Systematic bias in genomic classification due to contaminating non-neoplastic tissue in breast tumor samples

Differences in 21-Gene and PAM50 Recurrence Scores in Younger and Black Women With Breast Cancer

PURPOSE

Genomic tests, such as the Oncotype Dx 21-gene and Prosigna risk of recurrence (ROR-P) assay, are commonly used for breast cancer prognostication. Emerging data suggest variability between assays, but this has not been compared in diverse populations.

MATERIALS AND METHODS

RNA sequencing was performed on 647 previously untreated stage I-III estrogen receptor-positive/human epidermal growth factor receptor 2-negative tumors in the Carolina Breast Cancer Study, which oversampled Black and younger women (age <50 years at diagnosis), using research versions of two common RNA-based prognostic assays: ROR-PR and the 21-gene recurrence score (RSR). Relative frequency differences and 95% CIs were estimated for associations with race and age, and hazards of 5-year local or distant recurrence were modeled with Cox regression. Proliferation and estrogen module scores from each assay, representing broad activity of genes in those pathways, were examined to guide interpretation of differences between tests.

RESULTS

Among both younger and older individuals, Black women had higher frequency of intermediate and high ROR-PR scores than non-Black women. Race was not significantly associated with RSR in either age group. High (hazard ratio [HR], 4.67 [95% CI, 1.73 to 12.70]) and intermediate (HR, 2.12 [95% CI, 0.98 to 4.62]) ROR-PR scores were associated with greater risk of recurrence, but RSR did not predict recurrence. RSR emphasized estrogen over proliferation modules, whereas ROR-PR emphasized proliferation. Higher proliferation scores were associated with younger age and Black race in both assays. Modifications to the RSR algorithm that increased emphasis on proliferation improved prognostication in this diverse population.

CONCLUSION

ROR-PR and the 21-gene RSR differentially emphasize estrogen-related and proliferative biology. The emphasis of 21-gene RS on estrogen-related biology and lower endocrine therapy initiation among Black women may contribute to poorer prognostic ability in heterogeneously treated populations.

Multiplexed RNA-FISH-guided Laser Capture Microdissection RNA Sequencing Improves Breast Cancer Molecular Subtyping, Prognostic Classification, and Predicts Response to Antibody Drug Conjugates

On a retrospective cohort of 1,082 FFPE breast tumors, we demonstrated the analytical validity of a test using multiplexed RNA-FISH-guided laser capture microdissection (LCM) coupled with RNA-sequencing (mFISHseq), which showed 93% accuracy compared to immunohistochemistry. The combination of these technologies makes strides in i) precisely assessing tumor heterogeneity, ii) obtaining pure tumor samples using LCM to ensure accurate biomarker expression and multigene testing, and iii) providing thorough and granular data from whole transcriptome profiling. We also constructed a 293-gene intrinsic subtype classifier that performed equivalent to the research based PAM50 and AIMS classifiers. By combining three molecular classifiers for consensus subtyping, mFISHseq alleviated single sample discordance, provided near perfect concordance with other classifiers (κ > 0.85), and reclassified 30% of samples into different subtypes with prognostic implications. We also use a consensus approach to combine information from 4 multigene prognostic classifiers and clinical risk to characterize high, low, and ultra-low risk patients that relapse early (< 5 years), late (> 10 years), and rarely, respectively. Lastly, to identify potential patient subpopulations that may be responsive to treatments like antibody drug-conjugates (ADC), we curated a list of 92 genes and 110 gene signatures to interrogate their association with molecular subtype and overall survival. Many genes and gene signatures related to ADC processing (e.g., antigen/payload targets, endocytosis, and lysosome activity) were independent predictors of overall survival in multivariate Cox regression models, thus highlighting potential ADC treatment-responsive subgroups. To test this hypothesis, we constructed a unique 19-feature classifier using multivariate logistic regression with elastic net that predicted response to trastuzumab emtansine (T-DM1; AUC = 0.96) better than either ERBB2 mRNA or Her2 IHC alone in the T-DM1 arm of the I-SPY2 trial. This test was deployed in a research-use only format on 26 patients and revealed clinical insights into patient selection for novel therapies like ADCs and immunotherapies and de-escalation of adjuvant chemotherapy.

Proteome deconvolution of liver biopsies reveals hepatic cell composition as an important marker of fibrosis

Human liver tissue is composed of heterogeneous mixtures of different cell types and their cellular stoichiometry can provide information on hepatic physiology and disease progression. Deconvolution algorithms for the identification of cell types and their proportions have recently been developed for transcriptomic data. However, no method for the deconvolution of bulk proteomics data has been presented to date. Here, we show that proteomes, which usually contain less data than transcriptomes, can provide useful information for cell type deconvolution using different algorithms. We demonstrate that proteomes from defined mixtures of cell lines, isolated primary liver cells, and human liver biopsies can be deconvoluted with high accuracy. In contrast to transcriptome-based deconvolution, liver tissue proteomes also provided information about extracellular compartments. Using deconvolution of proteomics data from liver biopsies of 56 patients undergoing Roux-en-Y gastric bypass surgery we show that proportions of immune and stellate cells correlate with inflammatory markers and altered composition of extracellular matrix proteins characteristic of early-stage fibrosis. Our results thus demonstrate that proteome deconvolution can be used as a molecular microscope for investigations of the composition of cell types, extracellular compartments, and for exploring cell-type specific pathological events. We anticipate that these findings will allow the refinement of retrospective analyses of the growing number of proteome datasets from various liver disease states and pave the way for AI-supported clinical and preclinical diagnostics.

Survivin Expression in Luminal Breast Cancer and Adjacent Normal Tissue for Immuno-Oncology Applications

Survivin (BIRC5) is a tumor-associated antigen (TAA) overexpressed in various tumors but present at low to undetectable levels in normal tissue. Survivin is known to have a high expression in breast cancer (e.g., Ductal Carcinoma in situ (DCIS) and triple negative breast cancer). Previous studies have not compared survivin expression levels in DCIS tumor samples to levels in adjacent, normal breast tissue from the same patient. To ensure the effective use of survivin as a target for T cell immunotherapy of breast cancer, it is essential to ascertain the varying levels of survivin expression between DCIS tumor tissue samples and the adjacent normal breast tissue taken from the same patient simultaneously. Next-generation sequencing of RNA (RNA-seq) in normal breast tissue and tumor breast tissue from five women presenting with DCIS for lumpectomy was used to identify sequence variation and expression levels of survivin. The identity of both tumor and adjacent normal tissue samples were corroborated by histopathology. Survivin was overexpressed in human breast tissue tumor samples relative to the corresponding adjacent human normal breast tissue. Wild-type survivin transcripts were the predominant species identified in all tumor tissue sequenced. This study demonstrates upregulated expression of wild type survivin in DCIS tumor tissue versus normal breast tissue taken from the same patient at the same time, and provides evidence that developing selective cytotoxic T lymphocyte (CTL) immunotherapy for DCIS targeting survivin warrants further study.

On data normalization and batch-effect correction for tumor subtyping with microRNA data

The discovery of new tumor subtypes has been aided by transcriptomics profiling. However, some new subtypes can be irreproducible due to data artifacts that arise from disparate experimental handling. To deal with these artifacts, methods for data normalization and batch-effect correction have been utilized before performing sample clustering for disease subtyping, despite that these methods were primarily developed for group comparison. It remains to be elucidated whether they are effective for sample clustering. We examined this issue with a re-sampling-based simulation study that leverages a pair of microRNA microarray data sets. Our study showed that (i) normalization generally benefited the discovery of sample clusters and quantile normalization tended to be the best performer, (ii) batch-effect correction was harmful when data artifacts confounded with biological signals, and (iii) their performance can be influenced by the choice of clustering method with the Prediction Around Medoid method based on Pearson correlation being consistently a best performer. Our study provides important insights on the use of data normalization and batch-effect correction in connection with the design of array-to-sample assignment and the choice of clustering method for facilitating accurate and reproducible discovery of tumor subtypes with microRNAs.

Molecular Skin Cancer Diagnosis: Promise and Limitations

Skin cancer is a significant and increasing global health burden. Although the current diagnostic workflow is robust and able to provide clinically actionable results, it is subject to notable limitations. The training and expertise required for accurate diagnoses using conventional skin cancer diagnostics are significant, and patient access to this workflow can be limited by geographic location or unforeseen events, such as coronavirus disease 2019 (COVID-19). Molecular biomarkers have transformed diagnostics and treatment delivery in oncology. With rapid advancements in molecular biology techniques, understanding of the underlying molecular mechanism of cancer pathologies has deepened, yielding biomarkers that can be used to monitor the course of malignant diseases. Herein, commercially available, clinically validated, and emerging skin cancer molecular biomarkers are reviewed. The qualities of an ideal molecular biomarker are defined. The potential benefits and limitations of applying molecular biomarker testing over the course of skin cancer from susceptibility to treatment are explored, with a view to outlining a future model of molecular biomarker skin cancer diagnostics.

