In nonmetastatic triple-negative breast cancer (TNBC) patients, we investigated whether circulating tumor DNA (ctDNA) detection can reflect the tumor response to neoadjuvant chemotherapy (NCT) and detect minimal residual disease after surgery.Ten milliliters of plasma were collected at 4 time points: before NCT; after 1 cycle; before surgery; after surgery. Customized droplet digital PCR (ddPCR) assays were used to track tumor protein p53 (TP53) mutations previously characterized in tumor tissue by massively parallel sequencing (MPS).Forty-six patients with nonmetastatic TNBC were enrolled. TP53 mutations were identified in 40 of them. Customized ddPCR probes were validated for 38 patients, with excellent correlation with MPS (r = 0.99), specificity (≥2 droplets/assay), and sensitivity (at least 0.1%). At baseline, ctDNA was detected in 27/36 patients (75%). Its detection was associated with mitotic index (P = 0.003), tumor grade (P = 0.003), and stage (P = 0.03). During treatment, we observed a drop of ctDNA levels in all patients but 1. No patient had detectable ctDNA after surgery. The patient with rising ctDNA levels experienced tumor progression during NCT. Pathological complete response (16/38 patients) was not correlated with ctDNA detection at any time point. ctDNA positivity after 1 cycle of NCT was correlated with shorter disease-free (P < 0.001) and overall (P = 0.006) survival.Customized ctDNA detection by ddPCR achieved a 75% detection rate at baseline. During NCT, ctDNA levels decreased quickly and minimal residual disease was not detected after surgery. However, a slow decrease of ctDNA level during NCT was strongly associated with shorter survival.
Abstract The gut microbiome is widely analyzed using high-throughput sequencing, such as 16S rRNA gene amplicon sequencing and shotgun metagenomic sequencing (SMS). DNA extraction is known to have a large impact on the metagenomic analyses. The aim of this study was to compare DNA extraction protocols for 16S sequencing. In that context, four commonly used DNA extraction methods were compared for the analysis of the gut microbiota. Commercial versions were evaluated against modified protocols using a stool preprocessing device (SPD, bioMérieux) upstream DNA extraction. Stool samples from nine healthy volunteers and nine patients with a Clostridium difficile infection were extracted with all protocols and 16S sequenced. Protocols were ranked using wet- and dry-lab criteria, including quality controls of the extracted genomic DNA, alpha-diversity, accuracy using a mock community of known composition and repeatability across technical replicates. SPD improved overall efficiency of three of the four tested protocols compared with their commercial version, in terms of DNA extraction yield, sample alpha -diversity, and recovery of Gram-positive bacteria. The best overall performance was obtained for the S-DQ protocol, SPD combined with the DNeasy PowerLyser PowerSoil protocol from QIAGEN. Based on this evaluation, we strongly believe that the use of such stool preprocessing device improves both the standardization and the quality of the DNA extraction in the human gut microbiome studies.
Abstract Background Several studies have documented the significant impact of methodological choices in microbiome analyses. The myriad of methodological options available complicate the replication of results and generally limit the comparability of findings between independent studies that use differing techniques and measurement pipelines. Here we describe the Mosaic Standards Challenge (MSC), an international interlaboratory study designed to assess the impact of methodological variables on the results. The MSC did not prescribe methods but rather asked participating labs to analyze 7 shared reference samples (5x human stool samples and 2x mock communities) using their standard laboratory methods. To capture the array of methodological variables, each participating lab completed a metadata reporting sheet that included 100 different questions regarding the details of their protocol. The goal of this study was to survey the methodological landscape for microbiome metagenomic sequencing (MGS) analyses and the impact of methodological decisions on metagenomic sequencing results. Results A total of 44 labs participated in the MSC by submitting results (16S or WGS) along with accompanying metadata; thirty 16S rRNA gene amplicon datasets and 14 WGS datasets were collected. The inclusion of two types of reference materials (human stool and mock communities) enabled analysis of both MGS measurement variability between different protocols using the biologically-relevant stool samples, and MGS bias with respect to ground truth values using the DNA mixtures. Owing to the compositional nature of MGS measurements, analyses were conducted on the ratio of Firmicutes: Bacteroidetes allowing us to directly apply common statistical methods. The resulting analysis demonstrated that protocol choices have significant effects, including both bias of the MGS measurement associated with a particular methodological choices, as well as effects on measurement robustness as observed through the spread of results between labs making similar methodological choices. In the analysis of the DNA mock communities, MGS measurement bias was observed even when there was general consensus among the participating laboratories. Conclusion This study was the result of a collaborative effort that included academic, commercial, and government labs. In addition to highlighting the impact of different methodological decisions on MGS result comparability, this work also provides insights for consideration in future microbiome measurement study design.
