UNASSIGNED: According to GLOBOCAN 2020, lymphoma ranked as the 9th most common cancer and the 12th leading cause of cancer-related deaths worldwide. Traditional diagnostic methods rely on the invasive excisional lymph node biopsy, which is an invasive approach with some limitations. Most lymphoma patients are diagnosed at an advanced stage since they are asymptomatic at the beginning, which has significantly impacted treatment efficacy and prognosis of the disease.
UNASSIGNED: This study assessed the performance and utility of a newly developed blood-based assay (SeekInCare) for lymphoma early detection. SeekInCare utilized protein tumor markers and a comprehensive set of cancer-associated genomic features, including copy number aberration (CNA), fragment size (FS), end motif, and lymphoma-related virus, which were profiled by shallow WGS of cfDNA.
UNASSIGNED: Protein marker CA125 could be used for lymphoma detection independent of gender, and the sensitivity was 27.8% at specificity of 98.0%. After integrating these multi-dimensional features, 77.8% sensitivity was achieved at specificity of 98.0%, while its NPV and PPV were both more than 92% for lymphoma detection. The sensitivity of early-stage (I-II) lymphoma was up to 51.3% (47.4% and 55.0% for stage I and II respectively). After 2 cycles of treatment, the molecular response of SeekInCare was correlated with the clinical outcome.
UNASSIGNED: In summary, a blood-based assay can be an alternative to detect lymphoma with adequate performance. This approach becomes particularly valuable in cases where obtaining tissue biopsy is difficult to obtain or inconclusive.

Cell-free DNA (cfDNA) provides a convenient diagnosis avenue for noninvasive cancer detection. The current methods are focused on identifying circulating tumor DNA (ctDNA)s genomic aberrations, e.g. mutations, copy number aberrations (CNAs) or methylation changes. In this study, we report a new computational method that unifies two orthogonal pieces of information, namely methylation and CNAs, derived from whole-genome bisulfite sequencing (WGBS) data to quantify low tumor content in cfDNA. It implements a Bayes model to enrich ctDNA from WGBS data based on hypomethylation haplotypes, and subsequently, models CNAs for cancer detection. We generated WGBS data in a total of 262 samples, including high-depth (>20×, deduped high mapping quality reads) data in 76 samples with matched triplets (tumor, adjacent normal and cfDNA) and low-depth (~2.5×, deduped high mapping quality reads) data in 186 samples. We identified a total of 54 Mb regions of hypomethylation haplotypes for model building, a vast majority of which are not covered in the HumanMethylation450 arrays. We showed that our model is able to substantially enrich ctDNA reads (tens of folds), with clearly elevated CNAs that faithfully match the CNAs in the paired tumor samples. In the 19 hepatocellular carcinoma cfDNA samples, the estimated enrichment is as high as 16 fold, and in the simulation data, it can achieve over 30-fold enrichment for a ctDNA level of 0.5% with a sequencing depth of 600×. We also found that these hypomethylation regions are also shared among many cancer types, thus demonstrating the potential of our framework for pancancer early detection.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide, and heterogeneity of HCC is the major barrier in improving patient outcome. To stratify HCC patients with different degrees of malignancy and provide precise treatment strategies, we reconstructed the tumor evolution trajectory with the help of scRNA-seq data and established a 30-gene prognostic model to identify the malignant state in HCC. Patients were divided into high-risk and low-risk groups. C-index and receiver operating characteristic (ROC) curve confirmed the excellent predictive value of this model. Downstream analysis revealed the underlying molecular and functional characteristics of this model, including significantly higher genomic instability and stronger proliferation/progression potential in the high-risk group. In summary, we established a novel prognostic model to overcome the barriers caused by HCC heterogeneity and provide the possibility of better clinical management for HCC patients to improve their survival outcomes.

Extrahepatic metastasis confers unfavorable patient prognosis in patients with hepatocellular carcinoma (HCC), however, reliable markers allowing prediction of extrahepatic metastasis at the time of initial diagnosis are still lacking. This study was to identify gene-level copy number aberrations (CNAs) related to extrahepatic metastasis-free survival of HCC patients, and further examine the associations between CNAs and gene expression. Array comparative genomic hybridization (aCGH) and expression array were used to analyze gene CNAs and expression levels, respectively. The associations between CNAs of a panel of 20 genes and extrahepatic metastasis-free survival were analyzed in 66 patients with follow-up period of 1.6-90.5 months. The gene expression levels between HCCs with and without gene CNA were compared in 109 patients with HCC. We observed that gains at MDM4 and BCL2L1, and losses at APC and FBXW7 were independent prognostic markers for extrahepatic metastasis-free survival of HCC patients. Integration analysis of aCGH and expression data showed that MDM4 and BCL2L1 were significantly upregulated in HCCs with gene gain, while APC and FBXW7 were significantly downregulated in HCCs with gene loss. We concluded that gene gains at MDM4 and BCL2L1, and losses at APC and FBXW7, with concordant expression changes, were associated with extrahepatic metastasis-free survival of HCC patients and have potential to act as novel prognostic markers.

