Gallbladder adenocarcinomas undergo subclonal diversification and selection from precancerous lesions to metastatic tumors

  1. Minsu Kang
  2. Hee Young Na
  3. Soomin Ahn  Is a corresponding author
  4. Ji-Won Kim  Is a corresponding author
  5. Sejoon Lee
  6. Soyeon Ahn
  7. Ju Hyun Lee
  8. Jeonghwan Youk
  9. Haesook T Kim
  10. Kui-Jin Kim
  11. Koung Jin Suh
  12. Jun Suh Lee
  13. Se Hyun Kim
  14. Jin Won Kim
  15. Yu Jung Kim
  16. Keun-Wook Lee
  17. Yoo-Seok Yoon
  18. Jee Hyun Kim
  19. Jin-Haeng Chung
  20. Ho-Seong Han
  21. Jong Seok Lee
  1. Department of Internal Medicine, Seoul National University Bundang Hospital, Republic of Korea
  2. Department of Pathology, Seoul National University Bundang Hospital, Republic of Korea
  3. Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea
  4. Genealogy Inc, Republic of Korea
  5. Center for Precision Medicine, Seoul National University Bundang Hospital, Republic of Korea
  6. Medical Research Collaboration Center, Seoul National University Bundang Hospital, Republic of Korea
  7. Department of Data Science, Dana Farber Cancer Institute, Harvard T.H. Chan School of Public Health, United States
  8. Biomedical Research Institute, Seoul National University Bundang Hospital, Republic of Korea
  9. Department of Surgery, Seoul National University Bundang Hospital, Republic of Korea

Abstract

We aimed to elucidate the evolutionary trajectories of gallbladder adenocarcinoma (GBAC) using multi-regional and longitudinal tumor samples. Using whole-exome sequencing data, we constructed phylogenetic trees in each patient and analyzed mutational signatures. A total of 11 patients including 2 rapid autopsy cases were enrolled. The most frequently altered gene in primary tumors was ERBB2 and TP53 (54.5%), followed by FBXW7 (27.3%). Most mutations in frequently altered genes in primary tumors were detectable in concurrent precancerous lesions (biliary intraepithelial neoplasia [BilIN]), but a substantial proportion was subclonal. Subclonal diversity was common in BilIN (n=4). However, among subclones in BilIN, a certain subclone commonly shrank in concurrent primary tumors. In addition, selected subclones underwent linear and branching evolution, maintaining subclonal diversity. Combined analysis with metastatic tumors (n=11) identified branching evolution in nine patients (81.8%). Of these, eight patients (88.9%) had a total of 11 subclones expanded at least sevenfold during metastasis. These subclones harbored putative metastasis-driving mutations in cancer-related genes such as SMAD4, ROBO1, and DICER1. In mutational signature analysis, six mutational signatures were identified: 1, 3, 7, 13, 22, and 24 (cosine similarity >0.9). Signatures 1 (age) and 13 (APOBEC) decreased during metastasis while signatures 22 (aristolochic acid) and 24 (aflatoxin) were relatively highlighted. Subclonal diversity arose early in precancerous lesions and clonal selection was a common event during malignant transformation in GBAC. However, selected cancer clones continued to evolve and thus maintained subclonal diversity in metastatic tumors.

Editor's evaluation

This is the first dedicated study of clonal evolution in gallbladder cancer that involves precancerous, transformed primary and metastatic lesions. The main insights include the finding of subclonal diversity in precancerous lesions, a degree of bottlenecking during transformation but maintenance of some clonal complexity through metastases.

https://doi.org/10.7554/eLife.78636.sa0

Introduction

Gallbladder adenocarcinoma (GBAC) is a malignant neoplasm that has a high incidence rate in Chile, India, Poland, Pakistan, Japan, and Korea (Roa et al., 2022; Valle et al., 2021; Bray et al., 2018; Bridgewater et al., 2014). Surgery is currently the only curative treatment modality for GBAC. However, because most patients are diagnosed at an advanced stage and thus inoperable, they receive palliative chemotherapy only. Therefore, the prognosis is poor with a median overall survival of only 11–15 months (Roa et al., 2022; Valle et al., 2021; Valle et al., 2010).

The recent advancement of massively parallel sequencing technology has enabled us to deeply understand the genome of a variety of cancers. In GBAC, tumor suppressor genes such as TP53, ARID1A, and SMAD4 and oncogenes such as ERBB2 (HER2), ERBB3, and PIK3CA are significantly mutated (Roa et al., 2022; Nepal et al., 2021; Wardell et al., 2018; Li et al., 2019; Nakamura et al., 2015; Lin et al., 2021; Narayan et al., 2019; Li et al., 2014). Of note, ERBB2 amplification and overexpression occur in approximately 6.9–28.6% of GBAC (Roa et al., 2022; Narayan et al., 2019; Nakazawa et al., 2005; Nam et al., 2016) and may have therapeutic implications.

Cancer cells undergo clonal evolution by acquiring additional mutations and thus exhibit more aggressive phenotypes, including invasion and metastasis (Turajlic et al., 2019; Greaves and Maley, 2012; Davis et al., 2017). Several large-scale studies have provided evidence of clonal evolution in some cancer types, including lung and kidney cancers (Jamal-Hanjani et al., 2017; Turajlic et al., 2018). However, no study has analyzed the patterns of clonal evolution from the initiation of carcinogenesis to distant metastasis in patients with GBAC.

This study aims to analyze the clonal evolutionary trajectories during carcinogenesis and metastasis of GBAC using multi-regional and longitudinal specimens including precancerous lesions (biliary intraepithelial neoplasia [BilIN]), primary tumors, and metastatic tumors from patients who underwent biopsy, surgery, and rapid autopsy.

Results

Baseline characteristics of patients with GBAC

A total of 11 patients, including 2 rapid autopsy cases (GB-A1 and GB-A2) and 9 surgery cases (GB-S1 – 9), were enrolled in this study (Table 1 and Figure 1A). There were 5 male and 6 female patients with a median age was 70 years (range, 59–75 years). Two patients had stage III and nine patients had IV disease at diagnosis. A total of 58 samples were analyzed, including 11 pairs of matched primary tumors and normal tissues, 6 concurrent BilIN, and 30 metastatic tumors. Among them, 15 samples obtained by rapid autopsy were fresh-frozen, and 43 samples obtained by surgery or biopsy were formalin-fixed paraffin-embedded (FFPE) (Supplementary file 1A). The median number of filtered somatic single nucleotide variants (SNVs) and small indels was 61 (range, 12–241). The number of metastatic tumors in each patient ranged from 1 to 11.

Figure 1 with 2 supplements see all
Clinical history of patients, mutational landscape, and study workflow.

