Systematic pan-cancer analysis of somatic allele frequency

Integrated variant allele frequency analysis pipeline and R package: easyVAF

Somatic sequence variants are associated with cancer diagnosis, prognostic stratification, and treatment response. Variant allele frequency (VAF), the percentage of sequence reads with a specific DNA variant over the read depth at that locus, has been used as a metric to quantify mutation rates in these applications. VAF has the potential for feature detection by reflecting changes in tumor clonal composition across treatments or time points. Although there are several packages, including Genome Analysis Toolkit and VarScan, designed for variant calling and rare mutation identification, there is no readily available package for comparing VAFs among and between groups to identify loci of interest. To this end, we have developed the R package easyVAF, which includes parametric and nonparametric tests to compare VAFs among multiple groups. It is accompanied by an interactive R Shiny app. With easyVAF, the investigator has the option between three statistical tests to maximize power while maintaining an acceptable type I error rate. This paper presents our proposed pipeline for VAF analysis, from quality checking to group comparison. We evaluate our method in a wide range of simulated scenarios and show that choosing the appropriate test to limit the type I error rate is critical. For situations where data is sparse, we recommend comparing VAFs with the beta-binomial likelihood ratio test over Fisher's exact test and Pearson's χ2 test.

Metastasis-related gene identification by compound constrained NMF and a semisupervised cluster approach using pancancer multiomics features

In recent years, with the gradual increase in pancancer-related research, more attention has been given to the field of pancancer metastasis. However, the molecular mechanism of pancancer metastasis is very unclear, and identification methods for pancancer metastasis-related genes are still lacking. In view of this research status, we developed a novel pipeline to identify pancancer metastasis-related genes based on compound constrained nonnegative matrix factorization (CCNMF). To solve the above problems, the following modules were designed. A correntropy operator and feature similarity fusion (FSF) were first adopted to process the multiomics features of genes; thus, the influences caused by irrelevant biomolecular patterns, manifested as non-Gaussian noise, were minimized. CCNMF was then adopted to handle the above features with compound constraints consisting of a gene relation network and a "metastasis-related" gene set, which maximizes the biological interpretability of the metafeatures generated by NMF. Since a negative set of pancancer "metastasis-related" genes could hardly be obtained, semisupervised analyses were performed on gene features acquired by each step in our pipeline to examine our method's effect. 83% of the 236 candidates identified by the above method were associated with the metastasis of one or more cancers, 71.9% candidates were identified immune-related in pancancer in addition to the hallmark genes. Our study provides an effective and interpretable method for identifying metastasis-related as well as immune-related genes, and the method is successfully applied to TCGA pancancer data.

Nucleotide-based genetic networks: Methods and applications

Genomic variations have been acclaimed as among the key players in understanding the biological mechanisms behind migration, evolution, and adaptation to extreme conditions. Due to stochastic evolutionary forces, the frequency of polymorphisms is affected by changes in the frequency of nearby polymorphisms in the same DNA sample, making them connected in terms of evolution. This article presents all the ingredients to understand the cumulative effects and complex behaviors of genetic variations in the human mitochondrial genome by analyzing co-occurrence networks of nucleotides, and shows key results obtained from such analyses. The article emphasizes recent investigations of these co-occurrence networks, describing the role of interactions between nucleotides in fundamental processes of human migration and viral evolution. The corresponding co-mutation-based genetic networks revealed genetic signatures of human adaptation in extreme environments. This article provides the methods of constructing such networks in detail, along with their graph-theoretical properties, and applications of the genomic networks in understanding the role of nucleotide co-evolution in evolution of the whole genome.

Whole exome sequencing of high-risk neuroblastoma identifies novel non-synonymous variants

Neuroblastoma (NBL), one of the main death-causing cancers in children, is known for its remarkable genetic heterogeneity and varied patient outcome spanning from spontaneous regression to widespread disease. Specific copy number variations and single gene rearrangements have been proven to be associated with biological behavior and prognosis; however, there is still an unmet need to enlarge the existing armamentarium of prognostic and therapeutic targets. We performed whole exome sequencing (WES) of samples from 18 primary tumors and six relapse samples originating from 18 NBL patients. Our cohort consists of 16 high-risk, one intermediate, and one very low risk patient. The obtained results confirmed known mutational hotspots in ALK and revealed other non-synonymous variants of NBL-related genes (TP53, DMD, ROS, LMO3, PRUNE2, ERBB3, and PHOX2B) and of genes cardinal for other cancers (KRAS, PIK3CA, and FLT3). Beyond, GOSeq analysis determined genes involved in biological adhesion, neurological cell-cell adhesion, JNK cascade, and immune response of cell surface signaling pathways. We were able to identify novel coding variants present in more than one patient in nine biologically relevant genes for NBL, including TMEM14B, TTN, FLG, RHBG, SHROOM3, UTRN, HLA-DRB1, OR6C68, and XIRP2. Our results may provide novel information about genes and signaling pathways relevant for the pathogenesis and clinical course in high-risk NBL.

