Large Genomic Databases Research Articles

BIOM-05. FUSION TRANSCRIPTOME LANDSCAPE IN GLIOBLASTOMA

Abstract BACKGROUND For most patients with glioblastoma (GBM), no truly effective salvage therapies exist. However, there are compelling examples in oncology where gene fusions lead to functioning oncoproteins that are highly effective targets (ie BCR-ABL1 in CML). Using a large genomic database, we sought to determine the prevalence of fusion transcripts in GBMs. METHODS A total of 4392 GBMs were tested at Caris Life Sciences (Phoenix, AZ). Mutations were detected by NextGen sequencing of DNA (592-gene panel or whole exome sequencing); gene fusions were detected by Whole Transcriptome Sequencing. All tumors were wild type for IDH1/2. Significance was tested by Fisher-Exact and ChiSquare and adjusted by Benjamini-Hochberg (q <0.05). RESULTS Overall, pathogenic fusions were found in 428 (9.7%). The most prevalent were FGFR3 [N=159 (37%), highest FGFR3:TACC3, N=134], MET [N=92 (21%), highest PTPRZ1:Met, N=31; ST7:MET N=25; CAPZA2: MET, N=23), EGFR [N=87(20%), highest EGFR:SETP14, N=21 and SEC61G: EGFR, N=19], NTRK2 (N=27), PDGFRA (N=23), ROS1 (N=14) and BRAF (N=10). Other actionable fusions including ALK (N=3), NTRK3 (N=3), RAF1(N=3), RET (N=3), NTRK1(N=2) were seen. Tumors with fusions were more likely to have pathogenic gene amplifications including: MET (7.5% vs. 0.7%), FGFR3 (5% vs. 0.2%), CDK4 (17% vs. 11%) and MDM2 (12% and 7.5%) but less frequent EGFR mutation (6.1% vs. 18%), amplification (6.1% vs. 18%) and EGFRvIII mutation (11.9% vs. 22.5%), RB1 (2.9% vs. 11.5%) and TP53mutation (22% vs. 30%) (all q<0.05). EGFR fusions are enriched with EGFR amplification (92%) and EGFRvIII mutation (51%); cMET fusion with MET amplification (40%). CONCLUSIONS Comprehensive molecular profiling reveals that approximately 10% of IDH WT GBMs carry oncogenic fusions that may be therapeutic targets. Broad spectrum of observed fusions underscores the need for novel clinical trial designs to allow efficient enrollment for prospective testing of potential targeted agents.

Prospective modeling and estimating the epidemiologically informative match rate within large foodborne pathogen genomic databases

ObjectivesMuch has been written about the utility of genomic databases to public health. Within food safety these databases contain data from two types of isolates—those from patients (i.e., clinical) and those from non-clinical sources (e.g., a food manufacturing environment). A genetic match between isolates from these sources represents a signal of interest. We investigate the match rate within three large genomic databases (Listeria monocytogenes, Escherichia coli, and Salmonella) and the smaller Cronobacter database; the databases are part of the Pathogen Detection project at NCBI (National Center for Biotechnology Information).ResultsCurrently, the match rate of clinical isolates to non-clinical isolates is 33% for L. monocytogenes, 46% for Salmonella, and 7% for E. coli. These match rates are associated with several database features including the diversity of the organism, the database size, and the proportion of non-clinical BioSamples. Modeling match rate via logistic regression showed relatively good performance. Our prediction model illustrates the importance of populating databases with non-clinical isolates to better identify a match for clinical samples. Such information should help public health officials prioritize surveillance strategies and show the critical need to populate fledgling databases (e.g., Cronobacter sakazakii).

Modulation of biophysical properties of nucleocapsid protein in the mutant spectrum of SARS-CoV-2.

Genetic diversity is a hallmark of RNA viruses and the basis for their evolutionary success. Taking advantage of the uniquely large genomic database of SARS-CoV-2, we examine the impact of mutations across the spectrum of viable amino acid sequences on the biophysical phenotypes of the highly expressed and multifunctional nucleocapsid protein. We find variation in the physicochemical parameters of its extended intrinsically disordered regions (IDRs) sufficient to allow local plasticity, but also observe functional constraints that similarly occur in related coronaviruses. In biophysical experiments with several N-protein species carrying mutations associated with major variants, we find that point mutations in the IDRs can have nonlocal impact and modulate thermodynamic stability, secondary structure, protein oligomeric state, particle formation, and liquid-liquid phase separation. In the Omicron variant, distant mutations in different IDRs have compensatory effects in shifting a delicate balance of interactions controlling protein assembly properties, and include the creation of a new protein-protein interaction interface in the N-terminal IDR through the defining P13L mutation. A picture emerges where genetic diversity is accompanied by significant variation in biophysical characteristics of functional N-protein species, in particular in the IDRs.

