Cancer Genomic Studies Research Articles

Towards functional maps of non-coding variants in cancer

Large scale cancer genomic studies in patients have unveiled millions of non-coding variants. While a handful have been shown to drive cancer development, the vast majority have unknown function. This review describes the challenges of functionally annotating non-coding cancer variants and understanding how they contribute to cancer. We summarize recently developed high-throughput technologies to address these challenges. Finally, we outline future prospects for non-coding cancer genetics to help catalyze personalized cancer therapy.

Ancestry and somatic profile predict acral melanoma origin and prognosis.

Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, here we characterise the genome and transcriptome of 128 acral melanoma tumours from 96 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF, NRAS or NF1. While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower number of structural variants. These BRAF-mutated tumours have a transcriptional profile similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and transcriptional profiling defined three expression clusters; these characteristics were associated with overall survival. We highlight novel low-frequency drivers, such as SPHKAP, which correlate with a distinct genomic profile and clinical characteristics. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

Abstract C153: Transcriptome-wide study identifies unique pathway functional clusters associated with breast cancer subtypes in population with high indigenous american ancestry

Abstract Purpose: Breast cancer incidence and outcomes differ by US census racial/ethnic category. Since large-scale genetic studies of human disease are predominately focused on populations of European ancestry, little is known about breast cancer molecular biology in Hispanic/Latinos which can widen cancer health disparities due to suboptimal translation of discoveries into clinical practice or public health policy. We aim to identify most relevant pathways in breast cancer subtype differentiation in breast cancer patients from Peru and compared them with those obtained analyzing the Cancer Genome Atlas (TCGA) Breast Cancer (BRCA) dataset and with the published results of the Molecular Profile of Breast Cancer Study (MPBCS), a multi-country Latin American cohort. Patients and Methods: Formalin fixed paraffin embedded tumor tissues samples were whole exome sequenced for a total of 271 patients recruited by the Peruvian Breast Cancer Genomics Study (PEGEN-BC). Quality control was conducted to remove genes with low counts for PEGEN and TCGA-BRCA cohorts, and only female stage II-III breast cancer patients were kept for downstream analysis. Intrinsic tumor subtypes were classified using the estrogen receptor (ER) status-adjusted PAM50 method with genefu package in R. Differential gene expression and pathway analyses between subtypes were performed by the GSEA 4.3.2 software. [LF1] [CX2] Statistical and biological significance was determined using p-values < 0.1 and absolute value of enrichment score > 1.5. Top 20 pathways for each cohort were selected and ranked for comparison. Results: PAM50 classification of the PEGEN-BC cohort defined 20.2% of tumors as Luminal A (LumA), 27.9% as Luminal B (LumB), 27.1% as HER2E, 22.5% as Basal and 2.3% as Normal. Transcriptomic pathway analysis showed that most of the significantly changed pathways were similar to previous findings in the literature, for example, proliferation pathways being upregulated in LumB, HER2E and Basal tumors, compared to the LumA subtype, and a strong dependency on the estrogen pathways for LumA and LumB. Within cohorts, we identified novel clusters of pathways that share related functions. For example, 6 out of the 20 top significant pathways in LumB vs. HER2E comparison for the PEGEN-BC cohort were glycan/glycosylation-related, suggesting the possibility of tumor immune response differences between LumB and HER2E due to glycan related pathway differences among Peruvian patients. Conclusions: We identified novel pathway clusters associated with specific breast cancer subtypes in a population with high Indigenous American ancestry from Peru. Further analyses will be conducted next to explore if the observed patterns were specific to genetic ancestry background or observed independently of genetic ancestry. Citation Format: Chenghuiyun Xu, Valentina A. Zavala, David Rocke, Xiaosong Huang, Sandro Casavilca-Zambrano, Jeannie Navarro-Vásquez, Ruddy Liendo-Picoaga, Monica Calderón, Guillermo Valencia, Patricia Rioja, Carlos A Castañeda, Zaida Morante, Silvia P. Neciosup, Hugo A Fuentes, Julio E. Abugattas, Jose M. Cotrina, Luis A. Salinas, Marco Gálvez-Nino, Jovanny Zabaleta, Andrea S. Llera, Tatiana Vidaurre, Laura Fejerman. Transcriptome-wide study identifies unique pathway functional clusters associated with breast cancer subtypes in population with high indigenous american ancestry [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C153.

Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies.

Variable selection methods have been extensively developed for and applied to cancer genomics data to identify important omics features associated with complex disease traits, including cancer outcomes. However, the reliability and reproducibility of the findings are in question if valid inferential procedures are not available to quantify the uncertainty of the findings. In this article, we provide a gentle but systematic review of high-dimensional frequentist and Bayesian inferential tools under sparse models which can yield uncertainty quantification measures, including confidence (or Bayesian credible) intervals, p values and false discovery rates (FDR). Connections in high-dimensional inferences between the two realms have been fully exploited under the "unpenalized loss function + penalty term" formulation for regularization methods and the "likelihood function × shrinkage prior" framework for regularized Bayesian analysis. In particular, we advocate for robust Bayesian variable selection in cancer genomics studies due to its ability to accommodate disease heterogeneity in the form of heavy-tailed errors and structured sparsity while providing valid statistical inference. The numerical results show that robust Bayesian analysis incorporating exact sparsity has yielded not only superior estimation and identification results but also valid Bayesian credible intervals under nominal coverage probabilities compared with alternative methods, especially in the presence of heavy-tailed model errors and outliers.