NSD1 Mutations in Sotos Syndrome Induce Differential Expression of Long Noncoding RNAs, miR646 and Genes Controlling the G2/M Checkpoint

An increasing amount of evidence indicates the critical role of the NSD1 gene in Sotos syndrome (SoS), a rare genetic disease, and in tumors. Molecular mechanisms affected by NSD1 mutations are largely uncharacterized. In order to assess the impact of NSD1 haploinsufficiency in the pathogenesis of SoS, we analyzed the gene expression profile of fibroblasts isolated from the skin samples of 15 SoS patients and of 5 healthy parents. We identified seven differentially expressed genes and five differentially expressed noncoding RNAs. The most upregulated mRNA was stratifin (SFN) (fold change, 3.9, Benjamini−Hochberg corrected p < 0.05), and the most downregulated mRNA was goosecoid homeobox (GSC) (fold change, 3.9, Benjamini−Hochberg corrected p < 0.05). The most upregulated lncRNA was lnc-C2orf84-1 (fold change, 4.28, Benjamini−Hochberg corrected p < 0.001), and the most downregulated lncRNA was Inc-C15orf57 (fold change, −0.7, Benjamini−Hochberg corrected p < 0.05). A gene set enrichment analysis reported the enrichment of genes involved in the KRAS and E2F signaling pathways, splicing regulation and cell cycle G2/M checkpoints. Our results suggest that NSD1 is involved in cell cycle regulation and that its mutation can induce the down-expression of genes involved in tumoral and neoplastic differentiation. The results contribute to defining the role of NSD1 in fibroblasts for the prevention, diagnosis and control of SoS.

Biased expression of mutant alleles in cancer-related genes in esophageal squamous cell carcinoma

BACKGROUND

Recent progress of large-scale international studies has provided comprehensive catalogs of somatic mutations in cancers. Additionally, it has become evident that allelic imbalance in the abundance of somatic mutations between DNA and RNA were pervasive in various types of cancer. However, the allelic imbalance of the abundance of somatic mutations in esophageal squamous cell carcinoma (ESCC) has not been fully analyzed.

METHODS

We performed exome sequencing for 25 Japanese patients with ESCC to detect a comprehensive catalog of somatic mutations in ESCC. Additionally, we performed mRNA sequencing to evaluate the allelic imbalance of the identified somatic mutations at the transcriptional level by comparing the mutant allele frequencies between RNA and DNA.

RESULTS

The exome sequencing showed that TP53 and ZNF750 were significantly mutated genes. The expression levels of TP53 and ZNF750 were different depending on the mutation status. In almost all the tumors with missense mutations in TP53 and ZNF750, the mutant allele frequencies were higher in the RNA sequencing than those in the exome sequencing, indicating that the mutant alleles were preferentially expressed. By examining the allelic imbalances for all the identified missense mutations, we demonstrated that genes showing preferential expressions of the mutant alleles were involved in the pathways including cell cycle, cell death, and chromatin modification.

CONCLUSIONS

The results of this study suggest that the allelic imbalance of the abundance of somatic mutations plays important roles in the initiation and progression of ESCC by modulating cancer-related biological pathways.

