Abstract 4252: Detecting microsatellite instability in ffpe tissue from crc subjects using next generation sequencing

Background

Microsatellite instability (MSI) is a hypermutable phenotype resulting from DNA mismatch repair deficiency and is observed in up to 10-15% of early-stage colorectal cancers (CRC). It manifests as abnormal lengthening or shortening of DNA repeats at specific genomic loci. MSI-High (MSI-H) CRC patients have been shown to respond favorably towards both chemotherapy and immunotherapy and have different prognosis than MSS patients and the FDA has approved Pembrolizumab as the first cancer therapy based on a class of biomarkers rather than a cancer type. With increasing options for targeted therapy in solid tumors, being able to combine MSI testing with other cancer biomarkers for approved therapies is beneficial. We show proof of concept of a pan-cancer gene panel using an NGS-based assay and bioinformatics workflow that enables classification of MSI status along with detection of SNVs, fusions, indels and CNVs from tumor tissue samples. Unlike the gold standard PCR-based test, our method does not require a matched normal sample, thus enabling MSI detection based on tumor tissue alone.

Methods

Our workflow combines whole genome library preparation, hybrid capture target enrichment, high-throughput sequencing and a proprietary analysis algorithm. We expanded the number of genomic loci to improve the sensitivity of MSI detection. The analysis algorithm was trained using DNA from pure and mixed cell lines, tumor tissue and normal samples with known MSI status to classify the molecular alterations between MSI-H and MSS genotypes from sequencing reads. The algorithm leverages repeat lengths in homopolymeric MSI loci in order to predict instability of individual loci followed by aggregation using a statistical framework to make the final call on MSI status.

Results

The above algorithm was evaluated on a cohort of 134 stage II and stage III CRC subjects who underwent curative intent surgery. MSI status for these samples was orthogonally tested using a PCR-based MSI Analysis System (31 positives, 103 negatives) and a dMMR system. Using a pre-determined threshold, the algorithm yielded 100% sensitivity and 100% specificity on the above cohort without using matched normal tissue. A second cohort evaluated includes 47 CRC patients that are enriched for MSI positive status by virtue of their selection criteria (BRAF positive). Very high concordance on MSI status with an orthogonal method was observed. Data demonstrating high concordance with a PCR_based MSI system will be available at the presentation.

Conclusions

Here we present an NGS-based assay and bioinformatics workflow with robust analytical performance for MSI detection in FFPE tissue samples without a paired normal.

Citation Format: Amrita Pati, Hao Wang, Hamid Mirebrahim, Seng Saelee, Joshua Lefkowitz, Sean Chien, Ashla Singh, Fergal Casey, Vera Rapoport, Xiaoju Max Ma, John Lee, Alex Lovejoy, Daniel Klass, Hans-Peter Adams. Detecting microsatellite instability in ffpe tissue from crc subjects using next generation sequencing [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 4252.

A comparative evaluation of genome assembly reconciliation tools

Background

The majority of eukaryotic genomes are unfinished due to the algorithmic challenges of assembling them. A variety of assembly and scaffolding tools are available, but it is not always obvious which tool or parameters to use for a specific genome size and complexity. It is, therefore, common practice to produce multiple assemblies using different assemblers and parameters, then select the best one for public release. A more compelling approach would allow one to merge multiple assemblies with the intent of producing a higher quality consensus assembly, which is the objective of assembly reconciliation.

Results

Several assembly reconciliation tools have been proposed in the literature, but their strengths and weaknesses have never been compared on a common dataset. We fill this need with this work, in which we report on an extensive comparative evaluation of several tools. Specifically, we evaluate contiguity, correctness, coverage, and the duplication ratio of the merged assembly compared to the individual assemblies provided as input.

Conclusions

None of the tools we tested consistently improved the quality of the input GAGE and synthetic assemblies. Our experiments show an increase in contiguity in the consensus assembly when the original assemblies already have high quality. In terms of correctness, the quality of the results depends on the specific tool, as well as on the quality and the ranking of the input assemblies. In general, the number of misassemblies ranges from being comparable to the best of the input assembly to being comparable to the worst of the input assembly.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1213-3) contains supplementary material, which is available to authorized users.