The spike-and-slab quantile LASSO for robust variable selection in cancer genomics studies.

Data irregularity in cancer genomics studies has been widely observed in the form of outliers and heavy-tailed distributions in the complex traits. In the past decade, robust variable selection methods have emerged as powerful alternatives to the nonrobust ones to identify important genes associated with heterogeneous disease traits and build superior predictive models. In this study, to keep the remarkable features of the quantile LASSO and fully Bayesian regularized quantile regression while overcoming their disadvantage in the analysis of high-dimensional genomics data, we propose the spike-and-slab quantile LASSO through a fully Bayesian spike-and-slab formulation under the robust likelihood by adopting the asymmetric Laplace distribution (ALD). The proposed robust method has inherited the prominent properties of selective shrinkage and self-adaptivity to the sparsity pattern from the spike-and-slab LASSO (Roc̆ková and George, J Am Stat Associat, 2018, 113(521): 431-444). Furthermore, the spike-and-slab quantile LASSO has a computational advantage to locate the posterior modes via soft-thresholding rule guided Expectation-Maximization (EM) steps in the coordinate descent framework, a phenomenon rarely observed for robust regularization with nondifferentiable loss functions. We have conducted comprehensive simulation studies with a variety of heavy-tailed errors in both homogeneous and heterogeneous model settings to demonstrate the superiority of the spike-and-slab quantile LASSO over its competing methods. The advantage of the proposed method has been further demonstrated in case studies of the lung adenocarcinomas (LUAD) and skin cutaneous melanoma (SKCM) data from The Cancer Genome Atlas (TCGA).

Discovery of Novel, Potent and Orally Bioavailable SMARCA2 PROTACs with Synergistic Anti-tumor Activity in Combination with KRAS G12C Inhibitors.

Cancer genomic studies have identified frequent mutations in subunits of the SWI/SNF chromatin remodeling complex including SMARCA4 in non-small cell lung cancer with a frequency of up to 33% in advanced stage disease, making it the most frequently mutated complex in lung cancer. We and others have identified SMARCA2 to be synthetic lethal to SMARCA4, indicating SMARCA2 is a high value therapeutic target. Here, we disclose the discovery and characterization of potent, selective and orally bioavailable Cereblon-based SMARCA2 PROTACs. Biochemically, YDR1 and YD54 are potent SMARCA2 degraders with an average DC 50 of 7.7nM and 3.5nM respectively in SMARCA4 mutant lung cancer cells. Phenotypically, both YDR1 and YD54 selectively inhibited growth of SMARCA4 mutant cancer cells. Further, we showed anti-tumor growth inhibitory activity of YDR1 and YD54 in SMARCA4 mutant xenograft models of lung cancer. Finally, we show that YDR1 and YD54 synergize with the KRAS G12C inhibitor sotorasib to inhibit growth of SMARCA4 and KRAS G12C co-mutant lung cancer cells. These findings provide additional evidence for the utility of single agent or combination regimens containing SMARCA2 PROTACs as synthetic lethal therapeutics against SMARCA4 mutant cancers.

A Metric to Characterize Differentially Methylated Region Sets Detected from Methylation Array Data

Background: Identifying differentially methylated region (DMR) is a basic but important task in epigenomics, which can help investigate the mechanisms of diseases and provide methylation biomarkers for screening diseases. A set of methods have been proposed to identify DMRs from methylation array data. However, it lacks effective metrics to characterize different DMR sets and enable a straight way for comparison. Methods: In this study, we introduce a metric, DMRn, to characterize DMR sets detected by different methods from methylation array data. To calculate DMRn, firstly, the methylation differences of DMRs are recalculated by incorporating the correlations between probes and their represented CpGs. Then, DMRn is calculated based on the number of probes and the dense of CpGs in DMRs with methylation differences falling in each interval. Result & Discussion: By comparing the DMRn of DMR sets predicted by seven methods on four scenario, the results demonstrate that DMRn can make an efficient guidance for selecting DMR sets, and provide new insights in cancer genomics studies by comparing the DMR sets from the related pathological states. For example, there are many regions with subtle methylation alteration in subtypes of prostate cancer are altered oppositely in the benign state, which may indicate a possible revision mechanism in benign prostate cancer. Conclusion: Futhermore, when applied to datasets that underwent different runs of batch effect removal, the DMRn can help to visualize the bias introduced by multi-runs of batch effect removal. The tool for calculating DMRn is available in the GitHub repository(https://github.com/xqpeng/DMRArrayMetric).

