It is now possible to comprehensively characterize the microbiota of the lungs using culture-independent, sequencing-based assays. Several sample types have been used to investigate the lung microbiota, each presenting specific challenges for preparation and analysis of microbial communities. Bronchoalveolar lavage fluid (BALF) enables the identification of microbiota specific to the lower lung but commonly has low bacterial density, increasing the risk of false-positive signal from contaminating DNA. The objectives of this study were to investigate the extent of contamination across a range of sample densities representative of BALF and identify features of contaminants that facilitate their removal from sequence data and aid in the interpretation of BALF sample 16S sequencing data.Using three mock communities across a range of densities ranging from 8E+ 02 to 8E+ 09 16S copies/ml, we assessed taxonomic accuracy and precision by 16S rRNA gene sequencing and the proportion of reads arising from contaminants. Sequencing accuracy, precision, and the relative abundance of mock community members decreased with sample input density, with a significant drop-off below 8E+ 05 16S copies/ml. Contaminant OTUs were commonly inversely correlated with sample input density or not reproduced between technical replicates. Removal of taxa with these features or physical concentration of samples prior to sequencing improved both sequencing accuracy and precision for samples between 8E+ 04 and 8E+ 06 16S copies/ml. For the lowest densities, below 8E+ 03 16S copies/ml BALF, accuracy and precision could not be significantly improved using these approaches. Using clinical BALF samples across a large density range, we observed that OTUs with features of contaminants identified in mock communities were also evident in low-density BALF samples.Relative abundance data and community composition generated by 16S sequencing of BALF samples across the range of density commonly observed in this sample type should be interpreted in the context of input sample density and may be improved by simple pre- and post-sequencing steps for densities above 8E+ 04 16S copies/ml. The human microbiome is composed of organ-specific microbiota, the composition and function of which have been associated with a broad array of human diseases –5. ComplSamples used to infer or directly measure the composition of the lung microbiota include oropharyngeal swabs or washes, sputum samples, bronchial aspirates, bronchoalveolar lavage fluids (BALF), and endobronchial biopsies . The micAnalysis of the bacterial communities from BALF samples is challenging, especially due to the low biomass commonly observed in these samples, making them more susceptible to artefacts introduced during sample processing and sequencing –19. The Using bacterial communities with defined composition (‘mock communities’) across the range of bacterial densities observed in BALF, we quantified the accuracy and precision of 16S rRNA gene sequencing for the characterization of bacterial communities, characterized the features of contaminants and mock community taxa, analysed the impact of simple pre- and post-sequencing techniques on these performance characteristics, and developed post-sequencing filtering approaches based on our observations. Our goal was to assess the performance of 16S rRNA gene sequencing across the range of densities observed in human BALF samples to calibrate the interpretation of observational studies from human cohorts.). BALF samples ranged from 1E+ 05 to 4.2E+ 08 16S rRNA copies per millilitre  and mock communities ~ 103–1010 16S copies/ml.In order to calibrate the input densities of our mock communities, we measured the densities of a set of BALF samples obtained from the Toronto Lung Transplant Program (TLTP) Biobank by 16S qPCR , and Rhizobium were the most abundant contaminating taxa in mock community samples with an input density below 8E+ 05 16S copies/ml for mock community 1 and 8E+ 06 16S copies/ml for mock communities 2 and 3. These three genera cumulatively represented 81.3% of the contaminating taxa in the mock samples across the whole dataset. We assessed the relationship between relative abundance of each operational taxonomic unit (OTU) and sample input density (measured with 16S qPCR) using a Spearman correlation and summarized the 45 most abundant OTUs in all 3 communities in Table rs < 0.98), whereas contaminants were negatively correlated (− 0.98 < rs < − 0.67), in agreement with prior studies [S. maltophilia) were positively correlated in samples in which they were mock community members, and negatively correlated in communities where they were contaminants.The relative abundance  studies . NotablyWe assessed sequencing accuracy by comparing taxonomic composition of a sample compared to the expected community composition  using Bray-Curtis dissimilarity . Bronchoscopies are commonly performed with 50–100 ml of saline but recovery is highly variable and resulting samples often range between 1 and 50 ml of fluid. Most common DNA extraction methods, such as those recommended for the Human Microbiome Project, have an upper limit for sample input volume, and this is particularly limiting for samples presenting low bacterial load such as BALF. We tested the impact of physical concentration on sequencing accuracy on a range of densities prepared from mock communities 1 and 3. At sample densities between 8E+ 03 16S copies/ml and 8E+ 05 16S copies/ml, concentration alone improved taxonomic similarity to the expected distribution of the input sample , but not at higher (> 8E+ 06 16S copies/ml) and the lowest (< 8E+ 03 16S copies/ml) input densities.We tested the effect of concentration on the sequencing accuracy for mock communities 1 and 3 Fig. . BronchoSince our approach allows us to definitively label taxa as a mock community member or a contaminant, we were able to identify features specific to contaminants, with the goal of using them to subsequently filter tables and improve sequencing accuracy and precision. Contaminants had several features that distinguished them from mock community members.One hundred and thirty-four out of 159 contaminant OTUs with a prevalence in the dataset above 50% were negatively correlated with sample input density Fig. a. At preThe number of non-reproducible OTUs across the range of densities increases with decreasing sample density Fig. b. The nuStreptococcus and Gemella) demonstrated Feature 2, but only in low abundance samples  and only in higher density samples (> 8E+ 04 16S copies/ml), indicating that relative abundance and input sample density must both be incorporated into interpretation of these taxa in BALF and that lack of control for input sample density may lead to a biased interpretation of relative abundance of these taxa.Multiple bacterial taxa such as nge Fig. . The lik We next analysed 50 post-transplant BALF samples obtained from surveillance bronchoscopies to identify whether OTUs in BALF in this population reflect the features of contaminants observed in our mock communities. Using 50 BALF samples, we assessed the correlation between relative abundance of each OTU and samples input density to identify contaminants.Acinetobacter, Bacillaceae, Bacillales, and Commamonadaceae) were negatively correlated with density, consistent with contaminant Feature 1, while taxa known to colonize the airways  were positively correlated with density. Interestingly, Ureaplasma was detected at high relative abundance in a single sample but lacked either feature of contaminants. All OTUs with positive or negative correlation with sample density are shown in Fig. The relative abundance across input densities of OTUs representing at least 1% of the overall sequence abundance are shown in Fig. We then assessed which OTUs presented the second feature of contaminants across the set of BALF samples Fig. c. Above U test; P < 0.01). At 4E+ 07 16S copies/ml, accuracy is slightly improved with a BCI of 0.09 for the filtered samples instead of 0.14 for the unfiltered samples. At the highest density (4E+ 08 16S copies/ml), filtered samples are similar to the unfiltered references. The dissimilarity between replicates remained similar across tested dilutions between filtered and unfiltered replicates.Using dilution series of two BALF samples presenting a density of ~ 4E+ 08 16S copies/ml, we assessed the effect of the removal of taxa with contaminant Features 1 or 2, or either feature combined filters on sequencing accuracy and precision Fig. d. SequenUnlike stool or other samples with consistently high bacterial loads, BALF have input bacterial densities that range over several orders of magnitude and include very low densities. The analysis of the microbiome present in low-density samples is particularly challenging due to the ubiquitous presence of contaminants , 19, 24.We found strong sample density-dependence of sequencing accuracy and precision. At a density below ~ 8E+ 06 16S copies/ml in unfiltered samples, the observed signal was derived from both bacterial taxa from the mock communities and contaminants. We identified features that distinguished contaminants from true positive taxa, including that they were negatively correlated with input density and that they showed low inter-replicate reproducibility. These features facilitated identification of a large proportion of contaminating OTUs and reads but could not be used to distinguish between taxa that are both common contaminants and airway pathogens/colonizers, indicating that current, commonly used amplicon sequencing methods will be limited for the quantitation of these taxa, especially in low-relative abundance or in low-density samples. In spite of these limitations, however, identification and removal of reads from these OTUs based on these features can improve accuracy without a significant impact on precision for samples with an input density above ~ 8E+ 04 16S copies/ml. It is important to note that with relative abundance compositional data, samples that are dissimilar based on density and relative abundance of contaminants may appear compositionally similar if reads from contaminants are removed. While this form of filtering may improve accuracy and precision, it potentially obscures differences between samples that may be informative, particularly those due to differences in the abundance of ‘true positive’ taxa that are lower in relative abundance in only one group of interest. This is an inherent limitation of relative abundance data but is an important consideration in the interpretation of samples of variable density and may need to be addressed with complementary methods .Our study has several important limitations. Not all known colonizers of the human airway nor all community composition types (e.g. taxon number or distribution) were represented in our mock communities. Our experiments represent only a single centre, and contaminants may vary by site and protocol-specific factors. Importantly, our method will not distinguish between contaminants introduced at the time of sample collection and those introduced during sample handling and processing.We recommend pre-screening of sample bacterial densities to predict expected sequencing accuracy and precision for any given sample set.For samples with densities comprised between 8E+ 04 and 8E+ 06 16S copies/ml BALF, we recommend sample concentration as well as the use of sequencing replicates and dilution series.Identification of OTUs inversely correlated with density or with poor technical replicability is a useful strategy to improve sequencing accuracy and precision. However, removal of reads should be considered within the context of analytical goals and the limitations associated with the use of relative abundance data. We suggest that removal of OTUs with features of contaminants be combined with other analytical approaches such as absolute quantitation of key taxa (e.g. by qPCR), quantitative normalization (e.g. with 16S qPCR), or comparisons of relative abundance data of only ‘true-positive’ taxa without removal of putative contaminants.Given the high precision between biological replicates for samples above 8E+ 06 16S copies/ml, sequencing replicates might not be necessary for all samples, and the sequencing strategy and costs can be optimized depending on input bacterial