Early detection and prognosis evaluation for hepatocellular carcinoma by circulating tumour DNA methylation: A multicentre cohort study.

BACKGROUND: Early diagnosis of hepatocellular carcinoma (HCC) can significantly improve patient survival. We aimed to develop a blood-based assay to aid in the diagnosis, detection and prognostic evaluation of HCC. METHODS: A three-phase multicentre study was conducted to screen, optimise and validate HCC-specific differentially methylated regions (DMRs) using next-generation sequencing and quantitative methylation-specific PCR (qMSP). RESULTS: Genome-wide methylation profiling was conducted to identify DMRs distinguishing HCC tumours from peritumoural tissues and healthy plasmas. The twenty most effective DMRs were verified and incorporated into a multilocus qMSP assay (HepaAiQ). The HepaAiQ model was trained to separate 293 HCC patients (Barcelona Clinic Liver Cancer (BCLC) stage 0/A, 224) from 266 controls including chronic hepatitis B (CHB) or liver cirrhosis (LC) (CHB/LC, 96), benign hepatic lesions (BHL, 23), and healthy controls (HC, 147). The model achieved an area under the curve (AUC) of 0.944 with a sensitivity of 86.0% in HCC and a specificity of 92.1% in controls. Blind validation of the HepaAiQ model in a cohort of 523 participants resulted in an AUC of 0.940 with a sensitivity of 84.4% in 205 HCC cases (BCLC stage 0/A, 167) and a specificity of 90.3% in 318 controls (CHB/LC, 100; BHL, 102; HC, 116). When evaluated in an independent test set, the HepaAiQ model exhibited a sensitivity of 70.8% in 65 HCC patients at BCLC stage 0/A and a specificity of 89.5% in 124 patients with CHB/LC. Moreover, HepaAiQ model was assessed in paired pre- and postoperative plasma samples from 103 HCC patients and correlated with 2-year patient outcomes. Patients with high postoperative HepaAiQ score showed a higher recurrence risk (Hazard ratio, 3.33, p < .001). CONCLUSIONS: HepaAiQ, a noninvasive qMSP assay, was developed to accurately measure HCC-specific DMRs and shows great potential for the diagnosis, detection and prognosis of HCC, benefiting at-risk populations.

Blood leukocytes as a non-invasive diagnostic tool for thyroid nodules: a prospective cohort study.

BACKGROUND: Thyroid nodule (TN) patients in China are subject to overdiagnosis and overtreatment. The implementation of existing technologies such as thyroid ultrasonography has indeed contributed to the improved diagnostic accuracy of TNs. However, a significant issue persists, where many patients undergo unnecessary biopsies, and patients with malignant thyroid nodules (MTNs) are advised to undergo surgery therapy. METHODS: This study included a total of 293 patients diagnosed with TNs. Differential methylation haplotype blocks (MHBs) in blood leukocytes between MTNs and benign thyroid nodules (BTNs) were detected using reduced representation bisulfite sequencing (RRBS). Subsequently, an artificial intelligence blood leukocyte DNA methylation (BLDM) model was designed to optimize the management and treatment of patients with TNs for more effective outcomes. RESULTS: The DNA methylation profiles of peripheral blood leukocytes exhibited distinctions between MTNs and BTNs. The BLDM model we developed for diagnosing TNs achieved an area under the curve (AUC) of 0.858 in the validation cohort and 0.863 in the independent test cohort. Its specificity reached 90.91% and 88.68% in the validation and independent test cohorts, respectively, outperforming the specificity of ultrasonography (43.64% in the validation cohort and 47.17% in the independent test cohort), albeit with a slightly lower sensitivity (83.33% in the validation cohort and 82.86% in the independent test cohort) compared to ultrasonography (97.62% in the validation cohort and 100.00% in the independent test cohort). The BLDM model could correctly identify 89.83% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. In micronodules, the model displayed higher specificity (93.33% in the validation cohort and 92.00% in the independent test cohort) and accuracy (88.24% in the validation cohort and 87.50% in the independent test cohort) for diagnosing TNs. This performance surpassed the specificity and accuracy observed with ultrasonography. A TN diagnostic and treatment framework that prioritizes patients is provided, with fine-needle aspiration (FNA) biopsy performed only on patients with indications of MTNs in both BLDM and ultrasonography results, thus avoiding unnecessary biopsies. CONCLUSIONS: This is the first study to demonstrate the potential of non-invasive blood leukocytes in diagnosing TNs, thereby making TN diagnosis and treatment more efficient in China.

