Analysis Of Genomic Data Research Articles

A gap analysis of UK biobank publications reveals SNPs associated with intrinsic subtypes of breast cancer

Breast cancer is a multifaceted disease and a leading cause of cancer morbidity and mortality in females across the globe. In 2020 alone, 2.3 million women were diagnosed and 685,000 died of breast cancer worldwide. With the number of diagnoses projected to increase to 3 million per year by 2040 it is essential that new methods of detection and disease stratification are sought to decrease this global cancer burden.Although significant improvements have been made in breast cancer diagnosis and treatment, the prognosis of breast cancer remains poor in some patient groups (i.e. triple negative breast cancer), necessitating research into better patient stratification, diagnosis and drug discovery. The UK Biobank, a comprehensive biomedical and epidemiological database with a wide variety of multiomics data (genomics, proteomics, metabolomics) offers huge potential to uncover groundbreaking discoveries in breast cancer research leading to improved patient stratification. Combining genomic, proteomic, and metabolic profiles of breast cancer in combination with histological classification, can aid treatment decisions through accurate diagnosis and prognosis prediction of tumor behaviour.Here, we systematically reviewed PubMed publications reporting the analysis of UK Biobank data in breast cancer research. Our analysis of UK Biobank studies in the past five years identified 125 publications, of which 76 focussed on genomic data analysis. Interestingly, only two studies reported the analysis of metabolomics and proteomics data, with none performing multiomics analysis of breast cancer. A meta-analysis of the 76 publications identified 2870 genetic variants associated with breast cancer across 445 genes. Subtype analysis revealed differential genetic alteration in 13 of the 445 genes and the identification of 59 well-established breast cancer genes. in differential pathways. Pathway interaction analyses illuminated their involvement in general cancer biomolecular pathways (e.g. DNA damage repair, Gene expression). While our meta-analysis only measured genetic differences in breast cancer due to current usage of UK Biobank data, minimal multi-omics analyses have been performed and the potential for harnessing multi-omics strategies within the UK Biobank cohort holds promise for unravelling the biological signatures of distinct breast cancer subtypes further in the future.

Gene cloning, expression, and enzyme kinetics analysis of Eimeria tenella 2- methylcitrate synthase

In prokaryotes and lower eukaryotes, 2-methylcitrate cycle (2-MCC) is the main pathway for propionate decomposition and transformation, but little is known about the 2-MCC pathway of Eimeria tenella. The analysis of genomic data found that the coding gene of 2- methylcitrate synthase (EC 2.3.3.5, PrpC) exists in E. tenella, which is a key enzyme of 2-MCC pathway. Through the search analysis of the database (ToxoDB), it was found that ETH_ 00026655 contains the complete putative sequence of EtprpC. In this study, we amplified the ORF sequence of EtprpC based on putative sequence. Then, prokaryotic expression, enzyme activity and kinetic analysis was performed. The results showed that the EtprpC ORF sequence was 1272 bp, encoding a 46.3 kDa protein comprising 424 amino acids. Enzyme activity assays demonstrate linearity between the initial reaction rate (OD/min) and EtPrpC concentration (ranging from 1.5 to 9 µg/reaction), with optimal enzyme activity observed at 41°C and pH 8.0. The results of enzymatic kinetic analysis showed that the Km of EtPrpC for propionyl-CoA, oxaloacetic acid, and acetyl-CoA was 5.239 ± 0.17 mM, 1.102 ± 0.08 μM, and 5.999 ± 1.24 μM, respectively. The Vmax was 191.11 ± 19.1 nmol/min/mg, 225.48 ± 14.4 nmol/min/mg, and 370.02 ± 25.8 nmol/min/mg when EtPrpC concentration at 4, 6, and 8 μg, respectively. Although the ability of EtPrpC to catalyze acetyl-CoA is only 0.11% of its ability to catalyze propionyl-CoA, it indicates that the 2-MCC pathway in E. tenella is similar to that in bacteria and may have a bypass function in the TCA cycle. This study can provide the theoretical foundation for the new drug targets and the development of new anticoccidial drugs.

Abstract PS06-08: Longitudinal Neoadjuvant and Post-operative Evaluation of Circulating Tumor DNA in Early Breast Cancer using a Tumor-Informed Assay: Updated Analysis of the TRACER Cohort

