An integrative genomic and transcriptomic analysis reveals molecular pathways and networks regulated by copy number aberrations in basal-like, HER2 and luminal cancers

Comparison of the mutational profiles of neuroendocrine breast tumours, invasive ductal carcinomas and pancreatic neuroendocrine carcinomas

The pathophysiology and the optimal treatment of breast neuroendocrine tumours (NETs) are unknown. We compared the mutational profiles of breast NETs (n = 53) with those of 724 publicly available invasive ductal carcinoma (IDC) and 98 pancreatic NET (PNET) cases. The only significantly different pathogenetic or unknown variant rate between breast NETs and IDCs was detected in the TP53 (11.3% in breast NETs and 41% in IDCs, adjusted p value 0.027) and ADCK2 (9.4% in breast NETs vs. 0.28% in IDCs, adjusted p value 0.045) genes. Between breast NETs and PNETs, different pathogenetic or unknown variant frequencies were detected in 30 genes. For example, MEN1 was mutated in only 6% of breast NETs and 37% in PNETs (adjusted p value 0.00050), and GATA3 pathogenetic or unknown variants were only found in 17.0% of breast NETs and 0% in PNETs (adjusted p value 0.0010). The most commonly affected oncogenic pathways in the breast NET cases were PI3K/Akt/mTOR, NOTCH and RTK-RAS pathways. Breast NETs had typically clock-like mutational signatures and signatures associated with defective DNA mismatch repair in their mutational landscape. Our results suggest that the breast NET mutational profile more closely resembles that of IDCs than that of PNETs. These results also revealed several potentially druggable targets, such as MMRd, in breast NETs. In conclusion, breast NETs are indeed a separate breast cancer entity, but their optimal treatment remains to be elucidated.

Novel applications of molecular imaging to guide breast cancer therapy

The goals of precision oncology are to provide targeted drug therapy based on each individual’s specific tumor biology, and to enable the prediction and early assessment of treatment response to allow treatment modification when necessary. Thus, precision oncology aims to maximize treatment success while minimizing the side effects of inadequate or suboptimal therapies. Molecular imaging, through noninvasive assessment of clinically relevant tumor biomarkers across the entire disease burden, has the potential to revolutionize clinical oncology, including breast oncology. In this article, we review breast cancer positron emission tomography (PET) imaging biomarkers for providing early response assessment and predicting treatment outcomes. For 2-¹⁸fluoro-2-deoxy-D-glucose (FDG), a marker of cellular glucose metabolism that is well established for staging multiple types of malignancies including breast cancer, we highlight novel applications for early response assessment. We then review current and future applications of novel PET biomarkers for imaging the steroid receptors, including the estrogen and progesterone receptors, the HER2 receptor, cellular proliferation, and amino acid metabolism.

Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration

Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods are expected to facilitate molecular subtyping of cancer. Most existing machine learning–based methods usually classify samples according to single omics data, fail to integrate multi-omics data to learn comprehensive representations of the samples, and ignore that information transfer and aggregation among samples can better represent them and ultimately help in classification. We propose a novel framework named multi-omics graph convolutional network (M-GCN) for molecular subtyping based on robust graph convolutional networks integrating multi-omics data. We first apply the Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) to select the molecular subtype-related transcriptomic features and then construct a sample–sample similarity graph with low noise by using these features. Next, we take the selected gene expression, single nucleotide variants (SNV), and copy number variation (CNV) data as input and learn the multi-view representations of samples. On this basis, a robust variant of graph convolutional network (GCN) model is finally developed to obtain samples’ new representations by aggregating their subgraphs. Experimental results of breast and stomach cancer demonstrate that the classification performance of M-GCN is superior to other existing methods. Moreover, the identified subtype-specific biomarkers are highly consistent with current clinical understanding and promising to assist accurate diagnosis and targeted drug development.

Non-conventional and Investigational PET Radiotracers for Breast Cancer: A Systematic Review

Breast cancer is one of the most common malignancies in women, with high morbidity and mortality rates. In breast cancer, the use of novel radiopharmaceuticals in nuclear medicine can improve the accuracy of diagnosis and staging, refine surveillance strategies and accuracy in choosing personalized treatment approaches, including radioligand therapy. Nuclear medicine thus shows great promise for improving the quality of life of breast cancer patients by allowing non-invasive assessment of the diverse and complex biological processes underlying the development of breast cancer and its evolution under therapy. This review aims to describe molecular probes currently in clinical use as well as those under investigation holding great promise for personalized medicine and precision oncology in breast cancer.

Methyl-lysine readers PHF20 and PHF20L1 define two distinct gene expression-regulating NSL complexes

The methyl-lysine readers plant homeodomain finger protein 20 (PHF20) and its homolog PHF20-like protein 1 (PHF20L1) are known components of the nonspecific lethal (NSL) complex that regulates gene expression through its histone acetyltransferase activity. In the current model, both PHF homologs coexist in the same NSL complex, although this was not formally tested; nor have the functions of PHF20 and PHF20L1 regarding NSL complex integrity and transcriptional regulation been investigated. Here, we perform an in-depth biochemical and functional characterization of PHF20 and PHF20L1 in the context of the NSL complex. Using mass spectrometry, genome-wide chromatin analysis, and protein-domain mapping, we identify the existence of two distinct NSL complexes that exclusively contain either PHF20 or PHF20L1. We show that the C-terminal domains of PHF20 and PHF20L1 are essential for complex formation with NSL, and the Tudor 2 domains are required for chromatin binding. The genome-wide chromatin landscape of PHF20-PHF20L1 shows that these proteins bind mostly to the same genomic regions, at promoters of highly expressed/housekeeping genes. Yet, deletion of PHF20 and PHF20L1 does not abrogate gene expression or impact the recruitment of the NSL complex to those target gene promoters, suggesting the existence of an alternative mechanism that compensates for the transcription of genes whose sustained expression is important for critical cellular functions. This work shifts the current paradigm and lays the foundation for studies on the differential roles of PHF20 and PHF20L1 in regulating NSL complex activity in physiological and diseases states.

Prognostic and Predictive Value of CCND1/Cyclin D1 Amplification in Breast Cancer With a Focus on Postmenopausal Patients: A Systematic Review and Meta-Analysis

BACKGROUND

Up to 80% of breast cancers (BCa) are estrogen receptor positive and current treatments target the estrogen receptor (endocrine therapies) and/or CDK4/6 (CDK4/6 inhibitors). CCND1 encodes the protein cyclin D1, responsible for regulation of G1 to S phase transition in the cell cycle. CCND1 amplification is common in BCa and contributes to increased cyclin D1 expression. As there are signalling interactions between cyclin D1 and the estrogen receptor, understanding the impact of CCND1 amplification on estrogen receptor positive patients' disease outcomes, is vital. This review aims to evaluate CCND1 amplification as a prognostic and predictive biomarker in BCa.

MATERIALS AND METHODS

Publications were retrieved from the databases: PubMed, MEDLINE, Embase and Cochrane library. Exclusion criteria were duplication, publication type, non-English language, in vitro and animal studies, not BCa, male BCa, premenopausal BCa, cohort size <35, CCND1 amplification not reported. Publications with cohort duplication, and inadequate recurrence free survival (RFS) and overall survival (OS) data, were also excluded. Included publications were assessed for Risk of Bias (RoB) using the Quality In Prognosis Studies tool. Statistical analyses (Inverse Variance and Mantel-Haenszel) were performed in Review Manager. The PROSPERO registration number is [CRD42020208179].

RESULTS

CCND1 amplification was significantly associated with positive estrogen receptor status (OR:1.70, 95% CI:1.19-2.43, p = 0.004) and cyclin D1 overexpression (OR: 5.64, 95% CI: 2.32-13.74, p=0.0001). CCND1 amplification was significantly associated with shorter RFS (OR: 1.64, 95% CI: 1.13-2.38, p = 0.009), and OS (OR: 1.51, 95% CI: 1.19-1.92, p = 0.0008) after removal of studies with a high RoB. In endocrine therapy treated patients specifically, CCND1 amplification predicted shorter RFS (HR: 2.59, 95% CI: 1.96-3.41, p < 0.00001) and OS (HR: 1.59, 95% CI: 1.00-2.49, p = 0.05) also after removal of studies with a high RoB.

