Making sense of cancer genomic data

Boosting predictive models and augmenting patient data with relevant genomic and pathway information

The recurrence of low-stage lung cancer poses a challenge due to its unpredictable nature and diverse patient responses to treatments. Personalized care and patient outcomes heavily rely on early relapse identification, yet current predictive models, despite their potential, lack comprehensive genetic data. This inadequacy fuels our research focus-integrating specific genetic information, such as pathway scores, into clinical data. Our aim is to refine machine learning models for more precise relapse prediction in early-stage non-small cell lung cancer. To address the scarcity of genetic data, we employ imputation techniques, leveraging publicly available datasets such as The Cancer Genome Atlas (TCGA), integrating pathway scores into our patient cohort from the Cancer Long Survivor Artificial Intelligence Follow-up (CLARIFY) project. Through the integration of imputed pathway scores from the TCGA dataset with clinical data, our approach achieves notable strides in predicting relapse among a held-out test set of 200 patients. By training machine learning models on enriched knowledge graph data, inclusive of triples derived from pathway score imputation, we achieve a promising precision of 82% and specificity of 91%. These outcomes highlight the potential of our models as supplementary tools within tumour, node, and metastasis (TNM) classification systems, offering improved prognostic capabilities for lung cancer patients. In summary, our research underscores the significance of refining machine learning models for relapse prediction in early-stage non-small cell lung cancer. Our approach, centered on imputing pathway scores and integrating them with clinical data, not only enhances predictive performance but also demonstrates the promising role of machine learning in anticipating relapse and ultimately elevating patient outcomes.

A novel defined programmed cell death related gene signature for predicting the prognosis of serous ovarian cancer

PURPOSE

This study aims to explore the contribution of differentially expressed programmed cell death genes (DEPCDGs) to the heterogeneity of serous ovarian cancer (SOC) through single-cell RNA sequencing (scRNA-seq) and assess their potential as predictors for clinical prognosis.

METHODS

SOC scRNA-seq data were extracted from the Gene Expression Omnibus database, and the principal component analysis was used for cell clustering. Bulk RNA-seq data were employed to analyze SOC-associated immune cell subsets key genes. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were utilized to calculate immune cell scores. Prognostic models and nomograms were developed through univariate and multivariate Cox analyses.

RESULTS

Our analysis revealed that 48 DEPCDGs are significantly correlated with apoptotic signaling and oxidative stress pathways and identified seven key DEPCDGs (CASP3, GADD45B, GNA15, GZMB, IL1B, ISG20, and RHOB) through survival analysis. Furthermore, eight distinct cell subtypes were characterized using scRNA-seq. It was found that G protein subunit alpha 15 (GNA15) exhibited low expression across these subtypes and a strong association with immune cells. Based on the DEGs identified by the GNA15 high- and low-expression groups, a prognostic model comprising eight genes with significant prognostic value was constructed, effectively predicting patient overall survival. Additionally, a nomogram incorporating the RS signature, age, grade, and stage was developed and validated using two large SOC datasets.

CONCLUSION

GNA15 emerged as an independent and excellent prognostic marker for SOC patients. This study provides valuable insights into the prognostic potential of DEPCDGs in SOC, presenting new avenues for personalized treatment strategies.

Computational analysis of protein-protein interactions of cancer drivers in renal cell carcinoma

Renal cell carcinoma (RCC) is the most common type of kidney cancer with rising cases in recent years. Extensive research has identified various cancer driver proteins associated with different subtypes of RCC. Most RCC drivers are encoded by tumor suppressor genes and exhibit enrichment in functional categories such as protein degradation, chromatin remodeling, and transcription. To further our understanding of RCC, we utilized powerful deep-learning methods based on AlphaFold to predict protein-protein interactions (PPIs) involving RCC drivers. We predicted high-confidence complexes formed by various RCC drivers, including TCEB1, KMT2C/D and KDM6A of the COMPASS-related complexes, TSC1 of the MTOR pathway, and TRRAP. These predictions provide valuable structural insights into the interaction interfaces, some of which are promising targets for cancer drug design, such as the NRF2-MAFK interface. Cancer somatic missense mutations from large datasets of genome sequencing of RCCs were mapped to the interfaces of predicted and experimental structures of PPIs involving RCC drivers, and their effects on the binding affinity were evaluated. We observed more than 100 cancer somatic mutations affecting the binding affinity of complexes formed by key RCC drivers such as VHL and TCEB1. These findings emphasize the importance of these mutations in RCC pathogenesis and potentially offer new avenues for targeted therapies.

Genomics in Clinical trials for Breast Cancer

Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress of genomic technology has transformed our understanding of the genetic landscape of breast cancer, opening new avenues for improved anti-cancer therapeutics. Genomics is critical in developing tailored therapeutics and identifying patients most benefit from these treatments. The next generation of breast cancer clinical trials has incorporated next-generation sequencing technologies into the process, and we have seen benefits. These innovations have led to the approval of better-targeted therapies for patients with breast cancer. Genomics has a role to play in clinical trials, including genomic tests that have been approved, patient selection and prediction of therapeutic response. Multiple clinical trials in breast cancer have been done and are still ongoing, which have applied genomics technology. Precision medicine can be achieved in breast cancer therapy with increased efforts and advanced genomic studies in this domain. Genomics studies assist with patient outcomes improvement and oncology advancement by providing a deeper understanding of the biology behind breast cancer. This article will examine the present state of genomics in breast cancer clinical trials.

S100A6 is a potential diagnostic and prognostic biomarker for human glioma

S100 calcium-binding protein A6 (S100A6) is a protein that belongs to the S100 family. The present study aimed to investigate the function of S100A6 in the diagnosis and survival prediction of glioma and elucidated the potential processes affecting glioma development. The Cancer Genome Atlas database was searched to identify the relationship among S100A6 expression, immune cell infiltration, clinicopathological parameters and glioma prognosis. Several clinical cases were used to verify these findings. S100A6 gene expression was high in glioma tissues, suggesting its diagnostic significance. In particular, S100A6 upregulation in glioma tissues exhibited a significant and positive correlation with the World Health Organization (WHO) grade, histological type, age, sex, primary treatment outcomes, 1p/19q codeletion, isocitrate dehydrogenase (IDH) status, overall survival (OS), progression-free interval and disease-specific survival. Kaplan-Meier and Cox regression analyses revealed that S100A6 gene expression can independently function as a risk factor affecting the prognosis of patients with glioma. Furthermore, Gene Ontology functional enrichment analysis revealed that S100A6 is implicated in immune responses and that the expression profiles of S100A6 are linked to the immune microenvironment. Furthermore, immunohistochemistry revealed that increased S100A6 protein levels are correlated with age, 1p/19q codeletion, IDH status, WHO grade and OS. The present findings suggest that increased S100A6 expression is an indicator of the dismal prognosis of patients with glioma and that it can be used as a potential diagnostic biomarker for this condition.

Computed cancer interactome explains the effects of somatic mutations in cancers

Protein-protein interactions (PPIs) are involved in almost all essential cellular processes. Perturbation of PPI networks plays critical roles in tumorigenesis, cancer progression, and metastasis. While numerous high-throughput experiments have produced a vast amount of data for PPIs, these data sets suffer from high false positive rates and exhibit a high degree of discrepancy. Coevolution of amino acid positions between protein pairs has proven to be useful in identifying interacting proteins and providing structural details of the interaction interfaces with the help of deep learning methods like AlphaFold (AF). In this study, we applied AF to investigate the cancer protein-protein interactome. We predicted 1,798 PPIs for cancer driver proteins involved in diverse cellular processes such as transcription regulation, signal transduction, DNA repair, and cell cycle. We modeled the spatial structures for the predicted binary protein complexes, 1,087 of which lacked previous 3D structure information. Our predictions offer novel structural insight into many cancer-related processes such as the MAP kinase cascade and Fanconi anemia pathway. We further investigated the cancer mutation landscape by mapping somatic missense mutations (SMMs) in cancer to the predicted PPI interfaces and performing enrichment and depletion analyses. Interfaces enriched or depleted with SMMs exhibit different preferences for functional categories. Interfaces enriched in mutations tend to function in pathways that are deregulated in cancers and they may help explain the molecular mechanisms of cancers in patients; interfaces lacking mutations appear to be essential for the survival of cancer cells and thus may be future targets for PPI modulating drugs.

