Melanoma expression analysis with Big Data technologies

Melanoma is a highly immunogenic tumor. Therefore, in recent years physicians have incorporated drugs that alter the immune system into their therapeutic arsenal against this disease, revolutionizing in the treatment of patients in an advanced stage of the disease. This has led us to explore and deepen our knowledge of the immunology surrounding melanoma, in order to optimize its approach. At present, immunotherapy for metastatic melanoma is based on stimulating an individual’s own immune system through the use of specific monoclonal antibodies. The use of immunotherapy has meant that many of patients with melanoma have survived and therefore it constitutes a present and future treatment in this field. At the same time, drugs have been developed targeting specific mutations, specifically BRAF, resulting in large responses in tumor regression (set up in this clinical study to 18 months), as well as a higher percentage of long-term survivors. The analysis of the gene expression changes and their correlation with clinical changes can be developed using the tools provided by those companies which currently provide gene expression platforms. The gene expression platform used in this clinical study is NanoString, which provides nCounter. However, nCounter has some limitations as the type of analysis is restricted to a predefined set, and the introduction of clinical features is a complex task. This paper presents an approach to collect the clinical information using a structured database and a Web user interface to introduce this information, including the results of the gene expression measurements, to go a step further than the nCounter tool. As part of this work, we present an initial analysis of changes in the gene expression of a set of patients before and after targeted therapy. This analysis has been carried out using Big Data technologies (Apache Spark) with the final goal being to scale up to large numbers of patients, even though this initial study has a limited number of enrolled patients (12 in the first analysis). This is not a Big Data problem, but the underlaying study aims at targeting 20 patients per year just in Málaga, and this could be extended to be used to analyze the 3.600 patients diagnosed with melanoma per year.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This work was funded in part by Grants TIN2014-58304-R (Ministerio de Ciencia e Innovación) and P11-TIC-7529 and P12-TIC-1519 (Plan Andaluz de Investigación, Desarrollo e Innovación). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Different Pathological Complete Response Rates According to PAM50 Subtype in HER2+ Breast Cancer Patients Treated With Neoadjuvant Pertuzumab/Trastuzumab vs. Trastuzumab Plus Standard Chemotherapy: An Analysis of Real-World Data.

Background: Double blockade with pertuzumab and trastuzumab combined with chemotherapy is the standard neoadjuvant treatment for HER2-positive early breast cancer. Data derived from clinical trials indicates that the response rates differ among intrinsic subtypes of breast cancer. The aim of this study is to determine if these results are valid in real-world patients. Methods: A total of 259 patients treated in eight Spanish hospitals were included and divided into two cohorts: Cohort A (132 patients) received trastuzumab plus standard neoadjuvant chemotherapy (NAC), and Cohort B received pertuzumab and trastuzumab plus NAC (122 patients). Pathological complete response (pCR) was defined as the complete disappearance of invasive tumor cells. Assignment of the intrinsic subtype was realized using the research-based PAM50 signature. Results: There were more HER2-enriched tumors in Cohort A (70 vs. 56%) and more basal-like tumors in Cohort B (12 vs. 2%), with similar luminal cases in both cohorts (luminal A 12 vs. 14%; luminal B 14 vs. 18%). The overall pCR rate was 39% in Cohort A and 61% in Cohort B. Better pCR rates with pertuzumab plus trastuzumab than with trastuzumab alone were also observed in all intrinsic subtypes (luminal PAM50 41 vs. 11.4% and HER2-enriched subtype 73.5 vs. 50%) but not in basal-like tumors (53.3 vs. 50%). In multivariate analysis the only significant variables related to pCR in both luminal PAM50 and HER2-enriched subtypes were treatment with pertuzumab plus trastuzumab (Cohort B) and histological grade 3. Conclusions: With data obtained from patients treated in clinical practice, it has been possible to verify that the addition of pertuzumab to trastuzumab and neoadjuvant chemotherapy substantially increases the rate of pCR, especially in the HER2-enriched subtype but also in luminal subtypes, with no apparent benefit in basal-like tumors