MicroRNA dysregulations in Merkel cell carcinoma: Molecular mechanisms and clinical applications

Merkel cell carcinoma (MCC) is an aggressive skin malignancy with two distinct etiologies. The first, which accounts for the highest proportion, is caused by Merkel cell polyomavirus (MCPyV), a DNA tumor virus. A second, UV-induced, MCC form has also been identified. Few MCC diagnostic, prognostic, and therapeutic options are available. MicroRNAs (miRNAs) are small noncoding RNA molecules, which play a key role in regulating various physiologic cellular functions including cell cycling, proliferation, differentiation, and apoptosis. Numerous miRNAs are dysregulated in cancer, by acting as either tumor suppressors or oncomiRs. The aim of this review is to collect, summarize, and discuss recent findings on miRNAs whose dysregulation has been assumed to play a role in MCC. The potential clinical application of miRNAs as diagnostic and prognostic biomarkers in MCC is also described. In the future, miRNAs will potentially gain clinical significance for the improvement of MCC diagnostic, prognostic, and therapeutic options.

Using Machine Vision of Glycolytic Elements to Predict Breast Cancer Recurrences: Design and Implementation

A major goal of biomedical research has been the early and quantitative identification of patients who will subsequently experience a cancer recurrence. In this review, I discuss the ability of glycolytic enzyme and transporter patterns within tissues to detect sub-populations of cells within ductal carcinoma in situ (DCIS) lesions that specifically precede cancer recurrences. The test uses conventional formalin fixed paraffin embedded tissue samples. The accuracy of this machine vision test rests on the identification of relevant glycolytic components that promote enhanced glycolysis (phospho-Ser226-glucose transporter type 1 (phospho-Ser226-GLUT1) and phosphofructokinase type L (PFKL)), their trafficking in tumor cells and tissues as judged by computer vision, and their high signal-to-noise levels. For each patient, machine vision stratifies micrographs from each lesion as the probability that the lesion originated from a recurrent sample. This stratification method removes overlap between the predicted recurrent and non-recurrent patients, which eliminates distribution-dependent false positives and false negatives. The method identifies computationally negative samples as non-recurrent and computationally positive samples are recurrent; computationally positive non-recurrent samples are likely due to mastectomies. The early phosphorylation and isoform switching events, spatial locations and clustering constitute important steps in metabolic reprogramming. This work also illuminates mechanistic steps occurring prior to a recurrence, which may contribute to the development of new drugs.

Feasibility of a novel non-invasive swab technique for serial whole-exome sequencing of cervical tumors during chemoradiation therapy

Background

Clinically relevant genetic predictors of radiation response for cervical cancer are understudied due to the morbidity of repeat invasive biopsies required to obtain genetic material. Thus, we aimed to demonstrate the feasibility of a novel noninvasive cervical swab technique to (1) collect tumor DNA with adequate throughput to (2) perform whole-exome sequencing (WES) at serial time points over the course of chemoradiation therapy (CRT).

Methods

Cervical cancer tumor samples from patients undergoing chemoradiation were collected at baseline, at week 1, week 3, and at the completion of CRT (week 5) using a noninvasive swab-based biopsy technique. Swab samples were analyzed with whole-exome sequencing (WES) with mutation calling using a custom pipeline optimized for shallow whole-exome sequencing with low tumor purity (TP). Tumor mutation changes over the course of treatment were profiled.

Results

216 samples were collected and successfully sequenced for 70 patients (94% of total number of tumor samples collected). A total of 33 patients had a complete set of samples at all four time points. The mean mapping rate was 98% for all samples, and the mean target coverage was 180. Estimated TP was greater than 5% for all samples. Overall mutation frequency decreased during CRT but mapping rate and mean target coverage remained at >98% and >180 reads at week 5.

Conclusion

This study demonstrates the feasibility and application of a noninvasive swab-based technique for WES analysis which may be applied to investigate dynamic tumor mutational changes during treatment to identify novel genes which confer radiation resistance.

Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study

Tumor purity is the percentage of cancer cells within a tissue section. Pathologists estimate tumor purity to select samples for genomic analysis by manually reading hematoxylin-eosin (H&E)-stained slides, which is tedious, time consuming, and prone to inter-observer variability. Besides, pathologists' estimates do not correlate well with genomic tumor purity values, which are inferred from genomic data and accepted as accurate for downstream analysis. We developed a deep multiple instance learning model predicting tumor purity from H&E-stained digital histopathology slides. Our model successfully predicted tumor purity in eight The Cancer Genome Atlas (TCGA) cohorts and a local Singapore cohort. The predictions were highly consistent with genomic tumor purity values. Thus, our model can be utilized to select samples for genomic analysis, which will help reduce pathologists' workload and decrease inter-observer variability. Furthermore, our model provided tumor purity maps showing the spatial variation within sections. They can help better understand the tumor microenvironment.

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MIL model successfully predicts a sample's tumor purity from histopathology slides

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MIL model learns to spatially resolve tumor purity from sample-level labels

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Tumor purity varies spatially within a sample

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Pathologists’ region selection is vital for correct percentage tumor nuclei estimation

Given some big data and coarse-level labels, extracting fine-level information is a demanding yet rewarding challenge in data science. This study develops a machine learning model utilizing big data and exploiting coarse-level labels to reveal fine-level details within the data. Although it can be applied to different data science tasks with enormous data and coarse labels, we applied it to a computational histopathology task with gigapixel histopathology slides and sample-level labels. Specifically, the model revealed spatial resolution of tumor purity within histopathology slides using only sample-level genomic tumor purity values during training. This can also be extended to other omics features, providing precious information about cancer biology and promising personalized, precision medicine. Such studies are of great clinical importance in discovering imaging biomarkers and better understanding the tumor microenvironment.

Understanding and overcoming tumor heterogeneity in metastatic breast cancer treatment

Rational development of targeted therapies has revolutionized metastatic breast cancer outcomes, although resistance to treatment remains a major challenge. Advances in molecular profiling and imaging technologies have provided evidence for the impact of clonal diversity in cancer treatment resistance, through the outgrowth of resistant clones. In this Review, we focus on the genomic processes that drive tumoral heterogeneity and the mechanisms of resistance underlying metastatic breast cancer treatment and discuss implications for future treatment strategies.

Sample Preparation Approach Influences PAM50 Risk of Recurrence Score in Early Breast Cancer

The PAM50 gene expression subtypes and the associated risk of recurrence (ROR) score are used to predict the risk of recurrence and the benefits of adjuvant therapy in early-stage breast cancer. The Prosigna assay includes the PAM50 subtypes along with their clinicopathological features, and is approved for treatment recommendations for adjuvant hormonal therapy and chemotherapy in hormone-receptor-positive early breast cancer. The Prosigna test utilizes RNA extracted from macrodissected tumor cells obtained from formalin-fixed, paraffin-embedded (FFPE) tissue sections. However, RNA extracted from fresh-frozen (FF) bulk tissue without macrodissection is widely used for research purposes, and yields high-quality RNA for downstream analyses. To investigate the impact of the sample preparation approach on ROR scores, we analyzed 94 breast carcinomas included in an observational study that had available gene expression data from macrodissected FFPE tissue and FF bulk tumor tissue, along with the clinically approved Prosigna scores for the node-negative, hormone-receptor-positive, HER2-negative cases (n = 54). ROR scores were calculated in R; the resulting two sets of scores from FFPE and FF samples were compared, and treatment recommendations were evaluated. Overall, ROR scores calculated based on the macrodissected FFPE tissue were consistent with the Prosigna scores. However, analyses from bulk tissue yielded a higher proportion of cases classified as normal-like; these were samples with relatively low tumor cellularity, leading to lower ROR scores. When comparing ROR scores (low, intermediate, and high), discordant cases between the two preparation approaches were revealed among the luminal tumors; the recommended treatment would have changed in a minority of cases.

Gene Expression Profiling for Differential Diagnosis of Liver Metastases: A Multicenter, Retrospective Cohort Study

Background

Liver metastases (LM) are the most common tumors encountered in the liver and continue to be a significant cause of morbidity and mortality. Identification of the primary tumor of any LM is crucial for the implementation of effective and tailored treatment approaches, which still represents a difficult problem in clinical practice.

Methods

The resection or biopsy specimens and associated clinicopathologic data were archived from seven independent centers between January 2017 and December 2020. The primary tumor sites of liver tumors were verified through evaluation of available medical records, pathological and imaging information. The performance of a 90-gene expression assay for the determination of the site of tumor origin was assessed.