A Systems Biology and LASSO-Based Approach to Decipher the Transcriptome-Interactome Signature for Predicting Non-Small Cell Lung Cancer

The lack of precise molecular signatures limits the early diagnosis of non-small cell lung cancer (NSCLC). The present study used gene expression data and interaction networks to develop a highly accurate model with the least absolute shrinkage and selection operator (LASSO) for predicting NSCLC. The differentially expressed genes (DEGs) were identified in NSCLC compared with normal tissues using TCGA and GTEx data. A biological network was constructed using DEGs, and the top 20 upregulated and 20 downregulated hub genes were identified. These hub genes were used to identify signature genes with penalized logistic regression using the LASSO to predict NSCLC. Our model’s development involved the following steps: (i) the dataset was divided into 80% for training (TR) and 20% for testing (TD1); (ii) a LASSO logistic regression analysis was performed on the TR with 10-fold cross-validation and identified a combination of 17 genes as NSCLC predictors, which were used further for development of the LASSO model. The model’s performance was assessed on the TD1 dataset and achieved an accuracy and an area under the curve of the receiver operating characteristics (AUC-ROC) of 0.986 and 0.998, respectively. Furthermore, the performance of the LASSO model was evaluated using three independent NSCLC test datasets (GSE18842, GSE27262, GSE19804) and achieved high accuracy, with an AUC-ROC of >0.99, >0.99, and 0.95, respectively. Based on this study, a web application called NSCLCpred was developed to predict NSCLC.

DRESIS: the first comprehensive landscape of drug resistance information

Widespread drug resistance has become the key issue in global healthcare. Extensive efforts have been made to reveal not only diverse diseases experiencing drug resistance, but also the six distinct types of molecular mechanisms underlying this resistance. A database that describes a comprehensive list of diseases with drug resistance (not just cancers/infections) and all types of resistance mechanisms is now urgently needed. However, no such database has been available to date. In this study, a comprehensive database describing drug resistance information named 'DRESIS' was therefore developed. It was introduced to (i) systematically provide, for the first time, all existing types of molecular mechanisms underlying drug resistance, (ii) extensively cover the widest range of diseases among all existing databases and (iii) explicitly describe the clinically/experimentally verified resistance data for the largest number of drugs. Since drug resistance has become an ever-increasing clinical issue, DRESIS is expected to have great implications for future new drug discovery and clinical treatment optimization. It is now publicly accessible without any login requirement at: https://idrblab.org/dresis/.

A novel defined cuproptosis-related gene signature for predicting the prognosis of lung adenocarcinoma

Lung adenocarcinoma (LUAD) has become the most prevalent histologic subset of primary lung cancer, and effective innovative prognostic models are needed to enhance the feasibility of targeted therapies for the disease. Programmed cell death (PCD) performs an integral function in the origin and treatment of cancer. Some PCD-related effective signatures for predicting prognosis in LUAD patients could provide potential therapeutic options in LUAD. A copper-dependent cell death referred to as cuproptosis is distinct from known PCD. However, whether cuproptosis is associated with LUAD patients' prognoses and the potential roles of cuproptosis-related genes involved is still unknown. For the prediction of LUAD prognosis, we developed a unique cuproptosis-associated gene signature. In The Cancer Genome Atlas (TCGA) cohort, the score derived from the risk signature on the basis of six cuproptosis-related genes was found to independently serve as a risk factor for anticipating lung cancer-related death. The differentially expressed genes between the high- and low-risk groups were linked to the cilium-related function. LUAD patients’ prognoses may now be predicted by a unique gene signature identified in this work. This discovery also provides a substantial foundation for future research into the links between cuproptosis-associated genes and cilium-related function in LUAD patients.

Activation of PPARγ in bladder cancer via introduction of the long arm of human chromosome 9

Bladder cancer is divided into two molecular subtypes, luminal and basal, which form papillary and nodular tumors, respectively, and are identifiable by gene expression profiling. Although loss of heterozygosity (LOH) of the long arm of human chromosome 9 (9q) has been observed in the early development of both types of bladder cancer, the functional significance of LOH remains to be clarified. The present study introduced human chromosome 9q into basal bladder cancer cell line, SCaBER, using microcell-mediated chromosome transfer to investigate the effect of LOH of 9q on molecular bladder cancer subtypes. These cells demonstrated decreased proliferation and migration capacity compared with parental and control cells. Conversely, transfer of human chromosome 4 did not change the cell phenotype. Expression level of peroxisome proliferator-activated receptor (PPAR)γ, a marker of luminal type, increased 3.0-4.4 fold in SCaBER cells altered with 9q compared with parental SCaBER cells. Furthermore, the expression levels of tumor suppressor PTEN, which regulates PPARγ, also increased in 9q-altered cells. These results suggested that human chromosome 9q may carry regulatory genes for PPARγ that are involved in the progression of neoplastic transformation of bladder cancer.

Distinct genetic landscape and a low response to doxorubicin in a luminal-A breast cancer cell line of Pakistani origin

BACKGROUND

Breast cancers exhibit genetic heterogeneity which causes differential responses to various chemotherapy agents. Given the unique demographic and genomic background in South Asia, genetic architecture in breast cancers is not fully explored.

METHODS AND RESULTS

In this study, we determined the genetic landscape of our previously established luminal-A subtype breast cancer cell line (BC-PAK1), and compared it with a Caucasian origin MCF7 breast cancer cell line of the same molecular subtype. Deep whole-exome sequencing (100X) was performed from early passages of the primary cancer cells using the Illumina NextSeq500. Data analysis with in silico tools showed novel non-silent somatic mutations previously not described in breast cancers, including a frameshift insertion (p.Ala1591AlafsTer28) in CIC, and a frameshift deletion (p.Lys333LysfsTer21) in PABPC1. Five genes CDC27, PIK3CG, ARAP3, RAPGEF1, and EFNA3, related with cell cycle pathway (hsa04110), ErbB signaling pathway (hsa04012), Ras signaling pathway (hsa04014), and Rap1 signaling pathway (hsa04015) were found to have recurrent non-silent somatic mutations. Further, the major contribution of COSMIC signatures 3 (failure of DNA double-strand break repair by homologous recombination), and 12 (transcriptional strand-bias for T>C substitutions) was observed. Also, the somatic mutations landscape in BC-PAK1 was found to be different as compared to the MCF7 cell line. The unique genetic landscape of BC-PAK1 might be responsible for significantly reduced response to doxorubicin than the MCF7 cell line.

CONCLUSION

This study presents a distinct genetic architecture in luminal-A breast cancer potentially responsible for differential response to chemotherapy. Further studies on large cohorts of breast cancer patients are suggested for implementation in personalized medicine.

SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis

Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised Multi-Omic Filtering that removes variables or features considered to be irrelevant noise. SuMO-Fil is intended to be performed prior to downstream analyses that detect supervised gene networks in sparse settings. We accomplish this by implementing variable filters based on low similarity across the datasets in conjunction with low similarity with the outcome. This approach can improve accuracy, as well as reduce run times for a variety of computationally expensive downstream analyses. This method has applications in a setting where the downstream analysis may include sparse canonical correlation analysis. Filtering methods specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. The SuMO-Fil method performs favorably by eliminating non-network features while maintaining important biological signal under a variety of different signal settings as compared to popular filtering techniques based on low means or low variances. We show that the speed and accuracy of methods such as supervised sparse canonical correlation are increased after using SuMO-Fil, thus greatly improving the scalability of these approaches.

Gene expression analysis of MCF7 cell lines of breast cancer treated with herbal extract of Cissampelos pareira revealed association with viral diseases

BACKGROUND

It is necessary to assess the cellular, molecular, and pathogenetic characteristics of COVID-19 and attention is required to understand highly effective gene targets and mechanisms. In this study, we suggest understandings into the fundamental pathogenesis of COVID-19 through gene expression analyses using the microarray data set GSE156445 publicly reachable at NIH/NCBI Gene Expression Omnibus database. The data set consists of MCF7 which is a human breast cancer cell line with estrogen, progesterone and glucocorticoid receptors. The cell lines treated with different quantities of Cissampelos pareira (Cipa). Cipa is a traditional medicinal plant which would possess an antiviral potency in preventing viral diseases such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

METHODS

Utilizing Biobase, GEOquery, gplots packages in R studio, the differentially expressed genes (DEGs) were identified. The gene ontology (GO) of pathway enrichments employed by utilizing DAVID and KEGG enrichment analyses were studied. We further constructed a human protein-protein interaction (PPI) network and performed, based upon that, a subnetwork module analysis for significant signaling pathways.