Genome resources for climate-resilient cowpea, an essential crop for food security

Cowpea (Vigna unguiculata L. Walp.) is a legume crop that is resilient to hot and drought-prone climates, and a primary source of protein in sub-Saharan Africa and other parts of the developing world. However, genome resources for cowpea have lagged behind most other major crops. Here we describe foundational genome resources and their application to the analysis of germplasm currently in use in West African breeding programs. Resources developed from the African cultivar IT97K-499-35 include a whole-genome shotgun (WGS) assembly, a bacterial artificial chromosome (BAC) physical map, and assembled sequences from 4355 BACs. These resources and WGS sequences of an additional 36 diverse cowpea accessions supported the development of a genotyping assay for 51 128 SNPs, which was then applied to five bi-parental RIL populations to produce a consensus genetic map containing 37 372 SNPs. This genetic map enabled the anchoring of 100 Mb of WGS and 420 Mb of BAC sequences, an exploration of genetic diversity along each linkage group, and clarification of macrosynteny between cowpea and common bean. The SNP assay enabled a diversity analysis of materials from West African breeding programs. Two major subpopulations exist within those materials, one of which has significant parentage from South and East Africa and more diversity. There are genomic regions of high differentiation between subpopulations, one of which coincides with a cluster of nodulin genes. The new resources and knowledge help to define goals and accelerate the breeding of improved varieties to address food security issues related to limited-input small-holder farming and climate stress.

De novo meta-assembly of ultra-deep sequencing data

We introduce a new divide and conquer approach to deal with the problem of de novo genome assembly in the presence of ultra-deep sequencing data (i.e. coverage of 1000x or higher). Our proposed meta-assembler Slicembler partitions the input data into optimal-sized ‘slices’ and uses a standard assembly tool (e.g. Velvet, SPAdes, IDBA_UD and Ray) to assemble each slice individually. Slicembler uses majority voting among the individual assemblies to identify long contigs that can be merged to the consensus assembly. To improve its efficiency, Slicembler uses a generalized suffix tree to identify these frequent contigs (or fraction thereof). Extensive experimental results on real ultra-deep sequencing data (8000x coverage) and simulated data show that Slicembler significantly improves the quality of the assembly compared with the performance of the base assembler. In fact, most of the times, Slicembler generates error-free assemblies. We also show that Slicembler is much more resistant against high sequencing error rate than the base assembler.

Availability and implementation: Slicembler can be accessed at http://slicembler.cs.ucr.edu/.

Contact:hamid.mirebrahim@email.ucr.edu

Sequencing of 15 622 gene-bearing BACs clarifies the gene-dense regions of the barley genome

Barley (Hordeum vulgare L.) possesses a large and highly repetitive genome of 5.1 Gb that has hindered the development of a complete sequence. In 2012, the International Barley Sequencing Consortium released a resource integrating whole-genome shotgun sequences with a physical and genetic framework. However, because only 6278 bacterial artificial chromosome (BACs) in the physical map were sequenced, fine structure was limited. To gain access to the gene-containing portion of the barley genome at high resolution, we identified and sequenced 15 622 BACs representing the minimal tiling path of 72 052 physical-mapped gene-bearing BACs. This generated ~1.7 Gb of genomic sequence containing an estimated 2/3 of all Morex barley genes. Exploration of these sequenced BACs revealed that although distal ends of chromosomes contain most of the gene-enriched BACs and are characterized by high recombination rates, there are also gene-dense regions with suppressed recombination. We made use of published map-anchored sequence data from Aegilops tauschii to develop a synteny viewer between barley and the ancestor of the wheat D-genome. Except for some notable inversions, there is a high level of collinearity between the two species. The software HarvEST:Barley provides facile access to BAC sequences and their annotations, along with the barley-Ae. tauschii synteny viewer. These BAC sequences constitute a resource to improve the efficiency of marker development, map-based cloning, and comparative genomics in barley and related crops. Additional knowledge about regions of the barley genome that are gene-dense but low recombination is particularly relevant.

When less is more: 'slicing' sequencing data improves read decoding accuracy and de novo assembly quality

MOTIVATION

As the invention of DNA sequencing in the 70s, computational biologists have had to deal with the problem of de novo genome assembly with limited (or insufficient) depth of sequencing. In this work, we investigate the opposite problem, that is, the challenge of dealing with excessive depth of sequencing.

RESULTS

We explore the effect of ultra-deep sequencing data in two domains: (i) the problem of decoding reads to bacterial artificial chromosome (BAC) clones (in the context of the combinatorial pooling design we have recently proposed), and (ii) the problem of de novo assembly of BAC clones. Using real ultra-deep sequencing data, we show that when the depth of sequencing increases over a certain threshold, sequencing errors make these two problems harder and harder (instead of easier, as one would expect with error-free data), and as a consequence the quality of the solution degrades with more and more data. For the first problem, we propose an effective solution based on 'divide and conquer': we 'slice' a large dataset into smaller samples of optimal size, decode each slice independently, and then merge the results. Experimental results on over 15 000 barley BACs and over 4000 cowpea BACs demonstrate a significant improvement in the quality of the decoding and the final assembly. For the second problem, we show for the first time that modern de novo assemblers cannot take advantage of ultra-deep sequencing data.

AVAILABILITY AND IMPLEMENTATION

Python scripts to process slices and resolve decoding conflicts are available from http://goo.gl/YXgdHT; software Hashfilter can be downloaded from http://goo.gl/MIyZHs

CONTACT

stelo@cs.ucr.edu or timothy.close@ucr.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.