Abstract 5163: A high sensitivity, tumor-informed liquid biopsy platform, designed to detect minimal residual disease at part per million resolution

Tumor-informed liquid biopsy approaches have proven promising for detecting minimal residual disease (MRD) and recurrence of cancer following surgical resection or other therapy. However, current liquid biopsy MRD assays typically detect ctDNA in a range above 30 to 300 parts per million (PPM), leaving a significant fraction of MRD cases undetected, particularly soon after surgery and in early stage cancers where ctDNA can be at very low levels. To address this, we have developed NeXT Personal™, a tumor-informed liquid biopsy assay that achieves sensitivity down to 1 PPM, therefore enabling earlier detection of MRD and recurrence.

NeXT Personal leverages tumor/normal whole genome sequencing to design personalized MRD liquid biopsy panels for each patient. The panel is composed of >1,200 somatic tumor variants enabling higher sensitivity MRD detection in plasma through tracking of larger numbers of high quality and lower noise variants. This allows the platform to achieve high sensitivity across cancer types and stages, including early stage cancers and low mutational burden tumors, utilizing ~4 mL of plasma. Two independent methods were used to establish utility and performance: a proprietary cell-line media system, and well-characterized matched tumor-normal-plasma patient samples. Samples were serially diluted to <1 PPM, with replicates used to confirm performance. Digital droplet polymerase chain reaction (ddPCR) was used to orthogonally validate platform performance to the limit of detection (LOD) of ddPCR.

Characterization of MRD LOD in three cell-line media systems, HCC1143, HCC38, and HCC1937, yielded accurate and reproducible detection of signal across a broad range of concentrations, to a lower limit of 1-2 PPM. We then used our platform to characterize MRD LOD in a set of serially diluted patient samples, demonstrating sensitivity down to as low as 1 PPM, with high specificity in normal control samples. Finally, we demonstrated the performance of NeXT Personal with matched tumor-normal-plasma patient samples (8 different cancer types, stages II-IV). In this series, NeXT Personal detected cancers down to 0.8 PPM with high specificity demonstrated across a set of healthy normal donor samples. We estimate that ~50% of the cases in this set of patients would not have been detected by other commercially available liquid biopsy MRD platforms.

NeXT Personal achieved highly sensitive and specific MRD detection, reproducibly demonstrating a LOD down to 1 PPM in different cancer types and cell line dilutions, representing approximately 10 to 100 times higher sensitivity than other liquid biopsy MRD approaches. The high sensitivity of NeXT Personal potentially enables MRD detection across a broad variety of cancers and stages, including typically challenging early stage, low mutational burden, and low-shedding cancers.

Citation Format: Sean Michael Boyle, Gabor Bartha, John Lyle, Jason Harris, Josette Northcott, Dan Norton, Rachel Marty Pyke, Fabio C. P. Navarro, Alexander Stram, Christian Haudenschild, Rose Santiago, Robin Li, Chris Nelson, Yelia Huo, Manju Chinnappa, Qi Zhang, Lloyd Hsu, John West, Richard O. Chen. A high sensitivity, tumor-informed liquid biopsy platform, designed to detect minimal residual disease at part per million resolution [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 5163.

FANCY: fast estimation of privacy risk in functional genomics data

MOTIVATION

Functional genomics data are becoming clinically actionable, raising privacy concerns. However, quantifying privacy leakage via genotyping is difficult due to the heterogeneous nature of sequencing techniques. Thus, we present FANCY, a tool that rapidly estimates the number of leaking variants from raw RNA-Seq, ATAC-Seq and ChIP-Seq reads, without explicit genotyping. FANCY employs supervised regression using overall sequencing statistics as features and provides an estimate of the overall privacy risk before data release.

RESULTS

FANCY can predict the cumulative number of leaking SNVs with an average 0.95 R2 for all independent test sets. We realize the importance of accurate prediction when the number of leaked variants is low. Thus, we develop a special version of the model, which can make predictions with higher accuracy when the number of leaking variants is low.

