Associations between genomic stratification of breast cancer and centrally reviewed tumour pathology in the METABRIC cohort

Identifying and targeting key driver genes for collagen production within the 11q13/14 breast cancer amplicon

Genetic studies indicate that breast cancer can be divided into several basic molecular groups. One of these groups, termed IntClust-2, is characterized by amplification of a small portion of chromosome 11 and has a median survival of only five years. Several cancer-relevant genes occupy this portion of chromosome 11, and it is thought that overexpression of a combination of driver genes in this region is responsible for the poor outcome of women in this group. In this study we used a gene editing method to knock out, one by one, each of 198 genes that are located within the amplified region of chromosome 11 and determined how much each of these genes contributed to the survival of breast cancer cells. In addition to well-known drivers such as CCND1 and PAK1 , we identified two different genes ( SERPINH1 and P4HA3 ), that encode proteins involved in collagen synthesis and organization. Using both in vitro and in vivo functional analyses, we determined that P4HA3 and/or SERPINH1 provide a critical driver function on IntClust-2 basic processes, such as viability, proliferation, and migration. Inhibiting these enzymes via genetic or pharmacologic means reduced collagen synthesis and impeded oncogenic signaling transduction in cell culture models, and a small-molecule inhibitor of P4HA3 was effective in treating 11q13 tumor growth in an animal model. As collagen has a well-known association with tissue stiffness and aggressive forms of breast cancer, we believe that the two genes we identified provide an opportunity for a new therapeutic strategy in IntClust-2 breast cancers.

Unveiling breast cancer risk profiles: a survival clustering analysis empowered by an online web application

Aim: To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. Materials & methods: Analysis is based on the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which contains 1726 subjects and 22 variables. Cox regression was used to identify survival risk factors for K-means clustering. Logrank tests and C-statistics were compared across different cluster numbers and Kaplan-Meier plots were presented. Results & conclusion: Our study fills an existing void by introducing a unique combination of unsupervised learning techniques and survival information on the clinician side, demonstrating the potential of survival clustering as a valuable tool in uncovering hidden structures based on distinct risk profiles.

Adaptive MCMC for Bayesian Variable Selection in Generalised Linear Models and Survival Models

Developing an efficient computational scheme for high-dimensional Bayesian variable selection in generalised linear models and survival models has always been a challenging problem due to the absence of closed-form solutions to the marginal likelihood. The Reversible Jump Markov Chain Monte Carlo (RJMCMC) approach can be employed to jointly sample models and coefficients, but the effective design of the trans-dimensional jumps of RJMCMC can be challenging, making it hard to implement. Alternatively, the marginal likelihood can be derived conditional on latent variables using a data-augmentation scheme (e.g., Pólya-gamma data augmentation for logistic regression) or using other estimation methods. However, suitable data-augmentation schemes are not available for every generalised linear model and survival model, and estimating the marginal likelihood using a Laplace approximation or a correlated pseudo-marginal method can be computationally expensive. In this paper, three main contributions are presented. Firstly, we present an extended Point-wise implementation of Adaptive Random Neighbourhood Informed proposal (PARNI) to efficiently sample models directly from the marginal posterior distributions of generalised linear models and survival models. Secondly, in light of the recently proposed approximate Laplace approximation, we describe an efficient and accurate estimation method for marginal likelihood that involves adaptive parameters. Additionally, we describe a new method to adapt the algorithmic tuning parameters of the PARNI proposal by replacing Rao-Blackwellised estimates with the combination of a warm-start estimate and the ergodic average. We present numerous numerical results from simulated data and eight high-dimensional genetic mapping data-sets to showcase the efficiency of the novel PARNI proposal compared with the baseline add-delete-swap proposal.

Quantification of subtype purity in Luminal A breast cancer predicts clinical characteristics and survival

PURPOSE

PAM50 profiling assigns each breast cancer to a single intrinsic subtype based on a bulk tissue sample. However, individual cancers may show evidence of admixture with an alternate subtype that could affect prognosis and treatment response. We developed a method to model subtype admixture using whole transcriptome data and associated it with tumor, molecular, and survival characteristics for Luminal A (LumA) samples.

METHODS

We combined TCGA and METABRIC cohorts and obtained transcriptome, molecular, and clinical data, which yielded 11,379 gene transcripts in common and 1,178 cases assigned to LumA. We used semi-supervised non-negative matrix factorization (ssNMF) to compute the subtype admixture proportions of the four major subtypes-pLumA, pLumB, pHER2, and pBasal-for each case and measured associations with tumor characteristics, molecular features, and survival.

