Early detection and stratification of colorectal cancer using plasma cell-free DNA fragmentomic profiling.

Timely accurate and cost-efficient detection of colorectal cancer (CRC) is of great clinical importance. This study aims to establish prediction models for detecting CRC using plasma cell-free DNA (cfDNA) fragmentomic features. Whole-genome sequencing (WGS) was performed on cfDNA from 620 participants, including healthy individuals, patients with benign colorectal diseases and CRC patients. Using WGS data, three machine learning methods were compared to build prediction models for the stratification of CRC patients. The optimal model to discriminate CRC patients of all stages from healthy individuals achieved a sensitivity of 92.31% and a specificity of 91.14%, while the model to separate early-stage CRC patients (stage 0-II) from healthy individuals achieved a sensitivity of 88.8% and a specificity of 96.2%. Additionally, the cfDNA fragmentation profiles reflected disease-specific genomic alterations in CRC. Overall, this study suggests that cfDNA fragmentation profiles may potentially become a noninvasive approach for the detection and stratification of CRC.

Customized m-RCNN and hybrid deep classifier for liver cancer segmentation and classification.

Diagnosing liver disease presents a significant medical challenge in impoverished countries, with over 30 billion individuals succumbing to it each year. Existing models for detecting liver abnormalities suffer from lower accuracy and higher constraint metrics. As a result, there is a pressing need for improved, efficient, and effective liver disease detection methods. To address the limitations of current models, this method introduces a deep liver segmentation and classification system based on a Customized Mask-Region Convolutional Neural Network (cm-RCNN). The process begins with preprocessing the input liver image, which includes Adaptive Histogram Equalization (AHE). AHE helps dehaze the input image, remove color distortion, and apply linear transformations to obtain the preprocessed image. Next, a precise region of interest is segmented from the preprocessed image using a novel deep strategy called cm-RCNN. To enhance segmentation accuracy, the architecture incorporates the ReLU activation function and the modified sigmoid activation function. Subsequently, a variety of features are extracted from the segmented image, including ResNet features, shape features (area, perimeter, approximation, and convex hull), and enhanced median binary pattern. These extracted features are then used to train a hybrid classification model, which incorporates classifiers like SqueezeNet and DeepMaxout models. The final classification outcome is determined by averaging the scores obtained from both classifiers.

Optical navigation robot-assisted puncture system for accurate lung nodule biopsy: an animal study.

Background: As lung cancer is one of the most significant factors seriously endangering human health, a robot-assisted puncture system with high accuracy and safety is urgently needed. The purpose of this investigation was to compare the safety and effectiveness of such a robot-assisted system to the conventional computed tomography (CT)-guided manual method for percutaneous lung biopsies (PLBs) in pigs. Methods: An optical navigation robot-assisted puncture system was developed and compared to the traditional CT-guided PLB using simulated lesions in experimental animals. A total of 30 pulmonary nodules were successfully created in 5 pigs (Wuzhishan pig, 1 male and 4 females). Of these, 15 were punctured by the optical navigation robot-assisted puncture system (robotic group), and 15 were manually punctured under CT guidance (manual group). The biopsy success rate, operation time, first needle tip-target point deviation, and needle adjustment times were compared between groups. Postoperative CT scans were performed to identify complications. Results: The single puncture success rate was higher in the robotic group (13/15; 86.7%) than in the manual group (8/15; 53.3%). The first puncture was closer to the target lesion (1.8±1.7 mm), and the operation time was shorter (7.1±3.7 minutes) in the robotic group than in the manual group (4.4±2.8 mm and 12.9±7.6 minutes, respectively). The angle deviation was smaller in the robotic group (3.26°±2.48°) than in the manual group (7.71°±3.86°). The robotic group displayed significant advantages (P<0.05). The primary complication in both groups was slight bleeding, with an incidence of 26.7% in the robotic group and 40.0% in the manual group. There was 1 case of pneumothorax in the manual group, and there were no deaths due to complications in either group. Conclusions: An optical navigation robot-assisted system for PLBs guided by CT images was developed and demonstrated. The experimental results indicate that the proposed system is accurate, efficient, and safe in pigs.

