Direct Transcriptional Consequences of Somatic Mutation in Breast Cancer

Copy number alteration is an independent prognostic biomarker in triple-negative breast cancer patients

BACKGROUND

Next-generation sequencing (NGS) has enabled comprehensive genomic profiling to identify gene alterations that play important roles in cancer biology. However, the clinical significance of these genomic alterations in triple-negative breast cancer (TNBC) patients has not yet been fully elucidated. The aim of this study was to clarify the clinical significance of genomic profiling data, including copy number alterations (CNA) and tumor mutation burden (TMB), in TNBC patients.

METHODS

A total of 47 patients with Stage I-III TNBC with genomic profiling of 435 known cancer genes by NGS were enrolled in this study. Disease-free survival (DFS) and overall survival (OS) were evaluated for their association to gene profiling data.

RESULTS

CNA-high patients showed significantly worse DFS and OS than CNA-low patients (p = 0.0009, p = 0.0041, respectively). TMB was not associated with DFS or OS in TNBC patients. Patients with TP53 alterations showed a tendency of worse DFS (p = 0.0953) and significantly worse OS (p = 0.0338) compared with patients without TP53 alterations. Multivariable analysis including CNA and other clinicopathological parameters revealed that CNA was an independent prognostic factor for DFS (p = 0.0104) and OS (p = 0.0306). Finally, multivariable analysis also revealed the combination of CNA-high and TP53 alterations is an independent prognostic factor for DFS (p = 0.0005) and OS (p = 0.0023).

CONCLUSIONS

We revealed that CNA, but not TMB, is significantly associated with DFS and OS in TNBC patients. The combination of CNA-high and TP53 alterations may be a promising biomarker that can inform beyond standard clinicopathologic factors to identify a subgroup of TNBC patients with significantly worse prognosis.

Widespread hypertranscription in aggressive human cancers

Cancers are often defined by the dysregulation of specific transcriptional programs; however, the importance of global transcriptional changes is less understood. Hypertranscription is the genome-wide increase in RNA output. Hypertranscription's prevalence, underlying drivers, and prognostic significance are undefined in primary human cancer. This is due, in part, to limitations of expression profiling methods, which assume equal RNA output between samples. Here, we developed a computational method to directly measure hypertranscription in 7494 human tumors, spanning 31 cancer types. Hypertranscription is ubiquitous across cancer, especially in aggressive disease. It defines patient subgroups with worse survival, even within well-established subtypes. Our data suggest that loss of transcriptional suppression underpins the hypertranscriptional phenotype. Single-cell analysis reveals hypertranscriptional clones, which dominate transcript production regardless of their size. Last, patients with hypertranscribed mutations have improved response to immune checkpoint therapy. Our results provide fundamental insights into gene dysregulation across human cancers and may prove useful in identifying patients who would benefit from novel therapies.

Neoplasia-associated Chromosome Translocations Resulting in Gene Truncation

Chromosomal translocations in cancer as well as benign neoplasias typically lead to the formation of fusion genes. Such genes may encode chimeric proteins when two protein-coding regions fuse in-frame, or they may result in deregulation of genes via promoter swapping or translocation of the gene into the vicinity of a highly active regulatory element. A less studied consequence of chromosomal translocations is the fusion of two breakpoint genes resulting in an out-of-frame chimera. The breaks then occur in one or both protein-coding regions forming a stop codon in the chimeric transcript shortly after the fusion point. Though the latter genetic events and mechanisms at first awoke little research interest, careful investigations have established them as neither rare nor inconsequential. In the present work, we review and discuss the truncation of genes in neoplastic cells resulting from chromosomal rearrangements, especially from seemingly balanced translocations.

Detecting Medium and Large Insertions and Deletions with transIndel

Insertions and deletions (indels) are primarily detected from DNA sequencing (DNA-seq) data, but their transcriptional consequences remain unexplored due to challenges in distinguishing medium- and large-sized indels from RNA splicing events in RNA-seq data. We introduce transIndel, a splice-aware algorithm that parses the chimeric alignments predicted by a short read aligner and reconstructs the mid-sized insertions and large deletions based on the linear alignments of split reads from DNA-seq or RNA-seq data. Here, we describe the method and provide a tutorial on the installation and application of transIndel.

The expressed mutational landscape of microsatellite stable colorectal cancers

Background

Colorectal cancer is the 2nd leading cause of cancer-related deaths with few patients benefiting from biomarker-guided therapy. Mutation expression is essential for accurate interpretation of mutations as biomarkers, but surprisingly, little has been done to analyze somatic cancer mutations on the expression level. We report a large-scale analysis of allele-specific mutation expression.

