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首页> 外文期刊>Clinical and Translational Medicine >Somatic mutations and CRISPR/Cas9 library screening integrated analysis identifies cervical cancer drug‐resistant pathways
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Somatic mutations and CRISPR/Cas9 library screening integrated analysis identifies cervical cancer drug‐resistant pathways

机译：体细胞突变和CRISPR / CAS9文库筛查综合分析识别宫颈癌耐药途径

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To the Editor: Cervical cancer ranks the fourth cause of cancer mortality in women worldwide. ~(1) Neoadjuvant chemotherapy has remarkable effects on advanced cervical cancer, ~(2) but 15%–34% of patients do not respond to drug treatment. ~(3) Using the integrated analysis of whole‐exome sequencing (WES) and CRISPR screening, our data explored the intrinsic mechanisms that contribute to chemoresistance. We performed WES analysis on 135 cervical cancer patients who were classified as responders or nonresponders to neoadjuvant chemotherapy from our previous research ~(4) (Figure? 1 ). A total of 38?884 somatic mutations and 13?058 nonsynonymous mutations were detected. Sixty significant copy number variation (CNV) events (19 amplifications, 41 deletions) were identified in 89 tumour samples (Figure? 2A and Data S1). Among them, eight CNV events (five amplifications, three deletions) had higher frequencies in drug‐resistant patients’ group, including 3q26.31 (amplification; odds ratio [OR]?=?2.18, p ?=?.047), 3q29 (amplification; OR?=?2.25, p? =?.032), 4p16.1 (deletion; OR?=?2.58, p ?=?.012), 8p23.1 (deletion; OR?=?2.62, p ?=?.0096), 12q13.3 (amplification; OR?=?4.59, p ?=?.00067), 14q11.2 (amplification; OR?=?2.66, p? =?.022), 19q13.31 (amplification; OR?=?2.64, p ?=?.021) and 22q11.21 (deletion; OR?=?3.25, p ?=?.0018) (Figure? 2A and Data S2). Here ORs were the ratio of the odds of treatment response in the presence of CNV and the odds of treatment response in the absence of CNV. FIGURE 1 Sample collection and tumour mutation burden in cervical cancer. (A) The cohort study included 135 patients with cervical cancer (FIGO stages IB1–IIB) who received neoadjuvant cisplatin‐based chemotherapy. Patients were classified into drug‐resistant patients’ group ( n ?=?48) and drug‐sensitive patients’ group ( n ?=?87) according to the disease progression after treatment. WES was performed for each prechemotherapy tumour tissue and peripheral blood pair. (B) Representative radiological and pathological images of drug‐resistant and drug‐sensitive patients. The white arrows indicated the lesion sites FIGURE 2 Somatic mutational landscape and clinical drug‐resistant genes. (A) Significant CNV events detected in high‐purity samples ( n ?=?89). Amplifications and deletions related to treatment response are labelled. (B) Somatic mutational landscape of 21 significant driver genes detected with MutSigCV or oncodriveCLUST algorithms sorted by their mutation frequencies. Nonsynonymous mutation counts are shown above. Treatment response, differentiation level, lymph node metastasis state, cancer stage, and APOBEC signature contribution are annotated below. Left bar chart: the odds ratio for drug resistance. Right bar chart: number of samples with mutations. (C) The counts of the genes mutated only in drug‐sensitive patients’ group, drug‐resistant patients’ group, and both groups; 1978 genes mutated only in drug‐resistant patients’ group were directly included in clinical candidate drug‐resistant gene set. (D) Treatment response odds ratio (OR) and Fisher's exact test significance for genes mutated both in drug‐sensitive and drug‐resistant patients’ groups. Total 1185 genes with an OR?