A Robust Brain Tumor Detector Using BiLSTM and Mayfly Optimization and Multi-Level Thresholding.

A brain tumor refers to an abnormal growth of cells in the brain that can be either benign or malignant. Oncologists typically use various methods such as blood or visual tests to detect brain tumors, but these approaches can be time-consuming, require additional human effort, and may not be effective in detecting small tumors. This work proposes an effective approach to brain tumor detection that combines segmentation and feature fusion. Segmentation is performed using the mayfly optimization algorithm with multilevel Kapur's threshold technique to locate brain tumors in MRI scans. Key features are achieved from tumors employing Histogram of Oriented Gradients (HOG) and ResNet-V2, and a bidirectional long short-term memory (BiLSTM) network is used to classify tumors into three categories: pituitary, glioma, and meningioma. The suggested methodology is trained and tested on two datasets, Figshare and Harvard, achieving high accuracy, precision, recall, F1 score, and area under the curve (AUC). The results of a comparative analysis with existing DL and ML methods demonstrate that the proposed approach offers superior outcomes. This approach has the potential to improve brain tumor detection, particularly for small tumors, but further validation and testing are needed before clinical use.

Detection and monitoring of HBV-related hepatocellular carcinoma from plasma cfDNA fragmentation profiles.

Most hepatocellular carcinomas (HCCs) are associated with hepatitis B virus infection (HBV) in China. Early detection of HCC can significantly improve prognosis but is not yet fully clinically feasible. This study aims to develop methods for detecting HCC and studying the carcinogenesis of HBV using plasma cell-free DNA (cfDNA) whole-genome sequencing (WGS) data. Low coverage WGS was performed for 452 participants, including healthy individuals, hepatitis B patients, cirrhosis patients, and HCC patients. Then the sequencing data were processed using various machine learning models based on cfDNA fragmentation profiles for cancer detection. Our best model achieved a sensitivity of 87.10% and a specificity of 88.37%, and it showed an increased sensitivity with higher BCLC stages of HCC. Overall, this study proves the potential of a non-invasive assay based on cfDNA fragmentation profiles for the detection and prognosis of HCC and provides preliminary data on the carcinogenic mechanism of HBV.

Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review.

Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat to people's health. Therefore, diagnosing lung nodules at an early stage is crucial to improving patient survival rates. Numerous computer-aided diagnosis (CAD) systems have been developed to detect and classify such nodules in their early stages. Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification. A number of review articles have considered various components of such systems, but this review focuses on segmentation and classification parts. Specifically, categorizing segmentation parts based on lung nodule type and network architectures, i.e., general neural network and multiview convolution neural network (CNN) architecture. Moreover, this work organizes related literature for classification of parts based on nodule or non-nodule and benign or malignant. The essential CT lung datasets and evaluation metrics used in the detection and diagnosis of lung nodules have been systematically summarized as well. Thus, this review provides a baseline understanding of the topic for interested readers.

Single-Stage Pulmonary Resection via a Combination of Single Hookwire Localization and Video-Assisted Thoracoscopic Surgery for Synchronous Multiple Pulmonary Nodules.