density.Finally, we encourage each laboratory to identify the specific performance characteristics of their own experimental environment and methods, using dilution series of samples with known composition or high input density, covering the entire range of sample densities in their sample set.Based on our observations, the following factors should be considered when analysing BALF, especially when bacterial density is low in some samples:To calibrate the range of densities of our mock communities, we retrieved a set of 51 selected post-transplantation BALF samples obtained from bronchoscopies from the Toronto Lung Transplant Program (TLTP) biobank, reasoning that this population is both highly sampled and has a diversity of both infectious and non-infectious complications of transplantation. Raw, unspun, and unfiltered, BALF samples were used for analyses. Our programme’s collection protocol was published previously .Pseudomonas aeruginosa str. PAO1, Burkholderia multivorans (ATCC 17616), Stenotrophomonas maltophila (ATCC 13637), Staphylococcus aureus (ATCC 12600), Fusobacterium nucleatum (ATCC 23726), Streptococcus anginosus (ATCC 33397), Bacillus halodurans (ATCC BAA-125), and Gemella haemolysans (ATCC 10379) were grown  overnight in Tryptic-Soy broth (TSB) at 37 °C and subsequently quantified on Tryptic-Soy agar plates. Culture broths were pooled to a density of ~ 8E+ 09 16S copies/ml. A series of seven tenfold dilutions was prepared resulting in sample densities ranging from 8E+ 09 to 8E+ 02 16S copies/ml. Three mock communities were prepared, the first being composed of an equimolar ratio of P. aeruginosa, B. multivorans, and S. maltophilia. The second community was composed of P. aeruginosa and B. multivorans, each accounting for 50% of the community composition. The third community was composed of S. aureus (55%), F. nucleatum (24%), B. halodurans (15%), S. anginosus (5.5%), and G. haemolysans (0.5%).Bacterial isolates were obtained from the American Type Culture Collection . Glycerol stocks were prepared upon arrival as recommended by ATCC. 2O) used in the extraction protocol (NTC1-2). The second control was an aliquot of TSB medium used to cultivate the mock species (NTC3-4). The third was elution buffer (H2O) which was extracted along other samples (NTC5-6). The final control was H2O which was concentrated and subsequently extracted (NTC7-8). All controls were sequenced in duplicate. All species included in the mock communities were also sequenced individually, and the resulting OTUs were used to differentiate mock species from contaminating taxa.Four types of negative controls were used in this study. The first was an aliquot of the elution buffer (HPseudomonas aeruginosa str. PAO1), and the number of 16S copies/ml was inferred using the URI Genomics & Sequencing Center online calculator (http://cels.uri.edu/gsc/cndna.html). 16S qPCR primers and conditions are described in Additional file Nucleic acids were isolated from 250 μl of sample using a PowerSoil DNA isolation kit  following the manufacturer’s instructions except for the elution step which was done in 60 μl purified water. Densities were measured using a 16S quantitative polymerase chain reaction  and a Samples were concentrated using Amicon Ultra-15 Centrifugal Filter Units with the 30-kDa filter . A tenfold concentration factor was obtained by concentrating 5 ml of sample into a volume of 500 μl.The V4 hypervariable region of the 16S rRNA gene was amplified using a universal forward sequencing primer and a uniquely barcoded reverse sequencing primer to allow for multiplexing . AmplifiThe UNOISE pipeline, available through USEARCH version 10.0.240, was used for sequence analysis –30. The We applied three different filtering strategies based on features observed in contaminating OTUs. This included (1) the identification and removal of contaminating taxa based on the negative correlation between their relative abundances and sample input densities (Feature 1), (2) the identification and removal of singletons in technical replicates (Feature 2), or (3) the combined removal of OTUs with either of these features. To apply Filter F1, we converted raw absolute abundance tables to relative abundances. We subsequently measured the Spearman correlation between relative abundances and sample input density and tested for significance for both the mock sample set and the BALF set, independently. OTUs presenting significant negative correlation were labelled as contaminants. For filter F2, we assessed whether each bacterial taxon was present in both technical replicates and subsequently removed those which were found to be singletons. For filter F3, we first applied F1, followed by F2.Bray-Curtis dissimilarity indices were calculated using the ‘dissimilarity’ function from the Vegan R package version 2.5-2 . WilcoxoAdditional file 1: Reproducibility of OTUs across duplicates.Additional file 2: 16S qPCR primers and conditions.Additional file 3: Study IDs and metadata.


Clinical benefits of recruitment maneuver in ARDS patients are controversial. A number of previous studies showed possible benefits; a large recent study reported that recruitment maneuver and PEEP titration may even be harmful. This meta-analysis was designed to compare the clinical utility of recruitment maneuver with low tidal volume ventilation in adult patients with ARDS.Randomized controlled trials comparing recruitment maneuver and lung protective ventilation strategy with lung protective strategy ventilation protocol alone in adult patients with ARDS has been included in this meta-analysis. PubMed and Cochrane Central Register of Controlled Trials were searched from inception to 10 November 2017 to identify potentially eligible trials. Pooled risk ratio (RR) and standardized mean difference (SMD) were calculated for binary and continuous variables respectively.p = 0.33], ICU mortality  and in-hospital mortality  were similar between recruitment maneuver group and standard lung protective ventilation group. Duration of hospital stay  and duration of ICU stays  were also similar between recruitment maneuver group and standard lung protective ventilation group. Risk of barotrauma was also similar.Data of 2480 patients from 7 randomized controlled trials have been included in this meta-analysis and systemic review. Reported mortality at the longest available follow-up [RR (95% CI) 0.93 ; Use of recruitment maneuver along with co-interventions such as PEEP titration does not provide any benefit in terms of mortality, length of ICU, and hospital stay in ARDS patients.The online version of this article (10.1186/s40560-018-0305-9) contains supplementary material, which is available to authorized users. Version 5.3. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014). Publication bias was assessed by visual inspection of funnel plot. A meta-regression was planned by metareg command in STATA version 13.0  in case of more than 10 trials is found for any outcome.For binary outcomes, we calculated the following:  the riskInitial searching of database revealed 9558 articles, and searching of the other sources revealed another 114 articles. After duplicate removal, 540 articles were assessed and 12 articles were screened from title and abstract to identify potentially eligible trials. Finally, data of 2480 patients from 7 randomized controlled trials from published full text  and abstp = 0.33; I2 = 43%; n = 2480], ICU mortality  and in-hospital mortality  were similar between recruitment maneuver group and standard lung protective ventilation group. A forest plot for odds ratio of mortality at different time points at individual study level and pooled analysis level has been provided in Fig. Reported mortality at the longest available follow-up  and duration of ICU stays  were similar between recruitment maneuver group and standard lung protective ventilation group. A forest plot for SMD in length of ICU stay and length of hospital stay at individual study level and pooled analysis level has been provided in Fig. Duration of hospital stay .Only four trials reported incidence of barotrauma from recruitment maneuver, and it was found to be similar with standard lung protective ventilation group [RR (95% CI) 1.27 ; Principal findings of this meta-analysis and systematic review are that recruitment maneuver neither provides any mortality benefit nor reduces length of hospital and ICU stays in adult patients with ARDS. Findings of this meta-analysis contradicts the reported mortality benefits of recruitment maneuver by Goligher et al.  in a metAnother Cochrane database systematic review  reportedObservational studies have found benefits of recruitment maneuver in ARDS patients in terms of oxygenation and lung compliance , 26. An In this meta-analysis, we have found that recruitment maneuver used along with or without PEEP titration does not provide any mortality benefit at any time points. Our results remain essentially similar even when the trial by Xi et al.  excludedOur meta-analysis has several limitations. We have found significant amount of statistical heterogeneity most of the all analyses which is probably due to a heterogeneity in patients’ selection and in the methods of recruitment maneuver application across the studies. As the number of the included trials were small in our meta-analysis, a meta-regression analysis was not possible. A visual inspection of the funnel plot also suggested that publication biases might also be present.Recruitment maneuver along with co-interventions such as PEEP titration does not provide any benefit in terms of mortality, length of ICU and hospital stay. Further studies are required to know the clinical benefits of recruitment maneuver without PEEP titration in ARDS patients.Additional file 1:Appendix. (DOCX 63 kb)


While whole-exome DNA sequencing is the most common technology to study genetic variants in tumors in known exonic regions, RNA-seq is cheaper, covers most of the same exonic regions, and is often more readily available. In this study, we show the utility of mRNA-seq-based variant analysis combined with targeted gene sequencing performed on both tumor and matched blood as an alternative when exome data is unavailable. We use the approach to study expressed variant profiles in the well-characterized University of Michigan (UM) head and neck squamous carcinoma (HNSCC) cohort (n = 36). We found that 441 out of 455 (~97%) identified cancer genes with an expressed variant in the UM cohort also harbor a somatic mutation in TCGA. Fourteen (39%) patients had a germline variant in a cancer-related Fanconi Anemia (FA) pathway gene. HPV-positive patients had more nonsynonymous, rare, and damaging (NRD) variants in those genes than HPV-negative patients. Moreover, the known mutational signatures for DNA mismatch repair and APOBEC activation were attributive to the UM expressed NRD variants, and the APOBEC signature contribution differed by HPV status. Our results provide additional support to certain TCGA findings and suggest an association of expressed variants in FA/DNA repair pathways with HPV-associated HNSCC tumorigenesis. These results will benefit future studies on this and other cohorts by providing the genetic variants of key cancer-related genes. However, compared to genome sequencing, RNA-seq is more popular due to its lower cost and its ability to address a range of hypotheses, including gene expression quantification, detection of alternative splicing, allele-specific expression, viral integration, gene fusion7 and RNA editing10. Therefore, a large number of samples have available RNA-seq data but not WGS or WES data, and calling genomic variants from these already available data can improve the genomic information for these existing samples. Although calling and interpreting variants from RNA-seq data remains challenging due to the intrinsic complexity in the transcriptome, advances in bioinformatics pipelines for RNA-seq variant calling, such as the GATK best practice pipeline tuned for RNA-seq variant detection, provides an alternative way to study genetic variation (https://software.broadinstitute.org/gatk//guide/article?id=3891). RNA-seq also has the disadvantage that nonsense mutations, deletions, and other mutations that destabilize the transcript will not be detected. However, RNA-seq has the advantage that