RESULTS

Luminal A cases in the lowest versus highest quartile for pLumA transcriptomic proportion had a 27% higher prevalence of stage > 1, nearly a threefold higher prevalence of TP53 mutation, and a hazard ratio of 2.08 for overall mortality. We found positive associations between pHER2 and HER2 positivity by IHC or FISH; between pLumB and PR negativity; and between pBasal and younger age, node positivity, TP53 mutation, and EGFR expression. Predominant basal admixture, in contrast to predominant LumB or HER2 admixture, was not associated with shorter survival.

CONCLUSION

Bulk sampling for genomic analyses provides an opportunity to expose intratumor heterogeneity, as reflected by subtype admixture. Our results elucidate the striking extent of diversity among LumA cancers and suggest that determining the extent and type of admixture holds promise for refining individualized therapy. LumA cancers with a high degree of basal admixture appear to have distinct biological characteristics that warrant further study.

Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer

Established prognostic tests based on limited numbers of transcripts can identify high-risk breast cancer patients yet are approved only for individuals presenting with specific clinical features or disease characteristics. Deep learning algorithms could hold potential for stratifying patient cohorts based on full transcriptome data, yet the development of robust classifiers is hampered by the number of variables in omics datasets typically far exceeding the number of patients. To overcome this hurdle, we propose a classifier based on a data augmentation pipeline consisting of a Wasserstein generative adversarial network (GAN) with gradient penalty and an embedded auxiliary classifier to obtain a trained GAN discriminator (T-GAN-D). Applied to 1244 patients of the METABRIC breast cancer cohort, this classifier outperformed established breast cancer biomarkers in separating low- from high-risk patients (disease specific death, progression or relapse within 10 years from initial diagnosis). Importantly, the T-GAN-D also performed across independent, merged transcriptome datasets (METABRIC and TCGA-BRCA cohorts), and merging data improved overall patient stratification. In conclusion, the reiterative GAN-based training process allowed generating a robust classifier capable of stratifying low- vs high-risk patients based on full transcriptome data and across independent and heterogeneous breast cancer cohorts.

Integrative multi-omics networks identify PKCδ and DNA-PK as master kinases of glioblastoma subtypes and guide targeted cancer therapy

Despite producing a panoply of potential cancer-specific targets, the proteogenomic characterization of human tumors has yet to demonstrate value for precision cancer medicine. Integrative multi-omics using a machine-learning network identified master kinases responsible for effecting phenotypic hallmarks of functional glioblastoma subtypes. In subtype-matched patient-derived models, we validated PKCδ and DNA-PK as master kinases of glycolytic/plurimetabolic and proliferative/progenitor subtypes, respectively, and qualified the kinases as potent and actionable glioblastoma subtype-specific therapeutic targets. Glioblastoma subtypes were associated with clinical and radiomics features, orthogonally validated by proteomics, phospho-proteomics, metabolomics, lipidomics and acetylomics analyses, and recapitulated in pediatric glioma, breast and lung squamous cell carcinoma, including subtype specificity of PKCδ and DNA-PK activity. We developed a probabilistic classification tool that performs optimally with RNA from frozen and paraffin-embedded tissues, which can be used to evaluate the association of therapeutic response with glioblastoma subtypes and to inform patient selection in prospective clinical trials.

Mucinous carcinoma of the breast: distinctive histopathologic and genetic characteristics

Mucinous carcinoma is a rare histologic type of breast cancer that, when classified with favorable histology, can be treated with different therapeutic options. This study reviews the histologic findings of mucinous carcinoma that support or exclude favorable histology and emphasizes the necessity of an appropriate gross examination with radiologic findings for an accurate diagnosis. In addition, unusual findings such as micropapillary arrangements and lobular differentiation in mucinous carcinoma and their implications for prognosis and treatment are reviewed. Mucinous carcinoma involves upregulation of MUC2, a mucus-associated gene common in mucinous carcinoma of the breast as well as various other organs. In mucinous carcinoma, the fraction of genome altered and tumor mutation burden are lower than those of invasive carcinoma of no special type, the most common histology of breast cancer. In addition, the genetic alterations found in mucinous carcinoma are diverse, unlike the pathognomonic genetic alterations observed in other histologic types of breast cancer. These genetic features support the importance of conventional microscopic evaluations for the pathologic differential diagnosis of mucinous carcinoma of the breast in routine practice. A variety of breast lesions, including mucinous cystadenocarcinoma and mucocele-like lesions, as well as mucinous carcinoma from other organs, can mimic mucinous carcinoma of the breast. In order to obtain an accurate pathologic diagnosis, careful evaluation of the overall histopathologic characteristics and ancillary testing are required to provide information on appropriate treatment and prognosis.