Molecular subtyping improves breast cancer diagnosis in the Copenhagen Breast Cancer Genomics Study.

BACKGROUNDIntrinsic molecular subtypes define distinct biological breast cancers and can be used to further improve diagnosis and risk allocation.METHODSThe Copenhagen Breast Cancer Genomics Study (CBCGS) prospectively included women diagnosed with breast cancer at Rigshospitalet from 2014 to 2021. Eligible patients were females with a primary invasive breast cancer (T1c, if N0M0; otherwise, any T, any N, or any M stage) and no prior malignancy. All patients underwent molecular profiling with the CIT256 and PAM50 molecular profile.RESULTSIn the study period, 2,816 patients were included in the CBCGS. Molecular subtyping showed an increase in nonluminal (molecular-apocrine, luminal C, and Basal-like) as compared with luminal (luminal A, luminal B, and Normal-like) subtypes with increasing stage from I to IV. Across all stages, we found a significant difference in survival among subtypes; 91% of patients with LumA were alive at 5 years compared with 91% for LumB, 84% for LumC, 82% for mApo, and 80% for Basal-like. We identified 442 tumors (16%) that were discordant in subtype between CIT256 and IHC. Discordant subtype proved to be a risk factor of death among patients with IHC luminal breast cancer (hazard ratio [HR], 2.08; 95% CI, 1.51-2.86) in a multivariable Cox regression analysis. Discordance occurred more often among patients with N3, stage IV, or grade III disease.CONCLUSIONOur findings indicate that molecular subtypes are a predominant classification for survival. Assessment is particularly crucial for patients with IHC luminal breast cancer with known high-risk factors, since they are at an increased risk of harboring an aggressive molecular subtype.

Abstract 2338: Benchmarking long- and short-read somatic structural variation callers using a multi-technology panel of six tumor/normal cell lines

Abstract Better structural variation (SV) detection could improve cancer diagnosis, treatment and prevention. Yet many cancer genomics studies have only reliably detected single nucleotide variants and small indels due to technological limitations of short-read sequencing. While structural variants can be inferred from discordant short read pairs, as much as 70% of SVs remain undetected because of the mappability difficulties. This is especially true for SVs in regions where GC content is greater than 45% or regions which harbor tandem repeats and segmental duplications. Advances in long-read sequencing enabled direct observation of SVs with recall and precision over 95% across a larger portion of the genome. To harness this technology, we developed Severus - a long-read SV caller that provides phased detection of both simple germline SVs as well as complex rearrangements commonly found in cancer genomes. Such rearrangements are built into a breakpoint graph to reconstruct derived tumor chromosome structure. To benchmark Severus and other long-read SV calling methods, we sequenced six tumor/normal cell line pairs using Nanopore, HiFi, short-read and Hi-C sequencing technologies. We combined the calls from multiple tools and technologies to generate benchmarking sets of somatic SV for each tumor cell line, and evaluated short- and long-read tools against this benchmark set. Current tools for SV merging and comparison (such as truvari) were primarily designed for long indels, rather than breakends that are more common in somatic SVs. Another challenge is that different tools may represent the same variants differently in a VCF output. To enable robust somatic SV outputs comparison, we developed a tool called Minda that translates an input VCF entries into a unified representation, and then compares the calls in either pairwise or multi-tool mode. As expected, in our benchmarking of cell six tumor cell line pairs, short-read methods had substantially lower recall and precision, compared to long-read methods. Among the long-read methods, Severus consistently had substantially better recall and precision, compared to Sniffles2, nanomonsv and SAVANA. An analysis of publicly available long-read sequencing of a melanoma cell line (for which curated benchmark SV set is available) produced consistent results. Citation Format: Ayse Keskus, Asher Bryant, Tanveer Ahmad, Ataberk Donmez, Isabel Rodriguez, Nicole Rossi, Yi Xie, Byunggil Yoo, Rose Milano, Hong Lou, Jimin Park, Joshua Gardner, Brandy McNulty, Karen Miga, Mike Dean, Midhat Farooqi, Benedict Paten, Mikhail Kolmogorov. Benchmarking long- and short-read somatic structural variation callers using a multi-technology panel of six tumor/normal cell lines [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 2338.

Abstract 3861: Associations of intragenic rearrangement burden with immune cell infiltration and response to immune checkpoint blockade in cancer

