Identifying Biomarkers to Pair with Targeting Treatments within Triple Negative Breast Cancer for Improved Patient Stratification.

The concept of precision medicine has been around for many years and recent advances in high-throughput sequencing techniques are enabling this to become reality. Within the field of breast cancer, a number of signatures have been developed to molecularly sub-classify tumours. Notable examples recently approved by National Institute for Health and Care Excellence in the UK to guide treatment decisions for oestrogen receptors (ER)+ human epidermal growth factor receptor 2 (HER2)- patients include Prosigna test, EndoPredict, and Oncotype DX. However, a population of still unmet need are those with triple negative breast cancer (TNBC). Accounting for 15-20% of patients, this population has comparatively poor prognosis and as yet no targeted treatment options. Studies have shown that some patients with TNBC respond favourably to DNA damaging drugs (carboplatin) or agents which inhibit DNA damage response (poly ADP ribose polymerase (PARP) inhibitors). Known to be a heterogeneous population, there is a need to identify further TNBC patients who may benefit from these treatments. A number of signatures have been identified based on association with treatment response or specific genetic features/pathways however many of these were not restricted to TNBC patients and as of yet are not common practice in the clinic

Heterocellular gene signatures reveal luminal-A breast cancer heterogeneity and differential therapeutic responses.

Breast cancer is a highly heterogeneous disease. Although differences between intrinsic breast cancer subtypes have been well studied, heterogeneity within each subtype, especially luminal-A cancers, requires further interrogation to personalize disease management. Here, we applied well-characterized and cancer-associated heterocellular signatures representing stem, mesenchymal, stromal, immune, and epithelial cell types to breast cancer. This analysis stratified the luminal-A breast cancer samples into five subtypes with a majority of them enriched for a subtype (stem-like) that has increased stem and stromal cell gene signatures, representing potential luminal progenitor origin. The enrichment of immune checkpoint genes and other immune cell types in two (including stem-like) of the five heterocellular subtypes of luminal-A tumors suggest their potential response to immunotherapy. These immune-enriched subtypes of luminal-A tumors (containing only estrogen receptor positive samples) showed good or intermediate prognosis along with the two other differentiated subtypes as assessed using recurrence-free and distant metastasis-free patient survival outcomes. On the other hand, a partially differentiated subtype of luminal-A breast cancer with transit-amplifying colon-crypt characteristics showed poor prognosis. Furthermore, published luminal-A subtypes associated with specific somatic copy number alterations and mutations shared similar cellular and mutational characteristics to colorectal cancer subtypes where the heterocellular signatures were derived. These heterocellular subtypes reveal transcriptome and cell-type based heterogeneity of luminal-A and other breast cancer subtypes that may be useful for additional understanding of the cancer type and potential patient stratification and personalized medicine

A Four-gene Decision Tree Signature Classification of Triple-negative Breast Cancer: Implications for Targeted Therapeutics.

The molecular complexity of triple-negative breast cancers (TNBCs) provides a challenge for patient management. We set out to characterize this heterogeneous disease by combining transcriptomics and genomics data, with the aim of revealing convergent pathway dependencies with the potential for treatment intervention. A Bayesian algorithm was used to integrate molecular profiles in two TNBC cohorts, followed by validation using five independent cohorts (n = 1,168), including three clinical trials. A four-gene decision tree signature was identified, which robustly classified TNBCs into six subtypes. All four genes in the signature (EXO1, TP53BP2, FOXM1, and RSU1) are associated with either genomic instability, malignant growth, or treatment response. One of the six subtypes, MC6, encompassed the largest proportion of tumors (∼50%) in early diagnosed TNBCs. In TNBC patients with metastatic disease, the MC6 proportion was reduced to 25%, and was independently associated with a higher response rate to platinum-based chemotherapy. In TNBC cell line data, platinum sensitivity was recapitulated, and a sensitivity to the inhibition of the phosphatase PPM1D was revealed. Molecularly, MC6-TNBCs displayed high levels of telomeric allelic imbalances, enrichment of CD4+ and CD8+ immune signatures, and reduced expression of genes negatively regulating the MAPK signaling pathway. These observations suggest that our integrative classification approach may identify TNBC patients with discernible and theoretically pharmacologically tractable features that merit further studies in prospective trials

Development and validation for research assessment of Oncotype DX® Breast Recurrence Score, EndoPredict® and Prosigna®.