Triple-Negative Breast Cancer Analysis Based on Metabolic Gene Classification and Immunotherapy

Triple negative breast cancer (TNBC) has negative expression of ER, PR and HER-2. TNBC shows high histological grade and positive rate of lymph node metastasis, easy recurrence and distant metastasis. Molecular typing based on metabolic genes can reflect deeper characteristics of breast cancer and provide support for prognostic evaluation and individualized treatment. Metabolic subtypes of TNBC samples based on metabolic genes were determined by consensus clustering. CIBERSORT method was applied to evaluate the score distribution and differential expression of 22 immune cells in the TNBC samples. Linear discriminant analysis (LDA) established a subtype classification feature index. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were generated to validate the performance of prognostic metabolic subtypes in different datasets. Finally, we used weighted correlation network analysis (WGCNA) to cluster the TCGA expression profile dataset and screen the co-expression modules of metabolic genes. Consensus clustering of the TCGA cohort/dataset obtained three metabolic subtypes (MC1, MC2, and MC3). The ROC analysis showed a high prognostic performance of the three clusters in different datasets. Specifically, MC1 had the optimal prognosis, MC3 had a poor prognosis, and the three metabolic subtypes had different prognosis. Consistently, the immune characteristic index established based on metabolic subtypes demonstrated that compared with the other two subtypes, MC1 had a higher IFNγ score, T cell lytic activity and lower angiogenesis score, T cell dysfunction and rejection score. TIDE analysis showed that MC1 patients were more likely to benefit from immunotherapy. MC1 patients were more sensitive to immune checkpoint inhibitors and traditional chemotherapy drugs Cisplatin, Paclitaxel, Embelin, and Sorafenib. Multiclass AUC based on RNASeq and GSE datasets were 0.85 and 0.85, respectively. Finally, based on co-expression network analysis, we screened 7 potential gene markers related to metabolic characteristic index, of which CLCA2, REEP6, SPDEF, and CRAT can be used to indicate breast cancer prognosis. Molecular classification related to TNBC metabolism was of great significance for comprehensive understanding of the molecular pathological characteristics of TNBC, contributing to the exploration of reliable markers for early diagnosis of TNBC and predicting metastasis and recurrence, improvement of the TNBC staging system, guiding individualized treatment.

A gene signature consisting of ubiquitin ligases and deubiquitinating enzymes of SKP2 is associated with clinical outcome in breast cancer

The ubiquitination of SKP2, an oncoprotein, is controlled by its E3 ligases, including APC/C^FZR1 and deubiquitinases such as USP10. We identified a two-gene signature for the ubiquitination of SKP2, consisting of the copy number of FZR1 compared to the copy number of USP10. The signature reflects the level of SKP2 activity, stratifying BC patients into two groups with significantly different protein levels of SKP2 ubiquitination substrate p27 (t-test p < 0.01) and recapitulating the expression patterns of SKP2 between tumor and normal tissue (Spearman’s ρ = 0.39.) The signature is also highly associated with clinical outcome in luminal BC but not other subtypes, characterizing patients into two groups with significantly different overall survival times (log-rank p = 0.006). In addition, it is dramatically associated with tumor grade (Chi-squared p = 6.7 × 10⁻³), stage (Chi-squared p = 1.6 × 10⁻¹¹), and the number of positive lymph nodes (negative binomial regression coefficient p = 2.0 × 10⁻³). Our study provides a rationale for targeting the SKP2 ubiquitination pathway in luminal BC and for further investigation of the use of ubiquitinase/deubiquitinase genes as prognosis and treatment biomarkers.