Result

A total of 130 LM covering 15 tumor types and 16 primary liver tumor specimens that met all quality control criteria were analyzed by the 90-gene expression assay. Among 130 LM cases, tumors were most frequently located in the colorectum, ovary and breast. Overall, the analysis of the 90-gene signature showed 93.1% and 100% agreement rates with the reference diagnosis in LM and primary liver tumor, respectively. For the common primary tumor types, the concordance rate was 100%, 95.7%, 100%, 93.8%, 87.5% for classifying the LM from the ovary, colorectum, breast, neuroendocrine, and pancreas, respectively.

Conclusion

The overall accuracy of 93.8% demonstrates encouraging performance of the 90-gene expression assay in identifying the primary sites of liver tumors. Future incorporation of the 90-gene expression assay in clinical diagnosis will aid oncologists in applying precise treatments, leading to improved care and outcomes for LM patients.

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.

Pathologic and molecular responses to neoadjuvant trastuzumab and/or lapatinib from a phase II randomized trial in HER2-positive breast cancer (TRIO-US B07)

In this multicenter, open-label, randomized phase II investigator-sponsored neoadjuvant trial with funding provided by Sanofi and GlaxoSmithKline (TRIO-US B07, Clinical Trials NCT00769470), participants with early-stage HER2-positive breast cancer (N = 128) were recruited from 13 United States oncology centers throughout the Translational Research in Oncology network. Participants were randomized to receive trastuzumab (T; N = 34), lapatinib (L; N = 36), or both (TL; N = 58) as HER2-targeted therapy, with each participant given one cycle of this designated anti-HER2 therapy alone followed by six cycles of standard combination chemotherapy with the same anti-HER2 therapy. The primary objective was to estimate the rate of pathologic complete response (pCR) at the time of surgery in each of the three arms. In the intent-to-treat population, we observed similar pCR rates between T (47%, 95% confidence interval [CI] 30-65%) and TL (52%, 95% CI 38-65%), and a lower pCR rate with L (25%, 95% CI 13-43%). In the T arm, 100% of participants completed all protocol-specified treatment prior to surgery, as compared to 69% in the L arm and 74% in the TL arm. Tumor or tumor bed tissue was collected whenever possible pre-treatment (N = 110), after one cycle of HER2-targeted therapy alone (N = 89), and at time of surgery (N = 59). Higher-level amplification of HER2 and hormone receptor (HR)-negative status were associated with a higher pCR rate. Large shifts in the tumor, immune, and stromal gene expression occurred after one cycle of HER2-targeted therapy. In contrast to pCR rates, the L-containing arms exhibited greater proliferation reduction than T at this timepoint. Immune expression signatures increased in all arms after one cycle of HER2-targeted therapy, decreasing again by the time of surgery. Our results inform approaches to early assessment of sensitivity to anti-HER2 therapy and shed light on the role of the immune microenvironment in response to HER2-targeted agents.

Benchmarking of cell type deconvolution pipelines for transcriptomics data

Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance.

Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance.

Development and Clinical Validation of a 90-Gene Expression Assay for Identifying Tumor Tissue Origin

The accurate identification of tissue origin in patients with metastatic cancer is critical for effective treatment selection but remains a challenge. The aim of this study is to develop a gene expression assay for tumor molecular classification and integrate it with clinicopathologic evaluations to identify the tissue origin for cancer of uncertain primary (CUP). A 90-gene expression signature, covering 21 tumor types, was identified and validated with an overall accuracy of 89.8% (95% CI, 0.87-0.92) in 609 tumor samples. More specifically, the classification accuracy reached 90.4% (95% CI, 0.87-0.93) for 323 primary tumors and 89.2% (95% CI, 0.85-0.92) for 286 metastatic tumors, with no statistically significant difference (P = 0.71). Furthermore, in a real-life cohort of 141 CUP patients, predictions by the 90-gene expression signature were consistent or compatible with the clinicopathologic features in 71.6% of patients (101/141). Findings suggest that this novel gene expression assay could efficiently predict the primary origin for a broad spectrum of tumor types and support its diagnostic utility of molecular classification in difficult-to-diagnose metastatic cancer. Additional studies are ongoing to further evaluate the clinical utility of this novel gene expression assay in predicting primary site and directing therapy for CUP patients.

Expression of ncRNAs on the DLK1-DIO3 Locus Is Associated With Basal and Mesenchymal Phenotype in Breast Epithelial Progenitor Cells

Epithelial-to-mesenchymal transition (EMT) and its reversed process mesenchymal-to-epithelial transition (MET) play a critical role in epithelial plasticity during development and cancer progression. Among important regulators of these cellular processes are non-coding RNAs (ncRNAs). The imprinted DLK1-DIO3 locus, containing numerous maternally expressed ncRNAs including the lncRNA maternally expressed gene 3 (MEG3) and a cluster of over 50 miRNAs, has been shown to be a modulator of stemness in embryonic stem cells and in cancer progression, potentially through the tumor suppressor role of MEG3. In this study we analyzed the expression pattern and functional role of ncRNAs from the DLK1-DIO3 locus in epithelial plasticity of the breast. We studied their expression in various cell types of breast tissue and revisit the role of the locus in EMT/MET using a breast epithelial progenitor cell line (D492) and its isogenic mesenchymal derivative (D492M). Marked upregulation of ncRNAs from the DLK1-DIO3 locus was seen after EMT induction in two cell line models of EMT. In addition, the expression of MEG3 and the maternally expressed ncRNAs was higher in stromal cells compared to epithelial cell types in primary breast tissue. We also show that expression of MEG3 is concomitant with the expression of the ncRNAs from the DLK1-DIO3 locus and its expression is therefore likely indicative of activation of all ncRNAs at the locus. MEG3 expression is correlated with stromal markers in normal tissue and breast cancer tissue and negatively correlated with the survival of breast cancer patients in two different cohorts. Overexpression of MEG3 using CRISPR activation in a breast epithelial cell line induced partial EMT and enriched for a basal-like phenotype. Conversely, knock down of MEG3 using CRISPR inhibition in a mesenchymal cell line reduced the mesenchymal and basal-like phenotype of the cell line. In summary our study shows that maternally expressed ncRNAs are markers of EMT and suggests that MEG3 is a novel regulator of EMT/MET in breast tissue. Nevertheless, further studies are needed to fully dissect the molecular pathways influenced by non-coding RNAs at the DLK1-DIO3 locus in breast tissue.

AR/ER Ratio Correlates with Expression of Proliferation Markers and with Distinct Subset of Breast Tumors

The co-expression of androgen (AR) and estrogen (ER) receptors, in terms of higher AR/ER ratio, has been recently associated with poor outcome in ER-positive (ER+) breast cancer (BC) patients. The aim of this study was to analyze if the biological aggressiveness, underlined in ER+ BC tumors with higher AR/ER ratio, could be due to higher expression of genes related to cell proliferation. On a cohort of 47 ER+ BC patients, the AR/ER ratio was assessed by immunohistochemistry and by mRNA analysis. The expression level of five gene proliferation markers was defined through TaqMan^®-qPCR assays. Results were validated using 979 BC cases obtained from gene expression public databases. ER+ BC tumors with ratios of AR/ER ≥ 2 have higher expression levels of cellular proliferation genes than tumors with ratios of AR/ER < 2, in both the 47 ER+ BC patients (P < 0.001) and in the validation cohort (P = 0.005). Moreover, BC cases with ratios of AR/ER ≥ 2 of the validation cohort were mainly assigned to luminal B and HER2-enriched molecular subtypes, typically characterized by higher proliferation and poorer prognosis. These data suggest that joint routine evaluation of AR and ER expression may identify a unique subset of tumors, which show higher levels of cellular proliferation and therefore a more aggressive behavior.

Bimodal age distribution at diagnosis in breast cancer persists across molecular and genomic classifications

PURPOSE

Female breast cancer demonstrates bimodal age frequency distribution patterns at diagnosis, interpretable as two main etiologic subtypes or groupings of tumors with shared risk factors. While RNA-based methods including PAM50 have identified well-established clinical subtypes, age distribution patterns at diagnosis as a proxy for etiologic subtype are not established for molecular and genomic tumor classifications.

METHODS

We evaluated smoothed age frequency distributions at diagnosis for Carolina Breast Cancer Study cases within immunohistochemistry-based and RNA-based expression categories. Akaike information criterion (AIC) values compared the fit of single density versus two-component mixture models. Two-component mixture models estimated the proportion of early-onset and late-onset categories by immunohistochemistry-based ER (n = 2860), and by RNA-based ESR1 and PAM50 subtype (n = 1965). PAM50 findings were validated using pooled publicly available data (n = 8103).