Purpose: To retrospectively analyze the incidence and predictors of complications related to hookwire localization in patients with single and multiple nodules, and to evaluate the usefulness of a single-stage surgical method of single hookwire localization combined with video-assisted thoracoscopic surgery (VATS) in synchronous multiple pulmonary nodules (SMPNs). Methods: A total of 200 patients who underwent computed tomography (CT)-guided hookwire localization and subsequent VATS resection were enrolled in this study. For each patient, only 1 indeterminate nodule was implanted with a hookwire. There were 145 patients in the single-nodule group (Group S) and 55 in the multiple-nodule group (Group M). Univariate and binary logistic regression analyses were used to assess incidence and predictors of complications associated with hookwire localization. Results: The technical success rate of hookwire implantation was 97.5%. The incidence of pneumothorax and hookwire dislodgement was 17.0% and 2.5%, respectively. Binary logistic regression analysis showed that 1 transpleural puncture through the pleura (odds ratio [OR] = 0.433, P = .033) was the only independent protective factor for pneumothorax, and pneumothorax (OR = 26.114, P < .01) was the only independent risk factor for dislodgement. The volume of blood loss during VATS was significantly higher in group M than in group S, and the time of postoperative hospitalization was significantly longer in group M than in group S. About 44 patients in group M with additional 58 nodules without localization had undergone direct surgical resection simultaneously, and bilateral surgery was performed in 13 patients (29.5%). The intrathoracic recurrence rate was 4.8% during follow-up CT. Conclusion: Single-stage surgery via an approach of single hookwire localization combined with VATS is feasible and safe for SMPNs.

Mutational mechanisms shaping the coding and noncoding genome of germinal center derived B-cell lymphomas.

B cells have the unique property to somatically alter their immunoglobulin (IG) genes by V(D)J recombination, somatic hypermutation (SHM) and class-switch recombination (CSR). Aberrant targeting of these mechanisms is implicated in lymphomagenesis, but the mutational processes are poorly understood. By performing whole genome and transcriptome sequencing of 181 germinal center derived B-cell lymphomas (gcBCL) we identified distinct mutational signatures linked to SHM and CSR. We show that not only SHM, but presumably also CSR causes off-target mutations in non-IG genes. Kataegis clusters with high mutational density mainly affected early replicating regions and were enriched for SHM- and CSR-mediated off-target mutations. Moreover, they often co-occurred in loci physically interacting in the nucleus, suggesting that mutation hotspots promote increased mutation targeting of spatially co-localized loci (termed hypermutation by proxy). Only around 1% of somatic small variants were in protein coding sequences, but in about half of the driver genes, a contribution of B-cell specific mutational processes to their mutations was found. The B-cell-specific mutational processes contribute to both lymphoma initiation and intratumoral heterogeneity. Overall, we demonstrate that mutational processes involved in the development of gcBCL are more complex than previously appreciated, and that B cell-specific mutational processes contribute via diverse mechanisms to lymphomagenesis.

Development and Analytical Validation of a Targeted Next-Generation Sequencing Panel to Detect Actionable Mutations for Targeted Therapy.

BACKGROUND: The ability to rapidly, inexpensively, and accurately identify cancer patients based on actionable genomic mutations in tumour specimens is becoming critically important in routine clinical diagnostics. Targeted panel sequencing is becoming popular because it enables comprehensive and cost-effective diagnosis. However, the implementation of a next-generation sequencing (NGS) assay in clinical settings requires careful analytical validation to demonstrate its ability to detect multiple genomic variants. MATERIALS AND METHODS: Here, we developed a custom-targeted NGS panel to identify actionable variants, including single nucleotide variants, insertions, and deletions; copy number variants; and gene fusions, across 73 genes for targeted cancer therapy. We implemented a practical validation strategy with diluted samples and reference standard samples that modelled key determinants of accuracy, including mutant allele frequency, insertion/deletion length, amplitude of copy number, and hotspot gene fusions. RESULTS: The analytical validation results demonstrated that our panel can identify different types of genomic alterations in these test samples with high levels of accuracy, sensitivity, and reproducibility. CONCLUSION: Our panel could be deployed as a routine clinical test to comprehensively detect actionable variants in cancer patients to guide targeted therapy decisions.

Benchmarking HLA genotyping and clarifying HLA impact on survival in tumor immunotherapy.

