Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings

Virological testing is central to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) containment, but many settings face severe limitations on testing. Group testing offers a way to increase throughput by testing pools of combined samples; however, most proposed designs have not yet addressed key concerns over sensitivity loss and implementation feasibility. Here, we combined a mathematical model of epidemic spread and empirically derived viral kinetics for SARS-CoV-2 infections to identify pooling designs that are robust to changes in prevalence and to ratify sensitivity losses against the time course of individual infections. We show that prevalence can be accurately estimated across a broad range, from 0.02 to 20%, using only a few dozen pooled tests and using up to 400 times fewer tests than would be needed for individual identification. We then exhaustively evaluated the ability of different pooling designs to maximize the number of detected infections under various resource constraints, finding that simple pooling designs can identify up to 20 times as many true positives as individual testing with a given budget. Crucially, we confirmed that our theoretical results can be translated into practice using pooled human nasopharyngeal specimens by accurately estimating a 1% prevalence among 2304 samples using only 48 tests and through pooled sample identification in a panel of 960 samples. Our results show that accounting for variation in sampled viral loads provides a nuanced picture of how pooling affects sensitivity to detect infections. Using simple, practical group testing designs can vastly increase surveillance capabilities in resource-limited settings.

Using viral load and epidemic dynamics to optimize pooled testing in resource constrained settings

Extensive virological testing is central to SARS-CoV-2 containment, but many settings face severe limitations on testing. Group testing offers a way to increase throughput by testing pools of combined samples; however, most proposed designs have not yet addressed key concerns over sensitivity loss and implementation feasibility. Here, we combine a mathematical model of epidemic spread and empirically derived viral kinetics for SARS-CoV-2 infections to identify pooling designs that are robust to changes in prevalence, and to ratify losses in sensitivity against the time course of individual infections. Using this framework, we show that prevalence can be accurately estimated across four orders of magnitude using only a few dozen pooled tests without the need for individual identification. We then exhaustively evaluate the ability of different pooling designs to maximize the number of detected infections under various resource constraints, finding that simple pooling designs can identify up to 20 times as many positives compared to individual testing with a given budget. We illustrate how pooling affects sensitivity and overall detection capacity during an epidemic and on each day post infection, finding that sensitivity loss is mainly attributed to individuals sampled at the end of infection when detection for public health containment has minimal benefit. Crucially, we confirm that our theoretical results can be accurately translated into practice using pooled human nasopharyngeal specimens. Our results show that accounting for variation in sampled viral loads provides a nuanced picture of how pooling affects sensitivity to detect epidemiologically relevant infections. Using simple, practical group testing designs can vastly increase surveillance capabilities in resource-limited settings.

Structural Alterations Driving Castration-Resistant Prostate Cancer Revealed by Linked-Read Genome Sequencing

Nearly all prostate cancer deaths are from metastatic castration-resistant prostate cancer (mCRPC), but there have been few whole-genome sequencing (WGS) studies of this disease state. We performed linked-read WGS on 23 mCRPC biopsy specimens and analyzed cell-free DNA sequencing data from 86 patients with mCRPC. In addition to frequent rearrangements affecting known prostate cancer genes, we observed complex rearrangements of the AR locus in most cases. Unexpectedly, these rearrangements include highly recurrent tandem duplications involving an upstream enhancer of AR in 70%-87% of cases compared with <2% of primary prostate cancers. A subset of cases displayed AR or MYC enhancer duplication in the context of a genome-wide tandem duplicator phenotype associated with CDK12 inactivation. Our findings highlight the complex genomic structure of mCRPC, nominate alterations that may inform prostate cancer treatment, and suggest that additional recurrent events in the non-coding mCRPC genome remain to be discovered.

