IntOGen: integration and data mining of multidimensional oncogenomic data

Genetic factors associated with reasons for clinical trial stoppage

Many drug discovery projects are started but few progress fully through clinical trials to approval. Previous work has shown that human genetics support for the therapeutic hypothesis increases the chance of trial progression. Here, we applied natural language processing to classify the free-text reasons for 28,561 clinical trials that stopped before their endpoints were met. We then evaluated these classes in light of the underlying evidence for the therapeutic hypothesis and target properties. We found that trials are more likely to stop because of a lack of efficacy in the absence of strong genetic evidence from human populations or genetically modified animal models. Furthermore, certain trials are more likely to stop for safety reasons if the drug target gene is highly constrained in human populations and if the gene is broadly expressed across tissues. These results support the growing use of human genetics to evaluate targets for drug discovery programs.

Machine learning model reveals roles of interferon‑stimulated genes in sorafenib‑resistant liver cancer

HCC (Hepatocellular carcinoma) is the most common malignant tumor; however, the molecular pathogenesis of these tumors is not well understood. Sorafenib, an approved treatment for HCC, inhibits angiogenesis and tumor cell proliferation. However, only ~30% of patients are sensitive to sorafenib and most show disease progression, indicating resistance to sorafenib. The present study used machine learning to investigate several mechanisms related to sorafenib resistance in liver cancer cells. This revealed that unphosphorylated interferon-stimulated genes (U-ISGs) were upregulated in sorafenib-resistant liver cancer cells, and the unphosphorylated ISGF3 (U-ISGF3; unphosphorylated STAT1, unphosphorylated STAT2 and IRF9) complex was increased in sorafenib-resistant liver cancer cells. Further study revealed that the knockdown of the U-ISGF3 complex downregulated U-ISGs. In addition, inhibition of the U-ISGF3 complex downregulated cell viability in sorafenib-resistant liver cancer cells. These results suggest that U-ISGF3 induced sorafenib resistance in liver cancer cells. Also, this mechanism may also be relevant to patients with sorafenib resistance.

ERCC2 mutations alter the genomic distribution pattern of somatic mutations and are independently prognostic in bladder cancer

Excision repair cross-complementation group 2 (ERCC2) encodes the DNA helicase xeroderma pigmentosum group D, which functions in transcription and nucleotide excision repair. Point mutations in ERCC2 are putative drivers in around 10% of bladder cancers (BLCAs) and a potential positive biomarker for cisplatin therapy response. Nevertheless, the prognostic significance directly attributed to ERCC2 mutations and its pathogenic role in genome instability remain poorly understood. We first demonstrated that mutant ERCC2 is an independent predictor of prognosis in BLCA. We then examined its impact on the somatic mutational landscape using a cohort of ERCC2 wild-type (n = 343) and mutant (n = 39) BLCA whole genomes. The genome-wide distribution of somatic mutations is significantly altered in ERCC2 mutants, including T[C>T]N enrichment, altered replication time correlations, and CTCF-cohesin binding site mutation hotspots. We leverage these alterations to develop a machine learning model for predicting pathogenic ERCC2 mutations, which may be useful to inform treatment of patients with BLCA.

Distinct aneuploid evolution of astrocytoma and glioblastoma during recurrence

Astrocytoma and glioblastoma (GB) are reclassified subtypes of adult diffuse gliomas based on distinct isocitrate dehydrogenase (IDH) mutation in the fifth edition of the WHO Classification of Tumors of the Central Nervous System. The recurrence of gliomas is a common and inevitable challenge, and analyzing the distinct genomic alterations in astrocytoma and GB could provide insights into their progression. This study conducted a longitudinal investigation, utilizing whole-exome sequencing, on 65 paired primary/recurrent gliomas. It examined chromosome arm aneuploidies, copy number variations (CNVs) of cancer-related genes and pathway enrichments during the relapse. The veracity of these findings was verified through the integration of our data with multiple public resources and by corroborative immunohistochemistry (IHC). The results revealed a greater prevalence of aneuploidy changes and acquired CNVs in recurrent lower grade astrocytoma than in relapsed grade 4 astrocytoma and GB. Larger aneuploidy changes were predictive of an unfavorable prognosis in lower grade astrocytoma (P < 0.05). Further, patients with acquired gains of 1q, 6p or loss of 13q at recurrence had a shorter overall survival in lower grade astrocytoma (P < 0.05); however, these prognostic effects were confined in grade 4 astrocytoma and GB. Moreover, acquired gains of 12 genes (including VEGFA) on 6p during relapse were associated with unfavorable prognosis for lower grade astrocytoma patients. Notably, elevated VEGFA expression during recurrence corresponded to poorer survival, validated through IHC and CGGA data. To summarize, these findings offer valuable insights into the progression of gliomas and have implications for guiding therapeutic approaches during recurrence.

Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes

Accurate prediction of the prognoses of cancer patients and identification of prognostic biomarkers are both important for the improved treatment of cancer patients, in addition to enhanced anticancer drugs. Many previous bioinformatic studies have been carried out to achieve this goal; however, there remains room for improvement in terms of accuracy. In this study, we demonstrated that patient-specific cancer driver genes could be used to predict cancer prognoses more accurately. To identify patient-specific cancer driver genes, we first generated patient-specific gene networks before using modified PageRank to generate feature vectors that represented the impacts genes had on the patient-specific gene network. Subsequently, the feature vectors of the good and poor prognosis groups were used to train the deep feedforward network. For the 11 cancer types in the TCGA data, the proposed method showed a significantly better prediction performance than the existing state-of-the-art methods for three cancer types (BRCA, CESC and PAAD), better performance for five cancer types (COAD, ESCA, HNSC, KIRC and STAD), and a similar or slightly worse performance for the remaining three cancer types (BLCA, LIHC and LUAD). Furthermore, the case study for the identified breast cancer and cervical squamous cell carcinoma prognostic genes and their subnetworks included several pathways associated with the progression of breast cancer and cervical squamous cell carcinoma. These results suggested that heterogeneous cancer driver information may be associated with cancer prognosis.

Pan-cancer clinical impact of latent drivers from double mutations

Here, we discover potential 'latent driver' mutations in cancer genomes. Latent drivers have low frequencies and minor observable translational potential. As such, to date they have escaped identification. Their discovery is important, since when paired in cis, latent driver mutations can drive cancer. Our comprehensive statistical analysis of the pan-cancer mutation profiles of ~60,000 tumor sequences from the TCGA and AACR-GENIE cohorts identifies significantly co-occurring potential latent drivers. We observe 155 same gene double mutations of which 140 individual components are cataloged as latent drivers. Evaluation of cell lines and patient-derived xenograft response data to drug treatment indicate that in certain genes double mutations may have a prominent role in increasing oncogenic activity, hence obtaining a better drug response, as in PIK3CA. Taken together, our comprehensive analyses indicate that same-gene double mutations are exceedingly rare phenomena but are a signature for some cancer types, e.g., breast, and lung cancers. The relative rarity of doublets can be explained by the likelihood of strong signals resulting in oncogene-induced senescence, and by doublets consisting of non-identical single residue components populating the background mutational load, thus not identified.

