Evaluation of multiple transcriptomic gene risk signatures in male breast cancer

Male breast cancer (BCa) is a rare disease accounting for less than 1% of all breast cancers and 1% of all cancers in males. The clinical management is largely extrapolated from female BCa. Several multigene assays are increasingly used to guide clinical treatment decisions in female BCa, however, there are limited data on the utility of these tests in male BCa. Here we present the gene expression results of 381 M0, ER+ve, HER2-ve male BCa patients enrolled in the Part 1 (retrospective analysis) of the International Male Breast Cancer Program. Using a custom NanoString™ panel comprised of the genes from the commercial risk tests Prosigna®, OncotypeDX®, and MammaPrint®, risk scores and intrinsic subtyping data were generated to recapitulate the commercial tests as described by us previously. We also examined the prognostic value of other risk scores such as the Genomic Grade Index (GGI), IHC4-mRNA and our prognostic 95-gene signature. In this sample set of male BCa, we demonstrated prognostic utility on univariate analysis. Across all signatures, patients whose samples were identified as low-risk experienced better outcomes than intermediate-risk, with those classed as high risk experiencing the poorest outcomes. As seen with female BCa, the concordance between tests was poor, with C-index values ranging from 40.3% to 78.2% and Kappa values ranging from 0.17 to 0.58. To our knowledge, this is the largest study of male breast cancers assayed to generate risk scores of the current commercial and academic risk tests demonstrating comparable clinical utility to female BCa.

Predictive and prognostic transcriptomic biomarkers in soft tissue sarcomas

Soft tissue sarcomas (STS) are rare and heterogeneous tumours comprising over 80 different histological subtypes. Treatment options remain limited in advanced STS with high rates of recurrence following resection of localised disease. Prognostication in clinical practice relies predominantly on histological grading systems as well as sarcoma nomograms. Rapid developments in gene expression profiling technologies presented opportunities for applications in sarcoma. Molecular profiling of sarcomas has improved our understanding of the cancer biology of these rare cancers and identified potential novel therapeutic targets. In particular, transcriptomic signatures could play a role in risk classification in sarcoma to aid prognostication. Unlike other solid and haematological malignancies, transcriptomic signatures have not yet reached routine clinical use in sarcomas. Herein, we evaluate early developments in gene expression profiling in sarcomas that laid the foundations for transcriptomic signature development. We discuss the development and clinical evaluation of key transcriptomic biomarker signatures in sarcomas, including Complexity INdex in SARComas (CINSARC), Genomic Grade Index, and hypoxia-associated signatures. Prospective validation of these transcriptomic signatures is required, and prospective trials are in progress to evaluate reliability for clinical application. We anticipate that integration of these gene expression signatures alongside existing prognosticators and other Omics methodologies, including proteomics and DNA methylation analysis, could improve the identification of ‘high-risk’ patients who would benefit from more aggressive or selective treatment strategies. Moving forward, the incorporation of these transcriptomic prognostication signatures in clinical practice will undoubtedly advance precision medicine in the routine clinical management of sarcoma patients.

The prognostic and predictive impact of genomic grade index (GGI) versus central grade or molecular class in intermediate-risk breast cancer (BC): Results from the EC-Doc trial.

