Epigenome erosion and SOX10 drive neural crest phenotypic mimicry in triple-negative breast cancer

Intratumoral heterogeneity is caused by genomic instability and phenotypic plasticity, but how these features co-evolve remains unclear. SOX10 is a neural crest stem cell (NCSC) specifier and candidate mediator of phenotypic plasticity in cancer. We investigated its relevance in breast cancer by immunophenotyping 21 normal breast and 1860 tumour samples. Nuclear SOX10 was detected in normal mammary luminal progenitor cells, the histogenic origin of most TNBCs. In tumours, nuclear SOX10 was almost exclusive to TNBC, and predicted poorer outcome amongst cross-sectional (p = 0.0015, hazard ratio 2.02, n = 224) and metaplastic (p = 0.04, n = 66) cases. To understand SOX10’s influence over the transcriptome during the transition from normal to malignant states, we performed a systems-level analysis of co-expression data, de-noising the networks with an eigen-decomposition method. This identified a core module in SOX10’s normal mammary epithelial network that becomes rewired to NCSC genes in TNBC. Crucially, this reprogramming was proportional to genome-wide promoter methylation loss, particularly at lineage-specifying CpG-island shores. We propose that the progressive, genome-wide methylation loss in TNBC simulates more primitive epigenome architecture, making cells vulnerable to SOX10-driven reprogramming. This study demonstrates potential utility for SOX10 as a prognostic biomarker in TNBC and provides new insights about developmental phenotypic mimicry—a major contributor to intratumoral heterogeneity

Breast cancer - pathology and genetics

In this article, we present a detailed overview of the field’s current understanding of breast cancer pathology, and genetics. We address the common and rare subtypes of breast cancer and provide an up-to-date summary of the genomic analyses performed for many of these subtypes. The risk factors for developing the different variants of breast cancer are considered, and the known predictive and prognostic biomarkers summarized

Digital spatial profiling application in breast cancer: a user’s perspective

The disciplines of oncology and pathology are at present experiencing a wave of changes as precision medicine becomes embedded as standard-of-care. Consequently, the need to assess increasing numbers of biomarkers simultaneously has become more urgent and recognising the vast intra-tumoural heterogeneity, including within the microenvironment, requires a complex dimensional understanding of the localisation of the biomarker expression. Digital spatial profiling (DSP; nanoString™) technology spatially resolves and digitally quantifies proteins in a highly multiplexed assay, underpinned by the nCounter® barcoding platform. We present the application of this technology to breast cancer samples. Applying the 'off the shelf' cancer panel and a custom-conjugated E-cadherin antibody, we quantify vast intra-tumoural heterogeneity in immunological and tumour markers, and demonstrate a need for focussed selection of target cell populations. The technology offers enormous potential not only for making research advances but also for improving standard operating procedures in diagnostic applications

Does the proliferation fraction help identify mature B cell lymphomas with double- and triple-hit translocations?

The entity 'B cell lymphoma, unclassifiable, with features intermediate between diffuse large B cell lymphoma (DLBCL) and Burkitt lymphoma (BL)' refers to B cell neoplasms that share overlapping characteristics of BL and DLBCL. A subset of these 'grey-zone lymphomas' possesses C-MYC and IGH translocations but, in addition, contains additional rearrangements of BCL2 and/or BCL6 genes. The aim of this study was to investigate if the proliferation fraction by Ki67 immunostaining can be used to identify such double-/triple-hit lymphomas.We studied 492 cases of mature aggressive B cell neoplasms by histology, immunohistochemistry and interphase fluorescence in-situ hybridization (FISH) using break-apart probes against C-MYC, BCL2, BCL6, IGH, MALT1, PAX5 and CCND1. Forty Burkitt lymphomas and 28 cases of MYC(+) double-/triple-hit lymphomas were identified. Of the latter, 77% and 54% displayed proliferation fractions exceeding 75% and 90%, respectively. With a cut-off of >75% by Ki67 immunostaining, the sensitivity and specificity for detection of MYC(+) double/triple translocations was 0.77 and 0.36. Raising the proliferation fraction criterion to >90% improved the specificity to 0.62 at the expense of a low sensitivity of 0.54.Immunostaining for Ki67 is not a useful approach to prescreen B cell lymphomas for MYC(+) double/triple translocations

Automated image based prominent nucleoli detection

Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection.Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli.The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects.Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings

Diagnostic and Prognostic Utility of a DNA Hypermethylated Gene Signature in Prostate Cancer

<div><p>We aimed to identify a <b>p</b>rostate cancer DNA <b>hy</b>permethylation <b>m</b>icro<b>a</b>rray signature (denoted as <b>PHYMA</b>) that differentiates prostate cancer from benign prostate hyperplasia (BPH), high from low-grade and lethal from non-lethal cancers. This is a non-randomized retrospective study in 111 local Asian men (87 prostate cancers and 24 BPH) treated from 1995 to 2009 in our institution. Archival prostate epithelia were laser-capture microdissected and genomic DNA extracted and bisulfite-converted. Samples were profiled using Illumina GoldenGate Methylation microarray, with raw data processed by GenomeStudio. A classification model was generated using support vector machine, consisting of a 55-probe DNA methylation signature of 46 genes. The model was independently validated on an internal testing dataset which yielded cancer detection sensitivity and specificity of 95.3% and 100% respectively, with overall accuracy of 96.4%. Second validation on another independent western cohort yielded 89.8% sensitivity and 66.7% specificity, with overall accuracy of 88.7%. A PHYMA score was developed for each sample based on the state of methylation in the PHYMA signature. Increasing PHYMA score was significantly associated with higher Gleason score and Gleason primary grade. Men with higher PHYMA scores have poorer survival on univariate (p = 0.0038, HR = 3.89) and multivariate analyses when controlled for (i) clinical stage (p = 0.055, HR = 2.57), and (ii) clinical stage and Gleason score (p = 0.043, HR = 2.61). We further performed bisulfite genomic sequencing on 2 relatively unknown genes to demonstrate robustness of the assay results. PHYMA is thus a signature with high sensitivity and specificity for discriminating tumors from BPH, and has a potential role in early detection and in predicting survival.</p></div

Heatmaps of PHYMA signature in Asian and western datasets.

<p>(a) Training dataset of local Asian population. (b) Testing dataset of local Asian population. (c) OHSU western cohort dataset. Each row represents a methylation probe and column a sample. The level of methylation varies from green (low β) to red (high β). Tumor samples showedmore aberrant DNA methylation compared to BPH tissue. All tumor samples showed comparatively higher DNA methylation compared to the non-tumor samples.</p

Baseline characteristics of 87 prostate cancer and 24 BPH patients included in the study.

<p>Baseline characteristics of 87 prostate cancer and 24 BPH patients included in the study.</p