Cis-regulatory mutations associate with transcriptional and post-transcriptional deregulation of gene regulatory programs in cancers

Most cancer alterations occur in the noncoding portion of the human genome, where regulatory regions control gene expression. The discovery of noncoding mutations altering the cells' regulatory programs has been limited to few examples with high recurrence or high functional impact. Here, we show that transcription factor binding sites (TFBSs) have similar mutation loads to those in protein-coding exons. By combining cancer somatic mutations in TFBSs and expression data for protein-coding and miRNA genes, we evaluate the combined effects of transcriptional and post-transcriptional alterations on the regulatory programs in cancers. The analysis of seven TCGA cohorts culminates with the identification of protein-coding and miRNA genes linked to mutations at TFBSs that are associated with a cascading trans-effect deregulation on the cells' regulatory programs. Our analyses of cis-regulatory mutations associated with miRNAs recurrently predict 12 mature miRNAs (derived from 7 precursors) associated with the deregulation of their target gene networks. The predictions are enriched for cancer-associated protein-coding and miRNA genes and highlight cis-regulatory mutations associated with the dysregulation of key pathways associated with carcinogenesis. By combining transcriptional and post-transcriptional regulation of gene expression, our method predicts cis-regulatory mutations related to the dysregulation of key gene regulatory networks in cancer patients.

Molecular Analysis of an Intestinal Neuroendocrine/Non-neuroendocrine Neoplasm (MiNEN) Reveals MLH1 Methylation-driven Microsatellite Instability and a Monoclonal Origin: Diagnostic and Clinical Implications

Mixed neuroendocrine/non-neuroendocrine neoplasms (MiNEN) are rare mixed epithelial neoplasms in which a neuroendocrine component is combined with a non-neuroendocrine component. Here, we provide the clinical, pathologic, and molecular report of a 73-year-old-man presenting with an intestinal MiNEN. The lesion was composed of a well-differentiated G3 neuroendocrine tumor and a colloid adenocarcinoma. The molecular characterization was performed using a multigene next-generation sequencing panel. The neoplasm displayed microsatellite instability due to MLH1 promoter methylation. The extended molecular profile documented the same mutations affecting ARID1A, ASXL1, BLM, and RNF43 genes in both components, indicating a monoclonal origin of the tumor. Regarding component-specific gene mutations, BRCA2 was specifically altered in the neuroendocrine area. It may represent a new actionable target for precision oncology in MiNEN, but the lack of its alteration in the colloid component calls for further considerations on intratumor heterogeneity. The most important finding with potential immediate implications regards the presence of microsatellite instability: it indicates that this molecular alteration should become part of the diagnostic algorithm for these rare neoplasms.

Juvenile polyposis diagnosed with an integrated histological, immunohistochemical and molecular approach identifying new SMAD4 pathogenic variants

Juvenile polyposis (JP) is a rare familial syndrome characterized by the development of numerous hamartomatous polyps of the gastrointestinal tract and by an increased risk of developing gastrointestinal cancers. It follows a pattern of autosomal dominant inheritance and is associated with germline variants of SMAD4 or BMPR1A genes. Differential diagnosis may be difficult based on histology alone, due to morphological similarities to other familial syndromes. Here we report a case of familial JP diagnosed in a 50-years woman with a familial history positive for gastrointestinal cancers and other tumor types. The patient presented with severe iron deficiency anemia and showed numerous polyps in the stomach and jejunum according to endoscopy and imaging. She underwent an intra-gastric laparoscopic removal of the major gastric polyp, followed by jejunal exploration and resection of a segment with multiple neoformations. Histological examination revealed the presence of hamartomatous polyposis. Gastric and intestinal samples were analyzed with next-generation sequencing. Molecular analysis showed that the patient harbored a germline splicing site variant of SMAD4, c.1139 + 3A > G, which was complemented by different somatic variants of the same gene in the different polyps. Immunohistochemistry for SMAD4 confirmed loss of protein expression in the polyps, with regular expression in normal cells. cDNA sequencing further confirmed the findings. We thus definitively diagnosed the woman as having JP thanks to an integrated approach based on histology, immunohistochemistry and molecular analysis. The identified variants, all previously reported as variants of unknown significance, were classified as pathogenic as they complemented each other leading to SMAD4 loss.

IDH-wild type glioblastomas featuring at least 30% giant cells are characterized by frequent RB1 and NF1 alterations and hypermutation

Giant cell glioblastoma (GC-GBM) is a rare variant of IDH-wt GBM histologically characterized by the presence of numerous multinucleated giant cells and molecularly considered a hybrid between IDH-wt and IDH-mutant GBM. The lack of an objective definition, specifying the percentage of giant cells required for this diagnosis, may account for the absence of a definite molecular profile of this variant. This study aimed to clarify the molecular landscape of GC-GBM, exploring the mutations and copy number variations of 458 cancer-related genes, tumor mutational burden (TMB), and microsatellite instability (MSI) in 39 GBMs dichotomized into having 30-49% (15 cases) or ≥ 50% (24 cases) GCs. The type and prevalence of the genetic alterations in this series was not associated with the GCs content (< 50% or ≥ 50%). Most cases (82% and 51.2%) had impairment in TP53/MDM2 and PTEN/PI3K pathways, but a high proportion also featured TERT promoter mutations (61.5%) and RB1 (25.6%) or NF1 (25.6%) alterations. EGFR amplification was detected in 18% cases in association with a shorter overall survival (P = 0.004). Sixteen (41%) cases had a TMB > 10 mut/Mb, including two (5%) that harbored MSI and one with a POLE mutation. The frequency of RB1 and NF1 alterations and TMB counts were significantly higher compared to 567 IDH wild type (P < 0.0001; P = 0.0003; P < 0.0001) and 26 IDH-mutant (P < 0.0001; P = 0.0227; P < 0.0001) GBMs in the TCGA PanCancer Atlas cohort. These findings demonstrate that the molecular landscape of GBMs with at least 30% giant cells is dominated by the impairment of TP53/MDM2 and PTEN/PI3K pathways, and additionally characterized by frequent RB1 alterations and hypermutation and by EGFR amplification in more aggressive cases. The high frequency of hypermutated cases suggests that GC-GBMs might be candidates for immune check-point inhibitors clinical trials.

The Interpretation of Sequence Variants in Myeloid Neoplasms

OBJECTIVES

To provide an overview of the challenges encountered during the interpretation of sequence variants detected by next-generation sequencing (NGS) in myeloid neoplasms, as well as the limitations of the technology with the goal of preventing the over- or undercalling of alterations that may have a significant effect on patient management.

METHODS

Review of the peer-reviewed literature on the interpretation, reporting, and technical challenges of NGS assays for myeloid neoplasms.

RESULTS

NGS has been integrated widely and rapidly into the standard evaluating of myeloid neoplasms. Review of the literature reveals that myeloid sequence variants are challenging to detect and interpret. Large insertions and guanine-cytosine-heavy areas prove technically challenging while frameshift and truncating alterations may be classified as variants of uncertain significance by tertiary analysis informatics pipelines due to their absence in the literature and databases.