(A) Clinical history of 11 patients is summarized in the swimmer plot. (B) The mutational landscape of 11 primary tumors (GB) is visualized and compared with the two previous studies on gallbladder adenocarcinoma (GBAC). (C) The process of constructing clonal evolution trajectories using multiple tumor samples is shown in the workflow. Ab, antibody; Abd, abdominal; Adj, adjuvant; BilIN, biliary intraepithelial neoplasia; CBD, common bile duct; CCRT, concurrent chemoradiotherapy; CNVs, copy number variations; COSMIC, Catalogue Of Somatic Mutations In Cancer; FL, 5-fluorouracil + leucovorin; FOLFIRI, 5-fluorouracil + leucovorin + irinotecan; GB, gallbladder; GP, gemcitabine + cisplatin; iFAM, infusional 5-fluorouracil + doxorubicin + mitomycin-C; LOH, loss of heterozygosity; mFOLFOX, modified 5-fluorouracil + leucovorin + oxaliplatin; MSKCC, Memorial Sloan Kettering Cancer Center; NE, not evaluable; SNVs, single nucleotide variants; WGD, whole genome doubling; XP, capecitabine + cisplatin; 5-FU, 5-fluorouracil.

Table 1
Baseline characteristics of 11 patients with GBAC.
Patient ID*SexAge at diagnosisECOG PS at diagnosisStage at diagnosisDifferentiation
GB-A1F701IVPD
GB-S1F661IVMD
GB-S2F661IVMD
GB-S3F751IIIMD
GB-A2M611IVMD
GB-S4M721IVMD
GB-S5F701IVPD
GB-S6M700IVMD
GB-S7F741IVPD
GB-S8M671IIIWD
GB-S9M590IVMD
  1. *

    In patient ID, ‘A’ indicates autopsy cases whereas ‘S’ indicates surgery cases.

  2. GBAC, gallbladder adenocarcinoma; ECOG PS, Eastern Cooperative Oncology Group performance status; M, male; F, female; PD, poorly differentiated; MD, moderately differentiated; WD, well-differentiated.

Mutational landscape and ploidy of primary tumors

The mutational landscape of the 11 primary tumors (GB) was analyzed and compared with previous literature (Figure 1B; Narayan et al., 2019; Li et al., 2014). The most frequently altered gene in the primary tumor was ERBB2 and TP53 (54.5%), followed by FBXW7 (27.3%). Of the six ERBB2 alterations, three were amplification and the other three were missense mutations classified as ‘pathogenic’ in the COSMIC (Catalogue Of Somatic Mutations In Cancer) v96 database. Among six TP53 mutations found in six patients, five were accompanied by loss of heterozygosity (LOH).

Ploidy was analyzed in 15 tumors of six patients with purity >0.4 because ploidy estimation was inaccurate when tumor purity is ≤0.4 (Favero et al., 2015). In two patients (GB-S6 and GB-S9, 33.3%), whole genome doubling (WGD) was detected in both the primary and metastatic tumors (Figure 1B). In GB-S5 patient, WGD was found in distant metastasis, but not in primary GBAC.

Somatic mutations developed at the precancerous stage

Multi-regional distribution and longitudinal evolution of clones were analyzed using PyClone (Figure 1—figure supplement 1 and Supplementary file 2; Roth et al., 2014) and CITUP (Malikic et al., 2015) and then visualized using MapScape and TimeScape (Smith et al., 2017; Figure 1C). Among six patients having concurrent BilIN tissues, two patients were excluded from the further analysis. One patient had low tumor purity of BilIN (0.03) and the other patient had different truncal mutations of BilIN and primary GBAC, suggesting different origins of the two tumors (Figure 1—figure supplement 2).

Most mutations in frequently altered genes in GBAC existed at the BilIN stage (10 of 13, 76.9%), but some of them were subclonal. In GB-A1 (Figure 2A), TP53 C100Y with LOH, KMT2C R909K, ERBB3 G284R, ARID2 F575fs with LOH, and CTNNB1 S37F with LOH were observed clonally in BilIN. In GB-S1 (Figure 2B), SAMD9 E612K was clonal whereas FBXW7 S18C, ARID1A S382N, and NF1 R440X were subclonal. In GB-S2 (Figure 2C), considering the cellular prevalence of mutations, it is speculated that the mutations developed in the order of CDKN2A R58fs with LOH, TP53 H82R with LOH, ERBB2 V777L, and GNAS G306R during carcinogenesis. In GB-S3 (Figure 2D), ERBB3 E332K was dominant in BilIN, while SMAD4 539_540del was not detected.

Spatial and temporal clonal evolution of four patients with gallbladder adenocarcinoma (GBAC) whose precancerous BilIN tissues were analyzed.

(A–D) The most probable phylogenetic trees and MapScape and TimeScape results are visualized for GB-A1 (A), GB-S1 (B), GB-S2 (C), and GB-S3 (D). In MapScape visualization (table), the numbers in the row indicate the time taken from diagnosis to tissue acquisition in months, and the columns indicate whether the tissue is a precancerous lesion, primary tumor, or metastatic lesion. Colors represent distinct clones and clonal prevalences per site were proportional to the corresponding-colored area of the cellular aggregate representation. In TimeScape visualization (schema), clonal prevalences (vertical axis) were plotted across time points (horizontal axis) for each clone (colors). Asterisks (*) in the clonal phylogenetic tree denote subclones that constituted <5% in the primary tumor and expanded more than sevenfold in the metastatic tumor. Diamond symbol (◇) is used if the mutation in the figure is classified as 'pathogenic' in Catalogue Of Somatic Mutations In Cancer (COSMIC) v96. Notable events were marked with arrows. The time from diagnosis of GBAC to tissue acquisition was indicated under the sample name. Chemotherapy history was indicated in gray color, where '#' represents the number of chemotherapy cycles. BilIN, biliary intraepithelial neoplasia; CBD, common bile duct; CN, copy number; GB, gallbladder; GP, gemcitabine + cisplatin; LN, lymph node; LOH, loss of heterozygosity.

Subclonal diversity and ‘selective sweep' phenomenon during the early stage of carcinogenesis

Branching evolution and subclonal diversity were commonly observed in the BilIN of the four patients (Figure 2). When compared with the concurrent primary tumors, one subclone commonly shrank in the primary tumors, while the other subclones that acquired additional mutations relatively expanded in the primary tumors, suggesting a selective sweep phenomenon (Merlo et al., 2006). Selected subclones underwent linear and branching evolution, and thus subclonal diversity was maintained after the BilIN stage. In GB-A1 (Figure 2A), clone A underwent branching evolution into B and G, and clone B linearly evolved into C and then D. Clone D, which acquired additional mutations, increased from 42.7% to 68.1% while clone G decreased from 55.7% to 2.8%. In GB-S1 (Figure 2B), clone D that acquired KMT2D E338K increased from 0.1% to 28.1%, while clone G decreased from 51.4% to 0%. In GB-S2 (Figure 2C), clone D acquired ERBB2 V777L, clone E acquired GNAS G306R, and clone G increased from 9.0%, 3.5%, and 0% to 45.5%, 22.3%, and 11.4%, respectively. In contrast, clone F decreased from 55.8% to 0.6%. In GB-S3 (Figure 2D), clone B that acquired SMAD4 539_540del increased from 0.1% to 65.6%, while clone D containing ERBB3 E332K decreased from 81.1% to 0.6%.

