bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 6198: TP53 mutational clusters stratify the Li-Fraumeni syndrome spectrum
Authors: Fischer Nicholas W. , Laverty Brianne , Kafri Ran , Maxwell Kara N. , Woodward Emma R. , Kratz Christian , Malkin David 
Published: March 2024

Abstract
Li-Fraumeni syndrome (LFS) is a highly penetrant cancer predisposition disorder caused by germline variants in the TP53 tumor suppressor gene. LFS has recently been redefined as a ‘spectrum’ disorder to reflect the highly variable cancer types with largely unpredictable ages-of-onset and disease severity. The broad functional gradient associated with different TP53 variants is thought to contribute to LFS heterogeneity, although it is still poorly understood and there is an unmet clinical need for risk stratification strategies to improve variant interpretation and patient care. Here, we performed an unsupervised cluster analysis leveraging p53 mutagenesis datasets (Kato et al., 2003 & Giacomelli et al., 2018) that revealed five TP53 mutational clusters with unique mutation patterns, structural features, and functional consequences. Stratifying germline carriers based on the five clusters exposed important clinical characteristics to consider for patient management, such as ages-of-onset, tumor type development, and survival outcomes. In particular, we discovered an osteosarcoma-prone subgroup comprised of monomeric mutant p53 carriers with aggressive cancer phenotypes. We also identified a cluster of TP53 variants found more often in non-cancer and healthy older populations, and carriers of these variants that developed cancer had less “LFS-like” phenotypes including an older age-of-onset and a significantly higher frequency of colorectal cancers. Remarkably, our TP53 variant clustering strategy could also stratify breast cancer survival among germline carriers. This work provides a new framework to delineate the LFS spectrum toward the development of machine learning-based approaches for personalized cancer surveillance plans.
Citation Format: Nicholas W. Fischer, Brianne Laverty, Ran Kafri, Kara N. Maxwell, Emma R. Woodward, Christian Kratz, David Malkin. TP53 mutational clusters stratify the Li-Fraumeni syndrome spectrum [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 6198.
Abstract 6218: Haplotype-resolved analysis of cancer genomes and epigenomes using Oxford Nanopore sequencing
Authors: Rescheneder Philipp , James Phill , McKenzie Sean , Talenti Andrea , Aganezov Sergey , Turner Dan , Juul Sissel 
Published: March 2024

Abstract
Cancer is a complex and dynamic disease driven by somatic genomic and epigenomic alterations that accumulate over time. These changes give rise to heterogeneous collections of cells or clones, each with distinct (epi)genomic profiles within a single tumour. Accurate characterisation of these changes is crucial for understanding the mechanisms driving the disease, identifying potential therapeutic targets, and personalising treatment strategies. Due to the technological constraints of short-read and array-based approaches, cancer research has historically had a strong focus on detecting small genomic changes like SNPs and small indels (SNVs) as well as the broad characterisation of large-scale copy number changes (CNVs), mostly ignoring other important variant classes and epigenetic modifications. Here we demonstrate how Oxford Nanopore native long-read sequencing enables the direct detection of not only SNVs, but also break point-resolved simple and complex structural variants (SVs and CNVs), haplotype phasing of all variant types, and identification of DNA modifications like 5-methylcytosine (5mC) and 5-hydroxy-methylcytosine (5hmC) from a single tumour-normal dataset. We illustrate how our end-to-end somatic workflow streamlines analysis for the different variant classes and benchmark somatic SNV and SV calling performance using well characterised cancer cell lines and in-silico benchmarking datasets at different sequencing depths. Finally, we use real-world tumour-normal pairs to showcase a comprehensive sample to answer tumour-normal analysis using Nanopore sequencing. This includes the characterisation of complex patterns of somatic SVs in the different cancer samples, the identification of unique 5mC methylation patterns, exploring characteristic differences in 5hmC levels between tumour and normal samples as well as showing how Nanopore long reads enable haplotype and clone specific CNV calling as well as phasing of genetic and epigenetic variation.
Citation Format: Philipp Rescheneder, Phill James, Sean McKenzie, Andrea Talenti, Sergey Aganezov, Dan Turner, Sissel Juul. Haplotype-resolved analysis of cancer genomes and epigenomes using Oxford Nanopore sequencing [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 6218.
Abstract 6240: Mutation impact on mRNA versus protein expression across human cancers
Authors: Liu Yuqi , Elmas Abdulkadir , Huang Kuan-lin 
Published: March 2024