Methods

Whole-exome and total RNA sequencing was performed on 137 samples from 121 microsatellite stable colorectal cancers, including multiregional samples of primary and metastatic tumors from 4 patients. Data were integrated with allele-specific resolution. Results were validated in an independent set of 241 colon cancers. Therapeutic associations were explored by pharmacogenomic profiling of 15 cell lines or patient-derived organoids.

Results

The median proportion of expressed mutations per tumor was 34%. Cancer-critical mutations had the highest expression frequency (gene-wise mean of 58%), independent of frequent allelic imbalance. Systematic deviation from the general pattern of expression levels according to allelic frequencies was detected, including preferential expression of mutated alleles dependent on the mutation type and target gene. Translational relevance was suggested by correlations of KRAS/NRAS or TP53 mutation expression levels with downstream oncogenic signatures (p < 0.03), overall survival among patients with stage II and III cancer (KRAS/NRAS: hazard ratio 6.1, p = 0.0070), and targeted drug sensitivity. The latter was demonstrated for EGFR and MDM2 inhibition in pre-clinical models.

Conclusions

Only a subset of mutations in microsatellite stable colorectal cancers were expressed, and the “expressed mutation dose” may provide an opportunity for more fine-tuned biomarker interpretations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00955-2.

Breast Cancer and the Other Non-Coding RNAs

Breast cancer is very heterogenous and the most common gynaecological cancer, with various factors affecting its development. While its impact on human lives and national health budgets is still rising in almost all global areas, many molecular mechanisms affecting its onset and development remain unclear. Conventional treatments still prove inadequate in some aspects, and appropriate molecular therapeutic targets are required for improved outcomes. Recent scientific interest has therefore focused on the non-coding RNAs roles in tumour development and their potential as therapeutic targets. These RNAs comprise the majority of the human transcript and their broad action mechanisms range from gene silencing to chromatin remodelling. Many non-coding RNAs also have altered expression in breast cancer cell lines and tissues, and this is often connected with increased proliferation, a degraded extracellular environment, and higher endothelial to mesenchymal transition. Herein, we summarise the known abnormalities in the function and expression of long non-coding RNAs, Piwi interacting RNAs, small nucleolar RNAs and small nuclear RNAs in breast cancer, and how these abnormalities affect the development of this deadly disease. Finally, the use of RNA interference to suppress breast cancer growth is summarised.

GeTallele: A Method for Analysis of DNA and RNA Allele Frequency Distributions

Variant allele frequencies (VAF) are an important measure of genetic variation that can be estimated at single-nucleotide variant (SNV) sites. RNA and DNA VAFs are used as indicators of a wide-range of biological traits, including tumor purity and ploidy changes, allele-specific expression and gene-dosage transcriptional response. Here we present a novel methodology to assess gene and chromosomal allele asymmetries and to aid in identifying genomic alterations in RNA and DNA datasets. Our approach is based on analysis of the VAF distributions in chromosomal segments (continuous multi-SNV genomic regions). In each segment we estimate variant probability, a parameter of a random process that can generate synthetic VAF samples that closely resemble the observed data. We show that variant probability is a biologically interpretable quantitative descriptor of the VAF distribution in chromosomal segments which is consistent with other approaches. To this end, we apply the proposed methodology on data from 72 samples obtained from patients with breast invasive carcinoma (BRCA) from The Cancer Genome Atlas (TCGA). We compare DNA and RNA VAF distributions from matched RNA and whole exome sequencing (WES) datasets and find that both genomic signals give very similar segmentation and estimated variant probability profiles. We also find a correlation between variant probability with copy number alterations (CNA). Finally, to demonstrate a practical application of variant probabilities, we use them to estimate tumor purity. Tumor purity estimates based on variant probabilities demonstrate good concordance with other approaches (Pearson's correlation between 0.44 and 0.76). Our evaluation suggests that variant probabilities can serve as a dependable descriptor of VAF distribution, further enabling the statistical comparison of matched DNA and RNA datasets. Finally, they provide conceptual and mechanistic insights into relations between structure of VAF distributions and genetic events. The methodology is implemented in a Matlab toolbox that provides a suite of functions for analysis, statistical assessment and visualization of Genome and Transcriptome allele frequencies distributions. GeTallele is available at: https://github.com/SlowinskiPiotr/GeTallele.

