A three-gene model to robustly identify breast cancer molecular subtypes

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.

Breast cancer histologic grading using digital microscopy: concordance and outcome association

Aims

Virtual microscopy utilising digital whole slide imaging (WSI) is increasingly used in breast pathology. Histologic grade is one of the strongest prognostic factors in breast cancer (BC). This study aims at investigating the agreement between BC grading using traditional light microscopy (LM) and digital WSI with consideration of reproducibility and impact on outcome prediction.

Methods

A large (n=1675) well-characterised cohort of BC originally graded by LM was re-graded using WSI. Two separate virtual-based grading sessions (V1 and V2) were performed with a 3-month washout period. Outcome was assessed using BC-specific and distant metastasis-free survival.

Results

The concordance between LM grading and WSI was strong (LM/WSI Cramer’s V: V1=0.576, and V2=0.579). The agreement regarding grade components was as follows: tubule formation=0.538, pleomorphism=0.422 and mitosis=0.514. Greatest discordance was observed between adjacent grades, whereas high/low grade discordance was uncommon (1.5%). The intraobserver agreement for the two WSI sessions was substantial for grade (V1/V2 Cramer’s V=0.676; kappa=0.648) and grade components (Cramer’s V T=0.628, p=0.573 and M=0.580). Grading using both platforms showed strong association with outcome (all p values <0.001). Although mitotic scores assessed using both platforms were strongly associated with outcome, WSI tends to underestimate mitotic counts.

Conclusions

Virtual microscopy is a reliable and reproducible method for assessing BC histologic grade. Regardless of the observer or assessment platform, histologic grade is a significant predictor of outcome. Continuing advances in imaging technology could potentially provide improved performance of WSI BC grading and in particular mitotic count assessment.

Training replicable predictors in multiple studies

This article considers replicability of the performance of predictors across studies. We suggest a general approach to investigating this issue, based on ensembles of prediction models trained on different studies. We quantify how the common practice of training on a single study accounts in part for the observed challenges in replicability of prediction performance. We also investigate whether ensembles of predictors trained on multiple studies can be combined, using unique criteria, to design robust ensemble learners trained upfront to incorporate replicability into different contexts and populations.

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

The integration of genomic and transcriptomic profiles of 2000 breast tumours from the METABRIC [Molecular Taxonomy of Breast Cancer International Consortium] cohort revealed ten subtypes, termed integrative clusters (IntClust/s), characterised by distinct genomic drivers. Central histopathology (N = 1643) review was undertaken to explore the relationship between these ten molecular subtypes and traditional clinicopathological features. IntClust subtypes were significantly associated with histological type, tumour grade, receptor status, and lymphocytic infiltration (p < 0.0001). Lymph node status and Nottingham Prognostic Index [NPI] categories were also significantly associated with IntClust subtype. IntClust 3 was enriched for tubular and lobular carcinomas, the latter largely accounting for the association with CDH1 mutations in this cluster. Mucinous carcinomas were not present in IntClusts 5 or 10, but did not show an association with any of the remaining IntClusts. In contrast, medullary-like cancers were associated with IntClust 10 (15/26). Hormone receptor-positive tumours were scattered across all IntClusts. IntClust 5 was dominated by HER2 positivity (127/151), including both hormone receptor-positive (60/72) and hormone receptor-negative tumours (67/77). Triple-negative tumours comprised the majority of IntClust 10 (132/159) and around a quarter of IntClust 4 (52/217). Whilst the ten IntClust subtypes of breast cancer show characteristic patterns of association with traditional clinicopathological variables, no IntClust can be adequately identified by these variables alone. Hence, the addition of genomic stratification has the potential to enhance the biological relevance of the current clinical evaluation and facilitate genome-guided therapeutic strategies.

A critical comparison of topology-based pathway analysis methods

One of the aims of high-throughput gene/protein profiling experiments is the identification of biological processes altered between two or more conditions. Pathway analysis is an umbrella term for a multitude of computational approaches used for this purpose. While in the beginning pathway analysis relied on enrichment-based approaches, a newer generation of methods is now available, exploiting pathway topologies in addition to gene/protein expression levels. However, little effort has been invested in their critical assessment with respect to their performance in different experimental setups. Here, we assessed the performance of seven representative methods identifying differentially expressed pathways between two groups of interest based on gene expression data with prior knowledge of pathway topologies: SPIA, PRS, CePa, TAPPA, TopologyGSA, Clipper and DEGraph. We performed a number of controlled experiments that investigated their sensitivity to sample and pathway size, threshold-based filtering of differentially expressed genes, ability to detect target pathways, ability to exploit the topological information and the sensitivity to different pre-processing strategies. We also verified type I error rates and described the influence of overexpression of single genes, gene sets and topological motifs of various sizes on the detection of a pathway as differentially expressed. The results of our experiments demonstrate a wide variability of the tested methods. We provide a set of recommendations for an informed selection of the proper method for a given data analysis task.

A novel genomic signature predicting FDG uptake in diverse metastatic tumors

BACKGROUND

Building a universal genomic signature predicting the intensity of FDG uptake in diverse metastatic tumors may allow us to understand better the biological processes underlying this phenomenon and their requirements of glucose uptake.

METHODS

A balanced training set (n = 71) of metastatic tumors including some of the most frequent histologies, with matched PET/CT quantification measurements and whole human genome gene expression microarrays, was used to build the signature. Selection of microarray features was carried out exclusively on the basis of their strong association with FDG uptake (as measured by SUVmean35) by means of univariate linear regression. A thorough bioinformatics study of these genes was performed, and multivariable models were built by fitting several state of the art regression techniques to the training set for comparison.

RESULTS

The 909 probes with the strongest association with the SUVmean35 (comprising 742 identifiable genes and 62 probes not matched to a symbol) were used to build the signature. Partial least squares using three components (PLS-3) was the best performing model in the training dataset cross-validation (root mean square error, RMSE = 0.443) and was validated further in an independent validation dataset (n = 13) obtaining a performance within the 95% CI of that obtained in the training dataset (RMSE = 0.645). Significantly overrepresented biological processes correlating with the SUVmean35 were identified beyond glycolysis, such as ribosome biogenesis and DNA replication (correlating with a higher SUVmean35) and cytoskeleton reorganization and autophagy (correlating with a lower SUVmean35).

CONCLUSIONS

PLS-3 is a signature predicting accurately the intensity of FDG uptake in diverse metastatic tumors. FDG-PET might help in the design of specific targeted therapies directed to counteract the identified malignant biological processes more likely activated in a tumor as inferred from the SUVmean35 and also from its variations in response to antineoplastic treatments.

Comparison of multiple transcriptomes exposes unified and divergent features of quiescent and activated skeletal muscle stem cells

BACKGROUND

Skeletal muscle satellite (stem) cells are quiescent in adult mice and can undergo multiple rounds of proliferation and self-renewal following muscle injury. Several labs have profiled transcripts of myogenic cells during the developmental and adult myogenesis with the aim of identifying quiescent markers. Here, we focused on the quiescent cell state and generated new transcriptome profiles that include subfractionations of adult satellite cell populations, and an artificially induced prenatal quiescent state, to identify core signatures for quiescent and proliferating.

METHODS

Comparison of available data offered challenges related to the inherent diversity of datasets and biological conditions. We developed a standardized workflow to homogenize the normalization, filtering, and quality control steps for the analysis of gene expression profiles allowing the identification up- and down-regulated genes and the subsequent gene set enrichment analysis. To share the analytical pipeline of this work, we developed Sherpa, an interactive Shiny server that allows multi-scale comparisons for extraction of desired gene sets from the analyzed datasets. This tool is adaptable to cell populations in other contexts and tissues.

