TWI829042B - 泛癌症早篩預測方法 - Google Patents
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Abstract
本發明提供一種泛癌症早篩預測方法,包括以下步驟。建立癌症患者族群及健康族群的微型核糖核酸表現圖譜數據庫,並據此建立泛癌症早篩預測方法。接下來,將受檢者的液態活檢樣品中的微型核糖核酸表現圖譜通過泛癌症早篩預測方法進行分析,以判斷受檢者是否可能已罹患癌症。
Description
本發明是有關於一種早篩方法,且特別是有關於一種泛癌症早篩預測方法。
微型核糖核酸(microRNA)是一個非編碼的核糖核酸(non-coding RNA),其長度約為18至25核苷酸,於演化過程中,高度地被保留下來,並且於細胞內的調控中扮演非常重要的角色。於西元1993年,微型核糖核酸首度於線蟲(
C. elegans)中被發現。陸陸續續,於人類或其它的物種中也發現了越來越多的微型核糖核酸。目前,人類細胞內中約有2,500個已知的微型核糖核酸,這些微型核糖核酸被證實能調控大於百分之五十訊息核糖核酸表現量(mRNA expression)。此外,不正常的微型核糖核酸表現量(microRNA expression)已被證實與許多疾病的生成息息相關,其中也包括了癌症病變、慢性疾病、自體免疫疾病等。
於過去的數年中,微型核糖核酸已獲得廣大的推崇,且被看好能當作新型分子檢測標靶。目前微型核糖核酸也已被證實能從細胞中分泌到血液,並形成蛋白核糖核酸複合物(protein-RNA complexes),確保不會被核糖核酸酶(RNase)降解。這樣的特徵也變得非常有價值,使得血液中游離微型核糖核酸變得相當容易被取得,並可透過檢測游離微型核醣核酸表現量(cell free miRNA profiling)來當作疾病初期診斷的依據。舉例來說,不同類型的癌症已被證實各自擁有獨特的游離微型核糖核酸表現量,或者被稱為微型核糖核酸特徵(miRNA signature),可被利用來當作癌症初期診斷的依據。
一直以來,具有便利性及高診斷率的非侵入性疾病檢測方法為醫學界不斷追求的目標。以癌症為例,為了提早找出潛在未發現與早期無症狀的癌症,可進行癌症篩檢(cancer screening)來達到此目的。癌症篩檢是指利用檢查、檢驗或其他方法,辨別可能罹患癌症或可能未罹患癌症之過程。
目前,病患可經由許多症狀或檢驗結果來檢測是否罹患癌症,但診斷惡性腫瘤最確定的方式就是經由病理醫師對活體組織進行切片或經手術取得的組織做病理檢測來證實癌細胞的存在,屬於侵入式的檢測方式。
此外,腫瘤標記檢測是指藉由檢測與惡性腫瘤細胞相關的特殊蛋白質的變化來判斷是否罹患癌症。然而,腫瘤標記檢測的靈敏度及專一性不佳,往往在腫瘤已發展到相當大小或已經轉移到其他器官時才能偵測到。
基於上述,開發出一種非侵入性、早期評估的泛癌症早篩預測方法,及早對受檢者是否罹患癌症進行檢測,以及早診斷治療,為目前所需研究的重要課題。
本發明提供一種泛癌症早篩預測方法,藉由分析受檢者的液態活檢樣品中的微型核糖核酸表現圖譜,以檢測早期癌症。
本發明的泛癌症早篩預測方法包括以下步驟。建立癌症患者族群及健康族群的微型核糖核酸表現圖譜數據庫,並據此建立泛癌症早篩預測方法。泛癌症早篩預測方法是基於SVM所建構的預測方法,將癌症患者族群及健康族群的液態活檢樣品微型核糖核酸表現圖譜藉由以下步驟建立:數據正規化、缺失值校正、數據縮放、預測建模以及交叉驗證。預測方法建立後,以受檢者液態活檢樣品的微型核糖核酸表現圖譜對受檢者進行預測,以作為癌症初期診斷的依據。其預測結果可透過混淆矩陣(Confusion Matrix)以評估泛癌症早篩預測方法的效能。
在本發明的一實施例中,微型核糖核酸表現圖譜通過qPCR、定序、微陣列晶片或RNA-DNA雜交捕獲技術來測定。
在本發明的一實施例中,微型核糖核酸表現圖譜通過對由液態活檢樣品中的微型核糖核酸所合成的cDNA進行qPCR來測定。
在本發明的一實施例中,微型核糖核酸表現圖譜包括多個微型核糖核酸的表現程度。
在本發明的一實施例中,多個微型核糖核酸包括至少167個微型核糖核酸。
在本發明的一實施例中,癌症初期診斷的類別包括頭頸癌、肺癌或乳癌。
在本發明的一實施例中,液態活檢樣品包括血漿、血清或尿液。
在本發明的一實施例中,正規化處理用以使每個樣本的實驗數據分布達到一致。
在本發明的一實施例中,缺失值校正將沒有訊號的生物標誌校正為所有樣本內微小核糖核酸生物標誌表現的循環閾值(Cq)最大值。