Abstract Background: ctDNA is detectable in early breast cancer (EBC) using sensitive assays and treatment-related changes in ctDNA are associated with clinical response. RaDaR® (NeoGenomics), a tumor-informed assay, has been shown to detect circulating tumor DNA (ctDNA) prior to clinical recurrence. We retrospectively quantified ctDNA using RaDaR in serial samples from a large cohort of patients with EBC receiving standard neoadjuvant therapy (NAT). Methods: Unselected patients with EBC were enrolled prior to NAT in the TRACER cohort from 2015 onwards. Plasma samples were collected at baseline, during treatment, perioperatively, and in follow-up. For patients with available tissue for assay generation, RaDaR was performed on all available plasma timepoints. Clinical and pathologic characteristics (assessed on core biopsy), treatment, and outcomes were recorded. Results: Of 128 patients evaluated, 9 (7.0%) were excluded from this analysis due to panel quality control metrics, leaving 119 patients (41 ER+, 32 TNBC, 46 HER2+) with 681 individual timepoints (median=6, range: 1-12). 103/119 patients (86%) received neoadjuvant anthracycline- and taxane-based chemotherapy. Median followup from diagnosis was 3.8 years (range: 0.6-6.3 years) and 16 recurrences have occurred (9 ER+, 6 TN, 1 HER2+). 114 patients had a baseline plasma sample collected prior to NAT, in which the detection rate was 77% (70% ER+, 90% TNBC, 76% HER2+), with a median estimated variant allele frequency (eVAF) of 0.0823% (range: 2.90E-5 - 7.5%). All patients with clinical recurrence had ctDNA detected at baseline. Baseline detection was associated with tumor grade (p=0.050) but not size (p=0.65) or clinical nodal status (p=0.36). There were non-significant associations between eVAF and grade (p=0.097) and eVAF and tumor size (p=0.086). Persistent ctDNA detection midway through neoadjuvant therapy (pre-cycle 5) was associated with an increased risk of recurrence in patients with ER+ (HR: 10.27, 95%CI: 1.61-65.4; p=0.014) and TNBC (HR: 20.17, 95%CI: 1.97-206.4; p=0.011). Residual cancer burden (RCB) status further stratified the risk of recurrence; those with RCB-2/3 disease and ctDNA detected pre-cycle 5 were at the highest risk. Few patients had detectable ctDNA in pre- or initial post-operative specimens, all of which had residual disease (non-pCR). 9/16 patients with clinical recurrence had evaluable post-operative and follow-up samples for lead time calculation; ctDNA was detected prior to recurrence in 7/9 (78 %), with a median lead time of 152 days (range: 13-699 days). Of the 2 patients without a positive test, one had an ipsilateral local recurrence (grade 2, 1.8 mm), the other had a negative, but borderline, test 72 days prior to recurrence (solitary 8 mm lung nodule on PET). Follow up is ongoing for two patients with ctDNA detected but no recurrence at data cutoff (time since last positive test: 0.60 and 1.78 years). Any ctDNA detection postoperatively or in follow-up was strongly associated with disease recurrence (HR: 37.35, 95%CI: 2.7-520.7; p< 0.0001). Conclusion: RaDaR detects ctDNA in most patients prior to the initiation of NAT for EBC. Changes in ctDNA levels during treatment and its presence are associated with clinical outcomes. Prospective evaluation and integration of RaDaR testing into clinical trials are warranted. Further analysis of ctDNA detection, clinical outcomes, and genomic data will be presented at the meeting. Citation Format: Mitchell Elliott, Jesus Fuentes Antras, Philippe Echelard, Aaron Dou, Zachary Veitch, Philippe Bedard, Eitan Amir, Michelle Nadler, Nicholas Meti, Nancy Gregorio, Elizabeth Shah, Celeste Yu, Nathan Campbell, Christodoulos Pipinikas, Karen Howarth, Lillian Siu, Hal Berman, David Cescon. Longitudinal Neoadjuvant and Post-operative Evaluation of Circulating Tumor DNA in Early Breast Cancer using a Tumor-Informed Assay: Updated Analysis of the TRACER Cohort [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS06-08.

GenCoder: A Novel Convolutional Neural Network Based Autoencoder for Genomic Sequence Data Compression.

Revolutionary advances in DNA sequencing technologies fundamentally change the nature of genomics. Today's sequencing technologies have opened into an outburst in genomic data volume. These data can be used in various applications where long-term storage and analysis of genomic sequence data are required. Data-specific compression algorithms can effectively manage a large volume of data. In recent times, deep learning has achieved great success in many compression tools and is gradually being used in genomic sequence compression. Significantly, autoencoder has been applied in dimensionality reduction, compact representations of data, and generative model learning. It can use convolutional layers to learn essential features from input data, which is better for image and series data. Autoencoder reconstructs the input data with some loss of information. Since accuracy is critical in genomic data, compressed genomic data must be decompressed without any information loss. We introduce a new scheme to address the loss incurred in the decompressed data of the autoencoder. This paper proposes a novel algorithm called GenCoder for reference-free compression of genomic sequences using a convolutional autoencoder and regenerating the genomic sequences from a latent code produced by the autoencoder, and retrieving original data losslessly. Performance evaluation is conducted on various genomes and benchmarked datasets. The experimental results on the tested data demonstrate that the deep learning model used in the proposed compression algorithm generalizes well for genomic sequence data and achieves a compression gain of 27% over the best state-of-the-art method.