Pan-Cancer Analysis Reveals the Diverse Landscape of Novel Sense and Antisense Fusion Transcripts

Gene fusions that contribute to oncogenicity can be explored for identifying cancer biomarkers and potential drug targets. To investigate the nature and distribution of fusion transcripts in cancer, we examined the transcriptome data of about 9,000 primary tumors from 33 different cancers in TCGA (The Cancer Genome Atlas) along with cell line data from CCLE (Cancer Cell Line Encyclopedia) using ChimeRScope, a novel fusion detection algorithm. We identified several fusions with sense (canonical, 39%) or antisense (non-canonical, 61%) transcripts recurrent across cancers. The majority of the recurrent non-canonical fusions found in our study are novel, unexplored, and exhibited highly variable profiles across cancers, with breast cancer and glioblastoma having the highest and lowest rates, respectively. Overall, 4,344 recurrent fusions were identified from TCGA in this study, of which 70% were novel. Additional analysis of 802 tumor-derived cell line transcriptome data across 20 cancers revealed significant variability in recurrent fusion profiles between primary tumors and corresponding cell lines. A subset of canonical and non-canonical fusions was validated by examining the structural variation evidence in whole-genome sequencing (WGS) data or by Sanger sequencing of fusion junctions. Several recurrent fusion genes identified in our study show promise for drug repurposing in basket trials and present opportunities for mechanistic studies.

Targeted-Gene Sequencing to Catch Triple Negative Breast Cancer Heterogeneity before and after Neoadjuvant Chemotherapy

Triple negative breast cancer (TNBC) patients not attaining pathological Complete Response (pCR) after neo-adjuvant chemotherapy (NAC) have poor prognosis. We characterized 19 patients for somatic mutations in primary tumor biopsy and residual disease (RD) at surgery by 409 cancer-related gene sequencing (IonAmpliSeqTM Comprehensive Cancer Panel). A median of four (range 1-66) genes was mutated in each primary tumor biopsy, and the most common mutated gene was TP53 followed by a long tail of low frequency mutations. There were no recurrent mutations significantly associated with pCR. However, half of patients with RD had primary tumor biopsy with mutations in genes related to the immune system compared with none of those achieving pCR. Overall, the number of mutations showed a downward trend in post- as compared to pre-NAC samples. PIK3CA was the most common altered gene after NAC. The mutational profile of TNBC during treatment as inferred from patterns of mutant allele frequencies in matched pre-and post-NAC samples showed that RD harbored alterations of cell cycle progression, PI3K/Akt/mTOR, and EGFR tyrosine kinase inhibitor-resistance pathways. Our findings support the use of targeted-gene sequencing for TNBC therapeutic development, as patients without pCR may present mutations of immune-related pathways in their primary tumor biopsy, or actionable targets in the RD.

Genomic and transcriptomic characterisation of undifferentiated pleomorphic sarcoma of bone

Undifferentiated pleomorphic sarcoma of bone (UPSb) is a rare primary bone sarcoma that lacks a specific line of differentiation. There is very little information about the genetic alterations leading to tumourigenesis or malignant transformation. Distinguishing between UPSb and other malignant bone sarcomas, including dedifferentiated chondrosarcoma and osteosarcoma, can be challenging due to overlapping features. To explore the genomic and transcriptomic landscape of UPSb tumours, whole-exome sequencing (WES) and RNA sequencing (RNA-Seq) were performed on UPSb tumours. All tumours lacked hotspot mutations in IDH1/2 132 or 172 codons, thereby excluding the diagnosis of dedifferentiated chondrosarcoma. Recurrent somatic mutations in TP53 were identified in four of 14 samples (29%). Moreover, recurrent mutations in histone chromatin remodelling genes, including H3F3A, ATRX and DOT1L, were identified in five of 14 samples (36%), highlighting the potential role of deregulated chromatin remodelling pathways in UPSb tumourigenesis. The majority of recurrent mutations in chromatin remodelling genes identified here are reported in COSMIC, including the H3F3A G34 and K36 hotspot residues. Copy number alteration analysis identified gains and losses in genes that have been previously altered in UPSb or UPS of soft tissue. Eight somatic gene fusions were identified by RNA-Seq, two of which, CLTC-VMP1 and FARP1-STK24, were reported previously in multiple cancers. Five gene fusions were genomically characterised. Hierarchical clustering analysis, using RNA-Seq data, distinctly clustered UPSb tumours from osteosarcoma and other sarcomas, thus molecularly distinguishing UPSb from other sarcomas. RNA-Seq expression profiling analysis and quantitative reverse transcription-polymerase chain reaction showed an elevated expression in FGF23, which can be a potential molecular biomarker for UPSb. To our knowledge, this study represents the first comprehensive WES and RNA-Seq analysis of UPSb tumours revealing novel protein-coding recurrent gene mutations, gene fusions and identifying a potential UPSb molecular biomarker, thereby broadening the understanding of the pathogenic mechanisms and highlighting the possibility of developing novel targeted therapeutics. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Overexpression of MUC1 predicts poor prognosis in patients with breast cancer

