Abstract Background Circulating tumor DNA (ctDNA) isolated from plasma contains genetic mutations that can be representative of those found in primary tumor tissue DNA. These samples can provide insights into tumoral heterogeneity in patients with advanced gastric cancer (AGC). Although trastuzumab has been shown to be effective in first-line therapy for patients with metastatic gastric cancer with overexpression of human epidermal growth factor receptor 2 (HER2), the mechanism of AGC resistance is incompletely understood. Methods In this prospective study, we used targeted capture sequencing to analyze 173 serial ctDNA samples from 39 AGC patients. We analyzed cancer cell fractions with PyClone to understand the clonal population structure in cancer, and monitored serial samples during therapy. Serial monitoring of ctDNA using the molecular tumor burden index (mTBI), identified progressive disease before imaging results (mean: 18 weeks). Findings We reconstructed the clonal structure of ctDNA during anti-HER2 treatment, and identified 32 expanding mutations potentially related to trastuzumab resistance. Multiple pathways activating in the same patients revealed heterogeneity in trastuzumab resistance mechanisms in AGC. In patients who received chemotherapy, mTBI was validated for the prediction of progressive disease, with a sensitivity of 94% (15/16). A higher mTBI (≥1%) in pretreatment ctDNA was also a risk factor for progression-free survival. Conclusions Analysis of ctDNA clones based on sequencing is a promising approach to clinical management, and may lead to improved therapeutic strategies for AGC patients. Fund This work was supported by grants from the National International Cooperation Grant (to J.X.; Project No. 2014DFB33160). Keywords: Advanced gastric cancer, Circulating tumor DNA (ctDNA), Trastuzumab, Resistance mechanism, Monitoring __________________________________________________________________ Research in context. Evidence before this study Trastuzumab has previously been shown to be an effective first-line therapy for patients with metastatic gastric cancer with overexpression of human epidermal growth factor receptor 2 (HER2); however, the mechanism of resistance and how resistant clones evolve in advanced gastric cancer (AGC) is incompletely understood. Circulating tumor DNA (ctDNA) sequencing, a method of liquid biopsy, provides a potential tool for real-time monitoring of the tumor during treatment. However, tumor heterogeneity limits the value of the ctDNA detecting method, which is based on a single gene or a few mutation positions, in predicting treatment outcomes or clinical prognoses. Added value of this study In this study, based on targeted capture sequencing and temporal evolution analyzing, ctDNA clonal mutations derived from tumor tissue were used in monitoring tumor burden, and 32 expanding mutations potentially related to trastuzumab resistance were identified. Moreover, in this study, we also confirmed that, in patients who received chemotherapy, mTBI was validated for the prediction of progressive disease with a sensitivity of 94% (15/16). A higher mTBI (≥1%) of the clonal structure in pretreatment ctDNA was also a risk factor for progression-free survival. Implications of all the available evidence This study provides a broad, clinically applicable method of using ctDNA to predict disease progression, and highlights the potential prognostic utility of mTBI for the identification of high-risk groups. It demonstrates for the first time that the co-occurrence of multiple resistant mutations in high-heterogeneity AGC, and suggests the possibility of combination of trastuzumab with other targeted agents. Alt-text: Unlabelled Box 1. Introduction Imaging methods combined with serum biomarkers have been commonly used to evaluate the efficacy of therapy for solid tumors [[51]1]. Serum-based protein biomarkers, such as carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), and carbohydrate antigen 19-9 (CA19-9), have limitations with regard to sensitivity and specificity [[52][2], [53][3], [54][4], [55][5]]. Imaging has limitations in measuring bone, pleural, and peritoneal disease [[56]6]. In addition, radiation exposure limits the frequency of using computerized tomography (CT) scans [[57]7]. There is an urgent need for a highly sensitive, standardized and validated blood-based assay that may be used for the accurate detection of early relapse and/or progression of disease. Non-invasive circulating tumor DNA (ctDNA) sequencing in plasma provides a potential tool for real-time monitoring of tumor load [[58][8], [59][9], [60][10]], and may be used in concert with imaging modalities to improve accuracy [[61]11] as well as to improve the early detection of non-measurable metastases and occult lesions [[62]12]. Molecular characteristics in ctDNA can provide clues for the temporal evolution of tumor-resistant clones [[63]13]. Although many studies have described the use of ctDNA in late-stage tumors, the detection of a single gene or a few mutation positions to predict treatment outcome or clinical prognosis has limited value because of tumor heterogeneity among patients [[64][14], [65][15], [66][16], [67][17], [68][18]]. These methods have been unable to detect mutations that occur in additional regions along with tumor evolution, which may cause secondary resistance. The current clinical application of whole-exome sequencing is limited by its high cost [[69]19]. In terms of calculating the ctDNA fraction, methodology based on mean variant allele frequency (VAF) of all detected mutations allows the use of ctDNA for molecular monitoring in patients, but could underestimate the ctDNA fraction in cell-free DNA (cfDNA) because of intra-tumor heterogeneity [[70]20]. Currently, no widely applicable and accurate ctDNA-based method has been developed to monitor ctDNA and to explain resistance mechanisms in highly heterogeneous solid tumors such as advanced gastric cancer (AGC). Studies on tumor evolution have provided evidence that clonal mutations (truncal mutations, occurring early and central on the phylogenetic trees) exist in all or almost all tumor cells [[71]21,[72]22]. Compared with subclonal mutations, these clonal mutations present a higher cancer cell fraction (CCF) and VAF in ctDNA when they are released