Life as an early career researcher: interview with Heloisa Helena Milioli.

Heloisa Helena Milioli speaks to Francesca Lake, Managing Editor: Heloisa received a BSc degree in Biological Sciences (2008) from the Universidade Federal de Santa Catarina (Brazil) and obtained a MSc degree in Genetics (2011) from Universidade Federal do Paraná (Brazil). In 2011 and 2012, she worked as a lecturer and tutor in the Department of Cell Biology, Embryology and Genetics (Universidade Federal de Santa Catarina). She moved to Australia in 2012 to obtain her PhD in Biological Sciences, with emphasis on Bioinformatics, from The University of Newcastle. Her doctoral work brings together new considerations in the breast cancer field by combining novel bioinformatics approaches with the study of intrinsic subtypes. She has been applying advanced methods and sophisticated algorithms in unconventional computer architecture for the molecular classification of breast cancer based on the genomic (single nucleotide polymorphisms, circulating nucleic acids and copy number variations) and transcriptomic (gene expression and miRNA) signatures. Fundamental research will allow her to identify biomarkers of use in translational medicine for the diagnosis, prognosis and disease management focused on group-based tailored therapies.

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

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

The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set.

BACKGROUND: The prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction. METHODS AND FINDINGS: The microarray transcriptome data sets used in this study are: the METABRIC breast cancer data recorded for over 2000 patients, and the public integrated source from ROCK database with 1570 samples. We first computed the CM1 score to identify the probes with highly discriminative patterns of expression across samples of each intrinsic subtype. We further assessed the ability of 42 selected probes on assigning correct subtype labels using 24 different classifiers from the Weka software suite. For comparison, the same method was applied on the list of 50 genes from the PAM50 method. CONCLUSIONS: The CM1 score portrayed 30 novel biomarkers for predicting breast cancer subtypes, with the confirmation of the role of 12 well-established genes. Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers are more consistent and homogeneous than the original PAM50 labels. The new subtypes show accurate distributions of current clinical markers ER, PR and HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the paradoxical attribution of the original labels reinforces the limitations of employing a single sample classifiers to predict breast cancer intrinsic subtypes.

Comparative proteomics of primary breast carcinomas and lymph node metastases outlining markers of tumor invasion.

BACKGROUND: Lymph node metastasis is an important clinicopathological parameter for breast cancer prognostication and treatment. Although the development of metastasis is common in axillary lymph nodes, the mechanisms underlying the locoregional spread are yet poorly understood. In the present study, we outline the involvement of proteins in tumor invasion by comparing the proteome profile of primary breast tumors (PBT) against that of lymph node metastasis (LNM). PATIENTS AND METHODS: The comparative proteome analyses of seven paired samples were performed using two-dimensional gel electrophoresis (2DE) and mass spectrometry (MS). RESULTS: Recurrent proteins were differentially expressed in PBT and LNM across patients. Higher levels of 1433G, 1433T, K2C8, PSME2, SNAA, TPM4, TRFE and VIME were observed in primary tumors compared to the metastatic site. On the other hand, higher levels of ALDH2 and GDIR2 were identified in metastasis related to tumors. These proteins provide a new insight on breast cancer research. CONCLUSION: Our achievements strengthened previous omics-based studies and also support the validation of potential markers of tumor invasion and metastasis.

The IMPAKT of breast cancer research: fundamental science and applied medicine.

The IMPAKT 2015 Breast Cancer Conference was designed for researchers and clinicians by the Breast International Group (BIG) and the European Society for Medical Oncology (ESMO). The event was held on 7-9 May in Brussels, Belgium, bringing together approximately 525 participants with a special interest in translational science and state-of-the-art applications in the clinical setting. Oncologists, pathologists and scientists collaborated to develop innovative ideas about breast cancer research and to enhance its relevance to patient care. This report highlights the most recent discussions in fundamental research and future clinical perspectives presented by professionals from around the world. It also covers the important issues regarding new technologies for biomarker discovery and the actual path to clinical utility.