MSX1+PDGFRAlow limb mesenchyme-like cells as an efficient stem cell source for human cartilage regeneration.

Degenerative bone disorders have a significant impact on global health, and regeneration of articular cartilage remains a challenge. Existing cell therapies using mesenchymal stromal cells (MSCs) have shown limited efficacy, highlighting the necessity for alternative stem cell sources. Here, we have identified and characterized MSX1+ mesenchymal progenitor cells in the developing limb bud with remarkable osteochondral-regenerative and microenvironment-adaptive capabilities. Single-cell sequencing further revealed the presence of two major cell compositions within the MSX1+ cells, where a distinct PDGFRAlow subset retained the strongest osteochondral competency and could efficiently regenerate articular cartilage in vivo. Furthermore, a strategy was developed to generate MSX1+PDGFRAlow limb mesenchyme-like (LML) cells from human pluripotent stem cells that closely resembled their mouse counterparts, which were bipotential in vitro and could directly regenerate damaged cartilage in a mouse injury model. Together, our results indicated that MSX1+PDGFRAlow LML cells might be a prominent stem cell source for human cartilage regeneration.

TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations.

The most widely practiced strategy for constructing the deep learning (DL) prediction model for drug resistance of Mycobacterium tuberculosis (MTB) involves the adoption of ready-made and state-of-the-art architectures usually proposed for non-biological problems. However, the ultimate goal is to construct a customized model for predicting the drug resistance of MTB and eventually for the biological phenotypes based on genotypes. Here, we constructed a DL training framework to standardize and modularize each step during the training process using the latest tensorflow 2 API. A systematic and comprehensive evaluation of each module in the three currently representative models, including Convolutional Neural Network, Denoising Autoencoder, and Wide & Deep, which were adopted by CNNGWP, DeepAMR, and WDNN, respectively, was performed in this framework regarding module contributions in order to assemble a novel model with proper dedicated modules. Based on the whole-genome level mutations, a de novo learning method was developed to overcome the intrinsic limitations of previous models that rely on known drug resistance-associated loci. A customized DL model with the multilayer perceptron architecture was constructed and achieved a competitive performance (the mean sensitivity and specificity were 0.90 and 0.87, respectively) compared to previous ones. The new model developed was applied in an end-to-end user-friendly graphical tool named TB-DROP (TuBerculosis Drug Resistance Optimal Prediction: https://github.com/nottwy/TB-DROP ), in which users only provide sequencing data and TB-DROP will complete analysis within several minutes for one sample. Our study contributes to both a new strategy of model construction and clinical application of deep learning-based drug-resistance prediction methods.

SingleScan: a comprehensive resource for single-cell sequencing data processing and mining.

Single-cell sequencing has shed light on previously inaccessible biological questions from different fields of research, including organism development, immune function, and disease progression. The number of single-cell-based studies increased dramatically over the past decade. Several new methods and tools have been continuously developed, making it extremely tricky to navigate this research landscape and develop an up-to-date workflow to analyze single-cell sequencing data, particularly for researchers seeking to enter this field without computational experience. Moreover, choosing appropriate tools and optimal parameters to meet the demands of researchers represents a major challenge in processing single-cell sequencing data. However, a specific resource for easy access to detailed information on single-cell sequencing methods and data processing pipelines is still lacking. In the present study, an online resource called SingleScan was developed to curate all up-to-date single-cell transcriptome/genome analyzing tools and pipelines. All the available tools were categorized according to their main tasks, and several typical workflows for single-cell data analysis were summarized. In addition, spatial transcriptomics, which is a breakthrough molecular analysis method that enables researchers to measure all gene activity in tissue samples and map the site of activity, was included along with a portion of single-cell and spatial analysis solutions. For each processing step, the available tools and specific parameters used in published articles are provided and how these parameters affect the results is shown in the resource. All information used in the resource was manually extracted from related literature. An interactive website was designed for data retrieval, visualization, and download. By analyzing the included tools and literature, users can gain insights into the trends of single-cell studies and easily grasp the specific usage of a specific tool. SingleScan will facilitate the analysis of single-cell sequencing data and promote the development of new tools to meet the growing and diverse needs of the research community. The SingleScan database is publicly accessible via the website at http://cailab.labshare.cn/SingleScan .

