Non-cancer Genes Research Articles

Opportunistic genomic screening has clinical utility: An interventional cohort study

Practice is shifting toward genome-first approaches, such as opportunistic screening for secondary findings (SFs). Analysis of SFs could be extended beyond medically actionable results to include non-medically actionable monogenic disease risks, carrier status, pharmacogenomic variants, and risk variants for common complex disease. However, evidence on the clinical utility of returning these results is lacking. We assessed the outcomes of opportunistic screening for a broad spectrum of SFs by evaluating the yield, impact on clinical management, and consistency between SFs and participants' clinical features and family history. Adult cancer patients had GS with the option to learn multiple categories of SFs. Outcomes data were collected through chart review and participant-reported measures up to one year after return of results. All participants (n=139, 85.6% female, average 54.6 years old) who elected to learn SFs had ≥1 variant reported (100% [139/139]). The yield of reportable findings was highest for pharmacogenomic variants (97.8% [135/138] of participants), followed by common disease risk variants (89.4% [118/132]), carrier status (89.3% [117/131]), and variants related to Mendelian (27.2% [34/125]), medically actionable (15.2% [21/138]), and early-onset neurodegenerative (2.6% [3/117]) disease risks. SFs from the ACMG list (v3.2, non-cancer genes) were reported in 1.4% (2/138) of participants. SFs across all categories demonstrated clinical utility by prompting management changes in 28.1% (39/139) of participants. Moreover, a considerable proportion of participants had suggestive clinical features (49.0% (24/49)]) or family history (21.8% (27/124)) potentially related to their SFs. Our findings indicate there are potential benefits from opportunistic screening for a broad range of SFs.

Universal germline testing for cancer susceptibility and actionable noncancer disorders among 19,842 patients: Initial findings from the City of Hope INSPIRE study.

10594 Background: Germline testing can be essential for risk stratification and therapeutic decision making. However, guideline-based testing misses ~50% of people with inherited cancer risk. To address this gap, we launched the INSPIRE study, offering universal germline testing (31 non-cancer and 154 cancer genes) to consented patients affected and unaffected with cancer. Methos: In 2020, we initiated a scalable in person/remote consent model without formal pre-test counseling. Consenting patients opted-in for one, both, or neither germline test. Research staff placed orders. Results were reviewed by a genetic counselor. Pathogenic/likely pathogenic (P/LP) and increased risk allele (IRA) results were disclosed by phone and scheduled for a genetic consult. Variant of uncertain significance (VUS)/negative results were disclosed by letter with a genetic consult available on request. Descriptive statistics and chi-squared tests evaluated a priori hypotheses regarding potential factors impacting genetic testing uptake and actionability. Results: We approached 26,920 patients: 22,115 (82%) enrolled, 1,830 (7%) declined, 2,975 (11%) deferred a decision. Consented patients opted for testing for germline cancer (21,871/ 99%), non-cancer (21,731/98%), or neither (160/0.7%). We have returned results to 19,842 patients: mean age 62, 69% female; 72% white, 14% Asian, 5% Black, 9% other; 24% Hispanic. Most patients (15,578) had cancer, 13% with multiple primaries. Among the 19,842 patients, 4,456 (23%) had at least one P/LP/IRA variant; 3,971 (20%) in a cancer risk gene and 589 (3%) in a non-cancer gene. Variant proportion by category was 1,466 (30%) high-penetrance, 847 (18%) moderate-penetrance, 1,127 (23%) low-penetrance, 1,282 (28%) recessive, and 34 (0.7%) preliminary evidence. Penetrance prevalence differed by sex: high penetrance variants were more common in females (33% vs. 27%; p=<0.001) and moderate penetrance variants were more common in males (21% vs. 17%; p=0.003). 16,883 (85%) individuals carry at least one VUS. Conclusions: A scalable model to increase germline genetic testing among unselected cancer and unaffected patients across >20 sites was highly feasible. A significant proportion of patients had actionable findings in cancer and non-cancer genes. Next steps include analyses to establish variant prevalence across groups, a longitudinal assessment of psychological outcomes and an evaluation of the impact of result disclosure on cost, patient care, and outcomes.