RESULTS

Breast cancers were best characterized by bimodal age distribution at diagnosis with incidence peaks near 45 and 65 years, regardless of molecular characteristics. However, proportional composition of early-onset and late-onset age distributions varied by molecular and genomic characteristics. Higher ER-protein and ESR1-RNA categories showed a greater proportion of late age-at-onset. Similarly, PAM50 subtypes showed a shifting age-at-onset distribution, with most pronounced early-onset and late-onset peaks found in Basal-like and Luminal A, respectively.

CONCLUSIONS

Bimodal age distribution at diagnosis was detected in the Carolina Breast Cancer Study, similar to national cancer registry data. Our data support two fundamental age-defined etiologic breast cancer subtypes that persist across molecular and genomic characteristics. Better criteria to distinguish etiologic subtypes could improve understanding of breast cancer etiology and contribute to prevention efforts.

The Impact of Stroma Admixture on Molecular Subtypes and Prognostic Gene Signatures in Serous Ovarian Cancer

BACKGROUND

Recent efforts to improve outcomes for high-grade serous ovarian cancer, a leading cause of cancer death in women, have focused on identifying molecular subtypes and prognostic gene signatures, but existing subtypes have poor cross-study robustness. We tested the contribution of cell admixture in published ovarian cancer molecular subtypes and prognostic gene signatures.

METHODS

Gene signatures of tumor and stroma were developed using paired microdissected tissue from two independent studies. Stromal genes were investigated in two molecular subtype classifications and 61 published gene signatures. Prognostic performance of gene signatures of stromal admixture was evaluated in 2,527 ovarian tumors (16 studies). Computational simulations of increasing stromal cell proportion were performed by mixing gene-expression profiles of paired microdissected ovarian tumor and stroma.

RESULTS

Recently described ovarian cancer molecular subtypes are strongly associated with the cell admixture. Tumors were classified as different molecular subtypes in simulations where the percentage of stromal cells increased. Stromal gene expression in bulk tumors was associated with overall survival (hazard ratio, 1.17; 95% confidence interval, 1.11-1.23), and in one data set, increased stroma was associated with anatomic sampling location. Five published prognostic gene signatures were no longer prognostic in a multivariate model that adjusted for stromal content.

CONCLUSIONS

Cell admixture affects the interpretation and reproduction of ovarian cancer molecular subtypes and gene signatures derived from bulk tissue. Elucidating the role of stroma in the tumor microenvironment and in prognosis is important.

IMPACT

Single-cell analyses may be required to refine the molecular subtypes of high-grade serous ovarian cancer.

Toward a Framework for Outcome-Based Analytical Performance Specifications: A Methodology Review of Indirect Methods for Evaluating the Impact of Measurement Uncertainty on Clinical Outcomes

BACKGROUND

For medical tests that have a central role in clinical decision-making, current guidelines advocate outcome-based analytical performance specifications. Given that empirical (clinical trial-style) analyses are often impractical or unfeasible in this context, the ability to set such specifications is expected to rely on indirect studies to calculate the impact of test measurement uncertainty on downstream clinical, operational, and economic outcomes. Currently, however, a lack of awareness and guidance concerning available alternative indirect methods is limiting the production of outcome-based specifications. Therefore, our aim was to review available indirect methods and present an analytical framework to inform future outcome-based performance goals.

CONTENT

A methodology review consisting of database searches and extensive citation tracking was conducted to identify studies using indirect methods to incorporate or evaluate the impact of test measurement uncertainty on downstream outcomes (including clinical accuracy, clinical utility, and/or costs). Eighty-two studies were identified, most of which evaluated the impact of imprecision and/or bias on clinical accuracy. A common analytical framework underpinning the various methods was identified, consisting of 3 key steps: (a) calculation of "true" test values; (b) calculation of measured test values (incorporating uncertainty); and (c) calculation of the impact of discrepancies between (a) and (b) on specified outcomes. A summary of the methods adopted is provided, and key considerations are discussed.

CONCLUSIONS

Various approaches are available for conducting indirect assessments to inform outcome-based performance specifications. This study provides an overview of methods and key considerations to inform future studies and research in this area.

Epigenome Mapping Identifies Tumor-Specific Gene Expression in Primary Rectal Cancer

Epigenetic alterations play a central role in cancer development and progression. The acetylation of histone 3 at lysine 27 (H3K27ac) specifically marks active genes. While chromatin immunoprecipitation (ChIP) followed by next-generation sequencing (ChIP-seq) analyses are commonly performed in cell lines, only limited data are available from primary tumors. We therefore examined whether cancer-specific alterations in H3K27ac occupancy can be identified in primary rectal cancer. Tissue samples from primary rectal cancer and matched mucosa were obtained. ChIP-seq for H3K27ac was performed and differentially occupied regions were identified. The expression of selected genes displaying differential occupancy between tumor and mucosa were examined in gene expression data from an independent patient cohort. Differential expression of four proteins was further examined by immunohistochemistry. ChIP-seq for H3K27ac in primary rectal cancer and matched mucosa was successfully performed and revealed differential binding on 44 regions. This led to the identification of genes with increased H3K27ac, i.e., RIPK2, FOXQ1, KRT23, and EPHX4, which were also highly upregulated in primary rectal cancer in an independent dataset. The increased expression of these four proteins was confirmed by immunohistochemistry. This study demonstrates the feasibility of ChIP-seq-based epigenome mapping of primary rectal cancer and confirms the value of H3K27ac occupancy to predict gene expression differences.

Computational deconvolution of transcriptomics data from mixed cell populations

Summary

Gene expression analyses of bulk tissues often ignore cell type composition as an important confounding factor, resulting in a loss of signal from lowly abundant cell types. In this review, we highlight the importance and value of computational deconvolution methods to infer the abundance of different cell types and/or cell type-specific expression profiles in heterogeneous samples without performing physical cell sorting. We also explain the various deconvolution scenarios, the mathematical approaches used to solve them and the effect of data processing and different confounding factors on the accuracy of the deconvolution results.

Contact

katleen.depreter@ugent.be.

Supplementary information

Supplementary data are available at Bioinformatics online.

PCA-PAM50 improves consistency between breast cancer intrinsic and clinical subtyping reclassifying a subset of luminal A tumors as luminal B

The PAM50 classifier is widely used for breast tumor intrinsic subtyping based on gene expression. Clinical subtyping, however, is based on immunohistochemistry assays of 3–4 biomarkers. Subtype calls by these two methods do not completely match even on comparable subtypes. Nevertheless, the estrogen receptor (ER)-balanced subset for gene-centering in PAM50 subtyping, is selected based on clinical ER status. Here we present a new method called Principle Component Analysis-based iterative PAM50 subtyping (PCA-PAM50) to perform intrinsic subtyping in ER status unbalanced cohorts. This method leverages PCA and iterative PAM50 calls to derive the gene expression-based ER status and a subsequent ER-balanced subset for gene centering. Applying PCA-PAM50 to three different breast cancer study cohorts, we observed improved consistency (by 6–9.3%) between intrinsic and clinical subtyping for all three cohorts. Particularly, a more aggressive subset of luminal A (LA) tumors as evidenced by higher MKI67 gene expression and worse patient survival outcomes, were reclassified as luminal B (LB) increasing the LB subtype consistency with IHC by 25–49%. In conclusion, we show that PCA-PAM50 enhances the consistency of breast cancer intrinsic and clinical subtyping by reclassifying an aggressive subset of LA tumors into LB. PCA-PAM50 code is available at ftp://ftp.wriwindber.org/.

Exploiting single-cell RNA sequencing data to link alternative splicing and cancer heterogeneity: A computational approach

Cell heterogeneity studies using single-cell sequencing are gaining great significance in the era of personalized medicine. In particular, characterization of tumor heterogeneity is an emergent issue to improve clinical oncology, since both inter- and intra-tumor level heterogeneity influence the utility and application of molecular classifications through specific biomarkers. Majority of studies have exploited gene expression to discriminate cell types. However, to provide a more nuanced view of the underlying differences, isoform expression and alternative splicing events have to be analyzed in detail. In this study, we utilize publicly available single cell and bulk RNA sequencing datasets of breast cancer cells from primary tumors and immortalized cell lines. Breast cancer is very heterogeneous with well defined molecular subtypes and was therefore chosen for this study. RNA-seq data were explored in terms of genes, isoforms abundance and splicing events. The study was conducted from an average based approach (gene level expression) to detailed and deeper ones (isoforms abundance/splicing events) to perform a comparative analysis, and, thus, highlight the importance of the splicing machinery in defining the tumor heterogeneity. Moreover, here we demonstrate how the investigation of gene isoforms expression can help to identify the appropriate in vitro models. We furthermore extracted marker isoforms, and alternatively spliced genes between and within the different single cell populations to improve the classification of the breast cancer subtypes.