RESULTS

A multi-scale analysis comprising eight datasets of quiescent satellite cells had 207 and 542 genes commonly up- and down-regulated, respectively. Shared up-regulated gene sets include an over-representation of the TNFα pathway via NFKβ signaling, Il6-Jak-Stat3 signaling, and the apical surface processes, while shared down-regulated gene sets exhibited an over-representation of Myc and E2F targets and genes associated to the G2M checkpoint and oxidative phosphorylation. However, virtually all datasets contained genes that are associated with activation or cell cycle entry, such as the immediate early stress response genes Fos and Jun. An empirical examination of fixed and isolated satellite cells showed that these and other genes were absent in vivo, but activated during procedural isolation of cells.

CONCLUSIONS

Through the systematic comparison and individual analysis of diverse transcriptomic profiles, we identified genes that were consistently differentially expressed among the different datasets and shared underlying biological processes key to the quiescent cell state. Our findings provide impetus to define and distinguish transcripts associated with true in vivo quiescence from those that are first responding genes due to disruption of the stem cell niche.

Identification of cancer genes that are independent of dominant proliferation and lineage programs

Large, multidimensional “landscaping” projects have provided datasets that can be mined to identify potential targets for subgroups of tumors. Here, we analyzed genomic and transcriptomic data from human breast tumors to identify genes whose expression is enriched in tumors harboring specific genetic alterations. However, this analysis revealed that two other factors, proliferation rate and tumor lineage, are more dominant factors in shaping tumor transcriptional programs than genetic alterations. This discovery shifted our attention to identifying genes that are independent of the dominant proliferation and lineage programs. A small subset of these genes represents candidate targets for combination cancer therapies because they are druggable, maintained after treatment with chemotherapy, essential for cell line survival, and elevated in drug-resistant stem-like cancer cells.

Large, multidimensional cancer datasets provide a resource that can be mined to identify candidate therapeutic targets for specific subgroups of tumors. Here, we analyzed human breast cancer data to identify transcriptional programs associated with tumors bearing specific genetic driver alterations. Using an unbiased approach, we identified thousands of genes whose expression was enriched in tumors with specific genetic alterations. However, expression of the vast majority of these genes was not enriched if associations were analyzed within individual breast tumor molecular subtypes, across multiple tumor types, or after gene expression was normalized to account for differences in proliferation or tumor lineage. Together with linear modeling results, these findings suggest that most transcriptional programs associated with specific genetic alterations in oncogenes and tumor suppressors are highly context-dependent and are predominantly linked to differences in proliferation programs between distinct breast cancer subtypes. We demonstrate that such proliferation-dependent gene expression dominates tumor transcriptional programs relative to matched normal tissues. However, we also identified a relatively small group of cancer-associated genes that are both proliferation- and lineage-independent. A subset of these genes are attractive candidate targets for combination therapy because they are essential in breast cancer cell lines, druggable, enriched in stem-like breast cancer cells, and resistant to chemotherapy-induced down-regulation.

A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity

Breast cancer (BC) comprises multiple distinct subtypes that differ genetically, pathologically, and clinically. Here, we describe a robust protocol for long-term culturing of human mammary epithelial organoids. Using this protocol, >100 primary and metastatic BC organoid lines were generated, broadly recapitulating the diversity of the disease. BC organoid morphologies typically matched the histopathology, hormone receptor status, and HER2 status of the original tumor. DNA copy number variations as well as sequence changes were consistent within tumor-organoid pairs and largely retained even after extended passaging. BC organoids furthermore populated all major gene-expression-based classification groups and allowed in vitro drug screens that were consistent with in vivo xeno-transplantations and patient response. This study describes a representative collection of well-characterized BC organoids available for cancer research and drug development, as well as a strategy to assess in vitro drug response in a personalized fashion.

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.

p-STAT3 in luminal breast cancer: Integrated RNA-protein pooled analysis and results from the BIG 2-98 phase III trial

In the present study, in order to investigate the role of signal transducer and activator of transcription 3 (STAT3) in estrogen receptor (ER)-positive breast cancer prognosis, we evaluated the phosphorylated STAT3 (p-STAT3) status and investigated its effect on the outcome in a pooled analysis and in a large prospective adjuvant trial. By using the TCGA repository, we developed gene signatures that reflected the level of p-STAT3. Using pooled analysis of the expression data from luminal breast cancer patients, we assessed the effects of the p-STAT3 expression signature on prognosis. We further validated the p-STAT3 prognostic effect using immunohistochemistry (IHC) and immunofluorescence staining of p-STAT3 tissue microarrays from a large randomised prospective trial. Our analysis demonstrated that p-STAT3 expression was elevated in luminal A-type breast cancer (Kruskal-Wallis test, P<10e-10) and was significantly associated with a good prognosis (log-rank, P<10e-10). Notably, the p-STAT3 expression signature identified patients with a good prognosis irrespective of the luminal subtype (log-rank: luminal A, P=0.026; luminal B, P=0.006). p-STAT3 staining by IHC in the stroma or tumour was detected in 174 out of 610 ER-positive samples (28.5%) from the BIG 2-98 randomised trial. With a median follow-up of 10.1 years, p-STAT3 was associated with a reduced risk of recurrence in ER-positive/HER2-negative breast cancer (Cox univariate HR, 0.66; 95% CI, 0.44-0.98; P=0.04). On the whole, our data indicate that p-STAT3 is associated with an improved outcome in ER-positive breast cancer.

Effect of Mammography Screening on Mortality by Histological Grade

Background: It has been asserted that mammography screening preferentially benefits those with less aggressive cancers, with lesser or no impact on more rapidly progressing and therefore more life-threatening tumors.Methods: We utilized data from the Swedish Two-County Trial, which randomized 77,080 women ages 40 to 74 to invitation to screening and 55,985 for usual care. We tabulated cancers by histologic grade and then compared mortality from cancers specific to histologic grade between the invited and control group using Poisson regression, with specific interest in the effect on mortality from grade 3 cancers. We used incidence-based mortality from tumors diagnosed within the screening phase of the trial. Finally, we cross-tabulated grade with tumor size and node status, to assess downstaging within tumor grades.Results: There was a major reduction in mortality from grade 3 tumors (RR = 0.65; 95% CI, 0.53-0.80; P < 0.001), and more deaths prevented from grade 3 tumors (n = 95) than grade 1 and 2 tumors combined (n = 48) in the invited group. The proportions of tumors ≥15 mm or larger and node-positive tumors were substantially reduced in the grade 3 tumors in the invited group.Conclusions: The combination of prevention of tumors progressing to grade 3 and detection at smaller sizes and lesser rates of lymph node metastases within grade 3 tumors results in a substantial number of deaths from grade 3 cancers being prevented by invitation to mammographic screening.Impact: Mammography screening prevents deaths from aggressive cancers. Cancer Epidemiol Biomarkers Prev; 27(2); 154-7. ©2017 AACR.

Analysis of breast cancer subtypes by AP-ISA biclustering

BACKGROUND

Gene expression profiling has led to the definition of breast cancer molecular subtypes: Basal-like, HER2-enriched, LuminalA, LuminalB and Normal-like. Different subtypes exhibit diverse responses to treatment. In the past years, several traditional clustering algorithms have been applied to analyze gene expression profiling. However, accurate identification of breast cancer subtypes, especially within highly variable LuminalA subtype, remains a challenge. Furthermore, the relationship between DNA methylation and expression level in different breast cancer subtypes is not clear.

RESULTS

In this study, a modified ISA biclustering algorithm, termed AP-ISA, was proposed to identify breast cancer subtypes. Comparing with ISA, AP-ISA provides the optimized strategy to select seeds and thresholds in the circumstance that prior knowledge is absent. Experimental results on 574 breast cancer samples were evaluated using clinical ER/PR information, PAM50 subtypes and the results of five peer to peer methods. One remarkable point in the experiment is that, AP-ISA divided the expression profiles of the luminal samples into four distinct classes. Enrichment analysis and methylation analysis showed obvious distinction among the four subgroups. Tumor variability within the Luminal subtype is observed in the experiments, which could contribute to the development of novel directed therapies.