it can be used to filter out mutations in genes that are not expressed in either normal or tumor tissue. This may be desirable since mRNA expression is required to produce proteins with altered or disrupted functionality, and the presence of mutant mRNA is essential for neoantigen production and prediction11. In light of this, identifying expressed mutations is important to determine altered proteins/pathways, and RNA sequencing (RNA-seq) is an appropriate approach for this task.Whole-genome sequencing (WGS) and whole-exome sequencing (WES) are the optimal approaches to elucidate somatic mutations that alter protein function or abundance in carcinogenesis14, with HPV− cases generally associated with worse outcome15. Genomic profiling of HNSCC by The Cancer Genome Atlas (TCGA) and other groups has significantly improved our understanding of HPV-related malignancy. We now appreciate tremendous inter- and intra-tumor heterogeneity among the patients. However, these results are complicated by multiple sites of specimen collection and unbalanced HPV status in the TCGA samples (427 HPV− and 99 HPV + samples)16. To systematically study the molecular profiles of HNSCC with balanced HPV status and from a single hospital with consistent sample collection and treatment protocols, we ascertained HNSCC patients with untreated oropharynx, oral cavity squamous, or larynx cell carcinoma from an ongoing survivorship cohort at the University of Michigan (UM HNSCC cohort). We comprehensively characterized the tumors from these patients, including mRNA-deep sequencing (mRNA-seq) data16. Here, we demonstrate the utility and reproducibility of identifying expressed variants via mRNA-seq data, combined with targeted gene sequencing to determine the germline versus somatic mutation status of known cancer and pathway-relevant genes. We show that although we cannot distinguish germline from somatic mutations for most genes, there are several useful analyses we can perform that shed light on carcinogenic mechanisms. We elucidate important pathways that differ by HPV status and are known to be associated with risk of HNSCC.Head and neck squamous cell carcinomas (HNSCC) represent a diverse group of cancers characterized by anatomical, phenotypic, etiological, biological and clinical heterogeneity. HNSCC develops via two primary carcinogenic routes: chemical carcinogenesis through excessive use of tobacco and alcohol or high risk human papillomavirus (HPV)-induced tumorigenesis. Patients with HPV-positive (HPV+) and HPV-negative (HPV−) HNSCCs have distinct clinical behavior and molecular profiles17  HNSCC cohort (n = 36) are representative of the head and neck cancer patient population in our larger cohort and the United States18, and the majority of nucleotide alterations were C > T (G > A).Using the GATK best practices pipeline for RNA-seq data, we identified a total of 839,836 germline and somatic variants in the 36 tumor RNA samples Fig. . After fFAT1 which was found mutated in 33%  and is known to be a tumor suppressor gene with somatic mutations in HNSCC as documented in COSMIC database. NOTCH2, TP53, NRG1 and MAP3K1 are also known tumor suppressor genes in multiple cancers and were identified as having recurrent NRD variants in our study. To evaluate the overall concordance between TCGA DNA-based findings and our RNA-seq based results, we extracted the 181 oncogenes and 63 tumor suppressor genes from the COSMIC database and evaluated the number of genes found to carry both TCGA somatic mutations and UM RNA NRD variants. In total, 63 oncogenes were identified to carry at least one UM RNA NRD variant and all of them also carried ≥1 TCGA somatic mutation; 28 tumor suppressor genes were found to carry UM RNA NRD variants and 27 of them also carried ≥1 TCGA somatic mutations , 441 of them also harbored a somatic mutation identified in the TCGA (~97%). Figure ns Table . The UM ons Fig. .Figure 29. For the 72 target panel genes, we examined the germline/somatic ratio with respect to each of the 5 categories of genes chosen for the panel  genes, APOBEC genes, TCGA HNSC genes and epigenetic regulators). These five categories and the specific genes in each were selected based on the combination of our previous findings and what was already known to be likely relevant to HNSCC carcinogenesis in the literature. We found that the general cancer-related genes had significantly more somatic mutations  than all other genes , with the odds ratio (OR) of 0.11 (Fisher’s exact test p = 1.92 × 10−8), whereas FA genes had only 1/24 somatic mutations and TCGA HN genes had 13/122 somatic mutations, both with significantly more germline mutations . All three APOBEC mutations were germline and 5 out of 6 mutations in epigenetic regulators were germline  and 3 were annotated as cancer-related by COSMIC database; five were predicted to be benign, 1 in FANCA was likely pathogenic, 5 conflicting or uncertain pathogenicity, and 7 benign/likely benign according to ClinVar clinical significance22 (Supplementary Table We distinguished germline from somatic mutations by using the targeted gene sequencing data covering 72 genes (see Methods) in tumor and blood paired samples from the same set of patients. Overall, 156 of the RNA NRD variants were located in the target panel genes, out of which 39 were found to be somatic mutations and 107 were determined to be germline mutations in the DNA samples, resulting in a total overlapping rate of 94% . However, we noticed that some FA genes such as PALB2 had variants only in HPV+ patients, and we found that patients with at least one variant in a cancer-related FA gene were slightly more likely to be HPV+, although not statistically significant . Cancer-related FA genes were defined as those known to be important in HNSCC or cancer in general, including those carrying recurrent mutations in cancers, having clinical effect when targeted by drugs, or being significantly associated with cancer-related citations in the literature (see Methods). We thus further tested whether the number of NRD variants in the FA pathway differed by HPV status. After adjusting for breadth of coverage, the number of NRD variants in cancer-related FA genes was found to be significantly associated with HPV status . This is interesting, in light of our previous finding that FA genes are up-regulated in HPV+ patients16. These findings suggest that the identified RNA NRD variants in FA genes were associated with carcinogenesis in the HPV+ HNSCC patients.Our group and others have shown that HNSCC tumors differ by HPV status in terms of their transcriptome profiles, copy number alterations, and genome-wide DNA methylation and histone modification profiles24. We investigated how the predefined mutational signatures are represented in the RNA NRD of the UM HNSCC cohort  Signature 1 (median coefficient = 0.41), the result of an endogenous mutational process initiated by spontaneous deamination of 5-methylcytosine, which is correlated with age of cancer of diagnosis; ii) Signature 3 (median coefficient = 0.12), which is associated with failure of DNA double-strand break-repair by homologous recombination (DNA-DSBR); and iii) Signature 6 (median coefficient = 0.17), that is associated with defective DNA mismatch repair (DNA-MMR) . The different combinations of mutation types, i.e. “mutational signatures”, often reflect different mutational processes. Previously the Stratton group identified 30 mutational signaturesort Fig. . The matMR) Fig. . MarkedlMR) Fig. , second CW > T or TCW > G motif, where W is A or T) regardless of HPV status  25, and it has been suggested that APOBEC3 proteins link viral infections to cancer development26. Moreover, the number of APOBEC-induced variants was identified to be significantly correlated with expression levels of APOBEC family genes at both overall and individual gene level, and this correlation was mainly due to the association in HPV+ patients, suggesting the APOBEC proteins do not play a major role in HPV− mutagenesis , and the expression of APOBEC3C was found to be the most attributive to the APOBEC-induced mutational burden , was significantly more attributive in HPV+ than HPV− patients . The contribution of Signature 13 was highly correlated with the direct estimate of the number of APOBEC-induced variants (T02) Fig. . All of sis Fig. . This is13.Most analyses of mutations in cancer focus only on somatic alterations. However, germline mutations are also important for cancer initiation and progression, including interplaying with the somatic mutations, which is often overlooked. Using mRNA-seq data from 36 University of Michigan HNSCC patients (18 HPV+ and 18 HPV−), we identified expressed germline and somatic variants that were rare and predicted to potentially have deleterious effects on the encoded proteins. We demonstrated that the majority of cancer genes harboring these variants were documented in the somatic mutation profile of HNSCC patients by TCGA. Many of the findings of TCGA using WES were also found in our study using mRNA-seq, including the APOBEC mutational signature in HPV+ patients. We complemented the mRNA-seq analysis with targeted gene sequencing data to determine germline versus somatic mutation status for a select set of 72 genes. Since HPV status is closely associated with the genomic landscape in HNSCC, we took advantage of the balanced HPV status in the UM HNSCC cohort and focused on the comparison of genetic profiles between HPV+ and HPV− patients. Our results reproduced known differences: that HPV− HNSCC inactivates the cell cycle suppressor TP53 (7 HPV− vs. 1 HPV+ HNSCCs harbor variants) and CDKN2A (4 HPV− vs. 2 HPV+ HNSCCs harbor variants) by nonsynonymous single nucleotide polymorphism29, and it has been shown that crosstalk exists between the FA and mismatch repair (MMR) pathways30. The number of expressed NRD variants in cancer-related FA genes was significantly higher in HPV+ patients, and patients with an FA variant were slightly younger than those with no FA gene variants . The expressed NRD variants identified in FA genes were mostly found to be germline mutations by the targeted gene sequencing, suggesting a genetic predisposition to HNSCC. In fact, FA genes with biallelic germline mutations are associated with a 700-fold increased risk of HNSCC32, with the most common site being the oral cavity. Based on our results we speculate that a heterozygous, germline FA mutation combined with a second risk factor, such as HPV infection or smoking, may increase the risk of HNSCC and at a slightly younger age. This hypothesis will need to be tested in a larger cohort with additional evidence to support the pathogenicity of their FA variants. One reason our study may have been able to detect a significant difference in age is due to our focus on oral cavity and oropharyngeal tumors, and our wide range of ages at diagnosis. In addition, the mutation status in cancer-related FA genes was found to differentiate the contribution of the APOBEC mutagen signature to the mutational distribution in HNSCC, consistent with previous studies showing that APOBEC proteins promote the cytidine deaminase-dependent DNA repair process and are involved in mutational double-strand DNA break repair following ionizing radiation treatments34. Previous analysis of TCGA data also detected evidence that HPV infection was strongly associated with APOBEC-mediated mutagenesis in HNSCC35. These findings point to the general importance of Fanconi Anemia and DNA repair in HNSCC pathogenesis and highlight the importance of integrating population studies of risk with somatic mutational analyses.The FA pathway coordinates elements of three basic DNA repair pathways including homologous recombination (HR), nucleotide excision repair (NER), and mutagenic translesion synthesis (TLS)i) two subtypes of HPV+ HNSCC were identified with distinct transcriptional profiles, which were associated with HPV characteristics, specific CNAs, PIK3CA mutations and pathway signatures16; ii) HPV integration in HNSCC was associated with worse survival outcomes, weakened immune response signatures and suggested novel candidate drivers7. The current work complements the previous studies from the perspective of genetic variation, providing a more comprehensive view of the molecular characteristics of this cohort on which future