia in 2012 [70, 71] and is currently being investigated for the treatment of ALL [72].…”

Section: Resultsmentioning

confidence: 99%

Path-based reasoning for biomedical knowledge graphs with BioPathNet

Hu,

Oleshko,

Firmani

et al. 2024

Preprint

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Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine. Traditional link prediction (LP) methods, using similarity metrics like Personalized PageRank, are limited in capturing the complexity of biological networks. Recently, representation-based learning techniques have emerged, mapping nodes to low-dimensional embeddings to enhance prediction accuracy. However, these methods often face challenges with interpretability and scalability in large, complex networks. Based on a representation of biological systems as knowledge graphs (KGs), which encode entities and their relationships as triplets, we propose here BioKGC, a novel graph neural network framework which builds upon the Neural Bellman-Ford Network (NBFNet). It addresses the limitations of previous methods by utilizing path-based reasoning for LP in biomedical knowledge graphs (KGs). Unlike node-embedding learning frameworks that optimize the embedding space based on single triplets, BioKGC learns representations between nodes by considering all relations along paths. This approach enhances prediction accuracy and interpretability, allowing for the visualization of influential paths and facilitating the validation of biological plausibility. BioKGC leverages a background regulatory graph (BRG) for enhanced message passing and implements a stringent negative sampling strategy to improve learning precision. In evaluations across various LP tasks — gene function annotation, drug-disease interaction prediction, synthetic lethality prediction, and lncRNA-mRNA regulatory relationship inference — BioKGC consistently outperformed state-of-the art methods. BioKGC outperformed knowledge graph embedding and GNN-based methods in gene function prediction, especially with BRG information. We demonstrated that BioKGC effectively predicts drug-disease interactions in zero-shot learning scenarios, surpassing state-of-the-art models like TxGNN. Additionally, BioKGC demonstrated robust performance in synthetic lethality prediction and the capacity for scoring novel lncRNA-mRNA interactions, showcasing its versatility in diverse biomedical applications. One of BioKGC’s key advantages is its interpretability, enabling researchers to trace prediction paths and gain insights into molecular mechanisms. Combined with its use of regulatory information for message passing, BioKGC is a powerful tool for predicting complex biological interactions, making it valuable for drug discovery and personalized medicine.

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Section: Resultsmentioning

confidence: 99%

Path-based reasoning for biomedical knowledge graphs with BioPathNet

Hu,

Oleshko,

Firmani

et al. 2024

Preprint

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“…CAMK2G is a subunit of calcium/calmodulin-dependent protein kinase II (CaMKII). The reactome database showed that CaMKII and Src participate together in several serine threonine kinase (RAF)-related pathways or complexes ( 39 ), and that both CAMK2G and Src are targets of tyrosine kinase inhibitors ( 40 ). These predicted target genes may be linked to confirmed target genes or new, yet-to-explored mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of long non-coding RNA MALAT1 in hepatocellular carcinoma: A study based on multi-omics analysis and RT-PCR validation

Liao¹,

Chen²,

Luo³

et al. 2023

Pathol. Oncol. Res.