Evolutionary trajectories and expansion of subclones during regional and distant metastasis

Combined analysis of regional and distant metastatic tumors revealed branching and linear evolution in nine patients (81.8%) and linear evolution only in two patients (18.2%). Of the nine patients with branching and linear evolution, eight (88.9%) had a total of 11 subclones expanded at least sevenfold in the regional or distant metastasis stage (Table 2). In GB-A1 (Figure 2A), clone E, which acquired JARID2 (Celik et al., 2018) K603Q with LOH, increased from 0.2% to 20.5% during common bile duct metastasis. In addition, clone F, which acquired SMAD4 (Narayan et al., 2019; Jamal-Hanjani et al., 2017; Yoon et al., 2021; Zhao et al., 2018) L414fs with LOH, expanded from 0.4% to 49.8% during omentum 1 metastasis and to 27.0% during omentum 2 metastasis. In GB-S1 (Figure 2B), clone B harboring SLIT3 F843I mutation evolved into E by additionally acquiring ROBO1 P1360Q mutation (Gara et al., 2015). In GB-A2 (Figure 3A), clone F, which acquired PRKCD (Griner and Kazanietz, 2007; Yoshida, 2007) I153L, expanded from 0.3% to 73.1% and 68.3% during metastasis to liver and mesentery, respectively. In addition, clone G acquired DICER1 (Foulkes et al., 2014) T519A and expanded from 2.8% to 39.9% and 61.5% during metastasis to the lung and chest wall, respectively. In the other five patients (Figures 2C and 3B–E), mutations in FBXW2 (Xu et al., 2017), KIAA0100 (Gara et al., 2018), CSMD2 (Gara et al., 2018), and OSCP1 (Yi et al., 2017) genes were observed.

Spatial and temporal clonal evolution of additional seven patients with gallbladder adenocarcinoma (GBAC).

(A–G) The most probable phylogenetic trees and MapScape and TimeScape results are visualized for GB-A2 (A), GB-S4 (B), GB-S5 (C), GB-S6 (D), GB-S7 (E), GB-S8 (F), and GB-S9 (G). In MapScape visualization (table), the numbers in the row indicate the time taken from diagnosis to tissue acquisition in months, and the columns indicate whether the tissue is a precancerous lesion, primary tumor, or metastatic lesion. Colors represent distinct clones and clonal prevalences per site were proportional to the corresponding-colored area of the cellular aggregate representation. In TimeScape visualization (schema), clonal prevalences (vertical axis) were plotted across time points (horizontal axis) for each clone (colors). Asterisks (*) in the clonal phylogenetic tree denote subclones that constituted <5% in the primary tumor and expanded more than sevenfold in the metastatic tumor. Diamond symbol (◇) is used if the mutation in the figure is classified as 'pathogenic' in Catalogue Of Somatic Mutations In Cancer (COSMIC) v96. Notable events were marked with arrows. The time from diagnosis of GBAC to tissue acquisition was indicated under the sample name. Chemotherapy history was indicated in gray color, where '#' represents the number of chemotherapy cycles. Adj, adjuvant; BilIN, biliary intraepithelial neoplasia; CN, copy number; FL, 5-fluorouracil + leucovorin; GB, gallbladder; GP, gemcitabine + cisplatin; LN, lymph node; LOH, loss of heterozygosity; WGD, whole genome doubling.

Table 2
List of subclones expanding during metastasis.
Patient IDSubcloneNo. of mutationsPutative driver mutationsClonal prevalence in primary tumorClonal prevalence in metastasisMetastatic organ
GB-A1E121JARID2 p.K603Q (LOH)0.2%20.5%CBD
F54SMAD4 p.L414fs (LOH)0.4%49.8%Omentum 1
27.0%Omentum 2
GB-S1E6ROBO1 p.P1360Q0.0%59.2%Distant LN
GB-S2H61.2%28.1%Regional LN
GB-A2F14PRKCD p.I153L0.3%73.1%Liver
68.3%Mesentery
G3DICER1 p.T519A2.8%39.9%Lung
61.5%Chest wall
GB-S4F32FBXW2 p.W450C0.0%34.1%Colon wall
H360.0%34.9%Distant LN
GB-S5B28KIAA0100 p.F5S1.2%54.4%Liver
CSMD2 p.E411K
GB-S6B7OSCP1 p.R351X1.1%60.2%Lung
GB-S7C53.3%24.7%Regional LN
  1. Putative driver mutations are indicated, and a full list of mutated genes is specified in Supplementary file 2.

  2. GB, gallbladder; LN, lymph node; CBD, common bile duct.

In GB-S8 and GB-S9, only linear evolution was identified (Figure 3F and G). In GB-S9, amplification of cell cycle-related oncogenes CDKN1B and CCNE1 was uniformly observed from primary GBAC to regional and distant metastatic tumors.

Polyclonal metastasis and intermetastatic heterogeneity

The metastatic lesions were uniformly polyclonal. In GB-A1, GB-A2, and GB-S4, which contained two or more distant metastatic lesions, the clonal composition of tissues obtained from the same or adjacent organs showed a similar tendency, while the clonal composition of anatomically distant organs was distinct from each other. In GB-A1 (Figure 2A), abdominal wall 1–4 did not contain clone C, and liver 1–3 did not contain clone F. In addition, omentum 1–2 had a high prevalence of clone F of over 27.0%. In GB-A2 (Figure 3A), as opposed to clone G, which was more prevalent in lung and chest wall lesions, clone F was more prevalent in liver and mesentery lesions. In clinical information of the GB-A2 patient, chest wall and mesentery metastases developed later than lung and liver metastases. In GB-S4 (Figure 3B), proportions of clones G and H were specifically high in distant LN and colon wall metastasis, respectively.

Mutational signatures during clonal evolution

We identified six mutational signatures (cosine similarity >0.9): signatures 1 (age), 3 (DNA double-strand break-repair), 7 (ultraviolet), 13 (APOBEC), 22 (aristolochic acid), and 24 (aflatoxin). Then, our results were compared with those of the Memorial Sloan Kettering Cancer Center (MSKCC) and Shanghai datasets (Narayan et al., 2019; Li et al., 2014). While MSKCC and Shanghai datasets consist merely of primary tumors, our dataset includes precancerous and metastatic lesions. Signatures 1, 3, and 13 were commonly dominant in all three datasets, while signatures 22 and 24 were exclusive in our dataset (Figure 4A). In our dataset, the limited number of SNVs and small indels per sample (median 61, range 12–241) made it difficult to compare among individual tumors (Figure 4—figure supplement 1). Therefore, we classified the mutations according to the timing of development during clonal evolution (Figure 4B): (1) early carcinogenesis (i.e., clone A in Figures 2 and 3), (2) late carcinogenesis (i.e., subclones which were not categorized in early carcinogenesis or metastasis), and (3) metastasis (i.e., subclones expanding during metastasis [Table 2]). At the metastasis phase, signatures 1 and 13 decreased while signatures 22 and 24 increased compared with early and late carcinogenesis. Then, the same analysis was performed according to the type of sample (Figure 4—figure supplement 2): (1) BilIN, (2) GB, (3) regional LN metastasis, and (4) distant metastasis. This mutational signature analysis was conducted using the published tool Mutalisk (Lee et al., 2018) and then validated with two additional tools, Signal (Degasperi et al., 2020) and MuSiCa (Díaz-Gay et al., 2018; Supplementary file 1B and Figure 4—figure supplement 3).

