Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota.

BACKGROUND: Gut dysbiosis has been associated with colorectal cancer (CRC), the third most prevalent cancer in the world. This study compares microbiota taxonomic and abundance results obtained by 16S rRNA gene sequencing (16S) and whole shotgun metagenomic sequencing to investigate their reliability for bacteria profiling. The experimental design included 156 human stool samples from healthy controls, advanced (high-risk) colorectal lesion patients (HRL), and CRC cases, with each sample sequenced using both 16S and shotgun methods. We thoroughly compared both sequencing technologies at the species, genus, and family annotation levels, the abundance differences in these taxa, sparsity, alpha and beta diversities, ability to train prediction models, and the similarity of the microbial signature derived from these models. RESULTS: As expected, the results showed that 16S detects only part of the gut microbiota community revealed by shotgun, although some genera were only profiled by 16S. The 16S abundance data was sparser and exhibited lower alpha diversity. In lower taxonomic ranks, shotgun and 16S highly differed, partially due to a disagreement in reference databases. When considering only shared taxa, the abundance was positively correlated between the two strategies. We also found a moderate correlation between the shotgun and 16S alpha-diversity measures, as well as their PCoAs. Regarding the machine learning models, only some of the shotgun models showed some degree of predictive power in an independent test set, but we could not demonstrate a clear superiority of one technology over the other. Microbial signatures from both sequencing techniques revealed taxa previously associated with CRC development, e.g., Parvimonas micra. CONCLUSIONS: Shotgun and 16S sequencing provide two different lenses to examine microbial communities. While we have demonstrated that they can unravel common patterns (including microbial signatures), shotgun often gives a more detailed snapshot than 16S, both in depth and breadth. Instead, 16S will tend to show only part of the picture, giving greater weight to dominant bacteria in a sample. Therefore, we recommend choosing one or another sequencing technique before launching a study. Specifically, shotgun sequencing is preferred for stool microbiome samples and in-depth analyses, while 16S is more suitable for tissue samples and studies with targeted aims.

Concomitant human papillomavirus (HPV) vaccination and screening for elimination of HPV and cervical cancer.

HPV vaccination with concomitant HPV-based screening of young women has been proposed for faster cervical cancer elimination. We describe the baseline results of a population-based trial of this strategy to reduce the incidence of HPV. All 89,547 women born 1994-1999 and resident in the capital region of Sweden were personally invited to concomitant HPV vaccination and HPV screening with 26,125 women (29.2%) enrolled between 2021-05-03 and 2022-12-31. Baseline HPV genotyping of cervical samples from the study participants finds, compared to pre-vaccination prevalences, a strong decline of HPV16 and 18 in birth cohorts previously offered vaccination, some decline for cross-protected HPV types but no decline for HPV types not targeted by vaccines. Our dynamic transmission modelling predicts that the trial could reduce the incidence of high-risk HPV infections among the 1994-1998 cohorts by 62-64% in 3 years. Baseline results are prevalences of HPV infection, validated transmission model projections, and power estimates for evaluating HPV incidence reductions at follow-up (+/-0.1% with 99.9% confidence). In conclusion, concomitant HPV vaccination and HPV screening appears to be a realistic option for faster cervical cancer elimination. Clinicaltrials.gov identifier: NCT04910802; EudraCT number: 2020-001169-34.

Assessment of bacterial and viral gut communities in healthy and tumoral colorectal tissue using RNA and DNA deep sequencing.

BACKGROUND: Colorectal cancer (CRC) is known to present a distinct microbiome profile compared to healthy mucosa. Non-targeted deep-sequencing strategies enable nowadays full microbiome characterization up to species level. AIM: We aimed to analyze both bacterial and viral communities in CRC using these strategies. MATERIALS & METHODS: We analyzed bacterial and viral communities using both DNA and RNA deep-sequencing (Novaseq) in colorectal tissue specimens from 10 CRC patients and 10 matched control patients. Following taxonomy classification using Kraken 2, different metrics for alpha and beta diversities as well as relative and differential abundance were calculated to compare tumoral and healthy samples. RESULTS: No viral differences were identified between tissue types, but bacterial species Polynucleobacter necessarius had a highly increased presence for DNA in tumors (p = 0.001). RNA analyses showed that bacterial species Arabia massiliensis had a highly decreased transcription in tumors (p = 0.002) while Fusobacterium nucleatum transcription was highly increased in tumors (p = 0.002). DISCUSSION: Sequencing of both DNA and RNA enables a wider perspective of micriobiome profiles. Lack of RNA transcription (Polynucleobacter necessarius) casts doubt on possible role of a microorganism in CRC. The association of F. nucleatum mainly with transcription, may provide further insights on its role in CRC. CONCLUSION: Joint assessment of the metagenome (DNA) and the metatranscriptome (RNA) at the species level provided a huge coverage for both bacteria and virus and identifies differential specific bacterial species as tumor associated.