Machine learning identified MDK score has prognostic value for idiopathic pulmonary fibrosis based on integrated bulk and single cell expression data.

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease that poses a significant challenge to medical professionals due to its increasing incidence and prevalence coupled with the limited understanding of its underlying molecular mechanisms. In this study, we employed a novel approach by integrating five expression datasets from bulk tissue with single-cell datasets; they underwent pseudotime trajectory analysis, switch gene selection, and cell communication analysis. Utilizing the prognostic information derived from the GSE47460 dataset, we identified 22 differentially expressed switch genes that were correlated with clinical indicators as important genes. Among these genes, we found that the midkine (MDK) gene has the potential to serve as a marker of Idiopathic pulmonary fibrosis because its cellular communicating genes are differentially expressed in the epithelial cells. We then utilized midkine and its cellular communication-related genes to calculate the midkine score. Machine learning models were further constructed through midkine and related genes to predict Idiopathic pulmonary fibrosis disease through the bulk gene expression datasets. The midkine score demonstrated a correlation with clinical indexes, and the machine learning model achieved an AUC of 0.94 and 0.86 in the Idiopathic pulmonary fibrosis classification task based on lung tissue samples and peripheral blood mononuclear cell samples, respectively. Our findings offer valuable insights into the pathogenesis of Idiopathic pulmonary fibrosis, providing new therapeutic directions and target genes for further investigation.

ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data.

BACKGROUND: High oncogene expression in cancer cells is a major cause of rapid tumor progression and drug resistance. Recent cancer genome research has shown that oncogenes as well as regulatory elements can be amplified in the form of extrachromosomal DNA (ecDNA) or subsequently integrated into chromosomes as homogeneously staining regions (HSRs). These genome-level variants lead to the overexpression of the corresponding oncogenes, resulting in poor prognosis. Most existing detection methods identify ecDNA using whole genome sequencing (WGS) data. However, these techniques usually detect many false positive regions owing to chromosomal DNA interference. RESULTS: In the present study, an algorithm called "ATACAmp" that can identify ecDNA/HSRs in tumor genomes using ATAC-seq data has been described. High chromatin accessibility, one of the characteristics of ecDNA, makes ATAC-seq naturally enriched in ecDNA and reduces chromosomal DNA interference. The algorithm was validated using ATAC-seq data from cell lines that have been experimentally determined to contain ecDNA regions. ATACAmp accurately identified the majority of validated ecDNA regions. AmpliconArchitect, the widely used ecDNA detecting tool, was used to detect ecDNA regions based on the WGS data of the same cell lines. Additionally, the Circle-finder software, another tool that utilizes ATAC-seq data, was assessed. The results showed that ATACAmp exhibited higher accuracy than AmpliconArchitect and Circle-finder. Moreover, ATACAmp supported the analysis of single-cell ATAC-seq data, which linked ecDNA to specific cells. CONCLUSIONS: ATACAmp, written in Python, is freely available on GitHub under the MIT license: https://github.com/chsmiss/ATAC-amp . Using ATAC-seq data, ATACAmp offers a novel analytical approach that is distinct from the conventional use of WGS data. Thus, this method has the potential to reduce the cost and technical complexity associated ecDNA analysis.

Chromothripsis is correlated with reduced cytotoxic immune infiltration and diminished responsiveness to checkpoint blockade immunotherapy.