MeIS: DNA methylation-based immune response signatures for thyroid nodule diagnostics.

CONTEXT: Accurately distinguishing between benign thyroid nodules (BTNs) and papillary thyroid cancers (PTCs) with current conventional methods poses a significant challenge. OBJECTIVE: We identify DNA methylation markers of immune response-related genes for distinguishing BTNs and PTCs. METHODS: In this study, we analyzed a public reduced representative bisulfite sequencing (RRBS) dataset and revealed distinct methylation patterns associated with immune signals in PTCs and BTNs. Based on these findings, we developed a diagnostic classifier named as the Methylation-based Immune Response Signature (MeIS), which was composed of fifteen DNA methylation markers associated with immune response-related genes. We validated the MeIS's performance in two independent cohorts: ZS's retrospective cohort (50 PTC and 18 BTN surgery-leftover samples) and ZS's preoperative cohort (31 PTC and 30 BTN fine-needle aspiration (FNA) samples). RESULTS: The MeIS classifier demonstrated significant clinical promise, achieving AUCs of 0.96, 0.98, 0.89 and 0.90 in the training set, validation set, ZS's retrospective cohort, and ZS's preoperative cohort, respectively. For the cytologically indeterminate thyroid nodules, in the ZS's retrospective cohort, MeIS exhibited a sensitivity of 91% and a specificity of 82%; in the ZS's preoperative cohort, MeIS achieved a sensitivity of 84% and a specificity of 74%. Additionally, combining MeIS and BRAFV600E detection improved the detecting performance of cytologically indeterminate thyroid nodules, yielding sensitivities of 98% and 87%, and specificities of 82% and 74% in the ZS's retrospective cohort and ZS's preoperative cohort, respectively. CONCLUSIONS: The fifteen markers we identified can be employed to improve the diagnostic of cytologically indeterminate thyroid nodules.

Circulating cell-free DNA-based multi-cancer early detection.

Patients benefit considerably from early detection of cancer. Existing single-cancer tests have various limitations, which could be effectively addressed by circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED). With sensitive detection and accurate localization of multiple cancer types at a very low and fixed false-positive rate (FPR), MCED has great potential to revolutionize early cancer detection. Herein, we review state-of-the-art approaches for cfDNA-based MCED and their limitations and discuss both technical and clinical challenges in the development and application of MCED tests. Given the constant improvements in technology and understanding of cancer biology, we propose that a cfDNA-based targeted sequencing assay that integrates multimodal features should be optimized for MCED.

Cross-platform comparisons for targeted bisulfite sequencing of MGISEQ-2000 and NovaSeq6000.