Mutations in the telomerase reverse transcriptase promoter and PIK3CA gene are common events in penile squamous cell carcinoma of Italian and Ugandan patients

Penile carcinoma develops either through human papillomavirus (HPV) related or unrelated carcinogenic pathways. Genetic alterations and nucleotide changes in coding regions (ie, TP53, CDKN2A, PIK3CA and NOTCH1) are main cancer driver events either in HPV positive or in HPV negative tumours. We investigated the presence of hotspot nucleotide mutations in TERT promoter (TERTp) and PIK3CA exon 9 and their relationship with HPV status in 69 penile cancer cases from Italian and Ugandan patients. Genetic variations and viral sequences have been characterised by end‐point polymerase chain reaction (PCR) and Sanger sequencing. The mutant allele frequencies (MAFs) of TERTp −124A/−146A and PIK3CA E545K have been determined by droplet digital PCR (ddPCR) assays. The results showed that TERTp mutations are highly prevalent in penile carcinoma (53.6%) and significantly more frequent in HPV negative (67.6%) than HPV positive (32.4%) cases (P = .0482). PIK3CA mutations were similarly distributed in virus‐related and unrelated cases (25.9% and 26.7%, respectively) and coexisted with TERTp changes in 15.8% of penile carcinoma samples. Notably, MAFs of co‐occurring mutations were frequently discordant indicating that PIK3CA E545K nucleotide changes are subsequent genetic events occurring in subclones of TERTp mutated cells. The frequencies of TERTp and PIK3CA mutations were higher among Italian compared to Ugandan cases and inversely correlated with the HPV status. In conclusion, TERTp mutations are very common in penile carcinoma and their coexistence with PIK3CA in a substantial number of cases may represent a novel oncogenic synergy relevant for patient stratification and use of therapeutic strategies against new actionable targets.

NSD1: A Lysine Methyltransferase between Developmental Disorders and Cancer

Recurrent epigenomic alterations associated with multiple human pathologies have increased the interest in the nuclear receptor binding SET domain protein 1 (NSD1) lysine methyltransferase. Here, we review the current knowledge about the biochemistry, cellular function and role of NSD1 in human diseases. Several studies have shown that NSD1 controls gene expression by methylation of lysine 36 of histone 3 (H3K36me1/2) in a complex crosstalk with de novo DNA methylation. Inactivation in flies and mice revealed that NSD1 is essential for normal development and that it regulates multiple cell type-specific functions by interfering with transcriptional master regulators. In humans, putative loss of function NSD1 mutations characterize developmental syndromes, such as SOTOS, as well as cancer from different organs. In pediatric hematological malignancies, a recurrent chromosomal translocation forms a NUP98-NSD1 fusion with SET-dependent leukemogenic activity, which seems targetable by small molecule inhibitors. To treat or prevent diseases driven by aberrant NSD1 activity, future research will need to pinpoint the mechanistic correlation between the NSD1 gene dosage and/or mutational status with development, homeostasis, and malignant transformation.

Machine learning algorithm improved automated droplet classification of ddPCR for detection of BRAF V600E in paraffin-embedded samples

Somatic mutations in cancer driver genes can help diagnosis, prognosis and treatment decisions. Formalin-fixed paraffin-embedded (FFPE) specimen is the main source of DNA for somatic mutation detection. To overcome constraints of DNA isolated from FFPE, we compared pyrosequencing and ddPCR analysis for absolute quantification of BRAF V600E mutation in the DNA extracted from FFPE specimens and compared the results to the qualitative detection information obtained by Sanger Sequencing. Sanger sequencing was able to detect BRAF V600E mutation only when it was present in more than 15% total alleles. Although the sensitivity of ddPCR is higher than that observed for Sanger, it was less consistent than pyrosequencing, likely due to droplet classification bias of FFPE-derived DNA. To address the droplet allocation bias in ddPCR analysis, we have compared different algorithms for automated droplet classification and next correlated these findings with those obtained from pyrosequencing. By examining the addition of non-classifiable droplets (rain) in ddPCR, it was possible to obtain better qualitative classification of droplets and better quantitative classification compared to no rain droplets, when considering pyrosequencing results. Notable, only the Machine learning k-NN algorithm was able to automatically classify the samples, surpassing manual classification based on no-template controls, which shows promise in clinical practice.