studies can be based.Comprehensive characterization of the 36 patient UM HNSCC cohort has allowed our group to define important genetic and epigenetic characteristics of HNSCC especially for HPV-associated tumors, resulting in several novel findings: 37. Although calling genetic variants from RNA-seq has been controversial due to the intrinsic complexity of transcriptomes and limitations in variant calling algorithms, significant effort has resulted in improvements to the analysis pipeline40. The GATK best practice of RNA-seq variant calling pipeline, a currently widely applied pipeline, effectively addresses the issue of mapping the alternative spliced transcripts to the reference genome by the STAR 2-pass approach and Split ‘N’ Trim technique41. To control the false positives and potential RNA editing events in our call set, we imposed a series of vigorous filtering, and limited the variants to nonsynonymous, rare and damaging ones in cancer-related genes, over 90% of which were successfully validated by the targeted gene sequencing. Since the NRD mutational burden inferred from RNA-seq is highly correlated to the number of covered bases, we introduced breadth of coverage as a covariate in linear models to investigate the relationship between NRD mutational burden and HPV status. One limitation of our study is the lack of genome-wide germline data. After applying filters to focus on damaging variants the final call set contained 73% germline mutations as indicated by the germline data for the 72 target panel genes. However, the proportion of somatic and germline mutations depends on the type of genes, and indeed cancer genes were found to harbor significantly more somatic mutations.While RNA-based variant calling may miss some important mutations that are not transcribed, it is a feasible approach to detecting expressed mutations given the large amount of existing RNA-seq data in cancer studies. Many studies have shown the value of using RNA-seq data to study genetic variantsIn conclusion, we used mRNA-seq data generated from the UM HNSCC cohort to identify expressed germline and somatic deleterious variants, and we validated the results by targeted gene sequencing on both somatic and germline samples. Although our study was limited by only being able to distinguish germline from somatic mutations for 72 genes of interest, we show that valid and useful conclusions can be made with combined NRD germline and somatic variants. The results recapitulate many TCGA findings and highlight the association between FA/DNA repair pathways and HPV status in HNSCC tumorigenesis, which complement our previous studies based on the same cohort. Based on this one-center high-quality HNSCC cohort, we have analyzed multi-dimensional molecular profiles that we hope will be valuable for future studies in HNSCC and other cancer sites.From 2011 to 2013, incident, previously untreated HNSCC patients with initial diagnosis of oropharynx, oral cavity or larynx tumors were screened for eligibility and approached to sign a written, informed consent for collection of tumor tissue and whole blood. Written informed consent was obtained from all patients, and the study was approved by the University of Michigan Institutional Review Board. The IRB Authorization Agreement has been approved. Tumor tissue and blood were immediately placed into a cryogenic storage tube and flash frozen in liquid nitrogen by surgical staff in the hospital procedures unit. Flash frozen tissue specimens were transported in liquid nitrogen and stored at −80 °C until prepared for histology. The frozen tissues were embedded in OCT media in vinyl cryomolds on dry ice and stored in −80 °C. H&E slides were sectioned from frozen tissue on a cryostat for each tumor specimen and assessed by a board-certified pathologist from the Pathology Department of the University of Michigan and assessed for degrees of cellularity and necrosis. Specimens exhibiting 70% or greater cellularity and less than 10% necrosis were selected for further study. In total, 36 patients were included in this study. All experimental protocols used to process the specimens were approved by University of Michigan Institutional Review Board, and all methods were carried out in accordance with relevant guidelines and regulations.16).Surface scrapings from the region of tissue identified as having 70% or greater cellularity were taken directly from the frozen tissue block using a sterile scalpel. Processing was performed over dry ice without allowing the tissue to thaw and frozen scrapings were placed into pre-chilled tubes on dry ice. Tissue scrapings were processed using the Qiagen AllPrep DNA/RNA/Protein Mini Kit  as per manufacturer protocol. DNA and RNA concentration and quality were assessed using a Nanodrop 2000 spectrophotometer . RNA quality and concentration was also assessed by Agilent Bioanalyzer. Whole blood DNA from the same patients was isolated using the Qiagen QIAamp Blood DNA Mini Kit , according to manufacturer protocol. RNA library construction and sequencing on Illumina HiSeq  using 100 nucleotide paired-end reads were performed by the University of Michigan DNA sequencing Core Facility . Briefly, paired-end reads were aligned to the human genome hg19 using STAR 2-pass42 and duplications were marked by Picard (http://broadinstitute.github.io/picard/). GATK was used for indel realignment and base recalibration. Variants were called for multiple samples using HaplotypeCaller . Variants which fell under any of the following criteria were filtered out: quality scores less than 25 (QUAL < 25), read depth less than 10 (DP < 10), strong strand bias (FS > 30), normalized quality score less than 2 (QD < 2.0), or variants part of a SNP cluster that defined as 2 SNPs within 35 bps indicating a false positive. To identify nonsynonymous, rare and damaging (NRD) variants, resulting variants were filtered by three steps: i) the variants predicted to disrupt the primary structure of the protein, i.e. nonsynonymous variants, were identified by SnpEff43; ii) rare variants defined as variants that are present in less than 5% of 1000 Genomes44 and NHLBI Exome Sequencing Project45 subjects annotated by ANNOVAR46; iii) damaging variants were identified by either PolyPhen v247 or SIFT48.Variant calling was performed by following GATK Best Practices for RNA-seq data to identify single nucleotide polymorphisms (SNPs) and small indels (49 (212 genes), 2) the gene was included in the Catalog of Somatic Mutations in Cancer (COSMIC), a large database of cancer somatic gene mutations curated by the Wellcome Trust Sanger Institute50 (547 genes), 3) the gene qualified as an “actionable target” or “pharmacogenetic target” in cancer by Wagle and colleagues51 (137 genes), 4) the gene was significantly often associated with the Medical Subject Heading (MeSH) terms “stem cell” or containing the keyword “neoplasm” in biomedical literature  or 5) the gene was defined as a “Mut-driver” by Vogelstein and colleagues54 (140 genes). In total, 1344 genes met this criteria and were defined as cancer genes in this study.To further prioritize cancer-associated mutations, we created a list of cancer-related genes derived from several public sources. We included genes that met any of the following criteria: 1) the gene was reported to harbor significant or recurrent mutations in head and neck cancerhttps://portal.gdc.cancer.gov), and compared with our called RNA NRD variants. The frequency of the genes that harbor both University of Michigan RNA NRD and TCGA DNA somatic mutations were reported.Somatic mutation annotation format (MAF) file (version 2.4) generated by Canada’s Michael Smith Genome Sciences Center (bcgsc.ca) was downloaded from the TCGA data portal (i) germline mutations were called by HaplotypeCaller GVCF pipeline (https://software.broadinstitute.org/gatk/best-practices/bp_3step.php?case=GermShortWGS), and (ii) somatic mutations were called using the Mutect2 pipeline (https://software.broadinstitute.org/gatk/best-practices/mutect2.php).A panel of 72 genes were selected for validation by targeted DNA sequencing. Out of the 72 gene panel, 12 were classified as general cancer-related genes, 14 were FA genes, 6 were APOBEC genes, 36 were TCGA head and neck cancer genes, and 4 were epigenetic regulator genes. In order to generate mutually exclusive gene groups for presentation, we prioritized them from highest to lowest in the following order: FA, APOBEC, Epigenetic regulators, cancer-related and TCGA HN genes , FANCD2, FANCE, FANCF, FANCG, FANCJ (BRIP1), FANCN (PALB2) and FANCS (BRCA1)56. Out of the 17 FA genes, 2 genes that do not contain any NRD variants were excluded from this study (RAD51C and XPF). The patients were categorized by FA gene mutation status, i.e. the patients with a germline or somatic mutation in at least one of the FA genes were considered as FA + , and the rest were FA−. To compare the RNA NRD mutational burden by different conditions, a linear regression model was used to model the number of NRD mutations in the 10 cancer-related FA genes as a function of HPV status, using the breadth of coverage as a covariate, i.e. the number of ≥10x covered sites.The FA pathway is comprised of approximately 17 genes24. The previously defined 30 mutational signatures were downloaded from COSMIC (http://cancer.sanger.ac.uk/cosmic/signatures). Assuming the mutational distribution of a single sample is a linear combination of the known 30 signatures, we used non-negative least squares (NNLS) method57 to decompose the mutational signatures (a 96 × 30 matrix) for the observed mutational distribution of each HNSCC patient (a 96 × 1 vector). The difference in the contribution (coefficient) of each known mutational signature by HPV or FA status was accessed by Wilcoxon rank-sum test with Benjamini-Hochberg correction.The NRD variants in each patient were categorized into one of the 96 possible categories: 6 classes of base substitution  × 16 combinations of bases immediately 5′ and 3′ to each mutation base (context information), and the frequency of each mutation category per sample was computedCW motif (where W corresponds to either A or T) to represent the APOBEC-induced mutational patterns and the mutations include C > T and C > G (TCW to TTW or TGW and the complementary WGA to WAA or WCA) changes27. The number of NRD mutations with any of the above patterns was summarized for each patient, representing the APOBEC-induced mutational burden. The expression levels  of all APOBEC family genes were extracted from the mRNA-seq data generated from the same set of cohort16, and 7 APOBEC genes  that have average CPM level greater than 1 were included in the analysis. The correlation between the expression level of individual APOBEC genes (or combined APOBEC genes) and the APOBEC-induced mutational burden were evaluated by Spearman correlation test. To identify the specific APOBEC genes whose expression levels significantly contribute to the APOBEC-induced mutations, a linear regression model was applied to assess the relationship between the individual gene’s expression levels and the APOBEC mutational burden.We chose the stringent TAll data has been deposited in the Gene expression Omnibus (GEO) under accession number: GSE74956 (RNA-seq) and SRP148108 (targeted gene sequencing).Supplementary MaterialTable S1Table S2Table S3Table S4Table S5