CONCLUSION

While a lack of standardised approach for the detection of CCND1 amplification is to be considered as a limitation, CCND1 amplification was found to be prognostic of shorter RFS and OS in BCa. CCND1 amplification is also predictive of reduced RFS and OS in endocrine therapy treated patients specifically. With standardised methods and cut offs for the detection of CCND1 amplification, CCND1 amplification would have potential as a predictive biomarker in breast cancer patients.

SYSTEMATIC REVIEW REGISTRATION

https://www.crd.york.ac.uk/prospero/, identifier CRD42020208179.

Radionuclide-Based Imaging of Breast Cancer: State of the Art

Simple Summary

Breast cancer is one of the most commonly diagnosed malignant tumors, possessing high incidence and mortality rates that threaten women’s health. Thus, early and effective breast cancer diagnosis is crucial for enhancing the survival rate. Radionuclide molecular imaging displays its advantages for detecting breast cancer from a functional perspective. Noninvasive visualization of biological processes with radionuclide-labeled small metabolic compounds helps elucidate the metabolic state of breast cancer, while radionuclide-labeled ligands/antibodies for receptor-targeted radionuclide molecular imaging is sensitive and specific for visualization of the overexpressed molecular markers in breast cancer. This review focuses on the most recent developments of novel radiotracers as promising tools for early breast cancer diagnosis.

Abstract

Breast cancer is a malignant tumor that can affect women worldwide and endanger their health and wellbeing. Early detection of breast cancer can significantly improve the prognosis and survival rate of patients, but with traditional anatomical imagine methods, it is difficult to detect lesions before morphological changes occur. Radionuclide-based molecular imaging based on positron emission tomography (PET) and single-photon emission computed tomography (SPECT) displays its advantages for detecting breast cancer from a functional perspective. Radionuclide labeling of small metabolic compounds can be used for imaging biological processes, while radionuclide labeling of ligands/antibodies can be used for imaging receptors. Noninvasive visualization of biological processes helps elucidate the metabolic state of breast cancer, while receptor-targeted radionuclide molecular imaging is sensitive and specific for visualization of the overexpressed molecular markers in breast cancer, contributing to early diagnosis and better management of cancer patients. The rapid development of radionuclide probes aids the diagnosis of breast cancer in various aspects. These probes target metabolism, amino acid transporters, cell proliferation, hypoxia, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), gastrin-releasing peptide receptor (GRPR) and so on. This article provides an overview of the development of radionuclide molecular imaging techniques present in preclinical or clinical studies, which are used as tools for early breast cancer diagnosis.

PIK3CA somatic alterations in invasive breast cancers: different spectrum from Caucasians to Chinese detected by next generation sequencing

Purpose

Somatic alteration of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) is a crucial therapeutic target in breast cancer (BC) and PI3Kα-specific inhibitor Alpelisib has been used in clinics. This study investigates the PIK3CA alterations in Chinese and Caucasians BC patients for the purpose of selecting anti-PI3K therapy.

Methods

The molecular profile of the PIK3CA gene was analyzed in 412 Chinese patients with untreated invasive BC using a 540 gene next-generation sequencing panel. The results were compared with data of the Caucasian BC patients in The Cancer Genome Atlas (TCGA-white).

Results

PIK3CA alterations were frequently found in BC of estrogen receptor (ER) positive (49.3%, p = 0.024), low ki67 proliferation index (58.3%, p = 0.007) and low pathological grade (grade I/II/III 80%, 53.4%, 35.9%, p < 0.001). Compared to TCGA-white, Chinese BC patients had a higher alteration frequency (45.6% vs. 34.7%, p < 0.001) with larger proportion of p.H1047R mutation among three common mutation sites (p.E545K, p.E542K and p.H1047R) (66.1% vs. 43.7%, p = 0.01). Across four molecular subtypes, ER + /human epidermal growth factor receptor 2 positive (HER2 +) tumors harbored the most PIK3CA alterations (51.6%), while ER-/HER2- harbored the least alteration (30.0%) but the most copy number amplification (19.05%).

Conclusion

PIK3CA alterations prevail in Chinese BC patients and have different molecular features compared to that of Caucasians. The results provide precise annotations of PIK3CA genomic alterations of Chinese in the context of application of PIK3CA inhibitor.

Conformational Selection in Ligand Recognition by the First Tudor Domain of PHF20L1

The first Tudor domain (Tudor1) of PHF20L1 recognizes (non)histone methylation to play versatile roles. However, the underlying ligand-recognition mechanism remains unknown as a closed state revealed in the free-form structure. NMR relaxation dispersion and molecular dynamics simulations suggest a pre-existing low-population conformation with a remarkable rearrangement of aromatic cage residues of PHF20L1 Tudor1. Such an open-form conformation is utilized to recognize lysine 142 methylated DNMT1, a cosolvent, and an NMR fragment screening hit, as revealed by the complex crystal structures. Intriguingly, the ligand binding capacity was enhanced by mutation that tunes up the open-state population only. The recognition of DNMT1 by PHF20L1 was further validated in cancer cells. This conformational selection mechanism will enable the discovery of small molecule inhibitors against the seemingly "undruggable" PHF20L1 Tudor1.

Circulating MicroRNAs as Prognostic and Therapeutic Biomarkers in Breast Cancer Molecular Subtypes

Breast cancer (BC) is a common and heterogeneous disease, of which six molecular subtypes, characterized by different biological features and clinical outcomes, were described. The identification of additional biomarkers able to further connote and distinguish the different BC subtypes is essential to improve the diagnostic, prognostic and therapeutic strategies in BC patients. MicroRNAs (miRNAs) are short non-coding RNA involved in several physiological and pathological processes, including cancer development and progression. In particular, circulating miRNAs, which can be found in an adequately stable structure in serum/plasma of cancer patients, are emerging as very promising non-invasive biomarkers. Several studies have analyzed the potential role of circulating miRNAs as prognostic and therapeutic markers in BC. In the present review we describe circulating miRNAs, identified as putative biomarker in BC, with special reference to different BC molecular subtypes.

Chemopreventive Property of Sencha Tea Extracts towards Sensitive and Multidrug-Resistant Leukemia and Multiple Myeloma Cells

The popular beverage green tea possesses chemopreventive activity against various types of tumors. However, the effects of its chemopreventive effect on hematological malignancies have not been defined. In the present study, we evaluated antitumor efficacies of a specific green tea, sencha tea, on sensitive and multidrug-resistant leukemia and a panel of nine multiple myelomas (MM) cell lines. We found that sencha extracts induced cytotoxicity in leukemic cells and MM cells to different extents, yet its effect on normal cells was limited. Furthermore, sencha extracts caused G2/M and G0/G1 phase arrest during cell cycle progression in CCRF/CEM and KMS-12-BM cells, respectively. Specifically, sencha-MeOH/H2O extracts induced apoptosis, ROS, and MMP collapse on both CCRF/CEM and KMS-12-BM cells. The analysis with microarray and COMPARE in 53 cell lines of the NCI panel revealed diverse functional groups, including cell morphology, cellular growth and proliferation, cell cycle, cell death, and survival, which were closely associated with anti-tumor effects of sencha tea. It is important to note that PI3K/Akt and NF-κB pathways were the top two dominant networks by ingenuity pathway analysis. We demonstrate here the multifactorial modes of action of sencha tea leading to chemopreventive effects of sencha tea against cancer.

