隨者深度定序法的出現，腫瘤內異質性，也就是在致癌過程中出現的細胞多樣性演化，已經可以藉由演化樹觀察及顯示，而演化樹的組成則是藉由血液或活檢 樣本的基因組分析的創始克隆以及亞克隆所組成，活檢樣本則是來自單一癌性病 變或是原發性腫瘤以及轉移性腫瘤。而驅動突變，也就是基因的畸形變異，促成癌症發生、演化以及轉移，顯示出多種變異等位基因頻率(VAF)特別是在罕見或亞克隆變異之中，已經可以藉由高覆蓋率的 NGS 技術可靠的偵測到。整合這些關於腫瘤異質性的研究明確地證明了精確腫瘤學治療的可預測性及限制，但假設牽 涉分子分析匹配治療的兩個潛在的臨床治療方案，一個是腫瘤內異質性的推斷，在藥物治療之前能夠提供足夠的分子基因組資訊來分析及預測癌症治療的預後情形，另一個則是發掘出有驅動性的突變針對於不同的克隆細胞群體的創始克隆，可能是治療癌症復發的靶標藥物。
雖然在大規模大腸直腸癌(CRC)的基因型分析出四種共同的分子亞型，但是在 常規藥物治療下，異質性的結果依舊被觀察出在復發及非復發病人之中。我們假設腫瘤異質性或許能顯著地有助於臨床上未達到的需求，然而現在並沒有任何全面性基因體分析針對大腸直腸癌演化發表的文獻，且具有生物上可解釋的發現，僅有一些簡單或是回顧文獻。在本研究中，我們對非復發型以及復發型CRC患者 的基因組中癌症基因進行深度定序，且具有詳細的臨床特徵資料。除了獲得高品質的基因組和臨床數據外，我們還建立並驗證了一個生物信息學的流程管道，用於整合驅動突變的發現，拷倍數的偵測，克隆群鑑定以及建立演化樹模型。最終 我們建立了一個臨床決策樹，由創始克隆的驅動基因(MYO18A)和亞克隆中的兩個 驅動基因(DDR3和FLT3)組成，用於腫瘤內異質性的 CRC 治療反應的可預測性。而這個預測 CRC 復發的決策模型在治療前可能是有益的，且有利於疾病預後。

With the advent of ultra-deep sequencing, tumor heterogeneity, evolved from cellular diversities during the process of carcinogenesis, has been observed and exhibited displayed by an evolution tree consists of a founding clone and sub-clones based on genomic profiling of blood or biopsy samples from a single cancerous lesion or from both primary tumor and metastases. The driver mutations, i.e. genetic aberrations arise and contribute to tumor carcinogenesis, evolution, and metastasis, show a variety of variant allele frequency (VAF) particularly in the rare or subclonal variants which can be reliably detected from high-coverage NGS. Consolidating these studies for tumor heterogeneity explicitly demonstrates the limits of treatment predictability in precision
oncology, but hypothetically implicate two potential clinical regimens for the molecular profiling matched therapy. One is the inference of intratumor heterogeneity before drug treatment might present sufficient molecularly genomic profiling for predicting the
prognosis of cancer therapeutics. The other is the discovery of actionable mutations driving the founding clone towards different clonal cell populations might be effective drug targets for combination therapy against cancer relapse or recurrence.
Although the genomic profiling for a large-scale population of colorectal cancer (CRC) leading to four consensus molecular subtypes, the heterogeneous outcomes
between recurrence and non-recurrence were observed in response to the conventional drug treatments. We hypothesized that the tumor heterogeneity might prominently contribute to this clinically unmet needs, however, no any comprehensive genomics study for CRC tumor evolution with biologically interpretable finding is published except some simple or review papers. In this study, we have deeply sequenced the cancer panel genes on the genome of non-recurrent and recurrent CRC patients with detail clinical characteristics. Besides obtaining high-quality genomic and clinical data, a bioinformatics pipeline has been built and validated for integrating driver mutation discovery, copy number variation detection, clonal population identification, and
evolution tree modeling. Eventually, we established a clinical decision tree, consists of a driver gene (MYO18A) in the founding clone and two driver genes (DDR2 and FLT3) in the sub-clone, for the predictability of CRC treatment response based on intratumor heterogeneity. This decision model for the prediction of CRC recurrence may be informative before treatment and beneficial to disease prognosis.

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