Abstract
Cancer mutations are often assumed to alter proteins, thus promoting tumorigenesis. However, how mutations affect protein expression has rarely been systematically investigated. We conduct a comprehensive analysis of mutation impacts on mRNA- and protein-level expressions of 953 cancer cases with paired genomics and global proteomic profiling across six cancer types. Protein-level impacts are validated for 47.2% of the somatic expression quantitative trait loci (seQTLs), including mutations from likely “long-tail” driver genes. Devising a statistical pipeline for identifying somatic protein-specific QTLs (spsQTLs), we reveal several gene mutations, including NF1 and MAP2K4 truncations and TP53 missenses showing disproportional influence on protein abundance not readily explained by transcriptomics. Cross-validating with data from massively parallel assays of variant effects (MAVE), TP53 missenses associated with high tumor TP53 proteins were experimentally confirmed as functional. Our study demonstrates the importance of considering protein-level expression to validate mutation impacts and identify functional genes and mutations.
Citation Format: Yuqi Liu, Abdulkadir Elmas, Kuan-lin Huang. Mutation impact on mRNA versus protein expression across human cancers [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 6240.
Abstract 6241: A large scale proteogenomic atlas for precision oncology research
Authors: Simchi Nitzan , Givton Ofer , Daitchman Dina , Seger Eran , Pevzner Kirill 
Published: March 2024

Abstract
Recent advances in the field of mass spectrometry proteomics have led to growth in proteomics research resulting in a massive and rapid increase in the volume of high-quality publicly available proteomics datasets. This growth creates an underutilized resource with potential for new discoveries. Effectively utilizing this vast resource of preclinical and clinical samples is a major challenge.
We utilize the growing availability of proteomics and post-translational modifications (PTMs) data for drug and biomarker discovery. This innovative computational approach enables the integration of preclinical and clinical data, both public and proprietary, with a specific emphasis on extracting valuable biological insights that are only observable at the protein level or through PTMs.
We developed a platform that combines multi-omic data curated from the public domain, together with internally generated high quality proteomic and phosphoproteomic data. The platform incorporates diverse clinical annotations, tumor and normal samples, drug sensitivity, patient outcomes and relevant bioinformatic knowledge bases.
The platform can be leveraged to generate insights addressing diverse clinical needs. For example, we demonstrate how our platform can be utilized to estimate the clinical prevalence of a certain protein or protein signature, in order to identify novel response biomarkers with the highest likelihood of success by confirming its presence and variance in clinical datasets. In addition, these insights enable novel understanding of response and resistance mechanisms, thus establishing a framework that expedites the generation of hypotheses for subsequent investigation and experimental validation.
Citation Format: Nitzan Simchi, Ofer Givton, Dina Daitchman, Eran Seger, Kirill Pevzner. A large scale proteogenomic atlas for precision oncology research [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 6241.
Abstract 6244: MultiTME: A Bayesian method for integration and analysis of multimodal, multi-patient spatial profiling data
Authors: Zhang Haoran , Quinn Jeff , Yoffe Liron , Mittal Vivek , Tansey Wesley 
Published: March 2024

Abstract
The role of the tumor microenvironment (TME) in cancer evolution and progression is complex and multi-faceted. Multiple cellular phenotypes and patterns of cell-cell communication have been implicated as drivers of drug resistance and metastasis. The spatial architecture of the TME is emerging as particularly important in characterizing the trajectory of tumor growth and response to therapy. Accordingly, there has been a rapid rise in technologies to spatially profile the TME. Each technology focuses on a different modality (e.g. transcriptomics, proteomics, metabolomics) and operates at a different granularity, from coarse-grained tissue spots aggregating dozens of cells to fine-grained subcellular resolution. There is currently no computational method capable of drawing unified inferences across modalities. We introduce MultiTME, a single, holistic generative model for jointly modeling multimodal data across multiple samples in a cohort. MultiTME addresses these challenges through a two-dimensional integration of both samples and multimodal data. MultiTME incorporates a cohort of tissue samples, enabling the joint modeling of different samples within a single analytical framework. This integration allows for a comprehensive analysis of the spatial distribution of cell types across multiple samples, enhancing the understanding of TME variability and the identification of robust biomarkers. MultiTME also integrates multimodal data with varying resolutions, such as Spatial Transcriptomics (ST), single-cell RNA sequencing (scRNA-seq), and Imaging Mass Cytometry (IMC), and provides a multi-layered understanding of the TME. This integration enables the modeling of TME at a finer scale and the discovery of cell types and subtypes that are not distinguishable using single-modal approaches. It also provides concurrent expression profiles across different modalities, leading to the identification of novel biomarkers. We demonstrate MultiTME's efficiency and scalability with a dataset of coupled ST, scRNA-seq, and IMC from a cohort of 8 lung cancer patients. MultiTME's capability to handle both intra-cohort and inter-modality variability offers a panoramic view of TME and helps identify biomarkers and therapeutic targets for cancer screening and treatment. MultiTME is open source.
Citation Format: Haoran Zhang, Jeff Quinn, Liron Yoffe, Vivek Mittal, Wesley Tansey. MultiTME: A Bayesian method for integration and analysis of multimodal, multi-patient spatial profiling data [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 6244.
Abstract 6257: Genome wide, high-throughput, high-resolution structural variation detection at low variant allele fraction for oncology samples
Authors: Hastie Alex R. , Lynch Pat 
Published: March 2024