into peripheral blood, compared with subclonal mutations (Fig. S1). With the development of a pan-cancer panel, targeted capture sequencing may cost-effectively detect gene regions to identify cancer-associated mutations in ctDNA for clonal structure reconstruction and the exploration of potential drug resistance mechanisms during treatment [[73]23]. The mean VAF of clonal mutations, defined as molecular tumor burden index (mTBI), could help standardize the applicability and accuracy of ctDNA in monitoring tumor burden in highly heterogeneous gastric carcinoma [[74]24]. Serial ctDNA sequencing may reveal drug-resistance mutations during treatment [[75]21]. In this study, we validated ctDNA clonal mutations derived from tumor tissue, and determined the mTBI of serial ctDNA samples in patients with AGC who were undergoing anti-human epidermal receptor 2 (HER2) treatment. We demonstrated that the mTBI could be used as a sensitive biomarker to predict disease progression compared to standard imaging modalities in AGC patients, and that targeted capture identified potential trastuzumab-resistant clones during disease progression. We further validated the feasibility of using mTBI in AGC patients receiving chemotherapy, and showed that mTBI can serve as a predictive marker of treatment outcome. 2. Materials and methods 2.1. Patients and samples Patients with AGC were drawn from the Fifth Medical Center, General Hospital of PLA, Beijing, China. The cohorts included 21 AGC patients receiving chemotherapy plus trastuzumab, and 18 AGC patients receiving chemotherapy alone. Patients all signed a written consent form prior to the study. Study protocols were approved by the respective institutional review boards. According to the Response Evaluation Criteria in Solid Tumors (RECIST), version 1.1, we evaluated the clinical response of all patients every 6–8 weeks. Serial peripheral blood (10 mL) and matched formalin-fixed, paraffin-embedded (FFPE) tissues were obtained (details in Supplementary Materials and Methods). 2.2. Pan-cancer panel sequencing DNA was isolated from all normal cell samples, serial peripheral blood, and matched tissues samples using commercial kits (Qiagen, Hilden, Germany). The KAPA Library Preparation Kit (Kapa Biosystems, Wilmington, MA, USA) was next used to prepare indexed Illumina NGS libraries. A custom SeqCap EZ Library (Roche NimbleGen, Madison, WI, USA) was used to perform target enrichment according to the manufacturer's protocol. Coordinates of selected regions and genes of each version are listed in Table S1. 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 frequencies. 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 ΔmTBI 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 acid 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 database [[76]25]. 2.6. Statistical analysis The linear association between CNV and ΔmTBI was tested with Pearson correlation analysis. Multivariate Cox proportional hazards analysis (enter method) was performed considering the clinical characteristics and mTBI at baseline. Kaplan-Meier survival plots were generated 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 <0.05. 3. Results 3.1. Patient characteristics and mutation detection Patients with AGC with HER2 overexpression, who received chemotherapy plus trastuzumab at the Affiliated Hospital, Academy of Military Medical Sciences, Beijing, China, were enrolled from July 2013 to January 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 ([77]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 coverage 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 detected in paired tissue and plasma samples, respectively. Most frequently, CNVs occurred in the ERBB2, CDK12, TOP2A, CCNE1, MET, and RARA genes. The CNV positive predictive values of ERBB2 obtained by sequencing, according to FISH results from 12 patients (6 patients with 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 influences CNV analysis in both tissue biopsy and plasma. Table 1. Clinical characteristics of patients with AGC. Characteristic Patients (n = 21) Age (years) Median (range) 58 (35–61) Sex, no. (%) Male 17 (81) Female 4 (19) ECOG performance status Median (range) 1 (0–2) Stage, no. (%) IIIA 2 (10) IIIC 2 (10) IV 17 (80) Tumor differentiation, no. (%) Well/Moderate 8 (38) Poor 13 (62) Lauren type, no. (%) Diffuse 4 (19) Intestinal 7 (33) Mixed 10 (48) Gastroesophageal junction involvement, no. (%) Yes 10 (48) No 11 (52) Liver involvement, no. (%) Yes 15 (71) No 6 (28) Lung involvement, no. (%) Yes 10 (48) No 11 (52) Prior chemotherapy regimens, no. Median (range) 0 (0–3) [78]Open in a new tab Abbreviations: ECOG, Eastern Cooperative Oncology Group 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. Mutations in 14 paired samples were clustered separately, using a Bayesian algorithm with PyClone [[79]26]. An average cluster number of 10 (2−21) was obtained in pretreatment ctDNA. CCF of each mutation was predicted by PyClone. Clusters with the highest average VAF were identified 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 ([80]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 sampling bias of selected tissue specimens, which confounds resolution of the clonal status of mutations, and illustrates the problems of using tissue alone as the gold standard [[81]27]. Fig. 1. [82]Fig. 1 [83]Open in a new tab Clonal mutations detected in paired samples and used to monitor serial ctDNA from patients who received chemotherapy plus trastuzumab. (A) Relationship between clonal mutations in baseline ctDNA and matched tissues. (B) Patient counts and fractions of detected TP53 mutation in different studies. (C) The mTBI of ctDNA before treatment and during progressive disease. Top: Tumor burden changed based on baseline status. The gray line indicates CT imaging results. The red line indicates ΔmTBI results. Progressive metastases are marked in red font at PD. Bottom: serial changes of CNV detected in ctDNA. Patient P07 received palliative surgery with continuous evaluation of PR. BL, baseline; PR, partial response; PD, progressive disease; NA, no available CT result; PS, palliative surgery; mTBI, molecular tumor burden index; CT, computed tomography. (For interpretation of the references to colour in this figure legend, the reader is referred to