SIAH and EGFR, Two RAS Pathway Biomarkers, are Highly Prognostic in Locally Advanced and Metastatic Breast Cancer.

BACKGROUND: Metastatic breast cancer exhibits diverse and rapidly evolving intra- and inter-tumor heterogeneity. Patients with similar clinical presentations often display distinct tumor responses to standard of care (SOC) therapies. Genome landscape studies indicate that EGFR/HER2/RAS "pathway" activation is highly prevalent in malignant breast cancers. The identification of therapy-responsive and prognostic biomarkers is paramount important to stratify patients and guide therapies in clinical oncology and personalized medicine. METHODS: In this study, we analyzed matched pairs of tumor specimens collected from 182 patients who received neoadjuvant systemic therapies (NST). Statistical analyses were conducted to determine whether EGFR/HER2/RAS pathway biomarkers and clinicopathological predictors, alone and in combination, are prognostic in breast cancer. FINDINGS: SIAH and EGFR outperform ER, PR, HER2 and Ki67 as two logical, sensitive and prognostic biomarkers in metastatic breast cancer. We found that increased SIAH and EGFR expression correlated with advanced pathological stage and aggressive molecular subtypes. Both SIAH expression post-NST and NST-induced changes in EGFR expression in invasive mammary tumors are associated with tumor regression and increased survival, whereas ER, PR, and HER2 were not. These results suggest that SIAH and EGFR are two prognostic biomarkers in breast cancer with lymph node metastases. INTERPRETATION: The discovery of incorporating tumor heterogeneity-independent and growth-sensitive RAS pathway biomarkers, SIAH and EGFR, whose altered expression can be used to estimate therapeutic efficacy, detect emergence of resistant clones, forecast tumor regression, differentiate among partial responders, and predict patient survival in the neoadjuvant setting, has a clear clinical implication in personalizing breast cancer therapy. FUNDING: This work was supported by the Dorothy G. Hoefer Foundation for Breast Cancer Research (A.H. Tang); Center for Innovative Technology (CIT)-Commonwealth Research Commercialization Fund (CRCF) (MF14S-009-LS to A.H. Tang), and National Cancer Institute (CA140550 to A.H. Tang).

Record linkage in Scotland and its applications to health research.

AIMS AND OBJECTIVES: This paper will focus on the key concepts behind record linkage and describe how probability matching of Scottish health records can be used for national health research. BACKGROUND: Record linkage can bring together two or more records relating to the same individual. This allows information from multiple sources to be joined together to produce richer data sets for research purposes and has wide applicability in public health and epidemiological research. The probability matching techniques underpinning record linkage bring together records on a patient basis using key identifying information on each record. Scotland has a strong track record for performing linkage for research purposes owing to routinely collected and well-maintained national administrative health data sets, the emergence of the Scottish record linkage system and organisations like the Information Services Division of NHS National Services Scotland who centrally hold permanently linked patient-based databases. Design. A record linkage retrospective population cohort study is described within this paper. METHODS: The paper will describe current linkage methodology before discussing typical applications in the setting of Information Services Division and focusing on a particular linkage study investigating rates and risk factors for gastroschisis. RESULTS: Conclusions from the gastroschisis study are typical of the types of important findings drawn from analysing linked health data. CONCLUSIONS: Scotland's good track record for linking records for health research is evidenced by the high volume of research projects, publications and findings resulting from probability matching of national health data. Relevance to clinical practice. Record linkage allows information relating to the same person held across different data sources to be brought together. Probabilistic record linkage can overcome data quality issues, producing accurate matches. This allows linked, analysable, patient-based databases, capable of answering complex research questions, to be produced from several data sources with wide applications in the field of health research.