BACKGROUND: With the developments of DNA sequencing technology, large amounts of sequencing data have been produced that provides unprecedented opportunities for advanced association studies between somatic mutations and cancer types/subtypes which further contributes to more accurate somatic mutation based cancer typing (SMCT). In existing SMCT methods however, the absence of high-level feature extraction is a major obstacle in improving the classification performance.
RESULTS: We propose DeepCNA, an advanced convolutional neural network (CNN) based classifier, which utilizes copy number aberrations (CNAs) and HiC data, to address this issue. DeepCNA first pre-process the CNA data by clipping, zero padding and reshaping. Then, the processed data is fed into a CNN classifier, which extracts high-level features for accurate classification. Experimental results on the COSMIC CNA dataset indicate that 2D CNN with both cell lines of HiC data lead to the best performance. We further compare DeepCNA with three widely adopted classifiers, and demonstrate that DeepCNA has at least 78% improvement of performance.
CONCLUSIONS: This paper demonstrates the advantages and potential of the proposed DeepCNA model for processing of somatic point mutation based gene data, and proposes that its usage may be extended to other complex genotype-phenotype association studies.

Copy number aberrations (CNAs) in chromosome arm 8q have been associated with unfavorable clinical outcomes of several cancers and progressive tumor characteristics of hepatocellular carcinoma (HCC). This study was to identify correlation of CNAs in 8q with clinical outcomes of HCC patients, and further screen for differentially expressed genes in outcome-related CNAs. Array comparative genomic hybridization and expression arrays were performed to detect CNAs and expression levels, respectively. The correlations between CNAs in 8q and outcomes were analyzed in 66 patients, with a median follow-up time of 45.0 months (range, 2.6-108.6 months). One hundred and nine cases were further evaluated to identify differentially expressed genes in the potential outcome-related CNAs. Copy number gain in 8q was observed in 22 (33.3%) of the 66 HCC cases. The most recurrent gains (with frequencies >20%) were 8q13.3-21.3,8q21.3-23.3,8q23.3-24.13,8q24.13-24.3, and 8q24.3. Survival analysis showed that 8q24.13-24.3 gain was significantly associated with reduced overall survival (jP=0.010). Multivariate Cox analysis identified 8q24.13-24.3 gain as an independent prognostic factor for poor overall survival (HR=2.47; 95% CI=1.16-5.26; Р=0.019). Apanel of 17 genes within the 8q24.13-24.3 region, including ATAD2,SQLE,PVT1,ASAP1, and NDRG1 were significantly upregulated in HCCs with 8q24.13-24.3 gain compared to those without. These results suggest that copy number gain at 8q24.13-24.3 is an unfavorable prognostic marker for HCC patients, and the potential oncogenes ATAD2,SQLE, PVT1, ASAP1,and NDRG1 within the regional gain, may contribute coordinately to the 8q24.13-24.3 gain-related poor prognosis.

The integration of DNA methylation and copy number alteration data promises to provide valuable insight into the underlying molecular mechanisms responsible for cancer initiation and progression. However, the generation and processing of these datasets are costly and time-consuming if carried out separately. The Illumina Infinium HumanMethylation450 BeadChip, initially designed for the evaluation of DNA methylation levels, allows copy number variant calling using bioinformatics tools.
A substantial amount of Infinium HumanMethylation450 data across various cancer types has been accumulated in recent years and is a valuable resource for large-scale data analysis. Here we present MethCNA, a comprehensive database for genomic and epigenomic data integration in human cancer. In the current release, MethCNA contains about 10,000 tumor samples representing 37 cancer types. All raw array data were collected from The Cancer Genome Atlas and NCBI Gene Expression Omnibus database and analyzed using a pipeline that integrated multiple computational resources and tools. The normalized copy number aberration data and DNA methylation alterations were obtained. We provide a user-friendly web-interface for data mining and visualization.
The Illumina Infinium HumanMethylation450 BeadChip enables the interrogation and integration of both genomic and epigenomic data from exactly the same DNA specimen, and thus can aid in distinguishing driver from passenger mutations in cancer. We expect MethCNA will enable researchers to explore DNA methylation and copy number alteration patterns, identify key oncogenic drivers in cancer, and assist in the development of targeted therapies. MethCNA is publicly available online at http://cgma.scu.edu.cn/MethCNA .