CONCLUSIONS

The analysis and interpretation of NGS results in myeloid neoplasia are challenging due to the varied number of detectable gene alterations. Familiarity with the genomic landscape of myeloid malignancies and knowledge of the tools available for the interpretation of sequence variants are essential to facilitate translation into clinical and therapy decisions.

Genomic characterization of hepatoid tumors: context matters

Hepatoid tumors (HT) are rare neoplasms morphologically resembling hepatocellular carcinoma, which arise in several organs other than the liver. A comprehensive molecular profile of this group of neoplasms is still lacking. Genomic characterization of 19 HTs from different organs (three colon HTs, four esophagogastric HTs, four biliary HTs, six genitourinary HTs, two lung HTs) was performed using a multigene next-generation sequencing panel. NGS unraveled a composite molecular profile of HT. Their genetic alterations were clearly clustered by tumor site: (i) colorectal HT displayed microsatellite instability, high tumor mutational burden, mutations in ARID1A/B genes and NCOA4-RET gene fusion (2/3 cases); (ii) gastric HT had TP53 mutations (2/4); (iii) biliary HT displayed loss of CDKN2A (3/4) and loss of chromosome 18 (2/4); (iv) genital HT showed gain of chromosome 12 (3/6); (v) lung HT had STK11 somatic mutations (2/2). The only commonly mutated gene occurring in HT of different sites was TP53 (8/19 cases: colon 2, esophagogastric 2, biliary 2, genital 1, lungs 1). This study shows that most genetic alterations of HT were clustered by site, indicating that context matters. The novel potential targets for HT precision oncology are also clustered based on the anatomic origin. This study shed light on the biology of these rare cancers and may have important consequences for treatment decisions and clinical trial selection for HT patients.

CELLector: Genomics-Guided Selection of Cancer In Vitro Models

Selecting appropriate cancer models is a key prerequisite for maximizing translational potential and clinical relevance of in vitro oncology studies. We developed CELLector: an R package and R Shiny application allowing researchers to select the most relevant cancer cell lines in a patient-genomic-guided fashion. CELLector leverages tumor genomics to identify recurrent subtypes with associated genomic signatures. It then evaluates these signatures in cancer cell lines to prioritize their selection. This enables users to choose appropriate in vitro models for inclusion or exclusion in retrospective analyses and future studies. Moreover, this allows bridging outcomes from cancer cell line screens to precisely defined sub-cohorts of primary tumors. Here, we demonstrate the usefulness and applicability of CELLector, showing how it can aid prioritization of in vitro models for future development and unveil patient-derived multivariate prognostic and therapeutic markers. CELLector is freely available at https://ot-cellector.shinyapps.io/CELLector_App/ (code at https://github.com/francescojm/CELLector and https://github.com/francescojm/CELLector_App).

Systematic Analysis of 4-gene Prognostic Signature in Patients with Diffuse Gliomas Based on Gene Expression Profiles

Background: Diffuse gliomas are a group of diseases that contain different degrees of malignancy and complex heterogeneity. Previous studies proposed biomarkers for certain grades of gliomas, but few of them have conducted a systematic analysis of different grades to search for molecular markers.

Methods: WGCNA was used to find significant genes associated with malignant progression of diffuse glioma in TCGA glioma sequencing expression data and the GEO expression profile-merge meta dataset. Lasso regression was used for potential model building and the best model was selected by CPE, IDI, and C_index. Risk score model was used to evaluate the gene signature prognostic power. Multi-omics data, including CNV, methylation, clinical traits, and mutation, were used for model evaluation.

Results: We found out 67 genes significantly associated with malignant progression of diffuse glioma by WGCNA. Next, we established a new 4 gene molecular marker (KDELR2, EMP3, TIMP1, and TAGLN2). Multivariate cox analysis identified the risk score of the 4 genes as an independent predictor of prognosis in patients with diffuse gliomas, and its predictive power was independent of the histopathological grades of glioma. Further, we had confirmed in five independent test datasets and the risk score remained good predictive power. The combination of the prognosis model with specific molecular characteristics possessed a better predictive power. Furthermore, we divided the low-risk group into three subtypes: LowRisk_IDH1^wt, LowRisk_IDH1^mut/ATRX^mut, and LowRisk_IDH1^mut/ATRX^wt by combining IDH1 mutation with ATRX mutation, which possessed obvious survival difference. In further analysis, we found that the 4 gene prognosis model possessed multi-omics features.

Conclusion: We established a malignant-related 4-gene molecular marker by glioma expression profile data from multiple microarrays and sequencing data. The four markers had good predictive power on the overall survival of glioma patients and were associated with gliomas' clinical and genetic backgrounds, including clinical features, gene mutation, methylation, CNV, signal pathways.

Solid Pseudopapillary Neoplasm of the Pancreas and Abdominal Desmoid Tumor in a Patient Carrying Two Different BRCA2 Germline Mutations: New Horizons from Tumor Molecular Profiling

This case report describes the history of a 41 year-old woman with a solid pseudopapillary neoplasm (SPN) of the pancreas and a metachronous abdominal desmoid tumor (DT) that occurred two years after the SPN surgical resection. At next-generation sequencing of 174 cancer-related genes, both neoplasms harbored a CTNNB1 somatic mutation which was different in each tumor. Moreover, two BRCA2 pathogenic mutations were found in both tumors, confirmed as germline by the sequencing of normal tissue. The BRCA2 mutations were c.631G>A, resulting in the amino-acid change p.V211I, and c.7008-2A>T, causing a splice acceptor site loss. However, as the two neoplasms showed neither loss of heterozygosity nor somatic mutation in the second BRCA2 allele, they cannot be considered as BRCA-dependent tumors. Nevertheless, this study highlights the important opportunities opened by extensive tumor molecular profiling. In this particular case, it permitted the detection of BRCA2-germline mutations, essential for addressing the necessary BRCA-related genetic counseling, surveillance, and screening for the patient and her family.

Mutational mechanisms shaping the coding and noncoding genome of germinal center derived B-cell lymphomas

B cells have the unique property to somatically alter their immunoglobulin (IG) genes by V(D)J recombination, somatic hypermutation (SHM) and class-switch recombination (CSR). Aberrant targeting of these mechanisms is implicated in lymphomagenesis, but the mutational processes are poorly understood. By performing whole genome and transcriptome sequencing of 181 germinal center derived B-cell lymphomas (gcBCL) we identified distinct mutational signatures linked to SHM and CSR. We show that not only SHM, but presumably also CSR causes off-target mutations in non-IG genes. Kataegis clusters with high mutational density mainly affected early replicating regions and were enriched for SHM- and CSR-mediated off-target mutations. Moreover, they often co-occurred in loci physically interacting in the nucleus, suggesting that mutation hotspots promote increased mutation targeting of spatially co-localized loci (termed hypermutation by proxy). Only around 1% of somatic small variants were in protein coding sequences, but in about half of the driver genes, a contribution of B-cell specific mutational processes to their mutations was found. The B-cell-specific mutational processes contribute to both lymphoma initiation and intratumoral heterogeneity. Overall, we demonstrate that mutational processes involved in the development of gcBCL are more complex than previously appreciated, and that B cell-specific mutational processes contribute via diverse mechanisms to lymphomagenesis.