CONCLUSIONS

Aiming at breast cancer subtype classification, a novel biclustering algorithm AP-ISA is proposed in this paper. AP-ISA classifies breast cancer into seven subtypes and we argue that there are four subtypes in luminal samples. Comparison with other methods validates the effectiveness of AP-ISA. New genes that would be useful for targeted treatment of breast cancer were also obtained in this study.

Gene expression modules in primary breast cancers as risk factors for organotropic patterns of first metastatic spread: a case control study

BACKGROUND

Metastases from primary breast cancers can involve single or multiple organs at metastatic disease diagnosis. Molecular risk factors for particular patterns of metastastic spread in a clinical population are limited.

METHODS

A case-control design including 1357 primary breast cancers was used to study three distinct clinical patterns of metastasis, which occur within the first six months of metastatic disease: bone and visceral metasynchronous spread, bone-only, and visceral-only metastasis. Whole-genome expression profiles were obtained using whole genome (WG)-DASL assays from formalin-fixed paraffin-embedded (FFPE) samples. A systematic protocol was developed for handling FFPE samples together with stringent data quality controls to identify robust expression profiling data. A panel of published and novel gene sets were tested for association with these specific patterns of metastatic spread and odds ratios (ORs) were calculated.

RESULTS

Metasynchronous metastasis to bone and viscera was found in all intrinsic breast cancer subtypes, while immunohistochemically (IHC)-defined receptor status and specific IntClust subgroups were risk factors for visceral-only or bone-only first metastases. Among gene modules, those related to proliferation increased the risk of metasynchronous metastasis (OR (95% CI) = 2.3 (1.1-4.8)) and visceral-only first metastasis (OR (95% CI) = 2.5 (1.2-5.1)) but not bone-only metastasis (OR (95% CI) = 0.97 (0.56-1.7)). A 21-gene module (BV) was identified in estrogen-receptor-positive breast cancers with metasynchronous metastasis to bone and viscera (area under the curve = 0.77), and its expression increased the risk of bone and visceral metasynchronous spread in this population. BV was further orthogonally validated with NanoString nCounter in primary breast cancers, and was reproducible in their matched lymph nodes metastases and an external cohort.

CONCLUSION

This case-control study of WG-DASL global expression profiles from FFPE tumour samples, after careful quality control and RNA selection, revealed that gene modules in the primary tumour have differing risks for clinical patterns of metasynchronous first metastases. Moreover, a novel gene module was identified as a putative risk factor for metasynchronous bone and visceral first metastatic spread, with potential implications for disease monitoring and treatment planning.

Identifying gene-environment interactions for prognosis using a robust approach

For many complex diseases, prognosis is of essential importance. It has been shown that, beyond the main effects of genetic (G) and environmental (E) risk factors, gene-environment (G × E) interactions also play a critical role. In practical data analysis, part of the prognosis outcome data can have a distribution different from that of the rest of the data because of contamination or a mixture of subtypes. Literature has shown that data contamination as well as a mixture of distributions, if not properly accounted for, can lead to severely biased model estimation. In this study, we describe prognosis using an accelerated failure time (AFT) model. An exponential squared loss is proposed to accommodate data contamination or a mixture of distributions. A penalization approach is adopted for regularized estimation and marker selection. The proposed method is realized using an effective coordinate descent (CD) and minorization maximization (MM) algorithm. The estimation and identification consistency properties are rigorously established. Simulation shows that without contamination or mixture, the proposed method has performance comparable to or better than the nonrobust alternative. However, with contamination or mixture, it outperforms the nonrobust alternative and, under certain scenarios, is superior to the robust method based on quantile regression. The proposed method is applied to the analysis of TCGA (The Cancer Genome Atlas) lung cancer data. It identifies interactions different from those using the alternatives. The identified markers have important implications and satisfactory stability.

Identification of potential tissue-specific cancer biomarkers and development of cancer versus normal genomic classifiers

Machine learning techniques for cancer prediction and biomarker discovery can hasten cancer detection and significantly improve prognosis. Recent “OMICS” studies which include a variety of cancer and normal tissue samples along with machine learning approaches have the potential to further accelerate such discovery. To demonstrate this potential, 2,175 gene expression samples from nine tissue types were obtained to identify gene sets whose expression is characteristic of each cancer class. Using random forests classification and ten-fold cross-validation, we developed nine single-tissue classifiers, two multi-tissue cancer-versus-normal classifiers, and one multi-tissue normal classifier. Given a sample of a specified tissue type, the single-tissue models classified samples as cancer or normal with a testing accuracy between 85.29% and 100%. Given a sample of non-specific tissue type, the multi-tissue bi-class model classified the sample as cancer versus normal with a testing accuracy of 97.89%. Given a sample of non-specific tissue type, the multi-tissue multi-class model classified the sample as cancer versus normal and as a specific tissue type with a testing accuracy of 97.43%. Given a normal sample of any of the nine tissue types, the multi-tissue normal model classified the sample as a particular tissue type with a testing accuracy of 97.35%. The machine learning classifiers developed in this study identify potential cancer biomarkers with sensitivity and specificity that exceed those of existing biomarkers and pointed to pathways that are critical to tissue-specific tumor development. This study demonstrates the feasibility of predicting the tissue origin of carcinoma in the context of multiple cancer classes.

Computational approach for deriving cancer progression roadmaps from static sample data

As with any biological process, cancer development is inherently dynamic. While major efforts continue to catalog the genomic events associated with human cancer, it remains difficult to interpret and extrapolate the accumulating data to provide insights into the dynamic aspects of the disease. Here, we present a computational strategy that enables the construction of a cancer progression model using static tumor sample data. The developed approach overcame many technical limitations of existing methods. Application of the approach to breast cancer data revealed a linear, branching model with two distinct trajectories for malignant progression. The validity of the constructed model was demonstrated in 27 independent breast cancer data sets, and through visualization of the data in the context of disease progression we were able to identify a number of potentially key molecular events in the advance of breast cancer to malignancy.

Molecular signatures in breast cancer

The use of molecular signatures to add value to standard clinical and pathological parameters has impacted clinical practice in many cancer types, but perhaps most notably in the breast cancer field. This is, in part, due to the considerable complexity of the disease at the clinical, morphological and molecular levels. The adoption of molecular profiling of DNA, RNA and protein continues to reveal important differences in the intrinsic biology between molecular subtypes and has begun to impact the way patients are managed. Several bioinformatic tools have been developed using DNA or RNA-based signatures to stratify the disease into biologically and/or clinically meaningful subgroups. Here, we review the approaches that have been used to develop gene expression signatures into currently available diagnostic assays (e.g., OncotypeDX® and Mammaprint®), plus we describe the latest work on genome sequencing, the methodologies used in the discovery process of mutational signatures, and the potential of these signatures to impact the clinic.

GRAPE: a pathway template method to characterize tissue-specific functionality from gene expression profiles

BACKGROUND

Personalizing treatment regimes based on gene expression profiles of individual tumors will facilitate management of cancer. Although many methods have been developed to identify pathways perturbed in tumors, the results are often not generalizable across independent datasets due to the presence of platform/batch effects. There is a need to develop methods that are robust to platform/batch effects and able to identify perturbed pathways in individual samples.