Association between Cyclooxygenase-2 and Indoleamine 2,3-Dioxygenase Expression in Breast Cancer Patients from Pakistan

Background:

Tumors use several immunosuppressive mechanisms to evade immune destruction. Cyclooxygenase-2 (COX-2) expression may be a driver of immunosuppression in breast cancer, but the mechanisms involved remain elusive. COX-2 expression induces the expression of indoleamine 2,3 dioxygenase (IDO) in tumor cells. IDO is an immunosuppressive enzyme which is involved in tumor immune escape mechanisms in breast cancer. Our aim was to evaluate the association between COX-2 and IDO expression to find evidence of immunosuppression in Pakistani breast cancer patients.

Methods:

Immunohistochemical analysis was performed to evaluate the expression of COX-2, IDO, estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) on formalin-fixed paraffin-embedded breast cancer tissues of 100 patients. Univariable and multivariable logistic regression model was used to identify the independent risk factors of COX-2.

Results:

A total of 100 patients were included with a mean age and standard deviation of 48.28 ± 11.83. A significant association was observed among COX-2, IDO, ER, PR and tumor grade. In multivariable analysis, three variables were identified as significant independent risk factors for high COX-2: IDO expression high; [adjusted odds ratio (AOR) 6.51; 95% confidence interval (CI) (2.00-21.20), p=0.001], ER; [AOR 5.62; 95% CI (1.80-17.84), p=0.002] and age [AOR 1.04; 95% CI (1.00-1.10), p=0.05] respectively.

Conclusion:

Our data showed that high IDO expression is associated with high COX-2 expression in Pakistani breast cancer patients. The co-expression of both enzymes may suggest their role in disease pathogenesis. Hence the concurrent targeting of COX-2 and IDO may be a promising therapy for breast cancer.

Breast Carcinoma: Updates in Molecular Profiling 2018

The most significant contribution of molecular subtyping of breast carcinomas has been the identification of estrogen-positive and estrogen-negative tumor subtypes. Knowledge of genetic alterations in these tumors will help clinicians identify novel therapeutic targets. Understanding the progression pathways involved in the transition of in situ carcinoma to invasive carcinoma might lead to efficient risk stratification in these patients. The Cancer Genome Analysis Network has collected genomic and epigenomic data to provide comprehensive information regarding carcinogenesis and pathway interactions. Such information improves understanding of the disease process and also provides more accurate information toward identifying targetable mutations for treatment.

pAKT pathway activation is associated with PIK3CA mutations and good prognosis in luminal breast cancer in contrast to p-mTOR pathway activation

Numerous studies have focused on the PI3K/AKT/mTOR pathway in estrogen receptor positive (ER) breast cancer (BC), as a linear signal transduction pathway and reported its association with worse clinical outcomes. We developed gene signatures that reflect the level of expression of phosphorylated-Serine473-AKT (pAKT) and phosphorylated-Serine2448-mTOR (p-mTOR) separately, capturing their corresponding level of pathway activation. Our analysis revealed that the pAKT pathway activation was associated with luminal A BC while the p-mTOR pathway activation was more associated with luminal B BC (Kruskal-Wallis test p < 10^-10). pAKT pathway activation was significantly associated with better outcomes (multivariable HR, 0.79; 95%CI, 0.74-0.85; p = 2.5 × 10^-10) and PIK3CA mutations (p = 0.0001) whereas p-mTOR pathway activation showed worse outcomes (multivariable HR,1.1; 95%CI, 1.1-1.2; p = 9.9 × 10^-4) and associated with p53 mutations (p = 0.04). in conclusion, our data show that pAKT and p-mTOR pathway activation have differing impact on prognosis and suggest that they are not linearly connected in luminal breast cancers.

Biological Aggressiveness of Subclinical No-Mass Ductal Carcinoma In Situ (DCIS) Can Be Reflected by the Expression Profiles of Epithelial-Mesenchymal Transition Triggers

Epithelial-mesenchymal transitions (EMTs) have been recently implicated in the process of cancer progression. The aim of this study was to assess how the preoperative expression patterns of EMT biomarkers correlate with the risk of postoperative invasion in ductal carcinoma in situ (DCIS) found on stereotactic breast biopsies. N-cadherin, Snail1, and secreted protein acidic and rich in cysteine (SPARC) immunoreactivity was observed in 8%, 62%, and 38% of tumors, respectively. Snail1 and SPARC expressions were significantly related to N-cadherin expression and to each other. The postoperative upgrading rate was associated with a positive preoperative expression of all biomarkers. Significance of Snail1 and SPARC persisted in multivariate analysis, but the impact of SPARC on invasion was more significant. When these two EMT triggers were considered together, the risk of invasion did not significantly differ between the subtypes of DCIS with single positive expression (SPARC−/Snail1+ vs. SPARC+/Snail1−). However, it was significantly lower in single-positive DCIS when compared to lesions of a double-positive profile (SPARC+/Snail1+). Moreover, there were no cases in the double-negative DCIS (SPARC−/Snail1−), with foci of infiltrating cancer found postoperatively in residual postbiopsy lesions. In contrast, DCIS with a combined high SPARC and Snail1 expression (intermediate or strong) had an invasive component in 66–100% of tumors.

Receptor-based Surrogate Subtypes and Discrepancies with Breast Cancer Intrinsic Subtypes: Implications for Image Biomarker Development

Purpose To determine the concordance and accuracy of imaging surrogates of immunohistochemical (IHC) markers and the molecular classification of breast cancer. Materials and Methods A total of 3050 patients from 17 public breast cancer data sets containing IHC marker receptor status (estrogen receptor/progesterone receptor/human epidermal growth factor receptor 2 [HER2]) and their molecular classification (basal-like, HER2-enriched, luminal A or B) were analyzed. Diagnostic accuracy and concordance as measured with the κ statistic were calculated between the IHC and molecular classifications. Simulations were performed to assess the relationship between accuracy of imaging-based IHC markers to predict molecular classification. A simulation was performed to examine effects of misclassification of molecular type on patient survival. Results Accuracies of intrinsic subtypes based on IHC subtype were 71.7% (luminal A), 53.7% (luminal B), 64.8% (HER2-enriched), and 81.7% (basal-like). The κ agreement was fair (κ = 0.36) for luminal A and HER2-enriched subtypes, good (κ = 0.65) for the basal-like subtype, and poor (κ = 0.09) for the luminal B subtypes. Introduction of image misclassification by simulation lowered image-true subtype accuracies and κ values. Simulation analysis showed that misclassification caused survival differences between luminal A and basal-like subtypes to decrease. Conclusion There is poor concordance between triple-receptor status and intrinsic molecular subtype in breast cancer, arguing against their use in the design of prognostic genomic-based image biomarkers. © RSNA, 2018 Online supplemental material is available for this article.

A genomic copy number signature predicts radiation exposure in post-Chernobyl breast cancer

Breast cancer is the second leading cause of cancer death among women worldwide and besides life style, age and genetic risk factors, exposure to ionizing radiation is known to increase the risk for breast cancer. Further, DNA copy number alterations (CNAs), which can result from radiation-induced double-strand breaks, are frequently occurring in breast cancer cells. We set out to identify a signature of CNAs discriminating breast cancers from radiation-exposed and non-exposed female patients. We analyzed resected breast cancer tissues from 68 exposed female Chernobyl clean-up workers and evacuees and 68 matched non-exposed control patients for CNAs by array comparative genomic hybridization analysis (aCGH). Using a stepwise forward-backward selection approach a non-complex CNA signature, that is, less than ten features, was identified in the training data set, which could be subsequently validated in the validation data set (p value < 0.05). The signature consisted of nine copy number regions located on chromosomal bands 7q11.22-11.23, 7q21.3, 16q24.3, 17q21.31, 20p11.23-11.21, 1p21.1, 2q35, 2q35, 6p22.2. The signature was independent of any clinical characteristics of the patients. In all, we identified a CNA signature that has the potential to allow identification of radiation-associated breast cancer at the individual level.

