Pan-cancer analysis of whole genomes

Current methods in translational cancer research

Recent developments in pre-clinical screening tools, that more reliably predict the clinical effects and adverse events of candidate therapeutic agents, has ushered in a new era of drug development and screening. However, given the rapid pace with which these models have emerged, the individual merits of these translational research tools warrant careful evaluation in order to furnish clinical researchers with appropriate information to conduct pre-clinical screening in an accelerated and rational manner. This review assesses the predictive utility of both well-established and emerging pre-clinical methods in terms of their suitability as a screening platform for treatment response, ability to represent pharmacodynamic and pharmacokinetic drug properties, and lastly debates the translational limitations and benefits of these models. To this end, we will describe the current literature on cell culture, organoids, in vivo mouse models, and in silico computational approaches. Particular focus will be devoted to discussing gaps and unmet needs in the literature as well as current advancements and innovations achieved in the field, such as co-clinical trials and future avenues for refinement.

Comprehensive analysis of cancer breakpoints reveals signatures of genetic and epigenetic contribution to cancer genome rearrangements

Understanding mechanisms of cancer breakpoint mutagenesis is a difficult task and predictive models of cancer breakpoint formation have to this time failed to achieve even moderate predictive power. Here we take advantage of a machine learning approach that can gather important features from big data and quantify contribution of different factors. We performed comprehensive analysis of almost 630,000 cancer breakpoints and quantified the contribution of genomic and epigenomic features-non-B DNA structures, chromatin organization, transcription factor binding sites and epigenetic markers. The results showed that transcription and formation of non-B DNA structures are two major processes responsible for cancer genome fragility. Epigenetic factors, such as chromatin organization in TADs, open/closed regions, DNA methylation, histone marks are less informative but do make their contribution. As a general trend, individual features inside the groups show a relatively high contribution of G-quadruplexes and repeats and CTCF, GABPA, RXRA, SP1, MAX and NR2F2 transcription factors. Overall, the cancer breakpoint landscape can be represented by well-predicted hotspots and poorly predicted individual breakpoints scattered across genomes. We demonstrated that hotspot mutagenesis has genomic and epigenomic factors, and not all individual cancer breakpoints are just random noise but have a definite mutation signature. Besides we found a long-range action of some features on breakpoint mutagenesis. Combining omics data, cancer-specific individual feature importance and adding the distant to local features, predictive models for cancer breakpoint formation achieved 70-90% ROC AUC for different cancer types; however precision remained low at 2% and the recall did not exceed 50%. On the one hand, the power of models strongly correlates with the size of available cancer breakpoint and epigenomic data, and on the other hand finding strong determinants of cancer breakpoint formation still remains a challenge. The strength of predictive signals of each group and of each feature inside a group can be converted into cancer-specific breakpoint mutation signatures. Overall our results add to the understanding of cancer genome rearrangement processes.

Small non-coding RNAs in human cancer: function, clinical utility, and characterization

Small non-coding RNAs (sncRNAs) play critical roles in multiple regulatory processes, including transcription, post-transcription, and translation. Emerging evidence reveals the critical roles of sncRNAs in cancer development and their potential role as biomarkers and/or therapeutic targets. In this paper, we review recent research on four sncRNA species with functional significance in cancer: small nucleolar RNAs, transfer RNA, small nuclear RNAs, and piwi-interacting RNAs. We introduce their functional roles in tumorigenesis and discuss the potential utility of sncRNAs as prognostic and diagnostic biomarkers and therapeutic targets. We further summarize approaches to characterize sncRNAs in a high-throughput manner, including the specific library construction and computational framework. Our review provides a perspective of the functions, clinical utility, and characterization of sncRNAs in cancer.

Clinical-grade whole-genome sequencing and 3' transcriptome analysis of colorectal cancer patients

BACKGROUND

Clinical-grade whole-genome sequencing (cWGS) has the potential to become the standard of care within the clinic because of its breadth of coverage and lack of bias towards certain regions of the genome. Colorectal cancer presents a difficult treatment paradigm, with over 40% of patients presenting at diagnosis with metastatic disease. We hypothesised that cWGS coupled with 3' transcriptome analysis would give new insights into colorectal cancer.

METHODS

Patients underwent PCR-free whole-genome sequencing and alignment and variant calling using a standardised pipeline to output SNVs, indels, SVs and CNAs. Additional insights into the mutational signatures and tumour biology were gained by the use of 3' RNA-seq.

RESULTS

Fifty-four patients were studied in total. Driver analysis identified the Wnt pathway gene APC as the only consistently mutated driver in colorectal cancer. Alterations in the PI3K/mTOR pathways were seen as previously observed in CRC. Multiple private CNAs, SVs and gene fusions were unique to individual tumours. Approximately 30% of patients had a tumour mutational burden of > 10 mutations/Mb of DNA, suggesting suitability for immunotherapy.

CONCLUSIONS

Clinical whole-genome sequencing offers a potential avenue for the identification of private genomic variation that may confer sensitivity to targeted agents and offer patients new options for targeted therapies.

Runx3 Induces a Cell Shape Change and Suppresses Migration and Metastasis of Melanoma Cells by Altering a Transcriptional Profile

Runt-related transcription factor-3 (Runx3) is a tumor suppressor, and its contribution to melanoma progression remains unclear. We previously demonstrated that Runx3 re-expression in B16-F10 melanoma cells changed their shape and attenuated their migration. In this study, we found that Runx3 re-expression in B16-F10 cells also suppressed their pulmonary metastasis. We performed microarray analysis and uncovered an altered transcriptional profile underlying the cell shape change and the suppression of migration and metastasis. This altered transcriptional profile was rich in Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) annotations relevant to adhesion and the actin cytoskeleton and included differentially expressed genes for some major extracellular matrix (ECM) proteins as well as genes that were inversely associated with the increase in the metastatic potential of B16-F10 cells compared to B16-F0 melanoma cells. Further, we found that this altered transcriptional profile could have prognostic value, as evidenced by myelin and lymphocyte protein (MAL) and vilin-like (VILL). Finally, Mal gene expression was correlated with metastatic potential among the cells and was targeted by histone deacetylase (HDAC) inhibitors in B16-F10 cells, and the knockdown of Mal gene expression in B16-F0 cells changed their shape and enhanced the migratory and invasive traits of their metastasis. Our study suggests that self-entrapping of metastatic Runx3-negative melanoma cells via adhesion and the actin cytoskeleton could be a powerful therapeutic strategy.

The evolving paradigm of biomarker actionability: Histology-agnosticism as a spectrum, rather than a binary quality

Precision medicine is progressively revolutionizing oncology, through the identification of biomarkers predictive of treatment response in cancer patients. For three of such biomarkers, namely NTRK-fusions, microsatellite instability and high tumor mutational burden, drugs have been approved by regulatory agencies regardless of tumor histology, realizing the paradigm of histology-agnostic actionability. Several additional biomarkers are being studied in a histology-agnostic manner, and may in the future expand this list. However, most available evidence suggest that histology-agnosticism may be the extreme of a continuous spectrum of actionability, rather than a binary quality. The present review recapitulates such evidence, highlighting opportunities and challenges posed by the emergence of the spectrum of biomarker actionability in the context of a prevalently histology-based oncology.