RESULTS

The study identified 418 differentially expressed genes (DEGs) using bioinformatics tools. The gene ontology of pathway enrichments employed by GO and KEGG enrichment analyses of down-regulated and up-regulated DEGs were studied. Gene expression analysis utilizing gene ontology and KEGG results uncovered biological and signaling pathways such as "cell adhesion molecules", "plasma membrane adhesion molecules", "synapse assembly", and "Interleukin-3-mediated signaling" which are mostly linked to COVID-19. Our results provide in silico evidence for candidate genes which are vital for the inhibition, adhesion, and encoding cytokine protein including LYN, IGFBP5, IL-1R1, and IL-13RA1 that may have strong biomarker potential for infectious diseases such as COVID-19 related therapy targets.

A clinical study on the factors associated with nasopharyngeal carcinoma among the Chinese population

Nasopharyngeal carcinoma (NC) arises from the nasopharynx epithelium and the majority of NC cases globally are within China and Southeast Asia. Both short palate lung and nasal epithelium clone 1 (SPLUNC1) and myelodysplasia syndrome 1-ectopic viral integration site 1 (MDS1-EVI1) play an important role in carcinogenesis and have been found to be associated with nasopharyngeal carcinoma. In spite of their role in NC, the association between these genes and their polymorphisms in the development of NC has thus far not been studied. In the present study, the relationship between SPLUNC1 (rs2752903, T>C) and MDS1-EVI1 (rs6774494, G>A) polymorphisms and their role in the development of NC among the Chinese population were investigated. From a Chinese population of 1,059 patients with NC and 891 controls, genotype frequencies and the distribution of SPLUNC1 and MDS1-EVI1 polymorphisms were analyzed for possible susceptibility to NC. It was observed that those with MDS1-EVI1 CC (OR, 2.76; 95% CI, 1.96-3.81) and MDS1-EVI1 CT (OR, 1.51; 95% CI, 1.22-2.14) polymorphisms had an increased risk of developing NC. Those with SPLUNC1 AA genotypes also observed a higher risk for NC compared with SPLUNC1 GG genotypes (OR, 2.15; 95% CI, 1.62-3.15). When observing the gene-gene interaction between SPLUNC1 and MDS1-EVI1 polymorphisms, it was found that the presence of both SPLUNC1 CC and MDS1-EVI1 AA alleles was associated with a higher risk for NC compared with those who did not carry both alleles (OR, 6.75; 95% CI, 3.41-12.11). The present study suggested that the association between SPLUNC1 (rs2752903, T>C) and MDS1-EVI1 (rs6774494, G>A) polymorphisms may be a potent risk factor in the occurrence of NC.

CRISPR-Cas9, A Promising Therapeutic Tool for Cancer Therapy: A Review

Cancer is one of the most leading causes of mortality all over the world and remains a foremost social and economic burden. Mutations in the genome of individuals are taking place more frequently due to the excessive progress of xenobiotics and industrialization in the present world. With the progress in the field of molecular biology, it is possible to alter the genome and to observe the functional changes derived from genetic modulation using gene-editing technologies. Several therapies have been applied for the treatment of malignancy which affect the normal body cells; however, more effort is required to develop vsome latest therapeutic approaches for cancer biology and oncology exploiting these molecular biology advances. Recently, the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) associated protein 9 (Cas9) system has emerged as a powerful technology for cancer therapy because of its great accuracy and efficiency. Genome editing technologies have demonstrated a plethora of benefits to the biological sciences. CRISPR- Cas9, a versatile gene editing tool, has become a robust strategy for making alterations to the genome of organisms and a potent weapon in the arsenal of tumor treatment. It has revealed an excellent clinical potential for cancer therapy by discovering novel targets and has provided the researchers with the perception about how tumors respond to drug therapy. Stern efforts are in progress to enhance its efficiency of sequence specific targeting and consequently repressing offtarget effects. CRISPR-Cas9 uses specific proteins to convalesce mutations at genetic level. In CRISPR-Cas9 system, RNA-guided Cas9 endonuclease harnesses gene mutation, DNA deletion or insertion, transcriptional activation or repression, multiplex targeting only by manipulating 20-nucleotide components of RNA. Originally, CRISPR-Cas9 system was used by bacteria for their defense against different bacteriophages, and recently this system is receiving noteworthy appreciation due to its emerging role in the treatment of genetic disorders and carcinogenesis. CRISPR-Cas9 can be employed to promptly engineer oncolytic viruses and immune cells for cancer therapeutic applications. More notably, it has the ability to precisely edit genes not only in model organisms but also in human being that permits its use in therapeutic analysis. It also plays a significant role in the development of complete genomic libraries for cancer patients. In this review, we have highlighted the involvement of CRISPR-Cas9 system in cancer therapy accompanied by its prospective applications in various types of malignancy and cancer biology. In addition, some other conspicuous functions of this unique system have also been discussed beyond genome editing.

Identification of significantly mutated subnetworks in the breast cancer genome

Recent studies showed that somatic cancer mutations target genes that are in specific signaling and cellular pathways. However, in each patient only a few of the pathway genes are mutated. Current approaches consider only existing pathways and ignore the topology of the pathways. For this reason, new efforts have been focused on identifying significantly mutated subnetworks and associating them with cancer characteristics. We applied two well-established network analysis approaches to identify significantly mutated subnetworks in the breast cancer genome. We took network topology into account for measuring the mutation similarity of a gene-pair to allow us to infer the significantly mutated subnetworks. Our goals are to evaluate whether the identified subnetworks can be used as biomarkers for predicting breast cancer patient survival and provide the potential mechanisms of the pathways enriched in the subnetworks, with the aim of improving breast cancer treatment. Using the copy number alteration (CNA) datasets from the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study, we identified a significantly mutated yet clinically and functionally relevant subnetwork using two graph-based clustering algorithms. The mutational pattern of the subnetwork is significantly associated with breast cancer survival. The genes in the subnetwork are significantly enriched in retinol metabolism KEGG pathway. Our results show that breast cancer treatment with retinoids may be a potential personalized therapy for breast cancer patients since the CNA patterns of the breast cancer patients can imply whether the retinoids pathway is altered. We also showed that applying multiple bioinformatics algorithms at the same time has the potential to identify new network-based biomarkers, which may be useful for stratifying cancer patients for choosing optimal treatments.

Developing and Using a Data Commons for Understanding the Molecular Characteristics of Germ Cell Tumors

Germ cell tumors (GCTs) are a rare disease, but they account for 15% of all malignancies diagnosed during adolescence. The biological mechanisms underpinning their development are only starting to be explored. Current GCT treatment may be associated with significant toxicity. Therefore, there is an urgent need to understand the molecular basis of GCT and identify biomarkers to tailor the therapy for individual patients. However, this research is severely hamstrung by the rarity of GCTs in individual hospitals/institutes. A publicly available genomic data commons with GCT datasets compiled from different institutes/studies would be a valuable resource to facilitate such research. In this study, we first reviewed publicly available web portals containing GCT genomics data, focusing on comparing data availability, data access, and analysis tools, and the limitations of using these resources for GCT molecular studies. Next, we specifically designed a GCT data commons with a web portal, GCT Explorer, to assist the research community to store, manage, search, share, and analyze data. The goal of this work is to facilitate GCT molecular basis exploration and translational research.

Whole transcriptome signature for prognostic prediction (WTSPP): application of whole transcriptome signature for prognostic prediction in cancer

Developing prognostic biomarkers for specific cancer types that accurately predict patient survival is increasingly important in clinical research and practice. Despite the enormous potential of prognostic signatures, proposed models have found limited implementations in routine clinical practice. Herein, we propose a generic, RNA sequencing platform independent, statistical framework named whole transcriptome signature for prognostic prediction to generate prognostic gene signatures. Using ovarian cancer and lung adenocarcinoma as examples, we provide evidence that our prognostic signatures overperform previous reported signatures, capture prognostic features not explained by clinical variables, and expose biologically relevant prognostic pathways, including those involved in the immune system and cell cycle. Our approach demonstrates a robust method for developing prognostic gene expression signatures. In conclusion, our statistical framework can be generally applied to all cancer types for prognostic prediction and might be extended to other human diseases. The proposed method is implemented as an R package (PanCancerSig) and is freely available on GitHub ( https://github.com/Cheng-Lab-GitHub/PanCancer_Signature ).