Figure 4 with 3 supplements see all
Mutational signature analysis.

(A–B) The 100% stacked bar plots compare the proportions of known COSMIC Mutational Signatures v2 within our dataset and two public (MSKCC and Shanhai) datasets (A), and each category split according to the timing of development during clonal evolution (B). The total number of mutations (N) and cosine similarity (CS) values of each category were noted. BilIN, biliary intraepithelial neoplasia; COSMIC, Catalogue Of Somatic Mutations In Cancer; GB, gallbladder; LN, lymph node; MSKCC, Memorial Sloan Kettering Cancer Center.

ERBB2 amplification during clonal evolution

In GB-A1, ERBB2 copy number gain might have initiated from the precancerous stage and further progressed during the malignant transformation of BilIN. ERBB2 copy number (Figure 5A and B) was 3 and 9 in concurrent BillN and primary tumors, respectively. The increased copy number of ERBB2 was maintained after distant metastasis (Figure 2A). HER2 (=ERBB2) silver in situ hybridization (SISH) (Figure 5C and D) was conducted and ERBB2 copy number per cell ranged from 1 to 5 in BilIN, and from 2 to 14 in primary GBAC. ERBB2/CEP17 ratio was 2.48 and 6.00 in BilIN and primary GBAC, respectively (Figure 5E). In addition, HER2 immunohistochemistry (IHC) (Figure 5F and G) was carried out to evaluate whether the ERBB2 amplification was correlated to HER2 protein expression levels on the membrane of tumor cells. The HER2 IHC results were 2+/3 in the BillN and 3+/3 in primary GBAC. In GB-S6 and GB-S7 (Figure 3D and E), ERBB2 amplification was uniformly identified from primary tumors to metastatic tumors.

ERBB2 copy number variation during neoplastic transformation of BilIN in GB-A1.

(A–G) ERBB2 gene amplification (A and B), HER2 SISH (C and D), and HER2 IHC (F and G) were compared between BilIN (A, C) and (F) and primary GBAC (B), (D), and (G) samples and the mean ERBB2/CEP17 ratio of BilIN and GB-A1 samples were compared by using the Wilcoxon rank-sum test (E). BilIN, biliary intraepithelial neoplasia; GBAC, gallbladder adenocarcinoma; IHC, immunohistochemistry; SISH, silver in situ hybridization.

Discussion

To the best of our knowledge, this is the first study to investigate clonal evolution from precancerous lesions to metastatic tumors in patients with GBAC. In this study, evolutionary trajectories of GBAC were inferred using multi-regional and longitudinal whole-exome sequencing (WES) data from precancerous lesions to primary and metastatic tumors. Based on these results, we derived comprehensive models of carcinogenesis and metastasis in GBAC.

In our analysis of carcinogenesis, we discovered three common themes. First, most mutations in frequently altered genes in primary GBAC are detected in concurrent BilIN (10 of 13, 76.9%), but a substantial proportion was subclonal. Second, branching evolution and subclonal diversity are commonly observed at the BilIN stage. Third, one subclone in BilIN commonly shrinks in the primary tumors, while the other subclones undergo linear and branching evolution, maintaining subclonal diversity after the BilIN stage. A previous study in colorectal cancer by Vogelstein and colleagues demonstrated a stepwise carcinogenesis model from the precancerous lesion, adenoma, to invasive carcinoma by the accumulation of mutations, called the adenoma-carcinoma sequence (Vogelstein et al., 1988). In addition, recent studies on esophageal squamous cell carcinoma have reported that not only dysplasia but also histologically normal epithelia frequently harbor cancer-driving mutations (Chen et al., 2017; Yokoyama et al., 2019).

A recent study on GBAC (Lin et al., 2021) reported that CTNNB1 mutation was frequently observed (5 out of 11) when BilIN and primary GBAC coexist. In addition, ERBB2 alteration was found in 36.4%. In our study, CTNNB1 S37F with LOH was observed in one (GB-A1) of four patients and 54.5% of patients had an ERBB2 alteration. On the other hand, two previous studies on GBAC (Narayan et al., 2019; Li et al., 2014) reported the prevalence of ERBB2 alteration as low as 7.9% and 9.4%, respectively. Race or lifestyle differences might have contributed to the difference.

In the analysis of metastasis, the following three phenomena are observed. First, subclonal expansion is frequent (8 of 11 patients, 72.7%) and some subclones expand substantially in metastatic tumors, leading to increased subclonal diversity. Previous studies suggest that subclonal diversity increases through branching evolution during progression and metastasis (Turajlic and Swanton, 2016; Hong et al., 2015; Yachida et al., 2010; Minussi et al., 2021). Second, metastases are polyclonal but metastatic lesions in one organ or adjacent organs show similar clonal compositions. Previously, it was thought that metastasis is initiated by the migration of a single cell to another organ (Nowell, 1976). However, recent data suggest that polyclonal seeding occurs due to the migration of a cluster of cancer cells (Turajlic et al., 2019; Cheung and Ewald, 2016; Ullah et al., 2018; Wei et al., 2017). Third, we found the possibility of metastasis to metastasis spread. In recent studies on prostate and breast cancers, metastasis-to-metastasis spread was frequent (Ullah et al., 2018; Gundem et al., 2015). Our study found the possibility of metastasis-to-metastasis spread in GB-A2 (Figure 3A). Although intratumoral heterogeneity of primary GBAC may make it difficult to draw a strong conclusion, our data may support the possibility of metastasis-to-metastasis spread.

Of the 11 expanded subclones at the metastasis stage, we described putative driver mutations in eight subclones based on the previous literature (Table 2). For example, SMAD4 mutations expanded during metastasis in GB-A1 and GB-S3 (Figure 2A and D) and have been associated with distant metastasis and poor prognosis in various cancers, including GBAC (Narayan et al., 2019; Jamal-Hanjani et al., 2017; Yoon et al., 2021; Zhao et al., 2018). In GB-S1 (Figure 2B), clone B containing SLIT3 F843I evolves into E during metastasis by acquiring ROBO1 P1360Q. The SLIT/ROBO pathway suppresses tumor progression by regulating invasion, migration, and apoptosis (Gara et al., 2015). In GB-A2 (Figure 3A), DICER1 T519A is found in lung and chest wall metastases. The DICER1 gene is associated with pleuropulmonary blastoma in children (Foulkes et al., 2014). In GB-S5 (Figure 3C), KIAA0100 F5S and CSMD2 E411K are found during metastasis. KIAA0100 and CSMD2 are frequently mutated during metastasis in adrenocortical carcinoma (Gara et al., 2018).