Breast cancer is the most commonly diagnosed cancer in females; thus, there is an urgent requirement to identify precise biomarkers for the diagnosis and treatment of the disease. Mucin 1 (MUC1) is a glycoprotein that has been demonstrated to be involved in the metastasis and invasion of multiple tumor types. Bioinformatics analyses were conducted to indicate the prognostic value of MUC1 in breast cancer. Additionally, the expression level of MUC1 was assessed using Oncomine analysis. Furthermore, PrognoScan was used to analyze the prognostic value of MUC1 in breast cancer. Mutations of MUC1 were analyzed by the Catalogue of Somatic Mutations in Cancer and cBioPortal databases. In addition, University of California, Santa Cruz (UCSC) was used to examine the methylation status of MUC1. Co-expression of MUC1 mRNA was detected with the cBioPortal, UCSC and Breast Cancer Gene-Expression Miner v4.0 datasets. The results demonstrated that MCU1 is frequently overexpressed in breast cancer and is negatively associated with CpG sites. Furthermore, pooled data indicated that abnormally high expression of MUC1 indicates poor prognosis. Additionally, upregulation of MUC1 expression is associated with estrogen receptor- and progesterone receptor-positive disease, aging and increased Scarff, Bloom and Richardson grade, but is not associated with triple-negative and basal-like status. Subsequent data mining across multiple large databases demonstrated a positive association between MUC1 mRNA expression and cyclic AMP-responsive element-binding protein 3-like 4 (CREB3L4) in breast cancer tissues. The present data indicated that the overexpression of MUC1 indicates a poor prognosis in patients with breast cancer and is associated with MUC1 promoter methylation status. Additionally, MUC1 positively correlated with CREB3L4 and may serve as a potential prognostic factor and therapy target for breast cancer.

Network integration of multi-tumour omics data suggests novel targeting strategies

We characterize different tumour types in search for multi-tumour drug targets, in particular aiming for drug repurposing and novel drug combinations. Starting from 11 tumour types from The Cancer Genome Atlas, we obtain three clusters based on transcriptomic correlation profiles. A network-based analysis, integrating gene expression profiles and protein interactions of cancer-related genes, allows us to define three cluster-specific signatures, with genes belonging to NF-κB signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis biological processes. These signatures have been characterized by different approaches based on mutational, pharmacological and clinical evidences, demonstrating the validity of our selection. Moreover, we define new pharmacological strategies validated by in vitro experiments that show inhibition of cell growth in two tumour cell lines, with significant synergistic effect. Our study thus provides a list of genes and pathways that could possibly be used, singularly or in combination, for the design of novel treatment strategies.

Tumours of different tissues can show similarities in genomic alterations. Here, the authors combine tumour transcriptome and protein interaction data in a network-based analysis of 11 tumours types, and identify clusters of tumours with specific signatures for multi-tumour drug targeting and survival prognosis.

ProteomeGenerator: A Framework for Comprehensive Proteomics Based on de Novo Transcriptome Assembly and High-Accuracy Peptide Mass Spectral Matching

Modern mass spectrometry now permits genome-scale and quantitative measurements of biological proteomes. However, analysis of specific specimens is currently hindered by the incomplete representation of biological variability of protein sequences in canonical reference proteomes and the technical demands for their construction. Here, we report ProteomeGenerator, a framework for de novo and reference-assisted proteogenomic database construction and analysis based on sample-specific transcriptome sequencing and high-accuracy mass spectrometry proteomics. This enables the assembly of proteomes encoded by actively transcribed genes, including sample-specific protein isoforms resulting from non-canonical mRNA transcription, splicing, or editing. To improve the accuracy of protein isoform identification in non-canonical proteomes, ProteomeGenerator relies on statistical target-decoy database matching calibrated using sample-specific controls. Its current implementation includes automatic integration with MaxQuant mass spectrometry proteomics algorithms. We applied this method for the proteogenomic analysis of splicing factor SRSF2 mutant leukemia cells, demonstrating high-confidence identification of non-canonical protein isoforms arising from alternative transcriptional start sites, intron retention, and cryptic exon splicing as well as improved accuracy of genome-scale proteome discovery. Additionally, we report proteogenomic performance metrics for current state-of-the-art implementations of SEQUEST HT, MaxQuant, Byonic, and PEAKS mass spectral analysis algorithms. Finally, ProteomeGenerator is implemented as a Snakemake workflow within a Singularity container for one-step installation in diverse computing environments, thereby enabling open, scalable, and facile discovery of sample-specific, non-canonical, and neomorphic biological proteomes.