Identification of a Twelve-microRNA Signature with Prognostic Value in Stage II Microsatellite Stable Colon Cancer.

We aimed to identify and validate a set of miRNAs that could serve as a prognostic signature useful to determine the recurrence risk for patients with COAD. Small RNAs from tumors of 100 stage II, untreated, MSS colon cancer patients were sequenced for the discovery step. For this purpose, we built an miRNA score using an elastic net Cox regression model based on the disease-free survival status. Patients were grouped into high or low recurrence risk categories based on the median value of the score. We then validated these results in an independent sample of stage II microsatellite stable tumor tissues, with a hazard ratio of 3.24, (CI95% = 1.05-10.0) and a 10-year area under the receiver operating characteristic curve of 0.67. Functional analysis of the miRNAs present in the signature identified key pathways in cancer progression. In conclusion, the proposed signature of 12 miRNAs can contribute to improving the prediction of disease relapse in patients with stage II MSS colorectal cancer, and might be useful in deciding which patients may benefit from adjuvant chemotherapy.

Improving Species Level-taxonomic Assignment from 16S rRNA Sequencing Technologies.

Analysis of the bacterial community from a 16S rRNA gene sequencing technologies requires comparing the reads to a reference database. The challenging task involved in annotation relies on the currently available tools and 16S rRNA databases: SILVA, Greengenes and RDP. A successful annotation depends on the quality of the database. For instance, Greengenes and RDP have not been updated since 2013 and 2016, respectively. In addition, the nature of 16S sequencing technologies (short reads) focuses mainly on the V3-V4 hypervariable region sequencing and hinders the species assignment, in contrast to whole shotgun metagenome sequencing. Here, we combine the results of three standard protocols for 16S rRNA amplicon annotation that utilize homology-based methods, and we propose a new re-annotation strategy to enlarge the percentage of amplicon sequence variants (ASV) classified up to the species level. Following the pattern (reference) method: DADA2 pipeline and SILVA v.138.1 reference database classification (Basic Protocol 1), our method maps the ASV sequences to custom nucleotide BLAST with the SILVA v.138.1 (Basic Protocol 2), and to the 16S database of Bacteria and Archaea of NCBI RefSeq Targeted Loci Project databases (Basic Protocol 3). This new re-annotation workflow was tested in 16S rRNA amplicon data from 156 human fecal samples. The proposed new strategy achieved an increase of nearly eight times the proportion of ASV classified at the species level in contrast to the reference method for the database used in the present research. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Sample inference and taxonomic profiling through DADA2 algorithm. Basic Protocol 2: Custom BLASTN database creation and ASV taxonomical assignment. Basic Protocol 3: ASV taxonomical assignment using NCBI RefSeq Targeted Loci Project database. Basic Protocol 4: Definitive selection of lineages among the three methods.

COLONOMICS - integrative omics data of one hundred paired normal-tumoral samples from colon cancer patients.

Colonomics is a multi-omics dataset that includes 250 samples: 50 samples from healthy colon mucosa donors and 100 paired samples from colon cancer patients (tumor/adjacent). From these samples, Colonomics project includes data from genotyping, DNA methylation, gene expression, whole exome sequencing and micro-RNAs (miRNAs) expression. It also includes data from copy number variation (CNV) from tumoral samples. In addition, clinical data from all these samples is available. The aims of the project were to explore and integrate these datasets to describe colon cancer at molecular level and to compare normal and tumoral tissues. Also, to improve screening by finding biomarkers for the diagnosis and prognosis of colon cancer. This project has its own website including four browsers allowing users to explore Colonomics datasets. Since generated data could be reuse for the scientific community for exploratory or validation purposes, here we describe omics datasets included in the Colonomics project as well as results from multi-omics layers integration.