Construction of diagnosis system and gene regulatory networks based on microarray analysis

A microarray analysis generally contains expression data of thousands of genes, but most of them are irrelevant to the disease of interest, making analyzing the genes concerning specific diseases complicated. Therefore, filtering out a few essential genes as well as their regulatory networks is critical, and a disease can be easily diagnosed just depending on the expression profiles of a few critical genes. In this study, a target gene screening (TGS) system, which is a microarray-based information system that integrates F-statistics, pattern recognition matching, a two-layer K-means classifier, a Parameter Detection Genetic Algorithm (PDGA), a genetic-based gene selector (GBG selector) and the association rule, was developed to screen out a small subset of genes that can discriminate malignant stages of cancers. During the first stage, F-statistic, pattern recognition matching, and a two-layer K-means classifier were applied in the system to filter out the 20 critical genes most relevant to ovarian cancer from 9600 genes, and the PDGA was used to decide the fittest values of the parameters for these critical genes. Among the 20 critical genes, 15 are associated with cancer progression. In the second stage, we further employed a GBG selector and the association rule to screen out seven target gene sets, each with only four to six genes, and each of which can precisely identify the malignancy stage of ovarian cancer based on their expression profiles. We further deduced the gene regulatory networks of the 20 critical genes by applying the Pearson correlation coefficient to evaluate the correlationship between the expression of each gene at the same stages and at different stages. Correlationships between gene pairs were calculated, and then, three regulatory networks were deduced. Their correlationships were further confirmed by the Ingenuity pathway analysis. The prognostic significances of the genes identified via regulatory networks were examined using online tools, and most represented biomarker candidates. In summary, our proposed system provides a new strategy to identify critical genes or biomarkers, as well as their regulatory networks, from microarray data.

Integrated genomics and functional validation identifies malignant cell specific dependencies in triple negative breast cancer

Triple negative breast cancers (TNBCs) lack recurrent targetable driver mutations but demonstrate frequent copy number aberrations (CNAs). Here, we describe an integrative genomic and RNAi-based approach that identifies and validates gene addictions in TNBCs. CNAs and gene expression alterations are integrated and genes scored for pre-specified target features revealing 130 candidate genes. We test functional dependence on each of these genes using RNAi in breast cancer and non-malignant cells, validating malignant cell selective dependence upon 37 of 130 genes. Further analysis reveals a cluster of 13 TNBC addiction genes frequently co-upregulated that includes genes regulating cell cycle checkpoints, DNA damage response, and malignant cell selective mitotic genes. We validate the mechanism of addiction to a potential drug target: the mitotic kinesin family member C1 (KIFC1/HSET), essential for successful bipolar division of centrosome-amplified malignant cells and develop a potential selection biomarker to identify patients with tumors exhibiting centrosome amplification.

Towards predictive biomarkers for immunotherapy response in breast cancer patients

Immunotherapy using anti-PD(L)1 has revolutionized treatment for various tumor types. Early data have shown durable responses in a small subgroup of breast cancer patients. So far, the response rates appear higher for breast tumors that are triple negative, PDL1-positive and/or harbor high levels of immune cells. Both comprehensive analyses of the breast tumor microenvironment and exploiting research on biomarkers in other cancer types, such as melanoma and lung cancer, may contribute to the discovery of accurate biomarkers to select breast cancer patients for immunotherapy. Here we summarize key features of the breast tumor microenvironment as well as putative predictive biomarkers established in other tumor types. Insights from both fields can guide future studies to enable personalized breast cancer immunotherapy.

A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: its application on PAM50 algorithm

Motivation

The PAM50 classifier is used to assign patients to the highest correlated breast cancer subtype irrespectively of the obtained value. Nonetheless, all subtype correlations are required to build the risk of recurrence (ROR) score, currently used in therapeutic decisions. Present subtype uncertainty estimations are not accurate, seldom considered or require a population-based approach for this context.

Results

Here we present a novel single-subject non-parametric uncertainty estimation based on PAM50's gene label permutations. Simulations results ( n  = 5228) showed that only 61% subjects can be reliably 'Assigned' to the PAM50 subtype, whereas 33% should be 'Not Assigned' (NA), leaving the rest to tight 'Ambiguous' correlations between subtypes. The NA subjects exclusion from the analysis improved survival subtype curves discrimination yielding a higher proportion of low and high ROR values. Conversely, all NA subjects showed similar survival behaviour regardless of the original PAM50 assignment. We propose to incorporate our PAM50 uncertainty estimation to support therapeutic decisions.

Availability and Implementation

Source code can be found in 'pbcmc' R package at Bioconductor.

Contacts

cristobalfresno@gmail.com or efernandez@bdmg.com.ar.

Supplementary information

Supplementary data are available at Bioinformatics online.

Breast Cancer Molecular Stratification: From Intrinsic Subtypes to Integrative Clusters

Breast carcinomas can be stratified into different entities based on clinical behavior, histologic features, and/or by biological properties. A classification of breast cancer should be based on underlying biology, which we know must be determined by the somatic genomic landscape of mutations. Moreover, because the latest generations of anticancer agents are founded on biological mechanisms, a detailed molecular stratification is a requirement for appropriate clinical management. Such stratification, based on genomic drivers, will be important for selecting patients for clinical trials. It will also facilitate the discovery of novel drivers, the study of tumor evolution, and the identification of mechanisms of treatment resistance. Assays for risk stratification have focused mainly on response prediction to existing treatment regimens. Molecular stratification based on gene expression profiling revealed that breast cancers could be classified in so-called intrinsic subtypes (luminal A and B, HER2-enriched, basal-like, and normal-like), which mostly corresponded to hormone receptor and HER2 status, and further stratified luminal tumors based on proliferation. The realization that a significant proportion of the gene expression landscape is determined by the somatic copy number alterations that drive expression in cis led to the newer classification of breast cancers into integrative clusters. This stratification of breast cancers into integrative clusters reveals prototypical patterns of single-nucleotide variants and is associated with distinct clinical courses and response to therapy.

Integrated genomic analysis of clear cell ovarian cancers identified PRKCI as a potential therapeutic target

Clear cell ovarian cancer (CCOC) is an epithelial ovarian cancer histotype with unique pathologic, biologic and clinical features. Despite its worse prognosis than serous ovarian cancer (SOC), the genomic landscape of CCOC is less well defined. Integrated genomic analysis of CCOC allows the identification of potential therapeutic targets to improve the treatment of this tumor. Using comparative genomic hybridization and gene expression profiling, we have screened 12 CCOC cell lines and 40 tumors to identify 45 amplified and overexpressed genes. Pathways analysis of these genes identified 19 genes with cancer-related functions. Of these, PRKCI is one of the most frequently amplified and overexpressed genes and its expression induced cancer cell proliferation and migration/invasion in vitro as well as tumor growth in vivo. Targeting PRKCI by small molecule inhibitor, sodium aurothiomalate (ATM), significantly reduced the in vivo tumor growth and may be a new therapeutic strategy to improve the treatment of CCOC.

Correlated S-palmitoylation profiling of Snail-induced epithelial to mesenchymal transition

Epithelial cells form spatially-organized adhesion complexes that establish polarity gradients, regulate cell proliferation, and direct wound healing. As cells accumulate oncogenic mutations, these key tumor suppression mechanisms are disrupted, eliminating many adhesion complexes and bypassing contact inhibition. The transcription factor Snail is often expressed in malignant cancers, where it promotes transcriptional reprogramming to drive epithelial-mesenchymal transition (EMT) and establishes a more invasive state. S-Palmitoylation describes the fatty-acyl post-translational modification of cysteine residues in proteins, and is required for membrane anchoring, trafficking, localization and function of hundreds of proteins involved in cell growth, polarity, and signaling. Since Snail-expression disrupts apico-basolateral cell polarity, we asked if Snail-dependent transformation induces proteome-wide changes in S-palmitoylation. MCF10A breast cancer cells were retrovirally transduced with Snail and correlated proteome-wide changes in protein abundance and S-palmitoylation were profiled by using stable isotope labeling in cell culture with amino acid (SILAC) mass spectrometry. This analysis identified increased levels of proteins involved in migration, glycolysis, and cell junction remodeling, and decreased levels of proteins involved in cell adhesion. Overall, protein S-palmitoylation is highly correlated with protein abundance, yet for a subset of proteins, this correlation is uncoupled. These findings suggest that Snail-overexpression affects the S-palmitoylation cycle of some proteins, which may participate in cell polarity and tumor suppression.