Modulation of biophysical properties of nucleocapsid protein in the mutant spectrum of SARS-CoV-2

Genetic diversity is a hallmark of RNA viruses and the basis for their evolutionary success. Taking advantage of the uniquely large genomic database of SARS-CoV-2, we examine the impact of mutations across the spectrum of viable amino acid sequences on the biophysical phenotypes of the highly expressed and multifunctional nucleocapsid protein. We find variation in the physicochemical parameters of its extended intrinsically disordered regions (IDRs) sufficient to allow local plasticity, but also observe functional constraints that similarly occur in related coronaviruses. In biophysical experiments with several N-protein species carrying mutations associated with major variants, we find that point mutations in the IDRs can have nonlocal impact and modulate thermodynamic stability, secondary structure, protein oligomeric state, particle formation, and liquid-liquid phase separation. In the Omicron variant, distant mutations in different IDRs have compensatory effects in shifting a delicate balance of interactions controlling protein assembly properties, and include the creation of a new protein-protein interaction interface in the N-terminal IDR through the defining P13L mutation. A picture emerges where genetic diversity is accompanied by significant variation in biophysical characteristics of functional N-protein species, in particular in the IDRs.

Comprehensive investigation of the mutagenic potential of six pesticides classified by IARC as probably carcinogenic to humans

Pesticides are significant environmental pollutants, and many of them possess mutagenic potential, which is closely linked to carcinogenesis. Here we tested the mutagenicity of all six pesticides classified probably carcinogenic (Group 2A) by the International Agency of Research on Cancer: 4,4′-DDT, captafol, dieldrin, diazinon, glyphosate and malathion. Whole genome sequencing of TK6 human lymphoblastoid cell clones following 30-day exposure at subtoxic concentrations revealed a clear mutagenic effect of treatment with captafol or malathion when added at 200 nM or 100 μM initial concentrations, respectively. Each pesticide induced a specific base substitution mutational signature: captafol increased C to A mutations primarily, while malathion induced mostly C to T mutations. 4,4′-DDT, dieldrin, diazinon and glyphosate were not mutagenic. Whereas captafol induced chromosomal instability, H2A.X phosphorylation and cell cycle arrest in G2/M phase, all indicating DNA damage, malathion did not induce DNA damage markers or cell cycle alterations despite its mutagenic effect. Hypersensitivity of REV1 and XPA mutant DT40 chicken cell lines suggests that captafol induces DNA adducts that are bypassed by translesion DNA synthesis and are targets for nucleotide excision repair. The experimentally identified mutational signatures of captafol and malathion could shed light on the mechanism of action of these compounds. The signatures are potentially suitable for detecting past exposure in tumour samples, but the reanalysis of large cancer genome databases did not reveal any evidence of captafol or malathion exposure.

Real-world impact of KRAS mutations on clinical outcomes in metastatic colorectal cancer treated with trifluridine-tipiracil in the USA.

