Comparison of Genetic Profiling between Primary Tumor and Circulating Tumor Cells Captured by Microfluidics in Epithelial Ovarian Cancer: Tumor Heterogeneity or Allele Dropout?

Epithelial ovarian cancer (EOC) is a leading cause of cancer mortality among women but unfortunately is usually not diagnosed until advanced stage. Early detection of EOC is of paramount importance to improve outcomes. Liquid biopsy of circulating tumor cells (CTCs) is emerging as one of the promising biomarkers for early detection of solid tumors. However, discrepancies in terms of oncogenomics (i.e., different genetic defects detected) between the germline, primary tumor, and liquid biopsy are a serious concern and may adversely affect downstream cancer management. Here, we illustrate the potential and pitfalls of CTCs by presenting two patients of Stage I EOC. We successfully isolated and recovered CTCs by a silicon-based nanostructured microfluidics system, the automated Cell RevealTM. We examined the genomics of CTCs as well as the primary tumor and germline control (peripheral blood mononuclear cells) by whole exome sequencing. Different signatures were then investigated by comparisons of identified mutation loci distinguishing those that may only arise in the primary tumor or CTCs. A novel model is proposed to test if the highly variable allele frequencies, between primary tumor and CTCs results, are due to allele dropout in plural CTCs or tumor heterogeneity. This proof-of-principle study provides a strategy to elucidate the possible cause of genomic discrepancy between the germline, primary tumor, and CTCs, which is helpful for further large-scale use of such technology to be integrated into clinical management protocols.

Uncertainty in estimating the number of contributors from simulated DNA mixture profiles, with and without allele dropout, from Chinese, Malay, Indian, and Caucasian ethnic populations

Determining the number of contributors (NOC) accurately in a forensic DNA mixture profile can be challenging. To address this issue, there have been various studies that examined the uncertainty in estimating the NOC in a DNA mixture profile. However, the focus of these studies lies primarily on dominant populations residing within Europe and North America. Thus, there is limited representation of Asian populations in these studies. Further, the effects of allele dropout on the NOC estimation has not been explored. As such, this study assesses the uncertainty of NOC in simulated DNA mixture profiles of Chinese, Malay, and Indian populations, which are the predominant ethnic populations in Asia. The Caucasian ethnic population was also included to provide a basis of comparison with other similar studies. Our results showed that without considering allele dropout, the NOC from DNA mixture profiles derived from up to four contributors of the same ethnic population could be estimated with confidence in the Chinese, Malay, Indian and Caucasian populations. The same results can be observed on DNA mixture profiles originating from a combination of differing ethnic populations. The inclusion of an overall 30% allele dropout rate increased the probability (risk) of underestimating the NOC in a DNA mixture profile; even a 3-person DNA mixture profile has a > 99% risk of underestimating the NOC as two or fewer contributors. However, such risks could be mitigated when the highly polymorphic SE33 locus was included in the dataset. Lastly there was a negligible level of risk in misinterpreting the NOC in a mixture profile as deriving from a single source profile. In summary, our studies showcased novel results representative of the Chinese, Malay, and Indian ethnic populations when examining the uncertainty in NOC estimation in a DNA mixture profile. Our results would be useful in the estimation of NOC in a DNA mixture profile in the Asian context.

Removing the bad apples: a simple bioinformatic method to improve loci-recovery in de novo RADseq data for non-model organisms

The restriction site-associated DNA (RADseq) family of protocols involves digesting DNA and sequencing the region flanking the cut site, thus providing a cost and time efficient way for obtaining thousands of genomic markers. However, when working with non-model taxa with few genomic resources, optimization of RADseq wet-lab and bioinformatic tools may be challenging, often resulting in allele dropout – that is when a given RADseq locus is not sequenced in one or more individuals resulting in missing data. Additionally, as datasets include divergent taxa, rates of dropout will increase since restriction sites may be lost due to mutation. Mitigating the impacts of allele dropout is crucial, as missing data may lead to incorrect inferences in population genetics and phylogenetics. Here, we demonstrate a simple pipeline for the optimization of RADseq datasets which involves reducing and analysing datasets at a population or species level. By running the software Stacks at this level, we were able to reliably identify and remove individuals with high levels of missing data (bad apples) likely stemming from artefacts in library preparation, DNA quality or sequencing artefacts. Removal of the bad apples generally led to an increase of loci and decrease of missing data in the final datasets, thus improving the biological interpretability of the data.

PhISCS - A Combinatorial Approach for Sub-perfect Tumor Phylogeny Reconstruction via Integrative use of Single Cell and Bulk Sequencing Data

Recent technological advances in single cell sequencing (SCS) provide high resolution data for studying intra-tumor heterogeneity and tumor evolution. Available computational methods for tumor phylogeny inference via SCS typically aim to identify the most likelyperfect phylogeny treesatisfyinginfinite sites assumption(ISA). However limitations of SCS technologies such as frequent allele dropout or highly variable sequence coverage, commonly result in mutational call errors and prohibit a perfect phylogeny. In addition, ISA violations are commonly observed in tumor phylogenies due to the loss of heterozygosity, deletions and convergent evolution. In order to address such limitations, we, for the first time, introduce a new combinatorial formulation that integrates single cell sequencing data with matching bulk sequencing data, with the objective of minimizing a linear combination of (i) potential false negatives (due to e.g. allele dropout or variance in sequence coverage) and (ii) potential false positives (due to e.g. read errors) among mutation calls, as well as (iii) the number of mutations that violate ISA - to define theoptimal sub-perfect phylogeny.Our formulation ensures that several lineage constraints imposed by the use of variant allele frequencies (VAFs, derived from bulk sequence data) are satisfied. We express our formulation both in the form of an integer linear program (ILP) and - for the first time in the context of tumor phylogeny reconstruction - a boolean constraint satisfaction problem (CSP) and solve them by leveraging state-of-the-art ILP/CSP solvers. The resulting method, which we name PhISCS, is the first to integrate SCS and bulk sequencing data under the finite sites model. Using several simulated and real SCS data sets, we demonstrate that PhISCS is not only more general but also more accurate than the alternative tumor phylogeny inference tools. PhISCS is very fast especially when its CSP based variant is used returns the optimal solution, except in rare instances for which it provides an optimality gap. PhISCS is available athttps://github.com/haghshenas/PhISCS.

Interlaboratory comparison of fig (Ficus carica L.) microsatellite genotyping data and determination of reference alleles

<p>Microsatellites have been identified as the marker of choice in plant genotyping projects. However, due to length discrepancies obtained between different laboratories for the same allele, interlaboratory comparison of fingerprinting results is often a difficult task. The objectives of this study were to compare genotyping results of two laboratories, to evaluate genetic parameters of microsatellite markers and to determine reference allele sizes for fig cultivars from the Istrian peninsula.</p><p>Genotyping results of ninety fig (<em>Ficus carica</em> L.) accessions were comparable between the laboratories despite differences observed when comparing electropherograms of different capillary electrophoresis systems. Differences in lengths of the same alleles were detected due to different PCR methods and laboratory equipment, but the distances between alleles of the same locus were preserved. However, locus FSYC01 exhibited one allele dropout which led to misidentification of 28 heterozygotes as homozygote individuals suggesting this locus as unreliable. Allele dropout was assigned to the tail PCR technology or to a touchdown PCR protocol.</p><p>Genotypes of twenty-four reference cultivars from the Istrian peninsula were confirmed by both laboratories. These results will contribute to the usage of markers with greater reliability, discrimination power and consequently, to more reliable standardization with other fig genotyping projects.</p>