Characterization of sequences, expression profiling, and natural allelic variation analysis of the MYC gene family in sorghum (Sorghum bicolor)

Sorghum aphid (Melanaphis sacchari) and head smut fungi (Sporisorium reilianum) infesting sorghum cause delayed growth and development, and reduce yield and quality. This study use bioinformatics and molecular biological approaches to profile the gene expression pattern during sorghum development and under pest infestation, and analyzed the natural allelic DNA variation of sorghum MYC gene family. The findings provide insights for potential application in breeding the stress resistant and high productivity sorghum varieties. The results indicated that there are 28 MYC genes identified in sorghum genome, distributed on 10 chromosomes. The bHLH_MYC_N and HLH domains are the conserved domains of the MYC gene in sorghum. Gene expression analysis showed that SbbHLH35.7g exhibited high expression levels in leaves, SbAbaIn showed strong expression in early grains, and SbMYC2.1g showed high expression levels in mature pollen. In anti-aphid strains at the 5-leaf stage, SbAbaIn, SbLHW.4g and SbLHW.2g were significantly induced in leaves, while SbbHLH35.7g displayed the highest expression level in panicle tissue, which was significantly induced by the infection of head smut. Promoter cis-element analysis identified methyl jasmonate (MJ), abscisic acid (ABA), salicylic acid (SA) and MYB-binding sites related to drought-stress inducibility. Furthermore, genomic resequencing data analysis revealed natural allelic DNA variations such as single nucleotide polymorphism (SNP) and insertion-deletion (INDEL) for the key SbMYCs. Protein interaction network analysis using STRING indicated that SbAbaIn interacts with TIFYdomain protein, and SbbHLH35.7g interacts with MDR and imporin. SbMYCs exhibited temporal and spatial expression patterns and played vital roles during the sorghum development. Infestation by sugarcane aphids and head smut fungi induced the expression of SbAbaIn and SbbHLH35.7g, respectively. SbAbaIn modulated the jasmonic acid (JA) pathway to regulate the expression of defensive genes, conferring resistance to insects. On the other hand, SbbHLH35.7g participated in detoxification reactions to defend against pathogens.

Bulk segregant mapping and transcriptome analyses reveal the molecular mechanisms of spinetoram resistance in Spodoptera frugiperda

The evolution of resistance to insecticides poses a significant threat to pest management programs. Understanding the molecular mechanisms underlying insecticide resistance is essential to design sustainable pest control and resistance management programs. The fall armyworm, Spodoptera frugiperda, is an important insect pest of many crops and has a remarkable ability to evolve resistance to insecticides. In this study, we employed bulk segregant analysis (BSA) combined with DNA and RNA sequencing to characterize the molecular basis of spinetoram resistance in S. frugiperda. Analysis of genomic data derived from spinetoram selected and unselected bulks and the spinetoram-resistant and susceptible parental strains led to the identification of a three-nucleotide deletion in the gene encoding the nicotinic acetylcholine receptor α6 subunit (nAChR α6). Transcriptome profiling identified the upregulation of few genes encoding detoxification enzymes associated with spinetoram resistance. Thus, spinetoram resistance in S. frugiperda appears to be mediated mainly by target site insensitivity with a minor role of detoxification enzymes. Our findings provide insight into the mechanisms underpinning resistance to spinetoram in S. frugiperda and will inform the development of strategies to control this highly damaging, globally distributed crop pest.

Detection of autism spectrum disorder-related pathogenic trio variants by a novel structure-based approach

BackgroundGlutamatergic synapse dysfunction is believed to underlie the development of Autism Spectrum Disorder (ASD) and Intellectual Disability (ID) in many individuals. However, identification of genetic markers that contribute to synaptic dysfunction in these individuals is notoriously difficult. Based on genomic analysis, structural modeling, and functional data, we recently established the involvement of the TRIO-RAC1 pathway in ASD and ID. Furthermore, we identified a pathological de novo missense mutation hotspot in TRIO’s GEF1 domain. ASD/ID-related missense mutations within this domain compromise glutamatergic synapse function and likely contribute to the development of ASD/ID. The number of ASD/ID cases with mutations identified within TRIO’s GEF1 domain is increasing. However, tools for accurately predicting whether such mutations are detrimental to protein function are lacking.MethodsHere we deployed advanced protein structural modeling techniques to predict potential de novo pathogenic and benign mutations within TRIO’s GEF1 domain. Mutant TRIO-9 constructs were generated and expressed in CA1 pyramidal neurons of organotypic cultured hippocampal slices. AMPA receptor-mediated postsynaptic currents were examined in these neurons using dual whole-cell patch clamp electrophysiology. We also validated these findings using orthogonal co-immunoprecipitation and fluorescence lifetime imaging (FLIM-FRET) experiments to assay TRIO mutant overexpression effects on TRIO-RAC1 binding and on RAC1 activity in HEK293/T cells.ResultsMissense mutations in TRIO’s GEF1 domain that were predicted to disrupt TRIO-RAC1 binding or stability were tested experimentally and found to greatly impair TRIO-9’s influence on glutamatergic synapse function. In contrast, missense mutations in TRIO’s GEF1 domain that were predicted to have minimal effect on TRIO-RAC1 binding or stability did not impair TRIO-9’s influence on glutamatergic synapse function in our experimental assays. In orthogonal assays, we find most of the mutations predicted to disrupt binding display loss of function but mutants predicted to disrupt stability do not reflect our results from neuronal electrophysiological data.LimitationsWe present a method to predict missense mutations in TRIO’s GEF1 domain that may compromise TRIO function and test for effects in a limited number of assays. Possible limitations arising from the model systems employed here can be addressed in future studies. Our method does not provide evidence for whether these mutations confer ASD/ID risk or the likelihood that such mutations will result in the development of ASD/ID.ConclusionsHere we show that a combination of structure-based computational predictions and experimental validation can be employed to reliably predict whether missense mutations in the human TRIO gene impede TRIO protein function and compromise TRIO’s role in glutamatergic synapse regulation. With the growing accessibility of genome sequencing, the use of such tools in the accurate identification of pathological mutations will be instrumental in diagnostics of ASD/ID.