Background: Chromothripsis caused massive, clustered genomic rearrangements is prevalent in cancer and is considered a new paradigm for tumorigenesis and progression. In this study, we investigated the association among chromothripsis, anti-tumor immune responses, and responsiveness to immune checkpoint blockade (ICB). Methods: Quantification of immune cell infiltration and functional enrichment of immune-related signaling pathways were performed in the discovery set (n = 9403) and the validation set (n = 1140). we investigated the association between chromothripsis and anti-tumor immune responses. In the immunotherapy cohort, copy number alteration-based chromothripsis scores (CPSs) were introduced to assess the extent of chromothripsis to evaluate its association with responsiveness to ICB. Results: In the discovery set and the validation set, the ratios of CD8+ T cells to Tregs, TAMs, and MDSCs were significantly lower in tumors with chromothripsis (P = 1.5 × 10-13, P = 5.4 × 10-8, and P = 1.2 × 10-4, respectively, TCGA; P = 1.0 × 10-13, P = 3.6 × 10-15, and P = 3.3 × 10-3, respectively, PCAWG). The relevant pathways underlying the antitumor immune effect were significantly enriched in tumors without chromothripsis. Chromothripsis can be used as an independent predictor, and patients with low-CPSs experienced longer overall survival (OS) after immunotherapy [HR, 1.90; 95% confidence interval, 1.10-3.28; P = 0.019]. Conclusions: Our findings highlight the reduced cytotoxic immune infiltration in tumors with chromothripsis and enhanced immunosuppression in the tumor microenvironment. Chromothripsis can thus be used as a potential indicator to help identify patients who will respond to ICB, which could complement established biomarkers.

ESCCdb: A comprehensive database and key regulator exploring platform based on cross dataset comparisons for esophageal squamous cell carcinoma.

Esophageal cancer is the seventh most prevalent and the sixth most lethal cancer. Esophageal squamous cell carcinoma (ESCC) is one of the major esophageal cancer subtypes that accounts for 87 % of the total cases. However, its molecular mechanism remains unclear. Here, we present an integrated database for ESCC called ESCCdb, which includes a total of 56 datasets and published studies from the GEO, Xena or SRA databases and related publications. It helps users to explore a particular gene with multiple graphical and interactive views with one click. The results comprise expression changes across 20 datasets, copy number alterations in 11 datasets, somatic mutations from 12 papers, related drugs derived from DGIdb, related pathways, and gene correlations. ESCCdb enables directly cross-dataset comparison of a gene's mutations, expressions and copy number changes in multiple datasets. This allows users to easily assess the alterations in ESCC. Furthermore, survival analysis, drug-gene relationships, and results from whole-genome CRISPR/Cas9 screening can help users determine the clinical relevance, derive functional inferences, and identify potential drugs. Notably, ESCCdb also enables the exploration of the correlation structure and identification of potential key regulators for a process. Finally, we identified 789 consistently differential expressed genes; we summarized recurrently mutated genes and genes affected by significant copy number alterations. These genes may be stable biomarkers or important players during ESCC development. ESCCdb fills the gap between massive omics data and users' needs for integrated analysis and can promote basic and clinical ESCC research. The database is freely accessible at http://cailab.labshare.cn/ESCCdb.

Multi-region sequencing depicts intratumor heterogeneity and clonal evolution in cervical cancer.

Cervical cancer is a heterogeneous malignancy mainly caused by human papillomavirus (HPV). While a few studies have revealed heterogeneity of cervical cancer in chromosome levels, the correlation between genetic heterogeneity and HPV integration in cervical cancer remains unknown. Here, we applied multi-region whole-exome sequencing and HPV integration analysis to explore intratumor heterogeneity in cervical cancer. We sequenced 20 tumor regions and 5 adjacent normal tissues from 5 cervical cancer patients, analysis based on somatic mutations and somatic copy number alterations (SCNAs) levels were performed. Variable heterogeneity was observed between the five patients with different tumor stages and HPV infection statuses. We found HPV integration has a positive effect on somatic mutation burden, but the relation to SCNAs remains unclear. Frequently mutated genes in cervical cancer were identified as trunk events, such as FBXW7, PIK3CA, FAT1 in somatic mutations and TP63, MECOM, PIK3CA, TBL1XR1 in SCNAs. New potential driver genes in cervical cancer were summarized including POU2F2, TCF7 and UBE2A. The SCNAs level has potential relation with tumor stage, and Signature 3 related to homologous recombination deficiency may be the appropriate biomarker in advanced cervical cancer. Mutation signature analysis also revealed a potential pattern that APOBEC-associated signature occurs in early stage and signatures associated with DNA damage repair arise at the later stage of cervical cancer evolution. In a conclusion, our study provides insights into the potential relationship between HPV infection and tumor heterogeneity. Those results enhanced our understanding of tumorigenesis and progression in cervical cancer.

