以生物路徑為基礎的方法發掘基因印記及預測臨床表徵

Pathway-based Approaches for Gene Signature Identification and Clinical Outcome Prediction

張雅媗(Ya-Hsuan Chang)

指導教授 ： 陳中明 ； 陳璿宇

國立臺灣大學/工學院/醫學工程學研究所/博士(2015年)

https://doi.org/10.6342/NTU.2015.01949

摘要

基因體學、表觀體學、轉錄體學及蛋白質體學等不同層面的分子，在細胞生長、分化、發育以及在醫學上，皆扮演很重要的角色。現今在微陣列技術以及次世代定序儀等高通量技術的高度發展下，可用來偵測不同層面分子的變異情形，這些技術所產生的大量資料稱為體學資料。在這些優越的生物技術之下，一次的實驗中就可以測量到成千上萬個分子變異。
因腫瘤異質性的關係，即使具有相同的臨床病理特徵，而病人的治療反應或者臨床表徵會呈現不同的結果。故在本研究中，將以生物路徑的分式重新分析公開資料庫的微陣列資料，找尋與治療反應或者臨床表徵相關的基因印記，以增加治療反應或臨床表徵的預測正確率。在論文中，將上述方法應用在找尋肺腺癌及兒童高危險性急性B前體淋巴性白血病的基因印記，故包含兩個研究，分別為 1. 以生物路徑為基礎的基因印記預測預測肺腺癌的臨床表徵 2. 細胞凋亡路徑的基因印記預測兒童高危險性急性B前體淋巴性白血病的治療反應及臨床表徵。

研究一、以生物路徑為基礎的基因印記預測預測肺腺癌的臨床表徵
肺腺癌被診斷時大都為晚期且預後很差，且具有相同臨床病理特徵的病人卻有不同的臨床表徵。在過往研究顯示，肺癌生物標誌在不同的研究中有不一致的結果且很難有臨床的應用。因此，本研究上使用變異數倒數加權法，評估基因表現橫跨四組肺癌資料的風險比值，以增加統計檢定力，並利用生物路徑分析，以獲得生物路徑為基礎的基因印記來預測肺癌預後。研究結果顯示，2,418個基因表現與肺癌存活有關，其中21、22、及31個gene分別在HMGB1/RAGE訊息傳導路、β-腎上腺素感受器調控的ERK生物路徑及clathrin包覆水泡的循環路徑，在四組資料皆顯著地與肺腺癌的預後相關(對數等級檢定的p值皆小於 0.05)。我們合併此3個生物路徑標誌的危險分數而得到一個合併的生物路徑危險分數(CPBR)，並使用另兩個獨立的資料組來驗證這3個生物路徑標誌及CPBR的正確性。在比較p值及風險比值下，結果顯示生物路徑標誌及CPBR在四組資料的結果比2,418個基因更具有一致性。因此生物路徑標誌較具有正確預測預後的能力。
研究二、細胞凋亡路徑的基因印記預測兒童高危險性急性B前體淋巴性白血病的治療反應及臨床表徵
急性淋巴性白血病是兒童常見的癌症且具有很好的治癒率，尤其是B前體型的淋巴性白血病。但其治療仍具有抗藥性並導致癌症復發與藥劑過量的問題。在本研究中，利用邏輯迴歸分析、微陣列顯著分析(SAM)及基因集分析等方法來找出與治療反應相關的生物路徑印記。結果顯示3,772個微陣列探針顯著地與治療反應相關，再進一步進行基因集生物路徑分析後，只有細胞凋亡生物路徑印記與治療反應有顯著的相關。將15個顯著差異表現基因組合成細胞凋亡生物路徑印記，預測治療反應，正確率為88%，此生物路徑印記再用另外兩組獨立資料來驗證。結果顯示，在第一組驗證資料中，細胞凋亡生物路徑印記顯著地與誘導性治療失敗時間相關(校正的風險比值為1.60，95%的信賴區間為1.13至2.27)。在第二組驗證資料中，細胞凋亡生物路徑印記顯著地與無癌症復發或死亡事件發生的存活時間相關(校正的風險比值為1.56，95%的信賴區間為1.13至2.16)，且生物路徑印記也與整體存活期相關(校正的風險比值為1.74，95%的信賴區間為1.24至2.45)。細胞凋亡生物路徑印記不僅能用來預測臨床治療結果，且能提供病人治療處置的分子導引。

總體來說，藉由高通量技術得到基因表現的變異，可藉由生物路徑方式來尋找基因印記，不僅可用來預測疾病的預後、治療反應以及副作用，且本研究結果經由大規模的前瞻性研究驗證其預測能後，將具有提供臨床醫療上分子導引或標靶藥物研發的潛力。