Systematic pan-cancer analysis of somatic allele frequency

Imbalanced expression of somatic alleles in cancer can suggest functional and selective features, and can therefore indicate possible driving potential of the underlying genetic variants. To explore the correlation between allele frequency of somatic variants and total gene expression of their harboring gene, we used the unique data set of matched tumor and normal RNA and DNA sequencing data of 5523 distinct single nucleotide variants in 381 individuals across 10 cancer types obtained from The Cancer Genome Atlas (TCGA). We analyzed the allele frequency in the context of the variant and gene functional features and linked it with changes in the total gene expression. We documented higher allele frequency of somatic variants in cancer-implicated genes (Cancer Gene Census, CGC). Furthermore, somatic alleles bearing premature terminating variants (PTVs), when positioned in CGC genes, appeared to be less frequently degraded via nonsense-mediated mRNA decay, indicating possible favoring of truncated proteins by the tumor transcriptome. Among the genes with multiple PTVs with high allele frequency, ARID1, TP53 and NSD1 were known key cancer genes. All together, our analyses suggest that high allele frequency of tumor somatic variants can indicate driving functionality and can serve to identify potential cancer-implicated genes.

Indel detection from DNA and RNA sequencing data with transIndel

Background

Insertions and deletions (indels) are a major class of genomic variation associated with human disease. Indels are primarily detected from DNA sequencing (DNA-seq) data but their transcriptional consequences remain unexplored due to challenges in discriminating medium-sized and large indels from splicing events in RNA-seq data.

Results

Here, we developed transIndel, a splice-aware algorithm that parses the chimeric alignments predicted by a short read aligner and reconstructs the mid-sized insertions and large deletions based on the linear alignments of split reads from DNA-seq or RNA-seq data. TransIndel exhibits competitive or superior performance over eight state-of-the-art indel detection tools on benchmarks using both synthetic and real DNA-seq data. Additionally, we applied transIndel to DNA-seq and RNA-seq datasets from 333 primary prostate cancer patients from The Cancer Genome Atlas (TCGA) and 59 metastatic prostate cancer patients from AACR-PCF Stand-Up- To-Cancer (SU2C) studies. TransIndel enhanced the taxonomy of DNA- and RNA-level alterations in prostate cancer by identifying recurrent FOXA1 indels as well as exitron splicing in genes implicated in disease progression.

Conclusions

Our study demonstrates that transIndel is a robust tool for elucidation of medium- and large-sized indels from DNA-seq and RNA-seq data. Including RNA-seq in indel discovery efforts leads to significant improvements in sensitivity for identification of med-sized and large indels missed by DNA-seq, and reveals non-canonical RNA-splicing events in genes associated with disease pathology.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4671-4) contains supplementary material, which is available to authorized users.

Pan-cancer analysis of somatic mutations and transcriptomes reveals common functional gene clusters shared by multiple cancer types

To discover functional gene clusters across cancers, we performed a systematic pan-cancer analysis of 33 cancer types. We identified genes that were associated with somatic mutations and were the cores of a co-expression network. We found that multiple cancer types have relatively exclusive hub genes individually; however, the hub genes cooperate with each other based on their functional relationship. When we built a protein-protein interaction network of hub genes and found nine functional gene clusters across cancer types, the gene clusters divided not only the region of the network map, but also the function of the network by their distinct roles related to the development and progression of cancer. This functional relationship between the clusters and cancers was underpinned by the high expression of module genes and enrichment of programmed cell death, and known candidate cancer genes. In addition to protein-coding hub genes, non-coding hub genes had a possible relationship with cancer. Overall, our approach of investigating cancer genes enabled finding pan-cancer hub genes and common functional gene clusters shared by multiple cancer types based on the expression status of the primary tumour and the functional relationship of genes in the biological network.

Comprehensive genomic transcriptomic tumor-normal gene panel analysis for enhanced precision in patients with lung cancer

A CMS approved test for lung cancer is based on tumor-only analysis of a targeted 35 gene panel, specifically excluding the use of the patient's normal germline tissue. However, this tumor-only approach increases the risk of mistakenly identifying germline single nucleotide polymorphisms (SNPs) as somatically-derived cancer driver mutations (false positives). 621 patients with 30 different cancer types, including lung cancer, were studied to compare the precision of tumor somatic variant calling in 35 genes using tumor-only DNA sequencing versus tumor-normal DNA plus RNA sequencing. When sequencing of lung cancer was performed using tumor genomes alone without normal germline controls, 94% of variants identified were SNPs and thus false positives. Filtering for common SNPs still resulted in as high as 48% false positive variant calling. With tumor-only sequencing, 29% of lung cancer patients had a false positive variant call in at least one of twelve genes with directly targetable drugs. RNA analysis showed 18% of true somatic variants were not expressed. Thus, sequencing and analysis of both normal germline and tumor genomes is necessary for accurate identification of molecular targets. Treatment decisions based on tumor-only analysis may result in the administration of ineffective therapies while also increasing the risk of negative drug-related side effects.