Obstructive sleep apnea (OSA) is a common disorder in Type 2 diabetes (T2D) patients further increasing their already high cardiovascular risk. As T2D patients typically not report OSA symptoms, systematic screening for OSA in this population is warranted. We aimed to determine the readiness of T2D patients to undergo screening and to compare their adherence to continuous positive airway pressure (CPAP) therapy with “regular” sleep clinic patients who typically seek medical advice on their own initiative. We therefore recruited 494 consecutive T2D patients and offered them OSA screening using home sleep monitoring (type IV device). All participants in high risk of moderate-to-severe OSA were recommended home sleep apnea testing (HSAT) followed by CPAP therapy. Patients were followed-up for 12 months and outcomes compared to 228 consecutive sleep clinic patients undergoing HSAT. Among 307 screened T2D patients, 94 (31%) were identified at high risk of moderate-to-severe OSA. Subsequently, 54 patients underwent HSAT, 51 were recommended, and 38 patients initiated CPAP (acceptance 75%). Among 228 sleep clinic patients, 92 (40%) were recommended and 74 patients initiated CPAP (acceptance 80%). After 1 year, 15 (39%) T2D and 29 (39%) sleep clinic patients showed good CPAP adherence (use ≥ 4 h/night ≥ 70% nights). In conclusion, 20 T2D patients needed to be screened in order to obtain one successfully treated patient. OSA screening in T2D patients identified 31% with moderate-to-severe OSA. Once diagnosed, their CPAP acceptance and adherence did not differ from sleep clinic patients. However, the reasons for the high dropout during the screening-diagnostic process impacting the overall success of the screening program need to be identified and addressed. Obstructive sleep apnea (OSA) is a common treatable disorder known to increase cardiovascular mortality –3. Even Given the impact of unrecognized and therefore untreated OSA in Type 2 diabetes patients, the International Diabetes Federation recommends screening Type 2 diabetes patients for OSA . As the Even though the consequences of untreated OSA and health benefits of CPAP are well-known and are easy to explain to patients, sleeping with a CPAP mask might present the patient with both psychological and physical discomfort. CPAP treatment requires a high degree of patient cooperation which becomes even more pronounced in screening-eligible Type 2 diabetes patients who were hitherto unaware of an additional health problem or did not consider OSA symptoms to be significant enough to, seek medical help and, in this case, were simply asked to follow their doctor's advice. Low CPAP acceptance and adherence is an ongoing challenge despite efforts to improve patient comfort and support, ranging from 25 to 73% –23 even In this study we hypothesized that Type 2 diabetes patients will show markedly lower acceptance and adherence to CPAP therapy as the OSA diagnosis resulted from a systematic screening performed in subjects who did not seek health care specialist for OSA related symptoms, in contrast to sleep clinic patients, who started CPAP treatment because they actively approached sleep physician. Additionally, we aimed to provide real-life data on the suitability, effectiveness and outcomes of systematic OSA screening in Type 2 diabetes patients by determining the adherence of patients with Type 2 diabetes to the systematic screening-diagnosis-therapy process in an outpatient clinics.Subjects were recruited in diabetes care outpatient clinics providing routine care to unselected patients with diabetes located in Prague, Czech Republic between March 2014 and March 2015. In total, 494 consecutive patients fulfilled the inclusion criteria–diagnosis of Type 2 diabetes mellitus and age 18–80 years. Eleven patients were subsequently excluded due to unstable psychiatric disorders (5 patients) or already diagnosed OSA (6 patients) resulting in 483 subjects included in the study.During regular scheduled visits (regular diabetes care), a physician educated all the subjects about OSA and the health risks associated with OSA as well as about the treatment options. At the same time, OSA screening by home sleep monitoring was offered in the form of a type IV device  that recorded hemoglobin saturation, heart rate and nasal airflow during sleep. The respiratory event index (REI) was determined and subjects were stratified into low or high risk (REI ≥ 15) for the presence of moderate-to-severe OSA. Patients in the high-risk group were referred to a sleep clinic for a diagnostic home sleep study using a portable sleep monitor that recorded hemoglobin saturation, heart rate, nasal airflow, chest and abdominal respiratory effort and ECG . Subsequently, patients with confirmed moderate-to-severe OSA were offered CPAP treatment according to AASM guidelines in an outpatient sleep center .The collaborating outpatient sleep center  retrospectively included 252 consecutive patients who underwent diagnostic home sleep study in 2014 and were 18–80 years old. Subsequently, 24 patients were excluded for previous experience with OSA diagnosis or CPAP therapy, leading to 228 patients being evaluated in the study. Patients with moderate-to-severe OSA defined as REI ≥ 15 (none of them treated for diabetes) were offered CPAP treatment according to AASM guidelines. This study was carried out in accordance with the recommendations of the Ethical Committee of the Third Faculty of Medicine, Charles University, Prague with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Ethical Committee of the Third Faculty of Medicine, Charles University, Prague.CPAP usage data of both, Type 2 diabetes and sleep clinic patients were analyzed at 3 and 12 months after establishing optimal mask fit, treatment pressure and regime (titration). Acceptance was defined as the patient's agreement to CPAP therapy after titration. Adherence was assessed using reports downloaded from CPAP machines. Patients using CPAP ≥4 h per ≥70% of nights were considered having “Good” adherence, while lower CPAP usage was considered as “Poor” adherence.Screening for the presence of OSA was performed using a type IV device  that recorded hemoglobin saturation, heart rate and nasal airflow during sleep in a home setting. Subjects were instructed to set-up the device and keep regular sleep habits. Support in the form of a non-stop phone help-line was established and the devices were returned to investigators the next morning. Automatic scoring of respiratory events with a 4% desaturation threshold was performed, apneas defined as a ≥90% reduction in airflow for at least 10 s and hypopneas defined as a ≥30% reduction in airflow for at least 10 s together with hemoglobin desaturation of ≥4%. Patients with REI ≥15 were considered as being at high risk of moderate-to-severe OSA. For 13 patients the oxygen desaturation index (ODI) was used due to a poor airflow signal.Sleep recordings were performed using a type III device that recorded hemoglobin saturation, heart rate, nasal airflow, ECG, chest and abdominal respiratory efforts  in a home setting. The recordings were evaluated by a board-certified sleep medicine physician according to AASM criteria (apnea defined by a ≥ 90% reduction in airflow for at least 10 s and hypopnea defined as a ≥ 30% reduction in airflow for at least 10 s together with ≥ 4% desaturation). Patients with moderate-to-severe OSA (REI ≥ 15) were recommended to initiate CPAP treatment.T-test and differences in frequencies were analyzed using a Chi-Square test. Data are presented as mean ± SEM, counts or proportions (%). Statistical significance was set to p < 0.05.Statistical analysis was performed using Prizm 5 for Windows Software . Differences in anthropometrical parameters between the patient groups were analyzed using a Out of 483 consecutive Type 2 diabetes patients who fulfilled the inclusion criteria, 321 patients consented to undergo OSA screening, resulting in 307 analyzed sleep recordings of an acceptable quality. Among successfully screened patients, 31% (63 men and 31 women) were identified as being in a high risk of moderate-to-severe OSA and thus invited for a diagnostic sleep study. However, such a sleep study was performed for only 60% of them due to the unwillingness of patients to further continue with the diagnostic process  and a 49% higher score in Epworth sleepiness scale  than the Type 2 diabetes patients who declined HSAT. No differences in anthropometric and demographic parameters or associated comorbidities were observed (Table The Type 2 diabetes patients who accepted the diagnostic home sleep apnea testing (HSAT) were characterized by 42% higher REI  was observed in sleep clinic patients−74 patients were treated out of 92 patients recommended for CPAP . A similar acceptance rate of 80% (p < 0.05), had a lower score in the Epworth sleepiness scale (7.7 ± 0.7 vs. 10.3 ± 0.7), and they were more frequently treated for hypertension and dyslipidemia. There were no significant differences in REI and time spent in saturation < 90% between the Type 2 diabetes and the sleep clinic patients who were recommended CPAP . This difference was not observed in Type 2 diabetes patients accepting and not accepting CPAP , where some patients with lower REI determined by screening had already declined diagnostic home sleep apnea testing. Hypoxic exposure evaluated by time spent at saturation < 90% correlated with CPAP acceptance in both Type 2 diabetes and sleep clinic patients . Some patients exhibited better adherence after 3 months than after 1 year of using CPAP and vice versa  and microvascular complications were not assessed, even though adequate CPAP adherence clearly represents a key factor determining its beneficial effects on glucose tolerance, insulin sensitivity , 10 and In conclusion, every third to fourth patient with Type 2 diabetes suffers from clinically significant obstructive sleep apnea syndrome indicated for CPAP treatment whilst not being aware of his/her condition. Once diagnosed, their acceptance and adherence to CPAP did not differ from sleep clinic patients even though originally they did not actively seek medical advice regarding OSA symptoms. As almost half of the patients identified to be at high risk of OSA through screening were unwilling to undergo the subsequent diagnostic process, our study implies that factors preventing Type 2 diabetes from further medical evaluation should be targeted, probably through more extensive patient education combined with complex psychological approaches. A consideration should be also given to the possibility of conducting screening and diagnostic sleep studies in a single step using appropriate home sleep apnea testing devices.KW and AP recruited subjects with Type 2 diabetes, performed screening sleep studies, gathered data, participated in data analysis, participated in manuscript preparation. VD participated in subject recruitment, served as a study coordinator, helped in performing diagnostic sleep studies, participated in data interpretation, participated in manuscript drafting. MP scored sleep studies, was responsible for CPAP therapy in all subjects, recruited non-diabetic subjects from the sleep clinic, participated in data interpretation and manuscript drafting. JP designed and supervised the study, analyzed data and participated in interpretation of the results, edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Diabetes is related with increased cancer mortality across multiple cancer types. Its role in lung cancer mortality is still unclear. We aim to determine the prognostic value of fasting plasma glucose (FPG) and diabetes mellitus in patients with locally advanced non-small cell lung cancer (NSCLC) treated with concurrent chemoradiotherapy.One-hundred seventy patients with stage III NSCLC received definitive concurrent chemoradiotherapy from 2010 to 2014. Clinico-pathological data and clinical outcome was retrospectively registered. Fifty-six patients (33%), met criteria for type 2 diabetes mellitus (T2DM) at baseline. The prognostic value of FPG and other clinical variables was assessed. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan–Meier method and Cox proportional models and log-rank test were used.p < 0.001) and 31 months  respectively, for patients with FPG < 7 mmol/L. In the multivariate analysis of the entire cohort adjusted by platinum compound and comorbidities, high levels of FPG as a continuous variable , the presence of comorbidity , and treatment with carboplatin  were independent predictors for shorter OS. In additional multivariate models considering non-diabetic patients as a reference group, diabetic patients with poor metabolic control (HbA1c > 8.5%)  and those receiving insulin  had significantly independent worse OS.With a median follow-up of 36 months, median PFS was 8.0 months and median OS was 15.0 months in patients with FPG ≥7 mmol/L compared to 20 months  contains supplementary material, which is available to authorized users. In this study, we determine the prognostic value of fasting plasma glucose (FPG) and Type 2 Diabetes Mellitus (T2DM) in patients with stage III non-small cell lung cancer treated with concurrent chemoradiotherapy. High FPG at baseline predicts worse clinical outcome independently of other clinical variables. Metabolic control and antidiabetic treatment might also influence outcome in diabetic patients.Diabetes has been associated with an increased incidence and mortality in many types of cancer –3. DiabeThe role of diabetes in the prognosis of patients with lung cancer has not been well established . Some clThe use of baseline glycemia as a continuous variable could avoid the problem of T2DM underdiagnosis. In fact, fasting plasma glucose (FPG) level has been independently associated with an increased risk of mortality in a cohort of patients with newly diagnosed NSCLC . In addiThe influence of antidiabetic therapy on cancer incidence and mortality has been the subject of many observational epidemiologic studies in large populations , 10. TheThe purpose of our study is to determine the influence of T2DM and FPG levels on survival in a cohort of patients with unresectable locally advanced NSCLC treated with concurrent chemoradiotherapy. The influence on overall survival (OS) of antidiabetic treatments and glycemic control among T2DM patients were also evaluated as secondary endpoints.Eligible patients for this study had histological or cytological confirmation of NSCLC, locally advanced disease based on clinical assessments: cardiopulmonary function, contrast thoracic computed tomography (CT) scan and positron-emitted tomography-CT (PET-CT) scan, magnetic resonance imaging (MRI) of the brain and selective mediastinal staging with endobronquial ultrasonography and/or endoscopic (EBUS/END) ultrasonography; had been considered candidates for non-surgical cancer treatment by the Multidisciplinary Thoracic Oncology Board and had received definitive concurrent chemoradiation. From January 2010 to December 2014 the medical records of 170 consecutive patients from our institution fulfilling all those criteria were reviewed. We collected the following data: age, sex, smoking history, weight loss during the 6 months previous to NSCLC diagnosis, Eastern Cooperative Oncology Group performance status (ECOG PS), histology and clinical stage according to the 7th edition of TNM classification , chemothPatients had been assessed before cancer treatment by a nutritionist plus/minus an endocrinologist both working as a team at our Institution. Patients were classified as having T2DM if the diagnosis was in their medical record, if they were on antidiabetic drugs or if they met the diagnostic criteria for diabetes according to the American Diabetes Association (ADA) Criteria from 2016 . FPG meap-value of less than 0.05 was considered statistically significant. All analyses were carried out using the SPSS 22.0 statistical software .Chi square test for categorical and T- Student test for continuous variables were used to compare the characteristics of T2DM and non-diabetic patients. Overall survival (OS) was defined as the time between lung cancer diagnosis and death, progression free survival (PFS) was defined as the time between histological diagnosis and radiological progression of the disease or death, whichever came first. Patients with no evaluable radiological images were censored. Kaplan–Meier method was used to estimate OS and PFS. The Cox proportional hazard regression model and the log-rank test were used to assess differences among prognostic factors. Multivariate survival analysis was performed using Cox Regression with the Forward Step model. A two-tailed p = 0.004) and to receive carboplatin regimens (51.7%) compared with non-diabetic patients (32.5%) (p = 0.019). Median number of cycles of chemotherapy and the total dose of radiation therapy received were similar in T2DM and nondiabetic patients. There were no statistically significant differences in comorbidities between T2DM and non-diabetic patients, with the exception of cardiovascular disease that was more common in diabetic patients .In this cohort of 170 patients, 56 (33%) met T2DM criteria. There were no patients with type 1 diabetes mellitus. Baseline characteristics, including gender, smoking history, ECOG PS, comorbidity and stage were similar in patients with T2DM and in non-diabetic patients  were receiving antidiabetic medication at lung cancer diagnosis. Twenty patients were receiving metformin alone or in combination with other oral hypoglycemic agents and 19 were on insulin treatment alone or with oral hypoglycemic drugs. Relevant baseline nutritional characteristics are shown in Additional file p = 0.402). There were no significant differences in the pattern of tumor progression among diabetic and non-diabetic patients (p = 0.274).After a median follow-up of 36 months, 47 patients (27.6%) were alive. Median PFS was 12 months (95% CI 9–15) and median OS was 26 months (95% CI 20–32) for the whole cohort. Major causes of death were tumor progression (80%), treatment-related adverse events (7.8%), cardiovascular disease (7%), other causes (3.5%) and second tumors (1.7%). There were no significant differences in causes of death between diabetic and non-diabetic patients  compared to patients with FPG < 7 mmol/L  compared to non-diabetic patients  and moderate metabolic control  compared to those with poor metabolic control  . Interestingly, differences in OS were observed according to antidiabetic treatment. Patients treated with insulin had significantly shorter median OS compared to those receiving metformin or not treated with antidiabetic agents  did not have significantly higher presence of comorbidity  was an independent prognostic factor, whereas T2DM diagnosis was not  and the presence of comorbidity  were independent prognostic factors for OS as well. However, weight loss was not associated with overall survival in the multivariate analysis.We performed a multivariate Cox regression analysis including all patients in the study. We introduced prognostic factors previously described in NSCLC patients . Pre-treatment FPG level   and insulin treatment  were independent prognostic factors when adjusted for the above-mentioned variables  expression and transactivation of EGF receptor . HyperglThe association of high FPG with outcome in lung cancer patients in a clinical setting has already been reported. In an unselected cohort of 342 patients with newly diagnosed NSCLC, Luo et al. observed that patients with FPG levels ≥7.0 mmol/L (≥ 126 mg/dl) had shorter survival outcome independently of other prognostic factors . In thisTo evaluate the influence of metabolic control of diabetes on cancer outcome, we explored the association of HbA1c (as a dichotomous variable) with the prognosis of lung cancer. We observed that diabetic patients with the poorest glycemic control (HbA1c > 8.5%) had the shortest survival, and that diabetic patients with good metabolic control (HbA1c < 7%) had a similar OS than non-diabetic patients. Moreover in a multivariate model poor metabolic control was independently related to prognosis. These findings underline the potential benefit of achieving good metabolic control to improve cancer outcome in diabetic patients .The influence of antidiabetic treatment on cancer prognosis is still controversial. Metformin is an antidiabetic drug that suppresses hepatic neoglucogenesis and improves insulin sensitivity. Preclinical data have shown that metformin has direct and indirect potent antitumor effects in several cancers, including lung. However the concentration of metformin used in these studies is about one thousand times higher than that achieved in the blood in humans . Some epA common concern when studying the relationship between diabetes and cancer prognosis is the higher prevalence of comorbidities in diabetic patients and the existence of competing causes of death. We did not identify significant differences in the cause of death between diabetic and non-diabetic patients. Although patients with T2DM had higher prevalence of overweight, body mass index (BMI) was not a significant prognostic factor in our study. We also collected basic malnutrition measures as weight loss before treatment and baseline albumin level as known prognostic factors. However, none of these factors was different between the T2DM and the non-diabetic patients or achieved significance in the survival analysis. Another prominent issue is that the presence of T2DM could lead to a less intensive anticancer treatment. Although diabetic patients were more likely treated with carboplatin than cisplatin and, carboplatin treatment was associated with unfavorable overall survival in the multivariate analysis, baseline FPG, metabolic control and insulin treatment remained independent prognostic factors after adjusting by platinum treatment.The major limitation of our study is the relatively low sample size, which could lead to residual confounding as we do not have enough power to identify some well-known risk factors and compromises the analysis of subsets of patients. Another weakness is the retrospective nature of the study, which means that there was not a dedicated pre-planned design of patient management and data collection. The strength of our study is the uniform clinical practice on cancer and diabetic management at a single institution. The most relevant finding is that baseline FPG level is a strong predictor of survival in a set of consecutive patients treated with definitive chemoradiotherapy. Our results on the prognostic value of metabolic control and antidiabetic treatment have potential clinical implications and are consistent with what has been reported in the literature, but should be regarded with caution due to the limitations of the study.Our data support that baseline FPG level may impact on the outcome of patients with locally advanced NSCLC. The validation of this association in a large cohort of locally advanced NSCLC patients is warranted. In the complex relationship between cancer and diabetes our results suggest that metabolic control and treatment of diabetes might influence cancer-related outcomes. Prospective research in the field of diabetic control and management in lung cancer patients is necessary to provide insight into these issues.Additional file 1:Table S1. Nutritional baseline characteristics in T2DM and non-diabetic patients. Table S2.Chemotherapy regimens given concurrently with thoracic radiotherapy in T2DM and non-diabetic patients. Table S3.Presence of comorbidity according to metabolic control in T2DM patients (n = 45). Table S4.Metabolic control based on HbA1c according to antidiabetic treatment (n = 45). Table S5.Multivariate analysis for Overall Survival for selected prognostic factors in NSCLC including T2DM as covariate. (DOCX 20 kb)