Abstract 3001: Broad/IBM Project: Discovery of treatment resistance mechanisms through use of liquid biopsy genomics services

The Broad/IBM Cancer Resistance Project has partnered with Broad Genomics to pilot the use of cutting edge sequencing technology for the analysis of cell free DNA in blood biopsies. Working closely with the Broad's Cancer Program, Broad Genomics has developed a suite of liquid biopsy sequencing products designed to provide optimal flexibility in conducting research studies with a broad range of applications including; biomarker discovery, treatment resistance monitoring, and detection of minimal residual disease (MRD) post-surgery. Cell-free DNA is extracted from the blood, and a dual unique-molecular-indexed library is created. From this library, low coverage whole genome (ultra-low-pass 0.1x coverage) data is generated to survey sample quality and evaluate the tumor fraction in the liquid specimen. Utilizing the same library, additional assays can be selected for processing based on the research aim (Targeted Panel Assays, MRD Detection or Whole Exomes). Since our approach utilizes the same genomic material for whole genome and targeted sequencing assays, it is possible to maximize the information learned from each valuable and limited liquid biopsy specimen. Our study design takes advantage of the discovery potential of combined tissue-based sequencing and serial liquid biopsy analysis to elucidate mechanisms of cancer resistance by tracking the evolution of clonal and subclonal populations in patients samples over time. This collaboration will utilize the ultra-low-pass sequencing and whole exome sequencing together with custom analysis pipelines to correlate the genomic events with patient clinical data. We aim to process 3,000 samples from 1,000 patients over the next 3 years. To date we have processed close to 500 samples through the ultra-low-pass pipeline and 100 samples through the whole exome sequencing pipeline (results to be provided).The ability to successfully investigate treatment resistant cancers from non-invasive liquid biopsies presents new opportunities for identifying markers, understanding dynamics and monitoring tumor dissemination and clonal evolution.

Citation Format: Gad Getz, Carrie Cibulskis, Ignaty Leshchiner, Megan Hanna, Dimitri Livitz, Kara Slowik, Chaya Levovitz, Filippo Utro, Kahn Rhrissorrakrai, Denisse Rotem, Gregory Gydush, Sarah C. Reed, Justin Rhoades, Gavin Ha, Samuel S. Freeman, Christopher Lo, Mark Fleharty, Justin Abreu, Katie Larkin, Michelle Cipicchio, Brendan Blumenstiel, Matt DeFelice, Jonna Grimsby, Susanna Hamilton, Niall Lennon, Viktor A. Adalsteinsson, Laxmi Parida. Broad/IBM Project: Discovery of treatment resistance mechanisms through use of liquid biopsy genomics services [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3001.

Defining the diverse spectrum of inversions, complex structural variation, and chromothripsis in the morbid human genome

Background

Structural variation (SV) influences genome organization and contributes to human disease. However, the complete mutational spectrum of SV has not been routinely captured in disease association studies.

Results

We sequenced 689 participants with autism spectrum disorder (ASD) and other developmental abnormalities to construct a genome-wide map of large SV. Using long-insert jumping libraries at 105X mean physical coverage and linked-read whole-genome sequencing from 10X Genomics, we document seven major SV classes at ~5 kb SV resolution. Our results encompass 11,735 distinct large SV sites, 38.1% of which are novel and 16.8% of which are balanced or complex. We characterize 16 recurrent subclasses of complex SV (cxSV), revealing that: (1) cxSV are larger and rarer than canonical SV; (2) each genome harbors 14 large cxSV on average; (3) 84.4% of large cxSVs involve inversion; and (4) most large cxSV (93.8%) have not been delineated in previous studies. Rare SVs are more likely to disrupt coding and regulatory non-coding loci, particularly when truncating constrained and disease-associated genes. We also identify multiple cases of catastrophic chromosomal rearrangements known as chromoanagenesis, including somatic chromoanasynthesis, and extreme balanced germline chromothripsis events involving up to 65 breakpoints and 60.6 Mb across four chromosomes, further defining rare categories of extreme cxSV.

Conclusions

These data provide a foundational map of large SV in the morbid human genome and demonstrate a previously underappreciated abundance and diversity of cxSV that should be considered in genomic studies of human disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1158-6) contains supplementary material, which is available to authorized users.

Punctuated evolution of prostate cancer genomes

The analysis of exonic DNA from prostate cancers has identified recurrently mutated genes, but the spectrum of genome-wide alterations has not been profiled extensively in this disease. We sequenced the genomes of 57 prostate tumors and matched normal tissues to characterize somatic alterations and to study how they accumulate during oncogenesis and progression. By modeling the genesis of genomic rearrangements, we identified abundant DNA translocations and deletions that arise in a highly interdependent manner. This phenomenon, which we term "chromoplexy," frequently accounts for the dysregulation of prostate cancer genes and appears to disrupt multiple cancer genes coordinately. Our modeling suggests that chromoplexy may induce considerable genomic derangement over relatively few events in prostate cancer and other neoplasms, supporting a model of punctuated cancer evolution. By characterizing the clonal hierarchy of genomic lesions in prostate tumors, we charted a path of oncogenic events along which chromoplexy may drive prostate carcinogenesis.