An integrative bioinformatics approach reveals coding and non-coding gene variants associated with gene expression profiles and outcome in breast cancer molecular subtypes

Background

Sequence variations in coding and non-coding regions of the genome can affect gene expression and signalling pathways, which in turn may influence disease outcome.

Methods

In this study, we integrated somatic mutations, gene expression and clinical data from 930 breast cancer patients included in the TCGA database. Genes associated with single mutations in molecular breast cancer subtypes were identified by the Mann-Whitney U-test and their prognostic value was evaluated by Kaplan-Meier and Cox regression analyses. Results were confirmed using gene expression profiles from the Metabric data set (n = 1988) and whole-genome sequencing data from the TCGA cohort (n = 117).

Results

The overall mutation rate in coding and non-coding regions were significantly higher in ER-negative/HER2-negative tumours (P = 2.8E–03 and P = 2.4E–07, respectively). Recurrent sequence variations were identified in non-coding regulatory regions of several cancer-associated genes, including NBPF1, PIK3CA and TP53. After multivariate regression analysis, gene signatures associated with three coding mutations (CDH1, MAP3K1 and TP53) and two non-coding variants (CRTC3 and STAG2) in cancer-related genes predicted prognosis in ER-positive/HER2-negative tumours.

Conclusions

These findings demonstrate that sequence alterations influence gene expression and oncogenic pathways, possibly affecting the outcome of breast cancer patients. Our data provide potential opportunities to identify non-coding variations with functional and clinical relevance in breast cancer.

Genetic Mechanisms of Immune Evasion in Colorectal Cancer

To understand the genetic drivers of immune recognition and evasion in colorectal cancer, we analyzed 1,211 colorectal cancer primary tumor samples, including 179 classified as microsatellite instability-high (MSI-high). This set includes The Cancer Genome Atlas colorectal cancer cohort of 592 samples, completed and analyzed here. MSI-high, a hypermutated, immunogenic subtype of colorectal cancer, had a high rate of significantly mutated genes in important immune-modulating pathways and in the antigen presentation machinery, including biallelic losses of B2M and HLA genes due to copy-number alterations and copy-neutral loss of heterozygosity. WNT/β-catenin signaling genes were significantly mutated in all colorectal cancer subtypes, and activated WNT/β-catenin signaling was correlated with the absence of T-cell infiltration. This large-scale genomic analysis of colorectal cancer demonstrates that MSI-high cases frequently undergo an immunoediting process that provides them with genetic events allowing immune escape despite high mutational load and frequent lymphocytic infiltration and, furthermore, that colorectal cancer tumors have genetic and methylation events associated with activated WNT signaling and T-cell exclusion.Significance: This multi-omic analysis of 1,211 colorectal cancer primary tumors reveals that it should be possible to better monitor resistance in the 15% of cases that respond to immune blockade therapy and also to use WNT signaling inhibitors to reverse immune exclusion in the 85% of cases that currently do not. Cancer Discov; 8(6); 730-49. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 663.

Molecular profiling of advanced breast cancer tumors is beneficial in assisting clinical treatment plans

We used data obtained by Caris Life Sciences, to evaluate the benefits of tailoring treatments for a breast carcinoma cohort by using tumor molecular profiles to inform decisions. Data for 92 breast cancer patients from the commercial Caris Molecular Intelligence database was retrospectively divided into two groups, so that the first always followed treatment recommendations, whereas in the second group all patients received at least one drug after profiling that was predicted to lack benefit. The biomarker and drug associations were based on tests including fluorescent in situ hybridization and DNA sequencing, although immunohistochemistry was the main test used.

Patients whose drugs matched those recommended according to their tumor profile had an average overall survival of 667 days, compared to 510 days for patients that did not (P=0.0316). In the matched treatment group, 26% of patients were deceased by the last time of monitoring, whereas this was 41% in the unmatched group (P=0.1257). We therefore confirm the ability of tumor molecular profiling to improve survival of breast cancer patients. Immunohistochemistry biomarkers for the androgen, estrogen and progesterone receptors were found to be prognostic for survival.