&?1 are coloured red and included in the clinical candidate drug‐resistant gene set. Critical genes PLXNB1 , PLXNB2 and SYNE1 were labelled. (E) Functional domains and somatic mutation positions schematics for treatment response‐related gene PLXNB2 . The grey dot represents the mutation in the drug‐resistant patients’ group, and the orange dot represents the mutation in the drug‐sensitive patients’ group. Numbers refer to amino acid residues and domains are depicted with various colours and annotations below. Two sensitive mutations in PLXNB2 were detected from one patient Seven thousand ninety‐two mutated genes were detected in 102 samples (102/135, 75.56%), with 21 driver genes identified. ~(5,6) Six were reported driver genes in previous study, ~(7,8) including PIK3CA ( n ?=?23, 17.03%), CASP8 ( n ?=?13, 9.62%), EP300 ( n ?=?11, 8.15%), FBXW7 ( n ?=?10, 7.41%), STK11 ( n ?=?6, 4.44%), and MAPK1 ( n ?=?5, 3.7%). In addition, we identified 15 novel driver genes including TTN ( n ?=?45, 33.33%), UBR4 ( n ?=?16, 11.85%), ACPP ( n ?=?9, 6.67%), BAP1 ( n ?=?7, 5.19%), and FBXW10 ( n ?=?7, 5.19%) (Figure? 2B and Data S3). We obtained a drug‐resistant gene set of 3163 genes, including 1978 genes mutated only in drug‐resistant patients’ group, and 1185 genes with higher mutation frequency in drug‐resistant patients’ group than in drug‐sensitive patients’ group (OR?&?1) (Figure? 2C,D ). Among the 3163 clinical drug‐resistant genes, there were 36 genes with significantly higher mutation frequencies (Data S4). Total 34 out of the 36 genes were only mutated in the drug‐resistant group, the other two PLXNB2 ( n ?=?7, 5.19%, OR?=?12.07; p ?=?.008) and SYNE1 ( n ?=?11, 8.15%, OR?=?5.52; p ?=?.017) were mutated in both drug‐resistant and ‐sensitive g
机译：致编辑：宫颈癌在全球妇女中排名第四次癌症死亡率。 〜（1）Neoadjuvant化疗对晚期宫颈癌具有显着影响，〜（2）但15％-34％的患者没有反应药物治疗。 〜（3）采用全外销测序（WES）和CRISPR筛选的综合分析，我们的数据探索了有助于化学抑制的内在机制。我们对从我们以前的研究中被归类为响应者或非反应者的135名宫颈癌患者进行了WES分析，从我们之前的研究中〜（4）（图？1）。共有38个？884个躯体突变和13℃，058个不现一体突变。在89个肿瘤样本中鉴定了六十次显着拷贝数变异（CNV）事件（19扩增，41次缺失）（图2A和数据S1）。其中，八个CNV事件（五种扩增，三次缺失）在耐药患者组中具有较高的频率，包括3Q26.31（扩增;赔率比[或] =？2.18，P？= _. 047），3Q29 （扩增;或？=？2.25，p？=Δ.032），4p16.1（删除;或？=？2.58，p？=Δ.012），8p23.1（删除;或？=？2.62，p ？= 0096），12Q13.3（扩增;或？4.59，P？=Δ00067），14Q11.2（扩增;或？=？2.66，P ?. 022），19Q13.31 （扩增;或？=？2.64，p？=Δ.021）和22Q11.21（删除;或？=？3.25，P？=Δ0018）（图？2A和数据S2）。在这里或在没有CNV的情况下，在CNV存在下，治疗反应的几率与治疗反应的几率的比率。图1宫颈癌中的样品收集和肿瘤突变负担。 （a）队列研究包括135例宫颈癌患者（FICO阶段IB1-IIB），他们接受了基于Neoadjuvant顺铂的化疗。根据治疗后的疾病进展，患者被分为耐药患者组（N？= 48）和药物敏感患者组（N？=？87）。对每个预充理肿瘤组织和外周血对进行WES。 （b）药物抗药性和药物敏感患者的代表性放射性和病理学图像。白色箭头表示病变网站图2躯体突变景观和临床耐药基因。 （a）在高纯度样本中检测到的显着的CNV事件（n？=α89）。标记与治疗响应有关的扩增和缺失。 （b）用突变频率排序的mutsigcv或onodriveClust算法检测到21种显着驾驶员基因的体细胞突变景观。上面显示了非纯突变计数。治疗响应，分化水平，淋巴结转移状态，癌症阶段和apobec签名贡献在下面注释。左栏图：耐药性的差距。右栏图：突变的样本数。 （c）仅在药物敏感患者组，耐药患者组和两组中突变的基因计数; 1978年仅在耐药患者组中突变的基因直接包含在临床候选药物抗性基因集中。 （d）治疗响应赔率比（或）和Fisher对药物敏感和耐药患者群体中突变的基因的确切试验意义。总共1185个基因，具有或Δ＆amp; Δ1是着色的红色，包括在临床候选药物抗性基因集中。标记临界基因PLXNB1，PLXNB2和SYNE1。 （e）官能结构域和体细胞突变位置治疗响应相关基因PLXNB2的原理图。灰点表示耐药患者组中的突变，橙点代表药物敏感患者组中的突变。数字是指氨基酸残基，并且域以各种颜色和注释描绘。在102个样品中检测到PLXNB2中的两个敏感突变在102个样品（102/135,75.56％）中检测到七千九十二次突变基因，鉴定了21例驾驶员基因。 〜（5,6）六次在先前研究中报道了司机基因，〜（7,8）包括pik3ca（n？= 23,17.03％），casp8（n？= 13,9.62％），EP300（n？ =？11,8.15％），FBXW7（n？= 10,7.41％），stk11（n？=Δ6,4.44％）和mapk1（n？=？5,3.7％）。此外，我们鉴定了15个新型驾驶员基因，包括TTN（n？= 45,33.33％），UBR4（n？=α16,11.85％），ACPP（n？=？9,6.67％），BAP1（n？ =？7,5.19％）和FBXW10（n？=？7,5.19％）（图？2B和数据S3）。我们获得了一种抗药性基因组3163基因，其中包括1978年的基因，仅在耐药患者组中突变，耐药患者组中突变频率高于毒性患者组（或？ ＆amp; ？1）（图？2c，d）。在3163个临床耐药基因中，突变频率明显高出36个基因（数据S4）。 36个基因中的34种仅在耐药基团中突变，另外两个PLXNB2（n？=Δ7,5.19％，或？= 12.07; p？=Δ=Δ= yn？= ？11,8.15％，或？=？5.52; p？=β.017）在耐药性和敏感的g中突变

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