e15566 Background: Defining the impact of KRAS mutation status on outcomes to standard of care treatments in metastatic colorectal cancer (mCRC) is of increasing clinical relevance. We interrogated a large real-world genomic database to further explore the association between KRAS mutations and benefit to trifluridine-tipiracil (FTD-TPI) in patients with mCRC. Methods: 19,364 patients with mCRC and known KRAS mutation status were analyzed by next-generation sequencing (NextSeq, 592 Genes; NovaSeq, whole exome sequencing (WES)) at Caris Life Sciences. Real-world overall survival (rwOS) and time on treatment (ToT) was inferred from insurance claims data and calculated from time of start of treatment with FTD-TPI to last contact and time from first to last treatment with FTD-TPI with comparison done by the Kaplan-Meier test, respectively. Chi-square test was applied where appropriate, with p-value adjusted for multiple comparisons (p < 0.05). Results: 764 of 10,571 (7.2%) patients with wild-type (WT) KRAS, 768 of 6,984 (11%) with KRAS G12 mutation, and 170 of 1,809 (9.4%) with KRAS G13 mutation received FTD-TPI. Age distribution was similar among groups. Sidedness of primary site was reported in 692 (40.3%) patients; an increased proportion of right-sided tumors was observed in KRAS G12 (53.4% vs. 28.3%, p < 0.0001) and G13 (56% vs 28.3%, p < 0.0001) groups compared to KRAS WT. An increased proportion of TP53 mutations in KRAS WT was observed compared to KRAS G12 mutation (87% vs. 70%, p < 0.0001) and KRAS G13 mutation groups (87% vs. 75%, p < 0.0001). KRAS G12 mutation was associated with shorter median rwOS compared to KRAS WT (7.33 vs. 8.43 months (mo), HR 1.13 (95% CI 1.11-1.42), p = 0.041). KRAS G13 mutation was associated with similar rwOS to KRAS WT. Median ToT was shorter in patients with KRAS G12 mutations than KRAS WT (1.8 vs. 2 mo, HR of 1.22 (95% CI 1.11-1.42), p < 0.001). Median ToT was longer in patients with KRAS G13 versus KRAS G12 (2.3 vs. 1.8 mo, HR 0.82 (95% CI 0.68-0.98), p = 0.032). Conclusions: To our knowledge, this is the largest primarily US-based cohort of mCRC treated with FTD-TPI to be reported. KRAS G12 mutation, but not KRAS G13 mutation, was associated with shorter rwOS and ToT compared to KRAS WT, despite enrichment of TP53 and BRAF mutations in the KRAS WT group. This finding is consistent with previously published analyses of large randomized prospective trials of FTD-TPI in mCRC and further support KRAS mutation status as a predictive biomarker in this setting. [Table: see text]

Estimates of primary ciliary dyskinesia prevalence: a scoping review.

Primary ciliary dyskinesia (PCD) is a rare multisystem genetic disease caused by dysfunctional motile cilia. Despite PCD being the second most common inherited airway disease after cystic fibrosis, PCD continues to be under-recognised globally owing to nonspecific clinical features and the lack of a gold standard diagnostic test. Commonly repeated prevalence estimates range from one in 10 000 to one in 20 000, based on regional epidemiological studies with known limitations. The purpose of this scoping review was to appraise the PCD literature, to determine the best available global PCD prevalence estimate and to inform the reader about the potential unmet health service needs in PCD. The primary objective of the present study was to systematically review the literature about PCD prevalence estimates. A scoping review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) methodology. Included studies estimated PCD prevalence and used cohort, clinical or genomic data. Case reports, conference abstracts, review articles, animal studies or non-English articles were excluded. A literature review identified 3484 unique abstracts; 34 underwent full-text review and eight met the inclusion/exclusion criteria. Seven articles were based on epidemiological studies of specific geographical regions and provided prevalence estimates that ranged from approximately one to 44.1 in 100 000. Only one study estimated global prevalence, using two large genomic databases, and calculated it to be ∼13.2 in 100 000 (based on pathogenic variants in 29 disease-causing genes). A population-based genomic approach for estimating global prevalence has found that PCD is much more prevalent than previously cited in the literature. This highlights the potential unmet health service needs of people living with PCD.

AnnoView enables large-scale analysis, comparison, and visualization of microbial gene neighborhoods.

The analysis and comparison of gene neighborhoods is a powerful approach for exploring microbial genome structure, function, and evolution. Although numerous tools exist for genome visualization and comparison, genome exploration across large genomic databases or user-generated datasets remains a challenge. Here, we introduce AnnoView, a web server designed for interactive exploration of gene neighborhoods across the bacterial and archaeal tree of life. Our server offers users the ability to identify, compare, and visualize gene neighborhoods of interest from 30 238 bacterial genomes and 1672 archaeal genomes, through integration with the comprehensive Genome Taxonomy Database and AnnoTree databases. Identified gene neighborhoods can be visualized using pre-computed functional annotations from different sources such as KEGG, Pfam and TIGRFAM, or clustered based on similarity. Alternatively, users can upload and explore their own custom genomic datasets in GBK, GFF or CSV format, or use AnnoView as a genome browser for relatively small genomes (e.g. viruses and plasmids). Ultimately, we anticipate that AnnoView will catalyze biological discovery by enabling user-friendly search, comparison, and visualization of genomic data. AnnoView is available at http://annoview.uwaterloo.ca.

