Genome-wide Association Study (GWAS) of Germline Copy Number Variations (CNVs) Reveal Genetic Risks of Prostate Cancer in Chinese population

A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network

Ovarian cancer is a tumor with different clinicopathological and molecular features, and the vast majority of patients have local or extensive spread at the time of diagnosis. Early diagnosis and prognostic prediction of patients can contribute to the understanding of the underlying pathogenesis of ovarian cancer and the improvement of therapeutic outcomes. The occurrence of ovarian cancer is influenced by multiple complex mechanisms, including the genome, transcriptome and proteome. Different types of omics analysis help predict the survival rate of ovarian cancer patients. Multi-omics data of ovarian cancer exhibit high-dimensional heterogeneity, and existing methods for integrating multi-omics data have not taken into account the variability and inter-correlation between different omics data. In this paper, we propose a deep learning model, MDCADON, which utilizes multi-omics data and cross-modal view correlation discovery network. We introduce random forest into LASSO regression for feature selection on mRNA expression, DNA methylation, miRNA expression and copy number variation (CNV), aiming to select important features highly correlated with ovarian cancer prognosis. A multi-modal deep neural network is used to comprehensively learn feature representations of each omics data and clinical data, and cross-modal view correlation discovery network is employed to construct the multi-omics discovery tensor, exploring the inter-relationships between different omics data. The experimental results demonstrate that MDCADON is superior to the existing methods in predicting ovarian cancer prognosis, which enables survival analysis for patients and facilitates the determination of follow-up treatment plans. Finally, we perform Gene Ontology (GO) term analysis and biological pathway analysis on the genes identified by MDCADON, revealing the underlying mechanisms of ovarian cancer and providing certain support for guiding ovarian cancer treatments.

Image-guided prostate biopsy robots: A review

At present, the incidence of prostate cancer (PCa) in men is increasing year by year. So, the early diagnosis of PCa is of great significance. Transrectal ultrasonography (TRUS)-guided biopsy is a common method for diagnosing PCa. The biopsy process is performed manually by urologists but the diagnostic rate is only 20%-30% and its reliability and accuracy can no longer meet clinical needs. The image-guided prostate biopsy robot has the advantages of a high degree of automation, does not rely on the skills and experience of operators, reduces the work intensity and operation time of urologists and so on. Capable of delivering biopsy needles to pre-defined biopsy locations with minimal needle placement errors, it makes up for the shortcomings of traditional free-hand biopsy and improves the reliability and accuracy of biopsy. The integration of medical imaging technology and the robotic system is an important means for accurate tumor location, biopsy puncture path planning and visualization. This paper mainly reviews image-guided prostate biopsy robots. According to the existing literature, guidance modalities are divided into magnetic resonance imaging (MRI), ultrasound (US) and fusion image. First, the robot structure research by different guided methods is the main line and the actuators and material research of these guided modalities is the auxiliary line to introduce and compare. Second, the robot image-guided localization technology is discussed. Finally, the image-guided prostate biopsy robot is summarized and suggestions for future development are provided.

CNV-PCC: An efficient method for detecting copy number variations from next-generation sequencing data

Copy number variations (CNVs) significantly influence the diversity of the human genome and the occurrence of many complex diseases. The next-generation sequencing (NGS) technology provides rich data for detecting CNVs, and the read depth (RD)-based approach is widely used. However, low CN (copy number of 3–4) duplication events are challenging to identify with existing methods, especially when the size of CNVs is small. In addition, the RD-based approach can only obtain rough breakpoints. We propose a new method, CNV-PCC (detection of CNVs based on Principal Component Classifier), to identify CNVs in whole genome sequencing data. CNV-PPC first uses the split read signal to search for potential breakpoints. A two-stage segmentation strategy is then implemented to enhance the identification capabilities of low CN duplications and small CNVs. Next, the outlier scores are calculated for each segment by PCC (Principal Component Classifier). Finally, the OTSU algorithm calculates the threshold to determine the CNVs regions. The analysis of simulated data results indicates that CNV-PCC outperforms the other methods for sensitivity and F1-score and improves breakpoint accuracy. Furthermore, CNV-PCC shows high consistency on real sequencing samples with other methods. This study demonstrates that CNV-PCC is an effective method for detecting CNVs, even for low CN duplications and small CNVs.