AVAILABILITY AND IMPLEMENTATION

A python and MATLAB implementation of FANCY, as well as custom scripts to generate the features can be found at https://github.com/gersteinlab/FANCY. We also provide jupyter notebooks so that users can optimize the parameters in the regression model based on their own data. An easy-to-use webserver that takes inputs and displays results can be found at fancy.gersteinlab.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Abstract 2227: Pan-cancer shedding patterns of tumor circulating cell free DNA

Introduction: Tumor circulating cell-free DNA (ctDNA) comprises DNA molecules cast from tumors that reach and survive in a patient's bloodstream. Given its non-invasive nature, the liquid biopsy of ctDNA has remarkable potential for diagnosis, prognosis, disease progression tracking, and treatment monitoring. Nonetheless, little is known about which tumor features yield higher representation of ctDNA in blood or even which regions in the tumor genome are more inclined to shed and thus be observed as ctDNA.

Typically, studies of ctDNA have focused on a limited and well-established set of genes and recurrent variants. However, these limited gene panels may not capture the breadth of genetic alterations that reflect tumor biology. Their limited footprint hinders a comprehensive understanding of tumor heterogeneity, mechanisms of resistance, and DNA shedding patterns. To address these limitations, we developed a whole-exome scale cfDNA platform, NeXT Liquid Biopsy™, that enables sensitive detection and tracking of mutations in over twenty thousand genes from plasma samples.

Results: Here we profile over 50 tumor, normal, and plasma matched samples using NeXT Liquid Biopsy to investigate pan-cancer patterns of DNA shedding. We observe varying levels of ctDNA shedding in plasma, suggesting that tracking tumors with an exome-scale set of variants, as opposed to with a targeted panel, can benefit greatly from the higher sensitivity and granularity of our enhanced exome sequencing. Next, we sought to investigate the patterns of somatic variants representation in plasma. For this, we integrate shedding ratios with transcriptome and epigenome data from healthy paired tissues and examine variant shedding biases associated with transcription rate and histone modifications in associated nucleosomes.

Conclusion: We sequenced and analyzed one of the largest tumor, normal, and plasma cohorts to date with comprehensive coverage of somatic variants across all human genes. Our results suggest that tracking exome-scale somatic variants adds invaluable information to understand a tumor's biology. Moreover, our comprehensive coverage of the tumor genome can be used to unveil biases of tumor genome shedding patterns. We are able to investigate the shedding ratio across distinct genomic features deriving, to our knowledge, the first exome scale pan-cancer shedding resource of the human genome.

Citation Format: Fabio C. P. Navarro, Simo Zhang, Mengyao Tan, Charles Abbott, Josette Northcott, John Lyle, Gabor Bartha, Jason Harris, John West, Richard Chen, Sean Michael Boyle. Pan-cancer shedding patterns of tumor circulating cell free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2227.

STK11/LKB1 Loss of Function Is Associated with Global DNA Hypomethylation and S-Adenosyl-Methionine Depletion in Human Lung Adenocarcinoma

STK11 (liver kinase B1, LKB1) is the fourth most frequently mutated gene in lung adenocarcinoma, with loss of function observed in up to 30% of all cases. Our previous work identified a 16-gene signature for LKB1 loss of function through mutational and nonmutational mechanisms. In this study, we applied this genetic signature to The Cancer Genome Atlas (TCGA) lung adenocarcinoma samples and discovered a novel association between LKB1 loss and widespread DNA demethylation. LKB1-deficient tumors showed depletion of S-adenosyl-methionine (SAM-e), which is the primary substrate for DNMT1 activity. Lower methylation following LKB1 loss involved repetitive elements (RE) and altered RE transcription, as well as decreased sensitivity to azacytidine. Demethylated CpGs were enriched for FOXA family consensus binding sites, and nuclear expression, localization, and turnover of FOXA was dependent upon LKB1. Overall, these findings demonstrate that a large number of lung adenocarcinomas exhibit global hypomethylation driven by LKB1 loss, which has implications for both epigenetic therapy and immunotherapy in these cancers. SIGNIFICANCE: Lung adenocarcinomas with LKB1 loss demonstrate global genomic hypomethylation associated with depletion of SAM-e, reduced expression of DNMT1, and increased transcription of repetitive elements.