No evidence of EMAST in whole genome sequencing data from 248 colorectal cancers

Microsatellite instability (MSI) is caused by defective DNA mismatch repair (MMR), and manifests as accumulation of small insertions and deletions (indels) in short tandem repeats of the genome. Another form of repeat instability, elevated microsatellite alterations at selected tetranucleotide repeats (EMAST), has been suggested to occur in 50% to 60% of colorectal cancer (CRC), of which approximately one quarter are accounted for by MSI. Unlike for MSI, the criteria for defining EMAST is not consensual. EMAST CRCs have been suggested to form a distinct subset of CRCs that has been linked to a higher tumor stage, chronic inflammation, and poor prognosis. EMAST CRCs not exhibiting MSI have been proposed to show instability of di- and trinucleotide repeats in addition to tetranucleotide repeats, but lack instability of mononucleotide repeats. However, previous studies on EMAST have been based on targeted analysis of small sets of marker repeats, often in relatively few samples. To gain insight into tetranucleotide instability on a genome-wide level, we utilized whole genome sequencing data from 227 microsatellite stable (MSS) CRCs, 18 MSI CRCs, 3 POLE-mutated CRCs, and their corresponding normal samples. As expected, we observed tetranucleotide instability in all MSI CRCs, accompanied by instability of mono-, di-, and trinucleotide repeats. Among MSS CRCs, some tumors displayed more microsatellite mutations than others as a continuum, and no distinct subset of tumors with the previously proposed molecular characters of EMAST could be observed. Our results suggest that tetranucleotide repeat mutations in non-MSI CRCs represent stochastic mutation events rather than define a distinct CRC subclass.

Recent Advances in Integrative Multi-Omics Research in Breast and Ovarian Cancer

The underlying molecular heterogeneity of cancer is responsible for the dynamic clinical landscape of this disease. The combination of genomic and proteomic alterations, including both inherited and acquired mutations, promotes tumor diversity and accounts for variable disease progression, therapeutic response, and clinical outcome. Recent advances in high-throughput proteogenomic profiling of tumor samples have resulted in the identification of novel oncogenic drivers, tumor suppressors, and signaling networks; biomarkers for the prediction of drug sensitivity and disease progression; and have contributed to the development of novel and more effective treatment strategies. In this review, we will focus on the impact of historical and recent advances in single platform and integrative proteogenomic studies in breast and ovarian cancer, which constitute two of the most lethal forms of cancer for women, and discuss the molecular similarities of these diseases, the impact of these findings on our understanding of tumor biology as well as the clinical applicability of these discoveries.

The Role of the Pathologist in the Next-Generation Era of Tumor Molecular Characterization

Current pathology practice is being shaped by the increasing complexity of modern medicine, in particular of precision oncology, and major technological advances. In the "next-generation technologies era", the pathologist has become the person responsible for the integration and interpretation of morphologic and molecular information and for the delivery of critical answers to diagnostic, prognostic and predictive queries, acquiring a prominent position in the molecular tumor boards.

Subtype-GAN: a deep learning approach for integrative cancer subtyping of multi-omics data

MOTIVATION

The discovery of cancer subtyping can help explore cancer pathogenesis, determine clinical actionability in treatment, and improve patients' survival rates. However, due to the diversity and complexity of multi-omics data, it is still challenging to develop integrated clustering algorithms for tumor molecular subtyping.

RESULTS

We propose Subtype-GAN, a deep adversarial learning approach based on the multiple-input multiple-output neural network to model the complex omics data accurately. With the latent variables extracted from the neural network, Subtype-GAN uses consensus clustering and the Gaussian Mixture model to identify tumor samples' molecular subtypes. Compared with other state-of-the-art subtyping approaches, Subtype-GAN achieved outstanding performance on the benchmark data sets consisting of ∼4,000 TCGA tumors from 10 types of cancer. We found that on the comparison data set, the clustering scheme of Subtype-GAN is not always similar to that of the deep learning method AE but is identical to that of NEMO, MCCA, VAE, and other excellent approaches. Finally, we applied Subtype-GAN to the BRCA data set and automatically obtained the number of subtypes and the subtype labels of 1031 BRCA tumors. Through the detailed analysis, we found that the identified subtypes are clinically meaningful and show distinct patterns in the feature space, demonstrating the practicality of Subtype-GAN.

AVAILABILITY

The source codes, the clustering results of Subtype-GAN across the benchmark data sets are available at https://github.com/haiyang1986/Subtype-GAN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Galaxy and MEAN Stack to Create a User-Friendly Workflow for the Rational Optimization of Cancer Chemotherapy

One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to subnetwork entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, the most relevant targets identified by the pipeline are stored in MongoDB and can be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.

Hawk-Seq™ differentiates between various mutations in Salmonella typhimurium TA100 strain caused by exposure to Ames test-positive mutagens

A precise understanding of differences in genomic mutations according to the mutagenic mechanisms detected in mutagenicity data is required to evaluate the carcinogenicity of environmental mutagens. Recently, we developed a highly accurate genome sequencing method, ‘Hawk-Seq™’, that enables the detection of mutagen-induced genome-wide mutations. However, its applicability to detect various mutagens and identify differences in mutational profiles is not well understood. Thus, we evaluated DNA samples from Salmonella typhimurium TA100 exposed to 11 mutagens, including alkylating agents, aldehydes, an aromatic nitro compound, epoxides, aromatic amines and polycyclic aromatic hydrocarbons (PAHs). We extensively analysed mutagen-induced mutational profiles and studied their association with the mechanisms of mutagens. Hawk-Seq™ sensitively detected mutations induced by all 11 mutagens, including one that increased the number of revertants by approximately 2-fold in the Ames test. Although the sensitivity for less water-soluble mutagens was relatively low, we increased the sensitivity to obtain high-resolution spectra by modifying the exposure protocol. Moreover, two epoxides indicated similar 6- or 96-dimensional mutational patterns; likewise, three S_N1-type alkylating agents indicated similar mutational patterns, suggesting that the mutational patterns are compound category specific. Meanwhile, an S_N2 type alkylating agent exhibited unique mutational patterns compared to those of the S_N1 type alkylating agents. Although the mutational patterns induced by aldehydes, the aromatic nitro compound, aromatic amines and PAHs did not differ substantially from each other, the maximum total base substitution frequencies (MTSFs) were similar among mutagens in the same structural groups. Furthermore, the MTSF was found to be associated with the carcinogenic potency of some direct-acting mutagens. These results indicate that our method can generate high-resolution mutational profiles to identify characteristic features of each mutagen. The detailed mutational data obtained by Hawk-Seq™ can provide useful information regarding mutagenic mechanisms and help identify its association with the carcinogenicity of mutagens without requiring carcinogenicity data.