CDK4/6 inhibitors: A potential therapeutic approach for triple negative breast cancer

Triple negative breast cancer (TNBC) cells lack expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER-2). Thus, TNBC does not respond to hormone-based therapy. TNBC is also an aggressive subtype associated with poorer prognoses compared to other breast cancers. Conventional chemotherapeutics are used to manage TNBC although systemic relapse is common with limited benefits being reported as well as adverse events being documented. Here, we discuss current therapies for TNBC in the neo- and adjuvant settings, as well as recent advancements in the targeting of PD-L1-positive tumors and inclusion of PARP inhibitors for TNBC patients with BRCA mutations. The recent development of cyclin-dependent kinase (CDK) 4/6 inhibitors in ER-positive breast cancers has demonstrated significant improvements in progression free survival in patients. Here, we review preclinical data of CDK 4/6 inhibitors and describe current clinical trials assessing these in TNBC disease.

The NFIB-ERO1A axis promotes breast cancer metastatic colonization of disseminated tumour cells

Metastasis is the main cause of deaths related to solid cancers. Active transcriptional programmes are known to regulate the metastatic cascade but the molecular determinants of metastatic colonization remain elusive. Using an inducible piggyBac (PB) transposon mutagenesis screen, we have shown that overexpression of the transcription factor nuclear factor IB (NFIB) alone is sufficient to enhance primary mammary tumour growth and lung metastatic colonization. Mechanistically and functionally, NFIB directly increases expression of the oxidoreductase ERO1A, which enhances HIF1α-VEGFA-mediated angiogenesis and colonization, the last and fatal step of the metastatic cascade. NFIB is thus clinically relevant: it is preferentially expressed in the poor-prognostic group of basal-like breast cancers, and high expression of the NFIB/ERO1A/VEGFA pathway correlates with reduced breast cancer patient survival.

Altered gene expression in glycolysis-cholesterol synthesis axis correlates with outcome of triple-negative breast cancer

Identification of molecular subtypes of clinically resectable triple-negative breast cancer (TNBC) is of great importance to achieve better clinical outcomes. Inter- and intratumor metabolic heterogeneity improves cancer survival, and the interaction of various metabolic pathways may affect treatment outcome of TNBC. We speculated that TNBC can be categorized into prognostic metabolic subtype according to the expression changes of glycolysis and cholesterol synthesis. The genome, transcriptome, and clinical data were downloaded from the Cancer Genome Atlas and Molecular Taxonomy of Breast Cancer International Consortium and subsequently analyzed by integrated bioinformatics methods. Four subtypes, namely, glycolytic, cholesterogenic, quiescent, and mixed, were classified according to the normalized median expressions of the genes involved in glycolysis and cholesterol synthesis. In the four subtypes, the cholesterogenic type was correlated with the shortest median survival (log rank P = 0.044), while patients with high-expressed glycolytic genes tended to have a longer survival. Tumors with PIK3CA amplification and dynein axonemal heavy chain 2 deletion exhibited higher expressions of cholesterogenic genes than other mutant oncogenes. The expressions of mitochondrial pyruvate carrier MPC1 and MPC2 were the lowest in quiescent tumor, and MPC2 expression was higher in cholesterogenic tumor compared with glycolytic or quiescent tumor (t-test P < 0.001). Glycolytic and cholesterogenic gene expressions were related to the expressions of prognostic genes in some other types of cancers. Classification of glycolytic and cholesterogenic pathways according to metabolic characteristics provides a new understanding to previously identified subtypes of TNBC and could improve personalized treatments based on tumor metabolic profiles.

Tumor microenvironment characterization in triple-negative breast cancer identifies prognostic gene signature

We aimed to elucidate the landscape of tumor microenvironment (TME) in triple-negative breast cancer (TNBC). Cohorts from Gene Expression Omnibus database (N = 107) and METABRIC (N = 299) were used as the training set and validation set, respectively. TME was evaluated via single-sample gene set enrichment analysis, and unsupervised clustering was used for cluster identification. Consequently, TNBC was classified into two distinct TME clusters (Cluster 1 and Cluster 2) according to predefined immune-related terms. Cluster 1 was characterized by low immune infiltration with poor prognosis; whereas, Cluster 2 was characterized by high immune infiltration with better survival probability. Further, Cluster 1 had larger tumor volumes, while Cluster 2 had smaller tumor volumes. Finally, a TME signature for prognosis stratification in TNBC was developed and validated. In summary, we comprehensively evaluated the TME of TNBC and constructed a TME signature that correlated with prognosis. Our results provide new insights for the immunotherapy of TNBC.

