• 2022-05
  • 2022-04
  • 2021-03
  • 2020-08
  • 2020-07
  • 2020-03
  • 2019-11
  • 2019-10
  • 2019-09
  • 2019-08
  • 2019-07
  • Latrunculin A br Identification of somatic mutations in tumor tissue and


    2.3. Identification of somatic mutations in tumor tissue and ctDNA
    Somatic mutations were detected in circulating and tissue DNA. Non-synonymous mutations annotated by ANNOVAR were used in clonal structure reconstruction (details in Supplementary Materials and Methods).
    2.4. Clone structure and mTBI analysis
    PyClone was employed to analyze the clonal structure, based on a Bayesian clustering method. An independent input was used to analyze the clonal structure in ctDNA and the tissue for ctDNA at baseline and matched tissue samples, respectively. For serial ctDNA, multiple inputs of each sample were used to analyze serial clonal population. Cancer cell fraction was calculated with the mean of predicted cellular frequen-cies. The cluster with the highest mean VAF was identified as the clonal cluster, and mutations in this cluster were clonal mutations. Meanwhile, other clusters and mutations were considered subclonal. In each ctDNA sample, mTBI was analyzed using the mean VAF of clonal mutations. The mTBI was calculated based on the mTBI of the first ctDNA sample.
    2.5. Pathway analysis of expanding clones
    SIFT and PolyPhen2 were used for predicting the functional impact of an amino Latrunculin A substitution caused by mutations. WebGestalt carried out pathway enrichment analysis to investigate the distribution of genes affected by somatic mutations and CNVs within the KEGG data-base [25].
    2.6. Statistical analysis
    The linear association between CNV and mTBI was tested with Pearson correlation analysis. Multivariate Cox proportional hazards analysis (enter method) was performed considering the clinical charac-teristics and mTBI at baseline. Kaplan-Meier survival plots were gener-ated for mTBI at baseline using log-rank tests. All statistical analyses were performed with SPSS (v.21.0; STATA, College Station, TX, USA) or GraphPad Prism (v. 6.0; GraphPad Software, La Jolla, CA, USA) software. Statistical significance was defined as a two-sided P-value of b0.05.
    3. Results
    3.1. Patient characteristics and mutation detection
    Patients with AGC with HER2 overexpression, who received chemo-therapy plus trastuzumab at the Affiliated Hospital, Academy of Military Medical Sciences, Beijing, China, were enrolled from July 2013 to Janu-ary 2017. HER2-positive status was defined as immunohistochemistry (IHC) 3+ or IHC 2+/fluorescence in situ hybridization (FISH)+. Tumor tissues and serial plasma samples were collected from twenty-one AGC patients (P01–P21) receiving chemotherapy plus trastuzumab (Table 1). Seventeen patients were confirmed by FISH. Eight patients (38%) were IHC 2+ and nine patients (43%) were IHC 3+. A mean of five (2–9) plasma samples were available for mutation detection. Targeted capture sequencing revealed a mean effective depth of cover-age of 464× in tissues and 1673× in plasma samples (Table S2). A total of 121 and 146 functional mutations were identified in 14 paired tissue and plasma samples, respectively, with a detection rate of 100% (Table S2). All pretreatment plasma samples presented at least one tumor-confirmed single nucleotide variant (SNV) or insertion-deletion (InDel). In addition, 31 and 36 copy number variations (CNVs) were de-tected in paired tissue and plasma samples, respectively. Most fre-quently, CNVs occurred in the ERBB2, CDK12, TOP2A, CCNE1, MET, and RARA genes. The CNV positive predictive values of ERBB2 obtained by se-quencing, according to FISH results from 12 patients (6 patients with
    Table 1
    Clinical characteristics of patients with AGC.
    Characteristic Patients (n = 21)
    Age (years)
    Male 17 (81) Female 4 (19) ECOG performance status
    Diffuse 4 (19) Intestinal 7 (33) Mixed 10 (48) Gastroesophageal junction involvement, no. (%)
    Abbreviations: ECOG, Eastern Cooperative Oncology Group 
    IHC 2+ and 6 patients with IHC 3+), were 58.33% (7/12 patients) and 66.67% (8/12 patients) in tissue and plasma, respectively. Four paired samples (P03, P13, P15, and P19) were negative, and another paired sample (P07) was plasma-positive but tissue-negative. The remaining two patients (P10 and P20) had immunohistochemistry (IHC) scores of 3+ and positive sequencing in both tissue and plasma samples (Fig. S2). These results suggest that intra-tumor heterogeneity influ-ences CNV analysis in both tissue biopsy and plasma.
    3.2. Consistency of clonal mutation between tissue and ctDNA
    We investigated whether clonal mutations in plasma samples were derived from the matched tumor that presented the highest CCF. Muta-tions in 14 paired samples were clustered separately, using a Bayesian algorithm with PyClone [26]. An average cluster number of 10 (2−21) was obtained in pretreatment ctDNA. CCF of each mutation was pre-dicted by PyClone. Clusters with the highest average VAF were identi-fied as clonal. The clonal cluster with the highest CCF contained 1–3 mutations. Of the total of 20 clonal mutations identified in ctDNA, 19 mutations were identified in tissue, with median CCF values of 89% (95% CI, 81%–93%) in ctDNA and 88% (95% CI, 81%–94%) in tissues (Fig. 1A). Only one mutation in P11 (PAX5 p.V132I) presented as a clonal mutation, with mutated TP53 and PTPRD in ctDNA but not in tissue (CCF in plasma = 91%, CCF in tissue = 28%). Further validation of this clonal mutation in ctDNA during the disease progression of P11 showed that PAX5 p.V132I was still clustered with mutated TP53 and PTPRD, with the highest CCF. This likely reflects what we already know about sam-pling bias of selected tissue specimens, which confounds resolution of the clonal status of mutations, and illustrates the problems of using tis-sue alone as the gold standard [27].