GenArchBench: A genomics benchmark suite for arm HPC processors

Arm usage has substantially grown in the High-Performance Computing (HPC) community. Japanese supercomputer Fugaku, powered by Arm-based A64FX processors, held the top position on the Top500 list between June 2020 and June 2022, currently sitting in the fourth position. The recently released 7th generation of Amazon EC2 instances for compute-intensive workloads (C7 g) is also powered by Arm Graviton3 processors. Projects like European Mont-Blanc and U.S. DOE/NNSA Astra are further examples of Arm irruption in HPC. In parallel, over the last decade, the rapid improvement of genomic sequencing technologies and the exponential growth of sequencing data has placed a significant bottleneck on the computational side. While most genomics applications have been thoroughly tested and optimized for x86 systems, just a few are prepared to perform efficiently on Arm machines. Moreover, these applications do not exploit the newly introduced Scalable Vector Extensions (SVE).This paper presents GenArchBench, the first genome analysis benchmark suite targeting Arm architectures. We have selected computationally demanding kernels from the most widely used tools in genome data analysis and ported them to Arm-based A64FX and Graviton3 processors. Overall, the GenArch benchmark suite comprises 13 multi-core kernels from critical stages of widely-used genome analysis pipelines, including base-calling, read mapping, variant calling, and genome assembly. Our benchmark suite includes different input data sets per kernel (small and large), each with a corresponding regression test to verify the correctness of each execution automatically. Moreover, the porting features the usage of the novel Arm SVE instructions, algorithmic and code optimizations, and the exploitation of Arm-optimized libraries. We present the optimizations implemented in each kernel and a detailed performance evaluation and comparison of their performance on four different HPC machines (i.e., A64FX, Graviton3, Intel Xeon Skylake Platinum, and AMD EPYC Rome). Overall, the experimental evaluation shows that Graviton3 outperforms other machines on average. Moreover, we observed that the performance of the A64FX is significantly constrained by its small memory hierarchy and latencies. Additionally, as proof of concept, we study the performance of a production-ready tool that exploits two of the ported and optimized genomic kernels.

Multiomics analysis to explore blood metabolite biomarkers in an Alzheimer’s Disease Neuroimaging Initiative cohort

Alzheimer's disease (AD) is a neurodegenerative disease that commonly causes dementia. Identifying biomarkers for the early detection of AD is an emerging need, as brain dysfunction begins two decades before the onset of clinical symptoms. To this end, we reanalyzed untargeted metabolomic mass spectrometry data from 905 patients enrolled in the AD Neuroimaging Initiative (ADNI) cohort using MS-DIAL, with 1,304,633 spectra of 39,108 unique biomolecules. Metabolic profiles of 93 hydrophilic metabolites were determined. Additionally, we integrated targeted lipidomic data (4873 samples from 1524 patients) to explore candidate biomarkers for predicting progressive mild cognitive impairment (pMCI) in patients diagnosed with AD within two years using the baseline metabolome. Patients with lower ergothioneine levels had a 12% higher rate of AD progression with the significance of P = 0.012 (Wald test). Furthermore, an increase in ganglioside (GM3) and decrease in plasmalogen lipids, many of which are associated with apolipoprotein E polymorphism, were confirmed in AD patients, and the higher levels of lysophosphatidylcholine (18:1) and GM3 d18:1/20:0 showed 19% and 17% higher rates of AD progression, respectively (Wald test: P = 3.9 × 10–8 and 4.3 × 10–7). Palmitoleamide, oleamide, diacylglycerols, and ether lipids were also identified as significantly altered metabolites at baseline in patients with pMCI. The integrated analysis of metabolites and genomics data showed that combining information on metabolites and genotypes enhances the predictive performance of AD progression, suggesting that metabolomics is essential to complement genomic data. In conclusion, the reanalysis of multiomics data provides new insights to detect early development of AD pathology and to partially understand metabolic changes in age-related onset of AD.