關鍵字

高通量技術 ； 生物路徑 ； 基因印記 ； 預後 ； 治療反應

並列摘要

Different molecular levels including genome, epigenome, transcriptome, and proteome play important roles in development, differentiation, growth etc. as well as in medicine. Up to date, high throughput technologies such as microarrays and next generation sequencers (NGS) can be used to measure variations of different molecular levels and above data was named “omic data”. It provided powerful tools to discovery huge amount of molecular changes at once.
Because of tumor heterogeneity, patients with the similar clinical pathology had the different treatment response and clinical outcome. Hence, in the studies, pathway-base analysis was used to reanalysis public datasets to explore signatures associated with treatment response or clinical outcome. These pathway-based signatures increased predict accuracy of treatment response or clinical outcome. In this dissertation, this methodology was used to find pathway-based signatures of lung adenocarcinoma and childhood high risk B-precursor acute lymphoblastic leukemia. Two studies were the 1st “Pathway-based gene signatures prediction clinical outcome of lung adenocarcinoma”, and 2nd “Apoptosis pathway signature for prediction of treatment response and clinical outcome in childhood high risk B-precursor acute lymphoblastic leukemia”.

Study 1: Pathway-based gene signatures prediction clinical outcome of lung adenocarcinoma.
Lung adenocarcinoma is often diagnosed at an advanced stage with poor prognosis. Patients with different clinical outcomes may have similar clinico-pathological characteristics. The results of previous studies for biomarkers for lung adenocarcinoma have generally been inconsistent and limited in clinical application. In this study, we used inverse-variance weighting to combine the hazard ratios for the four datasets and performed pathway analysis to identify prognosis-associated gene signatures. A total of 2,418 genes were found to be significantly associated with overall survival. Of these, a 21-gene signature in the HMGB1/RAGE signalling pathway, a 22-gene signature in the beta-adrenergic receptor regulation of ERK pathway, and a 31-gene signature in the clathrin-coated vesicle cycle pathway were significantly associated with prognosis of lung adenocarcinoma across all four datasets (all p-value< 0.05, log-rank test). We combined the scores for the three pathways to derive a combined pathway-based risk (CPBR) score. Then, three pathway-based signatures and the CPBR score were validated in two independent cohorts. Considering p-values and hazard ratios, three pathway-based signatures and CPBR score had more statistical significant than 2418 genes and showed the consistent results in four datasets. Pathway-based signatures portend the better prediction power for prognosis.

Study 2: Apoptosis pathway signature for prediction of treatment response and clinical outcome in childhood high risk B-precursor acute lymphoblastic leukemia
The most common cancer in children is acute lymphoblastic leukemia (ALL) and it had high cure rate, especially for B-precursor ALL. However, relapse due to drug resistance and overdose treatment reach the limitations in patient managements. In this study, integration of gene expression microarray data, logistic regression, analysis of microarray (SAM) method, and gene set analysis were performed to discover treatment response associated pathway-based signatures in the original cohort. Results showed that 3,72 probes were significantly associated with treatment response. After pathway analysis, only apoptosis pathway had significant association with treatment response. Apoptosis pathway signature (APS) derived from 15 significantly expressed genes had 88% accuracy for treatment response prediction. The APS was further validated in two independent cohorts. Results also showed that APS was significantly associated with induction failure time (adjusted hazard ratio [HR] =1.60, 95% confidence interval [CI] = [1.13, 2.27]) in the first cohort and significantly associated with event-free survival (adjusted HR=1.56, 95% CI= [1.13, 2.16]) or overall survival in the second cohort (adjusted HR=1.74, 95% CI= [1.24, 2.45]). APS not only can predict clinical outcome, but also provide molecular guidance of patient management.

In conclusions, pathway-based analysis could be applied in gene expression profiling measured from high throughput technologies to identified signatures. Pathway-based signatures could not only provide prediction abilities for prognosis, treatment response, and adverse effect, and they may provide potential molecular guidance in clinical practice or targets of drug development after validation in large prospective cohorts.

並列關鍵字

high-throughput technology ； prognosis ； pathway-based analysis ； gene signature ； treatment response

參考文獻

1 Chin, L. & Gray, J. W. Translating insights from the cancer genome into clinical practice. Nature 452, 553-563 (2008).

2 Ludwig, J. A. & Weinstein, J. N. Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer 5, 845-856 (2005).

3 Kulasingam, V. & Diamandis, E. P. Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nature clinical practice. Oncology 5, 588-599 (2008).

4 Mehta, S. et al. Predictive and prognostic molecular markers for cancer medicine. Ther Adv Med Oncol 2, 125-148 (2010).

5 Abaci, N. et al. Mitochondrial mutations in patients with congenital heart defects by next generation sequencing technology. Cardiol Young 25, 705-711 (2015).