TP53 mutations are associated with an increased risk of early-onset breast cancer. Traditionally, it was not standard practice to offer TP53 genetic testing due to the low mutation detection rate and limited options regarding preventive screening. Recent guidelines recommend that all women diagnosed with breast cancer before the age of 31, irrespective of family history, should be offered TP53 genetic testing. This study aims to gain more knowledge on the attitudes and experiences among genetics professionals regarding the timing and content of genetic counselling of young breast cancer patients for Li–Fraumeni syndrome (LFS). We conducted a nationwide online survey among genetics professionals who provide cancer genetic counselling in the Netherlands. Fifty-seven professionals completed the questionnaire . Most respondents reported that they discuss the option of TP53 genetic testing—simultaneously with BRCA 1/2—during the initial counselling visit, especially in case of referral for treatment-focused genetic counselling. There was a general consensus about ten information items that should be discussed during counselling. Sixty-one percent of genetics professionals did not encounter difficulties in providing genetic counselling for LFS, but a substantial minority (29%) did. This study offers valuable insight, which will be useful for clinical practice. Studies which address young breast cancer patients’ attitudes and preferences regarding the timing and content of counselling are warranted to further determine the most appropriate genetic counselling strategy for these women.Germline  BRCA1, BRCA2 and CHEK2* 1100delC mutations [TP53 associated with Li–Fraumeni syndrome (LFS) [Women in the Netherlands have a 12%, or a 1-in-8, lifetime risk of being diagnosed with breast cancer , 4. Earlutations . Howeverme (LFS) .TP53 mutation detection rates are reported in early-onset breast cancer patients with a family history suggestive of LFS or a personal history of an additional LFS-related tumour [TP53 genetic testing to all early-onset breast cancer patients, irrespective of family history [LFS is a rare, inherited cancer syndrome characterized by a very high risk of a wide variety of early-onset neoplasms, including sarcoma, breast cancer, brain tumours and adrenocortical carcinoma . Breast d tumour . Howeverd tumour –16. Ther history , 15, 16.TP53 germline mutation testing in women with breast cancer before the age of 30 [TP53 genetic testing should be offered to all breast cancer patients diagnosed before the age of 31 [TP53 genetic counselling and the role of specialized professionals in psychosocial support. In addition, we collected nationwide laboratory records from all young breast cancer patients tested for TP53 mutations in the Netherlands in order to assess the prevalence of TP53 germline mutations. The results of this study are published separately [In the Netherlands, the general recommendation in 2005 and 2010 was to consider ge of 30 , 18. Howge of 31 . We hypoA cross-sectional online survey was conducted among all professionals  involved in cancer genetic counselling in the Netherlands. Study invitations with a link to the online survey were sent by mail. The initial time limit for completing the questionnaire was two weeks. Non-responders received up to two email reminders in an attempt to increase the response rate. Furthermore, key professionals  were asked to encourage their colleagues to complete the survey. Data were collected in a 1 month period between March and April, 2017.To identify potential respondents, email addresses of clinical geneticists and genetic counsellors with special expertise in cancer genetics were collected by using the member registration of the Dutch Cancer Genetics Group  and the member registration of the Dutch Association of Genetic Counsellors (NVGC). Hospitals were contacted for missing data. Email addresses of clinical geneticists-in-training with some experience in oncogenetic counselling were gathered during an education meeting. In total, the contact details of 106 genetics professionals were collected, including 50 clinical geneticists, 32 genetic counsellors and 24 clinical geneticists-in-training.BRCA 1/2-, CHEK2 and TP53-mutations during the initial counselling visit. Professionals without experience in this field could directly continue with the fourth section of the questionnaire. This final section assessed participants’ attitudes and views regarding the genetic counselling of young breast cancer patients for LFS. This included statements regarding information that could be provided for counselees (content of genetic counselling). Genetics professionals answered by providing a rating of importance to specific information. Other topics were the role of specialized professionals in psychosocial support and timing of TP53 genetic counselling and testing. Questions were all closed-ended except for questions asking respondents to elaborate on their close-ended answers. Five-point Likert-type scales were used to rate the level of agreement  and importance  in the majority of items assessing attitudes and views. Other questions utilized pre-given categorical response options or dichotomous responses (“yes” or “no”).Questionnaire items were developed from unpublished results of a previous single-centre pilot study , including semi-structured interviews with genetics professionals, to identify key topics. A draft survey was designed using Research Online, a web-based secure and reliable survey tool that complies with the Good Clinical Practice guidelines for electronic data collecting . The finStatistical analyses were performed using the IBM Statistical Package for the Social Sciences (SPSS) 21.0. Descriptive statistics (percentages) were used for the analysis of all close-ended questions (quantitative data). For data analysis and interpretation of questions assessing genetics professionals’ attitudes, response categories for (agreement) Likert-type scale items were merged. Ratings 1 (strongly disagree) and 2 (disagree) were combined to construct a ‘(strongly) disagree’ category, and ratings 4 (agree) and 5 (strongly agree) were merged to construct a corresponding ‘(strongly) agree’ category. For interpretation of reported rates of importance regarding information items that could be discussed with counsellees, ratings 1 (very important) and 2 (important) were combined. In order to make recommendations for clinical practice, a threshold of 75% of respondents who rated the information item as ‘(very) important’ was considered to be ‘consensus’.Fifty-seven of 106 genetics professionals (54%) participated in the questionnaire Table ”.TP53 genetic testing to at least ten young breast cancer patients. Genetics professionals were asked for reasons for not discussing LFS with all women who were counselled for early-onset breast cancer. The most reported reasons were changes in national or hospital-based guidelines. Discussing LFS with all young breast cancer patients has not always been part of standard care. Previously, genetic testing for TP53 was only offered if a suggestive family history of LFS was present. Furthermore, two respondents preferred to wait for BRCA genetic test results before offering TP53 genetic testing. TP53 genetic testing is no longer indicated in case a BRCA mutation is detected.Ninety-six percent of respondents (55/57) have counselled at least one woman diagnosed with breast cancer before the age of 30 Table . LFS wasBRCA1/2, CHEK2 and TP53 during the initial counselling visit when women are referred for treatment-focused genetic counselling and testing  noted they were able to pay enough attention to the decision-making process. Approximately half of respondents (55%) did not have difficulties providing accurate genetic counselling for BRCA1/2, CHEK2 and TP53 during a single visit. Fourteen genetics professionals  experience difficulties in providing accurate genetic counselling for these four genes during a single visit.Respondents were asked to evaluate their experiences. The majority of counsellors (76%)  agreed with the statement ‘In general I am able to provide sufficient information about clinical and genetic aspects of TP53 mutation detection rate, as well as the high risk of developing cancer in individuals with LFS, was rated as important by every professional. Mentioning specific odds (expressed as percentages) of mutation detection rates and cancer risks was considered (very) important by respectively 33 and 26% of respondents. With regard to other information items, almost all participants considered discussing possible consequences for family members, limited screening options as well as the option of additional support by a psychosocial professional as (very) important. Fewer participants (35%) considered mentioning the name of the genetic disorder to be important.Genetics professionals rated the importance of 17 information items about LFS that could be discussed with young breast cancer patients Table . Ten infTP53 genetic testing  and patient’s request for additional psychosocial support (n = 5). Only two percent of respondents agreed that all young breast cancer patients who are offered TP53 genetic testing should receive additional support by a psychosocial professional before TP53 genetic testing is performed  additional support by a psychosocial professional  would be desirable. Respondents were allowed to choose more than one response category. The vast majority of respondents (88%) reported that additional support by a psychosocial professional is advisable in case it is questionable whether the counsellee understands the implications of ed Table , A.TP53 genetic testing should preferably be discussed after BRCA genetic test results are known.’ A similar number of participants agreed with the second statement . Less than one-third of respondents (32%) agreed that, in case of a regular referral, TP53 genetic testing should preferably be performed after a second counselling visit. For all three statements, approximately one-fourth of respondents selected the ‘neutral’ response option.Respondents were also asked about the extent of their agreement (or disagreement) with three statements about the timing of genetic counselling and testing for LFS Table , B. The BRCA1/2 with young breast cancer patients’. Only two clinical-geneticists-in-training agreed with this statement (Table TP53 genetic testing. Nonetheless the majority (61%) disagreed. Genetics professionals who reported that they encounter difficulties were asked to explain their responses. Table Almost all 96%) respondents  disagreed with the statement ‘I (would) encounter difficulties in discussing the option of % respondTP53 genetic testing—simultaneously with BRCA 1/2—during the initial counselling visit, especially in case of referral for treatment-focused genetic counselling. Furthermore, there was a general consensus about ten information items that should be discussed with counsellees. Almost two-thirds of genetics professionals did not encounter difficulties in providing genetic counselling of young breast cancer patients for LFS. However, a substantial minority did.To our knowledge, this is the first reported survey about the attitudes and experiences of genetics professionals regarding genetic counselling of young breast cancer patients for LFS. To summarize, most respondents reported that they discuss the option of This study aimed to make recommendations for clinical practice regarding the content and timing of genetic counselling and indications for additional psychosocial support. Table TP53 genetic testing should receive this support before TP53 genetic testing is performed. These findings suggest that genetics professionals feel capable of identifying counsellees who need additional psychosocial support. Nonetheless, there seems to be general consensus among respondents (≥ 75%) that additional support by a psychosocial professional should be provided in two specific situations. Firstly, in case it is questionable whether the counsellee has understood the implications of TP53 genetic testing, and secondly, in case the counsellee has doubts about performing TP53 genetic testing. Our results partly differ from recommendations from the Dutch patient association ‘Stichting Diagnose Kanker’ (SDK) and European Li–Fraumeni Families Foundation (ELFF), as mentioned in the medical standard ‘Li–Fraumeni Syndroom’. A substantial minority of respondents (approximately one-third) did not consider ‘the detection of a TP53 mutation’ as an indication for specialized psychosocial support. Whereas, the medical standard recommends that specialized psychosocial counselling should always be provided in case a TP53 mutation is detected [Almost all respondents indicated that it is important to discuss the option of additional support by a psychosocial professional during genetic counselling. However, the majority of genetics professionals disagree that all young breast cancer patients who are offered diagnostic detected . A plausTP53 germline mutations [TP53 genetic testing is being discussed with young breast cancer patients more often. Although, most respondents did not encounter difficulties in providing genetic counselling of young breast cancer patients for LFS, a substantial minority did. The most frequently noted reasons were limited screening options, severity and diversity of tumour spectrum and the psychosocial distress that might be induced.The results of our retrospective laboratory records review showed an increase in the number of early-onset breast cancer patients tested for utations . This suTP53 mutations carriers are offered an annual surveillance program, including physical examination, blood tests, whole body MRI (WB-MRI) and breast MRI in female patients. Recently, the initial findings of this surveillance program were published [TP53 mutation carriers. Data on TP53 carriers’ experiences with the annual surveillance program, including the psychosocial issues, are currently being collected.In recent years, multiple suggestions for clinical surveillance of individuals with LFS have been