Triple negative breast cancer: proposals for a pragmatic definition and implications for patient management and trial design.

In trials in triple negative breast cancer (TNBC), oestrogen and progesterone receptor negativity should be defined as < 1% positive cells. Negativity is a ratio of <2 between Her2 gene copy number and centromere of chromosome 17 or a copy number of 4 or less. In routine practice, immunohistochemistry is acceptable given stringent quality assurance. Triple negativity emerging after neoadjuvant treatment differs from primary TN and such patients should not enter TNBC trials. Patients relapsing with TN metastases should be eligible even if their primary was positive. Rare TN subtypes such as apocrine, adenoid-cystic and low-grade metaplastic tumours should be excluded. TN and basal-like (BL) signatures overlap but are not equivalent. Since the significance of basal cytokeratin or EGFR overexpression is not known and we lack validated assays, these features should not be used to subclassify TN tumours. Tissue collection in trials is mandatory so the effect on outcome of different tumour phenotypes and BRCA mutation can be explored. No prospective studies have established that TN tumours have particular sensitivity or resistance to any specific chemotherapy agent or radiation. TNBC patients should be treated according to tumour and clinical characteristics

Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer

<p>Abstract</p> <p>Purpose</p> <p>To determine whether functional proteomics improves breast cancer classification and prognostication and can predict pathological complete response (pCR) in patients receiving neoadjuvant taxane and anthracycline-taxane-based systemic therapy (NST).</p> <p>Methods</p> <p>Reverse phase protein array (RPPA) using 146 antibodies to proteins relevant to breast cancer was applied to three independent tumor sets. Supervised clustering to identify subgroups and prognosis in surgical excision specimens from a training set (n = 712) was validated on a test set (n = 168) in two cohorts of patients with primary breast cancer. A score was constructed using ordinal logistic regression to quantify the probability of recurrence in the training set and tested in the test set. The score was then evaluated on 132 FNA biopsies of patients treated with NST to determine ability to predict pCR.</p> <p>Results</p> <p>Six breast cancer subgroups were identified by a 10-protein biomarker panel in the 712 tumor training set. They were associated with different recurrence-free survival (RFS) (log-rank p = 8.8 E-10). The structure and ability of the six subgroups to predict RFS was confirmed in the test set (log-rank p = 0.0013). A prognosis score constructed using the 10 proteins in the training set was associated with RFS in both training and test sets (p = 3.2E-13, for test set). There was a significant association between the prognostic score and likelihood of pCR to NST in the FNA set (p = 0.0021).</p> <p>Conclusion</p> <p>We developed a 10-protein biomarker panel that classifies breast cancer into prognostic groups that may have potential utility in the management of patients who receive anthracycline-taxane-based NST.</p

An Integrated Approach to the Prediction of Chemotherapeutic Response in Patients with Breast Cancer

BACKGROUND: A major challenge in oncology is the selection of the most effective chemotherapeutic agents for individual patients, while the administration of ineffective chemotherapy increases mortality and decreases quality of life in cancer patients. This emphasizes the need to evaluate every patient's probability of responding to each chemotherapeutic agent and limiting the agents used to those most likely to be effective. METHODS AND RESULTS: Using gene expression data on the NCI-60 and corresponding drug sensitivity, mRNA and microRNA profiles were developed representing sensitivity to individual chemotherapeutic agents. The mRNA signatures were tested in an independent cohort of 133 breast cancer patients treated with the TFAC (paclitaxel, 5-fluorouracil, adriamycin, and cyclophosphamide) chemotherapy regimen. To further dissect the biology of resistance, we applied signatures of oncogenic pathway activation and performed hierarchical clustering. We then used mRNA signatures of chemotherapy sensitivity to identify alternative therapeutics for patients resistant to TFAC. Profiles from mRNA and microRNA expression data represent distinct biologic mechanisms of resistance to common cytotoxic agents. The individual mRNA signatures were validated in an independent dataset of breast tumors (P = 0.002, NPV = 82%). When the accuracy of the signatures was analyzed based on molecular variables, the predictive ability was found to be greater in basal-like than non basal-like patients (P = 0.03 and P = 0.06). Samples from patients with co-activated Myc and E2F represented the cohort with the lowest percentage (8%) of responders. Using mRNA signatures of sensitivity to other cytotoxic agents, we predict that TFAC non-responders are more likely to be sensitive to docetaxel (P = 0.04), representing a viable alternative therapy. CONCLUSIONS: Our results suggest that the optimal strategy for chemotherapy sensitivity prediction integrates molecular variables such as ER and HER2 status with corresponding microRNA and mRNA expression profiles. Importantly, we also present evidence to support the concept that analysis of molecular variables can present a rational strategy to identifying alternative therapeutic opportunities