Abstract While immune checkpoint blockade (ICB) induces durable cancer remission, only a small subset of patients can receive benefits, whereas a substantial portion of patients encounter severe immune-related adverse events. Further, about 4-29% of patients experience dramatic acceleration of disease progression after ICB treatment. Predictive biomarkers of ICB responses are in high demand to ensure patient benefits and avoid tragic clinical outcomes. In addition to PD-L1 tests, several genetic biomarkers are associated with ICB effectiveness, such as tumor mutation burden (TMB), somatic copy number aberrations, indels, and microsatellite instability. However, none of these neoantigen markers are sufficient to discriminate ICB responders in diverse cancer entities of distinct genetic profiles. In particular, TMB fails to predict in the cancer types with low simple mutation rates, such as triple-negative breast cancer (TNBC), ovarian cancer, and esophageal cancers. In TNBC, high TMB only accounts for 5-10% of patients, whereas a much larger proportion of tumors are inflamed (26-50%), which suggests a possible uncharacterized major source of neoantigens. Similarly, TMB is not predictive of ICB response in ovarian cancers according to the IMagyn050 trial. In esophageal cancer, TMB is not associated with T cell infiltration and does not predict ICB response. Our analyses of Pan-cancer whole genomes revealed that intragenic rearrangement (IGR) burden is a pivotal contributor to immune infiltration in breast, ovarian, esophageal, and endometrial cancers, which correlates with increased Macrophage M1 and CD8+ T cell signatures. IGRs result in exon duplications or deletions through rearrangements of a genomic distance of tens of kilobases. Multivariate regression against spatially counted tumor infiltrated lymphocytes in breast, endometrial, and ovarian cancers suggest that IGR burden is a more influential covariate than other genetic aberrations in these cancers. In the MEDI4736 trial evaluating durvalumab in esophageal adenocarcinoma, IGR burden correlates with patient benefits. In the IMVigor210 trial evaluating atezolizumab in urothelial carcinoma, IGR burden increases with platinum exposure and predicts patient benefits for TMB-low, platinum-exposed tumors. Altogether, we demonstrate that IGR burden contributes to T-cell inflammation and predicts ICB benefits in TMB-low, IGR-dominant tumors, or in platinum-exposed tumors. IGRs as a cryptic class of structural variants have been heretofore overlooked in cancer genomics studies. The association of IGRs with ICB response has not been previously reported in cancer in general. The development of IGR burden-based predictive marker could increase effectiveness of ICB therapy, reduce severe side-effects and unnecessary costs. Citation Format: Han Zhang, Sanghoon Lee, Renee R. Muthakana, Binfeng Lu, David N Boone, Daniel Lee, Xiaosong Wang. Associations of intragenic rearrangement burden with immune cell infiltration and response to immune checkpoint blockade in cancer [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 3861.

Abstract 2159: Testing of an educational tool kit for the Community Genetic Navigation Engagement Specialists, COGENES, Training Program

Abstract Introduction: There is a lack of culturally sensitive materials to educate the Hispanic/Latino/a/x (H/L) community on colorectal cancer (CRC), which is the second and third leading cause of cancer deaths among H/L men and women in the United States, respectively. For these reasons, an Educational Tool Kit was created for the Community Genetic Navigation Specialists (CoGENEs) program at the Center for Optimizing Engagement of Hispanic Colorectal Cancer Patients in Cancer Genomic Characterization Studies (COPECC) at the University of Southern California (USC). CoGENES is a train the trainer program geared towards the development of a local workforce of trained community engagement specialists, promotores de salud (community health workers) that will act as liaison agents to increase knowledge and create community awareness on genetic research, clinical genetic testing, counseling, biospecimen donation and participation in clinical trials for H/Ls CRC patients, and community at large. Methods: The Tool Kit consisted of a newly developed educational handbook focused on CRC prevention, genetics, counseling, tumor testing, and patient navigation, as well as existing materials with resources for CRC and counseling. To test the handbook, five focus groups in Spanish/English were conducted with 44 participants, recruited via community agencies/clinics serving Los Angeles. Optimization probes focused on readability, knowledge, acceptability, barriers, and recommended changes. Content analysis was conducted with focus group transcripts to inform how to optimize the handbook. A Materials Review Optimization Scorecard was developed to evaluate materials on content, literacy, graphics, layout and typography, learning, stimulation and motivation, and cultural and language appropriateness. Fifteen pieces in the handbook were each rated by three community health workers. A final suitability score was calculated for each piece. Results: Overall, the materials were well received, and participants understood the overall main message of the materials. Feedback included refining terminology, providing additional headings and subtitles, and clarifying images to optimize the illustration of mutations. Final suitability scores from community health educators ranged from 80 to 99% for all materials, translating into “superior materials.” Conclusion: Overall materials were well received, and they were rated as “superior materials.” Participants’ feedback from CoGENES is shared with the Engagement Optimization Unit (EOU) and Patient Engagement Unit (PEU) to optimize ongoing education and research at COPECC. The MR-OS tool will be shared with other PEU’s at other center’s to encourage it use when developing educational materials. Citation Format: Yaneth L. Rodriguez, Janet Rodriguez, Charité Ricker, Julie Culver, Daisy Hernandez, Natalia Guierrez, Yvonne Cardona, Rosa Barahona, Bianca Rosales, Mariana Stern, Lourdes A. Baezconde-Garbanati. Testing of an educational tool kit for the Community Genetic Navigation Engagement Specialists, COGENES, Training Program [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 2159.