BACKGROUND: An accurate and reproducible next-generation sequencing platform is essential to identify malignancy-related abnormal DNA methylation changes and translate them into clinical applications including cancer detection, prognosis, and surveillance. However, high-quality DNA methylation sequencing has been challenging because poor sequence diversity of the bisulfite-converted libraries severely impairs sequencing quality and yield. In this study, we tested MGISEQ-2000 Sequencer's capability of DNA methylation sequencing with a published non-invasive pancreatic cancer detection assay, using NovaSeq6000 as the benchmark. RESULTS: We sequenced a series of synthetic cell-free DNA (cfDNA) samples with different tumor fractions and found MGISEQ-2000 yielded data with similar quality as NovaSeq6000. The methylation levels measured by MGISEQ-2000 demonstrated high consistency with NovaSeq6000. Moreover, MGISEQ-2000 showed a comparable analytic sensitivity with NovaSeq6000, suggesting its potential for clinical detection. As to evaluate the clinical performance of MGISEQ-2000, we sequenced 24 clinical samples and predicted the pathology of the samples with a clinical diagnosis model, PDACatch classifier. The clinical model performance of MGISEQ-2000's data was highly consistent with that of NovaSeq6000's data, with the area under the curve of 1. We also tested the model's robustness with MGISEQ-2000's data when reducing the sequencing depth. The results showed that MGISEQ-2000's data showed matching robustness of the PDACatch classifier with NovaSeq6000's data. CONCLUSIONS: Taken together, MGISEQ-2000 demonstrated similar data quality, consistency of the methylation levels, comparable analytic sensitivity, and matching clinical performance, supporting its application in future non-invasive early cancer detection investigations by detecting distinct methylation patterns of cfDNAs.

Early detection and stratification of lung cancer aided by a cost-effective assay targeting circulating tumor DNA (ctDNA) methylation.

BACKGROUND: Detection of lung cancer at earlier stage can greatly improve patient survival. We aim to develop, validate, and implement a cost-effective ctDNA-methylation-based plasma test to aid lung cancer early detection. METHODS: Case-control studies were designed to select the most relevant markers to lung cancer. Patients with lung cancer or benign lung disease and healthy individuals were recruited from different clinical centers. A multi-locus qPCR assay, LunaCAM, was developed for lung cancer alertness by ctDNA methylation. Two LunaCAM models were built for screening (-S) or diagnostic aid (-D) to favor sensitivity or specificity, respectively. The performance of the models was validated for different intended uses in clinics. RESULTS: Profiling DNA methylation on 429 plasma samples including 209 lung cancer, 123 benign diseases and 97 healthy participants identified the top markers that detected lung cancer from benign diseases and healthy with an AUC of 0.85 and 0.95, respectively. The most effective methylation markers were verified individually in 40 tissues and 169 plasma samples to develop LunaCAM assay. Two models corresponding to different intended uses were trained with 513 plasma samples, and validated with an independent collection of 172 plasma samples. In validation, LunaCAM-S model achieved an AUC of 0.90 (95% CI: 0.88-0.94) between lung cancer and healthy individuals, whereas LunaCAM-D model stratified lung cancer from benign pulmonary diseases with an AUC of 0.81 (95% CI: 0.78-0.86). When implemented sequentially in the validation set, LunaCAM-S enables to identify 58 patients of lung cancer (90.6% sensitivity), followed by LunaCAM-D to remove 20 patients with no evidence of cancer (83.3% specificity). LunaCAM-D significantly outperformed the blood test of carcinoembryonic antigen (CEA), and the combined model can further improve the predictive power for lung cancer to an overall AUC of 0.86. CONCLUSIONS: We developed two different models by ctDNA methylation assay to sensitively detect early-stage lung cancer or specifically classify lung benign diseases. Implemented at different clinical settings, LunaCAM models has a potential to provide a facile and inexpensive avenue for early screening and diagnostic aids for lung cancer.

Cell-free DNA methylation biomarker for the diagnosis of papillary thyroid carcinoma.