Multi-gene prognostic signatures including the Oncotype® DX Recurrence Score (RS), EndoPredict® (EP) and Prosigna® (Risk Of Recurrence, ROR) are widely used to predict the likelihood of distant recurrence in patients with oestrogen-receptor-positive (ER+), HER2-negative breast cancer. Here, we describe the development and validation of methods to recapitulate RS, EP and ROR scores from NanoString expression data. RNA was available from 107 tumours from postmenopausal women with early-stage, ER+, HER2- breast cancer from the translational Arimidex, Tamoxifen, Alone or in Combination study (TransATAC) where previously these signatures had been assessed with commercial methodology. Gene expression was measured using NanoString nCounter. For RS and EP, conversion factors to adjust for cross-platform variation were estimated using linear regression. For ROR, the steps to perform subgroup-specific normalisation of the gene expression data and calibration factors to calculate the 46-gene ROR score were assessed and verified. Training with bootstrapping (n = 59) was followed by validation (n = 48) using adjusted, research use only (RUO) NanoString-based algorithms. In the validation set, there was excellent concordance between the RUO scores and their commercial counterparts (rc(RS) = 0.96, 95% CI 0.93-0.97 with level of agreement (LoA) of -7.69 to 8.12; rc(EP) = 0.97, 95% CI 0.96-0.98 with LoA of -0.64 to 1.26 and rc(ROR) = 0.97 (95% CI 0.94-0.98) with LoA of -8.65 to 10.54). There was also a strong agreement in risk stratification: (RS: κ = 0.86, p < 0.0001; EP: κ = 0.87, p < 0.0001; ROR: κ = 0.92, p < 0.001). In conclusion, the calibrated algorithms recapitulate the commercial RS and EP scores on individual biopsies and ROR scores on samples based on subgroup-centreing method using NanoString expression data

Characterization of MTAP gene expression in breast cancer patients and cell lines

MTAP is a ubiquitously expressed gene important for adenine and methionine salvage. The gene is located at 9p21, a chromosome region often deleted in breast carcinomas, similar to CDKN2A, a recognized tumor suppressor gene. Several research groups have shown that MTAP acts as a tumor suppressor, and some therapeutic approaches were proposed based on a tumors\ub4 MTAP status. We analyzed MTAP and CDKN2A gene (RT-qPCR) and protein (western-blotting) expression in seven breast cancer cell lines and evaluated their promoter methylation patterns to better characterize the contribution of these genes to breast cancer. Cytotoxicity assays with inhibitors of de novo adenine synthesis (5-FU, AZA and MTX) after MTAP gene knockdown showed an increased sensitivity, mainly to 5-FU. MTAP expression was also evaluated in two groups of samples from breast cancer patients, fresh tumors and paired normal breast tissue, and from formalin-fixed paraffin embedded (FFPE) core breast cancer samples diagnosed as Luminal-A tumors and triple negative breast tumors (TNBC). The difference of MTAP expression between fresh tumors and normal tissues was not statistically significant. However, MTAP expression was significantly higher in Luminal-A breast tumors than in TNBC, suggesting the lack of expression in more aggressive breast tumors and the possibility of using the new approaches based on MTAP status in TNB

MicroRNA expression as risk biomarker of breast cancer metastasis : a pilot retrospective case-cohort study

Background: MicroRNAs (miRNAs) are small, non-coding RNA molecules involved in post-transcriptional gene regulation and have recently been shown to play a role in cancer metastasis. In solid tumors, especially breast cancer, alterations in miRNA expression contribute to cancer pathogenesis, including metastasis. Considering the emerging role of miRNAs in metastasis, the identification of predictive markers is necessary to further the understanding of stage-specific breast cancer development. This is a retrospective analysis that aimed to identify molecular biomarkers related to distant breast cancer metastasis development. Methods: A retrospective case cohort study was performed in 64 breast cancer patients treated during the period from 1998-2001. The case group (n = 29) consisted of patients with a poor prognosis who presented with breast cancer recurrence or metastasis during follow up. The control group (n = 35) consisted of patients with a good prognosis who did not develop breast cancer recurrence or metastasis. These patient groups were stratified according to TNM clinical stage (CS) I, II and III, and the main clinical features of the patients were homogeneous. MicroRNA profiling was performed and biomarkers related to metastatic were identified independent of clinical stage. Finally, a hazard risk analysis of these biomarkers was performed to evaluate their relation to metastatic potential. Results: MiRNA expression profiling identified several miRNAs that were both specific and shared across all clinical stages (p <= 0.05). Among these, we identified miRNAs previously associated with cell motility (let-7 family) and distant metastasis (hsa-miR-21). In addition, hsa-miR-494 and hsa-miR-21 were deregulated in metastatic cases of CSI and CSII. Furthermore, metastatic miRNAs shared across all clinical stages did not present high sensitivity and specificity when compared to specific-CS miRNAs. Between them, hsa-miR-183 was the most significative of CSII, which miRNAs combination for CSII (hsa-miR-494, hsa-miR-183 and hsa-miR-21) was significant and were a more effective risk marker compared to the single miRNAs. Conclusions: Women with metastatic breast cancer, especially CSII, presented up-regulated levels of miR-183, miR-494 and miR-21, which were associated with a poor prognosis. These miRNAs therefore represent new risk biomarkers of breast cancer metastasis and may be useful for future targeted therapies.We thank the Researcher Support Center of Barretos Cancer Hospital, especially the statistician Zanardo C. for assisting in the statistical analysis.This study received financial support from Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (Fapesp, Proc: 10/ 16796-0, Sao Paulo, Brazil)

Prognostic Value of Intrinsic Subtypes in Hormone Receptor-Positive Metastatic Breast Cancer Treated With Letrozole With or Without Lapatinib.