Analysis, identification and visualization of subgroups in genomics

Motivation

Cancer is a complex and heterogeneous disease involving multiple somatic mutations that accumulate during its progression. In the past years, the wide availability of genomic data from patients’ samples opened new perspectives in the analysis of gene mutations and alterations. Hence, visualizing and further identifying genes mutated in massive sets of patients are nowadays a critical task that sheds light on more personalized intervention approaches.

Results

Here, we extensively review existing tools for visualization and analysis of alteration data. We compare different approaches to study mutual exclusivity and sample coverage in large-scale omics data. We complement our review with the standalone software AVAtar (‘analysis and visualization of alteration data’) that integrates diverse aspects known from different tools into a comprehensive platform. AVAtar supplements customizable alteration plots by a multi-objective evolutionary algorithm for subset identification and provides an innovative and user-friendly interface for the evaluation of concurrent solutions. A use case from personalized medicine demonstrates its unique features showing an application on vaccination target selection.

Availability

AVAtar is available at: https://github.com/sysbio-bioinf/avatar

Contact

hans.kestler@uni-ulm.de, phone: +49 (0) 731 500 24 500, fax: +49 (0) 731 500 24 502

Molecular docking and machine learning analysis of Abemaciclib in colon cancer

BACKGROUND

The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is personalised opens the doors to the design and discovery of new specific treatments for each patient. In this context, this work offers new ways to reuse existing databases and work to create added value in research. Three published signatures with significante prognostic value in Colon Adenocarcinoma (COAD) were indentified. These signatures were combined in a new meta-signature and validated with main Machine Learning (ML) and conventional statistical techniques. In addition, a drug repurposing experiment was carried out through Molecular Docking (MD) methodology in order to identify new potential treatments in COAD.

RESULTS

The prognostic potential of the signature was validated by means of ML algorithms and differential gene expression analysis. The results obtained supported the possibility that this meta-signature could harbor genes of interest for the prognosis and treatment of COAD. We studied drug repurposing following a molecular docking (MD) analysis, where the different protein data bank (PDB) structures of the genes of the meta-signature (in total 155) were confronted with 81 anti-cancer drugs approved by the FDA. We observed four interactions of interest: GLTP - Nilotinib, PTPRN - Venetoclax, VEGFA - Venetoclax and FABP6 - Abemaciclib. The FABP6 gene and its role within different metabolic pathways were studied in tumour and normal tissue and we observed the capability of the FABP6 gene to be a therapeutic target. Our in silico results showed a significant specificity of the union of the protein products of the FABP6 gene as well as the known action of Abemaciclib as an inhibitor of the CDK4/6 protein and therefore, of the cell cycle.

CONCLUSIONS

The results of our ML and differential expression experiments have first shown the FABP6 gene as a possible new cancer biomarker due to its specificity in colonic tumour tissue and no expression in healthy adjacent tissue. Next, the MD analysis showed that the drug Abemaciclib characteristic affinity for the different protein structures of the FABP6 gene. Therefore, in silico experiments have shown a new opportunity that should be validated experimentally, thus helping to reduce the cost and speed of drug screening. For these reasons, we propose the validation of the drug Abemaciclib for the treatment of colon cancer.

Patient-specific multi-omics models and the application in personalized combination therapy

The rapid advancement of high-throughput technologies and sharp decrease in cost have opened up the possibility to generate large amount of multi-omics data on an individual basis. The development of high-throughput -omics, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics, enables the application of multi-omics technologies in the clinical settings. Combination therapy, defined as disease treatment with two or more drugs to achieve efficacy with lower doses or lower drug toxicity, is the basis for the care of diseases like cancer. Patient-specific multi-omics data integration can help the identification and development of combination therapies. In this review, we provide an overview of different -omics platforms, and discuss the methods for multi-omics, high-throughput, data integration, personalized combination therapy.

PANOPLY: Omics-Guided Drug Prioritization Method Tailored to an Individual Patient

PURPOSE

The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and prioritize drug targets and cancer therapy regimens.

MATERIALS AND METHODS

The PANOPLY approach integrates clinical data with germline and somatic features obtained from multiomics platforms and applies machine learning and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow uses the following four steps: selection of matched controls to the patient of interest; identification of patient-specific genomic events; identification of suitable drugs using the driver-gene network and random forest analyses; and provision of an integrated multiomics case report of the patient with prioritization of anticancer drugs.

RESULTS

The PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study that collected clinical and genomic data as well as patient-derived xenografts to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant patient-derived xenograft model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor ( P < .05). We also applied PANOPLY to in-house and publicly accessible multiomics tumor data sets with therapeutic response or survival data available.

CONCLUSION

PANOPLY shows promise as a means to prioritize drugs on the basis of clinical and multiomics data for an individual patient with cancer. Additional studies are needed to confirm this approach.

AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU

Background

The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model’s complexity. In our system Aikyatan, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications’ combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes).

Results

We develop a suite of ML models, under the banner Aikyatan, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-‘a-vis the competition. Specifically, Aikyatan-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS.

Conclusions

Our exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. Our Aikyatan suite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at: https://bitbucket.org/cellsandmachines/aikyatan.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3049-1) contains supplementary material, which is available to authorized users.

Circulating tumor cells exit circulation while maintaining multicellularity, augmenting metastatic potential

Metastasis accounts for the majority of all cancer deaths, yet the process remains poorly understood. A pivotal step in the metastasis process is the exiting of tumor cells from the circulation, a process known as extravasation. However, it is unclear how tumor cells extravasate and whether multicellular clusters of tumor cells possess the ability to exit as a whole or must first disassociate. In this study, we use in vivo zebrafish and mouse models to elucidate the mechanism tumor cells use to extravasate. We found that circulating tumor cells exit the circulation using the recently identified extravasation mechanism, angiopellosis, and do so as both clusters and individual cells. We further show that when melanoma and cervical cancer cells utilize this extravasation method to exit as clusters, they exhibit an increased ability to form tumors at distant sites through the expression of unique genetic profiles. Collectively, we present a new model for tumor cell extravasation of both individual and multicellular circulating tumor cells.This article has an associated First Person interview with the first author of the paper.

Precision oncology: lessons learned and challenges for the future

The decreasing cost of and increasing capacity of DNA sequencing has led to vastly increased opportunities for population-level genomic studies to discover novel genomic alterations associated with both Mendelian and complex phenotypes. To translate genomic findings clinically, a number of health care institutions have worked collaboratively or individually to initiate precision medicine programs. These precision medicine programs involve designing patient enrollment systems, tracking electronic health records, building biobank repositories, and returning results with actionable matched therapies. As cancer is a paradigm for genetic diseases and new therapies are increasingly tailored to attack genetic susceptibilities in tumors, these precision medicine programs are largely driven by the urgent need to perform genetic profiling on cancer patients in real time. Here, we review the current landscape of precision oncology and highlight challenges to be overcome and examples of benefits to patients. Furthermore, we make suggestions to optimize future precision oncology programs based upon the lessons learned from these "first generation" early adopters.