MALAT1 modulates alternative splicing by cooperating with the splicing factors PTBP1 and PSF.

Understanding how long noncoding RNAs (lncRNAs) cooperate with splicing factors (SFs) in alternative splicing (AS) control is fundamental to human biology and disease. We show that metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a well-documented AS-implicated lncRNA, regulates AS via two SFs, polypyrimidine tract-binding protein 1 (PTBP1) and PTB-associated SF (PSF). MALAT1 stabilizes the interaction between PTBP1 and PSF, thereby forming a functional module that affects a network of AS events. The MALAT1-stabilized PTBP1/PSF interaction occurs in multiple cellular contexts; however, the functional module, relative to MALAT1 only, has more dominant pathological significance in hepatocellular carcinoma. MALAT1 also stabilizes the PSF interaction with several heterogeneous nuclear ribonucleoparticle proteins other than PTBP1, hinting a broad role in AS control. We present a model in which MALAT1 cooperates with distinct SFs for AS regulation and pose that, relative to analyses exclusively performed for lncRNAs, a comprehensive consideration of lncRNAs and their binding partners may provide more information about their biological functions.

Dental niche cells directly contribute to tooth reconstitution and morphogenesis.

Mammalian teeth develop from the inductive epithelial-mesenchymal interaction, an important mechanism shared by many organs. The cellular basis for such interaction remains elusive. Here, we generate a dual-fluorescence model to track and analyze dental cells from embryonic to postnatal stages, in which Pitx2+ epithelium and Msx1+ mesenchyme are sufficient for tooth reconstitution. Single-cell RNA sequencing and spatial mapping further revealed critical cellular dynamics during molar development, where tooth germs are organized by Msx1+Sdc1+ dental papilla and surrounding dental niche. Surprisingly, niche cells are more efficient in tooth reconstitution and can directly regenerate papilla cells through interaction with dental epithelium. Finally, from the dental niche, we identify a group of previously unappreciated migratory Msx1+ Sox9+ cells as the potential cell origin for dental papilla. Our results indicate that the dental niche cells directly contribute to tooth organogenesis and provide critical insights into the essential cell composition for tooth engineering.

Heterozygotic Brca1 mutation initiates mouse genome instability at embryonic stage.

BRCA1 mutation is the genetic predisposition in causing genome instability towards cancer. BRCA1 mutation is predominantly germline inherited at the fertilization. However, when the inherited mutation initiates genome instability in the mutation carriers remains largely elusive. We used a heterozygotic Brca1-knockout mouse as a model to investigate the issue. Through whole-genome sequencing and bioinformatics analysis, we monitored genome status across the developmental stages from embryo to adulthood in the mouse model. We observed that genome instability as reflected by structural variation, indel and copy number variation already appeared at 10.5-day embryo and progressively towards adulthood. We also observed that the genome instability was not linearly accumulated but dynamically changed along the developmental process, affecting many oncogenic genes and pathways including DNA damage repair, estrogen signaling, and oncogenesis. We further observed that many genome abnormalities in the cancer caused by Brca1 mutation were originated at embryonic stage, and Trp53 (TP53) mutation was not essential for the Brca1 mutation-caused genome instability in the non-cancer cells. Our study revealed that heterozygotic Brca1 mutation alone can cause genome instability at embryonic stage, highlighting that prevention of BRCA1 mutation-related cancer in humans may need to start earlier than currently considered.

PCR Primer Design for the Rapidly Evolving SARS-CoV-2 Genome.

Real-time quantitative PCR is currently the most widely used method for the human pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) identification. Due to the rapid evolution of the SARS-CoV-2 genome, novel mutations on the primer binding sites will cause the failure of PCR. Therefore, in addition to a well-designed primer set, these primers need to be updated and evaluated regularly to ensure that the rapidly evolving genome primers can be amplified. In this protocol, (1) we firstly use assembled genome sequences in the SARS-CoV-2 database to identify and characterize indels and point mutations; (2) design primers skipping the sites of mutations; (3) check the coverage of the primers with the daily update SARS-CoV-2 database; (4) redesign them if novel mutations found in the primer binding sites. Although this protocol takes SARS-CoV-2 as an example, it is suitable for other species that have genomes accumulating mutations over time.