To mock or not: a comprehensive comparison of mock IP and DNA input for ChIP-seq

Chromatin immunoprecipitation (IP) followed by sequencing (ChIP-seq) is the gold standard to detect transcription-factor (TF) binding sites in the genome. Its success depends on appropriate controls removing systematic biases. The predominantly used controls, i.e. DNA input, correct for uneven sonication, but not for nonspecific interactions of the IP antibody. Another type of controls, 'mock' IP, corrects for both of the issues, but is not widely used because it is considered susceptible to technical noise. The tradeoff between the two control types has not been investigated systematically. Therefore, we generated comparable DNA input and mock IP experiments. Because mock IPs contain only nonspecific interactions, the sites predicted from them using DNA input indicate the spurious-site abundance. This abundance is highly correlated with the 'genomic activity' (e.g. chromatin openness). In particular, compared to cell lines, complex samples such as whole organisms have more spurious sites-probably because they contain multiple cell types, resulting in more expressed genes and more open chromatin. Consequently, DNA input and mock IP controls performed similarly for cell lines, whereas for complex samples, mock IP substantially reduced the number of spurious sites. However, DNA input is still informative; thus, we developed a simple framework integrating both controls, improving binding site detection.

GENCODE 2021

The GENCODE project annotates human and mouse genes and transcripts supported by experimental data with high accuracy, providing a foundational resource that supports genome biology and clinical genomics. GENCODE annotation processes make use of primary data and bioinformatic tools and analysis generated both within the consortium and externally to support the creation of transcript structures and the determination of their function. Here, we present improvements to our annotation infrastructure, bioinformatics tools, and analysis, and the advances they support in the annotation of the human and mouse genomes including: the completion of first pass manual annotation for the mouse reference genome; targeted improvements to the annotation of genes associated with SARS-CoV-2 infection; collaborative projects to achieve convergence across reference annotation databases for the annotation of human and mouse protein-coding genes; and the first GENCODE manually supervised automated annotation of lncRNAs. Our annotation is accessible via Ensembl, the UCSC Genome Browser and https://www.gencodegenes.org.

Origins and characterization of variants shared between databases of somatic and germline human mutations

Background

Mutations arise in the human genome in two major settings: the germline and the soma. These settings involve different inheritance patterns, time scales, chromatin structures, and environmental exposures, all of which impact the resulting distribution of substitutions. Nonetheless, many of the same single nucleotide variants (SNVs) are shared between germline and somatic mutation databases, such as between the gnomAD database of 120,000 germline exomes and the TCGA database of 10,000 somatic exomes. Here, we sought to explain this overlap.

Results

After strict filtering to exclude common germline polymorphisms and sites with poor coverage or mappability, we found 336,987 variants shared between the somatic and germline databases. A uniform statistical model explains 34% of these shared variants; a model that incorporates the varying mutation rates of the basic mutation types explains another 50% of shared variants; and a model that includes extended nucleotide contexts (e.g. surrounding 3 bases on either side) explains an additional 4% of shared variants. Analysis of read depth finds mixed evidence that up to 4% of the shared variants may represent germline variants leaked into somatic call sets. 9% of the shared variants are not explained by any model. Sequencing errors and convergent evolution did not account for these. We surveyed other factors as well: Cancers driven by endogenous mutational processes share a greater fraction of variants with the germline, and recently derived germline variants were more likely to be somatically shared than were ancient germline ones.

Conclusions

Overall, we find that shared variants largely represent bona fide biological occurrences of the same variant in the germline and somatic setting and arise primarily because DNA has some of the same basic chemical vulnerabilities in either setting. Moreover, we find mixed evidence that somatic call-sets leak appreciable numbers of germline variants, which is relevant to genomic privacy regulations. In future studies, the similar chemical vulnerability of DNA between the somatic and germline settings might be used to help identify disease-related genes by guiding the development of background-mutation models that are informed by both somatic and germline patterns of variation.

Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition

About half of all cancers have somatic integrations of retrotransposons. Here, to characterize their role in oncogenesis, we analyzed the patterns and mechanisms of somatic retrotransposition in 2,954 cancer genomes from 38 histological cancer subtypes within the framework of the Pan-Cancer Analysis of Whole Genomes (PCAWG) project. We identified 19,166 somatically acquired retrotransposition events, which affected 35% of samples and spanned a range of event types. Long interspersed nuclear element (LINE-1; L1 hereafter) insertions emerged as the first most frequent type of somatic structural variation in esophageal adenocarcinoma, and the second most frequent in head-and-neck and colorectal cancers. Aberrant L1 integrations can delete megabase-scale regions of a chromosome, which sometimes leads to the removal of tumor-suppressor genes, and can induce complex translocations and large-scale duplications. Somatic retrotranspositions can also initiate breakage-fusion-bridge cycles, leading to high-level amplification of oncogenes. These observations illuminate a relevant role of L1 retrotransposition in remodeling the cancer genome, with potential implications for the development of human tumors.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

Comprehensive functional genomic resource and integrative model for the human brain

Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with >88% reconstruction accuracy). It also allows building of a gene regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.