Abstract 4017: Dissecting the clonal hierarchy of cancer-driving genomic lesions

Characterizing the genomic evolution of cancer is critical to understanding disease progression and identifying potential therapeutic targets. By examining the clonal hierarchy of genomic lesions in common tumors, it would be possible to reconstruct the path of oncogenic events that drive carcinogenesis. Reliable assessment of such paths from high-throughput genome sequencing data is complicated by the admixture of normal DNA in tumor samples and by reduced data signal for highly subclonal events. We introduce an approach that exploits individuals’ genetic background by using the abundant germline SNP genotype data provided by whole genome sequence coverage to assess the clonality of genomic alterations, including copy number changes, rearrangements, and point mutations.

We developed a novel algorithm, CLONET (CLONality Estimate in Tumors), which analyzes patient-specific heterozygous SNP loci (informative SNPs) and mono-allelic somatic deletions to assess levels of stromal DNA admixture and infer the clonal status of each aberration. For every mono allelic deletion, CLONET assesses the allelic fractions of informative SNPs to determine the apparent proportion of normal cells DNA. Next, through a conservative use of simulation-based error estimates, deletions with the lowest proportions of normal DNA reads are considered clonal. For point mutations, the tumor allelic fraction is corrected for stromal DNA admixture level and subclonality is inferred when it differs significantly from the expected value for clonal lesions. Similarly, the proportions of reads that span each side of a putative breakpoint involved in a rearrangement are matched against the expected values. CLONET also addresses tumor aneuploidy by searching for chromosomes with coverage and allelic fractions of informative SNPs not consistent with a diploid genome.

CLONET was tested on 55 whole genome sequences from prostate cancers, a highly heterogeneous tumor type, to catalogue the accumulation of somatic alterations during oncogenesis and progression. In 98% of the cases CLONET made confident assessment of admixture and clonality. We observed consistent clonal lesions involving NKX3-1, the 3Mb region between TMPRSS2 and ERG and FOXP1, as well as early point mutations in SPOP and FOXA1. Overall, we observed a higher rate of subclonal protein-coding point mutation versus deletions (p-value &lt; 10−7). We validated this approach by IHC and FISH for predicted clonal and sub-clonal events. A predicted subclonal homozygous deletion of CHD1 was confirmed by FISH that demonstrated the presence of both nuclei with homozygous and with hemizygous deletion of CHD1. Finally, to assess the general validity of CLONET, we analyzed data from 53 additional tumor genomes, including 25 melanomas and 28 lung adenocarcinomas.

In summary, our results imply the existence of consensus paths of tumor carcinogenesis that favor dysregulation of cancer genes in a defined sequence.

Citation Format: Davide Prandi, Sylvan C. Baca, Michael S. Lawrence, Juan Miguel Mosquera, Alessandro Romanel, Yotam Drier, Kyung Park, Naoki Kitabayashi, Theresa Y. MacDonald, Eliezer Van Allen, Gregory V. Kryukov, Jean-Philippe Theurillat, T. David Soong, Elizabeth Nickerson, Daniel Auclair, Ashutosh Tewari, Himisha Beltran, Robert C. Onofrio, Gunther Boysen, Candace Guiducci, Christopher E. Barbieri, Kristian Cibulskis, Andrey Sivachenko, Scott L. Carter, Gordon Saksena, Douglas Voet, Alex H. Ramos, Wendy Winckler, Michelle Cipicchio, Kristin Ardlie, Philip W. Kantoff, Michael F. Berger, Stacey B. Gabriel, Todd R. Golub, Matthew Meyerson, Eric S. Lander, Olivier Elemento, Gad Getz, Francesca Demichelis, Mark A. Rubin, Levi A. Garraway. Dissecting the clonal hierarchy of cancer-driving genomic lesions. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4017. doi:10.1158/1538-7445.AM2013-4017