'Omics Approaches in Breast Cancer Research and Clinical Practice

Our understanding of the natural history of breast cancer has evolved alongside technologies to study its genomic, transcriptomic, proteomic, and metabolomics landscapes. These technologies have helped decipher multiple molecular pathways dysregulated in breast cancer. First-generation 'omics analyses considered each of these dimensions individually, but it is becoming increasingly clear that more holistic, integrative approaches are required to fully understand complex biological systems. The 'omics represent an exciting era of discovery in breast cancer research, although important issues need to be addressed to realize the clinical utility of these data through precision cancer care. How can the data be applied to predict response to molecular-targeted therapies? When should treatment decisions be based on tumor genetics rather than histology? And with the sudden explosion of "big data" from large 'omics consortia and new precision clinical trials, how do we now negotiate evidence-based pathways to clinical translation through this apparent sea of opportunity? The aim of this review is to provide a broad overview of 'omics technologies used in breast cancer research today, the current state-of-play in terms of applying this new knowledge in the clinic, and the practical and ethical issues that will be central to the public discussion on the future of precision cancer care.

Three-dimensional modelling identifies novel genetic dependencies associated with breast cancer progression in the isogenic MCF10 model

The initiation and progression of breast cancer from the transformation of the normal epithelium to ductal carcinoma in situ (DCIS) and invasive disease is a complex process involving the acquisition of genetic alterations and changes in gene expression, alongside microenvironmental and recognized histological alterations. Here, we sought to comprehensively characterise the genomic and transcriptomic features of the MCF10 isogenic model of breast cancer progression, and to functionally validate potential driver alterations in three-dimensional (3D) spheroids that may provide insights into breast cancer progression, and identify targetable alterations in conditions more similar to those encountered in vivo. We performed whole genome, exome and RNA sequencing of the MCF10 progression series to catalogue the copy number and mutational and transcriptomic landscapes associated with progression. We identified a number of predicted driver mutations (including PIK3CA and TP53) that were acquired during transformation of non-malignant MCF10A cells to their malignant counterparts that are also present in analysed primary breast cancers from The Cancer Genome Atlas (TCGA). Acquisition of genomic alterations identified MYC amplification and previously undescribed RAB3GAP1-HRAS and UBA2-PDCD2L expressed in-frame fusion genes in malignant cells. Comparison of pathway aberrations associated with progression showed that, when cells are grown as 3D spheroids, they show perturbations of cancer-relevant pathways. Functional interrogation of the dependency on predicted driver events identified alterations in HRAS, PIK3CA and TP53 that selectively decreased cell growth and were associated with progression from preinvasive to invasive disease only when cells were grown as spheroids. Our results have identified changes in the genomic repertoire in cell lines representative of the stages of breast cancer progression, and demonstrate that genetic dependencies can be uncovered when cells are grown in conditions more like those in vivo. The MCF10 progression series therefore represents a good model with which to dissect potential biomarkers and to evaluate therapeutic targets involved in the progression of breast cancer. © 2016 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Sense-antisense gene-pairs in breast cancer and associated pathological pathways

More than 30% of human protein-coding genes form hereditary complex genome architectures composed of sense-antisense (SA) gene pairs (SAGPs) transcribing their RNAs from both strands of a given locus. Such architectures represent important novel components of genome complexity contributing to gene expression deregulation in cancer cells. Therefore, the architectures might be involved in cancer pathways and, in turn, be used for novel drug targets discovery. However, the global roles of SAGPs in cancer pathways has not been studied. Here we investigated SAGPs associated with breast cancer (BC)-related pathways using systems biology, prognostic survival and experimental methods. Gene expression analysis identified 73 BC-relevant SAGPs that are highly correlated in BC. Survival modelling and metadata analysis of the 1161 BC patients allowed us to develop a novel patient prognostic grouping method selecting the 12 survival-significant SAGPs. The qRT-PCR-validated 12-SAGP prognostic signature reproducibly stratified BC patients into low- and high-risk prognostic subgroups. The 1381 SAGP-defined differentially expressed genes common across three studied cohorts were identified. The functional enrichment analysis of these genes revealed the GABPA gene network, including BC-relevant SAGPs, specific gene sets involved in cell cycle, spliceosomal and proteasomal pathways. The co-regulatory function of GABPA in BC cells was supported using siRNA knockdown studies. Thus, we demonstrated SAGPs as the synergistically functional genome architectures interconnected with cancer-related pathways and associated with BC patient clinical outcomes. Taken together, SAGPs represent an important component of genome complexity which can be used to identify novel aspects of coordinated pathological gene networks in cancers.

Deciphering and Targeting Oncogenic Mutations and Pathways in Breast Cancer

: Advances in DNA and RNA sequencing revealed substantially greater genomic complexity in breast cancer than simple models of a few driver mutations would suggest. Only very few, recurrent mutations or copy-number variations in cancer-causing genes have been identified. The two most common alterations in breast cancer are TP53 (affecting the majority of triple-negative breast cancers) and PIK3CA (affecting almost half of estrogen receptor-positive cancers) mutations, followed by a long tail of individually rare mutations affecting <1%-20% of cases. Each cancer harbors from a few dozen to a few hundred potentially high-functional impact somatic variants, along with a much larger number of potentially high-functional impact germline variants. It is likely that it is the combined effect of all genomic variations that drives the clinical behavior of a given cancer. Furthermore, entirely new classes of oncogenic events are being discovered in the noncoding areas of the genome and in noncoding RNA species driven by errors in RNA editing. In light of this complexity, it is not unexpected that, with the exception of HER2 amplification, no robust molecular predictors of benefit from targeted therapies have been identified. In this review, we summarize the current genomic portrait of breast cancer, focusing on genetic aberrations that are actively being targeted with investigational drugs.

IMPLICATIONS FOR PRACTICE

Next-generation sequencing is now widely available in the clinic, but interpretation of the results is challenging, and its impact on treatment selection is often limited. This work provides an overview of frequently encountered molecular abnormalities in breast cancer and discusses their potential therapeutic implications. This review emphasizes the importance of administering investigational targeted therapies, or off-label use of approved targeted drugs, in the context of a formal clinical trial or registry programs to facilitate learning about the clinical utility of tumor target profiling.

ID4 controls luminal lineage commitment in normal mammary epithelium and inhibits BRCA1 function in basal-like breast cancer

Inhibitor of differentiation (ID) proteins are key regulators of development and tumorigenesis. One member of this family, ID4, controls lineage commitment during mammary gland development by acting upstream of key developmental pathways. Recent evidence suggests an emerging role for ID4 as a lineage-dependent proto-oncogene that is overexpressed and amplified in a subset of basal-like breast cancers (BLBCs), conferring poor prognosis. Several lines of evidence suggest ID4 may suppress BRCA1 function in BLBC and in doing so, define a subset of BLBC patients who may respond to therapies traditionally used in BRCA1-mutant cancers. This review highlights recent advances in our understanding of the requirement for ID4 in mammary lineage commitment and the role for ID4 in BLBC. We address current shortfalls in this field and identify important areas of future research.