RESULTS

We present Gene-Ranking Analysis of Pathway Expression (GRAPE) as a novel method to identify abnormal pathways in individual samples that is robust to platform/batch effects in gene expression profiles generated by multiple platforms. GRAPE first defines a template consisting of an ordered set of pathway genes to characterize the normative state of a pathway based on the relative rankings of gene expression levels across a set of reference samples. This template can be used to assess whether a sample conforms to or deviates from the typical behavior of the reference samples for this pathway. We demonstrate that GRAPE performs well versus existing methods in classifying tissue types within a single dataset, and that GRAPE achieves superior robustness and generalizability across different datasets. A powerful feature of GRAPE is the ability to represent individual gene expression profiles as a vector of pathways scores. We present applications to the analyses of breast cancer subtypes and different colonic diseases. We perform survival analysis of several TCGA subtypes and find that GRAPE pathway scores perform well in comparison to other methods.

CONCLUSIONS

GRAPE templates offer a novel approach for summarizing the behavior of gene-sets across a collection of gene expression profiles. These templates offer superior robustness across distinct experimental batches compared to existing methods. GRAPE pathway scores enable identification of abnormal gene-set behavior in individual samples using a non-competitive approach that is fundamentally distinct from popular enrichment-based methods. GRAPE may be an appropriate tool for researchers seeking to identify individual samples displaying abnormal gene-set behavior as well as to explore differences in the consensus gene-set behavior of groups of samples. GRAPE is available in R for download at https://CRAN.R-project.org/package=GRAPE .

Ror2 Signaling and Its Relevance in Breast Cancer Progression

Breast cancer is a heterogeneous disease and has been classified into five molecular subtypes based on gene expression profiles. Signaling processes linked to different breast cancer molecular subtypes and different clinical outcomes are still poorly understood. Aberrant regulation of Wnt signaling has been implicated in breast cancer progression. In particular Ror1/2 receptors and several other members of the non-canonical Wnt signaling pathway were associated with aggressive breast cancer behavior. However, Wnt signals are mediated via multiple complex pathways, and it is clinically important to determine which particular Wnt cascades, including their domains and targets, are deregulated in poor prognosis breast cancer. To investigate activation and outcome of the Ror2-dependent non-canonical Wnt signaling pathway, we overexpressed the Ror2 receptor in MCF-7 and MDA-MB231 breast cancer cells, stimulated the cells with its ligand Wnt5a, and we knocked-down Ror1 in MDA-MB231 cells. We measured the invasive capacity of perturbed cells to assess phenotypic changes, and mRNA was profiled to quantify gene expression changes. Differentially expressed genes were integrated into a literature-based non-canonical Wnt signaling network. The results were further used in the analysis of an independent dataset of breast cancer patients with metastasis-free survival annotation. Overexpression of the Ror2 receptor, stimulation with Wnt5a, as well as the combination of both perturbations enhanced invasiveness of MCF-7 cells. The expression-responsive targets of Ror2 overexpression in MCF-7 induced a Ror2/Wnt module of the non-canonical Wnt signaling pathway. These targets alter regulation of other pathways involved in cell remodeling processing and cell metabolism. Furthermore, the genes of the Ror2/Wnt module were assessed as a gene signature in patient gene expression data and showed an association with clinical outcome. In summary, results of this study indicate a role of a newly defined Ror2/Wnt module in breast cancer progression and present a link between Ror2 expression and increased cell invasiveness.

Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer

Single-cell transcriptome profiling of tumour tissue isolates allows the characterization of heterogeneous tumour cells along with neighbouring stromal and immune cells. Here we adopt this powerful approach to breast cancer and analyse 515 cells from 11 patients. Inferred copy number variations from the single-cell RNA-seq data separate carcinoma cells from non-cancer cells. At a single-cell resolution, carcinoma cells display common signatures within the tumour as well as intratumoral heterogeneity regarding breast cancer subtype and crucial cancer-related pathways. Most of the non-cancer cells are immune cells, with three distinct clusters of T lymphocytes, B lymphocytes and macrophages. T lymphocytes and macrophages both display immunosuppressive characteristics: T cells with a regulatory or an exhausted phenotype and macrophages with an M2 phenotype. These results illustrate that the breast cancer transcriptome has a wide range of intratumoral heterogeneity, which is shaped by the tumour cells and immune cells in the surrounding microenvironment.

Genetic heterogeneity in breast cancer has been demonstrated at a single-cell resolution with high levels of genome coverage. Here, the authors perform transcriptome analysis of 515 single cells from 11 patients and define core gene expression signatures for subtype-specific single breast cancer cells and tumour-infiltrating immune cells.

The E2F4 prognostic signature predicts pathological response to neoadjuvant chemotherapy in breast cancer patients

Background

Neoadjuvant chemotherapy is a key component of breast cancer treatment regimens and pathologic complete response to this therapy varies among patients. This is presumably due to differences in the molecular mechanisms that underlie each tumor’s disease pathology. Developing genomic clinical assays that accurately categorize responders from non-responders can provide patients with the most effective therapy for their individual disease.

Methods

We applied our previously developed E2F4 genomic signature to predict neoadjuvant chemotherapy response in breast cancer. E2F4 individual regulatory activity scores were calculated for 1129 patient samples across 5 independent breast cancer neoadjuvant chemotherapy datasets. Accuracy of the E2F4 signature in predicting neoadjuvant chemotherapy response was compared to that of the Oncotype DX and MammaPrint predictive signatures.

Results

In all datasets, E2F4 activity level was an accurate predictor of neoadjuvant chemotherapy response, with high E2F4 scores predictive of achieving pathologic complete response and low scores predictive of residual disease. These results remained significant even after stratifying patients by estrogen receptor (ER) status, tumor stage, and breast cancer molecular subtypes. Compared to the Oncotype DX and MammaPrint signatures, our E2F4 signature achieved similar performance in predicting neoadjuvant chemotherapy response, though all signatures performed better in ER+ tumors compared to ER- ones. The accuracy of our signature was reproducible across datasets and was maintained when refined from a 199-gene signature down to a clinic-friendly 33-gene panel.

Conclusion

Overall, we show that our E2F4 signature is accurate in predicting patient response to neoadjuvant chemotherapy. As this signature is more refined and comparable in performance to other clinically available gene expression assays in the prediction of neoadjuvant chemotherapy response, it should be considered when evaluating potential treatment options.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3297-2) contains supplementary material, which is available to authorized users.

Basal-like breast cancer: molecular profiles, clinical features and survival outcomes

BACKGROUND

Basal-like constitutes an important molecular subtype of breast cancer characterised by an aggressive behaviour and a limited therapy response. The outcome of patients within this subtype is, however, divergent. Some individuals show an increased risk of dying in the first five years, and others a long-term survival of over ten years after the diagnosis. In this study, we aim at identifying markers associated with basal-like patients' survival and characterising subgroups with distinct disease outcome.

METHODS

We explored the genomic and transcriptomic profiles of 351 basal-like samples from the METABRIC and ROCK data sets. Two selection methods, labelled Differential and Survival filters, were employed to determine genes/probes that are differentially expressed in tumour and control samples, and are associated with overall survival. These probes were further used to define molecular subgroups, which vary at the microRNA level and in DNA copy number.

RESULTS

We identified the expression signature of 80 probes that distinguishes between two basal-like subgroups with distinct clinical features and survival outcomes. Genes included in this list have been mainly linked to cancer immune response, epithelial-mesenchymal transition and cell cycle. In particular, high levels of CXCR6, HCST, C3AR1 and FPR3 were found in Basal I; whereas HJURP, RRP12 and DNMT3B appeared over-expressed in Basal II. These genes exhibited the highest betweenness centrality and node degree values and play a key role in the basal-like breast cancer differentiation. Further molecular analysis revealed 17 miRNAs correlated to the subgroups, including hsa-miR-342-5p, -150, -155, -200c and -17. Additionally, increased percentages of gains/amplifications were detected on chromosomes 1q, 3q, 8q, 10p and 17q, and losses/deletions on 4q, 5q, 8p and X, associated with reduced survival.