A Deep Neural Network for Gastric Cancer Prognosis Prediction Based on Biological Information Pathways

Background

Gastric cancer (GC) is one of the deadliest cancers in the world, with a 5-year overall survival rate of lower than 20% for patients with advanced GC. Genomic information is now frequently employed for precision cancer treatment due to the rapid advancements of high-throughput sequencing technologies. As a result, integrating multiomics data to construct predictive models for the GC patient prognosis is critical for tailored medical care.

Results

In this study, we integrated multiomics data to design a biological pathway-based gastric cancer sparse deep neural network (GCS-Net) by modifying the P-NET model for long-term survival prediction of GC. The GCS-Net showed higher accuracy (accuracy = 0.844), area under the curve (AUC = 0.807), and F1 score (F1 = 0.913) than traditional machine learning models. Furthermore, the GCS-Net not only enables accurate patient survival prognosis but also provides model interpretability capabilities lacking in most traditional deep neural networks to describe the complex biological process of prognosis. The GCS-Net suggested the importance of genes (UBE2C, JAK2, RAD21, CEP250, NUP210, PTPN1, CDC27, NINL, NUP188, and PLK4) and biological pathways (Mitotic Anaphase, Resolution of Sister Chromatid Cohesion, and SUMO E3 ligases) to GC, which is consistent with the results revealed in biological- and medical-related studies of GC.

Conclusion

The GCS-Net is an interpretable deep neural network built using biological pathway information whose structure represents a nonlinear hierarchical representation of genes and biological pathways. It can not only accurately predict the prognosis of GC patients but also suggest the importance of genes and biological pathways. The GCS-Net opens up new avenues for biological research and could be adapted for other cancer prediction and discovery activities as well.

Isoflurane Promotes Cell Proliferation, Invasion, and Migration by Regulating BACH1 and miR-375 in Prostate Cancer Cells In Vitro

The aim of this study was to investigate the mechanism of isoflurane in proliferation, invasion, and migration in prostate cancer (PC) cells in vitro by regulating BACH1 and miR-375. The effect of different concentrations of isoflurane (0%, 0.5%, 1%, and 2%) on PC cell proliferation (PC3 and 22RV1) was measured. After PC cells and normal human prostate stromal immortalized WPMY-1 cells were treated with isoflurane, BACH1 and miR-375 expression was measured. Subsequently, PC3 and 22RV1 cells underwent gain- and loss-of-function assays with or without 4-h 2% isoflurane pretreatment. The levels of miR-375, BACH1, and PTEN were assessed. The binding of BACH1 to miR-375 promoter was detected by ChIP assay. Dual-luciferase reporter assay detected the targeting relationship of miR-375 with BACH1 and PTEN. Isoflurane promoted PC3 and 22RV1 cell proliferation. In addition, isoflurane elevated the levels of BACH1 and miR-375 in a dosage-dependent manner in PC cells. Transfection with miR-375 inhibitor or sh-BACH1 repressed PC cell proliferation, invasion, and migration, while exposure to 2% isoflurane for 4 h before transfection counteracted the inhibitory effects of sh-BACH1 or miR-375 inhibitor on PC cells. PTEN expression was suppressed after 2% isoflurane treatment, but the transfection with miR-375 inhibitor partly abrogated this suppressive effect in PC cells. Moreover, BACH1 bound to miR-375 and miR-375 negatively targeted PTEN. miR-375 mimic could partially reverse the inhibitory effects of sh-BACH1 on the proliferation, invasion, and migration of isoflurane-treated PC cells. Isoflurane facilitated PC cell proliferation, migration, and invasion by activating BACH1 to upregulate miR-375.