Cancer Hallmarks, Biomarkers and Breast Cancer Molecular Subtypes

Breast cancer is a complex disease encompassing multiple tumor entities, each characterized by distinct morphology, behavior and clinical implications. Besides estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2, novel biomarkers have shown their prognostic and predictive values, complicating our understanding towards to the heterogeneity of such cancers. Ten cancer hallmarks have been proposed by Weinberg to characterize cancer and its carcinogenesis. By reviewing biomarkers and breast cancer molecular subtypes, we propose that the divergent outcome observed from patients stratified by hormone status are driven by different cancer hallmarks. 'Sustaining proliferative signaling' further differentiates cancers with positive hormone receptors. 'Activating invasion and metastasis' and 'evading immune destruction' drive the differentiation of triple negative breast cancers. 'Resisting cell death', 'genome instability and mutation' and 'deregulating cellular energetics' refine breast cancer classification with their predictive values. 'Evading growth suppressors', 'enabling replicative immortality', 'inducing angiogenesis' and 'tumor-promoting inflammation' have not been involved in breast cancer classification which need more focus in the future biomarker-related research. This review novels in its global view on breast cancer heterogeneity, which clarifies many confusions in this field and contributes to precision medicine.

Chronic oxidative stress promotes H2AX protein degradation and enhances chemosensitivity in breast cancer patients

Anti‐cancer drugs often increase reactive oxygen species (ROS) and cause DNA damage. Here, we highlight a new cross talk between chronic oxidative stress and the histone variant H2AX, a key player in DNA repair. We observe that persistent accumulation of ROS, due to a deficient JunD‐/Nrf2‐antioxidant response, reduces H2AX protein levels. This effect is mediated by an enhanced interaction of H2AX with the E3 ubiquitin ligase RNF168, which is associated with H2AX poly‐ubiquitination and promotes its degradation by the proteasome. ROS‐mediated H2AX decrease plays a crucial role in chemosensitivity. Indeed, cycles of chemotherapy that sustainably increase ROS reduce H2AX protein levels in Triple‐Negative breast cancer (TNBC) patients. H2AX decrease by such treatment is associated with an impaired NRF2‐antioxidant response and is indicative of the therapeutic efficiency and survival of TNBC patients. Thus, our data describe a novel ROS‐mediated regulation of H2AX turnover, which provides new insights into genetic instability and treatment efficacy in TNBC patients.

Computational pathology: Exploring the spatial dimension of tumor ecology

Tumors are evolving ecosystems where cancer subclones and the microenvironment interact. This is analogous to interaction dynamics between species in their natural habitats, which is a prime area of study in ecology. Spatial statistics are frequently used in ecological studies to infer complex relations including predator-prey, resource dependency and co-evolution. Recently, the emerging field of computational pathology has enabled high-throughput spatial analysis by using image processing to identify different cell types and their locations within histological tumor samples. We discuss how these data may be analyzed with spatial statistics used in ecology to reveal patterns and advance our understanding of ecological interactions occurring among cancer cells and their microenvironment.

An Integrative Analysis to Identify Driver Genes in Esophageal Squamous Cell Carcinoma

Background

Few driver genes have been well established in esophageal squamous cell carcinoma (ESCC). Identification of the genomic aberrations that contribute to changes in gene expression profiles can be used to predict driver genes.

Methods

We searched for driver genes in ESCC by integrative analysis of gene expression microarray profiles and copy number data. To narrow down candidate genes, we performed survival analysis on expression data and tested the genetic vulnerability of each genes using public RNAi screening data. We confirmed the results by performing RNAi experiments and evaluating the clinical relevance of candidate genes in an independent ESCC cohort.

Results

We found 10 significantly recurrent copy number alterations accompanying gene expression changes, including loci 11q13.2, 7p11.2, 3q26.33, and 17q12, which harbored CCND1, EGFR, SOX2, and ERBB2, respectively. Analysis of survival data and RNAi screening data suggested that GRB7, located on 17q12, was a driver gene in ESCC. In ESCC cell lines harboring 17q12 amplification, knockdown of GRB7 reduced the proliferation, migration, and invasion capacities of cells. Moreover, siRNA targeting GRB7 had a synergistic inhibitory effect when combined with trastuzumab, an anti-ERBB2 antibody. Survival analysis of the independent cohort also showed that high GRB7 expression was associated with poor prognosis in ESCC.

Conclusion

Our integrative analysis provided important insights into ESCC pathogenesis. We identified GRB7 as a novel ESCC driver gene and potential new therapeutic target.

Distinct EMT programs control normal mammary stem cells and tumour-initiating cells

Tumour-initiating cells (TICs) are responsible for metastatic dissemination and clinical relapse in a variety of cancers. Analogies between TICs and normal tissue stem cells have led to the proposal that activation of the normal stem-cell program within a tissue serves as the major mechanism for generating TICs. Supporting this notion, we and others previously established that the Slug epithelial-to-mesenchymal transition-inducing transcription factor (EMT-TF), a member of the Snail family, serves as a master regulator of the gland-reconstituting activity of normal mammary stem cells, and that forced expression of Slug in collaboration with Sox9 in breast cancer cells can efficiently induce entrance into the TIC state. However, these earlier studies focused on xenograft models with cultured cell lines and involved ectopic expression of EMT-TFs, often at non-physiological levels. Using genetically engineered knock-in reporter mouse lines, here we show that normal gland-reconstituting mammary stem cells residing in the basal layer of the mammary epithelium and breast TICs originating in the luminal layer exploit the paralogous EMT-TFs Slug and Snail, respectively, which induce distinct EMT programs. Broadly, our findings suggest that the seemingly similar stem-cell programs operating in TICs and normal stem cells of the corresponding normal tissue are likely to differ significantly in their details.

Tumor characterization and stratification by integrated molecular profiles reveals essential pan-cancer features

BACKGROUND

Identification of tumor heterogeneity and genomic similarities across different cancer types is essential to the design of effective stratified treatments and for the discovery of treatments that can be extended to different types of tumors. However, systematic investigations on comprehensive molecular profiles have not been fully explored to achieve this goal.

RESULTS

Here, we performed a network-based integrative pan-cancer genomic analysis on >3000 samples from 12 cancer types to uncover novel stratifications among tumors. Our study not only revealed recurrently reported cross-cancer similarities, but also identified novel ones. The macro-scale stratification demonstrates strong clinical relevance and reveals consistent risk tendency among cancer types. The micro-scale stratification shows essential pan-cancer heterogeneity with subgroup-specific gene network characteristics and biological functions.

CONCLUSIONS

In summary, our comprehensive network-based pan-cancer stratification provides valuable information about inter- and intra- cancer stratification for patient clinical assessments and therapeutic strategies.

Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer

BACKGROUND

Recent advances in high-throughput technology and the emergence of large-scale genomic datasets have enabled detection of genomic features that affect clinical outcomes. Although many previous computational studies have analysed the effect of each single gene or the additive effects of multiple genes on the clinical outcome, less attention has been devoted to the identification of gene-gene interactions of general type that are associated with the clinical outcome. Moreover, the integration of information from multiple molecular profiles adds another challenge to this problem. Recently, network-based approaches have gained huge popularity. However, previous network construction methods have been more concerned with the relationship between features only, rather than the effect of feature interactions on clinical outcome.

METHODS

We propose a mutual information-based integrative network analysis framework (MINA) that identifies gene pairs associated with clinical outcome and systematically analyses the resulting networks over multiple genomic profiles. We implement an efficient non-parametric testing scheme that ensures the significance of detected gene interactions. We develop a tool named MINA that automates the proposed analysis scheme of identifying outcome-associated gene interactions and generating various networks from those interacting pairs for downstream analysis.

RESULTS

We demonstrate the proposed framework using real data from ovarian cancer patients in The Cancer Genome Atlas (TCGA). Statistically significant gene pairs associated with survival were identified from multiple genomic profiles, which include many individual genes that have weak or no effect on survival. Moreover, we also show that integrated networks, constructed by merging networks from multiple genomic profiles, demonstrate better topological properties and biological significance than individual networks.

CONCLUSIONS

We have developed a simple but powerful analysis tool that is able to detect gene-gene interactions associated with clinical outcome on multiple genomic profiles. By being network-based, our approach provides a better insight into the underlying gene-gene interaction mechanisms that affect the clinical outcome of cancer patients.

