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.

An Update on the Molecular Pathology of Metaplastic Breast Cancer.

Metaplastic breast cancer (MpBC) is a fascinating morphologic sub-type of breast cancer, characterised by intra-tumoural heterogeneity. By definition, these tumors show regions of metaplasia that can present as spindle, squamous, chondroid or even osseous differentiation. MpBC are typically triple-negative, and are therefore not targetable with hormone therapy or anti-HER2 therapies, leaving only chemotherapeutics for management. MpBC are known for their aggressive course and poor response to chemotherapy. We review herein the pathology and molecular landscape of MpBC and discuss opportunities for targetted therapies as well as immunotherapies.

Metaplastic breast cancers frequently express immune checkpoint markers FOXP3 and PD-L1.

BACKGROUND: Metaplastic breast carcinoma encompasses a heterogeneous group of tumours with differentiation into squamous and/or spindle, chondroid, osseous or rhabdoid mesenchymal-looking elements. Emerging immunotherapies targeting Programmed Death Ligand 1 (PD-L1) and immune-suppressing T cells (Tregs) may benefit metaplastic breast cancer patients, which are typically chemo-resistant and do not express hormone therapy targets. METHODS: We evaluated the immunohistochemical expression of PD-L1 and FOXP3, and the extent of tumour infiltrating lymphocytes (TILs) in a large cohort of metaplastic breast cancers, with survival data. RESULTS: Metaplastic breast cancers were significantly enriched for PD-L1 positive tumour cells, compared to triple-negative ductal breast cancers (P < 0.0001), while there was no significant difference in PD-L1 positive TILs. Metaplastic breast cancers were also significantly enriched for TILs expressing FOXP3, with FOXP3 positive intra-tumoural TILs (iTILs) associated with an adverse prognostic outcome (P = 0.0226). Multivariate analysis identified FOXP3 iTILs expression status as an important independent prognostic factor for patient survival. CONCLUSIONS: Our findings indicate the clinical significance and prognostic value of FOXP3, PD-1/PD-L1 checkpoint and TILs in metaplastic breast cancer and confirm that a subset of metaplastics may benefit from immune-based therapies.

Tradeoff between metabolic i-proteasome addiction and immune evasion in triple-negative breast cancer.

In vitro studies have suggested proteasome inhibitors could be effective in triple-negative breast cancer (TNBC). We found that bortezomib and carfilzomib induce proteotoxic stress and apoptosis via the unfolded protein response (UPR) in TNBC cell lines, with sensitivity correlated with expression of immuno-(PSMB8/9/10) but not constitutive-(PSMB5/6/7) proteasome subunits. Equally, the transcriptomes of i-proteasome-high human TNBCs are enriched with UPR gene sets, and the genomic copy number landscape reflects positive selection pressure favoring i-proteasome activity, but in the setting of adjuvant treatment, this is actually associated with favorable prognosis. Tumor expression of PSMB8 protein (ß5i) is associated with levels of MHC-I, interferon-γ-inducible proteasome activator PA28ß, and the densities of stromal antigen-presenting cells and lymphocytes (TILs). Crucially, TILs were protective among TNBCs that maintain high ß5i but did not stratify survival amongst ß5i-low TNBCs. Moreover, ß5i expression was lower in brain metastases than in patient-matched primary breast tumors (n = 34; P = 0.007), suggesting that suppression contributes to immune evasion and metastatic progression. Hence, inhibiting proteasome activity could be counterproductive in the adjuvant treatment setting because it potentiates anti-TNBC immunity.

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.

Phenotypic and molecular dissection of metaplastic breast cancer and the prognostic implications.