In mutational signature analysis, considering the evolutionary trajectories in cancers, we suggest that the timing of development during clonal evolution (Figure 4B) is better classification criterion than the type of sample (Figure 4—figure supplement 2). Our data indicate that the importance of signatures 1 and 13 decreased during metastasis while the roles of signatures 22 and 24 were relatively highlighted. Aristolochic acid is an ingredient of oriental herbal medicine (Debelle et al., 2008; Hoang et al., 2013). In addition, aflatoxin is known to be contained in soybean paste and soy sauce (Ok et al., 2007). Taken together, the two carcinogens might have little impact on the early stage of cancer development, but their impacts might be highlighted in overt cancer cells.

In this cancer precision medicine era, targeted sequencing data of a single specimen are not enough to determine whether the detected mutations are clonal or subclonal. This proof-of-concept study may enable us to deeply understand the clonal evolution in GBAC. Moreover, we found that some of the mutations were clonal while a substantial proportion was subclonal, which is usually not an effective druggable target. For example, if a drug targeting ERBB2 p.V777L, a pathogenic mutation, is administered to GB-S2 patient (Figure 2C), the therapeutic effect will be limited in subclones without the ERBB2 p.V777L mutation, especially from regional metastasis. Therefore, we believe that our study highlights the importance of precise genomic analysis of multi-regional and longitudinal samples in individual cancer patients. However, one caveat is that we cannot easily apply this to real-world patients because multi-regional and longitudinal tumor biopsies may not be feasible in most patients unless they underwent surgery and repeated biopsies. Recent studies using circulating tumor DNA have shown the possibility of easily detecting mutations involved in cancer development and progression (Ignatiadis et al., 2021). By detecting clonal mutations from the carcinogenesis stage in healthy individuals, we can diagnose GBAC at an early stage. In addition, by detecting subclonal mutations in patients with GBAC, we can monitor the expanding subclones during follow-up, which enables us to detect cancer progression earlier.

This study has several limitations. First, it is not possible to obtain samples through frequent biopsies whenever desired. Thus, tumor samples were not acquired according to their developmental sequence. Second, due to intratumoral heterogeneity, the clonal composition of a small piece of the tumor may not reflect that of the entire lesion (Marusyk et al., 2012). Third, as the number of analyzed samples was different among patients, an accessibility bias was inevitable – the more samples the patient has, the more clones we can identify.

In conclusion, subclonal diversity developed early in precancerous lesions and clonal selection was a common event during malignant transformation in GBAC. However, cancer clones continued to evolve in metastatic tumors and thus maintained subclonal diversity. Our novel approach may help us to understand the GBAC of individual patients and to move forward to precision medicine that enables early detection of carcinogenesis and metastasis, and effective targeted therapy in these patients.

Methods

Patients and tumor samples

Patients were eligible for this study if they were diagnosed with GBAC and received surgery between 2013 and 2018 at Seoul National University Bundang Hospital (SNUBH), Seongnam, Korea. Of the 272 patients who underwent surgery, 9 patients who were able to obtain ≥3 types of tissue from among BilIN, GBAC, regional LN metastasis, and distant metastasis and died at the time of analysis were enrolled in the study. In addition, two patients who donated their bodies for rapid autopsy were included in the study. Our board-certified pathologists (Prof. Soomin Ahn and Hee Young Na) identified and dissected BilIN and GBAC lesions from tissue slides. Patients’ clinical information was obtained through retrospective medical record reviews. This study was approved by the Institutional Review Board (IRB) of SNUBH (IRB No. B-1902/522-303). Informed consent was waived because of the retrospective and anonymous nature of the study.

WES of GBAC

DNA was extracted from fresh-frozen or FFPE tissues. Library preparation and exome capture was carried out using Agilent SureSelectXT Human All Exon V6 (Santa Clara, CA, USA). WES was conducted with a paired-end, 100 bp using Illumina NovaSeq 6000 (San Diego, CA, USA). The depth of coverage of tumors and normal control samples were at least 300× and 200×, respectively.

Analysis of WES data

WES data of GBAC and matched normal samples were analyzed using the Genomon2 pipeline (Institute of Medical Science, University of Tokyo, Tokyo, Japan; https://genomon.readthedocs.io/ja/latest/, accessed on February 1, 2022). In brief, sequencing reads from adapter-trimmed.fastq files were aligned to the human reference genome GRCh37 (hg19) without the ‘chr’ prefix using Burrows-Wheeler Aligner version 0.7.12, with default settings. SNVs and small indels were called by eliminating polymorphisms and sequencing errors and filtered by pre-specified criteria used in previous literature analyzed with the Genomon2 pipeline (Yokoyama et al., 2019; Kakiuchi et al., 2020; Ochi et al., 2021): (a) only exonic or splicing sites were included; (b) synonymous SNVs, unknown variants, or those without proper annotation were excluded; (c) polymorphisms in dbSNP 131 were excluded; (d) p-values <0.01 from Fisher’s exact test were included; (e) simple repeat sequences were excluded; (f) strand ratio between positive-strand and negative-strand should not be 0 or 1 in tumor samples; (g) the number of variant reads should exceed 4 in tumor samples. For each patient, filtered variant lists of tumor samples were merged. Then, the merged list of target variants was manually called in each.bam file using bam-readcount version 0.8.0 (https://github.com/genome/bam-readcount; Khanna et al., 2022a; Khanna et al., 2022b, accessed on February 1, 2022) with Phred score and mapping quality of more than 30 and 60, respectively.

For samples with tumor purity >0.4, the ploidy of tumor cells was estimated using Sequenza version 3.0.0 to identify WGD (Favero et al., 2015). Copy number variations were analyzed using the Control-FREEC version 11.5 (Boeva et al., 2012). Gene amplification was defined as a copy number ≥6 (Wolff et al., 2018). In brief, the aligned.bam files were converted to.pileup.gz format using SAMtools version 1.9 (Li, 2011). The .pileup.gz files of data from GBAC and matched normal samples were analyzed using Control-FREEC with default settings. These datasets were then used to statistically infer clonal population structure using PyClone version 0.13.0 with the ‘pyclone_beta_binomial’ model (Roth et al., 2014). Clonal phylogeny was inferred by CITUP version 0.1.0 (Malikic et al., 2015) using cellular prevalence values of each cluster which were generated by PyClone. CITUP uses information from multiple samples and can infer clonal populations and their frequencies while satisfying phylogenetic constraints. To ensure accurate tree construction, clusters containing only one mutation were excluded from the input to CITUP if the mutation’s role is unclear from previous literature. This filter removed 42 mutations from a total of 1577 mutations, representing less than 2.7% of all clustered mutations. In addition, in GB-A2, clusters which limited to only one organ were excluded from the analysis because the calculation for the phylogenetic tree using CITUP took more than 1 month when the number of clusters was ≥14. Analyzed results were visualized using MapScape for multi-regional specimens and TimeScape for longitudinal or putative longitudinal datasets, as appropriate (Smith et al., 2017). In all analysis steps, the data were adjusted for the tumor purity values of each tumor.