Integrative analyses identify modulators of response to neoadjuvant aromatase inhibitors in patients with early breast cancer

INTRODUCTION

Aromatase inhibitors (AIs) are a vital component of estrogen receptor positive (ER+) breast cancer treatment. De novo and acquired resistance, however, is common. The aims of this study were to relate patterns of copy number aberrations to molecular and proliferative response to AIs, to study differences in the patterns of copy number aberrations between breast cancer samples pre- and post-AI neoadjuvant therapy, and to identify putative biomarkers for resistance to neoadjuvant AI therapy using an integrative analysis approach.

METHODS

Samples from 84 patients derived from two neoadjuvant AI therapy trials were subjected to copy number profiling by microarray-based comparative genomic hybridisation (aCGH, n=84), gene expression profiling (n=47), matched pre- and post-AI aCGH (n=19 pairs) and Ki67-based AI-response analysis (n=39).

RESULTS

Integrative analysis of these datasets identified a set of nine genes that, when amplified, were associated with a poor response to AIs, and were significantly overexpressed when amplified, including CHKA, LRP5 and SAPS3. Functional validation in vitro, using cell lines with and without amplification of these genes (SUM44, MDA-MB134-VI, T47D and MCF7) and a model of acquired AI-resistance (MCF7-LTED) identified CHKA as a gene that when amplified modulates estrogen receptor (ER)-driven proliferation, ER/estrogen response element (ERE) transactivation, expression of ER-regulated genes and phosphorylation of V-AKT murine thymoma viral oncogene homolog 1 (AKT1).

CONCLUSIONS

These data provide a rationale for investigation of the role of CHKA in further models of de novo and acquired resistance to AIs, and provide proof of concept that integrative genomic analyses can identify biologically relevant modulators of AI response.

Wip1 phosphatase in breast cancer

Understanding the factors contributing to tumor initiation, progression and evolution is of paramount significance. Among them, wild-type p53-induced phosphatase 1 (Wip1) is emerging as an important oncogene by virtue of its negative control on several key tumor suppressor pathways. Originally discovered as a p53-regulated gene, Wip1 has been subsequently found amplified and more recently mutated in a significant fraction of human cancers including breast tumors. Recent development in the field further uncovered the utility of anti-Wip1-directed therapies in delaying tumor onset or in reducing the tumor burden. Furthermore, Wip1 could be an important factor that contributes to tumor heterogeneity, suggesting that its inhibition may decrease the rate of cancer evolution. These effects depend on several signaling pathways modulated by Wip1 phosphatase in a spatial and temporal manner. In this review we discuss the recent development in understanding how Wip1 contributes to tumorigenesis with its relevance to breast cancer.

[PARP inhibitors and radiotherapy: rational and prospects for a clinical use]

Poly(ADP-ribosyl)ation is a ubiquitous protein modification involved in the regulation of many cellular processes that is carried out by the poly(ADP-ribose) polymerase (PARP) family. The PARP-1, PARP-2 and PARP-3 are the only PARPs known to be activated by DNA damage. The absence of PARP-1 and PARP-2, that are both activated by DNA damage and participate in DNA damage repair processes, results in hypersensitivity to ionizing radiation and alkylating agents. PARP inhibitors that compete with NAD(+) at the enzyme's activity site can be used in BRCA-deficient cells as single agent therapies acting through the principle of synthetic lethality exploiting these cells deficient DNA double-strand break repair. Preclinical data showing an enhancement of the response of tumors to radiation has been documented for several PARP inhibitors. However, whether this is due exclusively to impaired DNA damage responses or whether tumor re-oxygenation contributes to this radio-sensitization via the vasoactive effects of the PARP inhibitors remains to be fully determined. These promising results have paved the way for the evaluation of PARP inhibitors in combination with radiotherapy in phase I and phase II clinical trials for malignant glioma, head and neck, and breast cancers. A number of challenges remain that are also reviewed in this article, including the optimization of treatment schedules for combined therapies and the validation of biomarkers that will identify which patients will most benefit from either PARP inhibitors in combination with radiotherapy.

Prohibitin expression is associated with high grade breast cancer but is not a driver of amplification at 17q21.33

AIMS

In a study of ductal carcinoma in situ of the breast, we identified five genes at chromosome 17q21.33 that were over-expressed in high grade cases, and showed a correlation between expression and gene copy number. The aim of this study was to investigate potential drivers of genomic amplification at 17q21.33.

METHODS

Analysis of high resolution comparative genomic hybridisation and published data specified a minimum region of amplification at 17q21.33. Prohibitin (PHB) expression was examined by immunohistochemistry in 285 invasive breast cancers. Gene copy number was examined by fluorescence in situ hybridisation.

RESULTS

The minimum region of amplification at 17q21.33 included ten genes with PHB selected as a candidate driver. Increased PHB expression was associated with higher grade breast cancer and poorer survival. Amplification of PHB was detected in 13 of 235 cases (5.5%) but was not associated with PHB expression. PHB amplification was most common in the ERBB2+ breast cancer subtype, although high expression was most prevalent in basal-like and luminal B cancers.

CONCLUSIONS

Amplification at 17q21.33 is a recurrent feature of breast cancer that forms part of a 'firestorm' pattern of genomic aberration. PHB is not a driver of amplification, however PHB may contribute to high grade breast cancer.

p-mTOR expression is associated with better prognosis in luminal breast carcinoma

AIMS

Despite considerable interest in the PI3K/AKT/mTOR pathway in breast carcinomas (BC), published data reports contradictory results regarding the association of phosphorylated mammalian target of Rapamycin (p-mTOR) expression with clinico-pathological features and prognosis in BC. Here, we evaluate the main clinico-pathological associations with p-mTOR expression in BC, with focus on the different molecular subtypes.

METHODS

In this retrospective study, 331 BC patients were included in final analysis. Outcome measures included disease-free survival (DFS) and overall survival (OS) times. Baseline data and outcome measures were compared between immunohistochemical p-mTOR expressing and non-expressing BCs. Subgroup analysis was performed to assess the effect of p-mTOR expression in the outcome for each BC molecular subtype.

RESULTS

43.8% of the tumours were positive for p-mTOR, with a significant correlation between p-mTOR expression with smaller (<2 cm) (p=0.021) and lower-grade tumours (p<0.001). Expression of p-mTOR was also associated with longer DFS (HR of 0.32, p<0.001) and OS (HR of 0.20, p<0.001). In a multivariable analysis, the HR remained significant with minimal change (HR=0.26, p=0.002 for OS; HR=0.40, p=0.002 for DFS). In subgroup analysis, luminal p-mTOR-expressing tumours demonstrated longer DFS and OS (HR 0.33, p=0.003; HR 0.20, p=0.003, respectively) independently of size, grade, lymph node status and Her-2 overexpression.

CONCLUSIONS

p-mTOR expression is associated with smaller, lower-grade and with luminal BC. In multivariable analysis, p-mTOR expression was associated with longer DFS and OS, independently of the size, grade and lymph node status, especially in luminal BCs.

Breast carcinoma: molecular profiling and updates

The most significant contribution of molecular subtyping of breast carcinomas has been the identification of estrogen-positive and estrogen-negative tumor subtypes, which are 2 distinct entities with differing prognoses and requiring different therapy. Molecular and genetic analyses can provide prognostic information; however, a thorough histopathologic evaluation with an evaluation of predictive biomarkers will provide similar information. Knowledge of genetic alterations in these tumors will help identify novel therapeutic targets, which might have an impact on prognosis. Understanding the progression pathways involved in the transition of in situ carcinoma to invasive carcinoma might lead to efficient risk stratification in these patients.

Lessons learned from the intrinsic subtypes of breast cancer in the quest for precision therapy

BACKGROUND

Wide variability in breast cancer, between patients and within each individual neoplasm, adds confounding complexity to the treatment of the disease. In clinical practice, hormone receptor status has been used to classify breast tumours and to guide treatment. Modern classification systems should take the wide tumour heterogeneity into account to improve patient outcome.