CONCLUSIONS

The proposed signature supports the existence of at least two subgroups of basal-like breast cancers with distinct disease outcome. The identification of patients at a low risk may impact the clinical decisions-making by reducing the prescription of high-dose chemotherapy and, consequently, avoiding adverse effects. The recognition of other aggressive features within this subtype may be also critical for improving individual care and for delineating more effective therapies for patients at high risk.

Targeted proteomics driven verification of biomarker candidates associated with breast cancer aggressiveness

Breast cancer is the most common and molecularly relatively well characterized malignant disease in women, however, its progression to metastatic cancer remains lethal for 78% of patients 5years after diagnosis. Novel markers could identify the high risk patients and their verification using quantitative methods is essential to overcome genetic, inter-tumor and intra-tumor variability and translate novel findings into cancer diagnosis and treatment. We recently identified 13 proteins associated with estrogen receptor, tumor grade and lymph node status, the key factors of breast cancer aggressiveness, using untargeted proteomics. Here we verified these findings in the same set of 96 tumors using targeted proteomics based on selected reaction monitoring with mTRAQ labeling (mTRAQ-SRM), transcriptomics and immunohistochemistry and validated in 5 independent sets of 715 patients using transcriptomics. We confirmed: (i) positive association of anterior gradient protein 2 homolog (AGR2) and periostin (POSTN) and negative association of annexin A1 (ANXA1) with estrogen receptor status; (ii) positive association of stathmin (STMN1), cofilin-1 (COF1), plasminogen activator inhibitor 1 RNA-binding protein (PAIRBP1) and negative associations of thrombospondin-2 (TSP2) and POSTN levels with tumor grade; and (iii) positive association of POSTN, alpha-actinin-4 (ACTN4) and STMN1 with lymph node status. This study highlights a panel of gene products that can contribute to breast cancer aggressiveness and metastasis, the understanding of which is important for development of more precise breast cancer treatment.

Weibull regression with Bayesian variable selection to identify prognostic tumour markers of breast cancer survival

As data-rich medical datasets are becoming routinely collected, there is a growing demand for regression methodology that facilitates variable selection over a large number of predictors. Bayesian variable selection algorithms offer an attractive solution, whereby a sparsity inducing prior allows inclusion of sets of predictors simultaneously, leading to adjusted effect estimates and inference of which covariates are most important. We present a new implementation of Bayesian variable selection, based on a Reversible Jump MCMC algorithm, for survival analysis under the Weibull regression model. A realistic simulation study is presented comparing against an alternative LASSO-based variable selection strategy in datasets of up to 20,000 covariates. Across half the scenarios, our new method achieved identical sensitivity and specificity to the LASSO strategy, and a marginal improvement otherwise. Runtimes were comparable for both approaches, taking approximately a day for 20,000 covariates. Subsequently, we present a real data application in which 119 protein-based markers are explored for association with breast cancer survival in a case cohort of 2287 patients with oestrogen receptor-positive disease. Evidence was found for three independent prognostic tumour markers of survival, one of which is novel. Our new approach demonstrated the best specificity.

Up-Regulation of RFC3 Promotes Triple Negative Breast Cancer Metastasis and is Associated With Poor Prognosis Via EMT

Triple-negative breast cancer (TNBC) was regarded as the most aggressive and mortal subtype of breast cancer (BC) since the molecular subtype system has been established. Abundant studies have revealed that epithelial-mesenchymal transition (EMT) played a pivotal role during breast cancer metastasis and progression, especially in TNBC. Herein, we showed that inhibition the expression of replication factor C subunit 3 (RFC3) significantly attenuated TNBC metastasis and progression, which was associated with EMT signal pathway. In TNBC cells, knockdown of RFC3 can down-regulate mesenchymal markers and up-regulate epithelial markers, significantly attenuated cell proliferation, migration and invasion. Additionally, silencing RFC3 expression can decrease nude mice tumor volume, weight and relieve lung metastasis in vivo. Furthermore, we also demonstrated that overexpression of RFC3 in TNBC showed increased metastasis, progression and poor prognosis. We confirmed all of these results by immunohistochemistry analysis in 127 human TNBC tissues and found that RFC3 expression was significantly associated with poor prognosis in TNBC. Taken all these findings into consideration, we can conclude that up-regulation of RFC3 promotes TNBC progression through EMT signal pathway. Therefore, RFC3 could be an independent prognostic factor and therapeutic target for TNBC.

The Cdc42/Rac1 regulator CdGAP is a novel E-cadherin transcriptional co-repressor with Zeb2 in breast cancer

The loss of E-cadherin causes dysfunction of the cell-cell junction machinery, which is an initial step in epithelial-to-mesenchymal transition (EMT), facilitating cancer cell invasion and the formation of metastases. A set of transcriptional repressors of E-cadherin (CDH1) gene expression, including Snail1, Snail2 and Zeb2 mediate E-cadherin downregulation in breast cancer. However, the molecular mechanisms underlying the control of E-cadherin expression in breast cancer progression remain largely unknown. Here, by using global gene expression approaches, we uncover a novel function for Cdc42 GTPase-activating protein (CdGAP) in the regulation of expression of genes involved in EMT. We found that CdGAP used its proline-rich domain to form a functional complex with Zeb2 to mediate the repression of E-cadherin expression in ErbB2-transformed breast cancer cells. Conversely, knockdown of CdGAP expression led to a decrease of the transcriptional repressors Snail1 and Zeb2, and this correlated with an increase in E-cadherin levels, restoration of cell-cell junctions, and epithelial-like morphological changes. In vivo, loss of CdGAP in ErbB2-transformed breast cancer cells impaired tumor growth and suppressed metastasis to lungs. Finally, CdGAP was highly expressed in basal-type breast cancer cells, and its strong expression correlated with poor prognosis in breast cancer patients. Together, these data support a previously unknown nuclear function for CdGAP where it cooperates in a GAP-independent manner with transcriptional repressors to function as a critical modulator of breast cancer through repression of E-cadherin transcription. Targeting Zeb2-CdGAP interactions may represent novel therapeutic opportunities for breast cancer treatment.

Identification of Genetic and Epigenetic Variants Associated with Breast Cancer Prognosis by Integrative Bioinformatics Analysis

INTRODUCTION

Breast cancer being a multifaceted disease constitutes a wide spectrum of histological and molecular variability in tumors. However, the task for the identification of these variances is complicated by the interplay between inherited genetic and epigenetic aberrations. Therefore, this study provides an extrapolate outlook to the sinister partnership between DNA methylation and single-nucleotide polymorphisms (SNPs) in relevance to the identification of prognostic markers in breast cancer. The effect of these SNPs on methylation is defined as methylation quantitative trait loci (meQTL).

MATERIALS AND METHODS

We developed a novel method to identify prognostic gene signatures for breast cancer by integrating genomic and epigenomic data. This is based on the hypothesis that multiple sources of evidence pointing to the same gene or pathway are likely to lead to reduced false positives. We also apply random resampling to reduce overfitting noise by dividing samples into training and testing data sets. Specifically, the common samples between Illumina 450 DNA methylation, Affymetrix SNP array, and clinical data sets obtained from the Cancer Genome Atlas (TCGA) for breast invasive carcinoma (BRCA) were randomly divided into training and test models. An intensive statistical analysis based on log-rank test and Cox proportional hazard model has established a significant association between differential methylation and the stratification of breast cancer patients into high- and low-risk groups, respectively.