Human leukocyte antigen (HLA) genotyping gains intensive attention due to its critical role in cancer immunotherapy. It is still a challenging issue to generate reliable HLA genotyping results through in silico tools. In addition, the survival impact of HLA alleles in tumor prognosis and immunotherapy remains controversial. In this study, the benchmarking of HLA genotyping on TCGA is performed and a 'Gun-Bullet' model which helps to clarify the survival impact of HLA allele is presented. The performance of HLA class I genotyping is generally better than class II. POLYSOLVER, OptiType, and xHLA perform generally better at HLA class I calling with an accuracy of 0.954, 0.949, and 0.937, respectively. HLA-HD obtained the highest accuracy of 0.904 on HLA class II alleles calling. Each HLA genotyping tool displayed specific error patterns. The ensemble HLA calling from the top-3 tools is superior to any individual one. HLA alleles show distinct survival impact among cancers. Cytolytic activity (CYT) was proposed as the underlying mechanism to interpret the survival impact of HLA alleles in the 'Gun-Bullet' model for fighting cancer. A strong HLA allele plus a high tumor mutation burden (TMB) could stimulate intensive immune CYT, leading to extended survival. We established an up to now most reliable TCGA HLA benchmark dataset, composing of concordance alleles generated from eight prevalently used HLA genotyping tools. Our findings indicate that reliable HLA genotyping should be performed based on concordance alleles integrating multiple tools and incorporating TMB background with HLA genotype, which helps to improve the survival prediction compared to HLA genotyping alone.

SinoDuplex: An Improved Duplex Sequencing Approach to Detect Low-frequency Variants in Plasma cfDNA Samples.

Accurate detection of low frequency mutations from plasma cell-free DNA in blood using targeted next generation sequencing technology has shown promising benefits in clinical settings. Duplex sequencing technology is the most commonly used approach in liquid biopsies. Unique molecular identifiers are attached to each double-stranded DNA template, followed by production of low-error consensus sequences to detect low frequency variants. However, high sequencing costs have hindered application of this approach in clinical practice. Here, we have developed an improved duplex sequencing approach called SinoDuplex, which utilizes a pool of adapters containing pre-defined barcode sequences to generate far fewer barcode combinations than with random sequences, and implemented a novel computational analysis algorithm to generate duplex consensus sequences more precisely. SinoDuplex increased the output of duplex sequencing technology, making it more cost-effective. We evaluated our approach using reference standard samples and cell-free DNA samples from lung cancer patients. Our results showed that SinoDuplex has high sensitivity and specificity in detecting very low allele frequency mutations. The source code for SinoDuplex is freely available at https://github.com/SinOncology/sinoduplex.

Epigenetic alterations are associated with tumor mutation burden in non-small cell lung cancer.

BACKGROUND: To profile genomic and epigenomic of a naïve Chinese non-small cell lung cancer (NSCLC) cohort and investigate the association between tumor mutation burden (TMB) and DNA methylation (DNAm) to explore potential alternative/complimentary biomarkers for NSCLC immunotherapies. METHODS: A total of 89 tumor tissues with matched normal tissues from Chinese NSCLC patients were collected and subjected to whole exome sequencing (WES). From comparison, each patient was evaluated for the TMB value and divided into high, medium and low TMB based on TMB tertile distribution and then relatively high and low TMB samples were selected and subjected to DNAm profiling. RESULTS: Patients in the low (n = 30), medium (n = 29), and high (n = 30) TMB tertiles had 1.1-2.5, 2.5-4.1, and 4.2-13.9 mutations/Mb, respectively. A statistical directly association between differential methylation probes (DMPs) and TMB level was observed in our cohort (r = 0.63, P value =0.0003) and this was confirmed by using TCGA NSCLC dataset (r = 0.43, P value =0.006). Relatively high TMB group (n = 16, 7.5-13.9 mutations/Mb) harbors more differential DMPs while less in relatively low TMB group (n = 13, 1.1-2.4 mutations/Mb). Eight hundred fifty-eight differential methylation regions (DMRs) were found in relatively high TMB group. In addition, 437 genes show DNAm aberrance status in high TMB patient group and 99 have been reported as its association with lung cancer. CONCLUSION: To our knowledge, this is the first report for direct link between the methylome alterations and TMB in NSCLCs. High TMB NSCLCs had more DNAm aberrance and copy number variations (CNVs). In addition, the TMB distribution of Chinese NSCLCs population is lower than that of TCGA.