Abstract 4884: Evaluating the utility of in silico variant annotation tools for cancer driver detection

Abstract In cancer genomics, precise variant annotation is crucial for clinical decisions, drug development, and research. The burgeoning genomic data offers an opportunity to use data-driven approaches to generate knowledge that supports clinical decisions. Particularly machine learning (ML) and Deep learning (DL), are becoming essential, as their application is fast, scalable, simple to implement, and generates reproducible results. The methods ranging from simple sequence-based alignment scoring to advanced algorithms like Logistic regression, Support vector machine, and Recurrent neural networks, have been employed by multiple variant annotation tools. This study compares in-silico methods available in Ensembl's Variant Effect Predictor (VEP) using a test dataset with COSMIC annotations. ML/DL success relies on robust training sets with comprehensive genomic variants features, including effects on transcription/translation, genomic context, annotation resources, in silico pathogenicity predictions, and population allele frequency. The training set, composed of known benign or pathogenic variants, serves as a reference for these algorithms to classify new and unseen variants. Our analysis reveals a limited concordance between the prediction algorithms. Despite comparable true/false positives/negatives, discrepancies persist in variant classification. Certain algorithms exhibit a propensity to over or under-call deleterious mutations. Some demonstrate a tendency to classify random variants in non-cancer genes as deleterious. Challenges include the absence of consensus on informative features, diverse training datasets, and restriction to well annotated proteins/transcripts. Balancing the sensitivity and false positives in detecting cancer drivers is crucial. Integrating individual prediction scores with ML algorithms enhances tool performance but comes with risk of error propagation, and limited accuracy. The study emphasises the need for context-specific variant classification tools, as many variants' impacts are cancer-type specific, and some may drive disease synergistically. Existing tools, designed for a "one variant - one score approach," struggle to capture complex associations, especially those dependent on changes in the tumour microenvironment. Highlighting areas for improvement, the study addresses the "black box" problem in decision processes. While limited interpretability might not hinder practical applications, tools should evolve to assess more complex associations guided by biology. Formal consensus, reference training datasets, and standards are deemed essential for developing next-generation tools. The envisioned context-dependent tools aim to streamline feature complexity, thereby mitigating the black box problem and advancing the accuracy and interpretability of cancer variant annotation. Citation Format: Madhumita, Zbyslaw Sondka, Jon Teague. Evaluating the utility of in silico variant annotation tools for cancer driver detection [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 4884.

Evolvability of cancer-associated genes under APOBEC3A/B selection

Evolvability is an emergent hallmark of cancer that depends on intra-tumor heterogeneity and genetic variation. Mutations generated by APOBEC3 contribute to genetic variation and tumor evolvability. However, the influence of APOBEC3 on the evolvability of the genome and its differential impact on cancer genes versus non-cancer genes remains unclear. Analyzing over 40,000 human protein-coding transcripts, we identified distinct distribution patterns of APOBEC3A/B TC motifs between cancer and non-cancer genes, suggesting unique associations with cancer. Studying a bat species with numerous APOBEC3 genes, we found distinct motif patterns in orthologs of cancer genes compared to non-cancer genes, as in humans, suggesting APOBEC3 evolution to reduce impacts on the genome rather than the converse. Simulations confirmed that APOBEC3-induced heterogeneity enhances cancer evolution through bimodal patterns of mutations in certain classes of genes. Our results suggest the bimodal distribution of APOBEC-induced mutations can significantly increase cancer heterogeneity.

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.

Somatic mutation distribution across tumour cohorts provides a signal for positive selection in cancer

Cancer gene discovery is reliant on distinguishing driver mutations from a multitude of passenger mutations in tumour genomes. While driver genes may be revealed based on excess mutation recurrence or clustering, there is a need for orthogonal principles. Here, we take advantage of the fact that non-cancer genes, containing only passenger mutations under neutral selection, exhibit a likelihood of mutagenesis in a given tumour determined by the tumour’s mutational signature and burden. This relationship can be disrupted by positive selection, leading to a difference in the distribution of mutated cases across a cohort for driver and passenger genes. We apply this principle to detect cancer drivers independently of recurrence in large pan-cancer cohorts, and show that our method (SEISMIC) performs comparably to traditional approaches and can provide resistance to known confounding mutational phenomena. Being based on a different principle, the approach provides a much-needed complement to existing methods for detecting signals of selection.