A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction.

Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin loops and structures. Our investigation incorporates two protein (CTCF and Cohesin) factor-specific loop interaction datasets from six distinct pipelines, amassing a comprehensive collection of 36 diverse datasets. Through a meticulous review of existing literature, we offer a holistic perspective on the methodologies, tools and algorithms underpinning the analysis of this multifaceted genomic feature. We illuminate the vast array of approaches deployed, encompassing pivotal aspects such as data preparation pipeline, preprocessing, statistical features and modelling techniques. Beyond this, we rigorously assess the strengths and limitations inherent in these bioinformatics pipelines, shedding light on the interplay between data quality and the performance of deep learning models, ultimately advancing our comprehension of genomic intricacies.

3Dmapper: a command line tool for BioBank-scale mapping of variants to protein structures.

The interpretation of genomic data is crucial to understand the molecular mechanisms of biological processes. Protein structures play a vital role in facilitating this interpretation by providing functional context to genetic coding variants. However, mapping genes to proteins is a tedious and error-prone task due to inconsistencies in data formats. Over the past two decades, numerous tools and databases have been developed to automatically map annotated positions and variants to protein structures. However, most of these tools are web-based and not well-suited for large-scale genomic data analysis. To address this issue, we introduce 3Dmapper, a stand-alone command-line tool developed in Python and R. It systematically maps annotated protein positions and variants to protein structures, providing a solution that is both efficient and reliable. https://github.com/vicruiser/3Dmapper.

ScAnnoX: an R package integrating multiple public tools for single-cell annotation.

Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite the existence of numerous single-cell annotation algorithms, a comprehensive tool for integrating and comparing these algorithms is also lacking. This study meticulously investigated a plethora of widely adopted single-cell annotation algorithms. Ten single-cell annotation algorithms were selected based on the classification of either reference dataset-dependent or marker gene-dependent approaches. These algorithms included SingleR, Seurat, sciBet, scmap, CHETAH, scSorter, sc.type, cellID, scCATCH, and SCINA. Building upon these algorithms, we developed an R package named scAnnoX for the integration and comparative analysis of single-cell annotation algorithms. The development of the scAnnoX software package provides a cohesive framework for annotating cells in scRNA-seq data, enabling researchers to more efficiently perform comparative analyses among the cell type annotations contained in scRNA-seq datasets. The integrated environment of scAnnoX streamlines the testing, evaluation, and comparison processes among various algorithms. Among the ten annotation tools evaluated, SingleR, Seurat, sciBet, and scSorter emerged as top-performing algorithms in terms of prediction accuracy, with SingleR and sciBet demonstrating particularly superior performance, offering guidance for users. Interested parties can access the scAnnoX package at https://github.com/XQ-hub/scAnnoX.

Abstract 594: ORIC-613, a potential first- and best-in-class, orally bioavailable, potent and selective PLK4 inhibitor with synthetic lethality in TRIM37 high cancer models

Abstract A synthetic lethal interaction between polo-like kinase 4 (PLK4) and elevated TRIM37 was discovered in breast cancer and neuroblastoma (Meitinger et al. 2020; Yeow et al. 2020). High TRIM37 renders cells dependent on PLK4, a kinase that controls centriole duplication. Amplification and copy number gains of the 17q23 amplicon, where TRIM37 resides, are common in breast cancer and neuroblastoma and associated with early relapse and poor prognosis, making PLK4 a promising new synthetic lethal target for these patients. The development candidate ORIC-613 is an orally bioavailable, potent and exquisitely selective small molecule inhibitor of PLK4, which is synthetic lethal in TRIM37 high cells. ORIC-613 is highly selective against the kinome, including against the closely related aurora and PLK families. ORIC-613 ADME/PK profile includes high cell permeability and low potential for drug-drug interactions based on CYP profiling. ORIC-613 blocked PLK4 trans-autophosphorylation of phosphodegrons, leading to stabilization of PLK4, thus directly demonstrating target engagement in cells. PLK4 protein stabilization correlated with cell viability for ORIC-613, providing a quantifiable pharmacodynamic (PD) association with antitumor activity. Cell viability assessment in cancer cell lines revealed that ORIC-613 showed greater potency in TRIM37 high versus TRIM37 low cell lines. Importantly, ORIC-613 induced significantly greater apoptosis in TRIM37 high versus low cancer cell lines as measured by caspase 3/7 assay. We confirmed that ORIC-613 activity is on-target using an engineered cell line system of PLK4 G95L, in which the leucine mutation maintains enzymatic function but blocks compound binding. In cell viability assays, ORIC-613 was potent in the parental cells and lost activity in G95L cells. Analysis of genomic data from tumors indicates that TRIM37 copy number amplification and gain is prevalent across numerous cancers. Oral dosing of ORIC-613 resulted in tumor growth inhibition and regressions in TRIM37 high xenograft breast tumors with no body weight loss. In summary, ORIC-613 is a potential first- and best-in-class development candidate, with exquisite selectivity for PLK4, which demonstrates synthetic lethality in TRIM37 high tumors and has the potential to benefit these patients. Citation Format: Kyle A. Edgar, Siobhan McRee, Gina Andretta, Chelsea Chen, Christophe Colas, Wei Fang, Wayne Kong, Jason Long, Fang Liu, Jared Moore, Aleskandr Pankov, Dan Shore, Joanne Tan, Robert Warne, Livia Ulicna, Rakesh Vekariya, Anneleen Daemen, F Romero, Melissa R. Junttila, Lori S. Friedman. ORIC-613, a potential first- and best-in-class, orally bioavailable, potent and selective PLK4 inhibitor with synthetic lethality in TRIM37 high cancer models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 594.