Mutational signature analysis was conducted using Mutalisk (Lee et al., 2018) and validated with Signal (Degasperi et al., 2020) and MuSiCa (Díaz-Gay et al., 2018). The COSMIC Mutational Signatures v2 (Alexandrov et al., 2013) was used as a reference. To compare with the other GBAC cohorts, we additionally analyzed two public datasets of GBAC from the MSKCC and the Shanghai group (Narayan et al., 2019; Li et al., 2014), which could be downloaded from the cBioPortal (https://www.cbioportal.org/, accessed on February 1, 2022; Cerami et al., 2012).

HER2 IHC and SISH

The histologic sections from individual FFPE tissues were deparaffinized and dehydrated. IHC and SISH analysis of HER2-positive cells was conducted by the board-certified pathologist using PATHWAY anti-HER2/neu antibody (4B5; rabbit monoclonal; Ventana Medical Systems, Tucson, AZ, USA) and a staining device (BenchMark XT, Ventana Medical Systems, Tuscon, AZ, USA), respectively, as previously described (Koh et al., 2019). Signals from 20 tumor cells were counted and a HER2/CEP17 ratio ≥2.0 was defined as HER2 amplification (Wolff et al., 2018). Wilcoxon rank-sum test is used to compare the mean HER2/CEP17 ratio of BilIN versus primary tumor.

Data availability

The raw sequence data underlying this manuscript are available as fastq files at the NCBI SRA database under the BioProject number PRJNA821382.

The following data sets were generated
The following previously published data sets were used
    1. Narayan RR
    (2018) cBioPortal
    ID Gallbladder-Cancer-(MSK). Gallbladder Cancer (MSK, Cancer 2018).
    1. Li M
    (2014) cBioPortal
    ID Gallbladder-Carcinoma-(Shanghai). Gallbladder Carcinoma (Shanghai, Nat Genet 2014).

References

Decision letter

  1. Samra Turajlic
    Reviewing Editor; The Francis Crick Institute, United Kingdom
  2. W Kimryn Rathmell
    Senior Editor; Vanderbilt University Medical Center, United States
  3. Srivatsan Raghavan
    Reviewer; Dana-Farber Cancer Institute, United States

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Gallbladder adenocarcinomas undergo subclonal diversification and selection from precancerous lesions to metastatic tumors" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and W Kimryn Rathmell as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Srivatsan Raghavan (Reviewer #1).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

Please answer reviewers' comments. Overall:

1. Please give inclusion, exclusion, and sample selection criteria indicating which are fresh frozen, which are FFPE and how many samples and sample types are included for each patient. Also samples sites and types of samples. (Surgical vs biopsy).

2. Please clarify the presentation of data and figures.

3. Please explain how you accounted for intratumour heterogeneity.

4. Please describe reconstructing clonal phylogenies and how you dealt with multiple samples per patient.

5. Please analyse FFPE samples with the appropriate adjustments for noise relative to fresh frozen.

6. Please discuss the seeming variation in results of mutational signature analysis as quoted in the abstract versus those in Figure 4.

7. Please re-consider the inference of met-met spread and whether it is really supported by the data.

Reviewer #1 (Recommendations for the authors):

1. In Figure 1A, it would be helpful to indicate the site of disease associated with each tissue sample, and also to indicate which specimens were biopsies versus resection tissue.

2. As discussed above in the public review, the authors show variation across different sites of disease and across time, but it is somewhat difficult to distinguish between these in the figures. It would be helpful if Figures 2 and 3 could more clearly distinguish between spatial and temporal variation in clonality, particularly in terms of tracing and more effectively visualizing clonal variation across space versus over time. In addition, the authors don't comment on genomic variation in relation to intervening therapy, and this would be helpful particularly if any notable patterns were observed that may have clinical implications.

3. P. 6, lines 7-11 – In patients with concurrent BilIN and GBAC in the same gall bladder resection specimen, how were these identified? Were these lesions observed by a pathologist in different locations and each isolated separately? Additional details in the methods section would be helpful in this regard.

4. P.8, lines 16-20 – "The metastatic lesions were uniformly polyclonal. In GB-A1, GB-A2, and GB-S4, which contained two or more distant metastatic lesions, the clonal compositions of metastatic lesions were heterogeneous. However, metastatic lesions in one organ or adjacent organs showed similar clonal compositions. In GB-A1 (Figure 2A), abdominal wall 1-4 did not contain clone C, and liver 1-3 did not contain clone F." These statements seem contradictory, particularly the statement referring to "similar clonal compositions" and the following one which provides a counterexample. The conclusion that metastatic lesions in nearby organs are similar is only supported by a few cases and it is unclear that the authors are really powered to state this definitively.

https://doi.org/10.7554/eLife.78636.sa1

Author response

Essential revisions:

Please answer reviewers' comments. Overall:

1. Please give inclusion, exclusion, and sample selection criteria indicating which are fresh frozen, which are FFPE and how many samples and sample types are included for each patient. Also samples sites and types of samples. (Surgical vs biopsy).

Thank you for the important comment.

1) We have added the inclusion criteria to the Methods section (Page 15, Line 3-7). There were no specific exclusion criteria and all the obtained samples from the participants were analyzed.

2) A total of 15 samples obtained by rapid autopsy were fresh-frozen, and 43 samples obtained by surgery or biopsy were formalin-fixed paraffin-embedded (FFPE) specimens. We mentioned this in the Results section (Page 5, Line 8-10).

3) We revised Supplementary File 1A. The number of samples, methods for sample acquisition (autopsy vs. surgery vs. biopsy), and type of sample (fresh-frozen vs. FFPE) per each patient are now shown in Supplementary File 1A.

2. Please clarify the presentation of data and figures.

Thank you for your advice. Throughout the revision process, meaningful updates were made, and as a result, the presentation of the data and figures has been made clearer. The important changes are summarized below.

– In Figure 1A, the anatomical information of the obtained samples has been indicated.

– In Figure 1B, the distinction between the tumor suppressor gene and oncogene was removed.

– In Figures 2 and 3, the time point at which the tissue was obtained and the anatomical location of the tissue are clearly separated.

– Figure 4B was moved to Figure 4—figure supplement 2.

3. Please explain how you accounted for intratumour heterogeneity.

Thank you for the important comment. The cellular prevalence of the truncal clone – clone A in every sample – was assumed to be 1.0, and the relative cellular prevalence of the remaining subclones was calculated. The relative proportions of multiple clones were visualized using MapScape and TimeScape (Smith MA et al., Nat Methods. 2017). In Figures 2 and 3, the clonal composition varied from sample to sample, and this is the intratumoral heterogeneity identified in our study.

4. Please describe reconstructing clonal phylogenies and how you dealt with multiple samples per patient.

Thank you for the constructive comment. CITUP (Malikic S et al., Bioinformatics. 2015) infers clonal populations and their frequencies while satisfying phylogenetic constraints and can exploit data from multiple samples. In our study, phylogenetic trees were constructed using the CITUP (0.1.0) command, ‘run_citup_qip.py’. We mentioned this in the Methods section (Page 16, Line 23 – Page 17, Line 1).