Comprehensive elaboration of database resources utilized in next-generation sequencing-based tumor somatic mutation detection

The rapid evolution of next-generation sequencing (NGS)-based tumor genomic profile detection and the emergence of molecularly targeted therapies have enabled precision oncology. In NGS-based analysis, various types of databases have been developed to perform different functions. However, many problems still exist when using these public databases. Therefore, it is important to better understand the characteristics and limitations of each database and have them complement each other to provide useful clinical evidence for NGS testing. In this review, we elaborate on the important role of databases and their concrete applications in NGS-based somatic mutation detection. We introduce the typically used databases for sequence alignment, variant filtration, and variant interpretation, and compare the differences between the databases with similar functions. Subsequently, we determine the limitations of each database and provide the corresponding solutions. Furthermore, we present an overview diagram to clearly illustrate the database used in the entire NGS-based somatic mutation detection pipeline.

ALPK1 hotspot mutation as a driver of human spiradenoma and spiradenocarcinoma

Spiradenoma and cylindroma are distinctive skin adnexal tumors with sweat gland differentiation and potential for malignant transformation and aggressive behaviour. We present the genomic analysis of 75 samples from 57 representative patients including 15 cylindromas, 17 spiradenomas, 2 cylindroma-spiradenoma hybrid tumors, and 24 low- and high-grade spiradenocarcinoma cases, together with morphologically benign precursor regions of these cancers. We reveal somatic or germline alterations of the CYLD gene in 15/15 cylindromas and 5/17 spiradenomas, yet only 2/24 spiradenocarcinomas. Notably, we find a recurrent missense mutation in the kinase domain of the ALPK1 gene in spiradenomas and spiradenocarcinomas, which is mutually exclusive from mutation of CYLD and can activate the NF-κB pathway in reporter assays. In addition, we show that high-grade spiradenocarcinomas carry loss-of-function TP53 mutations, while cylindromas may have disruptive mutations in DNMT3A. Thus, we reveal the genomic landscape of adnexal tumors and therapeutic targets.

Exploration of prognosis-related microRNA and transcription factor co-regulatory networks across cancer types

The study of cancer prognosis serves as an important part of cancer research. Large-scale cancer studies have identified numerous genes and microRNAs (miRNAs) associated with prognosis. These informative genes and miRNAs represent potential biomarkers to predict survival and to elucidate the molecular mechanism of tumour progression. MiRNAs and transcription factors (TFs) can work cooperatively as essential mediators of gene expression, and their dysregulation affects cancer prognosis. A panoramic view of cancer prognosis at the system level, considering the co-regulation roles of miRNA and TF, remains elusive. Here, we establish 12 prognosis-related miRNA-TF co-regulatory networks. The characteristics of prognostic target genes and their regulators in the network are depicted. Although the target genes and co-regulatory patterns exhibit cancer-specific properties, some miRNAs and TFs are highly conserved across cancers. We illustrate and interpret the roles of these conserved regulators by building a model associated with cancer hallmarks, functional enrichment analysis, network community detection, and exhaustive literature research. The elaborated system-level prognostic miRNA-TF co-regulation landscape, including the highlighted roles of conserved regulators, provides a novel and powerful insights into further biological and medical discoveries.

Applying Expression Profile Similarity for Discovery of Patient-Specific Functional Mutations

The progress of cancer genome sequencing projects yields unprecedented information of mutations for numerous patients. However, the complexity of mutation profiles of cancer patients hinders the further understanding to mechanisms of oncogenesis. One basic question is how to find mutations with functional impacts. In this work, we introduce a computational method to predict functional somatic mutations of each patient by integrating mutation recurrence with expression profile similarity. With this method, the functional mutations are determined by checking the mutation enrichment among a group of patients with similar expression profiles. We applied this method to three cancer types and identified the functional mutations. Comparison of the predictions for three cancer types suggested that most of the functional mutations were cancer-type-specific with one exception to p53. By checking predicted results, we found that our method effectively filtered non-functional mutations resulting from large protein sizes. In addition, this method can also perform functional annotation to each patient to describe their association with signalling pathways or biological processes. In breast cancer, we predicted "cell adhesion" and other terms to be significantly associated with oncogenesis.

Involvement of hedgehog pathway in early onset, aggressive molecular subtypes and metastatic potential of breast cancer

Background

Dysregulation of hedgehog pathway is observed in numerous cancers. Relevance of hedgehog pathway genes in cancer cohort and inhibition of its downstream effector (GLI1) towards metastasis in cell lines are explored in the study.

Method

One hundred fifty fresh tumours of breast cancer patients were collected for the study. Based on differential expression, panel of 6 key regulators of the pathway (SHH, DHH, IHH, PTCH1, SMO and GLI1) in microarray datasets were identified. Expressional profiles of aforementioned genes were later correlated with clinico-pathological parameters in Pakistani breast cancer cohort at transcript and protein levels. In addition, GLI1 over expressing breast cancer cell lines (MDA-MB-231 and MCF-7) were treated with GANT61 to explore its probable effects on metastasis.

Result

SHH, DHH, PTCH1 and GLI1 were significantly over-expressed in tumours as compared with respective normal mammary tissues. A significant correlation of SHH, DHH and GLI1 expression with advanced tumour size, stages, grades, nodal involvement and distant metastasis was observed (p < 0.05). Over-expression of SHH, DHH and GLI1 was significantly related with patients having early onset and pre-menopausal status. Of note, hedgehog pathway was frequently up regulated in luminal B and triple negative breast cancer affected women. In addition, positive correlations were observed among aforementioned members of pathway and Ki67 (r-value: 0.63–0.78) emphasizing their role towards disease progression. Exposure of GANT61 (inhibitor for GLI1) significantly restricted cell proliferation, reduced cell motility and invasion.

Conclusion

Role of activated hedgehog pathway in breast cancer metastasis provides a novel target for cancer therapy against aggressive cancer subtypes.

Electronic supplementary material

The online version of this article (10.1186/s12964-017-0213-y) contains supplementary material, which is available to authorized users.

