Citations

Abstract With the rapid advancements in large language model technology and the emergence of bioinformatics‐specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide‐ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications.

Abstract Background Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors. Methods We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set. Results We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls. Conclusions In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats).

Abstract Purpose: Aberrant activation of EGFR is a hallmark of glioblastoma. However, EGFR inhibitors exhibit at best modest efficacy in glioblastoma. This is in sharp contrast with the observations in EGFR-mutant lung cancer. We examined whether activation of functionally redundant receptor tyrosine kinases (RTKs) conferred resistance to EGFR inhibitors in glioblastoma. Experimental Design: We collected a panel of patient-derived glioblastoma xenograft (PDX) lines that maintained expression of wild-type or mutant EGFR in serial xenotransplantation and tissue cultures. Using this physiologically relevant platform, we tested the abilities of several RTK ligands to protect glioblastoma cells against an EGFR inhibitor, gefitinib. Based on the screening results, we further developed a combination therapy cotargeting EGFR and insulin receptor (InsR)/insulin-like growth factor 1 receptor (IGF1R). Results: Insulin and IGF1 induced significant protection against gefitinib in the majority of EGFR-dependent PDX lines with one exception that did not express InsR or IGF1R. Blockade of the InsR/IGF1R pathway synergistically improved sensitivity to gefitinib or dacomitinib. Gefitinib alone effectively attenuated EGFR activities and the downstream MEK/ERK pathway. However, repression of AKT and induction of apoptosis required concurrent inhibition of both EGFR and InsR/IGF1R. A combination of gefitinib and OSI-906, a dual InsR/IGF1R inhibitor, was more effective than either agent alone to treat subcutaneous glioblastoma xenograft tumors. Conclusions: Our results suggest that activation of the InsR/IGF1R pathway confers resistance to EGFR inhibitors in EGFR-dependent glioblastoma through AKT regulation. Concurrent blockade of these two pathways holds promise to treat EGFR-dependent glioblastoma. Clin Cancer Res; 22(7); 1767–76. ©2015 AACR.