Abstract
Structural variation detection remains an elusive undertaking. For decades, G-banded karyotyping has remained the standard practice for nearly all hematological malignancy samples. Cancers are diseases of the genome and structural variants (SVs) are a hallmark of nearly all cancers, are often drivers of oncogenicity, and can sometimes be targeted for treatment, as highlighted by the discovery of the Philadelphia chromosome and later the development of targeted kinase inhibitors. One of the most important limitations of karyotyping, its requirement for cells to divide in culture, has prevented its application for most solid tumors and therefore, there is considerably less understanding of the impact of SVs in solid tumors. Optical genome mapping is a technique that is revolutionizing cytogenomics and unlocking the understanding of SVs in the genome at a level never seen before. It not only can detect nearly all variants that a karyotype can, but it can provide 10,000x higher resolution and does not require cell culture. Because of this, it is being adopted for oncology studies across hematological malignancies and solid tumors. This study demonstrates the performance of a new high throughput Stratys instrument for generation of optical genome map data at extraordinarily high coverage depth. With this instrument, 12 samples can be loaded at once, each loaded onto its own cartridge and the instrument can collect 400x genome coverage for each of these samples in 24 hours. The instrument has random access to the cartridges so that completed samples can be swapped out to facilitate optimal instrument run efficiency. To date, this system has been demonstrated to run 60 samples in a week, each at 400x coverage. Data from the instrument can be processed by Bionano Access™ and VIA™ software to call SVs, copy number variants (CNVs), regions of loss of heterozygosity (LOH), on a genome wide basis, and to classify those variants automatically according to guidelines such as the National Comprehensive Cancer Network (NCCN), World Health Organization (WHO), and National Health Services (NHS, United Kingdom). This new high throughput system with advanced bioinformatic data analysis has the potential to further transform structural variation analysis in cancer research.
Citation Format: Alex R. Hastie, Pat Lynch. Genome wide, high-throughput, high-resolution structural variation detection at low variant allele fraction for oncology samples [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 6257.
Abstract 6275: Exposure to neighborhood violence rewires glucocorticoid receptor binding and drives proliferation and invasiveness in lung tumor samples
Authors: Heath Hannah , Yoo Jin Young , Sharma Vani , McGee Hannah , Soliman Aiman , Mohamed Abeer , Matthews Alicia K. , Winn Robert , Madak-Erdogan Zeynep , Kim Sage 
Published: March 2024

Abstract
Background: Despite smoking less, Black men experience higher incidence of lung cancer compared to white men. This health disparity is even more prevalent in Black men from Chicago, suggesting local neighborhood factors impacting tumorigenesis. Preliminary data indicate that, compared to white men, Black men are more likely to reside in neighborhoods with high levels of violence and have elevated levels of hair cortisol.
Objective: To understand the link between lung tumorigenesis and exposure to neighborhood violence, our objective is to investigate the chromatin recruitment of the receptor for cortisol, called the glucocorticoid receptor (GR), and to correlate this recruitment with target gene expression levels.
Methods: Utilizing CUT & RUN, we identified the gene binding sites of GR using 15 lung tumor and corresponding healthy tissue samples from lung cancer patients living in Chicago. GR recruitment to chromatin was correlated with the neighborhood homicide rate obtained using patients’ zip codes. Spatial transcriptome profiles for these 15 lung tumor samples were obtained from the Space Ranger pipeline and gene expression hot spots of GR target genes were visualized using Loupe Browser (10x Genomics).
Results: GR recruitment to regulatory regions of chromatin increases with greater zip code level violence rates. In patients from high violence neighborhoods, tumor samples had higher magnitude of binding compared to normal neighboring tissue. In tumor samples from patients living in high violence compared to low violence neighborhoods, GR binding is seen in genes that are associated with increased tumor aggressiveness. Hot-spot analysis of these genes found strong co-expression of genes associated with proliferation and invasion in samples from high violence but not low violence neighborhoods.
Conclusion: Exposure to neighborhood violence may impact tumor biology via increased GR recruitment to genes associated with proliferation and invasion.
Citation Format: Hannah Heath, Jin Young Yoo, Vani Sharma, Hannah McGee, Aiman Soliman, Abeer Mohamed, Alicia K. Matthews, Robert Winn, Zeynep Madak-Erdogan, Sage Kim. Exposure to neighborhood violence rewires glucocorticoid receptor binding and drives proliferation and invasiveness in lung tumor samples [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 6275.
Abstract 6346: IgG engineered to target PIGR-mediated transcytosis specifically targets intracellular oncoproteins and abrogated tumor growth
Authors: Galindo Carmen Maria Anadon , Lopez-Bailon Luis U. , Chaurio Ricardo A. , Mine Jessica A. , Plappert Noah E. , Conejo-Garcia Jose R. 
Published: March 2024