Transcriptomic landscape of breast cancers through mRNA sequencing

Breast cancer is a heterogeneous disease with a poorly defined genetic landscape, which poses a major challenge in diagnosis and treatment. By massively parallel mRNA sequencing, we obtained 1.2 billion reads from 17 individual human tissues belonging to TNBC, Non-TNBC, and HER2-positive breast cancers and defined their comprehensive digital transcriptome for the first time. Surprisingly, we identified a high number of novel and unannotated transcripts, revealing the global breast cancer transcriptomic adaptations. Comparative transcriptomic analyses elucidated differentially expressed transcripts between the three breast cancer groups, identifying several new modulators of breast cancer. Our study also identified common transcriptional regulatory elements, such as highly abundant primary transcripts, including osteonectin, RACK1, calnexin, calreticulin, FTL, and B2M, and “genomic hotspots” enriched in primary transcripts between the three groups. Thus, our study opens previously unexplored niches that could enable a better understanding of the disease and the development of potential intervention strategies

An integrative genomic and proteomic analysis of PIK3CA, PTEN and AKT mutations in breast cancer

Phosphatidylinositol-3-kinase (PI3K)/AKT pathway aberrations are common in cancer. By applying mass spectroscopy-based sequencing and reverse phase protein arrays to 547 human breast cancers and 41 cell lines, we determined the subtype specificity and signaling effects of PIK3CA, AKT and PTEN mutations, and the effects of PIK3CA mutations on responsiveness to PI3K inhibition in-vitro and on outcome after adjuvant tamoxifen. PIK3CA mutations were more common in hormone receptor positive (33.8%) and HER2-positive (24.6%) than in basal-like tumors (8.3%). AKT1 (1.4%) and PTEN (2.3%) mutations were restricted to hormone receptor-positive cancers with PTEN protein levels also being significantly lower in hormone receptor-positive cancers. Unlike AKT1 mutations, PIK3CA (39%) and PTEN (20%) mutations were more common in cell lines than tumors, suggesting a selection for these but not AKT1 mutations during adaptation to culture. PIK3CA mutations did not have a significant impact on outcome in 166 hormone receptor-positive breast cancer patients after adjuvant tamoxifen. PIK3CA mutations, in comparison with PTEN loss and AKT1 mutations, were associated with significantly less and indeed inconsistent activation of AKT and of downstream PI3K/AKT signaling in tumors and cell lines, and PTEN loss and PIK3CA mutation were frequently concordant, suggesting different contributions to pathophysiology. PTEN loss but not PIK3CA mutations rendered cells sensitive to growth inhibition by the PI3K inhibitor LY294002. Thus, PI3K pathway aberrations likely play a distinct role in the pathogenesis of different breast cancer subtypes. The specific aberration may have implications for the selection of PI3K-targeted therapies in hormone receptor-positive breast cancer

A clinically relevant gene signature in triple negative and basal-like breast cancer

Introduction: Current prognostic gene expression profiles for breast cancer mainly reflect proliferation status and are most useful in ER-positive cancers. Triple negative breast cancers (TNBC) are clinically heterogeneous and prognostic markers and biology-based therapies are needed to better treat this disease. Methods: We assembled Affymetrix gene expression data for 579 TNBC and performed unsupervised analysis to define metagenes that distinguish molecular subsets within TNBC. We used n = 394 cases for discovery and n = 185 cases for validation. Sixteen metagenes emerged that identified basal-like, apocrine and claudin-low molecular subtypes, or reflected various non-neoplastic cell populations, including immune cells, blood, adipocytes, stroma, angiogenesis and inflammation within the cancer. The expressions of these metagenes were correlated with survival and multivariate analysis was performed, including routine clinical and pathological variables. Results: Seventy-three percent of TNBC displayed basal-like molecular subtype that correlated with high histological grade and younger age. Survival of basal-like TNBC was not different from non basal-like TNBC. High expression of immune cell metagenes was associated with good and high expression of inflammation and angiogenesis-related metagenes were associated with poor prognosis. A ratio of high B-cell and low IL-8 metagenes identified 32% of TNBC with good prognosis (hazard ratio (HR) 0.37, 95% CI 0.22 to 0.61; P < 0.001) and was the only significant predictor in multivariate analysis including routine clinicopathological variables. Conclusions: We describe a ratio of high B-cell presence and low IL-8 activity as a powerful new prognostic marker for TNBC. Inhibition of the IL-8 pathway also represents an attractive novel therapeutic target for this disease