Abstract 3851: Microenvironmental alterations co-occur with mosaic somatic clonal expansions in normal tissues

Abstract Clonal hematopoiesis confers an increased risk for myeloid neoplasia, but progression to cancer requires additional alterations. Recently described clonal expansion (CE) of mutant skin and esophagus suggests CE is pervasive across tissues. Immune evasion and angiogenesis, reliant on the microenvironment (ME), are cancer hallmarks, yet ME changes in the setting of CE have not been systematically studied. Using GTEX RNA-Seq data (25 normal tissues, n = 6,511), previously reported somatic variants (Yizhak K, Science 2019) and normalized gene expression values were downloaded. Samples were grouped by the presence of: non-synonymous mutation (NSM) in a COSMIC Cancer Gene Census gene (CGC-m), NSM in a non-cancer gene or no NSM (non-m). ME scores, i.e. stromal and immune cell abundances for all samples, were estimated using Xcell and CIBERSORTx. Our analyses focused on tissues with at least 10 CGC-m, especially skin and esophagus which had the most CGC-m cases (77/600, 12% and 166/512, 32% respectively; median :4.6%). We also compared ME scores from skin and esophagus with corresponding cancer data from TCGA. Differential gene expression (DEG) testing between CGC-m and non-m samples, Gene Set Enrichment Analysis (GSEA), and hypoxia scores (Bhandari V, Nature Comm 2020) based on findings (below) were generated. ME scores significantly differed in the CGC-m vs. non-m cohorts, with increased epithelial and lymphatic endothelial cells (skin, esophagus), megakaryocytes (skin, prostate) and neutrophils (prostate, lung) (Wilcoxon FDR p-value < 0.01). Skin showed reduced fibroblasts and esophagus, increased endothelial cells and melanocytes and lower pericytes and erythrocytes. Comparing TCGA and GTEX data, melanoma showed similar but more marked epithelial cell increase and fibroblast reduction. Esophageal carcinoma showed similar but more pronounced reductions in pericytes and erythrocytes. DEG testing between CGC-m and non-m identified: in esophagus, overexpression of genes aberrant in carcinomas (ERBB2, CDH1, SOX2 and TP63; p-adjusted < 0.01), downregulation of pro-angiogenic factors and those involved in pericyte recruitment (FGF2, ANGPT1 and PDGFB); in skin, extracellular matrix protein, ELN was overexpressed. GSEA analysis of differentially expressed genes identified dysregulation of mesenchymal-epithelial transition and myogenesis pathways in both tissues and angiogenesis-related pathway in esophagus. Hypoxia scores were markedly higher in CGC-m vs. non-m esophagus samples (Wilcoxon FDR p-value < 0.01). These results suggest ME alterations participate in initial stages of cancer progression and are tissue-specific. High hypoxia scores support a hypothesis of its early initiating role in CE-related angiogenesis in esophagus. Studying these changes may reveal novel avenues for early cancer intervention, immune or other targeted therapies and enhancement of conventional therapies. Citation Format: Chetan Munugala, Jieting Hu, Eirini Christodoulou, Venkata Yellapantula. Microenvironmental alterations co-occur with mosaic somatic clonal expansions in normal tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3851.

Abstract 5219: Frequency of germline mutations in Mexican children with cancer

Abstract Childhood cancer is a leading cause of death by disease in children from 5 -14 ages, for whom there are no prevention strategies. Due to early-age of diagnosis and short-time environmental factors exposition, increasing evidence suggests childhood cancer could have stronger association with germline alterations in predisposition cancer genes. It has been estimated that up to 30% all childhood cancer diagnosis may be caused by germline variants, but their frequency and distribution remain unclear. Elucidating the prevalence and role of germline mutations in the development of childhood cancer is crucial for understanding its bases, causes, cancer-risk reduction plan, surveillance, and treatment. Here, we present a pilot study on cancer children’s patients and their relatives. A total of 40 Mexican-mestizo children with any cancer diagnosis were recruited from several Institutions of Health in Mexico and a sample of saliva or blood was obtained. Whole Exome Sequencing was performed and bioinformatic analysis was carried out following GATK best practices for germline genetic variants. Additionally, we evaluated a risk selection tool for recognition of genetic predisposition in pediatric cancer patients. Pathogenic or likely-pathogenic genetic variants (mutations) were identified from the application of a filtering chain based on ACMG guidelines, and they were subsequently validated by Sanger sequencing. Germline mutations in MSH6, NF1, MUTYH, CDKN2A, CHEK2, DICER1, FANCA and SBDS genes were identified in 5/40 (12.5%) patients with cancer, only 3 of 5 index cases (60%) reported family history of cancer and all patients meet at least one criterion of the risk selection tool. Two patients were carriers of one mutation, while in three patients were identified 2 pathogenic variants. Additionally, we identified four nonsense candidate genetic variants on EWSR1, FANCL, EPCAM and HRAS genes and two pathogenic variants in non-cancer predisposition gene, ABCA4. Our results show for the first time a higher frequency (12.5%) of germline mutations in cancer predisposition genes in Mexican-mestizo children with cancer. The lack of family history in two carriers showed the need to establish standardized suspicion criteria for identification of children with higher risk of cancer who can benefitfrom genetic testing. Further characterization of additional genetic variants identified without clinical classification will be important to evaluate their real contribution, as well as the influence of environmental factors on de novo mutation promotion. Citation Format: Oscar Alonso Luna, Jorge Meléndez Zajgla, Marta Zapata Tarres, Luis Enrique Juárez Villegas, Elvia Cristina Mendoza Caamal, Elianeth Rey Helo, Socorro Aida Borges Yanez, Gabriela Elisa Mercado Celis, Rodrigo Barquera Lozano. Frequency of germline mutations in Mexican children with cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5219.