BACKGROUND: Cell-free DNA (cfDNA) is being explored as biomarker for non-invasive diagnosis of cancer. We aimed to establish a cfDNA-based DNA methylation marker panel to differentially diagnose papillary thyroid carcinoma (PTC) from benign thyroid nodule (BTN). METHODS: 220 PTC- and 188 BTN patients were enrolled. Methylation markers of PTC were identified from patients' tissue and plasma by reduced representation bisulfite sequencing and methylation haplotype analyses. They were combined with PTC markers from literatures and were tested on additional PTC and BTN samples to verify PTC-detecting ability using targeted methylation sequencing. Top markers were developed into ThyMet and were tested in 113 PTC and 88 BTN cases to train and validate a PTC-plasma classifier. Integration of ThyMet and thyroid ultrasonography was explored to improve accuracy. FINDINGS: From 859 potential PTC plasma-discriminating markers that include 81 markers identified by us, the top 98 most PTC plasma-discriminating markers were selected for ThyMet. A 6-marker ThyMet classifier for PTC plasma was trained. In validation it achieved an Area Under the Curve (AUC) of 0.828, similar to thyroid ultrasonography (0.833) but at higher specificity (0.722 and 0.625 for ThyMet and ultrasonography, respectively). A combinatorial classifier by them, ThyMet-US, improved AUC to 0.923 (sensitivity = 0.957, specificity = 0.708). INTERPRETATION: The ThyMet classifier improved the specificity of differentiating PTC from BTN over ultrasonography. The combinatorial ThyMet-US classifier may be effective in preoperative diagnosis of PTC. FUNDING: This work was supported by the grants from National Natural Science Foundation of China (82072956 and 81772850).

Deficiency of primate-specific SSX1 induced asthenoteratozoospermia in infertile men and cynomolgus monkey and tree shrew models.

Primate-specific genes (PSGs) tend to be expressed in the brain and testis. This phenomenon is consistent with brain evolution in primates but is seemingly contradictory to the similarity of spermatogenesis among mammals. Here, using whole-exome sequencing, we identified deleterious variants of X-linked SSX1 in six unrelated men with asthenoteratozoospermia. SSX1 is a PSG expressed predominantly in the testis, and the SSX family evolutionarily expanded independently in rodents and primates. As the mouse model could not be used for studying SSX1, we used a non-human primate model and tree shrews, which are phylogenetically similar to primates, to knock down (KD) Ssx1 expression in the testes. Consistent with the phenotype observed in humans, both Ssx1-KD models exhibited a reduced sperm motility and abnormal sperm morphology. Further, RNA sequencing indicated that Ssx1 deficiency influenced multiple biological processes during spermatogenesis. Collectively, our experimental observations in humans and cynomolgus monkey and tree shrew models highlight the crucial role of SSX1 in spermatogenesis. Notably, three of the five couples who underwent intra-cytoplasmic sperm injection treatment achieved a successful pregnancy. This study provides important guidance for genetic counseling and clinical diagnosis and, significantly, describes the approaches for elucidating the functions of testis-enriched PSGs in spermatogenesis.

Early detection and prognosis prediction for colorectal cancer by circulating tumour DNA methylation haplotypes: A multicentre cohort study.

Background: Early detection and prognosis prediction of colorectal cancer (CRC) can significantly reduce CRC-related mortality. Recently, circulating tumour DNA (ctDNA) methylation has shown good application foreground in the early detection and prognosis prediction of multiple tumours. Methods: This multicentre cohort study evaluated ctDNA methylation haplotype patterns based on archived plasma samples (collected between 2010 and 2018) from 1138 individuals at two medical centres: Fudan University Shanghai Cancer Center (Shanghai, China) and Southern Medical University Nanfang Hospital (Guangzhou, Guangdong, China), including 366 healthy individuals, 182 patients with advanced adenoma (AA), and 590 patients with CRC. Samples were processed using the ColonES assay, a targeted bisulfite sequencing method that detects ctDNA methylation haplotype patterns in 191 genomic regions. Among these 1138 samples, 748 were used to develop a classification model, and 390 served as a blinded cohort for independent validation. The study is registered at https://register.clinicaltrials.gov with the unique identifier NCT03737591. Results: The model obtained from unblinded samples discriminated patients with CRC or AA from normal controls with high accuracy. In the blinded validation set, the ColonES assay achieved sensitivity values of 79.0% (95% confidence interval (CI), 66%-88%) in AA patients and 86.6% (95% CI, 81%-91%) in CRC patients with a specificity of 88.1% (95% CI, 81%-93%) in healthy individuals. The model area under the curve (AUC) for the blinded validation set was 0.903 for AA samples and 0.937 for CRC samples. Additionally, the prognosis of patients with high preoperative ctDNA methylation levels was worse than that of patients with low ctDNA methylation levels (p = 0.001 for relapse-free survival and p = 0.004 for overall survival). Interpretation: We successfully developed and validated an accurate, noninvasive detection method based on ctDNA methylation haplotype patterns that may enable early detection and prognosis prediction for CRC. Funding: The Grant of National Natural Science Foundation of China (No.81871958), National Natural Science Foundation of China (No. 82203215), Shanghai Science and Technology Committee (No. 19140902100), Scientific Research Fund of Fudan University (No.IDF159052), Shanghai Municipal Health Commission (SHWJRS 2021-99), and Shanghai Sailing Program (22YF1408800).

Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction.

BACKGROUND AND AIM: Preoperative evaluation of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is important for surgical strategy determination. We aimed to develop and establish a preoperative predictive model for MVI status based on DNA methylation markers. METHODS: A total of 35 HCC tissues and the matched peritumoral normal liver tissues as well as 35 corresponding HCC patients' plasma samples and 24 healthy plasma samples were used for genome-wide methylation sequencing and subsequent methylation haplotype block (MHB) analysis. Predictive models were constructed based on selected MHB markers and 3-cross validation was used. RESULTS: We grouped 35 HCC patients into 2 categories, including the MVI- group with 17 tissue and plasma samples, and MVI + group with 18 tissue and plasma samples. We identified a tissue DNA methylation signature with an AUC of 98.0% and a circulating free DNA (cfDNA) methylation signature with an AUC of 96.0% for HCC detection. Furthermore, we established a tissue DNA methylation signature for MVI status prediction, and achieved an AUC of 85.9%. Based on the MVI status predicted by the DNA methylation signature, the recurrence-free survival (RFS) and overall survival (OS) were significantly better in the predicted MVI- group than that in the predicted MVI + group. CONCLUSIONS: In this study, we identified a cfDNA methylation signature for HCC detection and a tissue DNA methylation signature for MVI status prediction with high accuracy.

Noninvasive detection of pancreatic ductal adenocarcinoma using the methylation signature of circulating tumour DNA.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has the lowest overall survival rate primarily due to the late onset of symptoms and rapid progression. Reliable and accurate tests for early detection are lacking. We aimed to develop a noninvasive test for early PDAC detection by capturing the circulating tumour DNA (ctDNA) methylation signature in blood. METHODS: Genome-wide methylation profiles were generated from PDAC and nonmalignant tissues and plasma. Methylation haplotype blocks (MHBs) were examined to discover de novo PDAC markers. They were combined with multiple cancer markers and screened for PDAC classification accuracy. The most accurate markers were used to develop PDACatch, a targeted methylation sequencing assay. PDACatch was applied to additional PDAC and healthy plasma cohorts to train, validate and independently test a PDAC-discriminating classifier. Finally, the classifier was compared and integrated with carbohydrate antigen 19-9 (CA19-9) to evaluate and maximize its accuracy and utility. RESULTS: In total, 90 tissues and 546 plasma samples were collected from 232 PDAC patients, 25 chronic pancreatitis (CP) patients and 323 healthy controls. Among 223 PDAC cases with known stage information, 43/119/38/23 cases were of Stage I/II/III/IV. A total of 171 de novo PDAC-specific markers and 595 multicancer markers were screened for PDAC classification accuracy. The top 185 markers were included in PDACatch, from which a 56-marker classifier for PDAC plasma was trained, validated and independently tested. It achieved an area under the curve (AUC) of 0.91 in both the validation (31 PDAC, 26 healthy; sensitivity = 84%, specificity = 89%) and independent tests (74 PDAC, 65 healthy; sensitivity = 82%, specificity = 88%). Importantly, the PDACatch classifier detected CA19-9-negative PDAC plasma at sensitivities of 75 and 100% during the validation and independent tests, respectively. It was more sensitive than CA19-9 in detecting Stage I (sensitivity = 80 and 68%, respectively) and early-stage (Stage I-IIa) PDAC (sensitivity = 76 and 70%, respectively). A combinatorial classifier integrating PDACatch and CA19-9 outperformed (AUC=0.94) either PDACatch (0.91) or CA19-9 (0.89) alone (p < 0.001). CONCLUSIONS: The PDACatch assay demonstrated high sensitivity for early PDAC plasma, providing potential utility for noninvasive detection of early PDAC and indicating the effectiveness of methylation haplotype analyses in discovering robust cancer markers.