RESULTS

The comprehensive assessment based on the conjoint effect of CpG–SNP pair has guided in delaminating the breast cancer patients into the high- and low-risk groups. In particular, the most significant association was found with respect to cg05370838–rs2230576, cg00956490–rs940453, and cg11340537–rs2640785 CpG–SNP pairs. These CpG–SNP pairs were strongly associated with differential expression of ADAM8, CREB5, and EXPH5 genes, respectively. Besides, the exclusive effect of SNPs such as rs10101376, rs140679, and rs1538146 also hold significant prognostic determinant.

CONCLUSIONS

Thus, the analysis based on DNA methylation and SNPs have resulted in the identification of novel susceptible loci that hold prognostic relevance in breast cancer.

The molecular basis of breast cancer pathological phenotypes

The histopathological evaluation of morphological features in breast tumours provides prognostic information to guide therapy. Adjunct molecular analyses provide further diagnostic, prognostic and predictive information. However, there is limited knowledge of the molecular basis of morphological phenotypes in invasive breast cancer. This study integrated genomic, transcriptomic and protein data to provide a comprehensive molecular profiling of morphological features in breast cancer. Fifteen pathologists assessed 850 invasive breast cancer cases from The Cancer Genome Atlas (TCGA). Morphological features were significantly associated with genomic alteration, DNA methylation subtype, PAM50 and microRNA subtypes, proliferation scores, gene expression and/or reverse-phase protein assay subtype. Marked nuclear pleomorphism, necrosis, inflammation and a high mitotic count were associated with the basal-like subtype, and had a similar molecular basis. Omics-based signatures were constructed to predict morphological features. The association of morphology transcriptome signatures with overall survival in oestrogen receptor (ER)-positive and ER-negative breast cancer was first assessed by use of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset; signatures that remained prognostic in the METABRIC multivariate analysis were further evaluated in five additional datasets. The transcriptomic signature of poorly differentiated epithelial tubules was prognostic in ER-positive breast cancer. No signature was prognostic in ER-negative breast cancer. This study provided new insights into the molecular basis of breast cancer morphological phenotypes. The integration of morphological with molecular data has the potential to refine breast cancer classification, predict response to therapy, enhance our understanding of breast cancer biology, and improve clinical management. This work is publicly accessible at www.dx.ai/tcga_breast. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Oestrogen receptor negativity in breast cancer: a cause or consequence?

Endocrine resistance, which occurs either by de novo or acquired route, is posing a major challenge in treating hormone-dependent breast cancers by endocrine therapies. The loss of oestrogen receptor α (ERα) expression is the vital cause of establishing endocrine resistance in this subtype. Understanding the mechanisms that determine the causes of this phenomenon are therefore essential to reduce the disease efficacy. But how we negate oestrogen receptor (ER) negativity and endocrine resistance in breast cancer is questionable. To answer that, two important approaches are considered: (1) understanding the cellular origin of heterogeneity and ER negativity in breast cancers and (2) characterization of molecular regulators of endocrine resistance. Breast tumours are heterogeneous in nature, having distinct molecular, cellular, histological and clinical behaviour. Recent advancements in perception of the heterogeneity of breast cancer revealed that the origin of a particular mammary tumour phenotype depends on the interactions between the cell of origin and driver genetic hits. On the other hand, histone deacetylases (HDACs), DNA methyltransferases (DNMTs), miRNAs and ubiquitin ligases emerged as vital molecular regulators of ER negativity in breast cancers. Restoring response to endocrine therapy through re-expression of ERα by modulating the expression of these molecular regulators is therefore considered as a relevant concept that can be implemented in treating ER-negative breast cancers. In this review, we will thoroughly discuss the underlying mechanisms for the loss of ERα expression and provide the future prospects for implementing the strategies to negate ER negativity in breast cancers.

Patterns of Immune Infiltration in Breast Cancer and Their Clinical Implications: A Gene-Expression-Based Retrospective Study

BACKGROUND

Immune infiltration of breast tumours is associated with clinical outcome. However, past work has not accounted for the diversity of functionally distinct cell types that make up the immune response. The aim of this study was to determine whether differences in the cellular composition of the immune infiltrate in breast tumours influence survival and treatment response, and whether these effects differ by molecular subtype.

METHODS AND FINDINGS

We applied an established computational approach (CIBERSORT) to bulk gene expression profiles of almost 11,000 tumours to infer the proportions of 22 subsets of immune cells. We investigated associations between each cell type and survival and response to chemotherapy, modelling cellular proportions as quartiles. We found that tumours with little or no immune infiltration were associated with different survival patterns according to oestrogen receptor (ER) status. In ER-negative disease, tumours lacking immune infiltration were associated with the poorest prognosis, whereas in ER-positive disease, they were associated with intermediate prognosis. Of the cell subsets investigated, T regulatory cells and M0 and M2 macrophages emerged as the most strongly associated with poor outcome, regardless of ER status. Among ER-negative tumours, CD8+ T cells (hazard ratio [HR] = 0.89, 95% CI 0.80-0.98; p = 0.02) and activated memory T cells (HR 0.88, 95% CI 0.80-0.97; p = 0.01) were associated with favourable outcome. T follicular helper cells (odds ratio [OR] = 1.34, 95% CI 1.14-1.57; p < 0.001) and memory B cells (OR = 1.18, 95% CI 1.0-1.39; p = 0.04) were associated with pathological complete response to neoadjuvant chemotherapy in ER-negative disease, suggesting a role for humoral immunity in mediating response to cytotoxic therapy. Unsupervised clustering analysis using immune cell proportions revealed eight subgroups of tumours, largely defined by the balance between M0, M1, and M2 macrophages, with distinct survival patterns by ER status and associations with patient age at diagnosis. The main limitations of this study are the use of diverse platforms for measuring gene expression, including some not previously used with CIBERSORT, and the combined analysis of different forms of follow-up across studies.

CONCLUSIONS

Large differences in the cellular composition of the immune infiltrate in breast tumours appear to exist, and these differences are likely to be important determinants of both prognosis and response to treatment. In particular, macrophages emerge as a possible target for novel therapies. Detailed analysis of the cellular immune response in tumours has the potential to enhance clinical prediction and to identify candidates for immunotherapy.

The Prognostic Role of Androgen Receptor in Patients with Early-Stage Breast Cancer: A Meta-analysis of Clinical and Gene Expression Data

Purpose: Androgen receptor (AR) expression has been observed in about 70% of patients with breast cancer, but its prognostic role remains uncertain.Experimental Design: To assess the prognostic role of AR expression in early-stage breast cancer, we performed a meta-analysis of studies that evaluated the impact of AR at the protein and gene expression level on disease-free survival (DFS) and/or overall survival (OS). Eligible studies were identified by systematic review of electronic databases using the MeSH-terms "breast neoplasm" and "androgen receptor" and were selected after a qualitative assessment based on the REMARK criteria. A pooled gene expression analysis of 35 publicly available microarray data sets was also performed from patients with early-stage breast cancer with available gene expression and clinical outcome data.Results: Twenty-two of 33 eligible studies for the clinical meta-analysis, including 10,004 patients, were considered as evaluable for the current study after the qualitative assessment. AR positivity defined by IHC was associated with improved DFS in all patients with breast cancer [multivariate (M) analysis, HR 0.46; 95% confidence interval (CI) 0.37-0.58, P < 0.001] and better OS [M-HR 0.53; 95% CI, 0.38-0.73, P < 0.001]. Thirty-five datasets including 7,220 patients were eligible for the pooled gene expression analysis. High AR mRNA levels were found to confer positive prognosis overall in terms of DFS (HR 0.82; 95% CI 0.72-0.92;P = 0.0007) and OS (HR 0.84; 95% CI, 0.75-0.94; P = 0.02) only in univariate analysis.Conclusions: Our analysis, conducted among more than 17,000 women with early-stage breast cancer included in clinical and gene expression analysis, demonstrates that AR positivity is associated with favorable clinical outcome. Clin Cancer Res; 23(11); 2702-12. ©2016 AACR.