Robustness of biomarker determination in breast cancer by RT-qPCR: impact of tumor cell content, DCIS and non-neoplastic breast tissue

Background

Tissue heterogeneity in formalin-fixed paraffin-embedded (FFPE) breast cancer specimens may affect the accuracy of reverse transcription quantitative real-time PCR (RT-qPCR). Herein, we tested the impact of tissue heterogeneity of breast cancer specimen on the RT-qPCR-based gene expression assay MammaTyper®.

Methods

MammaTyper® quantifies the mRNA expression of the four biomarkers ERBB2, ESR1, PGR, and MKI67. Based on pre-defined cut-off values, this molecular in vitro diagnostic assay permits binary marker classification and determination of breast cancer subtypes as defined by St Gallen 2013. In this study, we compared data from whole FFPE sections with data obtained in paired RNA samples after enrichment for invasive carcinoma via macro- or laser-capture micro-dissection.

Results

Compared to whole sections, removal of surrounding adipose tissue by macrodissection generated mean absolute 40-ddCq differences of 0.28–0.32 cycles for all four markers, with ≥90% concordant binary classifications. The mean raw marker Cq values in the adipose tissue were delayed by 6 to 7 cycles compared with the tumor-enriched sections, adding a trivial linear fold change of 1.0078 to 1.0156. Comparison of specimens enriched for invasive tumor with whole sections with as few as 20% tumor cell content resulted in mean absolute differences that remained on average below 0.59 Cq. The mean absolute difference between whole sections containing up to 60% ductal carcinoma in situ (DCIS) and specimens after dissection of DCIS was only 0.16–0.25 cycles, although there was a tendency for higher gene expression in DCIS. Observed variations were related to small size of samples and proximity of values to the limit of detection.

Conclusion

Expression of ESR1, PGR, ERBB2 and MKI67 by MammaTyper® is robust in clinical FFPE samples. Assay performance was unaffected by adipose tissue and was stable in samples with as few as 20% tumor cell content and up to 60% DCIS.

Electronic supplementary material

The online version of this article (10.1186/s13000-018-0760-6) contains supplementary material, which is available to authorized users.

Overview on Clinical Relevance of Intra-Tumor Heterogeneity

Today, clinical evaluation of tumor heterogeneity is an emergent issue to improve clinical oncology. In particular, intra-tumor heterogeneity (ITH) is closely related to cancer progression, resistance to therapy, and recurrences. It is interconnected with complex molecular mechanisms including spatial and temporal phenomena, which are often peculiar for every single patient. This review tries to describe all the types of ITH including morphohistological ITH, and at the molecular level clonal ITH derived from genomic instability and nonclonal ITH derived from microenvironment interaction. It is important to consider the different types of ITH as a whole for any patient to investigate on cancer progression, prognosis, and treatment opportunities. From a practical point of view, analytical methods that are widely accessible today, or will be in the near future, are evaluated to investigate the complex pattern of ITH in a reproducible way for a clinical application.

Outcomes of single versus double hormone receptor-positive breast cancer. A GEICAM/9906 sub-study

BACKGROUND

Retrospective data suggest better outcomes for patients with double hormonal receptor (oestrogen [ER] and progesterone receptor [PgR])-positive (dHR+) early breast cancer, compared with single hormonal receptor-positive, sHR+, (ER+/PgR- or ER-/PgR+) disease. Here, we evaluate the classification according to intrinsic subtypes and clinical outcomes of sHR+ versus dHR+ in HER2-negative breast cancer patients enrolled in GEICAM/9906 study (NCT00129922).

METHODS

Archival tumours were retrieved retrospectively for the analysis of ER, PgR and HER2 status and classified into intrinsic subtypes using the PAM50 gene expression assay. Disease-free survival (DFS) and overall survival (OS) were explored using a Cox proportional hazard analysis.

RESULTS

Data on intrinsic subtypes were available in 571 (50%) patients with ER+ and/or PR+, and HER2-negative primary tumours. The incidence of luminal A and luminal B subtypes were 52%/36% in dHR+ tumours (ER+/PgR+), and 15%/58% in ER+/PgR-tumours. ER-/PgR+ tumours were mainly luminal A (52%). Compared with ER+/PgR+ patients, DFS was similar in ER-/PgR+ (hazard ratio [HR] 1.15, 95% confidence interval [CI] 0.57-2.34, p = 0.70) but worse in ER+/PgR- patients (HR 1.60, 95% CI 1.12-2.28, p < 0.01). Similar results were observed for OS (HR 1.50, p = 0.30 and HR 1.86, p < 0.01, respectively).

CONCLUSIONS

The ER+/PgR- group is characterised by higher proliferation and worse outcomes. In spite of the ER-/PgR+ subgroup resembles ER+/PgR+ disease in terms of molecular subtypes and outcomes, the small number of patients in this subgroup prevents from drawing any conclusions.

TRIAL REGISTRATION

EudraCT: 2005-003108-12 (retrospectively registered 28/06/2005). CLINICALTRIALS.

GOV IDENTIFIER

NCT00129922 (retrospectively registered 10/08/2005).

Frequency of breast cancer subtypes among African American women in the AMBER consortium

Background

Breast cancer subtype can be classified using standard clinical markers (estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2)), supplemented with additional markers. However, automated biomarker scoring and classification schemes have not been standardized. The aim of this study was to optimize tumor classification using automated methods in order to describe subtype frequency in the African American Breast Cancer Epidemiology and Risk (AMBER) consortium.

Methods

Using immunohistochemistry (IHC), we quantified the expression of ER, PR, HER2, the proliferation marker Ki67, and two basal-like biomarkers, epidermal growth factor receptor (EGFR) and cytokeratin (CK)5/6, in 1381 invasive breast tumors from African American women. RNA-based (prediction analysis of microarray 50 (PAM50)) subtype, available for 574 (42%) cases, was used to optimize classification. Subtype frequency was calculated, and associations between subtype and tumor characteristics were estimated using logistic regression.

Results

Relative to ER, PR and HER2 from medical records, central IHC staining and the addition of Ki67 or combined tumor grade improved accuracy for classifying PAM50-based luminal subtypes. Few triple negative cases (< 2%) lacked EGFR and CK5/6 expression, thereby providing little improvement in accuracy for identifying basal-like tumors. Relative to luminal A subtype, all other subtypes had higher combined grade and were larger, and ER-/HER2+ tumors were more often lymph node positive and late stage tumors. The frequency of basal-like tumors was 31%, exceeded only slightly by luminal A tumors (37%).

Conclusions

Our findings indicate that automated IHC-based classification produces tumor subtype frequencies approximating those from PAM50-based classification and highlight high frequency of basal-like and low frequency of luminal A breast cancer in a large study of African American women.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-0939-5) contains supplementary material, which is available to authorized users.

Desmoglein 2 promotes vasculogenic mimicry in melanoma and is associated with poor clinical outcome

Tumors can develop a blood supply not only by promoting angiogenesis but also by forming vessel-like structures directly from tumor cells, known as vasculogenic mimicry (VM). Understanding mechanisms that regulate VM is important, as these might be exploitable to inhibit tumor progression. Here, we reveal the adhesion molecule desmoglein 2 (DSG2) as a novel mediator of VM in melanoma. Analysis of patient-derived melanoma cell lines and tumor tissues, and interrogation of The Cancer Genome Atlas (TCGA) data, revealed that DSG2 is frequently overexpressed in primary and metastatic melanomas compared to normal melanocytes. Notably, this overexpression was associated with poor clinical outcome. DSG2⁺ melanoma cells self-organized into tube-like structures on Matrigel, indicative of VM activity, which was inhibited by DSG2 knockdown or treatment with a DSG2-blocking peptide. Mechanistic studies revealed that DSG2 regulates adhesion and cell-cell interactions during tube formation, but does not control melanoma cell viability, proliferation or motility. Finally, analysis of patient tumors revealed a correlation between DSG2 expression, VM network density and expression of VM-associated genes. These studies identify DSG2 as a key regulator of VM activity in human melanoma and suggest this molecule might be therapeutically targeted to reduce tumor blood supply and metastatic spread.

Accurity: accurate tumor purity and ploidy inference from tumor-normal WGS data by jointly modelling somatic copy number alterations and heterozygous germline single-nucleotide-variants

Motivation

Tumor purity and ploidy have a substantial impact on next-gen sequence analyses of tumor samples and may alter the biological and clinical interpretation of results. Despite the existence of several computational methods that are dedicated to estimate tumor purity and/or ploidy from The Cancer Genome Atlas (TCGA) tumor-normal whole-genome-sequencing (WGS) data, an accurate, fast and fully-automated method that works in a wide range of sequencing coverage, level of tumor purity and level of intra-tumor heterogeneity, is still missing.