CRISPR and transposon in vivo screens for cancer drivers and therapeutic targets

Human cancers harbor substantial genetic, epigenetic, and transcriptional changes, only some of which drive oncogenesis at certain times during cancer evolution. Identifying the cancer-driver alterations amongst the vast swathes of "passenger" changes still remains a major challenge. Transposon and CRISPR screens in vivo provide complementary methods for achieving this, and each platform has its own advantages. Here, we review recent major technological breakthroughs made with these two approaches and highlight future directions. We discuss how each genetic screening platform can provide unique insight into cancer evolution, including intra-tumoral heterogeneity, metastasis, and immune evasion, presenting transformative opportunities for targeted therapeutic intervention.

The GATA3 X308_Splice breast cancer mutation is a hormone context-dependent oncogenic driver

As the catalog of oncogenic driver mutations is expanding, it becomes clear that alterations in a given gene might have different functions and should not be lumped into one class. The transcription factor GATA3 is a paradigm of this. We investigated the functions of the most common GATA3 mutation (X308_Splice) and five additional mutations, which converge into a neoprotein that we called "neoGATA3," associated with excellent prognosis in patients. Analysis of available molecular data from >3000 breast cancer patients revealed a dysregulation of the ER-dependent transcriptional response in tumors carrying neoGATA3-generating mutations. Mechanistic studies in vitro showed that neoGATA3 interferes with the transcriptional programs controlled by estrogen and progesterone receptors, without fully abrogating them. ChIP-Seq analysis indicated that ER binding is reduced in neoGATA3-expressing cells, especially at distal regions, suggesting that neoGATA3 interferes with the fine tuning of ER-dependent gene expression. This has opposite outputs in distinct hormonal context, having pro- or anti-proliferative effects, depending on the estrogen/progesterone ratio. Our data call for functional analyses of putative cancer drivers to guide clinical application.

Correlation between telomerase reverse transcriptase messenger RNA expression and survival of patients with papillary thyroid carcinoma

BACKGROUND

Telomerase reverse transcriptase promoter mutations were recently found to be associated with poorer prognosis in patients with papillary thyroid carcinoma. Correlation between telomerase reverse transcriptase messenger RNA expression and survival of patients with papillary thyroid carcinoma has not been determined.

METHODS

Clinical information, somatic mutations, and RNA sequencing of 492 papillary thyroid carcinoma patients were obtained from The Cancer Genome Atlas. Correlations between messenger RNA expression and clinicopathologic variables were evaluated. Recursive partitioning regression trees were used to find cutoffs predicting survival. Differentially expressed gene analysis was performed by Edge-R, and Database for Annotation, Visualization and Integrated Discovery 6.7 was used to pathway analysis.

RESULTS

Telomerase reverse transcriptase messenger RNA expression was positively correlated with stages II and IV and high MACIS Prognostic Score for Papillary Thyroid Carcinoma. Using a telomerase reverse transcriptase messenger RNA level of 2.854 as a cutoff, patients with higher telomerase reverse transcriptase messenger RNA expression showed poorer overall survival (hazard ratio = 20.7). The higher telomerase reverse transcriptase messenger RNA group showed upregulation of 2,255 genes, with enrichment of carcinogenic pathways.

CONCLUSION

Higher telomerase reverse transcriptase messenger RNA expression was associated with poorer survival in patients with papillary thyroid carcinoma and was a better predictor for death than telomerase reverse transcriptase promoter mutations. Measuring telomerase reverse transcriptase messenger RNA expression in thyroid cancer tissue may allow early identification of papillary thyroid carcinoma patients with worse overall survival.

Identification of Prognostic Organic Cation and Anion Transporters in Different Cancer Entities by In Silico Analysis

The information derived from next generation sequencing technology allows the identification of deregulated genes, gene mutations, epigenetic modifications, and other genomic events that are associated with a given tumor entity. Its combination with clinical data allows the prediction of patients’ survival with a specific gene expression pattern. Organic anion transporters and organic cation transporters are important proteins that transport a variety of substances across membranes. They are also able to transport drugs that are used for the treatment of cancer and could be used to improve treatment. In this study, we have made use of publicly available data to analyze if the expression of organic anion transporters or organic cation transporters have a prognostic value for a given tumor entity. The expression of most organic cation transporters is prognostic favorable. Within the organic anion transporters, the ratio between favorable and unfavorable organic anion transporters is nearly equal for most tumor entities and only in liver cancer is the number of unfavorable genes two times higher compared to favorable genes. Within the favorable genes, UNC13B, and SFXN2 cover nine cancer types and in the same way, SLC2A1, PLS3, SLC16A1, and SLC16A3 within the unfavorable set of genes and could serve as novel target structures.

Driving toward precision medicine for B cell lymphomas: Targeting the molecular pathogenesis at the gene level

Lymphomas are a diverse group of hematologic malignancies that arise from either T cell, B cell or the natural killer cell lineage. B cell lymphomas arise from gene mutations with critical functions during normal B cell development. Recent advances in the understanding of molecular pathogenesis demonstrate that many different recurrent genomic and molecular abnormalities and dysregulated oncogenic regulatory pathways exist for many subtypes of B cell lymphomas, both across and within histological subtypes. Pathogenetic processes such as (1) chromosomal aberrations, for example, t(14;18) in follicular lymphoma, t(11;14) in mantle cell lymphoma, t(8;14) in Burkitt lymphoma; dysregulations in signaling pathways of (2) nuclear factor- κB (NF-κB); (3) B cell receptor (BCR); (4) Janus kinase/signal transducers and transcription activators (JAK-STAT); (5) impaired apoptosis/cell cycle regulation due to mutated, rearranged or amplified MYC, BCL-2, BCL-6 proto-oncogenes; (6) epigenetic aberrations may contribute to pathogenesis. More studies are under way to elucidate the molecular heterogeneity underlying many types of lymphomas that account for variable responses to treatment, generation of subclones and treatment resistance. Although significant research is still needed, targeted therapy promises to provide new options for the treatment of patients with lymphomas. This article provides a non-exhaustive overview on the current understanding on the genetics of pathogenesis of B cell lymphomas and their therapeutic implications.

Postzygotic Somatic Mutations in the Human Brain Expand the Threshold-Liability Model of Schizophrenia

The search for what causes schizophrenia has been onerous. This research has included extensive assessment of a variety of genetic and environmental factors using ever emerging high-resolution technologies and traditional understanding of the biology of the brain. These efforts have identified a large number of schizophrenia-associated genes, some of which are altered by mutational and epi-mutational mechanisms in a threshold liability model of schizophrenia development. The results, however, have limited predictability and the actual cause of the disease remains unknown. This current state asks for conceptualizing the problem differently in light of novel insights into the nature of mutations, the biology of the brain and the fine precision and resolution of emerging technologies. There is mounting evidence that mutations acquired during postzygotic development are more common than germline mutations. Also, the postzygotic somatic mutations including epimutations (PZMs), which often lead to somatic mosaicism, are relatively common in the mammalian brain in comparison to most other tissues and PZMs are more common in patients with neurodevelopmental mental disorders, including schizophrenia. Further, previously inaccessible, detection of PZMs is becoming feasible with the advent of novel technologies that include single-cell genomics and epigenomics and the use of exquisite experimental designs including use of monozygotic twins discordant for the disease. These developments allow us to propose a working hypothesis and expand the threshold liability model of schizophrenia that already encompasses familial genetic, epigenetic and environmental factors to include somatic de novo PZMs. Further, we offer a test for this expanded model using currently available genome sequences and methylome data on monozygotic twins discordant for schizophrenia (MZD) and their parents. The results of this analysis argue that PZMs play a significant role in the development of schizophrenia and explain extensive heterogeneity seen across patients. It also offers the potential to convincingly link PZMs to both nervous system health and disease, an area that has remained challenging to study and relatively under explored.