在本發明的一實施例中,數據縮放用以標準化數據的數值範圍以使數據具有零均值(zero-mean)和單位變異數(unit-variance)。
基於上述,本發明提供一種非侵入性、早期評估的泛癌症早篩預測方法,將受檢者的液態活檢樣品中的微型核糖核酸表現圖譜通過泛癌症早篩預測方法進行分析,因此,能夠即時且有效率地對早期癌症進行評估篩檢,更可改善習知癌症篩檢的便利性及診出率。
為讓本發明的上述特徵和優點能更明顯易懂,下文特舉實施例,並作詳細說明如下。
以下,將詳細描述本發明的實施例。然而,這些實施例為例示性,且本發明揭露不限於此。
本發明提供一種泛癌症早篩預測方法。下文中,先針對說明書內文所使用的名詞加以定義說明。
「cDNA」(complementary DNA,互補DNA)是指利用逆轉錄酶對RNA模板進行逆轉錄所產生的互補DNA。
「qPCR」或「real-time quantitative PCR」(即時定量聚合酶鏈鎖反應)是指使用PCR以擴增並同時定量目標DNA的實驗方法。利用多種測定化學物質來進行定量(包括諸如SYBR
®green的螢光染料或Taqman探針的螢光報告寡核苷酸探針等),隨著每次擴增循環之後反應中積累的擴增DNA來對其進行即時定量。
術語「表現」是指生物樣品,例如液態活檢樣品中的RNA分子的轉錄和/或積累。在此上下文中,術語「miRNA表現」是指生物樣本中的一或多個miRNA,且可通過使用所屬領域中已知的合適方法來檢測miRNA表現。
術語「微型核糖核酸」(“microRNA”或“miRNA”)是指從內源基因衍生的一類長度為大約18個到25個核苷酸的非編碼RNA。miRNA通過與其目標mRNA的3'非轉譯區(UTR)進行鹼基配對來作為基因表現的轉錄後調控因子,以用於mRNA降解或轉譯抑制。
術語「核酸」、「核苷酸」以及「多核苷酸」可互換地使用且是指呈單鏈或雙鏈形式的DNA或RNA的聚合物。除非另外指出,否則這些術語涵蓋含有天然核苷酸的已知類似物的多核苷酸,所述多核苷酸具有與參考核酸相似的結合特性且以與天然存在的核苷酸相似的方式進行代謝。
術語「引子」是指寡核苷酸,當在誘導引子延伸產物的合成的條件下,例如在核苷酸和聚合誘導劑(如DNA或核糖核酸聚合酶)的存在下且在合適溫度、pH、金屬離子濃度以及鹽濃度下放置所述寡核苷酸時,所述寡核苷酸用以引發互補核酸鏈的合成。
術語「探針」是指包括多核苷酸的結構,其含有與存在於目標核酸分析物(例如,核酸擴增產物)中的核酸序列互補的核酸序列。探針的多核苷酸區可由DNA和/或RNA和/或合成核苷酸類似物構成。探針的長度通常與其用於專一性檢測目標核酸的所有或部分目標序列相容。
術語「標靶」(targeting)是指選擇與所關注核酸序列雜交的合適核苷酸序列。
本發明提供一種泛癌症早篩預測方法,包括以下步驟。建立癌症患者族群及健康族群的微型核糖核酸表現圖譜數據庫,並據此建立泛癌症早篩預測方法。接下來,將受檢者的液態活檢樣品中的微型核糖核酸表現圖譜通過泛癌症早篩預測方法進行分析,以作為癌症初期診斷的依據。更詳細而言,液態活檢樣品可包括血漿、血清或尿液,但本發明並不以此為限。
本發明的泛癌症早篩預測方法是以監督式學習的支持向量機(Support Vector Machine,簡稱SVM)作為建模基礎。原始SVM是由Vladimir N. Vapnik和Alexey Ya. Chervonenkis於1963年發明。1992年,Bernhard E. Boser、Isabelle M. Guyon和Vladimir N. Vapnik提出了一種藉由核技巧(kernel trick)將超平面最大化間隔(maximum-margin hyperplanes)的方法來建立非線性分類器。目前SVM分類器標準的前身由Corinna Cortes和Vladimir N. Vapnik於1993年提出,並於1995年發表。(https://link.springer.com/article/10.1007%2FBF00994018)
在本實施例中,依據三萬多篇文獻建立與疾病相關的微型核醣核酸資訊庫,並篩選出與癌症高度相關的167個微型核糖核酸。所篩選出的與癌症高度相關的167個微型核醣核酸如下方表1中所示。
[表1]
167 miRNA 生物標記( Biomarker ) | ||||
hsa-let-7d-3p | hsa-miR-214-3p | hsa-let-7b-5p | hsa-miR-20b-5p | hsa-miR-451a |
