HER-2 status of circulating tumor cells in a metastatic breast cancer cohort: A comparative study on characterization techniques

Background Personalized targeted treatment in metastatic breast cancer relies on accurate assessment of molecular aberrations, e.g. overexpression of Human Epidermal growth factor Receptor 2 (HER-2). Molecular interrogation of circulating tumor cells (CTCs) can provide an attractive alternative for real-time biomarker assessment. However, implementation of CellSearch-based HER-2 analysis has been limited. Immunofluorescent (IF) image interpretation is crucial, as different HER-2 categories have been described. Major questions in CTC research are how these IF categories reflect gene expression and amplification, and if we should consider ‘medium’ HER-2 expressing CTCs for patient selection. Methods Tumor cells from spiked cell lines (n = 8) and CTCs (n = 116 samples) of 85 metastatic breast cancer patients were enriched using CellSearch. Comparative analysis of HER-2 expression by IF imaging (ACCEPT, DEPArray, and visual scoring) with qRT-PCR and HER-2/neu FISH was performed. Results Automated IF HER-2-profiling by DEPArray and ACCEPT delivered comparable results. There was a 98% agreement between 17 trained observers (visual scoring) and ACCEPT considering HER-2neg and HER-2high expressing CTCs. However, 89% of HER-2med expressing CTCs by ACCEPT were scored negative by observers. HER-2high expressing tumor cells demonstrated HER-2/neu gene amplification, whereas HER-2neg and HER-2med expressing tumor cells and CTCs by ACCEPT were copy-number neutral. All patients with HER-2-positive archival tumors had �1 HER-2high expressing CTCs, while 80% of HER-2- negative patients did not. High relative gene expression of HER-2 measured on enriched CTC lysates correlated with having �1 HER-2high expressing CTCs. Conclusion Automated images analysis has enormous potential for clinical implementation. HER-2 characterization and clinical trial design should be focused on HER-2high expressing CTCs

Decreased expression of ABAT and STC2 hallmarks ER-positive inflammatory breast cancer and endocrine therapy resistance in advanced disease

Background: Patients with Estrogen Receptor α-positive (ER+) Inflammatory Breast Cancer (IBC) are less responsive to endocrine therapy compared with ER+ non-IBC (nIBC) patients. The study of ER+ IBC samples might reveal biomarkers for endocrine resistant breast cancer. Materials & methods: Gene expression profiles of ER+ samples from 201 patients were explored for genes that discriminated between IBC and nIBC. Classifier genes were applied onto clinically annotated expression data from 947 patients with ER+ breast cancer and validated with RT-qPCR for 231 patients treated with first-line tamoxifen. Relationships with metastasis-free survival (MFS) and progression-free survival (PFS) following adjuvant and first-line endocrine treatment, respectively, were investigated using Cox regression analysis. Results: A metagene of six genes including the genes encoding for 4-aminobutyrate aminotransferase (ABAT) and Stanniocalcin-2 (STC2) were identified to distinguish 22 ER+ IBC from 43 ER+ nIBC patients and remained discriminatory in an independent series of 136 patients. The metagene and two genes were not prognostic in 517 (neo)adjuvant untreated lymph node-negative ER+ nIBC breast cancer patients. Only ABAT was related to outcome in 250 patients treated with adjuvant tamoxifen. Three independent series of in total 411 patients with advanced disease showed increased metagene scores and decreased expression of ABAT and STC2 to be correlated with poor first-line endocrine therapy outcome. The biomarkers remained predictive for first-line tamoxifen treatment outcome in multivariate analysis including traditional factors or published signatures. In an exploratory analysis, ABAT and STC2 protein expression levels had no relation with PFS after first-line tamoxifen. Conclusions: This study utilized ER+ IBC to identify a metagene including ABAT and STC2 as predictive biomarkers for endocrine therapy resistance

International consensus guidelines for scoring the histopathological growth patterns of liver metastasis

BACKGROUND: Liver metastases present with distinct histopathological growth patterns (HGPs), including the desmoplastic, pushing and replacement HGPs and two rarer HGPs. The HGPs are defined owing to the distinct interface between the cancer cells and the adjacent normal liver parenchyma that is present in each pattern and can be scored from standard haematoxylin-and-eosin-stained (H&E) tissue sections. The current study provides consensus guidelines for scoring these HGPs. METHODS: Guidelines for defining the HGPs were established by a large international team. To assess the validity of these guidelines, 12 independent observers scored a set of 159 liver metastases and interobserver variability was measured. In an independent cohort of 374 patients with colorectal liver metastases (CRCLM), the impact of HGPs on overall survival after hepatectomy was determined. RESULTS: Good-to-excellent correlations (intraclass correlation coefficient >0.5) with the gold standard were obtained for the assessment of the replacement HGP and desmoplastic HGP. Overall survival was significantly superior in the desmoplastic HGP subgroup compared with the replacement or pushing HGP subgroup (P=0.006). CONCLUSIONS: The current guidelines allow for reproducible determination of liver metastasis HGPs. As HGPs impact overall survival after surgery for CRCLM, they may serve as a novel biomarker for individualised therapies

Pathway-based subnetworks enable cross-disease biomarker discovery.

Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery

Landscape of somatic mutations in 560 breast cancer whole-genome sequences.