Race-specific alterations in DNA methylation among middle-aged African Americans and Whites with metabolic syndrome

Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors for all-cause mortality, cardiovascular disease, and cancer. Identifying epigenetic alterations associated with MetS in African Americans (AAs) and Whites may provide insight into genes that influence its differential health outcomes. We examined DNA methylation (DNAm) and performed an epigenome-wide association study (EWAS) of MetS among AAs and Whites with and without MetS. We assessed age, race and poverty status associated DNAm among AAs (n = 225) and White (n = 233) adults using NCEP-ATP III guidelines. Genome-wide DNAm measurement was assessed using Illumina Infinium Methylation EPIC BeadChip. Differentially methylated positions (DMPs) and differentially methylated regions (DMRs) were identified using dmpFinder and bumphunter. EWAS was performed using CpGassoc. We found significant DMPs associated with age, poverty status and MetS in each race. GSTT1(Glutathione S-Transferase Theta 1) was one of the top-hypermethylated genes and MIPEP (Mitochondrial Intermediate Peptidase) was one of the most hypomethylated genes when comparing AAs with and without MetS. PPP1R13L (Protein Phosphatase 1 Regulatory Subunit 13 Like) was the top hypermethylated and SCD (stearoyl-CoA desaturase-1) was one of the most hypomethylated genes for Whites with and without MetS. EWAS results showed that DNAm differences might contribute to MetS risk among Whites and AAs since different genes were identified in AAs and Whites. We replicated previously identified MetS associated genes and found that Thioredoxin-interacting protein (TXN1P) was statistically significantly differentially expressed only in Whites. Our results may be useful in further studies of genes underlying differences in MetS among AAs and Whites.

MRTFB suppresses colorectal cancer development through regulating SPDL1 and MCAM

Myocardin-related transcription factor B (MRTFB) is a candidate tumor-suppressor gene identified in transposon mutagenesis screens of the intestine, liver, and pancreas. Using a combination of cell-based assays, in vivo tumor xenograft assays, and Mrtfb knockout mice, we demonstrate here that MRTFB is a human and mouse colorectal cancer (CRC) tumor suppressor that functions in part by inhibiting cell invasion and migration. To identify possible MRTFB transcriptional targets, we performed whole transcriptome RNA sequencing in MRTFB siRNA knockdown primary human colon cells and identified 15 differentially expressed genes. Among the top candidate tumor-suppressor targets were melanoma cell adhesion molecule (MCAM), a known tumor suppressor, and spindle apparatus coiled-coil protein 1 (SPDL1), which has no confirmed role in cancer. To determine whether these genes play a role in CRC, we knocked down the expression of MCAM and SPDL1 in human CRC cells and showed significantly increased invasion and migration of tumor cells. We also showed that Spdl1 expression is significantly down-regulated in Mrtfb knockout mouse intestine, while lower SPDL1 expression levels are significantly associated with reduced survival in CRC patients. Finally, we show that depletion of MCAM and SPDL1 in human CRC cells significantly increases tumor development in xenograft assays, further confirming their tumor-suppressive roles in CRC. Collectively, our findings demonstrate the tumor-suppressive role of MRTFB in CRC and identify several genes, including 2 tumor suppressors, that act downstream of MRTFB to regulate tumor growth and survival in CRC patients.

DMCM: a Data-adaptive Mutation Clustering Method to identify cancer-related mutation clusters

Motivation

Functional somatic mutations within coding amino acid sequences confer growth advantage in pathogenic process. Most existing methods for identifying cancer-related mutations focus on the single amino acid or the entire gene level. However, gain-of-function mutations often cluster in specific protein regions instead of existing independently in the amino acid sequences. Some approaches for identifying mutation clusters with mutation density on amino acid chain have been proposed recently. But their performance in identification of mutation clusters remains to be improved.

Results

Here we present a Data-adaptive Mutation Clustering Method (DMCM), in which kernel density estimate (KDE) with a data-adaptive bandwidth is applied to estimate the mutation density, to find variable clusters with different lengths on amino acid sequences. We apply this approach in the mutation data of 571 genes in over twenty cancer types from The Cancer Genome Atlas (TCGA). We compare the DMCM with M2C, OncodriveCLUST and Pfam Domain and find that DMCM tends to identify more significant clusters. The cross-validation analysis shows DMCM is robust and cluster cancer type enrichment analysis shows that specific cancer types are enriched for specific mutation clusters.

Availability and implementation

DMCM is written in Python and analysis methods of DMCM are written in R. They are all released online, available through https://github.com/XinguoLu/DMCM.

Supplementary information

Supplementary data are available at Bioinformatics online.

circRNA circAF4 functions as an oncogene to regulate MLL-AF4 fusion protein expression and inhibit MLL leukemia progression

Background

Circular RNAs (circRNAs) represent a type of endogenous noncoding RNAs that are generated by back-splicing events and favor repetitive sequences. Recent studies have reported that cancer-associated chromosomal translocations could juxtapose distant complementary repetitive intronic sequences, resulting in the aberrant formation of circRNAs. However, among the reported fusion genes, only a small number of circRNAs were found to originate from fusion regions during gene translocation. We question if circRNAs could also originate from fusion partners during gene translocation.

Methods

Firstly, we designed divergent primers for qRT-PCR to identify a circRNA circAF4 in AF4 gene and investigated the expression pattern in different types of leukemia samples. Secondly, we designed two small interfering RNAs specially targeting the back-spliced junction point of circAF4 for functional studies. CCK8 cell proliferation and cell cycle assay were performed, and a NOD-SCID mouse model was used to investigate the contribution of circAF4 in leukemogenesis. Finally, luciferase reporter assay, AGO2 RNA immunoprecipitation (RIP), and RNA Fluorescent in Situ Hybridization (FISH) were performed to confirm the relationship of miR-128-3p, circAF4, and MLL-AF4 expression.

Results

We discovered a circRNA, named circAF4, originating from the AF4 gene, a partner of the MLL fusion gene in MLL-AF4 leukemia. We showed that circAF4 plays an oncogenic role in MLL-AF4 leukemia and promotes leukemogenesis in vitro and in vivo. More importantly, knockdown of circAF4 increases the leukemic cell apoptosis rate in MLL-AF4 leukemia cells, while no effect was observed in leukemia cells that do not carry the MLL-AF4 translocation. Mechanically, circAF4 can act as a miR-128-3p sponge, thereby releasing its inhibition on MLL-AF4 expression. We finally analyzed most of the MLL fusion genes loci and found that a number of circRNAs could originate from these partners, suggesting the potential roles of fusion gene partner-originating circRNAs (named as FP-circRNAs) in leukemia with chromosomal translocations.

Conclusion

Our findings demonstrate that the abnormal elevated expression of circAF4 regulates the cell growth via the circAF4/miR-128-3p/MLL-AF4 axis, which could contribute to leukemogenesis, suggesting that circAF4 may be a novel therapeutic target of MLL-AF4 leukemia.

Lung Carcinoma Presenting as a Superior Vena Cava Syndrome, Burnt and Twice Reborn as Adrenal and Facial Tumors

Eighty-five percent of all lung cancers are Non-Small-Cell Lung Carcinoma (NSCLC) with common sites of metastasis to the adrenal glands and liver. Onset is insidious, and seventy-five percent of patients have either regional or distant metastases at initial presentation. The five-year relative survival rate is four and a half percent with a distantly spread disease based on recent studies. Here we present a unique case of a ten-year survival with NSCLC initially presenting as a Superior Vena Cava Syndrome and reoccurring with adrenal gland, bone, and CNS lesions. The patient presented with SVC caused by lung cancer and underwent chemo and radiotherapy with complete response in 2010. Five years later, the same cancer returned disguised as an adrenal tumor. In 2017, the patient came in with facial neuropathy, shooting pains, sinus headaches, eyelid concerns, and active tumoral activity was detected in the middle cranial fossa, involving parotid glands and the vertebral column. Craniotomy revealed a metastatic poorly differentiated adenocarcinoma that extended through foramen ovale and rotundum to the infratemporal fossa and caused left-sided facial paralysis, hearing loss and numbness in CN V2 - V3 distribution. Considering that the patient has experienced several recurrences of disease on standard protocols and is not a candidate for targeted molecular therapies, an immunotherapy trial was suggested as the next step. The natural history of this disease is remarkable in terms of metastatic sites, paraneoplastic manifestations, and a substantially prolonged lifespan. Thus, more studies of similar cases will advance our understanding of tumor genetics and immunotherapy allowing the greater benefit to future patients.