Results

We describe a computational method called Accurity that infers tumor purity, tumor cell ploidy and absolute allelic copy numbers for somatic copy number alterations (SCNAs) from tumor-normal WGS data by jointly modelling SCNAs and heterozygous germline single-nucleotide-variants (HGSNVs). Results from both in silico and real sequencing data demonstrated that Accurity is highly accurate and robust, even in low-purity, high-ploidy and low-coverage settings in which several existing methods perform poorly. Accounting for tumor purity and ploidy, Accurity significantly increased signal/noise gaps between different copy numbers. We are hopeful that Accurity is of clinical use for identifying cancer diagnostic biomarkers.

Availability and implementation

Accurity is implemented in C++/Rust, available at http://www.yfish.org/software/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Enrichment of high-grade tumors in breast cancer gene expression studies

Purpose

Gene expression (GE) profiling for breast cancer classification and prognostication has become increasingly used in clinical diagnostics. GE profiling requires a reasonable tumor cell percentage and high-quality RNA. As a consequence, a certain amount of samples drop out. If tumor characteristics are different between samples included and excluded from GE profiling, this can lead to bias. Therefore, we assessed whether patient and tumor characteristics differ between tumors suitable or unsuitable for generating GE profiles in breast cancer.

Methods

In a consecutive cohort of 738 breast cancer patients who received neoadjuvant chemotherapy at the Netherlands Cancer Institute, GE profiling was performed. We compared tumor characteristics and treatment outcome between patients included and excluded from GE profiling. Results were validated in an independent cohort of 812 patients treated with primary surgery.

Results

GE analysis could be performed in 53% of the samples. Patients with tumor GE profiles more often had high-grade tumors [odds ratio 2.57 (95%CI 1.77–3.72), p < 0.001] and were more often lymph node positive [odds ratio 1.50 (95%CI 1.03–2.19), p = 0.035] compared to the group for which GE profiling was not possible. In the validation cohort, tumors suitable for gene expression analysis were more often high grade.

Conclusions

In our gene expression studies, tumors suitable for GE profiling had more often an unfavorable prognostic profile. Due to selection of samples with a high tumor percentage, we automatically select for tumors with specific features, i.e., tumors with a higher grade and lymph node involvement. It is important to be aware of this phenomenon when performing gene expression analysis in a research or clinical context.

Electronic supplementary material

The online version of this article (10.1007/s10549-017-4622-9) contains supplementary material, which is available to authorized users.

Impact of Tumor Purity on Immune Gene Expression and Clustering Analyses across Multiple Cancer Types

Surgical archives of tumor specimens are often impure. The presence of RNA transcripts from nontumor cells, such as immune and stromal cells, can impede analyses of cancer expression profiles. To systematically analyze the impact of tumor purity, the gene expression profiles and tumor purities were obtained for 7,794 tumor specimens across 21 tumor types (available in The Cancer Genome Atlas consortium). First, we observed that genes with roles in immunity and oxidative phosphorylation were significantly inversely correlated and correlated with the tumor purity, respectively. The expression of genes implicated in immunotherapy and specific immune cell genes, along with the abundance of immune cell infiltrates, was substantially inversely correlated with tumor purity. This relationship may explain the correlation between immune gene expression and mutation burden, highlighting the need to account for tumor purity in the evaluation of expression markers obtained from bulk tumor transcriptome data. Second, examination of cluster membership of gene pairs, with or without controlling for tumor purity, revealed that tumor purity may have a substantial impact on gene clustering across tumor types. Third, feature genes for molecular taxonomy were analyzed for correlation with tumor purity, and for some tumor types, feature genes representing the mesenchymal and classical subtypes were inversely correlated and correlated with tumor purity, respectively. Our findings indicate that tumor purity is an important confounder in evaluating the correlation between gene expression and clinicopathologic features such as mutation burden, as well as gene clustering and molecular taxonomy. Cancer Immunol Res; 6(1); 87-97. ©2017 AACR.

Systematic review of the clinical and economic value of gene expression profiles for invasive early breast cancer available in Europe

Gene expression profiles with prognostic capacities have shown good performance in multiple clinical trials. However, with multiple assays available and numerous types of validation studies performed, the added value for daily clinical practice is still unclear. In Europe, the MammaPrint, OncotypeDX, PAM50/Prosigna and Endopredict assays are commercially available. In this systematic review, we aim to assess these assays on four important criteria: Assay development and methodology, clinical validation, clinical utility and economic value. We performed a literature search covering PubMed, Embase, Web of Science and Cochrane, for studies related to one or more of the four selected assays. We identified 147 papers for inclusion in this review. MammaPrint and OncotypeDX both have evidence available, including level IA clinical trial results for both assays. Both assays provide prognostic information. Predictive value has only been shown for OncotypeDX. In the clinical utility studies, a higher reduction in chemotherapy was achieved by OncotypeDX, although the number of available studies differ considerably between tests. On average, economic evaluations estimate that genomic testing results in a moderate increase in total costs, but that these costs are acceptable in relation to the expected improved patient outcome. PAM50/prosigna and EndoPredict showed comparable prognostic capacities, but with less economical and clinical utility studies. Furthermore, for these assays no level IA trial data are available yet. In summary, all assays have shown excellent prognostic capacities. The differences in the quantity and quality of evidence are discussed. Future studies shall focus on the selection of appropriate subgroups for testing and long-term outcome of validation trials, in order to determine the place of these assays in daily clinical practice.

Complementary Value of Contralateral Parenchymal Enhancement on DCE-MRI to Prognostic Models and Molecular Assays in High-risk ER+/HER2- Breast Cancer

Purpose: To determine whether markers of healthy breast stroma are able to select a subgroup of patients at low risk of death or metastasis from patients considered at high risk according to routine markers of the tumor.Experimental Design: Patients with ER⁺/HER2^- breast cancer were consecutively included for retrospective analysis. The contralateral parenchyma was segmented automatically on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), where upon the average of the top-10% late enhancement was calculated. This contralateral parenchymal enhancement (CPE) was analyzed with respect to routine prognostic models and molecular assays (Nottingham Prognostic Index, Dutch clinical chemotherapy-selection guidelines, 70-gene signature, and 21-gene recurrence score). CPE was split in tertiles and tested for overall and distant disease-free survival. CPE was adjusted for patient and tumor characteristics, as well as systemic therapy, using inverse probability weighting (IPW). Subanalyses were performed in patients at high risk according to prognostic models and molecular assays.Results: Four-hundred-and-fifteen patients were included, constituting the same group in which the association between CPE and survival was discovered. Median follow-up was 85 months, 34/415(8%) patients succumbed. After IPW-adjustment for patient and tumor characteristics, patients with high CPE had significantly better overall survival than those with low CPE in groups at high risk according to the Nottingham Prognostic Index [HR (95% CI): 0.08 (0.00-0.40), P < 0.001]; Dutch clinical guidelines [HR (95% CI): 0.22 (0.00-0.81), P = 0.021]; and 21-gene recurrence score [HR (95% CI): 0.14 (0.00-0.84), P = 0.030]. One group showed a trend [70-gene signature: HR (95% CI): 0.25 (0.00-1.02), P = 0.054].Conclusions: In patients at high risk based on the tumor, subgroups at relatively low risk were identified using pretreatment enhancement of the stroma on breast DCE-MRI. Clin Cancer Res; 23(21); 6505-15. ©2017 AACR.

The effects of lymph node status on predicting outcome in ER+ /HER2- tamoxifen treated breast cancer patients using gene signatures

BACKGROUND

Lymph node (LN) status is the most important prognostic variable used to guide ER positive (+) breast cancer treatment. While a positive nodal status is traditionally associated with a poor prognosis, a subset of these patients respond well to treatment and achieve long-term survival. Several gene signatures have been established as a means of predicting outcome of breast cancer patients, but the development and indication for use of these assays varies. Here we compare the capacity of two approved gene signatures and a third novel signature to predict outcome in distinct LN negative (-) and LN+ populations. We also examine biological differences between tumours associated with LN- and LN+ disease.

METHODS

Gene expression data from publically available data sets was used to compare the ability of Oncotype DX and Prosigna to predict Distant Metastasis Free Survival (DMFS) using an in silico platform. A novel gene signature (Ellen) was developed by including patients with both LN- and LN+ disease and using Prediction Analysis of Microarrays (PAM) software. Gene Set Enrichment Analysis (GSEA) was used to determine biological pathways associated with patient outcome in both LN- and LN+ tumors.

RESULTS

The Oncotype DX gene signature, which only used LN- patients during development, significantly predicted outcome in LN- patients, but not LN+ patients. The Prosigna gene signature, which included both LN- and LN+ patients during development, predicted outcome in both LN- and LN+ patient groups. Ellen was also able to predict outcome in both LN- and LN+ patient groups. GSEA suggested that epigenetic modification may be related to poor outcome in LN- disease, whereas immune response may be related to good outcome in LN+ disease.