Abstract 1202: Signatures of selection for biallelic inactivation in tumor suppressor genes across cancer types

Abstract Background: The canonical model of tumor suppressor gene (TSG)-mediated oncogenesis, Knudson’s “two hit hypothesis,” posits that loss of both alleles is necessary for TSG inactivation. Many key TSGs accordingly exhibit near universal biallelic loss in associated cancers (e.g., APC in colorectal cancer and RB1 in retinoblastoma). However, because the majority of large-scale cancer genomics studies have utilized non-allele-specific copy number analysis methods, neither the extent of biallelic inactivation across TSGs, nor patterns of selective pressure for biallelic inactivation of TSGs, nor the functional and translational consequences of biallelic inactivation, are well-understood. Methods: Here, through allele-specific analysis of sequencing data from 48,179 cancer patients, we define the prevalence, selective pressure for, and functional consequences of biallelic inactivation across TSGs. We classify TSGs according to their patterns of selection for or against biallelic inactivation across cancer types. Finally, we investigate in detail the association of biallelic status with gene expression and clinical outcome for two TSGs, KEAP1 (in lung adenocarcinoma) and APC (in lung and prostate adenocarcinoma). Results: TSGs largely assort into distinct classes associated with either pan-cancer (Class I) or lineage-specific (Class II) patterns of biallelic loss, while some TSGs, mostly transcription factors, were predominantly monoallelic (Class III/IV). We discover that gene expression and co-mutation patterns indicate selection for biallelic losses in noncanonical contexts, including APC in lung and prostate adenocarcinomas. Selection for biallelic inactivation is also significantly elevated among variants of unknown significance (VUS) of several TSGs, including in KEAP1 in lung adenocarcinoma, nominating these VUS as worthy of in depth functional characterization. Patients with KEAP1 VUS also show similarly poor survival and response to chemoimmunotherapy to patients with known KEAP1 driver mutations, indicating that KEAP1 VUSs phenocopy established KEAP1 oncogenic alleles, and that zygosity, rather than variant classification, is prognostic of therapeutic response. Conclusion: TSGs vary significantly in their patterns of biallelic inactivation, and can be classified according to whether and how consistently, across cancer types, they are selected for loss of both copies. Additionally, taking into account the zygosity of TSG alteration events is often necessary to understand their phenotypic and clinical consequences, and can be useful in identifying novel driver events. Citation Format: Mark R. Zucker, Maria A. Perry, Arielle Elkrief, Anton Safonov, Rohit Thummalapalli, Samuel I. Gould, Debyani Chakravarty, Rose Brannon, Marc Ladanyi, Pedram Razavi, Mark T. Donoghue, Yonina R. Murciano-Goroff, Francisco J. Sánchez-Rivera, Yuan Chen, Ronglai Shen, Sarat Chandarlapaty, David B. Solit, Nikolaus Schultz, Michael F. Berger, Adam J. Schoenfeld, Jason Chang, Ed Reznik, Chaitanya Bandlamudi. Signatures of selection for biallelic inactivation in tumor suppressor genes across cancer types [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 1202.

Abstract 6246: De novo mutation discovery in a mouse model and a human patient sample of non-Hodgkin's lymphoma

Abstract Cancer encompasses many different diseases, all typically involving an accumulation of genetic alterations. However, despite the genomic complexity of this group of diseases, cancer genomic studies typically rely on sequence alignment to a "reference genome" prior to analysis. Such approaches can provide gene expression quantification of known, annotated genes. However, they miss the detection of fusion genes, novel transcripts, virus insertions, and other genetic insults that may be present in cancer beyond an altered gene expression profile. Here, we develop a workflow for de novo identification and quantification of "outside of reference genome" genetic aberrations. This workflow leverages klue, a method we developed that is based on the compact de Bruijn graph, which can extract sequences uniquely found in a "tumor" sample but not in a "normal" control sample from genomic sequencing data. We first applied this method to an RNA-seq experiment from the EμSRα-tTA/TetO-MYC mouse model, which is a MYC-driven autochthonous transgenic mouse model of T-cell acute lymphoblastic lymphoma (T-ALL). We extracted k-mer contigs present in the mouse tumor samples but are present neither in the spleens of wild-type mice of the same genetic background nor in the standard mouse reference genome assembly. We then mapped RNA-seq reads from the T-ALL mouse tumor samples to these contigs. The contigs with the highest number of mapped reads (i.e. the highest counts) were run through BLAST to determine their identities. The mapped contigs with the highest counts were the tTA transgenic element sequence and the human MYC transgene. This result showcases this workflow's success in isolating and quantifying "reference-absent” tumor-unique sequences. Next, we applied klue to whole genome sequencing (WGS), whole exome sequencing (WES), and RNA-seq data from a patient with B-cell follicular lymphoma (with matched normal specimen) from the Texas Cancer Research Biobank, as this data is unrestricted open access. Tumor-unique contigs in sequencing reads were extracted and analyzed with BLAST and BLAT. In one case, a nonsynonymous point mutation in the SPI1 (also known as PU.1) oncogene was found. The sequencing reads from the tumor sample showed both the "normal variant" of SPI1 as well as the mutant SPI1:H268P, suggesting a heterozygous mutation. Altogether, these results suggest that the idea of first identifying tumor-unique sequences then doing post hoc analysis of those sequences is a viable approach for de novo mutation discovery in cancer genomics. Citation Format: Delaney K. Sullivan, Eissa Albinali, Mayuko Boffelli, Lior Pachter. De novo mutation discovery in a mouse model and a human patient sample of non-Hodgkin's lymphoma [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 6246.