Transcriptional-Readthrough RNAs Reflect the Phenomenon of "A Gene Contains Gene(s)" or "Gene(s) within a Gene" in the Human Genome, and Thus Are Not Chimeric RNAs

Tens of thousands of chimeric RNAs, i.e., RNAs with sequences of two genes, have been identified in human cells. Most of them are formed by two neighboring genes on the same chromosome and are considered to be derived via transcriptional readthrough, but a true readthrough event still awaits more evidence and trans-splicing that joins two transcripts together remains as a possible mechanism. We regard those genomic loci that are transcriptionally read through as unannotated genes, because their transcriptional and posttranscriptional regulations are the same as those of already-annotated genes, including fusion genes formed due to genetic alterations. Therefore, readthrough RNAs and fusion-gene-derived RNAs are not chimeras. Only those two-gene RNAs formed at the RNA level, likely via trans-splicing, without corresponding genes as genomic parents, should be regarded as authentic chimeric RNAs. However, since in human cells, procedural and mechanistic details of trans-splicing have never been disclosed, we doubt the existence of trans-splicing. Therefore, there are probably no authentic chimeras in humans, after readthrough and fusion-gene derived RNAs are all put back into the group of ordinary RNAs. Therefore, it should be further determined whether in human cells all two-neighboring-gene RNAs are derived from transcriptional readthrough and whether trans-splicing truly exists.

Actionable gene alterations in an Asian population with triple-negative breast cancer

Purpose

It has been suggested that the biological characteristics of breast cancer may differ among different geographic or ethnic populations. Indeed, triple-negative breast cancer (TNBC), the most lethal breast cancer subgroup, has been reported to show a higher incidence in Japan than in the US. However, most genomic studies of these tumors are from Western countries and the genomic landscape of TNBC in an Asian population has not been thoroughly investigated. Here, we sought to elucidate the geographic and ethnic diversity of breast cancer by examining actionable driver alterations in TNBC tumors from Japanese patients and comparing them with The Cancer Genome Atlas (TCGA) database, which gather data primarily from non-Asian patients.

Materials and Methods

We performed comprehensive genomic profiling, including an analysis of 435 known cancer genes on Japanese TNBC patients (N=53) and compared the results to independent data obtained from TCGA (N=123).

Results

Driver alterations were identified in 51 out of 53 Japanese patients (96%). Although the overall alteration spectrum of Japanese patients was similar to that of the TCGA, we found significant differences in the frequencies of alterations in MYC and PTK2. We identified three patients (5.7%) with a high tumor mutation burden, although no microsatellite instability was observed in any of the Japanese patients. Importantly, pathway analysis revealed that 66.0% (35/53) of Japanese patients, as well as 66.7% (82/123) of the TCGA cohort, had alterations in at least one actionable gene targetable by an FDA-approved drug.

Conclusion

Our study identified actionable driver alterations in Japanese patients with TNBC, revealing new opportunities for targeted therapies in Asian patients.

Identification of cancer genes that are independent of dominant proliferation and lineage programs

Large, multidimensional “landscaping” projects have provided datasets that can be mined to identify potential targets for subgroups of tumors. Here, we analyzed genomic and transcriptomic data from human breast tumors to identify genes whose expression is enriched in tumors harboring specific genetic alterations. However, this analysis revealed that two other factors, proliferation rate and tumor lineage, are more dominant factors in shaping tumor transcriptional programs than genetic alterations. This discovery shifted our attention to identifying genes that are independent of the dominant proliferation and lineage programs. A small subset of these genes represents candidate targets for combination cancer therapies because they are druggable, maintained after treatment with chemotherapy, essential for cell line survival, and elevated in drug-resistant stem-like cancer cells.

Large, multidimensional cancer datasets provide a resource that can be mined to identify candidate therapeutic targets for specific subgroups of tumors. Here, we analyzed human breast cancer data to identify transcriptional programs associated with tumors bearing specific genetic driver alterations. Using an unbiased approach, we identified thousands of genes whose expression was enriched in tumors with specific genetic alterations. However, expression of the vast majority of these genes was not enriched if associations were analyzed within individual breast tumor molecular subtypes, across multiple tumor types, or after gene expression was normalized to account for differences in proliferation or tumor lineage. Together with linear modeling results, these findings suggest that most transcriptional programs associated with specific genetic alterations in oncogenes and tumor suppressors are highly context-dependent and are predominantly linked to differences in proliferation programs between distinct breast cancer subtypes. We demonstrate that such proliferation-dependent gene expression dominates tumor transcriptional programs relative to matched normal tissues. However, we also identified a relatively small group of cancer-associated genes that are both proliferation- and lineage-independent. A subset of these genes are attractive candidate targets for combination therapy because they are essential in breast cancer cell lines, druggable, enriched in stem-like breast cancer cells, and resistant to chemotherapy-induced down-regulation.