MERIT: Systematic Analysis and Characterization of Mutational Effect on RNA Interactome Topology

The interaction between RNA-binding proteins (RBPs) and RNA plays an important role in regulating cellular function. However, decoding genome-wide protein-RNA regulatory networks as well as how cancer-related mutations impair RNA regulatory activities in hepatocellular carcinoma (HCC) remains mostly undetermined. We explored the genetic alteration patterns of RBPs and found that deleterious mutations are likely to occur on the surface of RBPs. We then constructed protein-RNA interactome networks by integration of target binding screens and expression profiles. Network analysis highlights regulatory principles among interacting RBPs. In addition, somatic mutations selectively target functionally important genes (cancer genes, core fitness genes, or conserved genes) and perturb the RBP-gene regulatory networks in cancer. These regulatory patterns were further validated using independent data. A computational method (Mutational Effect on RNA Interactome Topology) and a web-based, user-friendly resource were further proposed to analyze the RBP-gene regulatory networks across cancer types. Pan-cancer analysis also suggests that cancer cells selectively target "vulnerability" genes to perturb protein-RNA interactome that is involved in cancer hallmark-related functions. Specifically, we experimentally validated four pairs of RBP-gene interactions perturbed by mutations in HCC, which play critical roles in cell proliferation. Based on the expression of perturbed RBP and target genes, we identified three subtypes of HCC with different survival rates. Conclusion: Our results provide a valuable resource for characterizing somatic mutation-perturbed protein-RNA regulatory networks in HCC, yielding valuable insights into the genotype-phenotype relationships underlying human cancer, and potential biomarkers for precision medicine.

Investigating the structural features of chromodomain proteins in the human genome and predictive impacts of their mutations in cancers

Epigenetic readers are specific proteins which recognize histone marks and represents the underlying mechanism for chromatin regulation. Histone H3 lysine methylation is a potential epigenetic code for the chromatin organization and transcriptional control. Recognition of histone methylation is achieved by evolutionary conserved reader modules known as chromodomain, identified in several proteins, and is involved in transcriptional silencing and chromatin remodelling. Genetic perturbations within the structurally conserved chromodomain could potentially mistarget the reader protein and impair their regulatory pathways, ultimately leading to cellular chaos by setting the stage for tumor development and progression. Here, we report the structural conservations associated with diverse functions, prognostic significance and functional consequences of mutations within chromodomain of human proteins in distinct cancers. We have extensively analysed chromodomain containing human proteins in terms of their structural-functional ability to act as a molecular switch in the recognition of methyl-lysine recognition. We further investigated the combinatorial potential, target promiscuity and binding specificity associated with their underlying mechanisms. Indeed, the molecular mechanism of epigenetic silencing significantly underlies a newer cancer therapy approach. We hope that a critical understanding of chromodomains will pave the way for novel paths of research providing newer insights into the designing of effective anti-cancer therapies.

Critical role of the fibroblast growth factor signalling pathway in Ewing's sarcoma octamer-binding transcription factor 4-mediated cell proliferation and tumorigenesis

Certain bone and soft tissue (BST) tumours harbour a chromosomal translocation [t(6;22)(p21;q12)], which fuses the Ewing's sarcoma (EWS) gene at 22q12 with the octamer-binding transcription factor 4 (Oct-4) gene at 6p21, resulting in the chimeric EWS-Oct-4 protein that possesses high transactivation ability. Although abnormal activation of signalling pathways can lead to human cancer development, the pathways underlying these processes in human BST tumours remain poorly explored. Here, we investigated the functional significance of fibroblast growth factor (FGF) signalling in human BST tumours. To identify the gene(s) involved in the FGF signalling pathway and potentially regulated by EWS-Oct-4 (also called EWS-POU5F1), we performed RNA-Seq analysis, electrophoretic mobility shift assays, chromatin immunoprecipitation assays, and xenograft assays. Treating GBS6 or ZHBTc4 cells-expressing EWS-Oct-4 with the small molecule FGF receptor (FGFR) inhibitors PD173074, NVPBGJ398, ponatinib, and dovitinib suppressed cellular proliferation. Gene expression analysis revealed that, among 22 Fgf and four Fgfr family members, Fgf-4 showed the highest upregulation (by 145-fold) in ZHBTc4 cells-expressing EWS-Oct-4. Computer-assisted analysis identified a putative EWS-Oct-4-binding site at +3017/+3024, suggesting that EWS-Oct-4 regulates Fgf-4 expression in human BST tumours. Fgf-4 enhancer constructs showed that EWS-Oct-4 transactivated the Fgf-4 gene reporter in vitro, and that overexpression of EWS-Oct-4 stimulated endogenous Fgf-4 gene expression in vivo. Finally, PD173074 significantly decreased tumour volume in mice. Taken together, these data suggest that FGF-4 signalling is involved in EWS-Oct-4-mediated tumorigenesis, and that its inhibition impairs tumour growth in vivo significantly.

Transcriptome analysis identifies metallothionein as biomarkers to predict recurrence in hepatocellular cacinoma

Background

Liver cancer is the fifth most common cancer, and hepatocellular carcinoma (HCC) is the major liver tumor type seen in adults. HCC is usually caused by chronic liver disease such as hepatitis B virus or hepatitis C virus infection. One of the promising treatments for HCC is liver transplantation, in which a diseased liver is replaced with a healthy liver from another person. However, recurrence of HCC after surgery is a significant problem. Therefore, it is important to discover reliable cellular biomarkers that can predict recurrence in HCC.

Methods

We analyzed previously published HCC RNA‐Seq data that includes 21 paired tumor and normal samples, in which nine tumors were recurrent after orthotopic liver transplantation and 12 were nonrecurrent tumors with their paired normal samples. We used both the reference genome and de novo transcriptome assembly based analyses to identify differentially expressed genes (DEG) and used RandomForest to discover biomarkers.

Results

We obtained 398 DEG using the Reference approach and 412 DEG using de novo assembly approach. Among these DEG, 258 genes were identified by both approaches. We further identified 30 biomarkers that could predict the recurrence. We used another independent HCC study that includes 50 patients normal and tumor samples. By using these 30 biomarkers, the prediction accuracy was 100% for normal condition and 98% for tumor condition. A group of Metallothionein was specifically discovered as biomarkers in both reference and de novo assembly approaches.

Conclusion

We identified a group of Metallothionein genes as biomarkers to predict recurrence. The metallothionein genes were all down‐regulated in tumor samples, suggesting that low metallothionein expression may be a promoter of tumor growth. In addition, using de novo assembly identified some unique biomarkers, further confirmed the necessity of conducting a de novo assembly in human cancer study.

Predicting stage-specific cancer related genes and their dynamic modules by integrating multiple datasets

BACKGROUND

The mechanism of many complex diseases has not been detected accurately in terms of their stage evolution. Previous studies mainly focus on the identification of associations between genes and individual diseases, but less is known about their associations with specific disease stages. Exploring biological modules through different disease stages could provide valuable knowledge to genomic and clinical research.

RESULTS

In this study, we proposed a powerful and versatile framework to identify stage-specific cancer related genes and their dynamic modules by integrating multiple datasets. The discovered modules and their specific-signature genes were significantly enriched in many relevant known pathways. To further illustrate the dynamic evolution of these clinical-stages, a pathway network was built by taking individual pathways as vertices and the overlapping relationship between their annotated genes as edges.

CONCLUSIONS

The identified pathway network not only help us to understand the functional evolution of complex diseases, but also useful for clinical management to select the optimum treatment regimens and the appropriate drugs for patients.