Abstract 583: Spatiotemporal analyses of longitudinal specimens reveal mechanisms of response and resistance to immune checkpoint blockade in metastatic melanoma

Abstract Although durable responses to immune checkpoint blockade (ICB) therapies exhibit by a subgroup of patients with metastatic melanoma, most of patients have no response or develop to acquired resistance to ICB therapies within few years. Molecular mechanisms of primary and acquired resistance to ICB therapies are still elusive, which warrants further elucidation. Here, we used a holistic approach to profile 35 longitudinal tumor specimens at pre-, on-, and post-treatment time points which were derived from 7 patients (3 responders and 4 non-responders) with metastatic melanoma who progressed on sequential ICB therapies, including anti-PD-1 and anti-CTLA-4 therapies. We integrated analyses of RNA sequencing, Whole-exome Sequencing (WES), NanoString nCounter vantage 3D, Cyclic Immunofluorescence (Cyc-IF), NanoString Digital Spatial Profiling (DSP) and Multiplex Immunohistochemistry (mIHC) data of these longitudinal tumor specimens to elucidate evolutionary trajectories that occurred in both the tumor and the tumor immune microenvironment (TiME) that may have orchestrated response and resistance to ICB therapies. Simultaneous analyses of genomic, transcriptomic and proteomic data identified the re-activation of MAPK-RAS as well as PI3K pathway upon or after ICB therapies, which may elucidate the mechanism of resistance to ICB therapies. Spatially-resolved, image-based immune monitoring analysis at single cell resolution revealed that the activation status of T cell markers and T-cell checkpoint is conversely associated with potential mechanism of resistance to ICB therapies. In summary, integrated analyses of muti-oics and spatially image-based data from longitudinal tumor specimens allow us not only to reveal the dynamic co-revolution of both tumor and immune cells in TiME following sequential ICB therapies but also to provide a comprehensive perspective to molecular mechanisms of response and resistance to ICB therapies in patients with metastatic melanoma. Citation Format: Gao Zhang, Lisa Coussens, Meenhard Herlyn, Keith T. Flaherty, Genevieve Boland, Gordon Mills. Spatiotemporal analyses of longitudinal specimens reveal mechanisms of response and resistance to immune checkpoint blockade in metastatic melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 583.

Abstract 7406: Visualization and analysis of cancer genomics data using UCSC Xena

Abstract UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. We support Mac, Windows, and Linux. Questions Xena can help you answer:* Is overexpression of this gene associated with lower/higher survival?* What genes are differentially expressed between these two groups of samples?* What is the relationship between mutation, copy number, expression, etc for this gene? Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links. Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, GSEA, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. New features include: * Genome-wide differential gene expression analysis * GSEA analysis * Select samples directly from the screen for filtering and creating subgroups * Loading of Microsoft Excel files Our beta prototype site for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Contact us for access to our beta prototype site. If you use us please cite our publication in Nature Biotechnology: https://www.nature.com/articles/s41587-020-0546-8 Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7406.

Abstract 3367: R-loop type determines the orientation of transcription-replication conflicts attenuated by transcription factor IIS