3'untranslated regions of tumor suppressor genes evolved specific features to favor cancer resistance.

Cancer-related genes have evolved specific genetic and genomic features to favor tumor suppression. Previously we reported that tumor suppressor genes (TSGs) acquired high promoter CpG dinucleotide frequencies during evolution to maintain high expression in normal tissues and resist cancer-specific downregulation. In this study, we investigated whether 3'untranslated regions (3'UTRs) of TSGs have evolved specific features to carry out similar functions. We found that 3'UTRs of TSGs, especially those involved in multiple histological types and pediatric cancers, are longer than those of non-cancer genes. 3'UTRs of TSGs also exhibit higher density of binding sites for RNA-binding proteins (RBPs), particularly those having high affinities to C-rich motifs. Both longer 3'UTR length and RBP binding sites enrichment are correlated with higher gene expression in normal tissues across tissue types. Moreover, both features together with the correlated N6-methyladenosine modification and the extent of protein-protein interactions are positively associated with the ability of TSGs to resist cancer-specific downregulation. These results were successfully validated with independent datasets. Collectively, these findings indicate that TSGs have evolved longer 3'UTR with increased propensity to RBP binding, N6-methyladenosine modification and protein-protein interactions for optimizing their tumor-suppressing functions.

The insertion and dysregulation of transposable elements in osteosarcoma and their association with patient event-free survival

The dysregulation of transposable elements (TEs) has been explored in a variety of cancers. However, TE activities in osteosarcoma (OS) have not been extensively studied yet. By integrative analysis of RNA-seq, whole-genome sequencing (WGS), and methylation data, we showed aberrant TE activities associated with dysregulations of TEs in OS tumors. Specifically, expression levels of LINE-1 and Alu of different evolutionary ages, as well as subfamilies of SVA and HERV-K, were significantly up-regulated in OS tumors, accompanied by enhanced DNA repair responses. We verified the characteristics of LINE-1 mediated TE insertions, including target site duplication (TSD) length (centered around 15 bp) and preferential insertions into intergenic and AT-rich regions as well as intronic regions of longer genes. By filtering polymorphic TE insertions reported in 1000 genome project (1KGP), besides 148 tumor-specific somatic TE insertions, we found most OS patient-specific TE insertions (3175 out of 3326) are germline insertions, which are associated with genes involved in neuronal processes or with transcription factors important for cancer development. In addition to 68 TE-affected cancer genes, we found recurrent germline TE insertions in 72 non-cancer genes with high frequencies among patients. We also found that +/− 500 bps flanking regions of transcription start sites (TSS) of LINE-1 (young) and Alu showed lower methylation levels in OS tumor samples than controls. Interestingly, by incorporating patient clinical data and focusing on TE activities in OS tumors, our data analysis suggested that higher TE insertions in OS tumors are associated with a longer event-free survival time.

Prospective genomic testing of unselected cancer patients yields insights about cancer susceptibility and noncancer disease with therapeutic implications.