The Impact of Variant Allele Frequency in EGFR Mutated NSCLC Patients on Targeted Therapy

EGFR mutations represent the most common currently targetable oncogenic driver in non-small cell lung cancer. There has been tremendous progress in targeting this alteration over the course of the last decade, and third generation tyrosine kinase inhibitors offer previously unseen survival rates among these patients. Nonetheless, a better understanding is still needed, as roughly a third of patients do not respond to targeted therapy and there is an important heterogeneity among responders. Allelic frequency, or the variant EGFR allele frequency, corresponds to the fraction of sequencing reads harboring the mutation. The allelic fraction is influenced by the proportion of tumor cells in the sample, the presence of copy number alterations but also, most importantly, by the proportion of cells within the tumor that carry the mutation. Mutations that occur early in tumor evolution, often called clonal or truncal, have a higher allelic frequency than late, subclonal mutations, and are more often drivers of cancer evolution and attractive therapeutic targets. Most, but not all, EGFR mutations are clonal. Although an exact estimate of clonal proportion is hard to derive computationally, the allelic frequency is readily available to clinicians and could be a useful surrogate. We hypothesized that tumors with low allelic frequency of the EGFR mutation will respond less favorably to targeted treatment.

Whole-slide laser microdissection for tumour enrichment

The practical application of genome-scale technologies to precision oncology research requires flexible tissue processing strategies that can be used to differentially select both tumour and normal cell populations from formalin-fixed, paraffin-embedded tissues. As tumour sequencing scales towards clinical implementation, practical difficulties in scheduling and obtaining fresh tissue biopsies at scale, including blood samples as surrogates for matched 'normal' DNA, have focused attention on the use of formalin-preserved clinical samples collected routinely for diagnostic purposes. In practice, such samples often contain both tumour and normal cells which, if correctly partitioned, could be used to profile both tumour and normal genomes, thus identifying somatic alterations. Here we report a semi-automated method for laser microdissecting entire slide-mounted tissue sections to enrich for cells of interest with sufficient yield for whole genome and transcriptome sequencing. Using this method, we demonstrated enrichment of tumour material from mixed tumour-normal samples by up to 67%. Leveraging new methods that allow for the extraction of high-quality nucleic acids from small amounts of formalin-fixed tissues, we further showed that the method was successful in yielding sequence data of sufficient quality for use in BC Cancer's Personalized OncoGenomics (POG) program. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

PESM: A novel approach of tumor purity estimation based on sample specific methylation sites

Background: Tumor purity is of great significance for the study of tumor genotyping and the prediction of recurrence, which is significantly affected by tumor heterogeneity. Tumor heterogeneity is the basis of drug resistance in various cancer treatments, and DNA methylation plays a core role in the generation of tumor heterogeneity. Almost all types of cancer cells are associated with abnormal DNA methylation in certain regions of the genome. The selection of tumor-related differential methylation sites, which can be used as an indicator of tumor purity, has important implications for purity assessment. At present, the selection of information sites mostly focuses on inter-tumor heterogeneity and ignores the heterogeneity of tumor growth space that is sample specificity. Results: Considering the specificity of tumor samples and the information gain of individual tumor sample relative to the normal samples, we present an approach, PESM, to evaluate the tumor purity through the specificity difference methylation sites of tumor samples. Applied to more than 200 tumor samples of Prostate adenocarcinoma (PRAD) and Kidney renal clear cell carcinoma (KIRC), it shows that the tumor purity estimated by PESM is highly consistent with other existing methods. In addition, PESM performs better than the method that uses the integrated signal of methylation sites to estimate purity. Therefore, different information sites selection methods have an important impact on the estimation of tumor purity, and the selection of sample specific information sites has a certain significance for accurate identification of tumor purity of samples.

Toward a More Precise Future for Oncology

Prospective molecular characterization of cancer has enabled physicians to define the genomic changes of each patient's tumor in real time and select personalized therapies based on these detailed portraits. Despite the promise of such an approach, previously unrecognized biological and therapeutic complexity is emerging. Here, we synthesize lessons learned and discuss the steps required to extend the benefits of genome-driven oncology, including proposing strategies for improved drug design, more nuanced patient selection, and optimized use of available therapies. Finally, we suggest ways that next-generation genome-driven clinical trials can evolve to accelerate our understanding of cancer biology and improve patient outcomes.

Differential Allele-Specific Expression Uncovers Breast Cancer Genes Dysregulated by Cis Noncoding Mutations

Identifying cancer-relevant mutations in noncoding regions is challenging due to the large numbers of such mutations, their low levels of recurrence, and difficulties in interpreting their functional impact. To uncover genes that are dysregulated due to somatic mutations in cis, we build upon the concept of differential allele-specific expression (ASE) and introduce methods to identify genes within an individual's cancer whose ASE differs from what is found in matched normal tissue. When applied to breast cancer tumor samples, our methods detect the known allele-specific effects of copy number variation and nonsense-mediated decay. Further, genes that are found to recurrently exhibit differential ASE across samples are cancer relevant. Genes with cis mutations are enriched for differential ASE, and we find 147 potentially functional noncoding mutations cis to genes that exhibit significant differential ASE. We conclude that differential ASE is a promising means for discovering gene dysregulation due to cis noncoding mutations.

Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAF_RNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAF_RNA estimation from current scRNA-seq datasets and shows that the 3′-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics.

Drug responses are conserved across patient-derived xenograft models of melanoma leading to identification of novel drug combination therapies

BACKGROUND

Patient-derived xenograft (PDX) mouse tumour models can predict response to therapy in patients. Predictions made from PDX cultures (PDXC) would allow for more rapid and comprehensive evaluation of potential treatment options for patients, including drug combinations.

METHODS

We developed a PDX library of BRAF-mutant metastatic melanoma, and a high-throughput drug-screening (HTDS) platform utilising clinically relevant drug exposures. We then evaluated 34 antitumor agents across eight melanoma PDXCs, compared drug response to BRAF and MEK inhibitors alone or in combination with PDXC and the corresponding PDX, and investigated novel drug combinations targeting BRAF inhibitor-resistant melanoma.

RESULTS

The concordance of cancer-driving mutations across patient, matched PDX and subsequent PDX generations increases as variant allele frequency (VAF) increases. There was a high correlation in the magnitude of response to BRAF and MEK inhibitors between PDXCs and corresponding PDXs. PDXCs and corresponding PDXs from metastatic melanoma patients that progressed on standard-of-care therapy demonstrated similar resistance patterns to BRAF and MEK inhibitor therapy. Importantly, HTDS identified novel drug combinations to target BRAF-resistant melanoma.

CONCLUSIONS

The biological consistency observed between PDXCs and PDXs suggests that PDXCs may allow for a rapid and comprehensive identification of treatments for aggressive cancers, including combination therapies.

Evidence for Etiologic Subtypes of Breast Cancer in the Carolina Breast Cancer Study

BACKGROUND

Distinctions in the etiology of triple-negative versus luminal breast cancer have become well established using immunohistochemical surrogates [notably estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)]. However, it is unclear whether established immunohistochemical subtypes are the sole or definitive means of etiologically subdividing breast cancers.

METHODS

We evaluated clinical biomarkers and tumor suppressor p53 with risk factor data from cases and controls in the Carolina Breast Cancer Study, a population-based study of incident breast cancers. For each individual marker and combinations of markers, we calculated an aggregate measure to distinguish the etiologic heterogeneity of different classification schema. To compare schema, we estimated subtype-specific case-control odds ratios for individual risk factors and fit age-at-incidence curves with two-component mixture models. We also evaluated subtype concordance of metachronous contralateral breast tumors in the California Cancer Registry.

RESULTS

ER was the biomarker that individually explained the greatest variability in risk factor profiles. However, further subdivision by p53 significantly increased the degree of etiologic heterogeneity. Age at diagnosis, nulliparity, and race were heterogeneously associated with ER/p53 subtypes. The ER-/p53+ subtype exhibited a similar risk factor profile and age-at-incidence distribution to the triple-negative subtype.

CONCLUSIONS

Clinical marker-based intrinsic subtypes have established value, yet other schema may also yield important etiologic insights.

IMPACT

Novel environmental or genetic risk factors may be identifiable by considering different etiologic schema, including cross-classification based on ER/p53.

The Many Faces of Gene Regulation in Cancer: A Computational Oncogenomics Outlook

Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular pathways depending on such control have been recognized as central to the characterization of the disease. More important though is the understanding of their causes, prognosis, and therapeutics. There is a multitude of factors associated with anomalous control of gene expression in cancer. Many of these factors are now amenable to be studied comprehensively by means of experiments based on diverse omic technologies. However, characterizing each dimension of the phenomenon individually has proven to fall short in presenting a clear picture of expression regulation as a whole. In this review article, we discuss some of the more relevant factors affecting gene expression control both, under normal conditions and in tumor settings. We describe the different omic approaches that we can use as well as the computational genomic analysis needed to track down these factors. Then we present theoretical and computational frameworks developed to integrate the amount of diverse information provided by such single-omic analyses. We contextualize this within a systems biology-based multi-omic regulation setting, aimed at better understanding the complex interplay of gene expression deregulation in cancer.