Zic Family Member 2 (ZIC2): a Potential Diagnostic and Prognostic Biomarker for Pan-Cancer

Background: As a transcription factor, Zinc finger protein ZIC2 can interact with various DNAs and proteins. Current studies have shown that ZIC2 plays an oncogene role in various cancers. In this study, we systematically characterize the prevalence and predictive value of ZIC2 expression across multiple cancer types. Methods: We mined several public databases, including Oncomine, the Cancer Genome Atlas (TCGA), cBioPortal, Kaplan-Meier Plotter and PrognoScan to evaluated the differentially expressed ZIC2 between tumor samples and normal control samples in pan-cancner, and then explored the association between ZIC2 expression and patient survival, prognosis and clinicopathologic stage. We also analyzed the relationship between tumor mutation burden (TMB), microsatellite instability (MSI), tumor microenvironment, tumor- and immune-related genes and ZIC2 expression. Finally, we explored the potential signaling pathway mechanism through gene set enrichment analysis (GSEA). Results: ZIC2 expression was higher in most cancer tissues compared with adjacent normal tissues. High ZIC2 expression was associated with worse prognosis and a higher clinicopathologic stage. ZIC2 expression was strongly associated with the TMB, MSI, tumor microenvironment and tumor- and immune-related genes. The GSEA revealed that multiple tumor- and immune-related pathways were differentially enriched in ZIC2 high or low expression phenotype. Conclusion: ZIC2 expression may be a potential prognostic molecular biomarker of poor survival in pan-cancer and may act as an oncogene with a strong effect in the processes of tumorigenesis and progression.

Aberrant phase separation and cancer

Eukaryotic cells are intracellularly divided into numerous compartments or organelles, which coordinate specific molecules and biological reactions. Membrane-bound organelles are physically separated by lipid bilayers from the surrounding environment. Biomolecular condensates, also referred to membraneless organelles, are micron-scale cellular compartments that lack membranous enclosures but function to concentrate proteins and RNA molecules, and these are involved in diverse processes. Liquid-liquid phase separation (LLPS) driven by multivalent weak macromolecular interactions is a critical principle for the formation of biomolecular condensates, and a multitude of combinations among multivalent interactions may drive liquid-liquid phase transition (LLPT). Dysregulation of LLPS and LLPT leads to aberrant condensate and amyloid formation, which causes many human diseases, including neurodegeneration and cancer. Here, we describe recent findings regarding abnormal forms of biomolecular condensates and aggregation via aberrant LLPS and LLPT of cancer-related proteins in cancer development driven by mutation and fusion of genes. Moreover, we discuss the regulatory mechanisms by which aberrant LLPS and LLPT occur in cancer and the drug candidates targeting these mechanisms. Further understanding of the molecular events regulating how biomolecular condensates and aggregation form in cancer tissue is critical for the development of therapeutic strategies against tumorigenesis.

Decoding the Evolutionary Response to Ensartinib in Patients With ALK-Positive NSCLC by Dynamic Circulating Tumor DNA Sequencing

INTRODUCTION

By implementing dynamic circulating tumor DNA (ctDNA) analysis, we explored the impact of TP53 mutations on tumor evolution and resistance mechanisms to ensartinib in patients with ALK-positive NSCLC.

METHODS

In a multicenter phase 2 trial, patients with ALK-positive NSCLC who progressed on crizotinib were treated with ensartinib. Blood samples for ctDNA analysis were collected at baseline, cycle 3 day 1, and progression disease (PD) and analyzed with a 212-gene panel.

RESULTS

A total of 440 samples were collected from 168 patients. Baseline TP53 mutations (20.2%) significantly correlated with inferior progression-free survival (4.2 mo versus 11.7 mo, p < 0.0001). Patients with TP53 mutations had higher mutation load than those without TP53 mutations at baseline (13.79 ± 3.72 versus 4.67 ± 0.39, p < 0.001). Although there was no significant difference in mutation load between these groups at cycle 3 day 1 (5.89 ± 2.25 versus 3.72 ± 0.62, p = 0.425), patients with mutated TP53 developed more mutations at PD (7.07 ± 1.25 versus 3.20 ± 0.33, p = 0.003). Frequency and abundance of secondary ALK mutations G1269A, G1202R, and E1210K increased markedly at PD than baseline. In patients without secondary ALK mutations, we identified ALK-independent resistance mechanisms including bypass signaling activation, downstream effector protein reactivation, epithelial-mesenchymal transformation, and epigenetic dysregulation.

CONCLUSIONS

Our study highlighted the advantage of ctDNA analysis for monitoring tumor evolution. TP53 mutations promoted genetic evolution and accelerated occurrence of resistance. We also unveiled ALK-dependent resistance mechanisms, mainly by G1269A, G1202R, and E1210K mutations, and ALK-independent resistance mechanisms to ensartinib.

Chromothripsis, DNA repair and checkpoints defects

Chromothripsis is a unique form of genome instability characterized by tens to hundreds of DNA double-strand breaks on one or very few chromosomes, followed by error-prone repair. The derivative chromosome(s) display massive rearrangements, which lead to the loss of tumor suppressor function and to the activation of oncogenes. Chromothripsis plays a major role in cancer as well as in other conditions, such as congenital diseases. In this review, we discuss the repair processes involved in the rejoining of the chromosome fragments, the role of DNA repair and checkpoint defects as a cause for chromothripsis as well as DNA repair defects resulting from chromothripsis. Finally, we consider clinical implications and potential therapeutic vulnerabilities that could be utilized to eliminate tumor cells with chromothripsis.

A pediatric brain tumor atlas of genes deregulated by somatic genomic rearrangement

The global impact of somatic structural variants (SSVs) on gene expression in pediatric brain tumors has not been thoroughly characterised. Here, using whole-genome and RNA sequencing from 854 tumors of more than 30 different types from the Children's Brain Tumor Tissue Consortium, we report the altered expression of hundreds of genes in association with the presence of nearby SSV breakpoints. SSV-mediated expression changes involve gene fusions, altered cis-regulation, or gene disruption. SSVs considerably extend the numbers of patients with tumors somatically altered for critical pathways, including receptor tyrosine kinases (KRAS, MET, EGFR, NF1), Rb pathway (CDK4), TERT, MYC family (MYC, MYCN, MYB), and HIPPO (NF2). Compared to initial tumors, progressive or recurrent tumors involve a distinct set of SSV-gene associations. High overall SSV burden associates with TP53 mutations, histone H3.3 gene H3F3C mutations, and the transcription of DNA damage response genes. Compared to adult cancers, pediatric brain tumors would involve a different set of genes with SSV-altered cis-regulation. Our comprehensive and pan-histology genomic analyses reveal SSVs to play a major role in shaping the transcriptome of pediatric brain tumors.

Protein dynamics analysis identifies candidate cancer driver genes and mutations in TCGA data

Recently, it has been showed that cancer missense mutations selectively target the neighborhood of hinge residues, which are key sites in protein dynamics. Here, we show that this approach can be extended to find previously unknown candidate mutations and genes. To this aim, we developed a computational pipeline to detect significantly enriched three-dimensional (3D) clustering of missense mutations around hinge residues. The hinge residues were detected by applying a Gaussian network model. By systematically analyzing the PanCancer compendium of somatic mutations in nearly 10 000 tumors from the Cancer Genome Atlas, we identified candidate genes and mutations in addition to well known ones. For instance, we found significantly enriched 3D clustering of missense mutations in known cancer genes including CDK4, CDKN2A, TCL1A, and MAPK1. Beside these known genes, we also identified significantly enriched 3D clustering of missense mutations around hinge residues in PLA2G4A, which may lead to excessive phosphorylation of the extracellular signal-regulated kinases. Furthermore, we demonstrated that hinge-based features improves pathogenicity prediction for missense mutations. Our results show that the consideration of clustering around hinge residues can help us explain the functional role of the mutations in known cancer genes and identify candidate genes.