ZFR promotes cell proliferation and tumor development in colorectal and liver cancers

Colorectal cancer (CRC) and liver cancer are the second and fourth leading causes of cancer-related deaths in the whole world, respectively, and each year over 1.6 million people die from these diseases. To identify driver genes in CRC and liver cancer, we have performed Sleeping Beauty transposon mutagenesis screens in mouse models. Zinc finger RNA binding protein, ZFR, was one of the novel candidate cancer genes identified in these forward genetic screens. Consistent with this discovery, a pan-cancer analysis of sequencing results of thousands of human cancer genomes demonstrated that ZFR is a potential potent oncogene. In this study, we aimed to investigate ZFR's roles in both types of cancer and found that overexpression of ZFR in CRC and liver cancer cells led to accelerated tumor development. Consistently, knockdown of ZFR resulted in significantly decelerated tumor development. ZFR overexpression also promoted tumor development of immortalized mouse liver cells. ZFR overexpression and shRNA knockdown led to accelerated and decelerated cell proliferation, respectively, indicating that ZFR promotes tumor development mainly by regulating cell proliferation. To identify ZFR's targets in transcription, we performed whole transcriptome sequencing using ZFR small interfering RNAs in a primary human colon cell line. All potential target genes were validated by real time PCR. FAM49B was a tumor suppressor candidate for ZFR targets. When we knocked down the expression of FAM49B in CRC and liver cancer cells, we observed significantly accelerated cell proliferation, consistent with the results with ZFR overexpression. The results presented here demonstrate the oncogenic role of ZFR in both CRC and liver cancer, providing a potential drug target for both cancers' treatment. We also identified ZFR's potential transcriptional targets, and further investigations on those targets, especially FAM49B, will help us understand more about the important role of ZFR in digestive system cancers.

Precise targeting of POLR2A as a therapeutic strategy for human triple negative breast cancer

TP53 is the most frequently mutated or deleted gene in triple negative breast cancer (TNBC). Both the loss of TP53 and the lack of targeted therapy are significantly correlated with poor clinical outcomes, making TNBC the only type of breast cancer that has no approved targeted therapies. Through in silico analysis, we identified POLR2A in the TP53-neighbouring region as a collateral vulnerability target in TNBC tumours, suggesting that its inhibition via small interfering RNA (siRNA) may be an amenable approach for TNBC targeted treatment. To enhance bioavailability and improve endo/lysosomal escape of siRNA, we designed pH-activated nanoparticles for augmented cytosolic delivery of POLR2A siRNA (siPol2). Suppression of POLR2A expression with the siPol2-laden nanoparticles leads to enhanced growth reduction of tumours characterized by hemizygous POLR2A loss. These results demonstrate the potential of the pH-responsive nanoparticle and the precise POLR2A targeted therapy in TNBC harbouring the common TP53 genomic alteration.

Comparable Genomic Copy Number Aberrations Differ across Astrocytoma Malignancy Grades

A collection of intracranial astrocytomas of different malignancy grades was analyzed for copy number aberrations (CNA) in order to identify regions that are driving cancer pathogenesis. Astrocytomas were analyzed by Array Comparative Genomic Hybridization (aCGH) and bioinformatics utilizing a Bioconductor package, Genomic Identification of Significant Targets in Cancer (GISTIC) 2.0.23 and DAVID software. Altogether, 1438 CNA were found of which losses prevailed. On our total sample, significant deletions affected 14 chromosomal regions, out of which deletions at 17p13.2, 9p21.3, 13q12.11, 22q12.3 remained significant even at 0.05 q-value. When divided into malignancy groups, the regions identified as significantly deleted in high grades were: 9p21.3; 17p13.2; 10q24.2; 14q21.3; 1p36.11 and 13q12.11, while amplified were: 3q28; 12q13.3 and 21q22.3. Low grades comprised significant deletions at 3p14.3; 11p15.4; 15q15.1; 16q22.1; 20q11.22 and 22q12.3 indicating their involvement in early stages of tumorigenesis. Significantly enriched pathways were: PI3K-Akt, Cytokine-cytokine receptor, the nucleotide-binding oligomerization domain (NOD)–like receptor, Jak-STAT, retinoic acid-inducible gene (RIG)-I-like receptor and Toll-like receptor pathways. HPV and herpex simplex infection and inflammation pathways were also represented. The present study brings new data to astrocytoma research amplifying the wide spectrum of changes that could help us identify the regions critical for tumorigenesis.

Genomics for All: International Open Science Genomics Projects and Capacity Building in the Developing World

Genomic medicine applications have the potential to considerably improve health care in developing countries in the coming years. However, if developing countries do not improve their capacity for research and development (R&D) in the field, they might be left out of the genomics revolution. Large-scale and widely accessible databases for storing and analyzing genomic data are crucial tools for the advancement of genomic medicine. Building developing countries' capacity in genomics is accordingly closely linked to their involvement in international human genomics research initiatives. The purpose of this paper is to conduct a pilot study on the impact of international open science genomics projects on capacity building in R&D in developing countries. Using indicators we developed in previous work to measure the performance of international open science genomics projects, we analyse the policies and practices of four key projects in the field: the International HapMap Project, the Human Heredity and Health in Africa Initiative, the Malaria Genomic Epidemiology Network and the Structural Genomics Consortium. The results show that these projects play an important role in genomics capacity building in developing countries, but play a more limited role with regard to the potential redistribution of the benefits of research to the populations of these countries. We further suggest concrete initiatives that could facilitate the involvement of researchers from developing countries in the international genomics research community and accelerate capacity building in the developing world.

Development of a risk scoring system for patients with papillary thyroid cancer

As the importance of personalized therapeutics in aggressive papillary thyroid cancer (PTC) increases, accurate risk stratification is required. To develop a novel prognostic scoring system for patients with PTC (n = 455), we used mRNA expression and clinical data from The Cancer Genome Atlas. We performed variable selection using Network-Regularized high-dimensional Cox-regression with gene network from pathway databases. The risk score was calculated using a linear combination of regression coefficients and mRNA expressions. The risk score and clinical variables were assessed by several survival analyses. The risk score showed high discriminatory power for the prediction of event-free survival as well as the presence of metastasis. In multivariate analysis, the risk score and presence of metastasis were significant risk factors among the clinical variables that were examined together. In the current study, we developed a risk scoring system that will help to identify suitable therapeutic options for PTC.

Temporal dynamics in meta longitudinal RNA-Seq data

Identification of differentially expressed genes has been a high priority task of downstream analyses to further advances in biomedical research. Investigators have been faced with an array of issues in dealing with more complicated experiments and metadata, including batch effects, normalization, temporal dynamics (temporally differential expression), and isoform diversity (isoform-level quantification and differential splicing events). To date, there are currently no standard approaches to precisely and efficiently analyze these moderate or large-scale experimental designs, especially with combined metadata. In this report, we propose comprehensive analytical pipelines to precisely characterize temporal dynamics in differential expression of genes and other genomic features, i.e., the variability of transcripts, isoforms and exons, by controlling batch effects and other nuisance factors that could have significant confounding effects on the main effects of interest in comparative models and may result in misleading interpretations.

The many faces of mathematical modelling in oncology

The application of modelling to solve problems in biology and medicine, and specifically in oncology and radiation therapy, is increasingly established and holds big promise. We provide an overview of the basic concepts of the field and its current state, along with new tools available and future directions for research. We will outline radiobiology models, examples of other anticancer therapy models, multiscale modelling, and we will discuss mechanistic and phenomenological approaches to modelling.

Trends in the characteristics of human functional genomic data on the gene expression omnibus, 2001-2017

The gene expression omnibus (GEO) is the world's largest public repository of functional genomic data. Despite its broad use in secondary genomic analyses, the temporal trends in the characteristics of genomic data on GEO, including experimental procedures, geographic origin, funder(s), and related disease, have not been examined. We identified 75,376 Series deposited to the GEO during 2001-2017 and built a database of all human genomic data (39,076 Series, 51.8% of all Series). Using the associated publications, we obtained funding information and identified the related disease area. Of the Series with classified disease areas, the two most common were cancer (n = 12,688, 32.5%) and immunologic diseases (n = 2,393, 6.1%), while the percentages of all other disease areas were below 5%, including neurological diseases (n = 1733, 4.4%), infectious diseases (n = 1225, 3.1%), diabetes (n = 828, 2.1%), and cardiovascular diseases (n = 299, 0.8%). In recent years, there has been a significant increase in the use of high-throughput sequencing (HTS), protein array and multiple-platform technologies, as well as in the proportion of North American deposits. Compared to those from other regions, North American deposits appeared to lead the shift from array-based to HTS technologies (odds ratio [OR], 95% confidence intervals [CI] = 3.39, 3.23-3.55, P = 9.40E-323), and were less likely to focus on a major disease area (OR = 0.64, 95% CI: 0.61-0.67, P = 5.02E-107), suggesting a greater emphasis on basic science in North America. Furthermore, the Series utilizing HTS were less likely to be disease-classified compared to other technologies (OR = 0.39, 95% CI: 0.37-0.41, P = 1.00E-322), suggesting a preferential use or adoption of HTS in basic science settings. Finally, funding from the NHGRI, NCI, NIEHS, and NCCR resulted in a higher number of GEO Series per grant than other NIH institutes, demonstrating different preferences on genomic studies among awardees of NIH institutes. Our findings demonstrate geographic, technological, and funding disparities in the trends of GEO deposit characteristics.