Proteomic Analysis Shows Constitutive Secretion of MIF and p53-associated Activity of COX-2-/- Lung Fibroblasts

The differential expression of two closelyassociated cyclooxygenase isozymes, COX-1 and COX-2, exhibited functions beyond eicosanoid metabolism. We hypothesized that COX-1 or COX-2 knockout lung fibroblasts may display altered protein profiles which may allow us to further differentiate the functional roles of these isozymes at the molecular level. Proteomic analysis shows constitutive production of macrophage migration inhibitory factor (MIF) in lung fibroblasts derived from COX-2^−/− but not wild-type (WT) or COX-1^−/− mice. MIF was spontaneously released in high levels into the extracellular milieu of COX2^−/− fibroblasts seemingly from the preformed intracellular stores, with no change in the basal gene expression of MIF. The secretion and regulation of MIF in COX-2^−/− was “prostaglandin-independent.” GO analysis showed that concurrent with upregulation of MIF, there is a significant surge in expression of genes related to fibroblast growth, FK506 binding proteins, and isomerase activity in COX-2^−/− cells. Furthermore, COX-2^−/− fibroblasts also exhibit a significant increase in transcriptional activity of various regulators, antagonists, and co-modulators of p53, as well as in the expression of oncogenes and related transcripts. Integrative Oncogenomics Cancer Browser (IntroGen) analysis shows downregulation of COX-2 and amplification of MIF and/or p53 activity during development of glioblastomas, ependymoma, and colon adenomas. These data indicate the functional role of the MIF-COX-p53 axis in inflammation and cancer at the genomic and proteomic levels in COX-2-ablated cells. This systematic analysis not only shows the proinflammatory state but also unveils a molecular signature of a pro-oncogenic state of COX-1 in COX-2 ablated cells.

Exome Sequencing Landscape Analysis in Ovarian Clear Cell Carcinoma Shed Light on Key Chromosomal Regions and Mutation Gene Networks

Previous studies have reported genome-wide mutation profile analyses in ovarian clear cell carcinomas (OCCCs). This study aims to identify specific novel molecular alterations by combined analyses of somatic mutation and copy number variation. We performed whole exome sequencing of 39 OCCC samples with 16 matching blood tissue samples. Four hundred twenty-six genes had recurrent somatic mutations. Among the 39 samples, ARID1A (62%) and PIK3CA (51%) were frequently mutated, as were genes such as KRAS (10%), PPP2R1A (10%), and PTEN (5%), that have been reported in previous OCCC studies. We also detected mutations in MLL3 (15%), ARID1B (10%), and PIK3R1 (8%), which are associations not previously reported. Gene interaction analysis and functional assessment revealed that mutated genes were clustered into groups pertaining to chromatin remodeling, cell proliferation, DNA repair and cell cycle checkpointing, and cytoskeletal organization. Copy number variation analysis identified frequent amplification in chr8q (64%), chr20q (54%), and chr17q (46%) loci as well as deletion in chr19p (41%), chr13q (28%), chr9q (21%), and chr18q (21%) loci. Integration of the analyses uncovered that frequently mutated or amplified/deleted genes were involved in the KRAS/phosphatidylinositol 3-kinase (82%) and MYC/retinoblastoma (75%) pathways as well as the critical chromatin remodeling complex switch/sucrose nonfermentable (85%). The individual and integrated analyses contribute details about the OCCC genomic landscape, which could lead to enhanced diagnostics and therapeutic options.

Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists

Widespread clinical laboratory implementation of next-generation sequencing-based cancer testing has highlighted the importance and potential benefits of standardizing the interpretation and reporting of molecular results among laboratories. A multidisciplinary working group tasked to assess the current status of next-generation sequencing-based cancer testing and establish standardized consensus classification, annotation, interpretation, and reporting conventions for somatic sequence variants was convened by the Association for Molecular Pathology with liaison representation from the American College of Medical Genetics and Genomics, American Society of Clinical Oncology, and College of American Pathologists. On the basis of the results of professional surveys, literature review, and the Working Group's subject matter expert consensus, a four-tiered system to categorize somatic sequence variations based on their clinical significances is proposed: tier I, variants with strong clinical significance; tier II, variants with potential clinical significance; tier III, variants of unknown clinical significance; and tier IV, variants deemed benign or likely benign. Cancer genomics is a rapidly evolving field; therefore, the clinical significance of any variant in therapy, diagnosis, or prognosis should be reevaluated on an ongoing basis. Reporting of genomic variants should follow standard nomenclature, with testing method and limitations clearly described. Clinical recommendations should be concise and correlate with histological and clinical findings.

GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome

Background

Recent large-scale undertakings such as ENCODE and Roadmap Epigenomics have generated experimental data mapped to the human reference genome (as genomic tracks) representing a variety of functional elements across a large number of cell types. Despite the high potential value of these publicly available data for a broad variety of investigations, little attention has been given to the analytical methodology necessary for their widespread utilisation.

Findings

We here present a first principled treatment of the analysis of collections of genomic tracks. We have developed novel computational and statistical methodology to permit comparative and confirmatory analyses across multiple and disparate data sources. We delineate a set of generic questions that are useful across a broad range of investigations and discuss the implications of choosing different statistical measures and null models. Examples include contrasting analyses across different tissues or diseases. The methodology has been implemented in a comprehensive open-source software system, the GSuite HyperBrowser. To make the functionality accessible to biologists, and to facilitate reproducible analysis, we have also developed a web-based interface providing an expertly guided and customizable way of utilizing the methodology. With this system, many novel biological questions can flexibly be posed and rapidly answered.

Conclusions

Through a combination of streamlined data acquisition, interoperable representation of dataset collections, and customizable statistical analysis with guided setup and interpretation, the GSuite HyperBrowser represents a first comprehensive solution for integrative analysis of track collections across the genome and epigenome. The software is available at: https://hyperbrowser.uio.no.

ProcessDriver: A computational pipeline to identify copy number drivers and associated disrupted biological processes in cancer

Copy number amplifications and deletions that are recurrent in cancer samples harbor genes that confer a fitness advantage to cancer tumor proliferation and survival. One important challenge in computational biology is to separate the causal (i.e., driver) genes from passenger genes in large, aberrated regions. Many previous studies focus on the genes within the aberration (i.e., cis genes), but do not utilize the genes that are outside of the aberrated region and dysregulated as a result of the aberration (i.e., trans genes). We propose a computational pipeline, called ProcessDriver, that prioritizes candidate drivers by relating cis genes to dysregulated trans genes and biological processes. ProcessDriver is based on the assumption that a driver cis gene should be closely associated with the dysregulated trans genes and biological processes, as opposed to previous studies that assume a driver cis gene should be the most correlated gene to the copy number of an aberrated region. We applied our method on breast, bladder and ovarian cancer data from the Cancer Genome Atlas database. Our results included previously known driver genes and cancer genes, as well as potentially novel driver genes. Additionally, many genes in the final set of drivers were linked to new tumor events after initial treatment using survival analysis. Our results highlight the importance of selecting driver genes based on their widespread downstream effects in trans.

Algorithmic methods to infer the evolutionary trajectories in cancer progression

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing data and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent work on the "selective advantage" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular, and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications because it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations, and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression as well as to suggest novel experimentally verifiable hypotheses.