Postmastectomy radiation therapy can improve survival for breast cancer patients with 1-3 positive axillary lymph nodes: a retrospective cohort study using the SEER database.

BACKGROUND: Postmastectomy radiation (PMRT) is an important adjuvant treatment for high-risk breast cancer. However, evidence concerning its efficacy in promoting survival of patients with 1-3 positive axillary lymph nodes remains insufficient. METHODS: We identified 57,793 patients, diagnosed from 2010-2015, from the Surveillance, Epidemiology, and End Results database, including 15,126 cases treated with beam radiation and 42,667 cases with none/unknown radiation. A Kaplan-Meier curve was utilized to compare survival of the two groups. We used univariate and multivariate Cox proportional hazard models to identify independent prognostic factors presented as hazard ratios (HRs) and 95% confidence intervals (CIs). For subgroup analysis, patients were stratified according to lymph node status, tumor size, and molecular subtypes. RESULTS: The PMRT group showed more aggressive clinicopathological features, including higher grades (P<0.001), larger tumor sizes (P<0.001), more lymph nodes (P<0.001), younger ages (P<0.001), more ER-negative cases (P<0.001), more PR-negative cases (P<0.001), and more HER2 overexpression (P<0.001). In addition, the PMRT group received more radical surgeries (P<0.001) and more chemotherapy (P<0.001). In the multivariate Cox proportional hazard regression analysis, the PMRT group exhibited improved survival in terms of breast cancer specific survival (BCSS) (HR, 0.74; 95% CI, 0.68-0.81; P<0.001) and overall survival (OS) (HR, 0.72; 95% CI, 0.67-0.78; P<0.001). After stratification according to positive axillary lymph nodes, the PMRT group showed improved BCSS and OS in the LN 1 to 3 subgroup (HR, 0.74; 95% CI, 0.64-0.85; P<0.001 and HR, 0.68; 95% CI, 0.60-0.78; P<0.001, respectively). For patients with 1-3 positive axillary lymph nodes and T1-2 tumors, the PMRT group still showed improved BCSS and OS (HR, 0.823; 95% CI, 0.69-0.99; P=0.04 and HR, 0.75; 95% CI, 0.64-0.88; P<0.001, respectively). In the subgroup analysis, PMRT remained a significant favorable prognostic factor in T2 and HER2-/HR+ subtype (P<0.05). CONCLUSIONS: This study suggests that PMRT can confer a survival benefit to breast cancer patients with 1-3 positive axillary lymph nodes, even with modern treatment options. Furthermore, for patients with 1-3 positive axillary lymph nodes and T1-2 tumors, PMRT can still provide survival benefits.

The Landscape of Somatic Copy Number Alterations in Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide. Somatic copy number alterations (CNAs) play a significant role in the development of this lethal cancer. In this study, we present a meta-analysis of CNAs for a total of 1,395 HNSCC samples. Publicly available R packages and in-house scripts were used for genomic array data processing, including normalization, segmentation and CNA calling. We detected 125 regions of significant gains or losses using GISTIC algorithm and found several potential driver genes in these regions. The incidence of chromothripsis in HNSCC was estimated to be 6%, and the chromosome pulverization hotspot regions were detected. We determined 323 genomic locations significantly enriched for breakpoints, which indicate HNSCC-specific genomic instability regions. Unsupervised clustering of genome-wide CNA data revealed a sub-cluster predominantly composed of nasopharynx tumors and presented a large proportion of HPV-positive samples. These results will facilitate the discovery of therapeutic candidates and extend our molecular understanding of HNSCC.

Insight into unique somitogenesis of yak (Bos grunniens) with one additional thoracic vertebra.