5. Please analyse FFPE samples with the appropriate adjustments for noise relative to fresh frozen.

We appreciate your valuable comment. According to previous literature (Chen G et al., Mol Diagn Ther. 2014, Moore DA et al., ESMO Open. 2019), sequencing artifacts induced by formalin fixation satisfy the following conditions: (1) sample specific mutations, (2) C:G > T:A variants, and (3) VAF < 5%. We summarized the mutations satisfying the above conditions in Author response table 1 (for review only). However, we did not remove these mutations in the analysis of the FFPE samples due to the following reasons: (1) it was impossible to determine which mutation is actual noise by formalin fixation, and (2) sample-specific subclones still existed with little influence on the phylogenetic tree structure even after all the potential artifacts have been eliminated.

Author response table 1
Proportion of mutations satisfying the conditions: (1) C:G > T:A variants, and (2) VAF < 5% in sample-specific clusters.
Patient IDSampleType of sampleSample specific clusters*Number of mutations in sample-specific clustersNumber of filtered mutations in sample-specific clustersPercentage (%)
GB-A1BilINFFPE23438.8
CBDFFPE31211310.7
GB-S1BilINFFPE29333.3
GBFFPE418738.9
Distant LNFFPE7600.0
GB-S2BilINFFPE68337.5
GBFFPE07228.6
GB-S3BilINFFPE37114.3
GBFFPE2300.0
GB-A2GBFresh-frozen24000.0
Lung (old)FFPE101715.9
LungFresh-frozen51500.0
Chest wallFresh-frozen32300.0
LiverFresh-frozen43100.0
MesenteryFresh-frozen68225.0
GB-S4Colon wallFFPE33600.0
Distant LNFFPE19222.2
GB-S5GBFFPE31300.0
GB-S6GBFFPE1500.0
LungFFPE2700.0
GB-S7GBFFPE01100.0
Regional LNFFPE3500.0
GB-S8GBFFPE235411.4
Regional LNFFPE08133.7

In FFPE samples, sample-specific mutations were found in 11 of 31 samples (35.5%). The number of possible artifacts satisfying the conditions was median 1/sample (range, 0–13/sample), and their proportion among sample-specific mutations was median 5.9% (range, 0–38.9%). In fresh frozen samples, sample-specific mutations were identified in 1 of 14 samples (7.1%). The number of mutations satisfying the conditions was 2, and their proportion among sample-specific mutations was 25%. Although the number of mutations satisfying the artifact conditions was higher in the FFPE samples than in the fresh frozen samples, there is no definitive way to determine which of these is real noise induced by formalin fixation. In addition, there was no sample-specific subclone consisting merely of mutations satisfying the above conditions.

BilIN, biliary intraepithelial neoplasia, GB, gallbladder; LN, lymph node; CBD, common bile duct; FFPE, formalin-fixed paraffin-embedded.

*See Figure 1—figure supplement 1.

See Supplementary File 2.

References for Essential Revisions #5

Chen G, Mosier S, Gocke CD, Lin MT, Eshleman JR. Cytosine deamination is a major cause of baseline noise in next-generation sequencing. Mol Diagn Ther. 2014;18(5):587-93.

Moore DA, Kushnir M, Mak G, Winter H, Curiel T, Voskoboynik M, et al. Prospective analysis of 895 patients on a UK Genomics Review Board. ESMO Open. 2019;4(2):e000469.

6. Please discuss the seeming variation in results of mutational signature analysis as quoted in the abstract versus those in Figure 4.

Thank you for the important comment. The sentences about mutational signatures in the abstract were a summary of Figure 4C and had been properly described. If Figure 4B confused you, we sincerely apologize to you for any possible misunderstandings. As stated in the manuscript, Figure 4B denotes mutational signature analysis according to the 4 types of samples (BilIN, GB, regional LN metastasis, and distant metastasis) while Figure 4C indicates the analyzed results according to the timing of development during clonal evolution (early carcinogenesis, late carcinogenesis, and metastasis). We suggest that the criterion of Figure 4C makes more sense considering the evolutionary trajectories in cancers. To avoid confusion, we moved Figure 4B to Figure 4—figure supplement 2 and revised the phrase "developmental stages of cancer" to "type of sample" in this figure.

7. Please re-consider the inference of met-met spread and whether it is really supported by the data.

Thank you for the important comment. We agree with reviewer #1's comment 3 that the rationale for metastasis-to-metastasis spread for GB-A1 is weak. Hence, this part was removed from the manuscript.

However, our data still suggests that the possibility of metastasis-to-metastasis spread is shown in GB-A2. In GB-A2, clone F, which is not present in GB, is dominant in the liver and mesentery, and this finding can be evidence of metastasis-to-metastasis. In addition, the clonal composition of GB is different from those of the lung and chest wall, while the clonal composition of the lung and chest wall is similar.

For this part, Figure 3A and its description in the manuscript have been updated (Page 12, Line 9-12).

Reviewer #1 (Recommendations for the authors):

1. In Figure 1A, it would be helpful to indicate the site of disease associated with each tissue sample, and also to indicate which specimens were biopsies versus resection tissue.

Thank you for the important comment. Reflecting on your comment, the anatomical information of the obtained samples has been updated in Figure 1A. As you can check by combining the color of the box (Surgery or Autopsy) and '' mark in Figure 1A, all were resection samples from surgery or autopsy except for the liver tissue of GB-S5 (biopsy). In addition, information on whether the tissue was obtained by resection or biopsy was added to Supplementary File 1A.

2. As discussed above in the public review, the authors show variation across different sites of disease and across time, but it is somewhat difficult to distinguish between these in the figures. It would be helpful if Figures 2 and 3 could more clearly distinguish between spatial and temporal variation in clonality, particularly in terms of tracing and more effectively visualizing clonal variation across space versus over time. In addition, the authors don't comment on genomic variation in relation to intervening therapy, and this would be helpful particularly if any notable patterns were observed that may have clinical implications.

Thank you for the important comment about (1) figures and (2) genomic variation about intervening therapy.

1) We have modified Figures 2 and 3 to distinguish between spatial and temporal variation more clearly.

2) Although it would have been better to compare genetic variation before and after treatment, especially when targeted therapies are used, only 2 out of 11 patients received targeted agents in our cohort. The GB-A1 patient was treated with varlitinib, both an EGFR and HER2 inhibitor, and then ceralasertib, which targets the DNA damage repair pathway. There was no difference in HER2 copy number before and after using varlitinib (Figure 2A). The number of mutations before the administration of ceralasertib was a median of 49 (range, 44–52), and the number of mutations after administration was a median of 60 (range, 48–104). However, it is difficult to conclude that the increased number of mutations was caused by ceralasertib because the administration period was as short as 2 months and the number of patients was only one. The GB-A2 patient was treated with a RON receptor tyrosine kinase inhibitor. After treatment, clone F containing PRKCD p.I153L expanded. However, it is difficult to give meaning to a single clinical case. Nevertheless, we agree with your opinion that we can get more clinical insights from sequencing multiple specimens before and after treatment.