Deciphering cell lineage specification of human lung adenocarcinoma with single-cell RNA sequencing.

Lung adenocarcinomas (LUAD) arise from precancerous lesions such as atypical adenomatous hyperplasia, which progress into adenocarcinoma in situ and minimally invasive adenocarcinoma, then finally into invasive adenocarcinoma. The cellular heterogeneity and molecular events underlying this stepwise progression remain unclear. In this study, we perform single-cell RNA sequencing of 268,471 cells collected from 25 patients in four histologic stages of LUAD and compare them to normal cell types. We detect a group of cells closely resembling alveolar type 2 cells (AT2) that emerged during atypical adenomatous hyperplasia and whose transcriptional profile began to diverge from that of AT2 cells as LUAD progressed, taking on feature characteristic of stem-like cells. We identify genes related to energy metabolism and ribosome synthesis that are upregulated in early stages of LUAD and may promote progression. MDK and TIMP1 could be potential biomarkers for understanding LUAD pathogenesis. Our work shed light on the underlying transcriptional signatures of distinct histologic stages of LUAD progression and our findings may facilitate early diagnosis.

DNA methylation patterns at and beyond the histological margin of early-stage invasive lung adenocarcinoma radiologically manifested as pure ground-glass opacity.

BACKGROUND: Early-stage lung cancers radiologically manifested as ground-glass opacities (GGOs) have been increasingly identified, among which pure GGO (pGGO) has a good prognosis after local resection. However, the optimal surgical margin is still under debate. Precancerous lesions exist in tumor-adjacent tissues beyond the histological margin. However, potential precancerous epigenetic variation patterns beyond the histological margin of pGGO are yet to be discovered and described. RESULTS: A genome-wide high-resolution DNA methylation analysis was performed on samples collected from 15 pGGO at tumor core (TC), tumor edge (TE), para-tumor tissues at the 5 mm, 10 mm, 15 mm, 20 mm beyond the tumor, and peripheral normal (PN) tissue. TC and TE were tested with the same genetic alterations, which were also observed in histologically normal tissue at 5 mm in two patients with lower mutation allele frequency. According to the difference of methylation profiles between PN samples, 2284 methylation haplotype blocks (MHBs), 1657 differentially methylated CpG sites (DMCs), and 713 differentially methylated regions (DMRs) were identified using reduced representation bisulfite sequencing (RRBS). Two different patterns of methylation markers were observed: Steep (S) markers sharply changed at 5 mm beyond the histological margin, and Gradual (G) markers changed gradually from TC to PN. S markers composed 86.2% of the tumor-related methylation markers, and G markers composed the other 13.8%. S-marker-associated genes enriched in GO terms that were related to the hallmarks of cancer, and G-markers-associated genes enriched in pathways of stem cell pluripotency and transcriptional misregulation in cancer. Significant difference in DNA methylation score was observed between peripheral normal tissue and tumor-adjacent tissues 5 mm further from the histological margin (p < 0.001 in MHB markers). DNA methylation score at and beyond 10 mm from histological margin is not significantly different from peripheral normal tissues (p > 0.05 in all markers). CONCLUSIONS: According to the methylation pattern observed in our study, it was implied that methylation alterations were not significantly different between tissues at or beyond P10 and distal normal tissues. This finding explained for the excellent prognosis from radical resections with surgical margins of more than 15 mm. The inclusion of epigenetic characteristics into surgical margin analysis may yield a more sensitive and accurate assessment of remnant cancerous and precancerous cells in the surgical margins.