Abstract 405: Translocation detection in cancer using low-pass pore-c sequencing

Abstract Complex chromosomal rearrangements, including translocations, play a critical role in oncogenesis and are often identified as recurrent genetic aberrations in hematologic malignancies and solid tumors[1]. Translocation detection by karyotyping of an individual’s metaphase chromosomes remains challenging for detection of balanced rearrangements, which have no gain or loss of genetic material. Rearrangement detection using whole-genome sequencing is a viable alternative, but it relies on breakpoint-spanning reads and requires high sequencing depth (30-60x) with long reads to achieve high sensitivity, especially in repetitive regions of the genome[2]. Here, we describe a workflow for translocation detection at low sequencing depth with Oxford Nanopore’s long read chromatin conformation capture technique, Pore-C[3]. Importantly, for translocation calling, Pore-C does not rely on breakpoint-spanning reads but rather the high intrachromosomal interaction frequency of genomic regions around the breakpoint, thus largely lowering sequencing depth requirements and reducing mapping issues in low complexity regions. We prepared, barcoded, and sequenced Pore-C libraries of 3 cancer cell lines including lung cancer (A549), Acute Monocytic Leukemia (THP-1), melanoma (COLO 829) and its matching normal (COLO 829 BL) on a single Q20+ MinION flow cell (FLO-MIN114). Genome-wide contact maps of low-pass data (<1.5 Gbps per sample) were compared against higher depth (>30 Gbps per sample) Hi-C maps [4], revealing that low-pass Pore-C successfully captured large-scale genomic rearrangement. For example, chromosomal pairs chr8-chr11 and chr15-chr19 in A549, chr9-chr11 and chr1-chr20 in THP1, and chr7-chr15 in Colo829 were detected at <0.5X depth of sequencing coverage. These translocation events were further validated by breakpoint analysis using adaptive sampling, where targeted regions were previously confirmed by independent studies[4],[5]. Low-pass Pore-C detects translocations in an unbiased manner and does not require prior knowledge of the translocation structure. Combined with its simple sample-preparation workflow and the capability to provide genome-wide copy-number information in a single experiment, it can serve as a cost-effective and comprehensive tool for cancer genomic studies. [1] Chromosomal translocations in human cancer, Nature, 372, 143 (1994) [2] Hi-C as a tool for precise detection and characterisation of chromosomal rearrangements and copy number variation in human tumors, Genome Biology, 18, 125 (2017) [3] Identifying synergistic high-order 3D chromatin conformations from genome-scale nanopore concatemer sequencing, Nature Biotechnology, 40, 1488(2022) [4] Chromosomal translocations detection in cancer cells using chromosomal conformation capture data, Genes (Basel), 13, 7 (2022) [5] A multi-platform reference for somatic structural variation detection, Cell Genomics, 2, 6 (2022) Citation Format: Scott Hickey, Xiaoguang Dai, Sergey Aganezov, John Beaulaurier, Eoghan Harrington, Sissel Juul. Translocation detection in cancer using low-pass pore-c 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 405.

Abstract 2145: Genetic ancestry associations with pancreatic cancer mutational profiles from a diverse 9,274-patient real-world cohort