Importance The value of the intrinsic subtypes of breast cancer (luminal A, luminal B, human epidermal growth factor receptor 2 [currently known as ERBB2, but referred to as HER2 in this study]-enriched, and basal-like) in the metastatic setting is currently unknown.Objective To evaluate the association of the intrinsic subtypes of breast cancer with outcome and/or benefit in hormone receptor (HR)-positive metastatic breast cancer.Design, setting, and participants Unplanned retrospective analysis of 821 tumor samples (85.7% primary and 14.3% metastatic) from the EGF30008 phase 3 clinical trial (NCT00073528), in which postmenopausal women with HR-positive invasive breast cancer and no prior therapy for advanced or metastatic disease were randomized to letrozole with or without lapatinib, an epidermal growth factor receptor (EGFR)/HER2 tyrosine kinase inhibitor. Tumor samples were classified into each subtype using the research-based PAM50 classifier. Prior neoadjuvant/adjuvant antiestrogen therapy was allowed. Patients with extensive symptomatic visceral disease were excluded. Treatment effects were evaluated using interaction tests.Main outcomes and measures Primary and secondary end points were progression-free survival and overall survival.Results The median (range) age was 62 (31-94) years. Intrinsic subtype was the strongest prognostic factor independently associated with progression-free survival and overall survival in all patients, and in patients with HER2-negative (n = 644) or HER2-positive (n = 157) diseases. Median progression-free survival differed across the intrinsic subtypes of clinically HER2-negative disease: luminal A (16.9 [95% CI, 14.1-19.9] months), luminal B (11.0 [95% CI, 9.6-13.6] months), HER2-enriched (4.7 [95% CI, 2.7-10.8] months), and basal-like (4.1 [95% CI, 2.5-13.8] months). Median OS also differed across the intrinsic subtypes: luminal A (45 [95% CI, 41-not applicable {NA}] months), luminal B (37 [95% CI, 31-42] months), HER2-enriched (16 [95% CI, 10-NA] months), and basal-like (23 [95% CI, 12-NA] months). Patients with HER2-negative/HER2-enriched disease benefited from lapatinib therapy (median PFS, 6.49 vs 2.60 months; progression-free survival hazard ratio, 0.238 [95% CI, 0.066-0.863]; interaction P = .02).Conclusions and relevance This is the first study to reveal an association between intrinsic subtype and outcome in first-line HR-positive metastatic breast cancer. Patients with HR-positive/HER2-negative disease with a HER2-enriched profile may benefit from lapatinib in combination with endocrine therapy. The clinical value of intrinsic subtyping in hormone receptor-positive metastatic breast cancer warrants further investigation, but patients with luminal A/HER2-negative metastatic breast cancer might be good candidates for letrozole monotherapy in the first-line setting regardless of visceral disease and number of metastases

Genomic Instability and <i>TP53</i> Genomic Alterations Associate With Poor Antiproliferative Response and Intrinsic Resistance to Aromatase Inhibitor Treatment.

Purpose Although aromatase inhibitor (AI) treatment is effective in estrogen receptor-positive postmenopausal breast cancer, resistance is common and incompletely explained. Genomic instability, as measured by somatic copy number alterations (SCNAs), is important in breast cancer development and prognosis. SCNAs to specific genes may drive intrinsic resistance, or high genomic instability may drive tumor heterogeneity, which allows differential response across tumors and surviving cells to evolve resistance to treatment rapidly. We therefore evaluated the relationship between SCNAs and intrinsic resistance to treatment as measured by a poor antiproliferative response.Patients and methods SCNAs were determined by single nucleotide polymorphism array in baseline and surgery core-cuts from 73 postmenopausal patients randomly assigned to receive 2 weeks of preoperative AI or no AI in the Perioperative Endocrine Therapy-Individualizing Care (POETIC) trial. Fifty-six samples from the AI group included 28 poor responders (PrRs, less than 60% reduction in protein encoded by the MKI67 gene [Ki-67]) and 28 good responders (GdRs, greater than 75% reduction in Ki-67). Exome sequencing was available for 72 pairs of samples.Results Genomic instability correlated with Ki-67 expression at both baseline (P P P = .048). The SCNA with the largest difference between GdRs and PrRs was loss of heterozygosity observed at 17p (false discovery rate, 0.08), which includes TP53. Nine of 28 PrRs had loss of wild-type TP53 as a result of mutations and loss of heterozygosity compared with three of 28 GdRs. In PrRs, somatic alterations of TP53 were associated with higher genomic instability, higher baseline Ki-67, and greater resistance to AI treatment compared with wild-type TP53.Conclusion We observed that primary tumors with high genomic instability have an intrinsic resistance to AI treatment and do not require additional evolution to develop resistance to estrogen deprivation therapy