High IL-1R8 expression in breast tumors promotes tumor growth and contributes to impaired antitumor immunity

Tumors develop numerous strategies to fine-tune inflammation and avoid detection and eradication by the immune system. The identification of mechanisms leading to local immune dysregulation is critical to improve cancer therapy. We here demonstrate that Interleukin-1 receptor 8 (IL-1R8 - previously known as SIGIRR/TIR8), a negative regulator of Toll-Like and Interleukin-1 Receptor family signaling, is up-regulated during breast epithelial cell transformation and in primary breast tumors. IL-1R8 expression in transformed breast epithelial cells reduced IL-1-dependent NF-κB activation and production of pro-inflammatory cytokines, inhibited NK cell activation and favored M2-like macrophage polarization. In a murine breast cancer model (MMTV-neu), IL-1R8-deficiency reduced tumor growth and metastasis and was associated with increased mobilization and activation of immune cells, such as NK cells and CD8+ T cells. Finally, immune-gene signature analysis in clinical specimens revealed that high IL-1R8 expression is associated with impaired innate immune sensing and T-cell exclusion from the tumor microenvironment. Our results indicate that high IL-1R8 expression acts as a novel immunomodulatory mechanism leading to dysregulated immunity with important implications for breast cancer immunotherapy.

FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods

Comprehensive and accurate identification of structural variations (SVs) from next generation sequencing data remains a major challenge. We develop FusorSV, which uses a data mining approach to assess performance and merge callsets from an ensemble of SV-calling algorithms. It includes a fusion model built using analysis of 27 deep-coverage human genomes from the 1000 Genomes Project. We identify 843 novel SV calls that were not reported by the 1000 Genomes Project for these 27 samples. Experimental validation of a subset of these calls yields a validation rate of 86.7%. FusorSV is available at https://github.com/TheJacksonLaboratory/SVE.

Gene copy-number polymorphism caused by retrotransposition in humans

The era of whole-genome sequencing has revealed that gene copy-number changes caused by duplication and deletion events have important evolutionary, functional, and phenotypic consequences. Recent studies have therefore focused on revealing the extent of variation in copy-number within natural populations of humans and other species. These studies have found a large number of copy-number variants (CNVs) in humans, many of which have been shown to have clinical or evolutionary importance. For the most part, these studies have failed to detect an important class of gene copy-number polymorphism: gene duplications caused by retrotransposition, which result in a new intron-less copy of the parental gene being inserted into a random location in the genome. Here we describe a computational approach leveraging next-generation sequence data to detect gene copy-number variants caused by retrotransposition (retroCNVs), and we report the first genome-wide analysis of these variants in humans. We find that retroCNVs account for a substantial fraction of gene copy-number differences between any two individuals. Moreover, we show that these variants may often result in expressed chimeric transcripts, underscoring their potential for the evolution of novel gene functions. By locating the insertion sites of these duplicates, we are able to show that retroCNVs have had an important role in recent human adaptation, and we also uncover evidence that positive selection may currently be driving multiple retroCNVs toward fixation. Together these findings imply that retroCNVs are an especially important class of polymorphism, and that future studies of copy-number variation should search for these variants in order to illuminate their potential evolutionary and functional relevance.

Recent studies of human genetic variation have revealed that, in addition to differing at single nucleotide polymorphisms, individuals differ in copy-number at many regions of the genome. These copy-number variants (CNVs) are caused by duplication or deletion events and often affect functional sequences such as genes. Efforts to reveal the functional impact of CNVs have identified many variants increasing the risk of various disorders, and some that are adaptive. However, these studies mostly fail to detect gene duplications caused by retrotransposition, in which an mRNA transcript is reverse-transcribed and reinserted into the genome, yielding a new intron-less gene copy. Here we describe a method leveraging next-generation sequence data to accurately detect gene copy-number variants caused by retrotransposition, or retroCNVs, and apply this method to hundreds of whole-genome sequences from three different human subpopulations. We find that these variants account for a substantial number of gene copy-number differences between individuals, and that gene retrotransposition may often result in both deleterious and beneficial mutations. Indeed, we present evidence that two of these new gene duplications may be adaptive. These results imply that retroCNVs are an especially important class of CNV and should be included in future studies of human copy-number variation.