Fecal Multidimensional Assay for Non-Invasive Detection of Colorectal Cancer: Fecal Immunochemical Test, Stool DNA Mutation, Methylation, and Intestinal Bacteria Analysis.

BACKGROUND: Fecal immunochemical test (FIT), DNA mutation, DNA methylation, and microbial dysbiosis all showed promising in colorectal cancer (CRC) non-invasive detection. We assessed CRC detection with an assay combining all these strategies and investigated the effect of clinical features on the performance of this comprehensive test. METHODS: We performed a multidimensional analysis study using stool samples collected from 108 patients with CRC, 18 patients with colorectal adenoma, and 36 individuals with no evidence of colorectal disease. The multidimensional analysis of stool samples including FIT, stool DNA (sDNA) tests for three methylated genes (Septin9, NDRG4, BMP3) and three mutated genes (KRAS, BRAF, PI3KCA) using next generation sequencing as well as detection of stool bacteria level of Fusobacterium nucleatum and Parvimonas micra using qPCR method. We used a linear support vector classification model to analyze the data. RESULTS: The sensitivity of FIT alone was 69.4% for CRC and 11.1% for adenoma. Separately, the sensitivity of the detection of intestinal bacteria, DNA mutation, and DNA methylation for CRC was 58.3, 50.0, and 51.9%, respectively. The combination of FIT and sDNA tests had a sensitivity of 81.5% for CRC (AUC: 0.93, better than FIT alone, P = 0.017) and 27.8% for adenoma with 94.4% specificity. Sensitivity of the multidimensional test to detect CRC with stage II (84.6%) and III (91.9%) CRC was relatively higher (88.2%) than that of patients with stage I (60.0%) and stage IV (75.0%) (P = 0.024). The rate of CRC detection increased with tumor size (P = 0.008) and age (P = 0.04). Interestingly, the rate of CRC detection was higher in smoking persons than non-smokers with marginal significance (P = 0.08). CONCLUSIONS: The multidimensional assay of stool samples combining FIT and stool DNA tests further improved the diagnostic sensitivity for CRC. This could provide new approach for improvement of CRC screening and further demonstrations are warranted.

DNA Methylation Haplotype Block Markers Efficiently Discriminate Follicular Thyroid Carcinoma from Follicular Adenoma.

CONTEXT: Follicular thyroid carcinoma (FTC) is the second most common type of thyroid carcinoma and must be pathologically distinguished from benign follicular adenoma (FA). Additionally, the clinical assessment of thyroid tumors with uncertain malignant potential (TT-UMP) demands effective indicators. OBJECTIVE: We aimed to identify discriminating DNA methylation markers between FA and FTC. METHODS: DNA methylation patterns were investigated in 33 FTC and 33 FA samples using reduced representation bisulfite sequencing and methylation haplotype block-based analysis. A prediction model was constructed and validated in an independent cohort of 13 FTC and 13 FA samples. Moreover, 36 TT-UMP samples were assessed using this model. RESULTS: A total of 70 DNA methylation markers, approximately half of which were located within promoters, were identified to be significantly different between the FTC and FA samples. All the Gene Ontology terms enriched among the marker-associated genes were related to "DNA binding," implying that the inactivation of DNA binding played a role in FTC development. A random forest model with an area under the curve of 0.994 was constructed using those markers for discriminating FTC from FA in the validation cohort. When the TT-UMP samples were scored using this model, those with fewer driver mutations also exhibited lower scores. CONCLUSION: An FTC-predicting model was constructed using DNA methylation markers, which distinguished between FA and FTC tissues with a high degree of accuracy. This model can also be used to help determine the potential of malignancy in TT-UMP.

Comprehensive analysis reveals distinct mutational signature and its mechanistic insights of alcohol consumption in human cancers.

