整合生物資訊分析與精準醫學概念以發展出應用在癌症檢測與治療方面上的實用解決方法

Abstract

數十年來，癌症一直是全球死因的頭號原因。癌症的發生是由於細胞內不斷累積的突變所造成，而數以萬計的研究也找出了與癌症發生相關的基因突變點位。但是，這群突變基因所扮演的角色仍不足以全面釐清罹癌的來龍去脈。因此，在現今科技的進步下，科學家利用生物晶片或是次世代定序等高通量技術進行大規模的基因研究，希望找出會造成基因功能產生顯著改變的變異。另一方面，隨著基因體學、蛋白質體學及分子檢測技術的發展，現今不僅僅根據癌症病理組織來源進行研究，也從小分子層面研究基因調控或是致病性，試圖更進一步找出影響調控機制的特定失常分子變異，以作為未來的檢測或是治療標的。精準醫學的概念是「對症下藥」，以生物資訊方法分析各種分子層面的資料，進而歸納出在特定的罹癌條件下最適合用於治療的藥物及療法。除了提昇治療效率以外，也可以降低病患接受不合適治療的發生機率。 本篇論文從美國癌症基因體圖譜計畫收集女性乳癌檢體的分子層面次世代定序資料，利用生物資訊分析方法找出可用於癌症檢測的突變生物標記及可作為新型癌症治療標靶的過度表現細胞膜受體。後續將進行生物實驗驗證分析結果，並與醫院進行合作再收集台灣女性乳癌檢體資料以進行臨床確認。希望未來可利用這套分析流程再找出其它癌症的生物標記，並將結果實際運用於臨床醫療上。

Cancer is a disease which can be ascribed to progressive accumulation of genetic aberrations, and it has been the leading cause of death worldwide for decades. Many studies have indicated some critical mutations in cancers in the past decades. However, functional roles of these mutations are insufficient to fully explain the whole picture of oncogenesis. In order to comprehensively identify the potential mechanisms of oncogenesis, a number of studies investigated mutation patterns and gene expression profiling by high-throughput technology, e.g. microarray or next generation sequencing. From high-throughput data analysis, many genes with significantly alterations are recognized. Currently, cancers are typically classified by pathologic criteria that rely heavily on the tissue site of origin. Owing to recent progress in genomics and proteomics, the focus on individual genes and cellular components as disease loci and drug targets has been a necessary step in understanding the basic mechanisms underlying tissue/organ physiology and drug action. Precision medicine is a concept of diagnosis based on individual characteristics that may fall into a limited number of sets of characteristics resulting in the same pathophysiology and therapy. Precision medicine utilizes both conventional medicine and cutting edge technology to concur the disease proven to be resistant to conventional medical techniques. In this work, it is focused on identification of molecular biomarkers of female breast cancer for cancer panel design and for identification of cancer-overexpressed membrane receptors as putative targeting agents in cancer therapy. Mutation profiles of whole-exome sequencing data and gene expression profiles of RNA-seq data were collected from TCGA. Generally, 58 mutation biomarkers were identified for breast cancer panel design and tens of breast cancer-overexpressed membrane receptors were identified as candidates for production of new antibody drugs. In the future, it is expected that precision medicine is practical to every patient whose genomic information is known so that the analyzed results will be used to accurately determine his cancer subtype, the drug with the best therapeutic effect for prescription, and the best targeting agent for targeted delivery.