Molecular intrinsic versus clinical subtyping in breast cancer: A comprehensive review

Breast cancer is a heterogeneous disease manifesting diversity at the molecular, histological and clinical level. The development of breast cancer classification was centered on informing clinical decisions. The current approach to the classification of breast cancer, which categorizes this disease into clinical subtypes based on the detection of estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and proliferation marker Ki67, is not ideal. This is manifested as a heterogeneity of therapeutic responses and outcomes within the clinical subtypes. The newer classification model, based on gene expression profiling (intrinsic subtyping) informs about transcriptional responses downstream from IHC single markers, revealing deeper appreciation for the disease heterogeneity and capturing tumor biology in a more comprehensive way than an expression of a single protein or gene alone. While accumulating evidences suggest that intrinsic subtypes provide clinically relevant information beyond clinical surrogates, it is imperative to establish whether the current conventional immunohistochemistry-based clinical subtyping approach could be improved by gene expression profiling and if this approach has a potential to translate into clinical practice.

Spliceosome-Associated microRNAs Signify Breast Cancer Cells and Portray Potential Novel Nuclear Targets

MicroRNAs (miRNAs) act as negative regulators of gene expression in the cytoplasm. Previous studies have identified the presence of miRNAs in the nucleus. Here we study human breast cancer-derived cell-lines (MCF-7 and MDA-MB-231) and a non-tumorigenic cell-line (MCF-10A) and compare their miRNA sequences at the spliceosome fraction (SF). We report that the levels of miRNAs found in the spliceosome, their identity, and pre-miRNA segmental composition are cell-line specific. One such miRNA is miR-7704 whose genomic position overlaps HAGLR, a cancer-related lncRNA. We detected an inverse expression of miR-7704 and HAGLR in the tested cell lines. Specifically, inhibition of miR-7704 caused an increase in HAGLR expression. Furthermore, elevated levels of miR-7704 slightly altered the cell-cycle in MDA-MB-231. Altogether, we show that SF-miR-7704 acts as a tumor-suppressor gene with HAGLR being its nuclear target. The relative levels of miRNAs found in the spliceosome fractions (e.g., miR-100, miR-30a, and let-7 family) in non-tumorigenic relative to cancer-derived cell-lines was monitored. We found that the expression trend of the abundant miRNAs in SF was different from that reported in the literature and from the observation of large cohorts of breast cancer patients, suggesting that many SF-miRNAs act on targets that are different from the cytoplasmic ones. Altogether, we report on the potential of SF-miRNAs as an unexplored route for cancerous cell state.

Advances in the Molecular Taxonomy of Breast Cancer

Breast cancers are heterogeneous with variable morphologic features, biologic behavior and response to therapy. Traditional histopathologic features such as size, grade, and lymph node status may be used to provide a general estimate of outcome, stratifying patients into broad prognostic groups with prescribed guidelines for therapy. With this approach however, up to 85% of breast cancer patients are overtreated, and at the other end of the spectrum, 20% of patients succumb to their disease despite receiving maximum therapy. The current routine testing for the Estrogen receptor (ER) and HER2 growth factor receptor (HER2) represents the earliest attempts to provide a targeted approach to breast cancer therapy, based on molecular drivers of the disease. The pioneering works by Perou and Sorlie et al using global gene expression profiling introduced a molecular taxonomy of breast cancer with associated prognostic implications. The Luminal, HER2-enriched, and Basal-like intrinsic subtypes are generally characterized by the presence or absence of ER and HER2. They have been further analyzed and refined using integration of genomic and transcriptomic data made possible by advancements in high throughput molecular techniques and bioinformatics. Indeed, an increased understanding of the genomic landscape of these subtypes, and the molecular basis of breast cancer growth regulation, holds the promise of a more personalized patient selection for specific targeted therapies and improved outcomes.

The Genomic Landscape of Mucinous Breast Cancer

Mucinous carcinoma of the breast (MCB) is a rare histologic form of estrogen receptor (ER)-positive/HER2-negative breast cancer (BC) characterized by tumor cells floating in lakes of mucin. We assessed the genomic landscape of 32 MCBs by whole-exome sequencing and/or RNA-sequencing. GATA3 (23.8%), KMT2C (19.0%), and MAP3K1 (14.3%) were the most frequently mutated genes in pure MCBs. In addition, two recurrent but not pathognomonic fusion genes, OAZ1-CSNK1G2 and RFC4-LPP, were detected in 3/31 (9.7%) and 2/31 (6.5%) samples, respectively. Compared with ER-positive/HER2-negative common forms of BC, MCBs displayed lower PIK3CA and TP53 mutation rates and fewer concurrent 1q gains and 16q losses. Clonal decomposition analysis of the mucinous and ductal components independently microdissected from five mixed MCBs revealed that they are clonally related and evolve following clonal selection or parallel evolution. Our findings indicate that MCB represents a genetically distinct ER-positive/HER2-negative form of BC.