Cancer survival prognosis with Deep Bayesian Perturbation Cox Network

BACKGROUND

The Cox proportional hazards model with neural networks is widely used to accurately predict survival outcome for choosing cancer treatment strategies. Although this method has shown outstanding performance in many tasks, it has encountered challenges when dealing with high-dimensional datasets. In this study, we point out that the Cox network has estimation bias in processing such datasets with a large number of censored samples. The estimation bias is composed of censored estimation bias and variance estimation bias, which limit the prediction performance of the model. In order to correct this bias, this paper proposes the Deep Bayesian Perturbation Cox Network (DBP), which introduces Bayesian prior knowledge about censored samples to optimize the training process of the neural network. Specifically, the model uses a sampling module called Bayesian Perturbation to approximate the prior knowledge, which can be used as a component for other Cox-based neural networks.

RESULTS

The comparison between DBP and the previous model in different kinds of genomic datasets demonstrates that our model has made significant improvements over previous state-of-the-art methods. In addition, the simulation experiments are performed to illustrate how the DBP method addresses the bias caused by Cox Network. In the case study, based on the predicted risks in BRCA data from TCGA, we identify 400 differential expressed genes and 20 KEGG pathways that are associated with breast cancer prognosis, among which 65% of the top 20 genes have been proved by literature review.

CONCLUSION

Overall, these results demonstrate that our proposed method is advanced and robust in datasets with a large proportion of censored samples. Besides, it can guide to discover disease-related genes.

Integrating multi-omics data through deep learning for accurate cancer prognosis prediction

BACKGROUND

Genomic information is nowadays widely used for precise cancer treatments. Since the individual type of omics data only represents a single view that suffers from data noise and bias, multiple types of omics data are required for accurate cancer prognosis prediction. However, it is challenging to effectively integrate multi-omics data due to the large number of redundant variables but relatively small sample size. With the recent progress in deep learning techniques, Autoencoder was used to integrate multi-omics data for extracting representative features. Nevertheless, the generated model is fragile from data noises. Additionally, previous studies usually focused on individual cancer types without making comprehensive tests on pan-cancer. Here, we employed the denoising Autoencoder to get a robust representation of the multi-omics data, and then used the learned representative features to estimate patients' risks.

RESULTS

By applying to 15 cancers from The Cancer Genome Atlas (TCGA), our method was shown to improve the C-index values over previous methods by 6.5% on average. Considering the difficulty to obtain multi-omics data in practice, we further used only mRNA data to fit the estimated risks by training XGboost models, and found the models could achieve an average C-index value of 0.627. As a case study, the breast cancer prognosis prediction model was independently tested on three datasets from the Gene Expression Omnibus (GEO), and shown able to significantly separate high-risk patients from low-risk ones (C-index>0.6, p-values<0.05). Based on the risk subgroups divided by our method, we identified nine prognostic markers highly associated with breast cancer, among which seven genes have been proved by literature review.

CONCLUSION

Our comprehensive tests indicated that we have constructed an accurate and robust framework to integrate multi-omics data for cancer prognosis prediction. Moreover, it is an effective way to discover cancer prognosis-related genes.

Genome-wide association study of germline copy number variations reveals an association with prostate cancer aggressiveness