Metaplastic breast carcinoma (MBC) is relatively rare but accounts for a significant proportion of global breast cancer mortality. This group is extremely heterogeneous and by definition exhibits metaplastic change to squamous and/or mesenchymal elements, including spindle, squamous, chondroid, osseous, and rhabdomyoid features. Clinically, patients are more likely to present with large primary tumours (higher stage), distant metastases, and overall, have shorter 5-year survival compared to invasive carcinomas of no special type. The current World Health Organisation (WHO) diagnostic classification for this cancer type is based purely on morphology - the biological basis and clinical relevance of its seven sub-categories are currently unclear. By establishing the Asia-Pacific MBC (AP-MBC) Consortium, we amassed a large series of MBCs (n = 347) and analysed the mutation profile of a subset, expression of 14 breast cancer biomarkers, and clinicopathological correlates, contextualising our findings within the WHO guidelines. The most significant indicators of poor prognosis were large tumour size (T3; p = 0.004), loss of cytokeratin expression (lack of staining with pan-cytokeratin AE1/3 antibody; p = 0.007), EGFR overexpression (p = 0.01), and for 'mixed' MBC, the presence of more than three distinct morphological entities (p = 0.007). Conversely, fewer morphological components and EGFR negativity were favourable indicators. Exome sequencing of 30 cases confirmed enrichment of TP53 and PTEN mutations, and intriguingly, concurrent mutations of TP53, PTEN, and PIK3CA. Mutations in neurofibromatosis-1 (NF1) were also overrepresented [16.7% MBCs compared to ∼5% of breast cancers overall; enrichment p = 0.028; mutation significance p = 0.006 (OncodriveFM)], consistent with published case reports implicating germline NF1 mutations in MBC risk. Taken together, we propose a practically minor but clinically significant modification to the guidelines: all WHO_1 mixed-type tumours should have the number of morphologies present recorded, as a mechanism for refining prognosis, and that EGFR and pan-cytokeratin expression are important prognostic markers. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Automated Renal Cancer Grading Using Nuclear Pleomorphic Patterns.

PURPOSE: Nuclear pleomorphic patterns are essential for Fuhrman grading of clear cell renal cell carcinoma (ccRCC). Manual observation of renal histopathologic slides may lead to subjective and inconsistent assessment between pathologists. An automated, image-based system that classifies ccRCC slides by quantifying nuclear pleomorphic patterns in an objective and consistent interpretable fashion can aid pathologists in histopathologic assessment. METHODS: In the current study, histopathologic tissue slides of 59 patients with ccRCC who underwent surgery at Singapore General Hospital were assembled retrospectively. An automated image classification pipeline detects and analyzes prominent nucleoli in ccRCC images to classify them as either low (Fuhrman grade 1 and 2) or high (Fuhrman grade 3 and 4). The pipeline uses machine learning and image pixel intensity-based feature extraction techniques for nuclear analysis. We trained classification systems that concurrently analyze different permutations of multiple prominent nucleoli image patches. RESULTS: Given the parameters for feature combination and extraction, we present experimental results across various configurations for the classification of a given ccRCC histopathologic image. We also demonstrate that the image score used by the pipeline, termed fraction value, is correlated ( R = 0.59) with an existing multigene assay-based scoring system that has previously been demonstrated to be a strong indicator of prognosis in patients with ccRCC. CONCLUSION: The current method provides an objective and fully automated way by which to process pathologic slides. The correlation study with a multigene assay-based scoring system also allows us to provide quantitative interpretation for already established nuclear pleomorphic patterns in ccRCC. This method can be extended to other cancers whose corresponding grading systems use nuclear pattern information.

Gland segmentation in prostate histopathological images.

Glandular structural features are important for the tumor pathologist in the assessment of cancer malignancy of prostate tissue slides. The varying shapes and sizes of glands combined with the tedious manual observation task can result in inaccurate assessment. There are also discrepancies and low-level agreement among pathologists, especially in cases of Gleason pattern 3 and pattern 4 prostate adenocarcinoma. An automated gland segmentation system can highlight various glandular shapes and structures for further analysis by the pathologist. These objective highlighted patterns can help reduce the assessment variability. We propose an automated gland segmentation system. Forty-three hematoxylin and eosin-stained images were acquired from prostate cancer tissue slides and were manually annotated for gland, lumen, periacinar retraction clefting, and stroma regions. Our automated gland segmentation system was trained using these manual annotations. It identifies these regions using a combination of pixel and object-level classifiers by incorporating local and spatial information for consolidating pixel-level classification results into object-level segmentation. Experimental results show that our method outperforms various texture and gland structure-based gland segmentation algorithms in the literature. Our method has good performance and can be a promising tool to help decrease interobserver variability among pathologists.

Automated image based prominent nucleoli detection.

INTRODUCTION: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

We aimed to identify a prostate cancer DNA hypermethylation microarray signature (denoted as PHYMA) 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.

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

AIMS: 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. METHODS AND RESULTS: 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. CONCLUSIONS: Immunostaining for Ki67 is not a useful approach to prescreen B cell lymphomas for MYC(+) double/triple translocations.