Comprehensive Landscape of BRAF Variant Classes, Clonalities, and Co-Mutations in Metastatic Colorectal Cancer Using ctDNA Profiling.

Although V600E accounts for the majority of the BRAF mutations in metastatic colorectal cancer (mCRC), non-V600 BRAF variants have been shown in recent years to represent a distinct molecular subtype. This study provides a comprehensive profile of BRAF variants in mCRC using a large genomic database of circulating tumor DNA (ctDNA) and analyzing clinical outcomes in a cohort of patients with atypical (non-V600) BRAF variants (aBRAF; class II, class III, unclassified). Overall, 1733 out of 14,742 mCRC patients in the ctDNA cohort had at least one BRAF variant. Patients with atypical BRAF variants tended to be younger and male. In contrast to BRAFV600E, BRAF class II and III variants and their co-occurrence with KRAS/NRAS mutations were increased at baseline and especially with those patients predicted to have prior anti-EGFR exposure. Our clinical cohort included 38 patients with atypical BRAF mCRC treated at a large academic referral center. While there were no survival differences between atypical BRAF classes, concurrent RAS mutations or liver involvement was associated with poorer prognosis. Notably, patients younger than 50 years of age had extremely poor survival. In these patients, the high-frequency KRAS/NRAS co-mutation and its correlation with poorer prognosis underlines the urgent need for novel therapeutic strategies. This study represents one of the most comprehensive characterizations to date of atypical BRAF variants, utilizing both ctDNA and clinical cohorts.

Genomic disparity impacts variant classification of cancer susceptibility genes in Turkish breast cancer patients.

Turkish genome is underrepresented in large genomic databases. This study aims to evaluate the effect of allele frequency in the Turkish population in determining the clinical utility of germline findings in breast cancer, including invasive lobular carcinoma (ILC), mixed invasive ductal and lobular carcinoma (IDC-L), and ductal carcinoma (DC). Two clinic-based cohorts from the Umraniye Research and Training Hospital (URTH) were used in this study: a cohort consisting of 132 women with breast cancer and a non-cancer cohort consisting of 492 participants. The evaluation of the germline landscape was performed by analysis of 27 cancer genes. The frequency and type of variants in the breast cancer cohort were compared to those in the non-cancer cohort to investigate the effect of population genetics. The variant allele frequencies in Turkish Variome and gnomAD were statistically evaluated. The genetic analysis identified 121 variants in the breast cancer cohort (actionable = 32, VUS = 89) and 223 variants in the non-cancer cohort (actionable = 25, VUS = 188). The occurrence of 21 variants in both suggested a possible genetic population effect. Evaluation of allele frequency of 121 variants from the breast cancer cohort showed 22% had a significantly higher value in Turkish Variome compared to gnomAD (p < 0.0001, 95% CI) with a mean difference of 60 times (ranging from 1.37-354.4). After adjusting for variant allele frequency using the ancestry-appropriate database, 6.7% (5/75) of VUS was reclassified to likely benign. To our knowledge, this is the first study of population genetic effects in breast cancer subtypes in Turkish women. Our findings underscore the need for a large genomic database representing Turkish population-specific variants. It further highlights the significance of the ancestry-appropriate population database for accurate variant assessment in clinical settings.

Speeding genomic island discovery through systematic design of reference database composition.

Genomic islands (GIs) are mobile genetic elements that integrate site-specifically into bacterial chromosomes, bearing genes that affect phenotypes such as pathogenicity and metabolism. GIs typically occur sporadically among related bacterial strains, enabling comparative genomic approaches to GI identification. For a candidate GI in a query genome, the number of reference genomes with a precise deletion of the GI serves as a support value for the GI. Our comparative software for GI identification was slowed by our original use of large reference genome databases (DBs). Here we explore smaller species-focused DBs. With increasing DB size, recovery of our reliable prophage GI calls reached a plateau, while recovery of less reliable GI calls (FPs) increased rapidly as DB sizes exceeded ~500 genomes; i.e., overlarge DBs can increase FP rates. Paradoxically, relative to prophages, FPs were both more frequently supported only by genomes outside the species and more frequently supported only by genomes inside the species; this may be due to their generally lower support values. Setting a DB size limit for our SMAll Ranked Tailored (SMART) DB design speeded runtime ~65-fold. Strictly intra-species DBs would tend to lower yields of prophages for small species (with few genomes available); simulations with large species showed that this could be partially overcome by reaching outside the species to closely related taxa, without an FP burden. Employing such taxonomic outreach in DB design generated redundancy in the DB set; as few as 2984 DBs were needed to cover all 47894 prokaryotic species. Runtime decreased dramatically with SMART DB design, with only minor losses of prophages. We also describe potential utility in other comparative genomics projects.