10603 Background: Clinicians have used strict criteria to determine eligibility for cancer susceptibility (CS) testing and have limited genetic assessment to cancer-related genes. However, half of all CS mutation carriers are missed by criteria-based testing and there may be an unrecognized opportunity to modify care for patients who have rare but actionable genetic disorders as defined by the American College of Medical Genetics (ACMG). With the aim of improving patient outcomes through precision genomics, we initiated an enterprise-wide program to offer somatic and germline sequencing to all patients. Methods: We offer consented patients clinical grade paired somatic & germline WES/ RNA seq and panel germline testing for cancer (156 genes) and ACMG disorders (59 genes). Results are reviewed by a Precision Oncology Tumor Board. Somatic results are returned by the treating team. Germline results are returned by phone (genetic counselor, GC) followed by a clinic visit (GC+MD) for those with pathogenic/likely pathogenic (P/LP) mutations and selected variants of uncertain significant. We evaluated the proportions of patients with somatic findings suggestive of germline conditions and those carrying P/LP mutations in CS and ACMG genes. Results: 1,804 patients enrolled and received somatic sequencing: 52% female; 51% non-Hispanic White/ 20% Hispanic White/ 18% Asian/ 4% Black/ 7% other; median age 64. Review of somatic data suggest that 14% have findings suggestive of germline conditions based on factors such as TMB, MSI, and young age. Of the patients offered germline testing, >95% opted to receive CS/ACMG results. To date, we have sequenced 684 patients for CS and 647 for ACMG. 18% of patients had P/LP mutations in CS genes and 4% had P/LP mutations in ACMG non-cancer genes (Table). Conclusions: Prospective somatic/germline sequencing of unselected cancer patients reveals tumor findings suggestive of germline disorders and identifies patients with CS and non-cancer genetic conditions. These findings highlight the promise of a comprehensive sequencing approach to help guide cancer treatment, management of unrecognized cancer risk and the need for concomitant management of rare disorders such as arrhythmogenic cardiomyopathy and susceptibility to adverse reactions with anesthesia.[Table: see text]

Multi-omic analysis suggests tumor suppressor genes evolved specific promoter features to optimize cancer resistance

Tumor suppressor genes (TSGs) exhibit distinct evolutionary features. We speculated that TSG promoters could have evolved specific features that facilitate their tumor-suppressing functions. We found that the promoter CpG dinucleotide frequencies of TSGs are significantly higher than that of non-cancer genes across vertebrate genomes, and positively correlated with gene expression across tissue types. The promoter CpG dinucleotide frequencies of all genes gradually increase with gene age, for which young TSGs have been subject to a stronger evolutionary pressure. Transcription-related features, namely chromatin accessibility, methylation and ZNF263-, SP1-, E2F4- and SP2-binding elements, are associated with gene expression. Moreover, higher promoter CpG dinucleotide frequencies and chromatin accessibility are positively associated with the ability of TSGs to resist downregulation during tumorigenesis. These results were successfully validated with independent datasets. In conclusion, TSGs evolved specific promoter features that optimized cancer resistance through achieving high expression in normal tissues and resistance to downregulation during tumorigenesis.

Immunogenomic landscape of gynecologic carcinosarcoma

ObjectiveCarcinosarcoma (CS) of the uterus or ovary is a rare, biphasic tumor comprising epithelial and mesenchymal elements, and exhibits more aggressive clinical features than its carcinoma counterpart. Four molecular subtypes of CS were recently established based on genomic aberration profiles (POLE, MSI, CNH, and CNL) and shown to be associated with multiple clinicopathological parameters, including patient outcomes. However, the role of the immune microenvironment in CS remains unclear. Here, we investigated the influence of the immune cells that infiltrate CS to better understand the immunological status of gynecological CS. MethodsTumor immune microenvironmental analyses on CS samples were performed using immune cell profiling with RNA-seq, transcriptomic subtyping with microenvironmental genes, and T-cell receptor repertoire assay. Carcinoma and sarcoma elements from CS samples were also assessed separately. ResultsRelying on estimations of tumor-infiltrating cell types from RNA-seq data, POLE and MSI (hypermutator) tumors showed an enrichment of M1 macrophages, plasma cells and CD8+ T cells, whereas CNH and CNL (non-hypermutator) tumors had high levels of M2 macrophages. Further subclassification by immune-related, non-cancer genes identified a fraction of tumors with distinct patient outcomes, particularly those with the CNH genomic aberration subtype. T-cell heterogeneity was independently correlated with prolonged progression-free survival. Differential analysis of carcinoma and sarcoma elements identified many shared mutations but there was little overlap in the T-cell receptor repertoire between the two elements. ConclusionsTumor immune microenvironmental analyses could offer potential clinical utility in the stratification of gynecological CS above classification by genomic aberration subtype alone.