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.

Re-definition of claudin-low as a breast cancer phenotype

The claudin-low breast cancer subtype is defined by gene expression characteristics and encompasses a remarkably diverse range of breast tumors. Here, we investigate genomic, transcriptomic, and clinical features of claudin-low breast tumors. We show that claudin-low is not simply a subtype analogous to the intrinsic subtypes (basal-like, HER2-enriched, luminal A, luminal B and normal-like) as previously portrayed, but is a complex additional phenotype which may permeate breast tumors of various intrinsic subtypes. Claudin-low tumors are distinguished by low genomic instability, mutational burden and proliferation levels, and high levels of immune and stromal cell infiltration. In other aspects, claudin-low tumors reflect characteristics of their intrinsic subtype. Finally, we explore an alternative method for identifying claudin-low tumors and thereby uncover potential weaknesses in the established claudin-low classifier. In sum, these findings elucidate the heterogeneity in claudin-low breast tumors, and substantiate a re-definition of claudin-low as a cancer phenotype.

In breast cancer, the claudin-low breast cancer subtype is remarkably diverse. Here, the authors propose that claudin-low is not a classical intrinsic breast cancer subtype, but rather a complex additional phenotype that can occur across intrinsic subtypes.

Breast Cancer: A Molecularly Heterogenous Disease Needing Subtype-Specific Treatments

Breast cancer is the most commonly occurring cancer in women. There were over two-million new cases in world in 2018. It is the second leading cause of death from cancer in western countries. At the molecular level, breast cancer is a heterogeneous disease, which is characterized by high genomic instability evidenced by somatic gene mutations, copy number alterations, and chromosome structural rearrangements. The genomic instability is caused by defects in DNA damage repair, transcription, DNA replication, telomere maintenance and mitotic chromosome segregation. According to molecular features, breast cancers are subdivided in subtypes, according to activation of hormone receptors (estrogen receptor and progesterone receptor), of human epidermal growth factors receptor 2 (HER2), and or BRCA mutations. In-depth analyses of the molecular features of primary and metastatic breast cancer have shown the great heterogeneity of genetic alterations and their clonal evolution during disease development. These studies have contributed to identify a repertoire of numerous disease-causing genes that are altered through different mutational processes. While early-stage breast cancer is a curable disease in about 70% of patients, advanced breast cancer is largely incurable. However, molecular studies have contributed to develop new therapeutic approaches targeting HER2, CDK4/6, PI3K, or involving poly(ADP-ribose) polymerase inhibitors for BRCA mutation carriers and immunotherapy.

Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer

Genomic alterations shape cell phenotypes and the structure of tumor ecosystems in poorly defined ways. To investigate these relationships, we used imaging mass cytometry to quantify the expression of 37 proteins with subcellular spatial resolution in 483 tumors from the METABRIC cohort. Single-cell analysis revealed cell phenotypes spanning epithelial, stromal and immune types. Distinct combinations of cell phenotypes and cell-cell interactions were associated with genomic subtypes of breast cancer. Epithelial luminal cell phenotypes separated into those predominantly impacted by mutations and those affected by copy number aberrations. Several features of tumor ecosystems, including cellular neighborhoods, were linked to prognosis, illustrating their clinical relevance. In summary, systematic analysis of single-cell phenotypic and spatial correlates of genomic alterations in cancer revealed how genomes shape both the composition and architecture of breast tumor ecosystems and will enable greater understanding of the phenotypic impact of genomic alterations.

