Rewiring ERBB3 and ERK signaling confers resistance to FGFR1 inhibition in gastrointestinal cancer harbored an ERBB3-E928G mutation

ERBB3 targeting: A promising approach to overcoming cancer therapeutic resistance

Human epidermal growth factor receptor-3 (ERBB3) is a member of the ERBB receptor tyrosine kinases (RTKs) and is expressed in many malignancies. Along with other ERBB receptors, ERBB3 is associated with regulating normal cell proliferation, apoptosis, differentiation, and survival, and has received increased research attention for its involvement in cancer therapies. ERBB3 expression or co-expression levels have been investigated as predictive factors for cancer prognosis and drug sensitivity. Additionally, the association between the elevated expression of ERBB3 and treatment failure in cancer therapy further established ERBB3-targeting therapy as a crucial therapeutic approach. This review delves into the molecular mechanisms of ERBB3-driven resistance to targeted therapeutics against ERBB2 and EGFR and other signal transduction inhibitors, endocrine therapy, chemotherapy, and radiotherapy. Using preclinical and clinical evidence, we synthesise and explicate how various aspects of aberrant ERBB3 activities-such as compensatory activation, signal crosstalk interactions, dysregulation in the endocytic pathway, mutations, ligand-independent activation, intrinsic kinase activity, and homodimerisation-can lead to resistance development and/or treatment failures. Several ERBB3-directed monoclonal antibodies, bispecific antibodies, and the emerging antibody-drug conjugate demonstrate encouraging clinical outcomes for improving therapeutic efficacy and overcoming resistance, especially when combined with other anti-cancer approaches. More research efforts are needed to identify appropriate biomarkers tailored for ERBB3-targeted therapies.

Extraction of Innate Immune Genes in Dairy Cattle and the Regulation of Their Expression in Early Embryos

As more and more of the available genomic data have been published, several databases have been developed for deciphering early mammalian embryogenesis; however, less research has been conducted on the regulation of the expression of natural immunity genes during early embryonic development in dairy cows. To this end, we explored the regulatory mechanism of innate immunity genes at the whole-genome level. Based on comparative genomics, 1473 innate immunity genes in cattle were obtained by collecting the latest reports on human innate immunity genes and updated bovine genome data for comparison, and a preliminary database of bovine innate immunity genes was constructed. In order to determine the regulatory mechanism of innate immune genes in dairy cattle early embryos, we conducted weighted co-expression network analysis of the innate immune genes at different developmental stages of dairy cattle early embryos. The results showed that specific module-related genes were significantly enriched in the MAPK signaling pathway. Protein-protein interaction (PPI) analysis showed gene interactions in each specific module, and 10 of the highest connectivity genes were chosen as potential hub genes. Finally, combined with the results for differential expressed genes (DEGs), ATF3, IL6, CD8A, CD69, CD86, HCK, ERBB3, LCK, ITGB2, LYN, and ERBB2 were identified as the key genes of innate immunity in dairy cattle early embryos. In conclusion, the bovine innate immunity gene set was determined and the co-expression network of innate immunity genes in the early embryonic stage of dairy cattle was constructed by comparing and analyzing the whole genome of bovines and humans. The findings in this study provide the basis for exploring the involvement and regulation of innate immune genes in the early embryonic development of dairy cattle.

Targeting FGFRs Using PD173074 as a Novel Therapeutic Strategy in Cholangiocarcinoma