A systems genomics approach to uncover the molecular properties of cancer genes

Genes involved in cancer are under constant evolutionary pressure, potentially resulting in diverse molecular properties. In this study, we explore 23 omic features from publicly available databases to define the molecular profile of different classes of cancer genes. Cancer genes were grouped according to mutational landscape (germline and somatically mutated genes), role in cancer initiation (cancer driver genes) or cancer survival (survival genes), as well as being implicated by genome-wide association studies (GWAS genes). For each gene, we also computed feature scores based on all omic features, effectively summarizing how closely a gene resembles cancer genes of the respective class. In general, cancer genes are longer, have a lower GC content, have more isoforms with shorter exons, are expressed in more tissues and have more transcription factor binding sites than non-cancer genes. We found that germline genes more closely resemble single tissue GWAS genes while somatic genes are more similar to pleiotropic cancer GWAS genes. As a proof-of-principle, we utilized aggregated feature scores to prioritize genes in breast cancer GWAS loci and found that top ranking genes were enriched in cancer related pathways. In conclusion, we have identified multiple omic features associated with different classes of cancer genes, which can assist prioritization of genes in cancer gene discovery.

Abstract A06: The added value of examining germline variants in a precision cancer therapy study

Abstract Introduction: Tumor profiling is becoming a more routine part of clinical care. Many academic centers and commercial entities offer tumor sequencing of cancer-related genes without matched germline profiling. We hypothesize that tumor-only sequencing may limit full clinical interpretation and have decreased sensitivity to identify significant germline variants. Methods: The Genomic Assessment Improves Novel Therapy (GAIN) Consortium is a clinical cancer genomics study for patients with high-risk solid malignancies. Patients in this study were selected for subanalysis if panel sequencing of 447 genes was performed on a tumor and interpreted by an expert panel prior to the availability of matched germline sequencing. Interpretation of tumor sequencing included both therapeutic recommendations and a curation of cancer-related variants of potential clinical significance if present in the germline. Germline sequencing was separately performed targeting 147 genes (a subset of the somatic panel) and analyzed with a germline-specific pipeline to identify and filter variants. We examined clinical recommendations in the somatic reports that were based on single-nucleotide variants identified from the 147 overlapping genes. We compared these interpretations with results from the matched germline data. Results: We identified 159 participants with somatic and germline sequencing reports meeting the eligibility criteria. Germline sequencing identified 38 pathogenic or likely pathogenic (P/LP) germline variants in 35 of 159 patients (22%). Of those 35 patients, 17 (49%) had a P/LP variant in an autosomal dominant cancer predisposition gene, 19 (54%) in an autosomal recessive gene, and 1 (2.9%) in a noncancer gene. Of the 38 total variants, 21 (55%) were identified by the analytic pipeline used for somatic sequencing and noted as potential germline variants in the somatic reports. Forty treatment recommendations were made from the somatic data within the overlapping genes. Ten (25%) treatment recommendations were based on variants that were later determined to be germline. These included variants in TP53, SDHA, SMARCA4, TSC2, FAM175A, CHEK2, and AKT1, many of which were noted in the somatic reports to be variants of uncertain significance or possibly germline. Conclusions: In this study, we found that clinically actionable germline variants were under-reported when relying on analytical pipelines and clinical interpretations developed for the analysis of tumor samples. In the absence of germline sequencing, we also found that cancer treatment recommendations can be made based on mutations identified from tumor sequencing that are germline variants. In many cases, these recommendations remain appropriate (e.g., PARP inhibitors for BRCA1/2) while in other cases germline data facilitated a more nuanced interpretation of actionability. These findings support the use of germline genetic testing and paired tumor-germline analysis in precision cancer medicine studies. Citation Format: Jaclyn Schienda, Catherine M. Clinton, Laura B. Corson, Alma Imamovic-Tuco, Navin Pinto, Luke Maese, Theodore W. Laetsch, AeRang Kim, Susan I. Vear, Margaret E. Macy, Mark A. Applebaum, Rochelle Bagatell, Amit J. Sabnis, Daniel A. Weiser, Julia L. Glade-Bender, Samuel L. Volchenboum, Wenjun Kang, Danielle Manning, Jonathan Nowak, Joshua Schiffman, Neal I. Lindeman, Alanna J. Church, Katherine A. Janeway, Brian D. Crompton, Junne Kamihara. The added value of examining germline variants in a precision cancer therapy study [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A06.