CONCLUSIONS

We demonstrate the importance of incorporating lymph node status during the development of prognostic gene signatures. Ellen may be a useful tool to predict outcome of patients regardless of lymph node status, or for those with unknown lymph node status. Finally we present candidate biological processes, unique to LN- and LN+ disease, that may indicate risk of relapse.

Practicability of detecting somatic point mutation from RNA high throughput sequencing data

Traditionally, somatic mutations are detected by examining DNA sequence. The maturity of sequencing technology has allowed researchers to screen for somatic mutations in the whole genome. Increasingly, researchers have become interested in identifying somatic mutations through RNAseq data. With this motivation, we evaluated the practicability of detecting somatic mutations from RNAseq data. Current somatic mutation calling tools were designed for DNA sequencing data. To increase performance on RNAseq data, we developed a somatic mutation caller GLMVC based on bias reduced generalized linear model for both DNA and RNA sequencing data. Through comparison with MuTect and Varscan we showed that GLMVC performed better for somatic mutation detection using exome sequencing or RNAseq data. GLMVC is freely available for download at the following website: https://github.com/shengqh/GLMVC/wiki.

Performance of Three-Biomarker Immunohistochemistry for Intrinsic Breast Cancer Subtyping in the AMBER Consortium

BACKGROUND

Classification of breast cancer into intrinsic subtypes has clinical and epidemiologic importance. To examine accuracy of IHC-based methods for identifying intrinsic subtypes, a three-biomarker IHC panel was compared with the clinical record and RNA-based intrinsic (PAM50) subtypes.

METHODS

Automated scoring of estrogen receptor (ER), progesterone receptor (PR), and HER2 was performed on IHC-stained tissue microarrays comprising 1,920 cases from the African American Breast Cancer Epidemiology and Risk (AMBER) consortium. Multiple cores (1-6/case) were collapsed to classify cases, and automated scoring was compared with the clinical record and to RNA-based subtyping.

RESULTS

Automated analysis of the three-biomarker IHC panel produced high agreement with the clinical record (93% for ER and HER2, and 88% for PR). Cases with low tumor cellularity and smaller core size had reduced agreement with the clinical record. IHC-based definitions had high agreement with the clinical record regardless of hormone receptor positivity threshold (1% vs. 10%), but a 10% threshold produced highest agreement with RNA-based intrinsic subtypes. Using a 10% threshold, IHC-based definitions identified the basal-like intrinsic subtype with high sensitivity (86%), although sensitivity was lower for luminal A, luminal B, and HER2-enriched subtypes (76%, 40%, and 37%, respectively).

CONCLUSION

Three-biomarker IHC-based subtyping has reasonable accuracy for distinguishing basal-like from nonbasal-like, although additional biomarkers are required for accurate classification of luminal A, luminal B, and HER2-enriched cancers.

IMPACT

Epidemiologic studies relying on three-biomarker IHC status for subtype classification should use caution when distinguishing luminal A from luminal B and when interpreting findings for HER2-enriched cancers.

Prediction of pathological complete response to neoadjuvant chemotherapy by magnetic resonance imaging in breast cancer patients

The purpose of this study was to evaluate whether the baseline breast MRI findings would be useful for the prediction for pathological complete response (pCR) by breast cancer patients to neoadjuvant chemotherapy. Primary breast cancer patients (stage II-III) preoperatively treated with sequential paclitaxel (12 cycles) and fluorouracil, epirubicin, and cyclophosphamide (4 cycles), followed by surgery were retrospectively enrolled, and 229 patients were eligible. Before chemotherapy, breast MRI studies were performed. Breast tumors were dichotomized into round + oval and irregular types based on MRI morphology. The round + oval tumors showed a significantly higher pCR rate than the irregular tumors (42.0% vs 17.3%; P < 0.001). In addition, PAM50 analysis revealed that basal and HER2-enriched tumors were significantly more prevalent among round + oval than irregular type tumors (P = 0.015). Baseline MRI morphology appears to be a significant predictor for pCR. The higher rate of the basal and HER2-enriched tumors among the round + oval tumors may explain their better chemo-sensitivity.

Systematic pan-cancer analysis of tumour purity

The tumour microenvironment is the non-cancerous cells present in and around a tumour, including mainly immune cells, but also fibroblasts and cells that comprise supporting blood vessels. These non-cancerous components of the tumour may play an important role in cancer biology. They also have a strong influence on the genomic analysis of tumour samples, and may alter the biological interpretation of results. Here we present a systematic analysis using different measurement modalities of tumour purity in >10,000 samples across 21 cancer types from the Cancer Genome Atlas. Patients are stratified according to clinical features in an attempt to detect clinical differences driven by purity levels. We demonstrate the confounding effect of tumour purity on correlating and clustering tumours with transcriptomics data. Finally, using a differential expression method that accounts for tumour purity, we find an immunotherapy gene signature in several cancer types that is not detected by traditional differential expression analyses.

The importance of the tumour microenvironment has now been realised, however the presence of non-tumour cells in cancer samples can complicate genomic analyses. Here, the authors estimate tumour purity in 10,000 samples from the TCGA dataset and can detect a signature of T cell activation.

The Effect of Acute and Chronic Social Stress on the Hippocampal Transcriptome in Mice

Psychogenic stress contributes to the formation of brain pathology. Using gene expression microarrays, we analyzed the hippocampal transcriptome of mice subjected to acute and chronic social stress of different duration. The longest period of social stress altered the expression of the highest number of genes and most of the stress-induced changes in transcription were reversible after 5 days of rest. Chronic stress affected genes involved in the functioning of the vascular system (Alas2, Hbb-b1, Hba-a2, Hba-a1), injury response (Vwf, Mgp, Cfh, Fbln5, Col3a1, Ctgf) and inflammation (S100a8, S100a9, Ctla2a, Ctla2b, Lcn2, Lrg1, Rsad2, Isg20). The results suggest that stress may affect brain functions through the stress-induced dysfunction of the vascular system. An important issue raised in our work is also the risk of the contamination of brain tissue samples with choroid plexus. Such contamination would result in a consistent up- or down-regulation of genes, such as Ttr, Igf2, Igfbp2, Prlr, Enpp2, Sostdc1, 1500015O10RIK (Ecrg4), Kl, Clic6, Kcne2, F5, Slc4a5, and Aqp1. Our study suggests that some of the previously reported, supposedly specific changes in hippocampal gene expression, may be a result of the inclusion of choroid plexus in the hippocampal samples.

Development and verification of the PAM50-based Prosigna breast cancer gene signature assay

BACKGROUND

The four intrinsic subtypes of breast cancer, defined by differential expression of 50 genes (PAM50), have been shown to be predictive of risk of recurrence and benefit of hormonal therapy and chemotherapy. Here we describe the development of Prosigna™, a PAM50-based subtype classifier and risk model on the NanoString nCounter Dx Analysis System intended for decentralized testing in clinical laboratories.

METHODS

514 formalin-fixed, paraffin-embedded (FFPE) breast cancer patient samples were used to train prototypical centroids for each of the intrinsic subtypes of breast cancer on the NanoString platform. Hierarchical cluster analysis of gene expression data was used to identify the prototypical centroids defined in previous PAM50 algorithm training exercises. 304 FFPE patient samples from a well annotated clinical cohort in the absence of adjuvant systemic therapy were then used to train a subtype-based risk model (i.e. Prosigna ROR score). 232 samples from a tamoxifen-treated patient cohort were used to verify the prognostic accuracy of the algorithm prior to initiating clinical validation studies.

RESULTS

The gene expression profiles of each of the four Prosigna subtype centroids were consistent with those previously published using the PCR-based PAM50 method. Similar to previously published classifiers, tumor samples classified as Luminal A by Prosigna had the best prognosis compared to samples classified as one of the three higher-risk tumor subtypes. The Prosigna Risk of Recurrence (ROR) score model was verified to be significantly associated with prognosis as a continuous variable and to add significant information over both commonly available IHC markers and Adjuvant! Online.

CONCLUSIONS

The results from the training and verification data sets show that the FDA-cleared and CE marked Prosigna test provides an accurate estimate of the risk of distant recurrence in hormone receptor positive breast cancer and is also capable of identifying a tumor's intrinsic subtype that is consistent with the previously published PCR-based PAM50 assay. Subsequent analytical and clinical validation studies confirm the clinical accuracy and technical precision of the Prosigna PAM50 assay in a decentralized setting.