Alcohol consumption is a critical risk factor for multiple types of cancer. A genome can be attacked and acquire numerous somatic mutations in the environment of alcohol exposure. Mutational signature has the capacity illustrating the complex somatic mutation patterns in cancer genome. Recent studies have discovered distinct mutational signatures associating with alcohol consumption in liver and esophageal cancers. However, their prevalence among diverse cancers, impact of genetic background and origin of alcohol-induced mutational signatures remain unclear. By a comprehensive bioinformatics analysis on somatic mutations from patients of four cancer types with drinking information, we identified nine mutational signatures (signatures B-J), among which signature J (similar to COSMIC signature 16) was distinctive to alcohol drinking. Signature J was associated with HNSC, ESCA and LIHC but not PAAD. Interestingly, patients with mutated allele rs1229984 in ADH1B had lower level of signature J while mutated allele rs671 in ALDH2 exhibited higher signature J abundance, suggesting acetaldehyde is one cause of signature J. Intriguingly, somatic mutations of three potential cancer driver genes (TP53, CUL3 and NSD1) were found the critical contributors for increased mutational load of signature J in alcohol consumption patients. Furthermore, signature J was enriched with early accumulated clonal mutations compared to mutations derived from late tumor growth. This study systematically characterized alcohol-related mutational signature and indicated mechanistic insights into the prevalence, origin and gene-environment interaction regarding the risk oncogenic mutations associated with alcohol intake.

DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity.

Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan.

Integrative Analysis of Somatic Mutations in Non-coding Regions Altering RNA Secondary Structures in Cancer Genomes.

RNA secondary structure may influence many cellular processes, including RNA processing, stability, localization, and translation. Single-nucleotide variations (SNVs) that alter RNA secondary structure, referred to as riboSNitches, are potentially causative of human diseases, especially in untranslated regions (UTRs) and noncoding RNAs (ncRNAs). The functions of somatic mutations that act as riboSNitches in cancer development remain poorly understood. In this study, we developed a computational pipeline called SNIPER (riboSNitch-enriched or depleted elements in cancer genomes), which employs MeanDiff and EucDiff to detect riboSNitches and then identifies riboSNitch-enriched or riboSNitch-depleted non-coding elements across tumors. SNIPER is available at github: https://github.com/suzhixi/SNIPER/ . We found that riboSNitches were more likely to be pathogenic. Moreover, we predicted several UTRs and lncRNAs (long non-coding RNA) that significantly enriched or depleted riboSNitches in cancer genomes, indicative of potential cancer driver or essential noncoding elements. Our study highlights the possibly neglected importance of RNA secondary structure in cancer genomes and provides a new strategy to identify new cancer-associated genes.

Olfactomedin domain-containing proteins: evolution, functional divergence, expression patterns and damaging SNPs.

Olfactomedin domain-containing proteins appear to facilitate neurodevelopment, cell adhesion, intercellular interactions, and protein-protein interactions, and the disruption of their expression will lead to dramatic developmental perturbations and lethality. The aim of the present work was to study how these genes evolved in metazoans and diverged after their duplication as well as to characterize their expression profiles and detrimental mutations. We conducted an exhaustive survey of olfactomedin domain-containing genes in genomic databases, identifying 235 olfactomedin-like (OLF) proteins in 29 representative species covering all the main metazoan lineages. Phylogenetic analyses allowed us to define nine different subfamilies of OLF genes, and subfamily IX, which specifically includes two immunoglobulin domains, was identified for the first time in arthropods. Functional divergence analysis suggested that the function of this arthropod-specific OLF subfamily might have diverged from that of other subfamilies. Expression pattern analysis of OLF genes in humans and rats showed that human OLF genes tended to be highly expressed in the brain, while rat OLF genes were inclined to be expressed in the ovary and brain. We used the SIFT and PolyPhen servers in dbNSFP to distinguish deleterious mutations from neutral mutations for each member of the OLF gene family. The results showed that OLFML2B contains the most destructive SNPs (up to 61), while none of the mutations in OLFM2, OLFM4 and LPHN2 were predicted to be harmful. Taken together, these findings may not only enhance understanding of the phylogenetic relationships of the OLF family but also aid future studies on OLF protein regulation of nervous system development and immune function.