Comprehensive characterization of protein-protein interactions perturbed by disease mutations

Technological and computational advances in genomics and interactomics have made it possible to identify how disease mutations perturb protein-protein interaction (PPI) networks within human cells. Here, we show that disease-associated germline variants are significantly enriched in sequences encoding PPI interfaces compared to variants identified in healthy participants from the projects 1000 Genomes and ExAC. Somatic missense mutations are also significantly enriched in PPI interfaces compared to noninterfaces in 10,861 tumor exomes. We computationally identified 470 putative oncoPPIs in a pan-cancer analysis and demonstrate that oncoPPIs are highly correlated with patient survival and drug resistance/sensitivity. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay, and also demonstrate the functional consequences of two of these on tumor cell growth. In summary, this human interactome network framework provides a powerful tool for prioritization of alleles with PPI-perturbing mutations to inform pathobiological mechanism- and genotype-based therapeutic discovery.

Mutational processes in cancer preferentially affect binding of particular transcription factors

Protein binding microarrays provide comprehensive information about the DNA binding specificities of transcription factors (TFs), and can be used to quantitatively predict the effects of DNA sequence variation on TF binding. There has also been substantial progress in dissecting the patterns of mutations, i.e., the "mutational signatures", generated by different mutational processes. By combining these two layers of information we can investigate whether certain mutational processes tend to preferentially affect binding of particular classes of TFs. Such preferential alterations of binding might predispose to particular oncogenic pathways. We developed and implemented a method, termed "Signature-QBiC", that integrates protein binding microarray data with the signatures of mutational processes, with the aim of predicting which TFs’ binding profiles are preferentially perturbed by particular mutational processes. We used Signature-QBiC to predict the effects of 47 signatures of mutational processes on 582 human TFs. Pathway analysis showed that binding of TFs involved in NOTCH1 signaling is strongly affected by the signatures of several mutational processes, including exposure to ultraviolet radiation. Additionally, toll-like-receptor signaling pathways are also vulnerable to disruption by this exposure. This study provides a novel overview of the effects of mutational processes on TF binding and the potential of these processes to activate oncogenic pathways through mutating TF binding sites.

Fusion Genes and RNAs in Cancer Development

Fusion RNAs are a hallmark of some cancers. They result either from chromosomal rearrangements or from splicing mechanisms that are non-chromosomal rearrangements. Chromosomal rearrangements that result in gene fusions are particularly prevalent in sarcomas and hematopoietic malignancies; they are also common in solid tumors. The splicing process can also give rise to more complex RNA patterns in cells. Gene fusions frequently affect tyrosine kinases, chromatin regulators, or transcription factors, and can cause constitutive activation, enhancement of downstream signaling, and tumor development, as major drivers of oncogenesis. In addition, some fusion RNAs have been shown to function as noncoding RNAs and to affect cancer progression. Fusion genes and RNAs will therefore become increasingly important as diagnostic and therapeutic targets for cancer development. Here, we discuss the function, biogenesis, detection, clinical relevance, and therapeutic implications of oncogenic fusion genes and RNAs in cancer development. Further understanding the molecular mechanisms that regulate how fusion RNAs form in cancers is critical to the development of therapeutic strategies against tumorigenesis.

Whole genome sequencing of skull-base chordoma reveals genomic alterations associated with recurrence and chordoma-specific survival

Chordoma is a rare bone tumor with an unknown etiology and high recurrence rate. Here we conduct whole genome sequencing of 80 skull-base chordomas and identify PBRM1, a SWI/SNF (SWItch/Sucrose Non-Fermentable) complex subunit gene, as a significantly mutated driver gene. Genomic alterations in PBRM1 (12.5%) and homozygous deletions of the CDKN2A/2B locus are the most prevalent events. The combination of PBRM1 alterations and the chromosome 22q deletion, which involves another SWI/SNF gene (SMARCB1), shows strong associations with poor chordoma-specific survival (Hazard ratio [HR] = 10.55, 95% confidence interval [CI] = 2.81-39.64, p = 0.001) and recurrence-free survival (HR = 4.30, 95% CI = 2.34-7.91, p = 2.77 × 10-6). Despite the low mutation rate, extensive somatic copy number alterations frequently occur, most of which are clonal and showed highly concordant profiles between paired primary and recurrence/metastasis samples, indicating their importance in chordoma initiation. In this work, our findings provide important biological and clinical insights into skull-base chordoma.

A Cluster-Based Approach for Identifying Prognostic microRNA Signatures in Digestive System Cancers

Cancer remains the second leading cause of death all over the world. Aberrant expression of miRNA has shown diagnostic and prognostic value in many kinds of cancer. This study aims to provide a novel strategy to identify reliable miRNA signatures and develop improved cancer prognostic models from reported cancer-associated miRNAs. We proposed a new cluster-based approach to identify distinct cluster(s) of cancers and corresponding miRNAs. Further, with samples from TCGA and other independent studies, we identified prognostic markers and validated their prognostic value in prediction models. We also performed KEGG pathway analysis to investigate the functions of miRNAs associated with the cancer cluster of interest. A distinct cluster with 28 cancers and 146 associated miRNAs was identified. This cluster was enriched by digestive system cancers. Further, we screened out 8 prognostic miRNA signatures for STAD, 5 for READ, 18 for PAAD, 24 for LIHC, 12 for ESCA and 18 for COAD. These identified miRNA signatures demonstrated strong abilities in discriminating the overall survival time between high-risk group and low-risk group (p-value < 0.05) in both TCGA training and test datasets, as well as four independent Gene Expression Omnibus (GEO) validation datasets. We also demonstrated that these cluster-based miRNA signatures are superior to signatures identified in single cancers for prognosis. Our study identified significant miRNA signatures with improved prognosis accuracy in digestive system cancers. It also provides a novel method/strategy for cancer prognostic marker selection and offers valuable methodological directions to similar research topics.