Cancer Treatment in the Genomic Era

The complexity of human cancer underlies its devastating clinical consequences. Drugs designed to target the genetic alterations that drive cancer have improved the outcome for many patients, but not the majority of them. Here, we review the genomic landscape of cancer, how genomic data can provide much more than a sum of its parts, and the approaches developed to identify and validate genomic alterations with potential therapeutic value. We highlight notable successes and pitfalls in predicting the value of potential therapeutic targets and discuss the use of multi-omic data to better understand cancer dependencies and drug sensitivity. We discuss how integrated approaches to collecting, curating, and sharing these large data sets might improve the identification and prioritization of cancer vulnerabilities as well as patient stratification within clinical trials. Finally, we outline how future approaches might improve the efficiency and speed of translating genomic data into clinically effective therapies and how the use of unbiased genome-wide information can identify novel predictive biomarkers that can be either simple or complex.

Micropapillary variant of mucinous carcinoma of the breast shows genetic alterations intermediate between those of mucinous carcinoma and micropapillary carcinoma

AIMS

Micropapillary variant of mucinous carcinoma of the breast (MPMC) is a rare histological form of oestrogen receptor (ER)-positive invasive carcinoma that is characterised by micropapillary clusters of tumour cells in lakes of extracellular mucin. The aims of this study were to determine the genetic alterations underpinning MPMCs, and to determine whether they overlap with those of mucinous carcinomas and/or invasive micropapillary carcinomas.

METHODS AND RESULTS

DNA from five MPMCs was subjected to whole-exome sequencing. Somatic mutations, copy number alterations and mutational signatures were determined with state-of-the-art bioinformatics methods. No mutations in genes significantly mutated in breast cancer, including TP53, PIK3CA, GATA3, and MAP3K1, were detected. We identified copy number alterations that have been reported in invasive micropapillary carcinomas, such as recurrent gains in 1q, 6p, 8q, and 10q, and recurrent losses in 16q, 11q, and 13q, as well as a recurrent 8p12-8p11.2 amplification encompassing FGFR1. Like mucinous carcinomas, three of the five MPMCs analysed lacked PIK3CA mutations, 1q gains, and 16q losses, which are the hallmark genetic alterations of ER-positive breast cancers, whereas two MPMCs harboured 16q losses and/or a complex pattern of copy number alterations similar to those found in breast-invasive micropapillary carcinomas.

CONCLUSIONS

MPMCs are heterogeneous at the genetic level; some tumours show a pattern of somatic genetic alterations similar to those of mucinous carcinomas, whereas others resemble invasive micropapillary carcinomas at the genetic level. These findings suggest that MPMCs may not constitute one histological subtype, but rather a convergent phenotype that can stem from mucinous carcinomas or invasive micropapillary carcinomas.

Array comparative genomic hybridization analysis discloses chromosome copy number alterations as indicators of patient outcome in lymph node-negative breast cancer

BACKGROUND

Patients with lymph node metastasis-negative (pN0) invasive breast cancer have favorable outcomes following initial treatment. However, false negatives which occur during routine histologic examination of lymph nodes are reported to underestimate the clinical stage of disease. To identify a high-risk group in pN0 invasive breast cancer, we examined copy number alterations (CNAs) of 800 cancer-related genes.

METHODS

Using array-based comparative genomic hybridization (CGH) in 51 pN0 cases (19 relapsed and 32 non-relapsed cases), the positivities of specific gene CNAs in the relapsed and non-relapsed groups were compared. An unsupervised hierarchical cluster analysis was then performed to identify case groups that were correlated with patient outcomes.

RESULTS

The cluster analysis identified three distinct clusters of cases: groups 1, 2, and 3. The major component was triple-negative cases (69%, 9 of 13) in group 1, luminal B-like (57%, 13 of 23) and HER2-overexpressing (26%, 6 of 23) subtypes in group 2, and luminal A-like subtype (60%, 9 of 15) in group 3. Among all 51 cases, those in group 1 showed significantly worse overall survival (OS) than group 2 (p = 0.014), and 5q15 loss was correlated with worse OS (p = 0.017). Among 19 relapsed cases, both OS and relapse-free survival (RFS) rates were significantly lower in group 1 than in group 2 (p = 0.0083 and 0.0018, respectively), and 5q15 loss, 12p13.31 gain, and absence of 16p13.3 gain were significantly correlated with worse OS and RFS (p = 0.019 and 0.0027, respectively).

CONCLUSIONS

As the target genes in these loci, NR2F1 (5q15), TNFRSF1A (12p13.31), and ABCA3 (16p13.3) were examined. 5q15 loss, 12p13.31 gain, and absence of 16q13.3 gain were potential indicators of high-risk recurrence and aggressive clinical behavior of pN0 invasive breast cancers.