Prostate cancer is a major health burden, being the second most commonly diagnosed malignancy in men worldwide. Overtreatment represents a major problem in prostate cancer therapy, leading to significant long-term quality-of-life effects for patients and a broad socio-ecological burden. Biomarkers that could facilitate risk stratification of prostate cancer aggressiveness at the time of diagnosis may help to guide clinical treatment decisions and reduce overtreatment. Previous research on genetic variations in prostate cancer has shown that germline copy number variations as well as somatic copy number alterations are commonly present in cancer patients, altering a greater portion of the cancer genome than any other type of genetic variation. To investigate the effect of germline copy number variations on cancer aggressiveness we have compared genome-wide screening data from genomic DNA isolated from the blood of 120 patients with aggressive prostate cancer, 231 patients with non-aggressive prostate cancer and 87 controls with benign prostatic hyperplasia from the Prostate Cancer Study of Austria biobank using the Affymetrix SNP 6.0 array. We could show that patients with an aggressive form of prostate cancer had a higher frequency of copy number variations [mean count of copy number segments (CNS) = 12.9, median count of CNS = 9] compared to patients with non-aggressive prostate cancer (mean count of CNS = 10.4, median count of CNS = 8) or control patients diagnosed with benign prostatic hyperplasia (mean count of CNS = 9.3, median count of CNS = 8). In general, we observed that copy number gain is a rarer event, compared to copy number loss within all three patient groups. Furthermore, we could show a significant effect of copy number losses located on chromosomes 8, 9 and 10 on prostate cancer aggressiveness (P = 0.040, P = 0.037 and P = 0.005, respectively). Applying a cross-validation analysis yielded an area under the curve of 0.63. Our study reports promising findings suggesting that copy number losses might play an important role in the establishment of novel biomarkers to predict prostate cancer aggressiveness at the time of diagnosis. Such markers could be used to facilitate risk stratification to reduce overtreatment of prostate cancer patients.

Hereditary prostate cancer - Primetime for genetic testing?

Prostate cancer (PCa) remains the most common cancer in men. The proportion of all PCa attributable to high-risk hereditary factors has been estimated to 5-15%. Recent landmark discoveries in PCa genetics led to the identification of germline mutations/alterations (eg. BRCA1, BRCA2, ATM or HOXB13), single nucleotide polymorphisms or copy number variations associated with PCa incidence and progression. However, offering germline testing to men with an assumed hereditary component is currently controversial. In the present review article, we provide an overview about the epidemiology and the genetic basis of PCa predisposition and critically discuss the significance and consequence in the clinical routine. In addition, we give an overview about genetic tests and report latest findings from ongoing clinical studies. Lastly, we discuss the impact of genetic testing in personalized therapy in advanced stages of the disease.

Combination of TMB and CNA Stratifies Prognostic and Predictive Responses to Immunotherapy Across Metastatic Cancer

PURPOSE

Although tumor mutation burden (TMB) has been well known to predict the response to immune checkpoint inhibitors (ICI), lack of randomized clinical trial data has restricted its clinical application. This study aimed to explore the significance and feasibility of biomarker combination based on TMB and copy-number alteration (CNA) for the prognosis of each tumor and prediction for ICI therapy in metastatic pan-cancer milieu.

EXPERIMENTAL DESIGN

Non-ICI-treated MSK pan-cancer cohort was used for prognosis analysis. Three independent immunotherapy cohorts, including non-small cell lung cancer (n = 240), skin cutaneous melanoma (n = 174), and mixed cancer (Dana-Farber, n = 98) patients from previous studies, were analyzed for efficacy of ICI therapy.

RESULTS

TMB and CNA showed optimized combination for the prognosis of most metastatic cancer types, and patients with TMB^lowCNA^low showed better survival. In the predictive analysis, both TMB and CNA were independent predictive factors for ICI therapy. Remarkably, when TMB and CNA were jointly analyzed, those with TMB^highCNA^low showed favorable responses to ICI therapy. Meanwhile, TMB^highCNA^low as a new biomarker showed better prediction for ICI efficacy compared with either TMB-high or CNA-low alone. Furthermore, analysis of the non-ICI-treated MSK pan-cancer cohort supported that the joint stratification of TMB and CNA can be used to categorize tumors into distinct sensitivity to ICI therapy across pan-tumors.

CONCLUSIONS

The combination of TMB and CNA can jointly stratify multiple metastatic tumors into groups with different prognosis and heterogeneous clinical responses to ICI treatment. Patients with TMB^highCNA^low cancer can be an optimal subgroup for ICI therapy.