The Circular RNA Landscape of Non-Small Cell Lung Cancer Cells.

The class of circular RNA (circRNA) is characterized by head-to-tail bonds between exons formed by backsplicing. Here, we provide a resource of circRNA expression in a comprehensive panel of 60 lung cancer and non-transformed cell lines (FL3C dataset). RNA sequencing after depletion of ribosomal RNA quantified the expression of circRNA and linear RNA. We detected 148,811 circular RNAs quantified by 2.8 million backsplicing reads originating from 12,251 genes. The number of identified circRNAs was markedly higher using rRNA depletion compared to public polyA-enriched RNA-seq datasets. CircRNAs almost never started in the first exon nor ended in the last exon and started more frequently in earlier exons. Most circRNAs showed high cell line specificity and correlated positively with their linear RNA counterpart. Known cancer genes produced more circRNAs than non-cancer genes. Subsets of circRNAs correlated with cell proliferation, histological subtype or genotype. CircTNFRSF21 was translated crossing the backsplice site in two different reading frames. Overexpression of circPVT1, circERBB2, circHIPK3, circCCNB1, circSMAD2, circTNFRSF21 and circKIF5B significantly increased colony formation. In conclusion, our data provide a comprehensive map of circRNA expression in lung cancer cells and global patterns of circRNA production as a useful resource for future research into lung cancer circRNAs.

Directional association test reveals high-quality putative cancer driver biomarkers including noncoding RNAs

BackgroundMost statistical methods used to identify cancer driver genes are either biased due to choice of assumed parametric models or insensitive to directional relationships important for causal inference. To overcome modeling biases and directional insensitivity, a recent statistical functional chi-squared test (FunChisq) detects directional association via model-free functional dependency. FunChisq examines patterns pointing from independent to dependent variables arising from linear, non-linear, or many-to-one functional relationships. Meanwhile, the Functional Annotation of Mammalian Genome 5 (FANTOM5) project surveyed gene expression at over 200,000 transcription start sites (TSSs) in nearly all human tissue types, primary cell types, and cancer cell lines. The data cover TSSs originated from both coding and noncoding genes. For the vast uncharacterized human TSSs that may exhibit complex patterns in cancer versus normal tissues, the model-free property of FunChisq provides us an unprecedented opportunity to assess the evidence for a gene’s directional effect on human cancer.ResultsWe first evaluated FunChisq and six other methods using 719 curated cancer genes on the FANTOM5 data. FunChisq performed best in detecting known cancer driver genes from non-cancer genes. We also show the capacity of FunChisq to reveal non-monotonic patterns of functional association, to which typical differential analysis methods such as t-test are insensitive. Further applying FunChisq to screen unannotated TSSs in FANTOM5, we predicted 1108 putative cancer driver noncoding RNAs, stronger than 90% of curated cancer driver genes. Next, we compared leukemia samples against other samples in FANTOM5 and FunChisq predicted 332/79 potential biomarkers for lymphoid/myeloid leukemia, stronger than the TSSs of all 87/100 known driver genes in lymphoid/myeloid leukemia.ConclusionsThis study demonstrated the advantage of FunChisq in revealing directional association, especially in detecting non-monotonic patterns. Here, we also provide the most comprehensive catalog of high-quality biomarkers that may play a causative role in human cancers, including putative cancer driver noncoding RNAs and lymphoid/myeloid leukemia specific biomarkers.

Somatic Alteration Burden Involving Non-Cancer Genes Predicts Prognosis in Early-Stage Non-Small Cell Lung Cancer.

The burden of somatic mutations and neoantigens has been associated with improved survival in cancer treated with immunotherapies, especially non-small cell lung cancer (NSCLC). However, there is uncertainty about their effect on outcome in early-stage untreated cases. We posited that the burden of mutations in a specific set of genes may also contribute to the prognosis of early NSCLC patients. From a small cohort of 36 NSCLC cases, we were able to identify somatic mutations and copy number alterations in 865 genes that contributed to patient overall survival. Simply, the number of altered genes (NAG) among these 865 genes was associated with longer disease-free survival (hazard ratio (HR) = 0.153, p = 1.48 × 10−4). The gene expression signature distinguishing patients with high/low NAG was also prognostic in three independent datasets. Patients with a high NAG could be further stratified based on the presence of immunogenic mutations, revealing a further subgroup of stage I NSCLC with even better prognosis (85% with >5 years survival), and associated with cytotoxic T-cell expression. Importantly, 95% of the highly-altered genes lacked direct relation to cancer, but were implicated in pathways regulating cell proliferation, motility and immune response.