hsa-miR-101-3p | hsa-miR-215-5p | hsa-let-7c-5p | hsa-miR-210-3p | hsa-miR-23a-3p |
hsa-miR-122-5p | hsa-miR-21-5p | hsa-let-7d-5p | hsa-miR-217 | hsa-let-7a-5p |
hsa-miR-1254 | hsa-miR-216a-5p | hsa-let-7f-5p | hsa-miR-221-3p | hsa-miR-103a-3p |
hsa-miR-125b-5p | hsa-miR-222-3p | hsa-let-7g-5p | hsa-miR-223-3p | hsa-miR-16-5p |
hsa-miR-126-3p | hsa-miR-22-3p | hsa-miR-100-5p | hsa-miR-27a-3p | hsa-miR-191-5p |
hsa-miR-1290 | hsa-miR-224-5p | hsa-miR-106a-5p | hsa-miR-29a-3p | hsa-miR-423-3p |
hsa-miR-129-5p | hsa-miR-24-3p | hsa-miR-106b-5p | hsa-miR-29b-3p | hsa-miR-423-5p |
hsa-miR-140-5p | hsa-miR-26a-5p | hsa-miR-10a-5p | hsa-miR-29c-3p | hsa-miR-93-5p |
hsa-miR-142-3p | hsa-miR-28-3p | hsa-miR-10b-5p | hsa-miR-302d-3p | hsa-miR-425-5p |
hsa-miR-143-3p | hsa-miR-299-5p | hsa-miR-1248 | hsa-miR-30a-5p | hsa-miR-1228-5p |
hsa-miR-144-3p | hsa-miR-29a-5p | hsa-miR-125a-5p | hsa-miR-30c-5p | |
hsa-miR-145-5p | hsa-miR-30b-5p | hsa-miR-127-3p | hsa-miR-30d-5p | |
hsa-miR-146a-5p | hsa-miR-31-5p | hsa-miR-128-3p | hsa-miR-30e-5p | |
hsa-miR-150-5p | hsa-miR-326 | hsa-miR-130a-3p | hsa-miR-31-3p | |
hsa-miR-151a-3p | hsa-miR-335-5p | hsa-miR-130b-3p | hsa-miR-320a | |
hsa-miR-152-3p | hsa-miR-338-5p | hsa-miR-133a-3p | hsa-miR-330-5p | |
hsa-miR-155-5p | hsa-miR-34a-5p | hsa-miR-133b | hsa-miR-376c-3p | |
hsa-miR-15a-5p | hsa-miR-361-5p | hsa-miR-134-5p | hsa-miR-411-5p | |
hsa-miR-15b-5p | hsa-miR-372-3p | hsa-miR-135a-5p | hsa-miR-429 | |
hsa-miR-17-3p | hsa-miR-373-3p | hsa-miR-135b-5p | hsa-miR-4306 | |
hsa-miR-181a-5p | hsa-miR-375 | hsa-miR-1-3p | hsa-miR-450a-5p | |
hsa-miR-181b-5p | hsa-miR-378a-5p | hsa-miR-141-3p | hsa-miR-486-5p | |
hsa-miR-182-5p | hsa-miR-382-5p | hsa-miR-146b-5p | hsa-miR-518a-5p | |
hsa-miR-183-5p | hsa-miR-409-3p | hsa-miR-17-5p | hsa-miR-520b | |
hsa-miR-193a-3p | hsa-miR-425-3p | hsa-miR-18a-5p | hsa-miR-539-5p | |