Abstract Pancreatic cancer is an aggressive malignancy associated with racial/ethnic disparities. Notably, Black patients have a higher incidence and mortality compared to their White counterparts. However, low representation of Black and Hispanic patients in cancer genomics studies hampers our understanding of genomic factors contributing to differential outcomes. Furthermore, comparisons between racial groups fail to capture differences in genomic ancestry. We studied the associations between genetic ancestry and race/ethnicity categories with mutational profiles, using a large, diverse real-world pancreatic cancer cohort. We analyzed 9,274 de-identified pancreatic cancer patients (8,483 with ductal adenocarcinoma, PDAC) who underwent tumor profiling with the Tempus xT 648-gene assay. We used 654 ancestry-informative markers to estimate continental ancestry for five regions: Africa (AFR), Americas (AMR), East Asia (EAS), Europe (EUR), and South Asia (SAS). We imputed Asian (ASN, n=431), Hispanic/Latino/Native American (HLN, n=593), Non-Hispanic Black (NHB, n=875) and Non-Hispanic White (NHW, n=7,309) categories from genetic ancestry using a previously published machine learning method. Associations between either race/ethnicity categories or ancestry proportions and somatic variants (copy number alterations or CNAs, small nonsynonymous, OncoKB L1/2 & R1, and predicted driver somatic mutations using boostDM) were assessed for 172 pancreatic cancer-related genes or gene sets using logistic regression models. Models with and without race/ethnicity or ancestry terms were compared with likelihood ratio (LR) tests, and p-values were adjusted to control the FDR at 5%. We tested for associations of race/ethnicity groups with TMB using a Kruskal-Wallis rank sum test. Tests of TMB and small variants used only patients with available matched normal tissue to avoid artifacts from misclassification of germline variants. Among pancreatic cancer patients, having either a CNA or small nonsynonymous mutation in a receptor tyrosine kinase (RTK) gene (ERBB2, EGFR, MET, FGFR1) was associated with genetic ancestry (LR adjusted p=0.027). Specifically, AMR ancestry was positively associated (OR=1.08 per doubling of ancestry proportion, p=0.004) and EUR negatively associated (OR=0.93, p=0.002). Associations in PDAC were similar but not significant. Race/ethnicity was associated with RTK changes in both groups, with ASN and HLN having more mutations than NHW, but the associations were not significant after correcting for multiple hypotheses. There was no association of race/ethnicity and TMB .Somatic changes in RTK genes were associated with AMR genetic ancestry; however, despite the relatively large cohort size, these results are modest, suggesting there is not a large somatic mutation component contributing to differences in pancreatic cancer outcomes by ancestry. Citation Format: Brooke Rhead, Vignesh Vudatha, Yannick Pouliot, Edward Williamns, Andrea N. Riner, Francisco M. De La Vega, Jose Trevino. Genetic ancestry associations with pancreatic cancer mutational profiles from a diverse 9,274-patient real-world cohort [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 2145.

Abstract 874: Expanded detection of BAP1 alterations in cancer and comparison of tumor type-specific gene expression scores

Abstract BAP1 is a tumor suppressor gene that was originally studied in uveal melanoma (UVM), kidney renal cell clear cell carcinoma (KIRC), and malignant mesothelioma (MESO). Early analyses focused on single-nucleotide variants, but other alteration types such as larger indels and gene-level copy number (CN) loss can also lead to loss of BAP1 expression. By incorporating these, we can improve our assessment of cancer type-specific consequences of BAP1 loss and broaden our findings beyond the initial small subset of cancer types which were studied in the context of mutations alone. We performed integrated multi-omic analyses using data from The Cancer Genome Atlas (TCGA), for 33 cancer types and more than 10,000 individuals. By combining and manually reviewing existing variant calls and new calls derived from a de novo local realignment pipeline across multiple independent variant callers including indel callers, we increased detection of high-quality somatic variant calls by 30% from 85 to 111, including 14 indels ≥5bp. Including CN loss alterations, we found a total of 1561 samples from 32 cancer types to be BAP1-altered, with alterations being predominantly CN-driven. We confirmed that expression of BAP1 was lower in altered versus unaltered tumors (p<0.001). Alteration frequencies across cancer types were highly variable, ranging from 0% in thyroid cancer to >30% in KIRC, cholangiocarcinoma (CHOL), UVM, MESO, lung squamous carcinoma (LUSC), and head-neck squamous cell carcinoma (HNSC). Tumor types such as CHOL, UVM, MESO, and liver hepatocellular carcinoma (LIHC) were relatively more mutation-driven whereas other tumor types like KIRC, LUSC, and HNSC were almost entirely CN-driven. To better understand the tissue and context-specific consequences of BAP1 alteration, we performed differential expression analyses across BAP1-altered and -unaltered samples. For each cancer type, we separately computed per-sample BAP1 alteration signature z-scores summarizing expression of the set of differentially expressed genes. In UVM, we observed distinct patterns of RNA expression, consistent with established literature on BAP1 loss and serving as a baseline for comparison for the biological effects of BAP1 loss in other less-studied cancer types. The BAP1 altered scores for 66% of cancer types were positively correlated with the UVM BAP1 altered signature, suggesting shared biological responses and affected pathways including immune responses, epithelial-mesenchymal transition, oxidative phosphorylation, and proliferation – all known roles of BAP1. In LIHC, we noted enrichment of bile duct gene expression in BAP1-altered tumors, suggesting a possible role for BAP1 in maintaining cell identity distinguishing hepatocytes and cholangiocytes. Our findings broadly emphasize the improvements that are gained by using new computational approaches in large cancer-genome studies such as TCGA. Citation Format: Ian R. Sturgill, Jesse R. Raab, Katherine A. Hoadley. Expanded detection of BAP1 alterations in cancer and comparison of tumor type-specific gene expression scores [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 874.

Epigenetic modifications in solid tumor metastasis in people of African ancestry.