Diversity of short linear interaction motifs in SARS-CoV-2 nucleocapsid protein.

Short linear motifs (SLiMs) are 3-10 amino acid long binding motifs in intrinsically disordered protein regions (IDRs) that serve as ubiquitous protein-protein interaction modules in eukaryotic cells. Through molecular mimicry, viruses hijack these sequence motifs to control host cellular processes. It is thought that the small size of SLiMs and the high mutation frequencies of viral IDRs allow rapid host adaptation. However, a salient characteristic of RNA viruses, due to high replication errors, is their obligate existence as mutant swarms. Taking advantage of the uniquely large genomic database of SARS-CoV-2, here, we analyze the role of sequence diversity in the presentation of SLiMs, focusing on the highly abundant, multi-functional nucleocapsid protein. We find that motif mimicry is a highly dynamic process that produces an abundance of motifs transiently present in subsets of mutant species. This diversity allows the virus to efficiently explore eukaryotic motifs and evolve the host-virus interface.

LSTM4piRNA: Efficient piRNA Detection in Large-Scale Genome Databases Using a Deep Learning-Based LSTM Network.

Piwi-interacting RNAs (piRNAs) are a new class of small, non-coding RNAs, crucial in the regulation of gene expression. Recent research has revealed links between piRNAs, viral defense mechanisms, and certain human cancers. Due to their clinical potential, there is a great interest in identifying piRNAs from large genome databases through efficient computational methods. However, piRNAs lack conserved structure and sequence homology across species, which makes piRNA detection challenging. Current detection algorithms heavily rely on manually crafted features, which may overlook or improperly use certain features. Furthermore, there is a lack of suitable computational tools for analyzing large-scale databases and accurately identifying piRNAs. To address these issues, we propose LSTM4piRNA, a highly efficient deep learning-based method for predicting piRNAs in large-scale genome databases. LSTM4piRNA utilizes a compact LSTM network that can effectively analyze RNA sequences from extensive datasets to detect piRNAs. It can automatically learn the dependencies among RNA sequences, and regularization is further integrated to reduce the generalization error. Comprehensive performance evaluations based on piRNAs from the piRBase database demonstrate that LSTM4piRNA outperforms current advanced methods and is well-suited for analysis with large-scale databases.

Investigation of blood group genotype prevalence in Korean population using large genomic databases

Blood group antigens, which are prominently expressed in red blood cells, are important in transfusion medicine. The advent of high-throughput genome sequencing technology has facilitated the prediction of blood group antigen phenotypes based on genomic data. In this study, we analyzed data from a large Korean population to provide an updated prevalence of blood group antigen phenotypes, including rare ones. A robust dataset comprising 72,291 single nucleotide polymorphism arrays, 5318 whole-exome sequences, and 4793 whole-genome sequences was extracted from the Korean Genome and Epidemiology Study, Genome Aggregation Database, and Korean Variant Archive and then analyzed. The phenotype prevalence of clinically significant blood group antigens, including MNSs, RHCE, Kidd, Duffy, and Diego, was predicted through genotype analysis and corroborated the existing literature. We identified individuals with rare phenotypes, including 369 (0.51%) with Fy(a−b+), 188 (0.26%) with Di(a+b−), and 16 (0.02%) with Jr(a−). Furthermore, we calculated the frequencies of individuals with extremely rare phenotypes, such as p (0.000004%), Kell-null (0.000310%), and Jk(a−b−) (0.000438%), based on allele frequency predictions. These findings offer valuable insights into the distribution of blood group antigens in the Korean population and have significant implications for enhancing the safety and efficiency of blood transfusion.