Differential clonal evolution in oesophageal cancers in response to neo-adjuvant chemotherapy

How chemotherapy affects carcinoma genomes is largely unknown. Here we report whole-exome and deep sequencing of 30 paired oesophageal adenocarcinomas sampled before and after neo-adjuvant chemotherapy. Most, but not all, good responders pass through genetic bottlenecks, a feature associated with higher mutation burden pre-treatment. Some poor responders pass through bottlenecks, but re-grow by the time of surgical resection, suggesting a missed therapeutic opportunity. Cancers often show major changes in driver mutation presence or frequency after treatment, owing to outgrowth persistence or loss of sub-clones, copy number changes, polyclonality and/or spatial genetic heterogeneity. Post-therapy mutation spectrum shifts are also common, particularly C>A and TT>CT changes in good responders or bottleneckers. Post-treatment samples may also acquire mutations in known cancer driver genes (for example, SF3B1, TAF1 and CCND2) that are absent from the paired pre-treatment sample. Neo-adjuvant chemotherapy can rapidly and profoundly affect the oesophageal adenocarcinoma genome. Monitoring molecular changes during treatment may be clinically useful.

p53MutaGene: an online tool to estimate the effect of p53 mutational status on gene regulation in cancer

p53MutaGene is the first online tool for statistical validation of hypotheses regarding the effect of p53 mutational status on gene regulation in cancer. This tool is based on several large-scale clinical gene expression data sets and currently covers breast, colon and lung cancers. The tool detects differential co-expression patterns in expression data between p53 mutated versus p53 normal samples for the user-specified genes. Statistically significant differential co-expression for a gene pair is indicative that regulation of two genes is sensitive to the presence of p53 mutations. p53MutaGene can be used in ‘single mode' where the user can test a specific pair of genes or in ‘discovery mode' designed for analysis of several genes. Using several examples, we demonstrate that p53MutaGene is a useful tool for fast statistical validation in clinical data of p53-dependent gene regulation patterns. The tool is freely available at http://www.bioprofiling.de/tp53

Noninvasive diagnosis and monitoring of mutations by deep sequencing of circulating tumor DNA in esophageal squamous cell carcinoma

Circulating tumor DNA (ctDNA) is becoming an important biomarker in noninvasive diagnosis and monitoring of tumor dynamics. This study tested the feasibility of plasma ctDNA for the non-invasive analysis of tumor mutations in esophageal squamous cell carcinoma (ESCC) by sequencing of tumor, tumor-adjacent, and normal tissue, as well as pre-surgery and post-surgery plasma. Exome sequencing of eight patients identified between 29 and 134 somatic mutations in ESCCs, many of which were also determined in ctDNA. Comparison of pre-surgery and post-surgery plasma has shown that mutations had reduced frequency or disappeared after surgery treatment. We further evaluated the TruSight Cancer sequencing panel by using it to detect mutations in the plasma of three patients. Tumor mutations were only found in one of them. To design a sequencing panel with improved targeting, we identified significantly mutated genes by meta-analysis of 532 ESCC genomes. Our results confirmed the well-known driver genes and found several uncharacterized genes. The new panel consisted of 90 recurrent genes, which theoretically achieved 94% and 75% of sensitivity when detecting at least 1 and 2 mutant genes in ESCC patients, respectively. Our results demonstrate the feasibility of using ctDNA to detect ESCCs and monitor treatment effect. The low-cost and sensitive target panel could facilitate clinical usage of ctDNA as a noninvasive biomarker.

Association analysis of the perturbation of interactions in biological pathways and anticancer drug activity

Understanding how different genomic mutational landscapes in patients with cancer lead to different responses to anticancer drugs is an important challenge for realizing precision medicine for cancer. Many studies have analyzed the comprehensive anticancer drug-response profiles and genomic profiles of cancer cell lines to identify the relationship between the anticancer drug response and genomic alternations. However, few studies have focused on interpreting these profiles with a network perspective. In this work, we analyzed genomic alterations in cancer cell lines by considering which interactions in the signaling pathway were perturbed by mutations. With our interaction-centric approach, we identified novel interaction/drug response associations for two drugs (afatinib and ixabepilone) for which no gene-centric association could be found. When we compared the performance of classifiers for predicting the responses to 164 drugs, the classifiers trained with interaction-centric features outperformed the classifiers trained with gene-centric features, despite the smaller number of features (p-value = 2.0 × 10(-3)). By incorporating the interaction information from signaling pathways, we revealed associations between genomic alterations and drug responses that could be missed when using a gene-centric approach.

Oncogenomic portals for the visualization and analysis of genome-wide cancer data

Somatically acquired genomic alterations that drive oncogenic cellular processes are of great scientific and clinical interest. Since the initiation of large-scale cancer genomic projects (e.g., the Cancer Genome Project, The Cancer Genome Atlas, and the International Cancer Genome Consortium cancer genome projects), a number of web-based portals have been created to facilitate access to multidimensional oncogenomic data and assist with the interpretation of the data. The portals provide the visualization of small-size mutations, copy number variations, methylation, and gene/protein expression data that can be correlated with the available clinical, epidemiological, and molecular features. Additionally, the portals enable to analyze the gathered data with the use of various user-friendly statistical tools. Herein, we present a highly illustrated review of seven portals, i.e., Tumorscape, UCSC Cancer Genomics Browser, ICGC Data Portal, COSMIC, cBioPortal, IntOGen, and BioProfiling.de. All of the selected portals are user-friendly and can be exploited by scientists from different cancer-associated fields, including those without bioinformatics background. It is expected that the use of the portals will contribute to a better understanding of cancer molecular etiology and will ultimately accelerate the translation of genomic knowledge into clinical practice.

Exome sequencing of osteosarcoma reveals mutation signatures reminiscent of BRCA deficiency

Osteosarcomas are aggressive bone tumours with a high degree of genetic heterogeneity, which has historically complicated driver gene discovery. Here we sequence exomes of 31 tumours and decipher their evolutionary landscape by inferring clonality of the individual mutation events. Exome findings are interpreted in the context of mutation and SNP array data from a replication set of 92 tumours. We identify 14 genes as the main drivers, of which some were formerly unknown in the context of osteosarcoma. None of the drivers is clearly responsible for the majority of tumours and even TP53 mutations are frequently mapped into subclones. However, >80% of osteosarcomas exhibit a specific combination of single-base substitutions, LOH, or large-scale genome instability signatures characteristic of BRCA1/2-deficient tumours. Our findings imply that multiple oncogenic pathways drive chromosomal instability during osteosarcoma evolution and result in the acquisition of BRCA-like traits, which could be therapeutically exploited.

Integrated analysis of oral tongue squamous cell carcinoma identifies key variants and pathways linked to risk habits, HPV, clinical parameters and tumor recurrence

Oral tongue squamous cell carcinomas (OTSCC) are a homogeneous group of tumors characterized by aggressive behavior, early spread to lymph nodes and a higher rate of regional failure. Additionally, the incidence of OTSCC among younger population (<50yrs) is on the rise; many of whom lack the typical associated risk factors of alcohol and/or tobacco exposure. We present data on single nucleotide variations (SNVs), indels, regions with loss of heterozygosity (LOH), and copy number variations (CNVs) from fifty-paired oral tongue primary tumors and link the significant somatic variants with clinical parameters, epidemiological factors including human papilloma virus (HPV) infection and tumor recurrence. Apart from the frequent somatic variants harbored in TP53, CASP8, RASA1, NOTCH and CDKN2A genes, significant amplifications and/or deletions were detected in chromosomes 6-9, and 11 in the tumors. Variants in CASP8 and CDKN2A were mutually exclusive. CDKN2A, PIK3CA, RASA1 and DMD variants were exclusively linked to smoking, chewing, HPV infection and tumor stage. We also performed a whole-genome gene expression study that identified matrix metalloproteases to be highly expressed in tumors and linked pathways involving arachidonic acid and NF-k-B to habits and distant metastasis, respectively. Functional knockdown studies in cell lines demonstrated the role of CASP8 in a HPV-negative OTSCC cell line. Finally, we identified a 38-gene minimal signature that predicts tumor recurrence using an ensemble machine-learning method. Taken together, this study links molecular signatures to various clinical and epidemiological factors in a homogeneous tumor population with a relatively high HPV prevalence.