hsa-miR-1972 | hsa-miR-452-3p | hsa-miR-18b-5p | hsa-miR-625-5p | |
hsa-miR-197-3p | hsa-miR-483-5p | hsa-miR-192-5p | hsa-miR-652-3p | |
hsa-miR-198 | hsa-miR-484 | hsa-miR-193b-3p | hsa-miR-660-5p | |
hsa-miR-199a-5p | hsa-miR-499a-5p | hsa-miR-195-5p | hsa-miR-663a | |
hsa-miR-19a-3p | hsa-miR-500a-5p | hsa-miR-196a-5p | hsa-miR-718 | |
hsa-miR-19b-3p | hsa-miR-574-3p | hsa-miR-196b-5p | hsa-miR-7-5p | |
hsa-miR-202-3p | hsa-miR-574-5p | hsa-miR-1973 | hsa-miR-760 | |
hsa-miR-203a-3p | hsa-miR-579-3p | hsa-miR-199a-3p | hsa-miR-885-5p | |
hsa-miR-205-5p | hsa-miR-589-5p | hsa-miR-200a-3p | hsa-miR-92a-3p | |
hsa-miR-206 | hsa-miR-593-5p | hsa-miR-200b-3p | hsa-miR-95-3p | |
hsa-miR-20a-5p | hsa-miR-596 | hsa-miR-200c-3p | hsa-miR-9-5p | |
hsa-miR-2114-3p | hsa-miR-601 | hsa-miR-208b-3p | hsa-miR-99b-5p | |
hsa-miR-940 | hsa-miR-1225-3p | hsa-miR-223-5p | hsa-miR-4772-3p |
在本實施例中,檢測血漿中微型核糖核酸的方法包括以下步驟:
1. 採集血液樣本
將抽血者皮膚以酒精擦拭採血部位,使用止血帶用活結方式綁在採血部位上方5公分至15公分處。以19G至22G針頭抽取10 ml全血至K
2EDTA真空採血管 (K
2EDTA BD Vacutainer tube),當血液流入採血管後,應立即鬆開止血帶。待抽血完成,立即將採血管輕輕上下顛倒混合5至8次,以確保抗凝劑完全發揮作用。將採血管置於室溫下保存,在採血後一小時內須完成血漿分離步驟。
2. 血漿分離方法
將採血管置於旋翼式轉子(Swinging-Bucket Rotor),以1200 xg於室溫下離心10分鐘。離心完成後,將上清液取出至新的15 ml離心管。將15 ml離心管以pipette吸放5次確保混勻,再均分至1.5 ml DNase/RNase-free eppendorf,以12,000 xg於室溫下離心10分鐘。離心完成後,取出上清液至新的15 ml離心管,避免取到1.5 ml eppendorf底部的白色沉澱物。將上清液pipette吸放5次確保混勻,分裝至1.5 ml DNA LoBind Tubes (Eppendorf, 22431021),立即置於-80度冰箱保存。
3. 微型核糖核酸萃取方法
於-80度冰箱取出血漿樣本,置於冰上解凍,解凍後依照Qiagen miRNeasy Serum/Plasma Kit所提供之操作手冊進行實驗,以30 μl Nuclease-free water進行回溶。
4. cDNA合成
取適量miRNA以Quarkbio microRNA Universal RT kit進行逆轉錄反應合成cDNA。
5. qPCR實驗
取適量cDNA以Quarkbio mirSCAN Panelchip
®所提供之操作手冊進行qPCR實驗。
在本實施例中,所選取樣本中的健康人和癌症患者是由醫師判定分類。每種癌症患者都是第1-2期未轉移的病人,且都是剛確診,尚未接受過治療。判定為新確診的癌症病患,會於其治療前抽血,以上述方法檢測血漿中167個微型核糖核酸的表現程度。判定為健康人者,亦抽血以上述方法檢測血漿中167個微型核糖核酸的表現程度。