Abstract
Despite their long half-life, therapeutic antibodies are considered ineffective against intracellular antigens due to their perceived inability to penetrate epithelial cells. Using recombinant antibodies targeting common mutations in oncogenes, we have demonstrated that dimeric IgA, but not the same antibody on an IgG backbone, penetrates human epithelial cancer cells through PIGR-dependent directional transcytosis, specifically neutralizing mutated oncodrivers and expelling antigens outside the cell, bound to secretory IgA. Accordingly, targeting of KRASG12D abrogated tumor cell proliferation in cell culture assays. In vivo, KRASG12D-specific dIgA1 limited the growth of RASG12D-mutated ovarian and lung carcinomas, including in syngeneic tumor-bearing immunocompetent mice. Moreover, dIgA targeting of KRASG12D restricted tumor growth more effectively than small-molecule KRASG12D inhibitors, supporting the potential of this approach for the treatment of human cancers. Since producing dimeric IgA is more challenging than producing monomeric IgG, and because the half-life of IgA is shorter (4-7 days) than IgG (21 days), we have developed a new approach combining KRASG12D specific IgG with a peptide that could join PIGR and penetrates tumor cells through transcytosis, that showed the same effect than IgA controlling tumor growth. Our results provide a rationale for developing modified IgG cell-penetrating antibodies to effectively target common mutations in intracellular oncogenes that drive many aggressive and frequent human cancers. In addition, further experiments suggest that these antibodies could be effectively used to target tumors that have become resistant to existing KRAS inhibitors.
Citation Format: Carmen Maria Anadon Galindo, Luis U. Lopez-Bailon, Ricardo A. Chaurio, Jessica A. Mine, Noah E. Plappert, Jose R. Conejo-Garcia. IgG engineered to target PIGR-mediated transcytosis specifically targets intracellular oncoproteins and abrogated tumor growth [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 6346.
Abstract 6358: Arming tumor-associated macrophages to inhibit cancer progression
Authors: Reddy Joshua P. , Yu Ziqi , Shepard Ryan M. , Von Schalscha Tami , McCormack Stephen J. , Weis Sara M. , Cheresh David A. , Wettersten Hiromi I. 
Published: March 2024

Abstract
Integrin αvβ3 is a marker of cancer progression in a range of epithelial cancers. In the 1990s, a humanized anti-αvβ3 antibody (Etaracizumab) was developed to target αvβ3+ cancer cells via NK cell-mediated cytotoxicity. While it demonstrated safety and some efficacy in clinical trials, our recent study of the immune microenvironment in αvβ3+ human epithelial tumors revealed increased tumor-associated macrophages (TAMs) but minimal NK cell accumulation. Accordingly, we re-engineered Etaracizumab to favor TAM engagement over NK cells and compared the efficacy of this antibody (ABT101) to that of Etaracizumab in various αvβ3+ epithelial cancer models. While ABT101 and Etaracizumab exhibited identical affinity for αvβ3, ABT 101 showed superior anti-tumor activity in vivo. Notably, depleting NK cells in mice had no impact on ABT101’s effectiveness, while macrophage depletion completely abolished its anti-tumor activity. Interestingly, tumors in mice treated with ABT101 displayed a significant increase in TAMs. Here we define an “antigen-effector cell matching” strategy that allows for maximal anti-tumor activity for various drug resistant epithelial cancers. ABT101 will enter clinical trials for patients with drug resistant lung cancer in the first quarter of 2024.
Citation Format: Joshua P. Reddy, Ziqi Yu, Ryan M. Shepard, Tami Von Schalscha, Stephen J. McCormack, Sara M. Weis, David A. Cheresh, Hiromi I. Wettersten. Arming tumor-associated macrophages to inhibit cancer progression [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 6358.