BACKGROUND: The yak is a species of livestock which is crucial for local communities of the Qinghai-Tibet Plateau and adjacent regions and naturally owns one more thoracic vertebra than cattle. Recently, a sub-population of yak termed as the Jinchuan yak has been identified with over half its members own a thoracolumbar vertebral formula of T15L5 instead of the natural T14L5 arrangement. The novel T15L5 positioning is a preferred genetic trait leading to enhanced meat and milk production. Selective breeding of this trait would have great agricultural value and exploration of the molecular mechanisms underlying this trait would both accelerate this process and provide us insight into the development and regulation of somitogenesis. RESULTS: Here we investigated the genetic background of the Jinchuan yak through resequencing fifteen individuals, comprising five T15L5 individuals and ten T14L5 individuals with an average sequencing depth of > 10X, whose thoracolumbar vertebral formulae were confirmed by anatomical observation. Principal component analysis, linkage disequilibrium analysis, phylogenetic analysis, and selective sweep analysis were carried out to explore Jinchuan yak's genetic background. Three hundred and thirty candidate markers were identified as associated with the additional thoracic vertebrae and target sequencing was used to validate seven carefully selected markers in an additional 51 Jinchuan yaks. The accuracies of predicting 15 thoracic vertebrae and 20 thoracolumbar vertebrae with these 7 markers were 100.00 and 33.33% despite they both could only represent 20% of all possible genetic diversity. Two genes, PPP2R2B and TBLR1, were found to harbour the most candidate markers associated with the trait and likely contribute to the unique somitic number and identity according to their reported roles in the mechanism of somitogenesis. CONCLUSIONS: Our findings provide a clear depiction of the Jinchuan yak's genetic background and a solid foundation for marker-assistant selection. Further exploitation of this unique population and trait could be promoted with the aid of our genomic resource.

Genome-wide somatic copy number alteration analysis and database construction for cervical cancer.

Cervical cancer is a common gynecological malignancy with high incidence and mortality. Somatic copy number alterations (CNAs) play an important role in identifying tumor suppressor genes and oncogenes and are a useful diagnostic indicator for many cancer types. However, the genomic landscape of CNAs in cervical cancer has not yet been comprehensively characterized. In the present study, we collected 974 cervical cancer samples from different data sources. All samples were analyzed by genomic arrays to obtain high-resolution CNAs. Focal genomic regions with CNA events and potential cancer driver genes were identified by GISTIC2.0. Meanwhile, we constructed a comprehensive cervical cancer database by PHP and self-written Perl and R scripts. In total, 54 recurrent regions of amplification and deletion were detected. Frequently altered tumor suppressor genes were found in these regions, including PIK3CA, ERBB2, EP300 and FBXW7. CNA hotspots and related enriched functional categories were also identified. The incidence of chromothripsis in cervical cancer was estimated to be 6.06%, and the chromosome pulverization hotspot regions were detected. Based on the curated data, we developed CNAdbCC (http://cailab.labshare.cn/CNAdbCC/), a comprehensive database for copy number alterations in cervical cancer. We provide a user-friendly Web interface for data mining and visualization. It is the most comprehensive public database devoted exclusively to genomic alterations in cervical cancer. These results extend our molecular understanding of cervical cancer. The database will enable researchers to explore specific CNA patterns in this lethal cancer and facilitate the discovery of therapeutic candidates.

Comprehensive identification and characterization of somatic copy number alterations in triple­negative breast cancer.

Triple­negative breast cancer (TNBC) accounts for ~15% of all breast cancer diagnoses each year. Patients with TNBC tend to have a higher risk for early relapse and a worse prognosis. TNBC is characterized by extensive somatic copy number alterations (CNAs). However, the DNA CNA profile of TNBC remains to be extensively investigated. The present study assessed the genomic profile of CNAs in 201 TNBC samples, aiming to identify recurrent CNAs that may drive the pathogenesis of TNBC. In total, 123 regions of significant amplification and deletion were detected using the Genomic Identification of Significant Targets in Cancer algorithm, and potential driver genes for TNBC were identified. A total of 31 samples exhibited signs of chromothripsis and revealed chromosome pulverization hotspot regions. The present study further determined 199 genomic locations that were significantly enriched for breakpoints, which indicated TNBC­specific genomic instability regions. Unsupervised hierarchical clustering of tumors resulted in three main subgroups that exhibited distinct CNA profiles, which may reveal the heterogeneity of molecular mechanisms in TNBC subgroups. These results will extend the molecular understanding of TNBC and will facilitate the discovery of therapeutic and diagnostic target candidates.