We analysed whole-genome sequences of 560 breast cancers to advance understanding of the driver mutations conferring clonal advantage and the mutational processes generating somatic mutations. We found that 93 protein-coding cancer genes carried probable driver mutations. Some non-coding regions exhibited high mutation frequencies, but most have distinctive structural features probably causing elevated mutation rates and do not contain driver mutations. Mutational signature analysis was extended to genome rearrangements and revealed twelve base substitution and six rearrangement signatures. Three rearrangement signatures, characterized by tandem duplications or deletions, appear associated with defective homologous-recombination-based DNA repair: one with deficient BRCA1 function, another with deficient BRCA1 or BRCA2 function, the cause of the third is unknown. This analysis of all classes of somatic mutation across exons, introns and intergenic regions highlights the repertoire of cancer genes and mutational processes operating, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer

Identification of cell-of-origin breast tumor subtypes in inflammatory breast cancer by gene expression profiling

Inflammatory breast cancer (IBC) is an aggressive form of locally advanced breast cancer with high metastatic potential. Most patients have lymph node involvement at the time of diagnosis and 1/3 of the patients have distant metastases. In a previous study, we demonstrated that IBC is a distinct form of breast cancer in comparison with non-IBC. The aim of this study was to investigate the presence of the different molecular subtypes in our data set of 16 IBC and 18 non-IBC specimen. Therefore, we selected an ‘intrinsic gene set’ of 144 genes, present on our cDNA chips and common to the ‘intrinsic gene set’ described by Sorlie et al. [PNAS, 2003]. This set of genes was tested for performance in the Norway/Stanford data set by unsupervised hierarchical clustering. Expression centroids were then calculated for the core members of each of the five subclasses in the Norway/Stanford data set and used to classify our own specimens by calculating Spearman correlations between each sample and each centroid. We identified the same cell-of-origin subtypes in IBC as those already described in non-IBC. The classification was in good agreement with immunohistochemical data for estrogen receptor protein expression and cytokeratin 5/6 protein expression. Confirmation was done by an alternative unsupervised hierarchical clustering method. The robustness of this classification was assessed by an unsupervised hierarchical clustering with an alternative gene set of 141 genes related to the cell-of-origin subtypes, selected using a discriminating score and iterative random permutation testing. The contribution of the different cell-of-origin subtypes to the IBC phenotype was investigated by principal component analysis. Generally, the combined ErbB2-overexpressing and basal-like cluster was more expressed in IBC compared to non-IBC, whereas the combined luminal A, luminal B and normal-like cluster was more pronounced in non-IBC compared to IBC. The presence of the same molecular cell-of-origin subtypes in IBC as in non-IBC does not exclude the specific molecular nature of IBC, since gene lists that characterize IBC and non-IBC are entirely different from gene lists that define the different cell-of-origin subtypes, as evidenced by principal component analysis.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44236/1/10549_2005_Article_9015.pd

Avelumab, an anti-PD-L1 antibody, in patients with locally advanced or metastatic breast cancer: a phase 1b JAVELIN Solid Tumor study

PURPOSE: Agents targeting programmed death receptor 1 (PD-1) or its ligand (PD-L1) have shown antitumor activity in the treatment of metastatic breast cancer (MBC). The aim of this study was to assess the activity of avelumab, a PD-L1 inhibitor, in patients with MBC. METHODS: In a phase 1 trial (JAVELIN Solid Tumor; NCT01772004), patients with MBC refractory to or progressing after standard-of-care therapy received avelumab intravenously 10 mg/kg every 2 weeks. Tumors were assessed every 6 weeks by RECIST v1.1. Adverse events (AEs) were graded by NCI-CTCAE v4.0. Membrane PD-L1 expression was assessed by immunohistochemistry (Dako PD-L1 IHC 73-10 pharmDx). RESULTS: A total of 168 patients with MBC, including 58 patients with triple-negative breast cancer (TNBC), were treated with avelumab for 2-50 weeks and followed for 6-15 months. Patients were heavily pretreated with a median of three prior therapies for metastatic or locally advanced disease. Grade >/= 3 treatment-related AEs occurred in 13.7% of patients, including two treatment-related deaths. The confirmed objective response rate (ORR) was 3.0% overall (one complete response and four partial responses) and 5.2% in patients with TNBC. A trend toward a higher ORR was seen in patients with PD-L1+ versus PD-L1- tumor-associated immune cells in the overall population (16.7% vs. 1.6%) and in the TNBC subgroup (22.2% vs. 2.6%). CONCLUSION: Avelumab showed an acceptable safety profile and clinical activity in a subset of patients with MBC. PD-L1 expression in tumor-associated immune cells may be associated with a higher probability of clinical response to avelumab in MBC

Overexpression of caveolin-1 and -2 in cell lines and in human samples of inflammatory breast cancer

Inflammatory breast cancer (IBC) is the most aggressive form of locally advanced breast cancer (LABC). The IBC phenotype is characterized by an infiltrative growth pattern, increased (lymph)angiogenesis and the propensity to invade dermal lymphatics. In pancreatic cancer, interactions between caveolin-1 and RhoC GTPase, a key molecule in causing the IBC phenotype, regulate tumour cell motility and invasion. In this study we sought to investigate the role of caveolin-1 and -2 in IBC cell lines and in human IBC samples.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44235/1/10549_2005_Article_9002.pd