This review focuses on the critical role of epigenetic modifications in solid tumor metastasis, particularly in people of African ancestry. Epigenetic alterations, such as DNA methylation, histone modifications, alterations in non-coding RNAs, and mRNA methylation, significantly influence gene expression, contributing to cancer development and progression. Despite the primary focus on populations of European, American, and Asian descent in most cancer research, this work emphasizes the importance of studying the unique genetic and epigenetic landscapes of African populations for a more inclusive approach in understanding and treating cancer. Insights from this review have the potential to pave the way for the development of effective, tailored treatments, and provide a richer resource for understanding cancer progression and metastasis. Specific focus was placed on the role of DNA methylation, histone modifications, non-coding RNAs, and mRNA methylation in solid tumor metastasis, including how these modifications contribute to the regulation of tumor suppressor genes and oncogenes, influence cellular pathways and signaling, and interact with the immune system. Moreover, this review elaborates on the development of epigenetic-targeted therapeutic strategies and the current advances in this field, highlighting the promising applications of these therapies in improving outcomes for African ancestry populations disproportionately affected by certain types of cancer. Nevertheless, this work acknowledges the challenges that lie ahead, particularly the under-representation of African populations in cancer genomic and epigenomic studies and the technical complications associated with detecting subtle epigenetic modifications. Emphasis is placed on the necessity for more inclusive research practices, the development of more robust and sensitive methods for detecting and interpreting epigenetic changes, and the understanding of the interplay between genetic and epigenetic variations. The review concludes with an optimistic outlook on the future of epigenetic research in People of African ancestry, urging the concerted efforts of researchers, clinicians, funding agencies, and policymakers to extend the benefits of this research to all populations.

Nonparametric Bayes multiresolution testing for high-dimensional rare events

In a variety of application areas, there is interest in assessing evidence of differences in the intensity of event realizations between groups. For example, in cancer genomic studies collecting data on rare variants, the focus is on assessing whether and how the variant profile changes with the disease subtype. Motivated by this application, we develop multiresolution nonparametric Bayes tests for differential mutation rates across groups. The multiresolution approach yields fast and accurate detection of spatial clusters of rare variants, and our nonparametric Bayes framework provides great flexibility for modelling the intensities of rare variants. Some theoretical properties are also assessed, including weak consistency of our Dirichlet Process-Poisson-Gamma mixture over multiple resolutions. Simulation studies illustrate excellent small sample properties relative to competitors, and we apply the method to detect rare variants related to common variable immunodeficiency from whole exome sequencing data on 215 patients and over 60,027 control subjects.

Singleton mutations in large-scale cancer genome studies: uncovering the tail of cancer genome.

Singleton or low-frequency driver mutations are challenging to identify. We present a domain driver mutation estimator (DOME) to identify rare candidate driver mutations. DOME analyzes positions analogous to known statistical hotspots and resistant mutations in combination with their functional and biochemical residue context as determined by protein structures and somatic mutation propensity within conserved PFAM domains, integrating the CADD scoring scheme. Benchmarked against seven other tools, DOME exhibited superior or comparable accuracy compared to all evaluated tools in the prediction of functional cancer drivers, with the exception of one tool. DOME identified a unique set of 32917 high-confidence predicted driver mutations from the analysis of whole proteome missense variants within domain boundaries across 1331 genes, including 1192 noncancer gene census genes, emphasizing its unique place in cancer genome analysis. Additionally, analysis of 8799 TCGA(The Cancer Genome Atlas) and in-house tumor samples revealed 847 potential driver mutations, with mutations in tyrosine kinase members forming the dominant burden, underscoring its higher significance in cancer. Overall, DOME complements current approaches for identifying novel, low-frequency drivers and resistant mutations in personalized therapy.

Frequent loss-of-function mutations in the AMPK-α2 catalytic subunit suggest a tumour suppressor role in human skin cancers.

The AMP-activated protein kinase (AMPK) is a sensor of cellular energy status activated by increases in AMP or ADP relative to ATP. Once activated, it phosphorylates targets that promote ATP-generating catabolic pathways or inhibit ATP-consuming anabolic pathways, helping to restore cellular energy balance. Analysis of human cancer genome studies reveals that the PRKAA2 gene (encoding the α2 isoform of the catalytic subunit) is often subject to mis-sense mutations in cancer, particularly in melanoma and non-melanoma skin cancers, where up to 70 mis-sense mutations have been documented, often accompanied by loss of the tumour suppressor NF1. Recently it has been reported that knockout of PRKAA2 in NF1-deficient melanoma cells promoted anchorage-independent growth in vitro, as well as growth as xenografts in immunodeficient mice in vivo, suggesting that AMPK-α2 can act as a tumour suppressor in that context. However, very few of the mis-sense mutations in PRKAA2 that occur in human skin cancer and melanoma have been tested to see whether they cause loss-of-function. We have addressed this by making most of the reported mutations and testing their activity when expressed in AMPK knockout cells. Of 55 different mis-sense mutations (representing 75 cases), 9 (12%) appeared to cause a total loss of activity, 18 (24%) a partial loss, 11 (15%) an increase in phenformin-stimulated kinase activity, while just 37 (49%) had no clear effect on kinase activity. This supports the idea that AMPK-α2 acts as a tumour suppressor in the context of human skin cancer.