Breast cancer subtype predictors revisited: from consensus to concordance?

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.

A comparison of rule-based and centroid single-sample multiclass predictors for transcriptomic classification

MOTIVATION

Gene expression-based multiclass prediction, such as tumor subtyping, is a non-trivial bioinformatic problem. Most classifier methods operate by comparing expression levels relative to other samples. Methods that base predictions on the expression pattern within a sample have been proposed as an alternative. As these methods are invariant to the cohort composition and can be applied to a sample in isolation, they can collectively be termed single sample predictors (SSP). Such predictors could potentially be used for preprocessing-free classification of new samples and be built to function across different expression platforms where proper batch and dataset normalization is challenging. Here, we evaluate the behavior of several multiclass SSPs based on binary gene-pair rules (k-Top Scoring Pairs, Absolute Intrinsic Molecular Subtyping and a new Random Forest approach) and compare them to centroids built with centered or raw expression values, with the criteria that an optimal predictor should have high accuracy, overcome differences in tumor purity, be robust across expression platforms and provide an informative prediction output score.

RESULTS

We found that gene-pair-based SSPs showed excellent performance on many expression-based classification tasks. The three methods differed in prediction score output, handling of tied scores and behavior in low purity samples. The k-Top Scoring Pairs and Random Forest approach both achieved high classification accuracy while providing an informative prediction score. Although gene-pair-based SSPs have been touted as being cross-platform compatible (through training on mixed platform data), out-of-the-box compatibility with a new dataset remains a potential issue that warrants cohort-to-cohort verification.

AVAILABILITY AND IMPLEMENTATION

Our R package 'multiclassPairs' (https://cran.r-project.org/package=multiclassPairs) (https://doi.org/10.1093/bioinformatics/btab088) is freely available and enables easy training, prediction, and visualization using the gene-pair rule-based Random Forest SSP method and provides additional multiclass functionalities to the switchBox k-Top-Scoring Pairs package.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A lncRNA landscape in breast cancer reveals a potential role for AC009283.1 in proliferation and apoptosis in HER2-enriched subtype

Breast cancer is the most commonly diagnosed neoplasm in women worldwide with a well-recognized heterogeneous pathology, classified into four molecular subtypes: Luminal A, Luminal B, HER2-enriched and Basal-like, each one with different biological and clinical characteristics. Long non-coding RNAs (lncRNAs) represent 33% of the human transcriptome and play critical roles in breast carcinogenesis, but most of their functions are still unknown. Therefore, cancer research could benefit from continued exploration into the biology of lncRNAs in this neoplasm. We characterized lncRNA expression portraits in 74 breast tumors belonging to the four molecular subtypes using transcriptome microarrays. To infer the biological role of the deregulated lncRNAs in the molecular subtypes, we performed co-expression analysis of lncRNA-mRNA and gene ontology analysis. We identified 307 deregulated lncRNAs in tumor compared to normal tissue and 354 deregulated lncRNAs among the different molecular subtypes. Through co-expression analysis between lncRNAs and protein-coding genes, along with gene enrichment analysis, we inferred the potential function of the most deregulated lncRNAs in each molecular subtype, and independently validated our results taking advantage of TCGA data. Overexpression of the AC009283.1 was observed in the HER2-enriched subtype and it is localized in an amplification zone at chromosome 17q12, suggesting it to be a potential tumorigenic lncRNA. The functional role of lncRNA AC009283.1 was examined through loss of function assays in vitro and determining its impact on global gene expression. These studies revealed that AC009283.1 regulates genes involved in proliferation, cell cycle and apoptosis in a HER2 cellular model. We further confirmed these findings through ssGSEA and CEMITool analysis in an independent HER2-amplified breast cancer cohort. Our findings suggest a wide range of biological functions for lncRNAs in each breast cancer molecular subtype and provide a basis for their biological and functional study, as was conducted for AC009283.1, showing it to be a potential regulator of proliferation and apoptosis in the HER2-enriched subtype.

Pregnancy and Breast Cancer

Breast cancer is the most commonly diagnosed type of cancer among women worldwide. The majority of breast cancers are sporadic and the etiology is not well understood. Several factors have been attributed to altering the risk of breast cancer. A full-term pregnancy is a crucial factor in altering the risk. Early full-term pregnancy has been shown to reduce the lifetime risk of breast cancer, while a later first full-term pregnancy increases breast cancer risk. Epidemiological and experimental data demonstrate that spontaneous or induced abortions do not significantly alter the risk of breast cancer. In this study, we briefly discuss the different types and stages of breast cancer, various risk factors, and potential mechanisms involved in early full-term pregnancy-induced protection against breast cancer. Understanding how early full-term pregnancy induces protection against breast cancer will help design innovative preventive and therapeutic strategies. This understanding can also help in the development of molecular biomarkers that can be of tremendous help in predicting the risk of breast cancer in the general population.

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.