在本實施例中,早期癌症篩檢的判別可包括癌症或健康人,例如可包括頭頸癌、肺癌、乳癌或健康人,但本發明並不以此為限,亦可包含其他癌症、腫瘤風險或可能罹癌的風險因子。更詳細而言,針對不同癌症族群與健康族群的微型核糖核酸資料庫與依其所建立的泛癌症早篩預測方法,依據受試者的微型核糖核酸表現圖譜,可將其診斷預測區分為乳癌、頭頸癌、肺癌或健康人,但本發明並不以此為限。更詳細而言,微型核糖核酸表現圖譜例如可通過qPCR、定序、微陣列晶片或RNA-DNA雜交捕獲技術來測定,較佳例如是通過對由液態活檢樣品中的微型核糖核酸所合成的cDNA進行qPCR來測定。
在本實施例中,泛癌症早篩預測方法為基於SVM所建構的演算法。更詳細而言,執行以下步驟來建構預測方法:數據正規化、缺失值校正、數據縮放、預測建模以及交叉驗證。
數據正規化
為使在統計特性中分佈相同,數據可通過百分位正規化(Quantile Normalization)來正規化,如博爾斯塔(Bolstad)等人,於 “A comparison of normalization methods for high density oligonucleotide array data based on variance and bias”,(Bioinformatics, 2003, 19(2):185-193)中所描述。
實驗檢測後對每個樣本的所有實驗原始數據進行正規化處理,讓每個樣本的實驗數據分布達到一致。
缺失值校正
如果實驗設計的內部控制組沒有受到任何非預期因素影響,實驗檢測沒有訊號的任一個微小核糖核酸生物標誌都將視為無訊號處理,並進行缺失值校正(Imputation)。缺失值校正的方式會將沒有訊號的生物標誌校正為所有樣本內微小核糖核酸生物標誌表現的循環閾值(Cq)最大值。
數據縮放
為確保目標函數恰當地作用,可標準化數據的數值範圍以使數據具有零均值(zero-mean)和單位變異數(unit-variance)。
預測建模
數據縮放後可用於進一步建構具有支援向量機(Support Vector Machine, SVM)的監督式學習分類模型。採用121例樣本(38例健康族群、18例頭頸癌、53例肺癌、12例乳癌)用來訓練模型,並透過k折交叉驗證來評估模型可行性並找出模型最佳參數。
交叉驗證
k折交叉驗證方法(例如,10折交叉驗證)可用於評估癌症早篩預測方法在最終完成之前的罹癌風險評估性能。在k折交叉驗證中,將原始樣本隨機地分割成k等分的子樣本。在k個子樣本中,其中一個子樣本保留作為測試模型的驗證數據以用於,且將其餘k-1個子樣本用作訓練數據。隨後重複進行交叉驗證過程k次(折),其中k個子樣本中的每一個都剛好使用一次作為驗證數據。來自等分數的k個結果隨後可被平均化(或以其它方式結合)以產生單一估算值。在驗證及優化之後,產出癌症早篩預測方法。依據以下混淆矩陣(Confusion Matrix)可得知模型交叉驗證預測結果之Sensitivity=80.62%、Specificity=93.32%、Positive Predictive Value=85.47%、Negative Predictive Value=93.57%及Accuracy=82.64%。混淆矩陣具有兩個維度的(實際和預測)列聯表,且兩維度中都有著一樣的類別的集合。病患實際分類的類別等於模型預測結果的類別(predicted condition),其為真陽性(true positive)和真陰性(true negative)。反之實際的類別不等於預測的類別,其為假陽性(false positive)和假陰性(false negative)。
Sensitivity (SEN) = TP / (TP + FN)
Specificity (SPE) = TN / (TN + FP)
Positive Predictive Value (PPV) = TP / (TP + FP)
Negative Predictive Value (NPV) = TN / (TN + FN)
Accuracy (ACC) = (TP + TN) / (TP + FP + TN + FN)
TP = True Positive
FP = False Positive
FN = False Negative
TN = True Negative
病患分類 | ||||||||||
健康族群 | 頭頸癌 | 肺癌 | 乳癌 | SEN | SPE | PPV | NPV | ACC | ||
模型預測結果 | 健康族群 | 34 | 3 | 7 | 0 | 89.47% | 87.95% | 77.27% | 94.81% | - |
頭頸癌 | 0 | 12 | 2 | 1 | 66.67% | 97.09% | 80.00% | 94.34% | - | |