Genomic characterization of thymic epithelial tumors in a real-world dataset

Thymic epithelial tumors (TETs) are rare neoplasms arising in the mediastinum, including thymic carcinomas and thymomas. Due to their rarity, little is known about the genomic profiles of TETs. Herein, we investigated the genomic characteristics of TETs evaluated in a large comprehensive genomic profiling database in a real-world setting. We included data from two different cohorts: Foundation Medicine Inc. (FMI) in the United States and the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) in Japan. Samples profiled were examined for all classes of alterations in 253 genes targeted across all assays. Tumor mutational burden (TMB) and microsatellite instability (MSI) were also evaluated. A total of 794 patients were collected in our study, including 722 cases from FMI and 72 cases from C-CAT. In the FMI data, CDKN2A (39.9%), TP53 (30.2%) and CDKN2B (24.6%) were frequently altered in thymic carcinoma, versus TP53 (7.8%), DNMT3A (6.8%), and CDKN2A (5.8%) in thymoma. TMB-high (≥10 mutations/Mb) and MSI were present in 7.0% and 2.3% of thymic carcinomas, and 1.6% and 0.3% of thymomas, respectively. Within C-CAT data, CDKN2A (38.5%), TP53 (36.5%) and CDKN2B (30.8%) were also frequently altered in thymic carcinoma, while alterations of TSC1, SETD2 and LTK (20.0% each) were found in thymoma. To the best of our knowledge, this is the largest cohort in which genomic alterations, TMB and MSI status of TETs were investigated. Potential targets for treatment previously unbeknownst in TETs are identified in this study, entailing newfound opportunities to advance therapeutic development.

CtDNA-based genomic landscape analysis to evaluate molecular brake and gatekeeper mutations in FGFR2 alterations.

3049 Background: FGFR2 and FGFR3 (FGFR2/3) gene alterations are oncogenic drivers found across various cancer types and have been successful targets of drug development. Several FGFR inhibitors (FGFRi) are approved for clinical use, and most are ATP-competitive, reversible inhibitors (1st-gen). Efficacy of the 1st-gen FGFRi can be limited by acquired resistance mutations in the FGFR kinase domain, typically molecular brake or gatekeeper mutations, which disrupt compounds binding through conformational changes or through direct steric hindrance, respectively. Irreversible (covalent) FGFRi, such as futibatinib, are in development to overcome kinase domain resistance mutations. However, preclinical evidence indicates that some of the known resistance mutations (eg. V564L) remain insensitive to futibatinib. Methods: To assess the landscape of FGFR-driven cancers and to better understand the prevalence of molecular brake and gatekeeper mutations, we analyzed the distribution of FGFR2/3 alterations in a large clinical genomic research database. This ctDNA-based genomic database was selected for the analysis given that liquid biopsy is a fit-for-purpose method to detect acquired resistance mutations. Results: We identified over 2,100 patients with FGFR2/3 alterations. Non-small cell lung cancer (NSCLC), breast, bladder, and cholangiocarcinoma (CCA) had the highest number of patients with FGFR2/3 alterations. 36% of all FGFR2 alterations across all cancer indications consisted of molecular brake or gatekeeper mutations. This finding was most enriched in CCA and breast cancer where 51% and 57%, respectively, of FGFR2 alterations were molecular brake or gatekeeper mutations. In breast cancer patients, molecular brake mutations were common and represent activating mutations in FGFRi-naïve patients. A small number of FGFR3 gene fusions were detected in CCA patients who had significantly shorter time on 1st-line treatment and trended toward shorter overall survival when compared to patients with FGFR2 fusions. We also identified CCA patients with FGFR2 fusions and one or more concurrent FGFR2 SNVs; primarily those in the kinase domain and associated with FGFRi resistance. Most of these putative cases of acquired resistance were polyclonal, with 2 or more (up to 8) FGFR2 SNVs coexisting with the fusion. Bladder cancer and NSCLC had the highest numbers of FGFR3 alterations. FGFR2/3 alterations were typically found without other driver mutations in CCA, bladder, and NSCLC patients. However, in breast there was a large overlap between FGFR2/3 alterations and other drivers. Conclusions: Herein, we identify a cohort of patients with FGFR2/3 molecular brake and gatekeeper mutations that may benefit from next-generation FGFR2/3-targeted therapies designed to inhibit a broad spectrum of both primary and secondary resistance mutations.

Association of CDK4 amplification with duration of response to bevacizumab in glioblastoma.