METHODS

This article reviews the identification of the intrinsic molecular subtypes of breast cancer, their prognostic and therapeutic implications, and the impact of tumour heterogeneity on cancer progression and treatment. The possibility of functionally addressing tumour-specific characteristics in in vivo models to inform decisions for precision therapies is also discussed.

RESULTS

Despite the robust breast tumour classification system provided by gene expression profiling, heterogeneity is also evident within these molecular portraits. A complicating factor in breast cancer classification is the process of selective clonality within developing neoplasms. Phenotypically and functionally distinct clones representing the intratumour heterogeneity might confuse molecular classification. Molecular portraits of the heterogeneous primary tumour might not necessarily reflect the subclone of cancer cells that causes the disease to relapse. Studies of reciprocal relationships between cancer cell subpopulations within developing tumours are therefore needed, and are possible only in genetically engineered mouse models or patient-derived xenograft models, in which the treatment-induced selection pressure on individual cell clones can be mimicked.

CONCLUSION

In the future, more refined classifications, based on integration of information at several molecular levels, are required to improve treatment guidelines. Large-scale translational research efforts paved the way for identification of the intrinsic subtypes, and are still fundamental for ensuring future progress in cancer care.

Carbonic anhydrase 9 expression increases with vascular endothelial growth factor-targeted therapy and is predictive of outcome in metastatic clear cell renal cancer

Background

There is a lack of biomarkers to predict outcome with targeted therapy in metastatic clear cell renal cancer (mccRCC). This may be because dynamic molecular changes occur with therapy.

Objective

To explore if dynamic, targeted-therapy-driven molecular changes correlate with mccRCC outcome.

Design, setting, and participants

Multiple frozen samples from primary tumours were taken from sunitinib-naïve (n = 22) and sunitinib-treated mccRCC patients (n = 23) for protein analysis. A cohort (n = 86) of paired, untreated and sunitinib/pazopanib-treated mccRCC samples was used for validation. Array comparative genomic hybridisation (CGH) analysis and RNA interference (RNAi) was used to support the findings.

Intervention

Three cycles of sunitinib 50 mg (4 wk on, 2 wk off).

Outcome measurements and statistical analysis

Reverse phase protein arrays (training set) and immunofluorescence automated quantitative analysis (validation set) assessed protein expression.

Results and limitations

Differential expression between sunitinib-naïve and treated samples was seen in 30 of 55 proteins (p < 0.05 for each). The proteins B-cell CLL/lymphoma 2 (BCL2), mutL homolog 1 (MLH1), carbonic anhydrase 9 (CA9), and mechanistic target of rapamycin (mTOR) (serine/threonine kinase) had both increased intratumoural variance and significant differential expression with therapy. The validation cohort confirmed increased CA9 expression with therapy. Multivariate analysis showed high CA9 expression after treatment was associated with longer survival (hazard ratio: 0.48; 95% confidence interval, 0.26–0.87; p = 0.02). Array CGH profiles revealed sunitinib was associated with significant CA9 region loss. RNAi CA9 silencing in two cell lines inhibited the antiproliferative effects of sunitinib. Shortcomings of the study include selection of a specific protein for analysis, and the specific time points at which the treated tissue was analysed.

Conclusions

CA9 levels increase with targeted therapy in mccRCC. Lower CA9 levels are associated with a poor prognosis and possible resistance, as indicated by the validation cohort.

Patient summary

Drug treatment of advanced kidney cancer alters molecular markers of treatment resistance. Measuring carbonic anhydrase 9 levels may be helpful in determining which patients benefit from therapy.

Differentiation and histogenesis of syringomatous tumour of the nipple and low-grade adenosquamous carcinoma: evidence for a common origin

AIMS

Syringomatous tumour of the nipple and low-grade adenosquamous carcinoma (LGAdSC) of the breast are regarded as distinct entities. To clarify the nature of these two lesions, we compared the expression of different lineage/differentiation markers in 12 syringomatous tumours of the nipple, nine LGAdSCs, and normal breast epithelium.

METHODS AND RESULTS

Using triple immunofluorescence labelling and quantitative RT-PCR for keratins, p63, and smooth muscle actin, we demonstrated that syringomatous tumour and LGAdSC contain p63+/K5/14+ tumour cells, K10+ squamous cells, and K8/18+ glandular cells, with intermediary cells being found in both lineages. Identical p63+/K5/14+ cells were also found in the normal breast duct epithelium.

CONCLUSIONS

Our data provide evidence that syringomatous tumour of the nipple and LGAdSC are identical or nearly identical lesions. They contain p63+/K5/14+ cells as the key cells from which the K10+ squamous lineage and the K8/18+ glandular lineage arise. On the basis of our findings in normal breast tissue and associated benign lesions, we suggest that p63+/K5/14+ cells of the normal breast duct epithelium or early related cells might play a key role in the neoplastic transformation of both syringomatous tumour and LGAdSC. We propose that the differentiation patterns found in both lesions reflect the early ontogenetic stages of the normal breast epithelium.

Characterization of the genomic features and expressed fusion genes in micropapillary carcinomas of the breast

Micropapillary carcinoma (MPC) is a rare histological special type of breast cancer, characterized by an aggressive clinical behaviour and a pattern of copy number aberrations (CNAs) distinct from that of grade- and oestrogen receptor (ER)-matched invasive carcinomas of no special type (IC-NSTs). The aims of this study were to determine whether MPCs are underpinned by a recurrent fusion gene(s) or mutations in 273 genes recurrently mutated in breast cancer. Sixteen MPCs were subjected to microarray-based comparative genomic hybridization (aCGH) analysis and Sequenom OncoCarta mutation analysis. Eight and five MPCs were subjected to targeted capture and RNA sequencing, respectively. aCGH analysis confirmed our previous observations about the repertoire of CNAs of MPCs. Sequencing analysis revealed a spectrum of mutations similar to those of luminal B IC-NSTs, and recurrent mutations affecting mitogen-activated protein kinase family genes and NBPF10. RNA-sequencing analysis identified 17 high-confidence fusion genes, eight of which were validated and two of which were in-frame. No recurrent fusions were identified in an independent series of MPCs and IC-NSTs. Forced expression of in-frame fusion genes (SLC2A1-FAF1 and BCAS4-AURKA) resulted in increased viability of breast cancer cells. In addition, genomic disruption of CDK12 caused by out-of-frame rearrangements was found in one MPC and in 13% of HER2-positive breast cancers, identified through a re-analysis of publicly available massively parallel sequencing data. In vitro analyses revealed that CDK12 gene disruption results in sensitivity to PARP inhibition, and forced expression of wild-type CDK12 in a CDK12-null cell line model resulted in relative resistance to PARP inhibition. Our findings demonstrate that MPCs are neither defined by highly recurrent mutations in the 273 genes tested, nor underpinned by a recurrent fusion gene. Although seemingly private genetic events, some of the fusion transcripts found in MPCs may play a role in maintenance of a malignant phenotype and potentially offer therapeutic opportunities.

DNA repair in cancer: emerging targets for personalized therapy

Genomic deoxyribonucleic acid (DNA) is under constant threat from endogenous and exogenous DNA damaging agents. Mammalian cells have evolved highly conserved DNA repair machinery to process DNA damage and maintain genomic integrity. Impaired DNA repair is a major driver for carcinogenesis and could promote aggressive cancer biology. Interestingly, in established tumors, DNA repair activity is required to counteract oxidative DNA damage that is prevalent in the tumor microenvironment. Emerging clinical data provide compelling evidence that overexpression of DNA repair factors may have prognostic and predictive significance in patients. More recently, DNA repair inhibition has emerged as a promising target for anticancer therapy. Synthetic lethality exploits intergene relationships where the loss of function of either of two related genes is nonlethal, but loss of both causes cell death. Exploiting this approach by targeting DNA repair has emerged as a promising strategy for personalized cancer therapy. In the current review, we focus on recent advances with a particular focus on synthetic lethality targeting in cancer.