Assessment of tumor mutation burden calculation from gene panel sequencing data.

Background: High tumor mutation burden (TMB) is an emerging selection biomarker for immune checkpoint blockade in tumors such as melanoma and non-small cell lung cancer. TMB is typically calculated from whole genome sequencing or whole exome sequencing (WES) data. Recently, clinical trials showed that TMB can also be estimated from targeted sequencing of a panel of only a few hundred genes of interest, which can be performed at a high depth for clinical applications.  Materials and methods: In this study, we systematically investigated the distribution of TMB and preferences at the gene and mutation level, as well as the correlation between TMB calculated by WES and panel sequencing data using somatic mutation data from 15 cancer types from The Cancer Genome Atlas (TCGA).  Results: We proposed a pan-cancer TMB panel and demonstrated that it had a higher correlation with WES than other panels. Our panel could serve as a reference data-set for TMB-oriented panel design to identify patients for immunotherapy.

BreakID: genomics breakpoints identification to detect gene fusion events using discordant pairs and split reads.

SUMMARY: Here we developed a tool called Breakpoint Identification (BreakID) to identity fusion events from targeted sequencing data. Taking discordant read pairs and split reads as supporting evidences, BreakID can identify gene fusion breakpoints at single nucleotide resolution. After validation with confirmed fusion events in cancer cell lines, we have proved that BreakID can achieve high sensitivity of 90.63% along with PPV of 100% at sequencing depth of 500× and perform better than other available fusion detection tools. We anticipate that BreakID will have an extensive popularity in the detection and analysis of fusions involved in clinical and research sequencing scenarios. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://github.com/SinOncology/BreakID. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SIRT7-dependent deacetylation of the U3-55k protein controls pre-rRNA processing.

SIRT7 is an NAD(+)-dependent protein deacetylase with important roles in ribosome biogenesis and cell proliferation. Previous studies have established that SIRT7 is associated with RNA polymerase I, interacts with pre-ribosomal RNA (rRNA) and promotes rRNA synthesis. Here we show that SIRT7 is also associated with small nucleolar RNP (snoRNPs) that are involved in pre-rRNA processing and rRNA maturation. Knockdown of SIRT7 impairs U3 snoRNA dependent early cleavage steps that are necessary for generation of 18S rRNA. Mechanistically, SIRT7 deacetylates U3-55k, a core component of the U3 snoRNP complex, and reversible acetylation of U3-55k modulates the association of U3-55k with U3 snoRNA. Deacetylation by SIRT7 enhances U3-55k binding to U3 snoRNA, which is a prerequisite for pre-rRNA processing. Under stress conditions, SIRT7 is released from nucleoli, leading to hyperacetylation of U3-55k and attenuation of pre-rRNA processing. The results reveal a multifaceted role of SIRT7 in ribosome biogenesis, regulating both transcription and processing of rRNA.

DNA methylome analysis in Burkitt and follicular lymphomas identifies differentially methylated regions linked to somatic mutation and transcriptional control.

Although Burkitt lymphomas and follicular lymphomas both have features of germinal center B cells, they are biologically and clinically quite distinct. Here we performed whole-genome bisulfite, genome and transcriptome sequencing in 13 IG-MYC translocation-positive Burkitt lymphoma, nine BCL2 translocation-positive follicular lymphoma and four normal germinal center B cell samples. Comparison of Burkitt and follicular lymphoma samples showed differential methylation of intragenic regions that strongly correlated with expression of associated genes, for example, genes active in germinal center dark-zone and light-zone B cells. Integrative pathway analyses of regions differentially methylated in Burkitt and follicular lymphomas implicated DNA methylation as cooperating with somatic mutation of sphingosine phosphate signaling, as well as the TCF3-ID3 and SWI/SNF complexes, in a large fraction of Burkitt lymphomas. Taken together, our results demonstrate a tight connection between somatic mutation, DNA methylation and transcriptional control in key B cell pathways deregulated differentially in Burkitt lymphoma and other germinal center B cell lymphomas.