Cholangiocarcinoma (CCA) is an architecturally complex tumour with high heterogeneity. Discovery at later stages makes treatment challenging. However, the lack of early detection methodologies and the asymptomatic nature of CCA make early diagnosis more difficult. Recent studies revealed the fusions in Fibroblast Growth Factor Receptors (FGFRs), a sub-family of RTKs, as promising targets for targeted therapy for CCA. Particularly, FGFR2 fusions have been of particular interest, as translocations have been found in approximately 13% of CCA patients. Pursuing this, Pemigatinib, a small-molecule inhibitor of FGFR, became the first targeted therapy drug to be granted accelerated approval by the FDA for treating CCA patients harbouring FGFR2 fusions who have failed first-line chemotherapy. However, despite the availability of Pemigatinib, a very limited group of patients benefit from this treatment. Moreover, as the underlying mechanism of FGFR signalling is poorly elucidated in CCA, therapeutic inhibitors designed to inhibit this pathway are prone to primary and acquired resistance, as witnessed amongst other Tyrosine Kinase Inhibitors (TKIs). While acknowledging the limited cohort that benefits from FGFR inhibitors, and the poorly elucidated mechanism of the FGFR pathway, we sought to characterise the potential of FGFR inhibitors in CCA patients without FGFR2 fusions. Here we demonstrate aberrant FGFR expression in CCA samples using bioinformatics and further confirm phosphorylated-FGFR expression in paraffinised CCA tissues using immunohistochemistry. Our results highlight p-FGFR as a biomarker to guide FGFR-targeted therapies. Furthermore, CCA cell lines with FGFR expression were sensitive to a selective pan-FGFR inhibitor, PD173074, suggesting that this drug can be used to suppress CCA cells irrespective of the FGFR2 fusions. Finally, the correlation analysis utilising publicly available cohorts suggested the possibility of crosstalk amongst the FGFR and EGFR family of receptors as they are significantly co-expressed. Accordingly, dual inhibition of FGFRs and EGFR by PD173074 and EGFR inhibitor erlotinib was synergistic in CCA. Hence, the findings from this study provide support for further clinical investigation of PD173074, as well as other FGFR inhibitors, to benefit a larger cohort of patients. Altogether, this study shows for the first time the potential of FGFRs and the importance of dual inhibition as a novel therapeutic strategy in CCA.

Tensor decomposition based on the potential low-rank and [Formula: see text]-shrinkage generalized threshold algorithm for analyzing cancer multiomics data

Tensor Robust Principal Component Analysis (TRPCA) has achieved promising results in the analysis of genomics data. However, the TRPCA model under the existing tensor singular value decomposition ([Formula: see text]-SVD) framework insufficiently extracts the potential low-rank structure of the data, resulting in suboptimal restored components. Simultaneously, the tensor nuclear norm (TNN) defined based on [Formula: see text]-SVD uses the same standard to handle various singular values. TNN ignores the difference of singular values, leading to the failure of the main information that needs to be well preserved. To preserve the heterogeneous structure in the low-rank information, we propose a novel TNN and extend it to the TRPCA model. Potential low-rank space may contain important information. We learn the low-rank structural information from the core tensor. The singular value space contains the association information between genes and cancers. The [Formula: see text]-shrinkage generalized threshold function is utilized to preserve the low-rank properties of larger singular values. The optimization problem is solved by the alternating direction method of the multiplier (ADMM) algorithm. Clustering and feature selection experiments are performed on the TCGA data set. The experimental results show that the proposed model is more promising than other state-of-the-art tensor decomposition methods.

RTK25: A Comprehensive Molecular Profiling Strategy in Cholangiocarcinoma Using an Integrated Bioinformatics Approach

Cholangiocarcinoma (CCA) is a heterogeneous group of malignancies that primarily originate from the bile duct. Tumor heterogeneity is a prime characteristic of CCA and considering the scarcity of approved targeted therapy drugs, this makes precision oncology impractical in CCA. Stratifying patients based on their molecular signature and biomarker-guided therapy may offer a conducive solution. Receptors tyrosine kinases (RTK) are potential targets for novel therapeutic strategies in CCA as RTK signaling is dysregulated in CCA. This study aims to identify targetable RTK profile in CCA using a bioinformatic approach. We discovered that CCA samples could be grouped into molecular subtypes based on the gene expression profile of selected RTKs (RTK25). Using the RTK25 gene list, we discovered five distinct molecular subtypes of CCA in this cohort. Tyrosine kinase inhibitors that target each RTK profile and their subsequent molecular signatures were also discovered. These results suggest that certain RTKs correlate with each other, indicating that tailored dual inhibition of RTKs may be more favorable than monotherapy. The results from this study can direct future investigative attention towards validating this concept in in vivo and in vitro systems. Ultimately, this will facilitate biomarker-guided clinical trials for the successful approval of novel therapeutic options in CCA.