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Background: This study aimed to explore the relationship between MALAT1 and the prognosis of patients with hepatocellular carcinoma (HCC).Methods: We constructed a MALAT1 protein-protein interaction network using the STRING database and a network of competing endogenous RNAs (ceRNAs) using the StarBase database. Using data from the GEPIA2 database, we studied the association between genes in these networks and survival of patients with HCC. The potential mechanisms underlying the relationship between MALAT1 and HCC prognosis were studied using combined data from RNA sequencing, DNA methylation, and somatic mutation data from The Cancer Genome Atlas (TCGA) liver cancer cohort. Tumor tissues and 19 paired adjacent non-tumor tissues (PANTs) from HCC patients who underwent radical resection were analyzed for MALAT1 mRNA levels using real-time PCR, and associations of MALAT1 expression with clinicopathological features or prognosis of patients were analyzed using log-rank test and Gehan-Breslow-Wilcoxon test.Results: Five interacting proteins and five target genes of MALAT1 in the ceRNA network significantly correlated with poor survival of patients with HCC (p < 0.05). High MALAT1 expression was associated with mutations in two genes leading to poor prognosis and may upregulate some prognostic risk genes through methylation. MALAT1 was significantly co-expressed with various signatures of genes involved in HCC progression, including the cell cycle, DNA damage repair, mismatch repair, homologous recombination, molecular cancer m6A, exosome, ferroptosis, infiltration of lymphocyte (p < 0.05). The expression of MALAT1 was markedly upregulated in HCC tissues compared with PANTs. In Kaplan-Meier analysis, patients with high MALAT1 expression had significantly shorter progression-free survival (PFS) (p = 0.033) and overall survival (OS) (p = 0.023) than those with low MALAT1 expression. Median PFS was 19.2 months for patients with high MALAT1 expression and 52.8 months for patients with low expression, while the corresponding median OS was 40.5 and 78.3 months. In subgroup analysis of patients with vascular invasion, cirrhosis, and HBsAg positive or AFP positive, MALAT1 overexpression was significantly associated with shorter PFS and OS. Models for predicting PFS and OS constructed based on MALAT1 expression and clinicopathological features had moderate predictive power, with areas under the receiver operating characteristic curves of 0.661–0.731. Additionally, MALAT1 expression level was significantly associated with liver cirrhosis, vascular invasion, and tumor capsular infiltration (p < 0.05 for all).Conclusion:MALAT1 is overexpressed in HCC, and higher expression is associated with worse prognosis. MALAT1 mRNA level may serve as a prognostic marker for patients with HCC after hepatectomy.

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“…This is well known for treatment of BCR-ABL1 translocated CML with imatinib. 18,19 In case CML-7 (Figure 3), an ABL1 p.E459K mutation is detectable in the DNA part of the analysis. This specific mutation mediates a molecular resistance against imatinib, but is sensitive against treatment with bosutinib.…”

Section: Discussionmentioning

confidence: 99%

Feasibility of Combined Detection of Gene Mutations and Fusion Transcripts in Bone Marrow Trephines from Leukemic Neoplasms

Bartels

Hasemeier

Vogtmann

et al. 2020

The Journal of Molecular Diagnostics

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Chromosomal translocations resulting in fusion genes represent important oncogenic drivers and potential therapeutic targets in rare leukemia subtypes. Formalin-fixed, paraffin-embedded trephines are frequently used in hematologic diagnostic tests and provide relevant access to leukemic cells for further studies, for example, phenotyping in bone marrow fibrosis. However, high-throughput molecular analysis of nucleic acids obtained from this material is challenging, especially the reliable detection of RNA transcripts. Sixty-three formalin-fixed, paraffin-embedded bone marrow trephines of patients with chronic eosinophilic leukemia, chronic myeloid leukemia, acute myeloid leukemia, and myeloproliferative neoplasms were analyzed for gene mutations and the presence of fusion transcripts with a commercial amplicon-based next-generation sequencing approach. Fusion transcripts relevant for diagnosis and therapy could be detected and validated (by RT-PCR) in 25 patients (39.7%). Retrospectively selected material, up to 10 years old, was used for this purpose, and only one sample failed in the RNA analysis (1.6%). This study concludes that amplicon-based fusion transcript detection in bone marrow trephines is feasible and that bone marrow trephines taken for histologic assessment can also be applied for high-throughput molecular analysis.

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Bosutinib for the treatment of Philadelphia chromosome-positive leukemias

Cited by 4 publications

References 39 publications

Path-based reasoning for biomedical knowledge graphs with BioPathNet

Path-based reasoning for biomedical knowledge graphs with BioPathNet

Prognostic value of long non-coding RNA MALAT1 in hepatocellular carcinoma: A study based on multi-omics analysis and RT-PCR validation

Feasibility of Combined Detection of Gene Mutations and Fusion Transcripts in Bone Marrow Trephines from Leukemic Neoplasms

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