Pathogenicity assessment of variants for breast cancer susceptibility genes based on BRCAness of tumor sample

Genes involved in the homologous recombination repair pathway—as exemplified by BRCA1, BRCA2, PALB2, ATM, and CHEK2—are frequently associated with hereditary breast and ovarian cancer syndrome. Germline mutations in the loci of these genes with loss of heterozygosity or additional somatic truncation at the WT allele lead to the development of breast cancers with characteristic clinicopathological features and prominent genomic features of homologous recombination deficiency, otherwise referred to as “BRCAness.” Although clinical genetic testing for these and other genes has increased the chances of identifying pathogenic variants, there has also been an increase in the prevalence of variants of uncertain significance, which poses a challenge to patient care because of the difficulties associated with making further clinical decisions. To overcome this challenge, we sought to develop a methodology to reclassify the pathogenicity of these unknown variants using statistical modeling of BRCAness. The model was developed with Lasso logistic regression by comparing 116 genomic attributes derived from 37 BRCA1/2 biallelic mutant and 32 homologous recombination‐quiescent breast cancer exomes. The model showed 95.8% and 86.7% accuracies in the training cohort and The Cancer Genome Atlas validation cohort, respectively. Through application of the model for variant reclassification of homologous recombination‐associated hereditary breast and ovarian cancer causal genes and further assessment with clinicopathological features, we finally identified one likely pathogenic and five likely benign variants. As such, the BRCAness model developed from the tumor exome was robust and provided a reasonable basis for variant reclassification.

Unravelling the Sequential Interplay of Mutational Mechanisms during Clonal Evolution in Relapsed Pediatric Acute Lymphoblastic Leukemia

Pediatric acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy and is characterized by clonal heterogeneity. Genomic mutations can increase proliferative potential of leukemic cells and cause treatment resistance. However, mechanisms driving mutagenesis and clonal diversification in ALL are not fully understood. In this proof of principle study, we performed whole genome sequencing of two cases with multiple relapses in order to investigate whether groups of mutations separated in time show distinct mutational signatures. Based on mutation allele frequencies at diagnosis and subsequent relapses, we clustered mutations into groups and performed cluster-specific mutational profile analysis and de novo signature extraction. In patient 1, who experienced two relapses, the analysis unraveled a continuous interplay of aberrant activation induced cytidine deaminase (AID)/apolipoprotein B editing complex (APOBEC) activity. The associated signatures SBS2 and SBS13 were present already at diagnosis, and although emerging mutations were lost in later relapses, the process remained active throughout disease evolution. Patient 2 had three relapses. We identified episodic mutational processes at diagnosis and first relapse leading to mutations resembling ultraviolet light-driven DNA damage, and thiopurine-associated damage at first relapse. In conclusion, our data shows that investigation of mutational processes in clusters separated in time may aid in understanding the mutational mechanisms and discovery of underlying causes.

SATB2 induction of a neural crest mesenchyme-like program drives melanoma invasion and drug resistance

Recent genomic and scRNA-seq analyses of melanoma demonstrated a lack of recurrent genetic drivers of metastasis, while identifying common transcriptional states correlating with invasion or drug resistance. To test whether transcriptional adaptation can drive melanoma progression, we made use of a zebrafish mitfa:BRAFV600E;tp53-/- model, in which malignant progression is characterized by minimal genetic evolution. We undertook an overexpression-screen of 80 epigenetic/transcriptional regulators and found neural crest-mesenchyme developmental regulator SATB2 to accelerate aggressive melanoma development. Its overexpression induces invadopodia formation and invasion in zebrafish tumors and human melanoma cell lines. SATB2 binds and activates neural crest-regulators, including pdgfab and snai2. The transcriptional program induced by SATB2 overlaps with known MITF^lowAXL^high and AQP1⁺NGFR1^high drug-resistant states and functionally drives enhanced tumor propagation and resistance to Vemurafenib in vivo. In summary, we show that melanoma transcriptional rewiring by SATB2 to a neural crest mesenchyme-like program can drive invasion and drug resistance in autochthonous tumors.

Systematic identification of non-coding somatic single nucleotide variants associated with altered transcription and DNA methylation in adult and pediatric cancers

Whole-genome sequencing combined with transcriptomics can reveal impactful non-coding single nucleotide variants (SNVs) in cancer. Here, we developed an integrative analytical approach that, as a first step, identifies genes altered in expression or DNA methylation in association with nearby somatic SNVs, in contrast to alternative approaches that first identify mutational hotspots. Using genomic datasets from the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium and the Children's Brain Tumor Tissue Consortium (CBTTC), we identified hundreds of genes and associated CpG islands for which the nearby presence of a non-coding somatic SNV recurrently associated with altered expression or DNA methylation, respectively. Genomic regions upstream or downstream of genes, gene introns and gene untranslated regions were all involved. The PCAWG adult cancer cohort yielded different significant SNV-expression associations from the CBTTC pediatric brain tumor cohort. The SNV-expression associations involved a wide range of cancer types and histologies, as well as potential gain or loss of transcription factor binding sites. Notable genes with SNV-associated increased expression include TERT, COPS3, POLE2 and HDAC2—involving multiple cancer types—MYC, BCL2, PIM1 and IGLL5—involving lymphomas—and CYHR1—involving pediatric low-grade gliomas. Non-coding somatic SNVs show a major role in shaping the cancer transcriptome, not limited to mutational hotspots.

Epigenome screening highlights that JMJD6 confers an epigenetic vulnerability and mediates sunitinib sensitivity in renal cell carcinoma

Aberrant epigenetic reprogramming represents a hallmark of renal cell carcinoma (RCC) tumorigenesis and progression. Whether there existed other epigenetic vulnerabilities that could serve as therapeutic targets remained unclear and promising. Here, we combined the clustered regularly interspaced short palindromic repeats functional screening results and multiple RCC datasets to identify JMJD6 as the potent target in RCC. JMJD6 expression correlated with poor survival outcomes of RCC patients and promoted RCC progression in vitro and in vivo. Mechanistically, aberrant p300 led to high JMJD6 expression, which activated a series of oncogenic crosstalk. Particularly, high-throughput sequencing data revealed that JMJD6 could assemble super-enhancers to drive a list of identity genes in kidney cancer, including VEGFA, β-catenin, and SRC. Moreover, this JMJD6-mediated oncogenic effect could be suppressed by a novel JMJD6 inhibitor (SKLB325), which was further demonstrated in RCC cells, patient-derived organoid models, and in vivo. Given the probable overlapped crosstalk between JMJD6 signature and tyrosine kinase inhibitors downstream targets, targeting JMJD6 sensitized RCC to sunitinib and was synergistic when they were combined together. Collectively, this study indicated that targeting JMJD6 was an effective approach to treat RCC patients.

Pan-cancer detection of driver genes at the single-patient resolution

BACKGROUND

Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions.

RESULTS

We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways.

CONCLUSIONS

sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types ( https://github.com/ciccalab/sysSVM2 ).

Glycoproteogenomics: Setting the Course for Next-generation Cancer Neoantigen Discovery for Cancer Vaccines

Molecular-assisted precision oncology gained tremendous ground with high-throughput next-generation sequencing (NGS), supported by robust bioinformatics. The quest for genomics-based cancer medicine set the foundations for improved patient stratification, while unveiling a wide array of neoantigens for immunotherapy. Upfront pre-clinical and clinical studies have successfully used tumor-specific peptides in vaccines with minimal off-target effects. However, the low mutational burden presented by many lesions challenges the generalization of these solutions, requiring the diversification of neoantigen sources. Oncoproteogenomics utilizing customized databases for protein annotation by mass spectrometry (MS) is a powerful tool toward this end. Expanding the concept toward exploring proteoforms originated from post-translational modifications (PTMs) will be decisive to improve molecular subtyping and provide potentially targetable functional nodes with increased cancer specificity. Walking through the path of systems biology, we highlight that alterations in protein glycosylation at the cell surface not only have functional impact on cancer progression and dissemination but also originate unique molecular fingerprints for targeted therapeutics. Moreover, we discuss the outstanding challenges required to accommodate glycoproteomics in oncoproteogenomics platforms. We envisage that such rationale may flag a rather neglected research field, generating novel paradigms for precision oncology and immunotherapy.