A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds

The inter- and intra-tumor heterogeneity of breast cancer needs to be adequately captured in pre-clinical models. We have created a large collection of breast cancer patient-derived tumor xenografts (PDTXs), in which the morphological and molecular characteristics of the originating tumor are preserved through passaging in the mouse. An integrated platform combining in vivo maintenance of these PDTXs along with short-term cultures of PDTX-derived tumor cells (PDTCs) was optimized. Remarkably, the intra-tumor genomic clonal architecture present in the originating breast cancers was mostly preserved upon serial passaging in xenografts and in short-term cultured PDTCs. We assessed drug responses in PDTCs on a high-throughput platform and validated several ex vivo responses in vivo. The biobank represents a powerful resource for pre-clinical breast cancer pharmacogenomic studies (http://caldaslab.cruk.cam.ac.uk/bcape), including identification of biomarkers of response or resistance.

Integrative proteomic and gene expression analysis identify potential biomarkers for adjuvant trastuzumab resistance: analysis from the Fin-her phase III randomized trial

Trastuzumab is a remarkably effective therapy for patients with human epidermal growth factor receptor 2 (HER2) - positive breast cancer (BC). However, not all women with high levels of HER2 benefit from trastuzumab.

By integrating mRNA and protein expression data from Reverse-Phase Protein Array Analysis (RPPA) in HER2-positive BC, we developed gene expression metagenes that reflect pathway activation levels. Next we assessed the ability of these metagenes to predict resistance to adjuvant trastuzumab using gene expression data from two independent datasets.

10 metagenes passed external validation (false discovery rate [fdr] < 0.05) and showed biological relevance with their pathway of origin. These metagenes were further screened for their association with trastuzumab resistance. An association with trastuzumab resistance was observed and validated only for the AnnexinA1 metagene (ANXA1). In the randomised phase III Fin-her study, tumours with low levels of the ANXA1 metagene showed a benefit from trastuzumab (multivariate: hazard ratio [HR] for distant recurrence = 0.16[95%CI 0.05–0.5]; p = 0.002; fdr = 0.03), while high expression levels of the ANXA1 metagene were associated with a lack of benefit to trastuzmab (HR = 1.29[95%CI 0.55–3.02]; p = 0.56). The association of ANXA1 with trastuzumab resistance was successfully validated in an independent series of subjects who had received trastuzumab with chemotherapy (Log Rank; p = 0.01).

In conclusion, in HER2-positive BC, some proteins are associated with distinct gene expression profiles. Our findings identify the ANXA1metagene as a novel biomarker for trastuzumab resistance.

Extensive Transcriptomic and Genomic Analysis Provides New Insights about Luminal Breast Cancers

Despite constituting approximately two thirds of all breast cancers, the luminal A and B tumours are poorly classified at both clinical and molecular levels. There are contradictory reports on the nature of these subtypes: some define them as intrinsic entities, others as a continuum. With the aim of addressing these uncertainties and identifying molecular signatures of patients at risk, we conducted a comprehensive transcriptomic and genomic analysis of 2,425 luminal breast cancer samples. Our results indicate that the separation between the molecular luminal A and B subtypes—per definition—is not associated with intrinsic characteristics evident in the differentiation between other subtypes. Moreover, t-SNE and MST-kNN clustering approaches based on 10,000 probes, associated with luminal tumour initiation and/or development, revealed the close connections between luminal A and B tumours, with no evidence of a clear boundary between them. Thus, we considered all luminal tumours as a single heterogeneous group for analysis purposes. We first stratified luminal tumours into two distinct groups by their HER2 gene cluster co-expression: HER2-amplified luminal and ordinary-luminal. The former group is associated with distinct transcriptomic and genomic profiles, and poor prognosis; it comprises approximately 8% of all luminal cases. For the remaining ordinary-luminal tumours we further identified the molecular signature correlated with disease outcomes, exhibiting an approximately continuous gene expression range from low to high risk. Thus, we employed four virtual quantiles to segregate the groups of patients. The clinico-pathological characteristics and ratios of genomic aberrations are concordant with the variations in gene expression profiles, hinting at a progressive staging. The comparison with the current separation into luminal A and B subtypes revealed a substantially improved survival stratification. Concluding, we suggest a review of the definition of luminal A and B subtypes. A proposition for a revisited delineation is provided in this study.

The Role of Proliferation in Determining Response to Neoadjuvant Chemotherapy in Breast Cancer: A Gene Expression-Based Meta-Analysis

PURPOSE

To provide further insight into the role of proliferation and other cellular processes in chemosensitivity and resistance, we evaluated the association of a diverse set of gene expression signatures with response to neoadjuvant chemotherapy (NAC) in breast cancer.

EXPERIMENTAL DESIGN

Expression data from primary breast cancer biopsies for 1,419 patients in 17 studies prior to NAC were identified and aggregated using common normalization procedures. Clinicopathologic characteristics, including response to NAC, were collected. Scores for 125 previously published breast cancer-related gene expression signatures were calculated for each tumor.

RESULTS

Within each receptor-based subgroup or PAM50 subtype, breast tumors with high proliferation signature scores were significantly more likely to achieve pathologic complete response to NAC. To distinguish "proliferation-associated" from "proliferation-independent" signatures, we used correlation and linear modeling approaches. Most signatures associated with response to NAC were proliferation associated: 90.5% (38/42) in ER⁺/HER2^- and 63.3% (38/60) in triple-negative breast cancer (TNBC). Proliferation-independent signatures predictive of response to NAC in ER⁺/HER2^- breast cancer were related to immune activity, while those in TNBC comprised a diverse set of signatures, including immune, DNA damage, signaling pathways (PI3K, AKT, Ras, and EGFR), and "stemness" phenotypes.

CONCLUSIONS

Proliferation differences account for the vast majority of predictive capacity of gene expression signatures in neoadjuvant chemosensitivity for ER⁺/HER2^- breast cancers and, to a lesser extent, TNBCs. Immune activation signatures are proliferation-independent predictors of pathologic complete response in ER⁺/HER2^- breast cancers. In TNBCs, significant proliferation-independent signatures include gene sets that represent a diverse set of cellular processes. Clin Cancer Res; 22(24); 6039-50. ©2016 AACR.

Refinement of Triple-Negative Breast Cancer Molecular Subtypes: Implications for Neoadjuvant Chemotherapy Selection

Triple-negative breast cancer (TNBC) is a heterogeneous disease that can be classified into distinct molecular subtypes by gene expression profiling. Considered a difficult-to-treat cancer, a fraction of TNBC patients benefit significantly from neoadjuvant chemotherapy and have far better overall survival. Outside of BRCA1/2 mutation status, biomarkers do not exist to identify patients most likely to respond to current chemotherapy; and, to date, no FDA-approved targeted therapies are available for TNBC patients. Previously, we developed an approach to identify six molecular subtypes TNBC (TNBCtype), with each subtype displaying unique ontologies and differential response to standard-of-care chemotherapy. Given the complexity of the varying histological landscape of tumor specimens, we used histopathological quantification and laser-capture microdissection to determine that transcripts in the previously described immunomodulatory (IM) and mesenchymal stem-like (MSL) subtypes were contributed from infiltrating lymphocytes and tumor-associated stromal cells, respectively. Therefore, we refined TNBC molecular subtypes from six (TNBCtype) into four (TNBCtype-4) tumor-specific subtypes (BL1, BL2, M and LAR) and demonstrate differences in diagnosis age, grade, local and distant disease progression and histopathology. Using five publicly available, neoadjuvant chemotherapy breast cancer gene expression datasets, we retrospectively evaluated chemotherapy response of over 300 TNBC patients from pretreatment biopsies subtyped using either the intrinsic (PAM50) or TNBCtype approaches. Combined analysis of TNBC patients demonstrated that TNBC subtypes significantly differ in response to similar neoadjuvant chemotherapy with 41% of BL1 patients achieving a pathological complete response compared to 18% for BL2 and 29% for LAR with 95% confidence intervals (CIs; [33, 51], [9, 28], [17, 41], respectively). Collectively, we provide pre-clinical data that could inform clinical trials designed to test the hypothesis that improved outcomes can be achieved for TNBC patients, if selection and combination of existing chemotherapies is directed by knowledge of molecular TNBC subtypes.