肺癌 | 4 | 3 | 44 | 1 | 83.02% | 88.24% | 84.62% | 86.96% | - | |
乳癌 | 0 | 0 | 0 | 10 | 83.33% | 100.00% | 100.00% | 98.20% | - | |
80.62% | 93.32% | 85.47% | 93.57% | 82.64% |
以下內容用以證明本發明所提出的泛癌症早篩預測方法能夠即時且有效率地對罹癌風險進行評估,必須說明的是,以下內容與前文所述實施例實驗檢測方式是相同的。
檢測受檢者血漿樣品中的微型核糖核酸表現圖譜以通過泛癌症早篩預測方法進行預測。預測結果將透過混淆矩陣(Confusion Matrix)來評估早篩預測方法的效能。
共計選取64例受檢者作為測試驗證(37例健康族群、27例肺癌)。依據以下混淆矩陣可得知模型預測結果之Sensitivity=83.98%、Specificity=92.28%、Positive Predictive Value=57.03%、Negative Predictive Value=92.08%及Accuracy=84.38%。
Sensitivity (SEN) = TP / (TP + FN)
Specificity (SPE) = TN / (TN + FP)
Positive Predictive Value (PPV) = TP / (TP + FP)
Negative Predictive Value (NPV) = TN / (TN + FN)
Accuracy (ACC) = (TP + TN) / (TP + FP + TN + FN)
TP = True Positive
FP = False Positive
FN = False Negative
TN = True Negative
病患分類 | ||||||||||
健康族群 | 頭頸癌 | 肺癌 | 乳癌 | SEN | SPE | PPV | NPV | ACC | ||
模型預測結果 | 健康族群 | 32 | 0 | 5 | 0 | 86.49% | 81.48% | 86.49% | 81.48% | - |
頭頸癌 | 1 | 0 | 0 | 0 | - | 98.44% | 0.00% | 100.00% | - | |
肺癌 | 4 | 0 | 22 | 0 | 81.48% | 89.19% | 84.62% | 86.84% | - | |
乳癌 | 0 | 0 | 0 | 0 | - | 100.00% | - | 100.00% | - | |
83.98% | 92.28% | 57.03% | 92.08% | 84.38% |
綜上所述,本發明提供一種非侵入性的泛癌症早篩預測方法,將受檢者液態活檢樣品中的微型核糖核酸表現圖譜通過泛癌症早篩預測法進行分析,因此,能夠即時且有效率地對早期癌症進行篩檢,更可改善習知癌症早篩技術的便利性及診出率,提供個人化的專業罹癌檢測監控。
雖然本發明已以實施例揭露如上,然其並非用以限定本發明,任何所屬技術領域中具有通常知識者,在不脫離本發明的精神和範圍內,當可作些許的更動與潤飾,故本發明的保護範圍當視後附的申請專利範圍所界定者為準。
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Claims (7)
- 如請求項1所述的泛癌症早篩預測方法,其中微型核糖核酸表現圖譜通過qPCR、定序、微陣列晶片或RNA-DNA雜交捕獲技術來測定。
- 如請求項2所述的泛癌症早篩預測方法,其中微型核糖核酸表現圖譜通過對由液態活檢樣品中的微型核糖核酸所合成的cDNA進行qPCR來測定。
- 如請求項1所述的泛癌症早篩預測方法,其中液態活檢樣品包括血漿、血清或尿液。
- 如請求項1所述的泛癌症早篩預測方法,其中所述正規化處理用以使每個樣本的實驗數據分布達到一致。
- 如請求項1所述的泛癌症早篩預測方法,其中所述缺失值校正將沒有訊號的生物標誌校正為所有樣本內微小核糖核酸生物標誌表現的循環閾值(Cq)最大值。
- 如請求項1所述的泛癌症早篩預測方法,其中所述數據縮放用以標準化數據的數值範圍以使數據具有零均值(zero-mean)和單位變異數(unit-variance)。
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