3. P. 6, lines 7-11 – In patients with concurrent BilIN and GBAC in the same gall bladder resection specimen, how were these identified? Were these lesions observed by a pathologist in different locations and each isolated separately? Additional details in the methods section would be helpful in this regard.

Thank you for the constructive comment. Our board-certified pathologists (Prof. Soomin Ahn and Hee Young Na) identified and dissected BilIN and GBAC lesions from tissue slides. We added this information to the Method section (Page 15, Line 8-9).

4. P.8, lines 16-20 – "The metastatic lesions were uniformly polyclonal. In GB-A1, GB-A2, and GB-S4, which contained two or more distant metastatic lesions, the clonal compositions of metastatic lesions were heterogeneous. However, metastatic lesions in one organ or adjacent organs showed similar clonal compositions. In GB-A1 (Figure 2A), abdominal wall 1-4 did not contain clone C, and liver 1-3 did not contain clone F." These statements seem contradictory, particularly the statement referring to "similar clonal compositions" and the following one which provides a counterexample. The conclusion that metastatic lesions in nearby organs are similar is only supported by a few cases and it is unclear that the authors are really powered to state this definitively.

We agree with your important comment. The contradictory and unclear sentences were corrected to convey the meaning accurately, and the overall statement was softened:

"The metastatic lesions were uniformly polyclonal. In GB-A1, GB-A2, and GB-S4, which contained two or more distant metastatic lesions, the clonal composition of tissues obtained from the same or adjacent organs showed a similar tendency, while the clonal composition of anatomically distant organs was distinct from each other." (Page 8, Line 13-16)

https://doi.org/10.7554/eLife.78636.sa2

Article and author information

Author details

  1. Minsu Kang

    Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Data curation, Software, Formal analysis, Validation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Hee Young Na
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6491-2277
  2. Hee Young Na

    Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Resources, Data curation, Formal analysis, Validation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Minsu Kang
    Competing interests
    No competing interests declared
  3. Soomin Ahn

    Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Validation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    suminy317@gmail.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1979-4010
  4. Ji-Won Kim

    1. Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    2. Genealogy Inc, Seoul, Republic of Korea
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    jiwonkim@snubh.org
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6426-9074
  5. Sejoon Lee

    Center for Precision Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Formal analysis, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  6. Soyeon Ahn

    Medical Research Collaboration Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Formal analysis, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  7. Ju Hyun Lee

    Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Formal analysis, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  8. Jeonghwan Youk

    Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Formal analysis, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  9. Haesook T Kim

    Department of Data Science, Dana Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, United States
    Contribution
    Formal analysis, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  10. Kui-Jin Kim

    Biomedical Research Institute, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Resources, Methodology, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  11. Koung Jin Suh

    Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Resources, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  12. Jun Suh Lee

    Department of Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Resources, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  13. Se Hyun Kim

    Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Resources, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2292-906X
  14. Jin Won Kim

    Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Resources, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  15. Yu Jung Kim

    Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Resources, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  16. Keun-Wook Lee

    Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Resources, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  17. Yoo-Seok Yoon

    Department of Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Resources, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  18. Jee Hyun Kim

    Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Resources, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  19. Jin-Haeng Chung

    Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Resources, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  20. Ho-Seong Han

    Department of Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Resources, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  21. Jong Seok Lee

    Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
    Contribution
    Resources, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared

Funding

Seoul National University Bundang Hospital Research Fund (No. 16-2021-001)

  • Ji-Won Kim

Small Grant for Exploratory Reserach (NRF-2018R1D1A1A02086240)

  • Ji-Won Kim

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This study was supported by a grant from Seoul National University Bundang Hospital Research Fund (No. 16-2021-001) and the Small Grant for Exploratory Research (SGER) program (NRF-2018R1D1A1A02086240) of the National Research Foundation (NRF), Korea. The authors would like to express the deepest respect to the two patients who donated their bodies for this study after death.

Ethics

Human subjects: This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Seoul National University Bundang Hospital (Number: B-1902/522-303). Informed consent was waived because of the retrospective and anonymous nature of the study.

Senior Editor

  1. W Kimryn Rathmell, Vanderbilt University Medical Center, United States

Reviewing Editor

  1. Samra Turajlic, The Francis Crick Institute, United Kingdom

Reviewer

  1. Srivatsan Raghavan, Dana-Farber Cancer Institute, United States

Publication history

  1. Received: March 14, 2022
  2. Preprint posted: April 1, 2022 (view preprint)
  3. Accepted: December 6, 2022
  4. Accepted Manuscript published: December 8, 2022 (version 1)
  5. Accepted Manuscript updated: December 16, 2022 (version 2)
  6. Version of Record published: December 21, 2022 (version 3)

Copyright

© 2022, Kang, Na et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Minsu Kang
  2. Hee Young Na
  3. Soomin Ahn
  4. Ji-Won Kim
  5. Sejoon Lee
  6. Soyeon Ahn
  7. Ju Hyun Lee
  8. Jeonghwan Youk
  9. Haesook T Kim
  10. Kui-Jin Kim
  11. Koung Jin Suh
  12. Jun Suh Lee
  13. Se Hyun Kim
  14. Jin Won Kim
  15. Yu Jung Kim
  16. Keun-Wook Lee
  17. Yoo-Seok Yoon
  18. Jee Hyun Kim
  19. Jin-Haeng Chung
  20. Ho-Seong Han
  21. Jong Seok Lee
(2022)
Gallbladder adenocarcinomas undergo subclonal diversification and selection from precancerous lesions to metastatic tumors
eLife 11:e78636.
https://doi.org/10.7554/eLife.78636

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    Deepti Prasad, Katharina Illek ... Anne-Kathrin Classen
    Research Article

    Tissue-intrinsic defense mechanisms eliminate aberrant cells from epithelia and thereby maintain the health of developing tissues or adult organisms. 'Interface surveillance' comprises one such distinct mechanism that specifically guards against aberrant cells which undergo inappropriate cell fate and differentiation programs. The cellular mechanisms which facilitate detection and elimination of these aberrant cells are currently unknown. We find that in Drosophila imaginal discs, clones of cells with inappropriate activation of cell fate programs induce bilateral JNK activation at clonal interfaces, where wild type and aberrant cells make contact. JNK-activation is required to drive apoptotic elimination of interface cells. Importantly, JNK-activity and apoptosis are highest in interface cells within small aberrant clones, which likely supports the successful elimination of aberrant cells when they arise. Our findings are consitent with a model where clone size affects the topology of interface contacts and thereby the strength of JNK activation in wild type and aberrant interface cells. Bilateral JNK activation is unique to 'interface surveillance' and is not observed in other tissue-intrinsic defense mechanisms, such as classical 'cell-cell competition'. Thus, bilateral JNK interface signaling provides an independent tissue-level mechanism to eliminate cells with inappropriate developmental fate but normal cellular fitness. Finally, oncogenic Ras-expressing clones activate 'interface surveillance' but evade elimination by bilateral JNK activation. Combined, our work establishes bilateral JNK interface signaling and interface apoptosis as a new hallmark of interface surveillance, and highlights how oncogenic mutations evade tumor suppressor function encoded by this tissue-intrinsic surveillance system.