James D. Brenton

The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups

The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ∼40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA–RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the ‘CNA-devoid’ subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.

Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA

Cancers acquire resistance to systemic treatment as a result of clonal evolution and selection. Repeat biopsies to study genomic evolution as a result of therapy are difficult, invasive and may be confounded by intra-tumour heterogeneity. Recent studies have shown that genomic alterations in solid cancers can be characterized by massively parallel sequencing of circulating cell-free tumour DNA released from cancer cells into plasma, representing a non-invasive liquid biopsy. Here we report sequencing of cancer exomes in serial plasma samples to track genomic evolution of metastatic cancers in response to therapy. Six patients with advanced breast, ovarian and lung cancers were followed over 1–2 years. For each case, exome sequencing was performed on 2–5 plasma samples (19 in total) spanning multiple courses of treatment, at selected time points when the allele fraction of tumour mutations in plasma was high, allowing improved sensitivity. For two cases, synchronous biopsies were also analysed, confirming genome-wide representation of the tumour genome in plasma. Quantification of allele fractions in plasma identified increased representation of mutant alleles in association with emergence of therapy resistance. These included an activating mutation in PIK3CA (phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha) following treatment with paclitaxel; a truncating mutation in RB1 (retinoblastoma 1) following treatment with cisplatin; a truncating mutation in MED1 (mediator complex subunit 1) following treatment with tamoxifen and trastuzumab, and following subsequent treatment with lapatinib, a splicing mutation in GAS6 (growth arrest-specific 6) in the same patient; and a resistance-conferring mutation in EGFR (epidermal growth factor receptor; T790M) following treatment with gefitinib. These results establish proof of principle that exome-wide analysis of circulating tumour DNA could complement current invasive biopsy approaches to identify mutations associated with acquired drug resistance in advanced cancers. Serial analysis of cancer genomes in plasma constitutes a new paradigm for the study of clonal evolution in human cancers.

Molecular Classification and Molecular Forecasting of Breast Cancer: Ready for Clinical Application?

Profiling breast cancer with expression arrays has become common, and it has been suggested that the results from early studies will lead to understanding of the molecular differences between clinical cases and allow individualization of care. We critically review two main applications of expression profiling; studies unraveling novel breast cancer classifications and those that aim to identify novel markers for prediction of clinical outcome. Breast cancer may now be subclassified into luminal, basal, and HER2 subtypes with distinct differences in prognosis and response to therapy. However, profiling studies to identify predictive markers have suffered from methodologic problems that prevent general application of their results. Future work will need to reanalyze existing microarray data sets to identify more representative sets of candidate genes for use as prognostic signatures and will need to take into account the new knowledge of molecular subtypes of breast cancer when assessing predictive effects.

Rethinking ovarian cancer: recommendations for improving outcomes.

There have been major advances in our understanding of the cellular and molecular biology of the human malignancies that are collectively referred to as ovarian cancer. At a recent Helene Harris Memorial Trust meeting, an international group of researchers considered actions that should be taken to improve the outcome for women with ovarian cancer. Nine major recommendations are outlined in this Opinion article.

Noninvasive Identification and Monitoring of Cancer Mutations by Targeted Deep Sequencing of Plasma DNA