Objective: To investigate the molecular markers of copy number aberrations (CNAs) of genes related to extrohepatic metastasis-free survival after the operation for hepatocellular carcinoma (HCC). Methods: The CNA status of 20 candidate genes in 66 HCC samples was detected by microarray comparative genomic hybridization. The associations between gene CNAs and extrohepatic metastasis-free survival were evaluated using the Cox regression model, Log-rank test, and Kaplan-Meier survival analysis. Results: Multivariate Cox analysis revealed that the independent risk factors for metastasis-free survival were MDM4 gain (hazard ratio [HR] = 2.74, 95% confidence interval [CI] = 1.18-6.37, P < 0.05), APC loss (HR = 8.43, 95% CI = 2.48-28.66, P < 0.01), and BCL2L1 gain (HR = 3.45, 95% CI = 1.13-10.52, P < 0.05) and the independent protective factor was FBXW7 loss (HR = 0.32, 95% CI = 0.12-0.89, P < 0.05). By stepwise Cox regression analysis, three CNAs related to metastasis-free survival were screened out: MDM4 gain (HR = 2.71, 95% CI = 1.11-6.64, P < 0.05), APC loss (HR = 7.19, 95% CI = 1.88-27.60, P < 0.005), and FBXW7 loss (HR = 0.16, 95% CI = 0.05-0.46, P < 0.01). There were significant differences in metastasis-free survival rate between the HCC patients with FBXW7 loss and without MDM4 gain or APC loss, those with MDM4 gain and/or APC loss and without FBXW7 loss, and those with other CNA combinations (log-rank test, P < 0.01). Conclusion: MDM4 gain, APC loss, and FBXW7 loss are the independent prognostic factors for extrohepatic metastasis-free survival after the operation for HCC and can be used to predict the risk of extrohepatic metastasis after the operation for HCC.
目的： 探讨肝细胞癌（HCC）术后无肝外转移生存相关的基因拷贝数变异（CNA）分子标志物。 方法： 采用微阵列比较基因组杂交技术检测66例HCC基因组DNA中的20个候选基因CNA，并与无肝外转移生存进行相关性分析。对数据采用Cox模型进行单因素、多因素及多元逐步回归生存分析。 结果： 多因素Cox分析显示，MDM4增益[风险比（HR）= 2.74, 95%可信区间（CI）为1.18～6.37 （P < 0.05）]、APC丢失（HR = 8.43, 95% CI为2.48～28.66, P < 0.01）和BCL2L1增益（HR = 3.45, 95% CI为1.13～10.52, P < 0.05）是无转移生存的独立危险因素，而FBXW7丢失（HR = 0.32, 95% CI为0.12～0.89, P < 0.05）是独立保护因素。多元逐步Cox回归分析筛出MDM4增益（HR = 2.71, 95% CI为1.11～6.64, P < 0.05）、APC丢失（HR = 7.19, 95% CI为1.88～27.60, P < 0.005）和FBXW7丢失（HR = 0.16, 95% CI为0.05～0.46, P < 0.01）等3个无转移生存相关CNAs。MDM4增益(-)/APC丢失(-)/FBXW7丢失(+)、MDM4增益(+)和（或）APC丢失(+)/FBXW7丢失(-)、其他组合等3组HCC患者术后无转移生存率差异有统计学意义（P < 0.01）。 结论： MDM4增益、APC丢失和FBXW7丢失是HCC术后无转移生存的独立预后因素，可用于HCC术后肝外转移的风险预判。.

Application of the Next generation sequencing (NGS) technology has demonstrated that most tumor samples exhibit intra-tumor heterogeneity. Here we proposed SAPPH (Somatic Aberrations Prediction for Paired Heterogeneous tumor samples), as a new method for estimating tumor somatic copy number aberrations as well as inferring tumor subclone proportions from heterogeneous tumor sequencing data. This method is based on CBS and local proportion clustering strategy. When SAPPH is applied on simulated tumor samples, the agreement between the results analyzed by SAPPH and the sequencing signals suggests that SAPPH can find the solution to best fit the signal distributions. We benchmark the performance of SAPPH and show that it outperforms existing method in estimating tumor copy number aberrations.

Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma (NHL). Patients with DLBCL harboring MYC aberrations concurrent with BCL2 or/and BCL6 aberrations constitute a specific group with extremely poor outcome. In this study, we retrospectively investigated the incidence and prognosis of MYC, BCL2, and BCL6 aberrations with DLBCL patients in Chinese population. We applied fluorescence in situ hybridization and immunohistochemical analysis in 246 DLBCL patients. The results showed that patients with MYC or BCL2 copy number aberration (CNA) had significantly worse overall survival (OS) and progression-free survival (PFS) than negative cases (P < 0.0001). Patients with both MYC and BCL2 CNA had similar outcomes to those with classic double hit lymphoma or protein double expression lymphoma (MYC and BCL2/BCL6 coexpression). By multivariate analysis, MYC CNA, BCL2 CNA and double CNA were the independent worse prognostic factors. In conclusions, patients with MYC or BCL2 CNA constituted a unique group with extremely poor outcome and may require more aggressive treatment regimens.