The mini-driver model of polygenic cancer evolution

Much of cancer genetics research has focused on the identification of the most-important somatic mutations ('major drivers') that cause tumour growth. However, many mutations found in cancer might not be major drivers or 'passenger' mutations, but instead might have relatively weak tumour-promoting effects. Our aim is to highlight the existence of these mutations (termed 'mini drivers' herein), as multiple mini-driver mutations might substitute for a major-driver change, especially in the presence of genomic instability or high mutagen exposure. The mini-driver model has clinical implications: for example, the effects of therapeutically targeting such genes may be limited. However, the main importance of the model lies in helping to provide a complete understanding of tumorigenesis, especially as we anticipate that an increasing number of mini-driver mutations will be found by cancer genome sequencing.

Colorectal cancer atlas: An integrative resource for genomic and proteomic annotations from colorectal cancer cell lines and tissues

In order to advance our understanding of colorectal cancer (CRC) development and progression, biomedical researchers have generated large amounts of OMICS data from CRC patient samples and representative cell lines. However, these data are deposited in various repositories or in supplementary tables. A database which integrates data from heterogeneous resources and enables analysis of the multidimensional data sets, specifically pertaining to CRC is currently lacking. Here, we have developed Colorectal Cancer Atlas (http://www.colonatlas.org), an integrated web-based resource that catalogues the genomic and proteomic annotations identified in CRC tissues and cell lines. The data catalogued to-date include sequence variations as well as quantitative and non-quantitative protein expression data. The database enables the analysis of these data in the context of signaling pathways, protein–protein interactions, Gene Ontology terms, protein domains and post-translational modifications. Currently, Colorectal Cancer Atlas contains data for >13 711 CRC tissues, >165 CRC cell lines, 62 251 protein identifications, >8.3 million MS/MS spectra, >18 410 genes with sequence variations (404 278 entries) and 351 pathways with sequence variants. Overall, Colorectal Cancer Atlas has been designed to serve as a central resource to facilitate research in CRC.

Public data and open source tools for multi-assay genomic investigation of disease

Molecular interrogation of a biological sample through DNA sequencing, RNA and microRNA profiling, proteomics and other assays, has the potential to provide a systems level approach to predicting treatment response and disease progression, and to developing precision therapies. Large publicly funded projects have generated extensive and freely available multi-assay data resources; however, bioinformatic and statistical methods for the analysis of such experiments are still nascent. We review multi-assay genomic data resources in the areas of clinical oncology, pharmacogenomics and other perturbation experiments, population genomics and regulatory genomics and other areas, and tools for data acquisition. Finally, we review bioinformatic tools that are explicitly geared toward integrative genomic data visualization and analysis. This review provides starting points for accessing publicly available data and tools to support development of needed integrative methods.

Computational methods and resources for the interpretation of genomic variants in cancer

The recent improvement of the high-throughput sequencing technologies is having a strong impact on the detection of genetic variations associated with cancer. Several institutions worldwide have been sequencing the whole exomes and or genomes of cancer patients in the thousands, thereby providing an invaluable collection of new somatic mutations in different cancer types. These initiatives promoted the development of methods and tools for the analysis of cancer genomes that are aimed at studying the relationship between genotype and phenotype in cancer. In this article we review the online resources and computational tools for the analysis of cancer genome. First, we describe the available repositories of cancer genome data. Next, we provide an overview of the methods for the detection of genetic variation and computational tools for the prioritization of cancer related genes and causative somatic variations. Finally, we discuss the future perspectives in cancer genomics focusing on the impact of computational methods and quantitative approaches for defining personalized strategies to improve the diagnosis and treatment of cancer.

Whole-genome characterization of chemoresistant ovarian cancer

Patients with high-grade serous ovarian cancer (HGSC) have experienced little improvement in overall survival, and standard treatment has not advanced beyond platinum-based combination chemotherapy, during the past 30 years. To understand the drivers of clinical phenotypes better, here we use whole-genome sequencing of tumour and germline DNA samples from 92 patients with primary refractory, resistant, sensitive and matched acquired resistant disease. We show that gene breakage commonly inactivates the tumour suppressors RB1, NF1, RAD51B and PTEN in HGSC, and contributes to acquired chemotherapy resistance. CCNE1 amplification was common in primary resistant and refractory disease. We observed several molecular events associated with acquired resistance, including multiple independent reversions of germline BRCA1 or BRCA2 mutations in individual patients, loss of BRCA1 promoter methylation, an alteration in molecular subtype, and recurrent promoter fusion associated with overexpression of the drug efflux pump MDR1.

Whole-genome characterization of chemoresistant ovarian cancer

Patients with high-grade serous ovarian cancer (HGSC) have experienced little improvement in overall survival, and standard treatment has not advanced beyond platinum-based combination chemotherapy, during the past 30 years. To understand the drivers of clinical phenotypes better, here we use whole-genome sequencing of tumour and germline DNA samples from 92 patients with primary refractory, resistant, sensitive and matched acquired resistant disease. We show that gene breakage commonly inactivates the tumour suppressors RB1, NF1, RAD51B and PTEN in HGSC, and contributes to acquired chemotherapy resistance. CCNE1 amplification was common in primary resistant and refractory disease. We observed several molecular events associated with acquired resistance, including multiple independent reversions of germline BRCA1 or BRCA2 mutations in individual patients, loss of BRCA1 promoter methylation, an alteration in molecular subtype, and recurrent promoter fusion associated with overexpression of the drug efflux pump MDR1.

Whole-genome characterization of chemoresistant ovarian cancer

Patients with high-grade serous ovarian cancer (HGSC) have experienced little improvement in overall survival, and standard treatment has not advanced beyond platinum-based combination chemotherapy, during the past 30 years. To understand the drivers of clinical phenotypes better, here we use whole-genome sequencing of tumour and germline DNA samples from 92 patients with primary refractory, resistant, sensitive and matched acquired resistant disease. We show that gene breakage commonly inactivates the tumour suppressors RB1, NF1, RAD51B and PTEN in HGSC, and contributes to acquired chemotherapy resistance. CCNE1 amplification was common in primary resistant and refractory disease. We observed several molecular events associated with acquired resistance, including multiple independent reversions of germline BRCA1 or BRCA2 mutations in individual patients, loss of BRCA1 promoter methylation, an alteration in molecular subtype, and recurrent promoter fusion associated with overexpression of the drug efflux pump MDR1.