DEP domain containing 1 predicts prognosis of hepatocellular carcinoma patients and regulates tumor proliferation and metastasis

DEP domain containing 1 (DEPDC1) protein is a novel oncoantigen upregulated in multiple types of cancers which present oncogenic activity and high immunogenicity. However, the function and therapeutic potential of DEPDC1 in hepatocellular carcinoma (HCC) remain unclear. In the present study, we showed that DEPDC1 was frequently upregulated in HCC and associated with cancer diagnosis and poor prognosis for HCC patients. Moreover, DEPDC1 promotes HCC cell proliferation in vitro as well as carcinogenesis in vivo. Notably, DEPDC1 overexpression also increases the neoplasm metastasis ability of HCC cells both in vivo and in vitro. Gene set enrichment analysis results showed that DEPDC1 expression is positively correlated with K‐RAS signal pathway, pathways in cancer and WNT/β‐catenin signal pathway, all of which are closely associated with specific cancer‐related gene sets. Our study provides the basis for further investigation of the molecular mechanism by which DEPDC1 promotes the development and metastasis of HCC.

Using Semantic Web Technologies to Enable Cancer Genomics Discovery at Petabyte Scale

Increased efforts in cancer genomics research and bioinformatics are producing tremendous amounts of data. These data are diverse in origin, format, and content. As the amount of available sequencing data increase, technologies that make them discoverable and usable are critically needed. In response, we have developed a Semantic Web-based Data Browser, a tool allowing users to visually build and execute ontology-driven queries. This approach simplifies access to available data and improves the process of using them in analyses on the Seven Bridges Cancer Genomics Cloud (CGC; www.cancergenomicscloud.org). The Data Browser makes large data sets easily explorable and simplifies the retrieval of specific data of interest. Although initially implemented on top of The Cancer Genome Atlas (TCGA) data set, the Data Browser's architecture allows for seamless integration of other data sets. By deploying it on the CGC, we have enabled remote researchers to access data and perform collaborative investigations.

Bayesian variable selection with graphical structure learning: Applications in integrative genomics

Significant advances in biotechnology have allowed for simultaneous measurement of molecular data across multiple genomic, epigenomic and transcriptomic levels from a single tumor/patient sample. This has motivated systematic data-driven approaches to integrate multi-dimensional structured datasets, since cancer development and progression is driven by numerous co-ordinated molecular alterations and the interactions between them. We propose a novel multi-scale Bayesian approach that combines integrative graphical structure learning from multiple sources of data with a variable selection framework—to determine the key genomic drivers of cancer progression. The integrative structure learning is first accomplished through novel joint graphical models for heterogeneous (mixed scale) data, allowing for flexible and interpretable incorporation of prior existing knowledge. This subsequently informs a variable selection step to identify groups of co-ordinated molecular features within and across platforms associated with clinical outcomes of cancer progression, while according appropriate adjustments for multicollinearity and multiplicities. We evaluate our methods through rigorous simulations to establish superiority over existing methods that do not take the network and/or prior information into account. Our methods are motivated by and applied to a glioblastoma multiforme (GBM) dataset from The Cancer Genome Atlas to predict patient survival times integrating gene expression, copy number and methylation data. We find a high concordance between our selected prognostic gene network modules with known associations with GBM. In addition, our model discovers several novel cross-platform network interactions (both cis and trans acting) between gene expression, copy number variation associated gene dosing and epigenetic regulation through promoter methylation, some with known implications in the etiology of GBM. Our framework provides a useful tool for biomedical researchers, since clinical prediction using multi-platform genomic information is an important step towards personalized treatment of many cancers.

Precision Medicine in Relapsed and Refractory Childhood Cancers: Single-center Experience, Literature Review, and Meta-analysis

OBJECTIVE

To date, the understanding of pediatric tumor genomics and how these genetic aberrations correlate with clinical outcome is lacking. Here, we report our experience with the next-generation sequencing (NGS) test program and discuss implications for the inclusion of molecular profiling into clinical pediatric oncology trials. We also aimed to explore studies on NGS in pediatric cancers and to quantify the variability of finding actionable mutations and the clinical implications.

METHODS

We present a retrospective case series of all patients whose tumor tissue underwent NGS tests during treatment in our department. We also reviewed the literature and carried out a meta-analysis to explore studies on NGS in pediatric cancers.

RESULTS

In 35/37 (94%) patients, we found at least one genomic alteration (GA); mean number of GAs per patient was 2 (range, 0-67), while 164 GAs were detected. Only 3 (8%) patients received precision medicine due to their GAs for a mean of 9 months (range, 5-14 months). Four studies were included in the meta-analysis. The pooled positive actionable mutation rate was 52% (95% CI 39%-66%), and the pooled rate of children who received precision medicine was 10% (95% CI 3%-20%).

CONCLUSIONS

In children and young adults with high-risk, recurrent, or refractory malignancies, tumor profiling results have clinical implications, despite barriers to the use of matched precision therapy.

CRIMEtoYHU: a new web tool to develop yeast-based functional assays for characterizing cancer-associated missense variants

Evaluation of the functional impact of cancer-associated missense variants is more difficult than for protein-truncating mutations and consequently standard guidelines for the interpretation of sequence variants have been recently proposed. A number of algorithms and software products were developed to predict the impact of cancer-associated missense mutations on protein structure and function. Importantly, direct assessment of the variants using high-throughput functional assays using simple genetic systems can help in speeding up the functional evaluation of newly identified cancer-associated variants. We developed the web tool CRIMEtoYHU (CTY) to help geneticists in the evaluation of the functional impact of cancer-associated missense variants. Humans and the yeast Saccharomyces cerevisiae share thousands of protein-coding genes although they have diverged for a billion years. Therefore, yeast humanization can be helpful in deciphering the functional consequences of human genetic variants found in cancer and give information on the pathogenicity of missense variants. To humanize specific positions within yeast genes, human and yeast genes have to share functional homology. If a mutation in a specific residue is associated with a particular phenotype in humans, a similar substitution in the yeast counterpart may reveal its effect at the organism level. CTY simultaneously finds yeast homologous genes, identifies the corresponding variants and determines the transferability of human variants to yeast counterparts by assigning a reliability score (RS) that may be predictive for the validity of a functional assay. CTY analyzes newly identified mutations or retrieves mutations reported in the COSMIC database, provides information about the functional conservation between yeast and human and shows the mutation distribution in human genes. CTY analyzes also newly found mutations and aborts when no yeast homologue is found. Then, on the basis of the protein domain localization and functional conservation between yeast and human, the selected variants are ranked by the RS. The RS is assigned by an algorithm that computes functional data, type of mutation, chemistry of amino acid substitution and the degree of mutation transferability between human and yeast protein. Mutations giving a positive RS are highly transferable to yeast and, therefore, yeast functional assays will be more predictable. To validate the web application, we have analyzed 8078 cancer-associated variants located in 31 genes that have a yeast homologue. More than 50% of variants are transferable to yeast. Incidentally, 88% of all transferable mutations have a reliability score >0. Moreover, we analyzed by CTY 72 functionally validated missense variants located in yeast genes at positions corresponding to the human cancer-associated variants. All these variants gave a positive RS. To further validate CTY, we analyzed 3949 protein variants (with positive RS) by the predictive algorithm PROVEAN. This analysis shows that yeast-based functional assays will be more predictable for the variants with positive RS. We believe that CTY could be an important resource for the cancer research community by providing information concerning the functional impact of specific mutations, as well as for the design of functional assays useful for decision support in precision medicine.