Identification of important genes and drug repurposing based on clinical-centered analysis across human cancers

Identification of the functional impact of mutated and altered genes in cancer is critical for implementing precision oncology and drug repurposing. In recent years, the emergence of multiomics data from large, well-characterized patient cohorts has provided us with an unprecedented opportunity to address this problem. In this study, we investigated survival-associated genes across 26 cancer types and found that these genes tended to be hub genes and had higher K-core values in biological networks. Moreover, the genes associated with adverse outcomes were mainly enriched in pathways related to genetic information processing and cellular processes, while the genes with favorable outcomes were enriched in metabolism and immune regulation pathways. We proposed using the number of survival-related neighbors to assess the impact of mutations. In addition, by integrating other databases including the Human Protein Atlas and the DrugBank database, we predicted novel targets and anticancer drugs using the drug repurposing strategy. Our results illustrated the significance of multidimensional analysis of clinical data in important gene identification and drug development.

The role of genomics in global cancer prevention

Despite improvements in the understanding of cancer causation, much remains unknown regarding the mechanisms by which genomic and non-genomic factors initiate carcinogenesis, drive cell invasion and metastasis, and enable cancer to develop. Technological advances have enabled the analysis of whole genomes, comprising thousands of tumours across populations worldwide, with the aim of identifying mutation signatures associated with particular tumour types. Large collaborative efforts have resulted in the identification and improved understanding of causal factors, and have shed light on new opportunities to prevent cancer. In this new era in cancer genomics, discoveries from studies conducted on an international scale can inform evidence-based strategies in cancer control along the cancer care continuum, from prevention to treatment. In this Review, we present the relevant history and emerging frontiers of cancer genetics and genomics from the perspective of global cancer prevention. We highlight the importance of local context in the adoption of new technologies and emergent evidence, with illustrative examples from worldwide. We emphasize the challenges in implementing important genomic findings in clinical settings with disparate resource availability and present a conceptual framework for the translation of such findings into clinical practice, and evidence-based policies in order to maximize the utility for a population.

Molecular characterization of a marine turtle tumor epizootic, profiling external, internal and postsurgical regrowth tumors

Sea turtle populations are under threat from an epizootic tumor disease (animal epidemic) known as fibropapillomatosis. Fibropapillomatosis continues to spread geographically, with prevalence of the disease also growing at many longer-affected sites globally. However, we do not yet understand the precise environmental, mutational and viral events driving fibropapillomatosis tumor formation and progression.Here we perform transcriptomic and immunohistochemical profiling of five fibropapillomatosis tumor types: external new, established and postsurgical regrowth tumors, and internal lung and kidney tumors. We reveal that internal tumors are molecularly distinct from the more common external tumors. However, they have a small number of conserved potentially therapeutically targetable molecular vulnerabilities in common, such as the MAPK, Wnt, TGFβ and TNF oncogenic signaling pathways. These conserved oncogenic drivers recapitulate remarkably well the core pan-cancer drivers responsible for human cancers. Fibropapillomatosis has been considered benign, but metastatic-related transcriptional signatures are strongly activated in kidney and established external tumors. Tumors in turtles with poor outcomes (died/euthanized) have genes associated with apoptosis and immune function suppressed, with these genes providing putative predictive biomarkers.Together, these results offer an improved understanding of fibropapillomatosis tumorigenesis and provide insights into the origins, inter-tumor relationships, and therapeutic treatment for this wildlife epizootic.

Advances in germline predisposition to acute leukaemias and myeloid neoplasms

Although much work has focused on the elucidation of somatic alterations that drive the development of acute leukaemias and other haematopoietic diseases, it has become increasingly recognized that germline mutations are common in many of these neoplasms. In this Review, we highlight the different genetic pathways impacted by germline mutations that can ultimately lead to the development of familial and sporadic haematological malignancies, including acute lymphoblastic leukaemia, acute myeloid leukaemia (AML) and myelodysplastic syndrome (MDS). Many of the genes disrupted by somatic mutations in these diseases (for example, TP53, RUNX1, IKZF1 and ETV6) are the same as those that harbour germline mutations in children and adolescents who develop these malignancies. Moreover, the presumption that familial leukaemias only present in childhood is no longer true, in large part due to the numerous studies demonstrating germline DDX41 mutations in adults with MDS and AML. Lastly, we highlight how different cooperating events can influence the ultimate phenotype in these different familial leukaemia syndromes.

My personal mutanome: a computational genomic medicine platform for searching network perturbing alleles linking genotype to phenotype

Massive genome sequencing data have inspired new challenges in personalized treatments and facilitated oncological drug discovery. We present a comprehensive database, My Personal Mutanome (MPM), for accelerating the development of precision cancer medicine protocols. MPM contains 490,245 mutations from over 10,800 tumor exomes across 33 cancer types in The Cancer Genome Atlas mapped to 94,563 structure-resolved/predicted protein-protein interaction interfaces ("edgetic") and 311,022 functional sites ("nodetic"), including ligand-protein binding sites and 8 types of protein posttranslational modifications. In total, 8884 survival results and 1,271,132 drug responses are obtained for these mapped interactions. MPM is available at https://mutanome.lerner.ccf.org .

Portals for Exploring Noncoding Variants in Pediatric Cancer

Zhou et al. present GenomePaint, a dynamic web-based data portal for exploring noncoding somatic alterations in cancer by genomic location. Multiple omics platforms - including whole-genome, whole-exome, transcriptome, and epigenome - can be visualized together. The portal incorporates data from >3800 pediatric tumors, and users may upload their own data.

Performances of Targeted RNA Sequencing for the Analysis of Fusion Transcripts, Gene Mutation, and Expression in Hematological Malignancies

RNA sequencing holds great promise to improve the diagnostic of hematological malignancies, because this technique enables to detect fusion transcripts, to look for somatic mutations in oncogenes, and to capture transcriptomic signatures of nosological entities. However, the analytical performances of targeted RNA sequencing have not been extensively described in diagnostic samples. Using a targeted panel of 1385 cancer-related genes in a series of 100 diagnosis samples and 8 controls, we detected all the already known fusion transcripts and also discovered unknown and/or unsuspected fusion transcripts in 12 samples. Regarding the analysis of transcriptomic profiles, we show that targeted RNA sequencing is performant to discriminate acute lymphoblastic leukemia entities driven by different oncogenic translocations. Additionally, we show that 86% of the mutations identified at the DNA level are also detectable at the messenger RNA (mRNA) level, except for nonsense mutations that are subjected to mRNA decay. We conclude that targeted RNA sequencing might improve the diagnosis of hematological malignancies. Standardization of the preanalytical steps and further refinements of the panel design and of the bioinformatical pipelines will be an important step towards its use in standard diagnostic procedures.