ABSTRACT There have been few studies on the concordance between phenotypic assays for predicting human immunodeficiency virus type 1 (HIV-1) coreceptor usage. The sensitivity of ultradeep pyrosequencing combined with genotyping tools is similar to that of phenotypic assays for detecting minor CXCR4-using variants. We evaluated the agreement between two phenotypic assays, the Toulouse tropism test (TTT) and the Trofile assay, and ultradeep pyrosequencing for determining the tropism of HIV-1 quasispecies. The concordance between the TTT and Trofile assays was assessed for 181 samples successfully phenotyped by both assays. The TTT was 86% concordant with the standard Trofile assay and 91.7% with its enhanced-sensitivity version. The concordance between phenotypic characterization of HIV-1 tropism and ultradeep pyrosequencing genotypic prediction was further studied in selected samples. The HIV-1 tropism inferred from ultradeep pyrosequencing of 11 samples phenotyped as X4 and dualtropic and 12 phenotyped as R5-tropic agreed closely with the results of phenotyping. However, ultradeep pyrosequencing detected minor CXCR4-using variants in 3 of 12 samples phenotyped as R5-tropic. Ultradeep pyrosequencing also detected minor CXCR4-using variants that had been missed by direct sequencing in 6 of 9 samples phenotyped as X4-tropic but genotyped as R5-tropic by direct sequencing. Ultradeep pyrosequencing was 87% concordant with the Trofile and TTT phenotypic assays and was in the same range of sensitivity (0.4%) than these two phenotypic assays (0.3 to 0.5%) for detecting minor CXCR4-using variants. Ultradeep pyrosequencing provides a new way to improve the performance of genotypic prediction of HIV-1 tropism to match that of the phenotypic assays.
Abstract Mus musculus is the classic mammalian model for biomedical research. Despite global efforts to standardize breeding and experimental procedures, the undefined composition and interindividual diversity of the microbiota of laboratory mice remains a limitation. In an attempt to standardize the gut microbiome in preclinical mouse studies, here we report the development of a simplified mouse microbiota composed of 15 strains from 7 of the 20 most prevalent bacterial families representative of the fecal microbiota of C57BL/6J Specific (and Opportunistic) Pathogen-Free (SPF/SOPF) animals and the derivation of a standardized gnotobiotic mouse model called GM15. GM15 recapitulates extensively the functionalities found in the C57BL/6J SOPF microbiota metagenome, and GM15 animals are phenotypically similar to SOPF or SPF animals in two different facilities. They are also less sensitive to the deleterious effects of post-weaning malnutrition. In this work, we show that the GM15 model provides increased reproducibility and robustness of preclinical studies by limiting the confounding effect of fluctuation in microbiota composition, and offers opportunities for research focused on how the microbiota shapes host physiology in health and disease.
Abstract Background Blood transcriptomic analysis is widely used to provide a detailed picture of a physiological state with potential outcomes for applications in diagnostics and monitoring of the immune response to vaccines. However, multi-species transcriptomic analysis is still a challenge from a technological point of view and a standardized workflow is urgently needed to allow interspecies comparisons. Results Here, we propose a single and complete total RNA-Seq workflow to generate reliable transcriptomic data from blood samples from humans and from animals typically used in preclinical models. Blood samples from a maximum of six individuals and four different species (rabbit, non-human primate, mouse and human) were extracted and sequenced in triplicates. The workflow was evaluated using different wet-lab and dry-lab criteria, including RNA quality and quantity, the library molarity, the number of raw sequencing reads, the Phred-score quality, the GC content, the performance of ribosomal-RNA and globin depletion, the presence of residual DNA, the strandness, the percentage of coding genes, the number of genes expressed, and the presence of saturation plateau in rarefaction curves. We identified key criteria and their associated thresholds to be achieved for validating the transcriptomic workflow. In this study, we also generated an automated analysis of the transcriptomic data that streamlines the validation of the dataset generated. Conclusions Our study has developed an end-to-end workflow that should improve the standardization and the inter-species comparison in blood transcriptomics studies. In the context of vaccines and drug development, RNA sequencing data from preclinical models can be directly compared with clinical data and used to identify potential biomarkers of value to monitor safety and efficacy.