Deep transcriptome profiling of ovarian cancer cells using next-generation sequencing approach.

The next-generation sequencing technology allows identification and cataloging of almost all mRNAs, even those with only one or a few transcripts per cell. To understand the chemotherapy response program in ovarian cancer cells at deep transcript sequencing levels, we applied two next-generation sequencing technologies to study two ovarian chemotherapy response models: the in vitro acquired cisplatin-resistant cell line model (IGROV-1-CP and IGROV1) and the in vivo ovarian cancer tissue resistant model. We identified 3,422 signatures (2,957 genes) that are significantly differentially expressed between IGROV1 and IGROV-1-CP cells (P < .001). Our database offers the first comprehensive view of the digital transcriptomes of ovarian cancer cell lines and tissues with different chemotherapy response phenotypes.

SOX2 targets fibronectin 1 to promote cell migration and invasion in ovarian cancer: new molecular leads for therapeutic intervention.

Abstract Ovarian cancer ranks as the second most common tumor of the female reproductive system, with a large burden on global public health. Therefore, the identification of novel molecular targets and diagnostics is an urgent need for many women affected by this disease. To this end, the human transcription factor SOX2 is involved in a wide range of pathophysiological roles, such as the maintenance of stem cell characteristics and carcinogenesis. To date, in most studies, SOX2 has been shown to promote the development of cancer, although its inhibitory roles in cancer have also been reported. However, to the best of our knowledge, the role of SOX2, specifically in ovarian cancer cells, has not been examined in detail. In this article, we report, for the first time, that SOX2 promotes migration, invasion, and clonal formation of ovarian cancer cells. We further observed that SOX2 targeted FN1, a key gene that regulates cell migration in ovarian cancer. Our findings collectively suggest that the SOX2-FN1 axis is a key pathway in mediating the migration and invasion of ovarian cancer cells. This pathway offers crucial molecular insights and promises to develop putative candidate therapeutic interventions in women with ovarian cancer.

Alternative polyadenylation in glioblastoma multiforme and changes in predicted RNA binding protein profiles.

Alternative polyadenylation (APA) is widely present in the human genome and plays a key role in carcinogenesis. We conducted a comprehensive analysis of the APA products in glioblastoma multiforme (GBM, one of the most lethal brain tumors) and normal brain tissues and further developed a computational pipeline, RNAelements (http://sysbio.zju.edu.cn/RNAelements/), using covariance model from known RNA binding protein (RBP) targets acquired by RNA Immunoprecipitation (RIP) analysis. We identified 4530 APA isoforms for 2733 genes in GBM, and found that 182 APA isoforms from 148 genes showed significant differential expression between normal and GBM brain tissues. We then focused on three genes with long and short APA isoforms that show inconsistent expression changes between normal and GBM brain tissues. These were myocyte enhancer factor 2D, heat shock factor binding protein 1, and polyhomeotic homolog 1 (Drosophila). Using the RNAelements program, we found that RBP binding sites were enriched in the alternative regions between the first and the last polyadenylation sites, which would result in the short APA forms escaping regulation from those RNA binding proteins. To the best of our knowledge, this report is the first comprehensive APA isoform dataset for GBM and normal brain tissues. Additionally, we demonstrated a putative novel APA-mediated mechanism for controlling RNA stability and translation for APA isoforms. These observations collectively lay a foundation for novel diagnostics and molecular mechanisms that can inform future therapeutic interventions for GBM.