Abstract During human Topoisomerase I (Top1) catalytic activity, a transient Top1-DNA cleavage complex (Top1cc) forms to allow DNA relaxation. Top1ccs can be stabilized by antitumor Top1 poisons, such as clinically-effective camptothecin analogs. Camptothecin-stabilized Top1ccs can lead to R-loop-dependent transcription-replication conflicts (TRCs), single-end DNA double-strand breaks (seDSBs) and genome instability in cancer cells. To define the genomic location of TRCs and the underlying mechanisms, we integrated genomic maps of Top1cc-induced DNA cleavage by END-seq and DNA:RNA hybrids by DRIP, determined the cell-cycle specificity of micronuclei formation and the effects of elongation transcription factor IIS (TFIIS). We have defined the genomic sites of both seDSBs and increased R-loop levels (gains) induced by Top1ccs. seDSBs are highly enriched at regions with R-loop gains at highly-transcribed genes in early replicating zones. Kinetics analyses of R-loops and genomic ChIP data of RNA polymerase II (RNAPII) revealed that R-loop gains can either be upstream or downstream of RNAPII. TFIIS effects showed that downstream R-loop gains can follow RNAPII backtracking and arrest. seDSB strandness showed that co-directional TRCs occur mainly at downstream hybrid-associated RNAPII, whereas head-on TRCs occur mainly at upstream hybrid-associated RNAPII. Therefore, Top1ccs can increase two structurally-different R-loops and R-loop-associated backtracked RNAPII causes co-directional TRCs at higher rate than head-on collisions. Moreover, Top1ccs trigger micronuclei formation at G1/early S, but not late S/G2, which are attenuated by TFIIS. Thus, our findings establish a role of RNAPII backtracking at highly-transcribed genes in Top1cc-induced co-directional TRCs and micronuclei formation. The findings can accelerate the development of new effective strategies to treat cancer patients. (Supported by Associazione Italiana per la Ricerca sul Cancro, Milan, Italy - IG 2019 - ID. 23032 to C.G. and by the European Union - NextGenerationEU through the Italian Ministry of University and Research under PNRR - M4C2-I1.3 Project PE_00000019 "HEAL ITALIA" to G.C., CUP J33C22002920006). Citation Format: Renée C. Duardo, Jessica Marinello, Marco Russo, Sara Morelli, Simona Pepe, Federico Guerra, Belen Gomez-González, Andrés Aguilera, Giovanni Capranico. R-loop type determines the orientation of transcription-replication conflicts attenuated by transcription factor IIS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3367.

Abstract 5133: Gastric cancer carrying FAT4 mutations associated with favorable prognosis in comparison to wild-type: Results from Real-World Cohorts

Abstract Introduction: Gastric cancer (GC) is a complex and heterogeneous disease with variable clinical outcomes. Identifying prognostic factors that can reliably predict patient outcomes is crucial for personalized treatment strategies. In this study, we aimed to investigate the impact of mutations in the FAT4 gene as potential prognostic factors in gastric cancer. Methods: We conducted a comprehensive analysis of genomic data from a cohort of 236 gastric cancer patients to identify mutations in the FAT4 gene and validated it using the TCGA cohort. The mutational landscape of FAT4 was analyzed using next-generation sequencing (NGS) techniques. Clinical data, including patient demographics, tumor characteristics, and survival outcomes, were collected and correlated with the presence of FAT4 cadherin mutations. Results: In the real-world cohort, the occurrence of mutations of FAT4 was 19.1% (45/236), while in the TCGA cohort, it was 21.0% (89/423). Specifically, the mutation rates in the single cadherin functional domain of FAT4 were 10.2% (24/236) in the real-world cohort and 8.0% (34/423) in the TCGA cohort. The mutation rates for multiple cadherin sites were 2.5% (6/236) and 2.1% (9/423) in the real-world and TCGA cohorts, respectively. Survival analysis revealed that patients with FAT4 mutations had a significantly better prognosis compared to those with wild-type FAT4 (p=0.0357). Patients with multiple cadherin mutations had a better prognosis compared to those with single cadherin mutations in real-world cohorts (p=0.0651). No significant difference was observed in the TCGA cohort between single and multiple cadherin mutations (p = 0.2003), although FAT4 mutations exhibited a significant improvement in prognosis compared to wild-type patients in the TCGA cohort (p = 0.0226). Conclusion: Our findings indicate that FAT4 mutations confer a favorable prognosis in gastric cancer patients. The presence of FAT4 mutations was associated with improved survival compared to patients with wild-type FAT4. These findings underscore the potential value of FAT4 mutation status as a prognostic factor, guiding personalized treatment approaches for individuals with gastric cancer. Citation Format: Shu Wang, Haoyuan Wang, Yuxuan Ma, Yuhao Wang, Yan Zhao, Chaosheng Peng, Weiming Duan, Feilong Zhao, Jianjun Yang. Gastric cancer carrying FAT4 mutations associated with favorable prognosis in comparison to wild-type: Results from Real-World Cohorts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5133.