2026 Background: Bevacizumab remains the standard second line therapy in glioblastoma (GBM) with reported improved progression free survival, but not overall survival. We investigated a large clinico-genomic database of GBM patients treated with bevacizumab for molecular alterations associated with treatment outcome. Methods: Molecular profiles of GBM were tested by next-generation sequencing (NGS) of DNA (592 genes, NextSeq or whole-exome sequencing, Novaseq) and RNA (whole transcriptome sequencing, NovaSeq) at Caris Life Sciences (Phoenix, AZ). Gene amplification was determined by NGS with a threshold of &gt; = 6 copies. Real-world survival information was obtained from insurance claims data. Time-on-treatment (TOT) of bevacizumab was calculated from start to finish of treatment while post-bevacizumab overall survival (bevOS) from start of bevacizumab to last day of contact. Short-term (ST) and long-term (LT) responders were defined as those with TOT ≤6 months and ≥1 year, respectively. Kaplan-Meier estimates were calculated and significance was determined as p values of &lt; 0.05. For molecular comparisons, Fisher’s exact tests and Mann-Whiney U were used when appropriate. Results: Among the total of 383 ST and 107 LT patients identified, no significant difference in gender or age were seen. The LT cohort had significantly longer bevOS compared to the ST cohort (HR 0.27, [95% CI: 0.28-0.35], p &lt; 0.0001). Interestingly, CDK4 amplification was seen in 23% (18/78) of LT patients but only 7% (21/300, p = 0.0002) of ST. When investigating the total cohort of 498 GBM treated with bevacizumab, CDK4 amplified patients demonstrated significantly improved TOT compared to non-amplified (HR: 0.639, [0.48-0.85, p = 0.002]). Conversely, the ST cohort had significantly more EGFR amplifications than the LT cohort (43%, 130/300 vs. 19%, 15/79, p &lt; 0.0001). In the total bevacizumab-treated cohort, EGFR-amplified GBM had significantly worse TOT on bevacizumab than non-EGFR amplified GBM (HR 1.387, [1.15-1.67], p &lt; 0.001). No associations were seen in other alterations explored including PIK3CA mutation (HR = 0.81 [0.587-1.113], p = 0.2), EGFR mutation (HR: 1.209, [0.977-1.495], p = 0.1) or RAF1 (HR: 0.647, [0.318-1.317], p = 0.2). Conclusions: Using a large clinical genomic database with GBM subjected to comprehensive molecular profiling, we demonstrated that amplification of CDK4 and EGFR were associated with long-term and short-term responses to bevacizumab, respectively. This warrants further investigation in independent cohorts controlled for age and other prognostic factors. If confirmed, a genetic basis for treatment optimization may provide meaningful clinical outcomes.

Not all treated KRAS-mutant pancreatic adenocarcinomas are equal: KRAS G12D and survival outcome.

4020 Background: KRAS is an oncogenic driver in pancreatic ductal adenocarcinoma (PDAC) with mutations identified in &gt; 90% of cases. G12D is the most frequent variant, followed by G12V and G12R. We recently reported on the prognostic impact of distinct KRAS mutations. The current study utilized a large clinical and genomic database, to further explore and characterize the prognostic and molecular differences between KRAS variants, focusing on KRAS G12D and G12R. Methods: PDAC samples were tested using whole transcriptome sequencing (WTS; Illumina NovaSeq) and NextGen DNA sequencing (NextSeq, 592 Genes and NovaSEQ, WES) at Caris Life Sciences (Phoenix, AZ). Transcriptomic signatures including MPAS (MAPK activation score), T-cell inflamed score and tumor micro environment (TME) characterization were calculated on WTS data. Significance was determined by X2 and Fisher-Exact and p-value was adjusted for multiple comparisons (q). Real-world overall survival (rwOS) obtained from insurance claims data was calculated from tissue collection to last contact (comparison done by Kaplan-Meier test). Results: 5,555 PDAC patients harboring either KRAS G12D (n = 2,671), G12V (n=1,871) G12R (n = 904) or G12C (n = 109) variants were identified. Patients with KRAS G12R mutant tumors had significantly longer OS compared to G12D (396 vs 311 days, HR 0.81, CI 0.74-0.88, p&lt;0.0001). There was no difference among KRAS variants in the rate of TP53, CDKN2A and SMAD4 mutations. ARID1A and KMT2D were more frequently mutated in KRAS G12D vs. G12R. The MPAS gene signature reflecting MAPK pathway activation trended lower in G12R vs. G12D. Expression of multiple genes in this pathway was statistically lower in the G12R cohort. Immune profiling suggested that: PDL1 expression is significantly lower in G12R vs G12D (13% vs 19%), TMB-H and dMMR were comparable in G12D vs G12R and several glucose and glutamine metabolism genes were significantly lower in expression when comparing G12R vs G12D - table. OS was improved within the G12R cohort for those patients on metformin (n=273 patients) (416 vs 388 days, HR 0.84, CI 0.72-0.99, p =0.037) while no difference was seen in those with KRAS G12D based on metformin use. Conclusions: Patients with G12D mutations have significantly lower survival compared to G12R. Significant molecular differences were seen in MAPK pathway gene expression, markers of immune activation, and genes involved in glucose and glutamine metabolism. Intriguingly, metformin use appeared to impact survival in the KRAS G12R subgroup. We aim to further explore distinct vulnerabilities based on MAPK pathway activation and dysregulated metabolism. Based on this data, future studies should address the KRAS mutation status and explore distinct therapeutic vulnerabilities. [Table: see text]