Biological Aspects of Cancer Health Disparities

Racial and ethnic disparities span the continuum of cancer care and are driven by a complex interplay among social, psychosocial, lifestyle, environmental, health system, and biological determinants of health. Research is needed to identify these determinants of cancer health disparities and to develop interventions to achieve cancer health equity. Herein, we focus on the overall burden of ancestry-related molecular alterations, the functional significance of the alterations in hallmarks of cancer, and the implications of the alterations for precision oncology and immuno-oncology. In conclusion, we reflect on the importance of estimating ancestry, improving diverse racial and ethnic participation in cancer clinical trials, and examining the intersection among determinants of cancer health disparities.

Inflammation-driven deaminase deregulation fuels human pre-leukemia stem cell evolution

Inflammation-dependent base deaminases promote therapeutic resistance in many malignancies. However, their roles in human pre-leukemia stem cell (pre-LSC) evolution to acute myeloid leukemia stem cells (LSCs) had not been elucidated. Comparative whole-genome and whole-transcriptome sequencing analyses of FACS-purified pre-LSCs from myeloproliferative neoplasm (MPN) patients reveal APOBEC3C upregulation, an increased C-to-T mutational burden, and hematopoietic stem and progenitor cell (HSPC) proliferation during progression, which can be recapitulated by lentiviral APOBEC3C overexpression. In pre-LSCs, inflammatory splice isoform overexpression coincides with APOBEC3C upregulation and ADAR1p150-induced A-to-I RNA hyper-editing. Pre-LSC evolution to LSCs is marked by STAT3 editing, STAT3β isoform switching, elevated phospho-STAT3, and increased ADAR1p150 expression, which can be prevented by JAK2/STAT3 inhibition with ruxolitinib or fedratinib or lentiviral ADAR1 shRNA knockdown. Conversely, lentiviral ADAR1p150 expression enhances pre-LSC replating and STAT3 splice isoform switching. Thus, pre-LSC evolution to LSCs is fueled by primate-specific APOBEC3C-induced pre-LSC proliferation and ADAR1-mediated splicing deregulation.

Comprehensive Review on the Clinical Relevance of Long Non-Coding RNAs in Cutaneous Melanoma

Cutaneous melanoma is considered a rare tumor, although it is one of the most common cancers in young adults and its incidence has risen in the last decades. Targeted therapy, with BRAF and MEK inhibitors, and immunotherapy revolutionized the treatment of metastatic melanoma but there is still a considerable percentage of patients with primary or acquired resistance to these therapies. Recently, oncology researchers directed their attention at the role of long non-coding RNAs (lncRNAs) in different types of cancers, including melanoma. lncRNAs are RNA transcripts, initially considered "junk sequences", that have been proven to have a crucial role in the fine regulation of physiological and pathological processes of different tissues. Furthermore, they are more expressed in tumors than protein-coding genes, constituting perfect candidates either as biomarkers (diagnostic, prognostic, predictive) or as therapeutic targets. In this work, we reviewed all the literature available for lncRNA in melanoma, elucidating all the potential roles in this tumor.

The clinicopathological significance of SWI/SNF alterations in gastric cancer is associated with the molecular subtypes

The clinicopathological significance of altered SWI/SNF complex has not been well evaluated in gastric cancer (GC). We examined SMARCA2, SMARCA4, SMARCB1 and ARID1A expression by immunohistochemistry in 1224 surgically resected GCs with subtyping into Epstein-Barr virus (EBV), microsatellite instability (MSI) and non-EBV/MSI Lauren histotypes. SWI/SNF mutations were investigated using the GC dataset of the TCGA Pan-Cancer Atlas. Clinicopathological association was assessed by statistical analysis. There were 427 cases (35%) of SWI/SNF-attenuated GC, including 344 SMARCA2 (28%), 28 SMARCA4 (2%), 11 SMARCB1 (1%) and 197 ARID1A (16%) cases. Simultaneous alterations of multiple subunits were observed. Compared to SWI/SNF-retained cases, SWI/SNF-attenuated GC exhibited a significant predilection to older ages, EBV and MSI genotypes, higher lymphatic invasion and less hematogenous recurrence (P < 0.05). SWI/SNF attenuation was an independent risk factor for short overall survival (P = 0.001, hazard ratio 1.360, 95% confidence interval 1.138-1.625). The survival impact stemmed from SMARCA2-attenuated GCs in stage III and non-EBV/MSI diffuse/mixed subtypes (P = 0.019 and < 0.001, respectively). ARID1A-lost/heterogeneous GCs were more aggressive in the EBV genotype (P = 0.016). SMARCB1 or SMARCA4 loss was not restricted to rhabdoid/undifferentiated carcinoma. In the TCGA dataset, 223 of 434 GCs (52%) harbored deleterious SWI/SNF mutations, including ARID1A (27%), SMARCA2 (9%), ARID2 (9%), ARID1B (8%), PBRM1 (7%), and SMARCA4 (7%). SWI/SNF-mutated GCs displayed a favorable outcome owing to the high percentage with the MSI genotype. In conclusion, SWI/SNF-altered GCs are common and the clinicopathological significance is related to the genotype.

Liquid biopsy enters the clinic - implementation issues and future challenges

Historically, studies of disseminated tumour cells in bone marrow and circulating tumour cells in peripheral blood have provided crucial insights into cancer biology and the metastatic process. More recently, advances in the detection and characterization of circulating tumour DNA (ctDNA) have finally enabled the introduction of liquid biopsy assays into clinical practice. The FDA has already approved several single-gene assays and, more recently, multigene assays to detect genetic alterations in plasma cell-free DNA (cfDNA) for use as companion diagnostics matched to specific molecularly targeted therapies for cancer. These approvals mark a tipping point for the widespread use of liquid biopsy in the clinic, and mostly in patients with advanced-stage cancer. The next frontier for the clinical application of liquid biopsy is likely to be the systemic treatment of patients with 'ctDNA relapse', a term we introduce for ctDNA detection prior to imaging-detected relapse after curative-intent therapy for early stage disease. Cancer screening and diagnosis are other potential future applications. In this Perspective, we discuss key issues and gaps in technology, clinical trial methodologies and logistics for the eventual integration of liquid biopsy into the clinical workflow.

Pathogenesis and Diagnosis of Genitourinary Cancer

Genitourinary (GU) cancers are among the most common malignant diseases in men [...].

Homologous recombination deficiency: how genomic signatures are generated

Cancer genomes harbor mutational and structural rearrangements that are jointly shaped by DNA damage and repair mechanisms. Accumulating evidence suggests that genetic alterations in DNA repair-defective tumors reflect the scars caused by the use of backup DNA repair mechanisms needed to maintain cellular viability. Detailed analysis of the patterns of mutations and structural rearrangements present in BRCA1/2-deficient tumors has allowed for the delineation of genomic signatures that reflect alternative repair with inactive homologous recombination (HR). Here we aim to summarize recent advances in the analysis of genomic signatures associated with HR-deficiency and examine recent studies that have shed light on the backup repair mechanisms responsible for genomic scarring in HR-deficient tumors.

Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type

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CUP occurs in as many as 3–5% of patients when standard diagnostic tests are not able to determine the origin of cancer.

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MI GPSai (Genomic Prevalence Score) is an AI that uses genomic and transcriptomic data to elucidate tumor origin.

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The algorithm was trained on molecular data from 57,489 cases and validated on 19,555 cases.

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MI GPSai predicted the tumor type out of 21 options in the labeled data set with an accuracy of over 94% on 93% of cases.

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When also considering the second highest prediction, the accuracy increases to 97%.

Cancer of Unknown Primary (CUP) occurs in 3–5% of patients when standard histological diagnostic tests are unable to determine the origin of metastatic cancer. Typically, a CUP diagnosis is treated empirically and has very poor outcomes, with median overall survival less than one year. Gene expression profiling alone has been used to identify the tissue of origin but struggles with low neoplastic percentage in metastatic sites which is where identification is often most needed. MI GPSai, a Genomic Prevalence Score, uses DNA sequencing and whole transcriptome data coupled with machine learning to aid in the diagnosis of cancer. The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai predicted the tumor type in the labeled data set with an accuracy of over 94% on 93% of cases while deliberating amongst 21 possible categories of cancer. When also considering the second highest prediction, the accuracy increases to 97%. Additionally, MI GPSai rendered a prediction for 71.7% of CUP cases. Pathologist evaluation of discrepancies between submitted diagnosis and MI GPSai predictions resulted in change of diagnosis in 41.3% of the time. MI GPSai provides clinically meaningful information in a large proportion of CUP cases and inclusion of MI GPSai in clinical routine could improve diagnostic fidelity. Moreover, all genomic markers essential for therapy selection are assessed in this assay, maximizing the clinical utility for patients within a single test.

Telomeres: history, health, and hallmarks of aging

The escalating social and economic burden of an aging world population has placed aging research at center stage. The hallmarks of aging comprise diverse molecular mechanisms and cellular systems that are interrelated and act in concert to drive the aging process. Here, through the lens of telomere biology, we examine how telomere dysfunction may amplify or drive molecular biological processes underlying each hallmark of aging and contribute to development of age-related diseases such as neurodegeneration and cancer. The intimate link of telomeres to aging hallmarks informs preventive and therapeutic interventions designed to attenuate aging itself and reduce the incidence of age-associated diseases.

3DIV update for 2021: a comprehensive resource of 3D genome and 3D cancer genome

Three-dimensional (3D) genome organization is tightly coupled with gene regulation in various biological processes and diseases. In cancer, various types of large-scale genomic rearrangements can disrupt the 3D genome, leading to oncogenic gene expression. However, unraveling the pathogenicity of the 3D cancer genome remains a challenge since closer examinations have been greatly limited due to the lack of appropriate tools specialized for disorganized higher-order chromatin structure. Here, we updated a 3D-genome Interaction Viewer and database named 3DIV by uniformly processing ∼230 billion raw Hi-C reads to expand our contents to the 3D cancer genome. The updates of 3DIV are listed as follows: (i) the collection of 401 samples including 220 cancer cell line/tumor Hi-C data, 153 normal cell line/tissue Hi-C data, and 28 promoter capture Hi-C data, (ii) the live interactive manipulation of the 3D cancer genome to simulate the impact of structural variations and (iii) the reconstruction of Hi-C contact maps by user-defined chromosome order to investigate the 3D genome of the complex genomic rearrangement. In summary, the updated 3DIV will be the most comprehensive resource to explore the gene regulatory effects of both the normal and cancer 3D genome. ‘3DIV’ is freely available at http://3div.kr.

Use of signals of positive and negative selection to distinguish cancer genes and passenger genes

A major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis. Most approaches focused on genes positively selected for mutations that drive carcinogenesis and neglected the role of negative selection. Some studies have actually concluded that negative selection has no role in cancer evolution. We have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human genes. Our analyses have confirmed that tumor suppressor genes are positively selected for inactivating mutations, oncogenes, however, were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations. Significantly, we have identified numerous human genes that show signs of strong negative selection during tumor evolution, suggesting that their functional integrity is essential for the growth and survival of tumor cells.

Multiple mutations within individual oncogenes

Recent studies of the cancer genome have identified numerous patients harboring multiple mutations (MM) within individual oncogenes. These MM (de novo MM) in cis synergistically activate the mutated oncogene and promote tumorigenesis, indicating a positive epistatic interaction between mutations. The relatively frequent de novo MM suggest that intramolecular positive epistasis is widespread in oncogenes. Studies also suggest that negative and higher-order epistasis affects de novo MM. Comparison of de novo MM and MM associated with drug-resistant secondary mutations (secondary MM) revealed several similarities with respect to allelic configuration, mutational selection and functionality of individual mutations. Conversely, they have several differences, most notably the difference in drug sensitivities. Secondary MM usually confer resistance to molecularly targeted therapies, whereas several de novo MM are associated with increased sensitivity, implying that both can be useful as therapeutic biomarkers. Unlike secondary MM in which specific secondary resistant mutations are selected, minor (infrequent) functionally weak mutations are convergently selected in de novo MM, which may provide an explanation as to why such mutations accumulate in cancer. The third type of MM is MM from different subclones. This type of MM is associated with parallel evolution, which may contribute to relapse and treatment failure. Collectively, MM within individual oncogenes are diverse, but all types of MM are associated with cancer evolution and therapeutic response. Further evaluation of oncogenic MM is warranted to gain a deeper understanding of cancer genetics and evolution.

Novel candidates of pathogenic variants of the BRCA1 and BRCA2 genes from a dataset of 3,552 Japanese whole genomes (3.5KJPNv2)

Identification of the population frequencies of definitely pathogenic germline variants in two major hereditary breast and ovarian cancer syndrome (HBOC) genes, BRCA1/2, is essential to estimate the number of HBOC patients. In addition, the identification of moderately penetrant HBOC gene variants that contribute to increasing the risk of breast and ovarian cancers in a population is critical to establish personalized health care. A prospective cohort subjected to genome analysis can provide both sets of information. Computational scoring and prospective cohort studies may help to identify such likely pathogenic variants in the general population. We annotated the variants in the BRCA1 and BRCA2 genes from a dataset of 3,552 whole-genome sequences obtained from members of a prospective cohorts with genome data in the Tohoku Medical Megabank Project (TMM) with InterVar software. Computational impact scores (CADD_phred and Eigen_raw) and minor allele frequencies (MAFs) of pathogenic (P) and likely pathogenic (LP) variants in ClinVar were used for filtration criteria. Familial predispositions to cancers among the 35,000 TMM genome cohort participants were analyzed to verify the identified pathogenicity. Seven potentially pathogenic variants were newly identified. The sisters of carriers of these moderately deleterious variants and definite P and LP variants among members of the TMM prospective cohort showed a statistically significant preponderance for cancer onset, from the self-reported cancer history. Filtering by computational scoring and MAF is useful to identify potentially pathogenic variants in BRCA genes in the Japanese population. These results should help to follow up the carriers of variants of uncertain significance in the HBOC genes in the longitudinal prospective cohort study.