Abstract 3532: Transforming genomic data into images for enhanced deep learning in precision oncology

Abstract Purpose: In the realm of cancer treatment and research, the analysis of genomic data stands as a cornerstone for diagnosis and therapy. Recognizing this, our study embarks on an ambitious journey to revolutionize how we interpret such data. By converting complex genomic data into a two-dimensional (2D) image format leveraging feature correlations, we pave the way for more profound insights and applications in clinical decision-making. This innovative approach utilizes 2D convolutional neural networks (CNNs) to analyze these structured images, promising a leap forward in the accuracy and utility of such data. Methodology: The core of our method involves a novel transformation of tabular genomic data into an image format. We start with m patient samples, each characterized by n genes, and calculate a pairwise correlation matrix to capture the intricate relationships between these genes. Concurrently, we generate an Euclidean distance matrix that represents the distances between n points in a 2D grid. The next step involves the optimization of the Gromov-Wasserstein discrepancy to align these two matrices, resulting in a transformation matrix, T. When applied to the tabular data, T transforms it into m distinct, informative images. These images are then processed using a 10-layer CNN, comprising a convolutional layer (3 × 3 kernel), three dense layers, two relu layers, and a dropout layer. Data Sources: We created images, termed 'genomaps', from genomic data collected from three diverse patient groups: 130 individuals (cancerous and normal) from Stanford Hospital, 230 breast cancer patients from The Cancer Genome Atlas (TCGA), and a larger group of 1572 patients from the Memorial Sloan Kettering (MSK) Cancer Center. The created genomaps are visually interpretable, offering clear differentiation between cancerous and non-cancerous samples, which is pivotal for accurate analysis. Outcomes: Our method demonstrates a remarkable 8% improvement in survival prediction accuracy in comparison to the existing methods. The employment of DeepSHAP in our analysis has allowed us to identify critical genes with greater precision than conventional methods. The p53 gene family, notably TP53 and TP63, was identified as significantly mutated in over 50% of all cancer types analyzed. Another critical discovery was the ERBB gene family, encompassing EGFR, ERBB2, ERBB3, ERBB4, which plays a dual role in tumor proliferation and influencing the immune response against tumors, a factor critical for immunotherapy strategies. Conclusion: This research introduces a transformative approach to represent genomic data, utilizing 2D CNNs for in-depth analysis. It sets a new benchmark in classification and regression accuracy, offering a more interpretable pathway for biomarker discovery, significantly contributing to the field of personalized medicine and cancer research. Citation Format: Md Tauhidul Islam, Lei Xing. Transforming genomic data into images for enhanced deep learning in precision oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3532.

Molecular genetic foundation of a sex-linked tailless trait in Hongshan chicken by whole genome data analysis

As a Chinese local chicken breed, Hongshan chickens have 2 kinds of tail feather phenotypes, normal and taillessness. Our previous studies showed that taillessness was a sex-linked dominant trait. Abnormal development of the tail vertebrae could be explained this phenomenon in some chicken breeds. However, the number of caudal vertebrae in rumpless Hongshan chickens was normal, so rumplessness in Hongshan chicken was not related to the development of the caudal vertebrae. Afterwards, we found that rumplessness in Hongshan was due to abnormal development of tail feather rather than abnormal development of caudal vertebrae. In order to understand the genetic foundation of the rumplessness of Hongshan chickens, we compared and reanalyzed 2 sets of data in normal and rumpless Hongshan chickens from our previous studies. By joint analysis of genome-wide selection signature analysis and genome-wide association approach, we found that 1 overlapping gene (EDIL3) and 16 peak genes (ENSGALG00000051843, ENSGALG00000053498, ENSGALG00000054800, KIF27, PTPRD, ENSGALG00000047579, ENSGALG00000041052, ARHGEF28, CAMK4, SERINC5, ENSGALG00000050776, ERCC8, MCC, ADAMTS19, ENSGALG00000053322, CHRNA8) located on the Z chromosome was associated with the rumpless trait. The results of this study furtherly revealed the molecular mechanism of the rumpless trait in Hongshan chickens, and identified the candidate genes associated with this trait. Our results will help to improve the shape of chicken tail feathers and to rise individual economic value in some specific market in China.

Genome-Wide Comparative Analysis of Five Amaranthaceae Species Reveals a Large Amount of Repeat Content.

Amaranthus is a genus of C4 dicotyledonous herbaceous plant species that are widely distributed in Asia, Africa, Australia, and Europe and are used as grain, vegetables, forages, and ornamental plants. Amaranth species have gained significant attention nowadays as potential sources of nutritious food and industrial products. In this study, we performed a comparative genome analysis of five amaranth species, namely, Amaranthus hypochondriacus, Amaranthus tuberculatus, Amaranthus hybridus, Amaranthus palmeri, and Amaranthus cruentus. The estimated repeat content ranged from 54.49% to 63.26% and was not correlated with the genome sizes. Out of the predicted repeat classes, the majority of repetitive sequences were Long Terminal Repeat (LTR) elements, which account for about 13.91% to 24.89% of all amaranth genomes. Phylogenetic analysis based on 406 single-copy orthologous genes revealed that A. hypochondriacus is most closely linked to A. hybridus and distantly related to A. cruentus. However, dioecious amaranth species, such as A. tuberculatus and A. palmeri, which belong to the subgenera Amaranthus Acnida, have formed their distinct clade. The comparative analysis of genomic data of amaranth species will be useful to identify and characterize agronomically important genes and their mechanisms of action. This will facilitate genomics-based, evolutionary studies, and breeding strategies to design faster, more precise, and predictable crop improvement programs.