The driver landscape of sporadic chordoma

Chordoma is a malignant, often incurable bone tumour showing notochordal differentiation. Here, we defined the somatic driver landscape of 104 cases of sporadic chordoma. We reveal somatic duplications of the notochordal transcription factor brachyury (T) in up to 27% of cases. These variants recapitulate the rearrangement architecture of the pathogenic germline duplications of T that underlie familial chordoma. In addition, we find potentially clinically actionable PI3K signalling mutations in 16% of cases. Intriguingly, one of the most frequently altered genes, mutated exclusively by inactivating mutation, was LYST (10%), which may represent a novel cancer gene in chordoma.Chordoma is a rare often incurable malignant bone tumour. Here, the authors investigate driver mutations of sporadic chordoma in 104 cases, revealing duplications in notochordal transcription factor brachyury (T), PI3K signalling mutations, and mutations in LYST, a potential novel cancer gene in chordoma.

Overexpressed somatic alleles are enriched in functional elements in Breast Cancer

Asymmetric allele content in the transcriptome can be indicative of functional and selective features of the underlying genetic variants. Yet, imbalanced alleles, especially from diploid genome regions, are poorly explored in cancer. Here we systematically quantify and integrate the variant allele fraction from corresponding RNA and DNA sequence data from patients with breast cancer acquired through The Cancer Genome Atlas (TCGA). We test for correlation between allele prevalence and functionality in known cancer-implicated genes from the Cancer Gene Census (CGC). We document significant allele-preferential expression of functional variants in CGC genes and across the entire dataset. Notably, we find frequent allele-specific overexpression of variants in tumor-suppressor genes. We also report a list of over-expressed variants from non-CGC genes. Overall, our analysis presents an integrated set of features of somatic allele expression and points to the vast information content of the asymmetric alleles in the cancer transcriptome.

A pan-cancer genome-wide analysis reveals tumour dependencies by induction of nonsense-mediated decay

Nonsense-mediated decay (NMD) eliminates transcripts with premature termination codons. Although NMD-induced loss-of-function has been shown to contribute to the genesis of particular cancers, its global functional consequence in tumours has not been characterized. Here we develop an algorithm to predict NMD and apply it on somatic mutations reported in The Cancer Genome Atlas. We identify more than 73 K mutations that are predicted to elicit NMD (NMD-elicit). NMD-elicit mutations in tumour suppressor genes (TSGs) are associated with significant reduction in gene expression. We discover cancer-specific NMD-elicit signatures in TSGs and cancer-associated genes. Our analysis reveals a previously unrecognized dependence of hypermutated tumours on hypofunction of genes that are involved in chromatin remodelling and translation. Half of hypermutated stomach adenocarcinomas are associated with NMD-elicit mutations of the translation initiators LARP4B and EIF5B. Our results unravel strong therapeutic opportunities by targeting tumour dependencies on NMD-elicit mutations.

Systems Approaches to Cancer Biology

Cancer systems biology aims to understand cancer as an integrated system of genes, proteins, networks, and interactions rather than an entity of isolated molecular and cellular components. The inaugural Systems Approaches to Cancer Biology Conference, cosponsored by the Association of Early Career Cancer Systems Biologists and the National Cancer Institute of the NIH, focused on the interdisciplinary field of cancer systems biology and the challenging cancer questions that are best addressed through the combination of experimental and computational analyses. Attendees found that elucidating the many molecular features of cancer inevitably reveals new forms of complexity and concluded that ensuring the reproducibility and impact of cancer systems biology studies will require widespread method and data sharing and, ultimately, the translation of important findings to the clinic. Cancer Res; 76(23); 6774-7. ©2016 AACR.

A proteogenomic approach for protein-level evidence of genomic variants in cancer cells

Variations in protein coding sequence may sometimes play important roles in cancer development. However, since variants may not express into proteins due to various cellular quality control systems, it is important to get protein-level evidence of the genomic variations. We present a proteogenomic strategy getting protein-level evidence of genomic variants, which we call sequential targeted LC-MS/MS based on prediction of peptide pI and Retention time (STaLPIR). Our approach shows improved peptide identification, and has the potential for the unbiased analysis of variant sequence as well as corresponding reference sequence. Integrated analysis of DNA, mRNA and protein suggests that protein expression level of the nonsynonymous variant is regulated either before or after translation, according to influence of the variant on protein function. In conclusion, our data provides an excellent approach getting direct evidence for the expression of variant protein forms from genome sequence data.