CancerTracer: a curated database for intrapatient tumor heterogeneity.

Comprehensive genomic analyses of cancers have revealed substantial intrapatient molecular heterogeneities that may explain some instances of drug resistance and treatment failures. Examination of the clonal composition of an individual tumor and its evolution through disease progression and treatment may enable identification of precise therapeutic targets for drug design. Multi-region and single-cell sequencing are powerful tools that can be used to capture intratumor heterogeneity. Here, we present a database we've named CancerTracer (http://cailab.labshare.cn/cancertracer): a manually curated database designed to track and characterize the evolutionary trajectories of tumor growth in individual patients. We collected over 6000 tumor samples from 1548 patients corresponding to 45 different types of cancer. Patient-specific tumor phylogenetic trees were constructed based on somatic mutations or copy number alterations identified in multiple biopsies. Using the structured heterogeneity data, researchers can identify common driver events shared by all tumor regions, and the heterogeneous somatic events present in different regions of a tumor of interest. The database can also be used to investigate the phylogenetic relationships between primary and metastatic tumors. It is our hope that CancerTracer will significantly improve our understanding of the evolutionary histories of tumors, and may facilitate the identification of predictive biomarkers for personalized cancer therapies.

Effect of radiotherapy on the survival of cervical cancer patients: An analysis based on SEER database.

Cervical cancer is among the most frequent cancer types in women worldwide. Radiotherapy, including external beam radiation and brachytherapy, is one of the commonly used treatment options for cervical cancer. However, the adverse effects of radiation therapy on cervical cancer survival have been poorly investigated with inconclusive results. Therefore, the aim of this study was to determine the suitable radiotherapy modality according to patients' characteristics. A retrospective survival analysis of 44,602 patients was performed using the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate proportional hazard Cox model was used to evaluate the prognostic impact of different radiotherapy modalities, primary surgery, age, TNM stage, and tumor size. Our results indicated that patients without primary surgery, diagnosed at older age (≥45 years' old), at advanced TNM stages (III/IV) or with larger tumor size (≥3 cm) could benefit from radiotherapy. However, radiotherapy was detrimental in patients with primary surgery, diagnosed at younger age (<45 years' old), at earlier TNM stages (I/II) or with smaller tumor size (<3 cm). In addition, external beam radiation was in most cases less effective compared with combined external beam and brachytherapy. These results highlighted the necessity of realizing personalized radiotherapy treatments for patients with cervical cancer.

MFEprimer-3.0: quality control for PCR primers.

Quality control (QC) for lab-designed primers is crucial for the success of a polymerase chain reaction (PCR). Here, we present MFEprimer-3.0, a functional primer quality control program for checking non-specific amplicons, dimers, hairpins and other parameters. The new features of the current version include: (i) more sensitive binding site search using the updated k-mer algorithm that allows mismatches within the k-mer, except for the first base at the 3' end. The binding sites of each primer with a stable 3' end are listed in the output; (ii) new algorithms for rapidly identifying self-dimers, cross-dimers and hairpins; (iii) the command-line version, which has an added option of JSON output to enhance the versatility of MFEprimer by acting as a QC step in the 'primer design → quality control → redesign' pipeline; (iv) a function for checking whether the binding sites contain single nucleotide polymorphisms (SNPs), which will affect the consistency of binding efficiency among different samples. In summary, MFEprimer-3.0 is updated with the well-tested PCR primer QC program and it can be integrated into various PCR primer design applications as a QC module. The MFEprimer-3.0 server is freely accessible without any login requirement at: https://mfeprimer3.igenetech.com/ and https://www.mfeprimer.com/. The source code for the command-line version is available upon request.