Australian public perspectives on genomic data governance: responsibility, regulation, and logistical considerations.

Genomic sequencing generates huge volumes of data, which may be collected or donated to form large genomic databases. Such information can be stored for future use, either for the data donor themselves or by researchers to help improve our understanding of the genetic basis of disease. Creating datasets of this magnitude and diversity is only possible if patients, their families, and members of the public worldwide share their data. However, there is no consensus on the best technical approach to data sharing that also minimises risks to individuals and exploration of stakeholders' views on aspects of genomic data governance models-the ways genomic data is stored, managed, shared and used-has been minimal. To address this need, we conducted focus groups with 39 members of the Australian public exploring their views and preferences for different aspects of genomic data governance models. We found that consent and control were essential to participants, as they wanted the option to choose who had access to their data and for what purposes. Critically, participants wanted a trustworthy body to enforce regulation of data storage, sharing and usage. While participants recognised the importance of data accessibility, they also expressed a strong desire for data security. Finally, financial responsibility for data storage raised concerns for inequity as well as organisations and individuals using data in ethically contentious ways to generate profit. Our findings highlight some of the trade-offs that need to be considered in the development of genomic data governance systems.

Evaluation of core genome and whole genome multilocus sequence typing schemes for Campylobacter jejuni and Campylobacter coli outbreak detection in the USA.

Campylobacter is a leading causing of bacterial foodborne and zoonotic illnesses in the USA. Pulsed-field gene electrophoresis (PFGE) and 7-gene multilocus sequence typing (MLST) have been historically used to differentiate sporadic from outbreak Campylobacter isolates. Whole genome sequencing (WGS) has been shown to provide superior resolution and concordance with epidemiological data when compared with PFGE and 7-gene MLST during outbreak investigations. In this study, we evaluated epidemiological concordance for high-quality SNP (hqSNP), core genome (cg)MLST and whole genome (wg)MLST to cluster or differentiate outbreak-associated and sporadic Campylobacter jejuni and Campylobacter coli isolates. Phylogenetic hqSNP, cgMLST and wgMLST analyses were also compared using Baker's gamma index (BGI) and cophenetic correlation coefficients. Pairwise distances comparing all three analysis methods were compared using linear regression models. Our results showed that 68/73 sporadic C. jejuni and C. coli isolates were differentiated from outbreak-associated isolates using all three methods. There was a high correlation between cgMLST and wgMLST analyses of the isolates; the BGI, cophenetic correlation coefficient, linear regression model R 2 and Pearson correlation coefficients were >0.90. The correlation was sometimes lower comparing hqSNP analysis to the MLST-based methods; the linear regression model R 2 and Pearson correlation coefficients were between 0.60 and 0.86, and the BGI and cophenetic correlation coefficient were between 0.63 and 0.86 for some outbreak isolates. We demonstrated that C. jejuni and C. coli isolates clustered in concordance with epidemiological data using WGS-based analysis methods. Discrepancies between allele and SNP-based approaches may reflect the differences between how genomic variation (SNPs and indels) are captured between the two methods. Since cgMLST examines allele differences in genes that are common in most isolates being compared, it is well suited to surveillance: searching large genomic databases for similar isolates is easily and efficiently done using allelic profiles. On the other hand, use of an hqSNP approach is much more computer intensive and not scalable to large sets of genomes. If further resolution between potential outbreak isolates is needed, wgMLST or hqSNP analysis can be used.