Sizable genomic regions were screened and low-frequency mutations were identified in circulating DNA of cancer patients using tagged-amplicon deep sequencing (TAm-Seq). Deep Sequencing Tumor DNA in Plasma Five liters of circulating blood contain millions of copies of the genome, broken into short fragments; in cancer patients, a small fraction is circulating tumor DNA (ctDNA). An even smaller number harbor mutations that affect cancer outcome. Looking for diagnostic answers in circulating DNA is a challenge, but Forshew, Murtaza, and colleagues have risen to the occasion by developing a tagged-amplicon deep sequencing (TAm-Seq) method that can amplify and sequence large genomic regions from even single copies of ctDNA. By sequencing such large regions, the authors were able to identify low-level mutations in the plasma of patients with high-grade serous ovarian carcinomas. Forshew et al. designed primers to amplify 5995 bases that covered select regions of cancer-related genes, including TP53, EGFR, BRAF, and KRAS. In plasma obtained from 38 patients with high levels of ctDNA, the authors were able to identify mutations in TP53 at allelic frequencies of 2% to 65%. In plasma samples from one patient, they also identified a de novo mutation in EGFR that had not been detected 15 months prior in the tumor mass itself. Finally, the TAm-Seq approach was used to sequence ctDNA in plasma samples collected from two women with ovarian cancer and one woman with breast cancer at different time points, tracking as many as 10 mutations in parallel. Forshew and coauthors showed that levels of mutant alleles reflected the clinical course of the disease and its treatment—for example, stabilized disease was associated with low allelic frequency, whereas patients at relapse exhibited a rise in frequency. Through several experiments, the authors were able to show that TAm-Seq is a viable method for sequencing large regions of ctDNA. Although this provides a new way to noninvasively identify gene mutations in our blood, TAm-Seq will need to achieve a more sensitive detection limit (<2% allele frequency) to identify mutations in the plasma of patients with less advanced cancers. Nevertheless, once optimized, this “liquid biopsy” approach will be amenable to personalized genomics, where the level and type of mutations in ctDNA would inform clinical decision-making on an individual basis. Plasma of cancer patients contains cell-free tumor DNA that carries information on tumor mutations and tumor burden. Individual mutations have been probed using allele-specific assays, but sequencing of entire genes to detect cancer mutations in circulating DNA has not been demonstrated. We developed a method for tagged-amplicon deep sequencing (TAm-Seq) and screened 5995 genomic bases for low-frequency mutations. Using this method, we identified cancer mutations present in circulating DNA at allele frequencies as low as 2%, with sensitivity and specificity of >97%. We identified mutations throughout the tumor suppressor gene TP53 in circulating DNA from 46 plasma samples of advanced ovarian cancer patients. We demonstrated use of TAm-Seq to noninvasively identify the origin of metastatic relapse in a patient with multiple primary tumors. In another case, we identified in plasma an EGFR mutation not found in an initial ovarian biopsy. We further used TAm-Seq to monitor tumor dynamics, and tracked 10 concomitant mutations in plasma of a metastatic breast cancer patient over 16 months. This low-cost, high-throughput method could facilitate analysis of circulating DNA as a noninvasive “liquid biopsy” for personalized cancer genomics.

Mutation of FOXL2 in granulosa-cell tumors of the ovary

BACKGROUND Granulosa-cell tumors (GCTs) are the most common type of malignant ovarian sex cord-stromal tumor (SCST). The pathogenesis of these tumors is unknown. Moreover, their histopathological diagnosis can be challenging, and there is no curative treatment beyond surgery. METHODS We analyzed four adult-type GCTs using whole-transcriptome paired-end RNA sequencing. We identified putative GCT-specific mutations that were present in at least three of these samples but were absent from the transcriptomes of 11 epithelial ovarian tumors, published human genomes, and databases of single-nucleotide polymorphisms. We confirmed these variants by direct sequencing of complementary DNA and genomic DNA. We then analyzed additional tumors and matched normal genomic DNA, using a combination of direct sequencing, analyses of restriction-fragment-length polymorphisms, and TaqMan assays. RESULTS All four index GCTs had a missense point mutation, 402C-->G (C134W), in FOXL2, a gene encoding a transcription factor known to be critical for granulosa-cell development. The FOXL2 mutation was present in 86 of 89 additional adult-type GCTs (97%), in 3 of 14 thecomas (21%), and in 1 of 10 juvenile-type GCTs (10%). The mutation was absent in 49 SCSTs of other types and in 329 unrelated ovarian or breast tumors. CONCLUSIONS Whole-transcriptome sequencing of four GCTs identified a single, recurrent somatic mutation (402C-->G) in FOXL2 that was present in almost all morphologically identified adult-type GCTs. Mutant FOXL2 is a potential driver in the pathogenesis of adult-type GCTs.