proposed , 23–26. ublished . MalignaTP53 mutations, irrespective of their carrier status, exhibit psychological distress [A few studies have reported that a substantial proportion of individuals in families with distress –30. HoweAlthough a large number of genetics professionals did participate in the questionnaire, the results of the survey might not be generalizable for all genetics professionals who provide cancer genetic counselling in the Netherlands. Forty-eight percent of eligible genetics professionals did not participate in the questionnaire and response rates differed between professions and hospitals. A detailed stratified analysis on profession was not possible due to the significant difference between the number of participants in each profession. Additionally, the possibility of a non-response bias cannot be excluded, as it is possible that professionals who have responded have stronger opinions than non-respondents. However, all nine hospitals were represented in the group of respondents and this study covered all three professions that provide genetic counselling. The response rate among clinical geneticists (supervisors of genetic counsellors and genetics residents) was relatively high (70%) and exceeded the mean response rate of 56% reported in a review of postal surveys conducted among healthcare professionals . FurtherThe number of early-onset breast cancer patients tested for LFS has increased over the past years.TP53 genetic testing—simultaneously with BRCA 1/2 and CHEK2—during the initial counselling visit, especially in case of referral for treatment-focused genetic counselling. Our recommendations regarding the content of genetic counselling for LFS and indications for additional psychosocial support will be useful for clinical practice. Studies which address young breast cancer patients’ experiences  are needed to further determine the most appropriate genetic counselling strategy for these women.The results of our nationwide survey suggest that most genetics professionals discuss the option of


Small RNAs, especially the microRNAs, have been revealed to play great roles in heart development and congenital heart defects. Several studies have shown dysregulated miRNAs in ventricular tissues of Tetralogy of Fallot (TOF) patients. In the present study, we conducted high throughput sequencing to obtain the global profiling of small RNA transcriptome in heart right ventricular samples from 10 age -matched TOF patients. These samples showed dominant composition of miRNA and mitochondrial associated RNAs. By sRNA cluster identification and differential gene expression analysis, significant sexual difference was discovered for sRNA expression in TOF patients. miR-1/miR-133, which have been identified as essential for cardiac development, account for the most variance of sRNA expression between sexes in TOF hearts. During the normal process of heart development and growth, a genetic network involving precise temporal and spatial gene regulation is required. Abnormity in the network might lead to failure of cell lineage specification, pattern formation and cell migration necessary for cardiac development, which finally resulting in congenital heart defects (CHD). Most CHD cases are sporadic, which are most often inherited from unaffected parents. De novo mutation, incomplete penetrance and recessive mutation have been suggested to contribute to CHD inheritance. Whereas, only a small fraction of the cases could be genetically diagnosed.1. Heart development depends on proper spatio-temporal expression of particular miRNAs. For example, miR-1 is the first miRNA reported as essential for heart development2, which is cardiac specific. Members from miR-1 family and miR-133 family could function as pairs from the same bicistronic transcripts. miR-1 transgenic mics display similar phenotype as Hand2 mutant mice3; The miRNA-17–92 cluster have been suggested function in promoting cardiac progenitor differentiation in a dose-dependent manner; disruption of miR-138 by morpholino and antagomir in zebrafish could result in expression of atrioventricular canal specific genes in ventricular chamber, thus lead to abnormal cardiac patterning.Recently, small non-coding RNAs (sRNAs) have been shown to play important parts in cardiac gene expression network. microRNA (miRNA) with an average length of ~22 nucleotides is the most intensively investigated class of sRNAs, which tune gene expression through post-transcriptional regulation by means of reducing mRNA stability or inhibiting translation4. TOF is caused by non-uniform separation between truncus and bulbus arteriosus during early embryonic development, resulting in malformation such as ventricular septal defect, aortic overriding, pulmonary artery or right ventricular outflow tract stenosis, and right ventricular hypertrophy. Although the molecular mechanisms of isolated TOF still remains unclear. Several evidence from microarray analysis have shown that miRNAs should be dysregulated in TOF heart tissues8. These results emphasized the importance of miRNA in TOF etiology. However, a comprehensive profiling of sRNAs in TOF hearts have not been reported up to date. Furthermore, sexual difference in gene expression have been identified in left heart ventricle tissues with no prior cardiovascular disease using Genotype-Tissue Expression (GTEx) data9. In fact, in the rat model miR-1 have been discovered to be responsible for sexual difference of Cx43 expression in cardiomyocytes under pathologic conditions. we hypothesize that small RNA expression profile in male and female individuals with TOF should be different10.Tetralogy of Fallot (TOF) is the most common cyanotic CHD with an incidence of 10% in all CHD patientsIn the present study, to get a full picture and characterize the sex differences of small RNA expression for TOF, we performed small RNA sequencing  on 10 age- matched  TOF right ventricular tissue samples. We found that TOF heart sRNA transcriptome have over represented composition of miRNAs and mitochondrion associated sRNAs, a significant difference of sRNA expression was also shown between male and female samples.Sequencing data for each sample have a Q20 value greater than 97% and Q30 value greater than 95% Table , suggestAs expected, the most abundant class of sequenced sRNAs in TOF right ventricles is miRNA (72.83%). A substantial proportion of sRNA originated from mitochondrion sRNAs, which might be a unique feature for heart tissues since cardiomyocytes enormous mitochondrion. Other sRNA classes such as piRNA and snoRNA made relatively few contribution to the sRNA composition for TOF heart Fig. . In consA total of 600 sRNA clusters were identified using the SeqCluster analysis pipeline. Most of the clusters were mapped onto miRNA precursors . Additionally, 9.5% and 9.0% of the clusters mapped onto piRNAs and tRNAs  with specificity and sensitivity of 0.8 in cross validation Fig. . The firBesides the identification of global patterns of sRNA clusters that could distinguish males from females. We also detected differential expression in single clusters between male and female TOF samples. Read counts were normalized  annotated with miR-1 and miR133 family. miR-1 have also been suggested might contribute to the development of TOF by regulating CX43 (connexin 43), which has been known to be involved in the development of conotruncal anomalies. Given the significant importance of this miRNA in heart development, we hypothesize that development of TOF in male and female patients have different molecular basis characterized by the expression of the miR-1/miR-133 cluster. It would be interesting to investigate that whether such difference exists in other types of CHD.Using the read count information of the sRNA clustering we detected, a model with good performance on predicting sample sexes could be built. The first component could significantly explain more than 90% of the response variability. Surprisingly, the most important variable in the model is cluster 242  did not separate male and female samples perfectly, with tof1 located at the outgroup. We further use a larger  and smaller  gene set to repeat such clustering, females and males could be separated unambiguously in both result . All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from the patients’ parents (patients were under 18 years old).Our subjects were patients with main cardiac malformation of TOF Table  under thTissue samples preserved in RNALater was lysed in Trizol (Ambion). Total RNA was extracted using the miRNeasy kit  according to the manufacturer’s instructions. The RNA degration and contamination was monitored by 1% agarose gels electrophoresis. The concentration of RNA samples were measured using Qubit RNA Assay Kit in Qubit 2.0 Flurometer . The RNA purity was checked using the NanoPhotometer spectrophotometer. The RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the 2100 Bioanalyzer instrument .A total amount of 3 μg total RNA for each sample was used for small RNA library construction. Sequencing libraries were generated using NEBNext Multiplex Small RNA Library Prep Set for Illumina  following manufacturer’s recommendations and index codes were ligated to attribute sequences to each sample. Briefly, NEB 3′ SR Adaptor was specifically added to 3′ end of miRNA, siRNA and piRNA. The SR RT Primer hybridized to the excess of 3′ SR Adaptor and transformed the single-stranded DNA adaptor into a double-stranded DNA molecular after 3′ ligation reaction. The first strand cDNA was then synthesized using M-MuLV Reverse Transcriptase (RNase H-). PCR amplification was performed using LongAmp Taq 2X Master Mix, SR primer for illumine and index primer. The PCR products were purified on an 8% polyacrylamide gel . DNA fragments corresponding to 140~160 bp were recovered and dissolved in 83 μL elution buffer. At last, library quality was determined on the Agilent Bioanalyzer 2100 system using DNA High Sensitivity Chips.The clustering of index-coded samples was performed on a cBot Cluster Generation System using TruSeq SR Cluster Kit v3-cBot-HS(Illumina) according to the manufacturers’ instructions. The library preparations were then sequenced on an Illumina Hiseq. 2500/2000 platform and 50 bp single-end reads were generated.11. Reads with low quality  were also removed.For raw reads of each sample, adapter sequences were trimmed cutadapt17. Sequences with adapter removal were then collapsed among samples, generating a set of unique reads with the corresponding counts. Sequences with more than 10 counts were used for further analysis. The reads were mapped onto the human genome (hg38) using STAR19 implementing 2-pass alignment mode. Clusters were then detected. Annotations used were GENCODE27 (GRCh38.p10), transposon annotations from Repbase20. snoRNA and miRNA annotations from the UCSC table browser21. In SeqCluster, a group of at least 10 overlapping sequences mapping onto a specific genomic site was defined as a hotspot and a cluster was defined as hotspots sharing any number of sequences. A cluster represents a group of a type of sRNAs that are consistently co-expressed.sRNA clusters were detected using the SeqCluster pipelineIn addition, we used miraligner for miRNA alignment and annotation using the miRBase dataset for each sample.22. We implemented this method in sRNA cluster and miRNAs we identified for the TOF samples using the R package mdatools.Partial least squares discriminant analysis (PLS-DA) is a method appropriate for dimension reduction for datasets with multicollineality and was successfully used in sRNA analysis23 for differential expression analysis between male and female samples. The analyses were conducted for read counts generated from SeqCluster and miraligner respectively. Clustering of gene sets among samples were visualized using the R package pheatmap .We used the R package DESeq2Differential expressed miRNAs were submitted to web based DIANA TOOLs miPath v3.0 for KEGG pathway enrichment analysis using the default parameters. Tarbase v7.0 and microT-CDS datasets were used as target information for miRNAs. Pathway union were used to generate pathway clustering heatmaps.24.A machine learning based tool-mirnovo, was used to predict the novel miRNA sequences from our sRNA dataSupplementary FiguresDataset 1Dataset 2Dataset 3Dataset 4