Quantification of HER family receptors in breast cancer

The clinical success of trastuzumab in breast cancer taught us that appropriate tumor evaluation is mandatory for the correct identification of patients eligible for targeted therapies. Although HER2 protein expression by immunohistochemistry (IHC) and gene amplification by fluorescence in situ hybridization (FISH) assays are routinely used to select patients to receive trastuzumab, both assays only partially predict response to the drug. In the case of epidermal growth factor receptor (EGFR), the link between the presence of the receptor or its amplification and response to anti-EGFR therapies could not be demonstrated. Even less is known for HER3 and HER4, mainly due to lack of robust and validated assays detecting these proteins. It is becoming evident that, besides FISH and IHC, we need better assays to quantify HER receptors and categorize the patients for individualized treatments. Here, we present the current available methodologies to measure HER family receptors and discuss the clinical implications of target quantification.

Metaplastic breast carcinomas display genomic and transcriptomic heterogeneity [corrected]

Metaplastic breast carcinoma is a rare and aggressive histologic type of breast cancer, preferentially displaying a triple-negative phenotype. We sought to define the transcriptomic heterogeneity of metaplastic breast cancers on the basis of current gene expression microarray-based classifiers, and to determine whether these tumors display gene copy number profiles consistent with those of BRCA1-associated breast cancers. Twenty-eight consecutive triple-negative metaplastic breast carcinomas were reviewed, and the metaplastic component present in each frozen specimen was defined (ie, spindle cell, squamous, chondroid metaplasia). RNA and DNA extracted from frozen sections with tumor cell content >60% were subjected to gene expression (Illumina HumanHT-12 v4) and copy number profiling (Affymetrix SNP 6.0), respectively. Using the best practice PAM50/claudin-low microarray-based classifier, all metaplastic breast carcinomas with spindle cell metaplasia were of claudin-low subtype, whereas those with squamous or chondroid metaplasia were preferentially of basal-like subtype. Triple-negative breast cancer subtyping using a dedicated website (http://cbc.mc.vanderbilt.edu/tnbc/) revealed that all metaplastic breast carcinomas with chondroid metaplasia were of mesenchymal-like subtype, spindle cell carcinomas preferentially of unstable or mesenchymal stem-like subtype, and those with squamous metaplasia were of multiple subtypes. None of the cases was classified as immunomodulatory or luminal androgen receptor subtype. Integrative clustering, combining gene expression and gene copy number data, revealed that metaplastic breast carcinomas with spindle cell and chondroid metaplasia were preferentially classified as of integrative clusters 4 and 9, respectively, whereas those with squamous metaplasia were classified into six different clusters. Eight of the 26 metaplastic breast cancers subjected to SNP6 analysis were classified as BRCA1-like. The diversity of histologic features of metaplastic breast carcinomas is reflected at the transcriptomic level, and an association between molecular subtypes and histology was observed. BRCA1-like genomic profiles were found only in a subset (31%) of metaplastic breast cancers, and were not associated with a specific molecular or histologic subtype.

Absolute assignment of breast cancer intrinsic molecular subtype

BACKGROUND

Massively parallel gene expression profiling has provided a more objective, molecular-level characterization of breast cancer subtypes. Several bioinformatics tools are available to infer patient subtype from a gene expression profile including the well-studied PAM50. The specific algorithmic methods used in these tools require access to a broad patient dataset. The choice of subtype for an individual is determined relative to all other patients across the panel, making subtypes heavily dependent on the composition of the dataset. Our aim was to develop a bioinformatics approach assigning absolute breast cancer subtypes, independent of dataset composition.

METHODS

Using a dataset of 4924 breast cancer patients, we defined a new bioinformatics approach: Absolute Intrinsic Molecular Subtyping (AIMS) that assigns subtype from a gene expression profile for an individual sample without the need for a large, diverse, and normalized dataset. We evaluated the agreement of AIMS with PAM50 and compared subtype assignment and prognostic value of the subtypes. We assessed AIMS' robustness using a benchmark set of tests including subtype reproducibility between technologies, gene removal, and normal gene expression contamination, and compared it with PAM50. All statistical tests, except where noted, were two-sided.

RESULTS

AIMS vastly agreed with PAM50, with 76% and 77% agreement for cross validation and the test set, respectively, and the prognostic capacity of the intrinsic subtypes was preserved. AIMS is fully stable, and its absolute nature enables its use on a wide range of datasets and technologies, including RNA-seq.

CONCLUSIONS

The instability of a breast cancer subtyping scheme like PAM50 could have important consequences in clinical management of patients. AIMS is a fully stable and robust subtyping scheme that recapitulates PAM50.

Tumor intrinsic subtype is reflected in cancer-adjacent tissue

INTRODUCTION

Overall survival of early-stage breast cancer patients is similar for those who undergo breast-conserving therapy (BCT) and mastectomy; however, 10% to 15% of women undergoing BCT suffer ipsilateral breast tumor recurrence. The risk of recurrence may vary with breast cancer subtype. Understanding the gene expression of the cancer-adjacent tissue and the stromal response to specific tumor subtypes is important for developing clinical strategies to reduce recurrence risk.

METHODS

We utilized two independent datasets to study gene expression data in cancer-adjacent tissue from invasive breast cancer patients. Complementary in vitro cocultures were used to study cell-cell communication between fibroblasts and specific breast cancer subtypes.

RESULTS

Our results suggest that intrinsic tumor subtypes are reflected in histologically normal cancer-adjacent tissue. Gene expression of cancer-adjacent tissues shows that triple-negative (Claudin-low or basal-like) tumors exhibit increased expression of genes involved in inflammation and immune response. Although such changes could reflect distinct immune populations present in the microenvironment, altered immune response gene expression was also observed in cocultures in the absence of immune cell infiltrates, emphasizing that these inflammatory mediators are secreted by breast-specific cells. In addition, although triple-negative breast cancers are associated with upregulated immune response genes, luminal breast cancers are more commonly associated with estrogen-response pathways in adjacent tissues.

CONCLUSIONS

Specific characteristics of breast cancers are reflected in the surrounding histologically normal tissue. This commonality between tumor and cancer-adjacent tissue may underlie second primaries and local recurrences.

IMPACT

Biomarkers derived from cancer-adjacent tissue may be helpful in defining personalized surgical strategies or in predicting recurrence risk.

Development of a robust RNA-based classifier to accurately determine ER, PR, and HER2 status in breast cancer clinical samples

Breast cancers are categorized into three subtypes based on protein expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2/ERBB2). Patients enroll onto experimental clinical trials based on ER, PR, and HER2 status and, as receptor status is prognostic and defines treatment regimens, central receptor confirmation is critical for interpreting results from these trials. Patients enrolling onto experimental clinical trials in the metastatic setting often have limited available archival tissue that might better be used for comprehensive molecular profiling rather than slide-intensive reconfirmation of receptor status. We developed a Random Forests-based algorithm using a training set of 158 samples with centrally confirmed IHC status, and subsequently validated this algorithm on multiple test sets with known, locally determined IHC status. We observed a strong correlation between target mRNA expression and IHC assays for HER2 and ER, achieving an overall accuracy of 97 and 96 %, respectively. For determining PR status, which had the highest discordance between central and local IHC, incorporation of expression of co-regulated genes in a multivariate approach added predictive value, outperforming the single, target gene approach by a 10 % margin in overall accuracy. Our results suggest that multiplexed qRT-PCR profiling of ESR1, PGR, and ERBB2 mRNA, along with several other subtype associated genes, can effectively confirm breast cancer subtype, thereby conserving tumor sections and enabling additional biomarker data to be obtained from patients enrolled onto experimental clinical trials.

Preanalytical variables and performance of diagnostic RNA-based gene expression analysis in breast cancer

Prognostic multigene expression assays have become widely available to provide additional information to standard clinical parameters and to support clinicians in treatment decisions. In this study, we analyzed the impact of variations in tissue handling on the diagnostic EndoPredict test results. EndoPredict is a quantitative reverse transcription PCR assay conducted on RNA from formalin-fixed, paraffin-embedded (FFPE) tissue that predicts the likelihood of distant recurrence in patients with ER-positive/HER2-negative breast cancer. In this study, we performed a total of 138 EndoPredict assays to study the effects of preanalytical variables such as time to fixation, fixation time, tumor cell content, and section storage time on the EndoPredict test results. A time to fixation of up to 12 h and fixation of up to 5 days did not affect the results of the gene expression test. Paired samples of FFPE sections with tumor cell content ranging from 15 to 95 % and tumor-enriched samples showed a correlation coefficient of 0.97. Test results of tissue sections that have been stored for 12 months at +4 or +20 °C showed a correlation of 0.99 when compared to results of nonstored sections. In conclusion, preanalytical tissue handling is not a critical factor for diagnostic gene expression analysis with the EndoPredict assay. The test can therefore be easily integrated into the standard workflow of molecular pathology.