Driver mutations in TP53 are ubiquitous in high grade serous carcinoma of the ovary

Numerous studies have tested the association between TP53 mutations in ovarian cancer and prognosis but these have been consistently confounded by limitations in study design, methodology, and/or heterogeneity in the sample cohort. High‐grade serous (HGS) carcinoma is the most clinically important histological subtype of ovarian cancer. As these tumours may arise from the ovary, Fallopian tube or peritoneum, they are collectively referred to as high‐grade pelvic serous carcinoma (HGPSC). To identify the true prevalence of TP53 mutations in HGPSC, we sequenced exons 2–11 and intron–exon boundaries in tumour DNA from 145 patients. HGPSC cases were defined as having histological grade 2 or 3 and FIGO stage III or IV. Surprisingly, pathogenic TP53 mutations were identified in 96.7% (n = 119/123) of HGPSC cases. Molecular and pathological review of mutation‐negative cases showed evidence of p53 dysfunction associated with copy number gain of MDM2 or MDM4, or indicated the exclusion of samples as being low‐grade serous tumours or carcinoma of uncertain primary site. Overall, p53 dysfunction rate approached 100% of confirmed HGPSCs. No association between TP53 mutation and progression‐free or overall survival was found. From this first comprehensive mapping of TP53 mutation rate in a homogeneous group of HGPSC patients, we conclude that mutant TP53 is a driver mutation in the pathogenesis of HGPSC cancers. Because TP53 mutation is almost invariably present in HGPSC, it is not of substantial prognostic or predictive significance. Copyright

The External RNA Controls Consortium: a progress report

Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.

A gene-expression signature to predict survival in breast cancer across independent data sets

Prognostic signatures in breast cancer derived from microarray expression profiling have been reported by two independent groups. These signatures, however, have not been validated in external studies, making clinical application problematic. We performed microarray expression profiling of 135 early-stage tumors, from a cohort representative of the demographics of breast cancer. Using a recently proposed semisupervised method, we identified a prognostic signature of 70 genes that significantly correlated with survival (hazard ratio (HR): 5.97, 95% confidence interval: 3.0–11.9, P=2.7e−07). In multivariate analysis, the signature performed independently of other standard prognostic classifiers such as the Nottingham Prognostic Index and the ‘Adjuvant!’ software. Using two different prognostic classification schemes and measures, nearest centroid (HR) and risk ordering (D-index), the 70-gene classifier was also found to be prognostic in two independent external data sets. Overall, the 70-gene set was prognostic in our study and the two external studies which collectively include 715 patients. In contrast, we found that the two previously described prognostic gene sets performed less optimally in external validation. Finally, a common prognostic module of 29 genes that associated with survival in both our cohort and the two external data sets was identified. In spite of these results, further studies that profile larger cohorts using a single microarray platform, will be needed before prospective clinical use of molecular classifiers can be contemplated.

Differential expression of selected histone modifier genes in human solid cancers

BackgroundPost-translational modification of histones resulting in chromatin remodelling plays a key role in the regulation of gene expression. Here we report characteristic patterns of expression of 12 members of 3 classes of chromatin modifier genes in 6 different cancer types: histone acetyltransferases (HATs)- EP300, CREBBP, and PCAF; histone deacetylases (HDACs)- HDAC1, HDAC2, HDAC4, HDAC5, HDAC7A, and SIRT1; and histone methyltransferases (HMTs)- SUV39H1 and SUV39H2. Expression of each gene in 225 samples (135 primary tumours, 47 cancer cell lines, and 43 normal tissues) was analysedby QRT-PCR, normalized with 8 housekeeping genes, and given as a ratio by comparison with a universal reference RNA.ResultsThis involved a total of 13,000 PCR assays allowing for rigorous analysis by fitting a linear regression model to the data. Mutation analysis of HDAC1, HDAC2, SUV39H1, and SUV39H2 revealed only two out of 181 cancer samples (both cell lines) with significant coding-sequence alterations. Supervised analysis and Independent Component Analysis showed that expression of many of these genes was able to discriminate tumour samples from their normal counterparts. Clustering based on the normalized expression ratios of the 12 genes also showed that most samples were grouped according to tissue type. Using a linear discriminant classifier and internal cross-validation revealed that with as few as 5 of the 12 genes, SIRT1, CREBBP, HDAC7A, HDAC5 and PCAF, most samples were correctly assigned.ConclusionThe expression patterns of HATs, HDACs, and HMTs suggest these genes are important in neoplastic transformation and have characteristic patterns of expression depending on tissue of origin, with implications for potential clinical application.