Progress in the liquid biopsy field, combined with the development of droplet digital PCR (ddPCR), has enabled noninvasive monitoring of mutations with high detection accuracy. However, current assays detect a restricted number of mutations per reaction. ddPCR is a recognized method for detecting alterations previously characterized in tumor tissues, but its use as a discovery tool when the mutation is unknown a priori remains limited.We established 2 ddPCR assays detecting all genomic alterations within KRAS exon 2 and EGFR exon 19 mutation hotspots, which are of clinical importance in colorectal and lung cancer, with use of a unique pair of TaqMan® oligoprobes. The KRAS assay scanned for the 7 most common mutations in codons 12/13 but also all other mutations found in that region. The EGFR assay screened for all in-frame deletions of exon 19, which are frequent EGFR-activating events.The KRAS and EGFR assays were highly specific and both reached a limit of detection of <0.1% in mutant allele frequency. We further validated their performance on multiple plasma and formalin-fixed and paraffin-embedded tumor samples harboring a panel of different KRAS or EGFR mutations.This method presents the advantage of detecting a higher number of mutations with single-reaction ddPCRs while consuming a minimum of patient sample. This is particularly useful in the context of liquid biopsy because the amount of circulating tumor DNA is often low. This method should be useful as a discovery tool when the tumor tissue is unavailable or to monitor disease during therapy.
Hepatitis B virus (HBV) infection affects 300 million individuals worldwide, representing a major factor for the development of hepatic complications. Although existing antivirals are effective in suppressing replication, eradication of HBV is not achieved. Therefore, a multi-faceted approach involving antivirals and immunomodulatory agents is required. Non-human primates are widely used in pre-clinical studies due to their close evolutionary relationship to humans. Nonetheless, it is fundamental to identify the differences in immune response between humans and these models. Thus, we performed a transcriptomic characterization and interspecies comparison of the early immune responses to HBV in human and cynomolgus macaques.We characterized early transcriptomic changes in human and cynomolgus B cells, T cells, myeloid and plasmacytoid dendritic cells (pDC) exposed to HBV ex vivo for 2 hours. Differentially-expressed genes were further compared to the profiles of HBV-infected patients using publicly-available single-cell data.HBV induced a wide variety of transcriptional changes in all cell types, with common genes between species representing only a small proportion. In particular, interferon gamma signaling was repressed in human pDCs. At the gene level, interferon gamma inducible protein 16 (IFI16) was upregulated in macaque pDCs, while downregulated in humans. Moreover, IFI16 expression in pDCs from chronic HBV-infected patients anti-paralleled serum HBsAg levels.Our characterization of early transcriptomic changes induced by HBV in humans and cynomolgus macaques represents a useful resource for the identification of shared and divergent host responses, as well as potential immune targets against HBV.
Abstract Several studies have documented the significant impact of methodological choices in microbiome analyses. The myriad of methodological options available complicate the replication of results and generally limit the comparability of findings between independent studies that use differing techniques and measurement pipelines. Here we describe the Mosaic Standards Challenge (MSC), an international interlaboratory study designed to assess the impact of methodological variables on the results. The MSC did not prescribe methods but rather asked participating labs to analyze 7 shared reference samples (5 × human stool samples and 2 × mock communities) using their standard laboratory methods. To capture the array of methodological variables, each participating lab completed a metadata reporting sheet that included 100 different questions regarding the details of their protocol. The goal of this study was to survey the methodological landscape for microbiome metagenomic sequencing (MGS) analyses and the impact of methodological decisions on metagenomic sequencing results. A total of 44 labs participated in the MSC by submitting results (16S or WGS) along with accompanying metadata; thirty 16S rRNA gene amplicon datasets and 14 WGS datasets were collected. The inclusion of two types of reference materials (human stool and mock communities) enabled analysis of both MGS measurement variability between different protocols using the biologically-relevant stool samples, and MGS bias with respect to ground truth values using the DNA mixtures. Owing to the compositional nature of MGS measurements, analyses were conducted on the ratio of Firmicutes: Bacteroidetes allowing us to directly apply common statistical methods. The resulting analysis demonstrated that protocol choices have significant effects, including both bias of the MGS measurement associated with a particular methodological choices, as well as effects on measurement robustness as observed through the spread of results between labs making similar methodological choices. In the analysis of the DNA mock communities, MGS measurement bias was observed even when there was general consensus among the participating laboratories. This study was the result of a collaborative effort that included academic, commercial, and government labs. In addition to highlighting the impact of different methodological decisions on MGS result comparability, this work also provides insights for consideration in future microbiome measurement study design.