Breast cancer subtype predictors revisited: from consensus to concordance?

BACKGROUND

At the molecular level breast cancer comprises a heterogeneous set of subtypes associated with clear differences in gene expression and clinical outcomes. Single sample predictors (SSPs) are built via a two-stage approach consisting of clustering and subtype predictor construction based on the cluster labels of individual cases. SSPs have been criticized because their subtype assignments for the same samples were only moderately concordant (Cohen's κ<0.6).

METHODS

We propose a semi-supervised approach where for five datasets, consensus sets were constructed consisting of those samples that were concordantly subtyped by a number of different predictors. Next, nine subtype predictors - three SSPs, three subtype classification models (SCMs) and three novel rule-based predictors based on the St. Gallen surrogate intrinsic subtype definitions (STGs) - were constructed on the five consensus sets and their associated consensus subtype labels. The predictors were validated on a compendium of over 4,000 uniformly preprocessed Affymetrix microarrays. Concordance between subtype predictors was assessed using Cohen's kappa statistic.

RESULTS

In this standardized setup, subtype predictors of the same type (either SCM, SSP, or STG) but with a different gene list and/or consensus training set were associated with almost perfect levels of agreement (median κ>0.8). Interestingly, for a given predictor type a change in consensus set led to higher concordance than a change to another gene list. The more challenging scenario where the predictor type, gene list and training set were all different resulted in predictors with only substantial levels of concordance (median κ=0.74) on independent validation data.

CONCLUSIONS

Our results demonstrate that for a given subtype predictor type stringent standardization of the preprocessing stage, combined with carefully devised consensus training sets, leads to predictors that show almost perfect levels of concordance. However, predictors of a different type are only substantially concordant, despite reaching almost perfect levels of concordance on training data.

Adenosine 2B Receptor Expression on Cancer Cells Promotes Metastasis

Adenosine plays an important role in inflammation and tumor development, progression, and responses to therapy. We show that an adenosine 2B receptor inhibitor (A2BRi) decreases both experimental and spontaneous metastasis and combines with chemotherapy or immune checkpoint inhibitors in mouse models of melanoma and triple-negative breast cancer (TNBC) metastasis. Decreased metastasis upon A2BR inhibition is independent of host A2BR and lymphocytes and myeloid cells. Knockdown of A2BR on mouse and human cancer cells reduces their metastasis in vivo and decreases their viability and colony-forming ability, while transiently delaying cell-cycle arrest in vitro The prometastatic activity of adenosine is partly tumor A2BR dependent and independent of host A2BR expression. In humans, TNBC cell lines express higher A2BR than luminal and Her2(+) breast cancer cell lines, and high expression of A2BR is associated with worse prognosis in TNBC. Collectively, high A2BR on mouse and human tumors promotes cancer metastasis and is an ideal candidate for therapeutic intervention. Cancer Res; 76(15); 4372-82. ©2016 AACR.

The calcium pump plasma membrane Ca(2+)-ATPase 2 (PMCA2) regulates breast cancer cell proliferation and sensitivity to doxorubicin

Regulation of Ca(2+) transport is vital in physiological processes, including lactation, proliferation and apoptosis. The plasmalemmal Ca(2+) pump isoform 2 (PMCA2) a calcium ion efflux pump, was the first protein identified to be crucial in the transport of Ca(2+) ions into milk during lactation in mice. In these studies we show that PMCA2 is also expressed in human epithelia undergoing lactational remodeling and also report strong PMCA2 staining on apical membranes of luminal epithelia in approximately 9% of human breast cancers we assessed. Membrane protein expression was not significantly associated with grade or hormone receptor status. However, PMCA2 mRNA levels were enriched in Basal breast cancers where it was positively correlated with survival. Silencing of PMCA2 reduced MDA-MB-231 breast cancer cell proliferation, whereas silencing of the related isoforms PMCA1 and PMCA4 had no effect. PMCA2 silencing also sensitized MDA-MB-231 cells to the cytotoxic agent doxorubicin. Targeting PMCA2 alone or in combination with cytotoxic therapy may be worthy of investigation as a therapeutic strategy in breast cancer. PMCA2 mRNA levels are also a potential tool in identifying poor responders to therapy in women with Basal breast cancer.

Anti-CSF-1 treatment is effective to prevent carcinoma invasion induced by monocyte-derived cells but scarcely by microglia

The mononuclear phagocytic system is categorized in three major groups: monocyte-derived cells (MCs), dendritic cells and resident macrophages. During breast cancer progression the colony stimulating factor 1 (CSF-1) can reprogram MCs into tumor-promoting macrophages in the primary tumor. However, the effect of CSF-1 during colonization of the brain parenchyma is largely unknown.

Thus, we analyzed the outcome of anti-CSF-1 treatment on the resident macrophage population of the brain, the microglia, in comparison to MCs, alone and in different in vitro co-culture models. Our results underline the addiction of MCs to CSF-1 while surprisingly, microglia were not affected. Furthermore, in contrast to the brain, the bone marrow did not express the alternative ligand, IL-34. Yet treatment with IL-34 and co-culture with carcinoma cells partially rescued the anti-CSF-1 effects on MCs. Further, MC-induced invasion was significantly reduced by anti-CSF-1 treatment while microglia-induced invasion was reduced to a lower extend. Moreover, analysis of lung and breast cancer brain metastasis revealed significant differences of CSF-1 and CSF-1R expression.

Taken together, our findings demonstrate not only differences of anti-CSF-1 treatment on MCs and microglia but also in the CSF-1 receptor and ligand expression in brain and bone marrow as well as in brain metastasis.

Head-to-head comparison of the impact of Aurora A, Aurora B, Repp86, CDK1, CDK2 and Ki67 expression in two of the most relevant gynaecological tumor entities

PURPOSE

The dysregulation of cell cycle kinases plays a crucial role in carcinogenesis and the expression of various kinases has been attributed to aggressive tumor growth and an unfavourable prognosis in oncological patients. We, therefore, aimed to evaluate the expression of Ki67 among five additional cell cycle kinases in a collective of mammary and ovarian tumor specimens and to find a correlation with clinicopathological parameters.

METHODS

76 mammary and 93 ovarian benign and malignant tumor samples were immunohistochemically stained and evaluated for the expression of Aurora A and B, Repp86, CDK1 and 2 (only breast specimens) and Ki67. The expression patterns of these cell cycle kinases were matched with retrospectively collected clinicopathological parameters.

RESULTS

All examined cell cycle kinases accurately discriminated benign from malignant breast and ovarian tissues. In breast cancer, Aurora A and B-, Repp86-, CDK2- and Ki67-expression was inversely associated with ER expression. No correlation with the HER2-status was found in our collective. Importantly, we found a significant correlation between the expression of Aurora A and CDK1 and axillary lymph node metastasis in breast cancer. Furthermore, a shortened disease free survival (DFS) upon expression of Aurora B and CDK2 was shown in breast cancer patients. None of the cell cycle kinases was associated with predictive or prognostic factors in epithelial ovarian cancer.

CONCLUSION

The prognostic value of the expression of Ki67 is overtrumped by alternative cell cycle kinases when it comes to prediction of axillary tumor spread and a shortened DFS, which might allow a further risk stratification in breast cancer patients.