1092 Background: Potential markers for adjuvant taxane-based chemotherapy (CTx) in early, intermediate-risk BC include histologic grade (HG), Ki-67, Genomic Grade (GG) or molecular classification. The randomized EC-Doc trial demonstrated improvements in DFS and OS for EC-Doc vs. FEC in patients with 1-3 positive LN. Methods: Centrally assessed protein expression data by IHC, histology/HG (n=772) and GG (n=476) were obtained. Luminal A/B classes were defined as: ER/PR+/KI-67<20/>20% and/or HER2+. Correlations/concordance of these factors were estimated; impact on DFS and value for predicting taxane-based CTx benefit was assessed. Results: Low, equivocal and high GG (GG-1/-EQ/-3) categories were observed among 54 (11%); 60 (13%); 358 patients (76%) and associated with decreasing 5-yr-DFS rates of 100%, 92% and 82% (p < 0.001).There is only 60% concordance between local (L)/central(C) HG assessments and 63% between GG and C-HG. 37.7% of GG-3 tumors were L- HG3 vs. 72% C-HG3; 79% of C-HG2 and 83% of L-HG2 tumors were re-classified (56-71% to GGI3). Only 5.6-6% of GG1 were HG1 by L/C-HG respectively.GG was prognostic only in L-HG subgroups.In univariate subgroup analyses, EC-Doc was significantly superior to FEC: C-G3 (HR=0.58, 0.39-0.93), high Ki-67 (HR=0.55, 0.32E0.92) and GG-3 (HR=0.58, 0.34-0.99) subgroups. In multivariate analyses of HR+ disease (including age, therapy, GGI category, tumor size, LN status, central/local HG, Ki-67, HER2), either C-HG3 / high Ki-67 (as dichotomous variables) or C-HG3 / GGI (as continuous variable) were identified as independent prognostic factors.In interaction analysis, only C-HG, luminal A subtype and interaction of luminal-B/therapy were significant. If local HG was included instead of C-HG, only GG was as independent prognostic factor. Conclusions: These data support GGI as independent prognostic factor in early HR+ BC and as predictive marker regarding taxane benefit by univariate analysis. There is heterogeneity between L/C-HG and GGI. C-HG and Ki-67 assessment appear similarly informative. A predictive effect regarding benefit of taxane-containing CTx was seen in the IHC-luminal B subtype.

Progression of genomic signatures in local and metastatic estrogen receptor-positive (ER+) breast cancer: Relevance to palliative treatment.

515 Background: Biological progression of ER+ breast cancer accelerates clinical progression and resistance to treatments. Methods: One laboratory used Affymetrix U133A gene expression microarrays to profile 588 biopsy samples from patients with ER+ breast cancer: 74 AJCC Stage I, 155 Stage IIA, 105 Stage IIB, 127 Stage III, 127 Stage IV (27 at presentation, 100 relapsed). We evaluated stage dependence of ER [ESR1, PGR, sensitivity to endocrine therapy (SET) index], proliferation [MKI67, AURKA, genomic grade index (GGI)], invasion [PLAU (uPA)], PI3-kinase (PIK3CA-GS), VEGF, genomic subtype [PAM50, 3-gene classifier (ESR1, ERBB2, AURKA)], and housekeeper control genes. Significance was evaluated through ordinal median regression (P < 0.002, for multiple testing) after adjusting for staging method (clinical or pathologic). Exploratory Cox regression analyses of progression-free survival (PFS) and overall survival (OS) were performed when treatment was hormonal therapy (HT, N=58) or chemotherapy (CT, N=27) after biopsy of metastatic ER+ breast cancer (MBC). Results: Stage progression was associated with reduced SET index and increased proliferation (GGI, MKI67, AURKA) and metabolism (GAPDH). These changes occurred between Stages IIB and III, and Stages III and IV. Luminal B and proliferation subtypes were more prevalent in Stage IV and less in Stage I. Interestingly, invasion (PLAU) genes were lower in MBC. Only SET index demonstrated a significant interaction with treatment (HT or CT) for MBC (PFS: p=0.018). SET was predictive of PFS and OS following HT, as a continuous score (PFS: HR=0.69, 95%CI 0.49 to 0.97, p=0.035; OS: HR=0.61, 95%CI 0.40 to 0.94, p=0.025) or dichotomized at median value (PFS: HR=0.43, 95%CI 0.24 to 0.76, p=0.003; OS: HR=0.37, 95%CI 0.18 to 0.77, p=0.006). Genomic subtype was prognostic for PFS irrespective of treatment type. High PIK3CA-GS expression predicted OS in the HT subset. Conclusions: Stage progression was associated with decreased ER-related transcription (SET) and increased proliferation, grade, higher risk subtype, and metabolism. In MBC samples, only SET index was predictive of PFS and OS with palliative hormonal therapy.