Abstract 2460: A comprehensive computational framework for interpretation of high-throughput cancer data using annotated pathways

Abstract Introduction: Identification of pathways that are associated with experimental gene expression profiles is critical for understanding the biological mechanisms in cancer studies. Current standard pathway analysis methods such as Over-representation Analysis and Gene Set Enrichment Analysis often only use the gene symbols of the annotated pathways and typically disregard the biological interactions. This approach is not sensitive to the dysregulation of central genes in the pathway networks whose perturbations may critically affect the biological mechanisms. Methods: In this study, we have developed a framework that leverages the networks of interactions from the KEGG database to infer the underlying pathways from high-throughput gene expression profiles of cancer data. This methodology uses novel network analysis models with optimized sensitivity towards genes with key positions in pathways, particularly cancer-genes. We have previously shown that our novel network models create a distinction between the topological position of cancer genes and non-cancer related genes in pathways. In this study, we have devised a statistical pipeline to incorporate the network evidences in refining standard pathway analysis models. We have also tested our model on a battery of multiple cancer datasets and incorporated synthetic data evaluation approaches to verify the performance of our model. Results: The results show that our network-based model is capable of detecting known and well-studied pathway associations when other methods fail to capture them. For example, our model identifies the PI3K-Akt signaling pathway from 274 samples (benign and malignant) from GSE9899 ovarian cancer dataset, when only a few number genes (19) are perturbed (adjusted p-value = 0.003). In comparison, over-representation analysis only produces an adjusted p-value of 0.15 using the same data for the same pathway. This observation outlines an instance where our model makes informative interpretations, given that the dysfunction of PI3K-Akt pathway is studied in ovarian cancer. On the same dataset, our methodology identifies the association of Ras signaling pathway with the ovarian cancer from only 7 perturbations (adjusted p-value = 0.034). In comparison, over-representation analysis produces an adjusted p-value of 1.00. Discussion: The network-based model of this study provides a new perspective for interpretation of high-throughput cancer data by accounting for the topological position of the genes with respect to their associated pathways. This methodology allows researchers to identify pathways that are associated with the perturbations. It also enables to evaluate the importance of the perturbation with regards to the organization of the pathways. This methodology is potentially beneficial to applications in biomarker discovery and drug target development. Citation Format: Pourya Naderi Yeganeh, M. Taghi Mostafavi. A comprehensive computational framework for interpretation of high-throughput cancer data using annotated pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2460.

Oncogenes expand during evolution to withstand somatic amplification

Cancer-related genes are under intense evolutionary pressure. We conjectured that gene size is an important determinant of amplification propensity for oncogenes and thus cancer susceptibility and therefore could be subject to natural selection. Gene information, including size and genomic locations, of all protein-coding genes were downloaded from Ensembl (release 87). Quantification of gene amplification was based on Genomic Identification of Significant Targets in Cancer scores obtained from available The Cancer Genome Atlas studies. Oncogenes are larger in size as compared with non-cancer genes (mean size: 92.1 kb versus 61.4 kb; P < 0.0001) in the human genome, which is contributed by both increased total exon size (mean size: 4.6 kb versus 3.4 kb; P < 0.0001) and higher intronic content (mean %: 84.8 versus 78.0; P < 0.01). Such non-random size distribution and intronic composition are conserved in mouse and Drosophila (all P < 0.0001). Stratification by gene age indicated that young oncogenes have been subject to a stronger evolutionary pressure for gene expansion than their non-cancer counterparts. Pan-cancer analysis demonstrated that larger oncogenes were amplified to a lesser extent. Tumor-suppressor genes also moved toward small oncogenes in the course of evolution. Oncogenes expand in size whereas tumor-suppressor genes move closer to small oncogenes in the course of evolution to withstand oncogenic somatic amplification. Our findings have shed new light on the previously unappreciated influence of gene size on oncogene amplification and elucidated how cancers have shaped our genome to its present configuration.