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Multi-region exome sequencing reveals the intratumoral heterogeneity of surgically resected small ce

Issuing time:2021-09-16 14:16

Abstract

Small cell lung cancer (SCLC) is a highly malignant tumor which is eventually refractory to any treatment. Intratumoral heterogeneity (ITH) may contribute to treatment failure. However, the extent of ITH in SCLC is still largely unknown. Here, we subject 120 tumor samples from 40 stage I-III SCLC patients to multi-regional whole-exome sequencing. The most common mutant genes are TP53 (88%) and RB1 (72%). We observe a medium level of mutational heterogeneity (0.30, range 0.0~0.98) and tumor mutational burden (TMB, 10.2 mutations/Mb, range 1.1~51.7). Our SCLC samples also exhibit somatic copy number variation (CNV) across all patients, with an average CNV ITH of 0.49 (range 0.02~0.99). In terms of mutation distribution, ITH, TMB, mutation clusters, and gene signatures, patients with combined SCLC behave roughly the same way as patients with pure SCLC. This condition also exists in smoking patients and patients with EGFR mutations. A higher TMB per cluster is associated with better disease-free survival while single-nucleotide variant ITH is linked to worse overall survival, and therefore these features may be used as prognostic biomarkers for SCLC. Together, these findings demonstrate the intratumoral genetic heterogeneity of surgically resected SCLC and provide insights into resistance to treatment.

Introduction

Lung cancer is the most prevalent cancer in the world, with 15% of patients diagnosed with the highly aggressive and metastatic malignancy small cell lung cancer (SCLC)1. About one-third of SCLC patients present with limited disease (LD) and the remaining patients are diagnosed with extensive disease (ED) SCLC at the time of initial diagnosis. The 5-year overall survival (OS) rate for ED SCLC is below 7%2. For SCLC patients, there has been no significant progress in the treatment modalities over the past decade. While the vast majority of patients are sensitive to chemotherapy and radiotherapy at the time of the initial treatment, all patients inevitably face the dilemma of chemoresistance and disease progression3. Recently, immunotherapy was approved for the comprehensive treatment of ED SCLC4,5,6,7,8. Yet, recurrence, drug resistance, and cancer as the cause of death are still common in the course of SCLC. How to improve a patient’s prognosis remains an unmet need for this recalcitrant malignancy.

An important factor in the failure of anticancer treatment is intratumor heterogeneity (ITH), which refers to distinct tumor cell populations (with different molecular and phenotypic profiles) within the same tumor specimen, resulting in differences in the tumor growth rate, invasion ability, drug sensitivity, and prognosis9. Next-generation sequencing (NGS) technology has been widely used for tumor genome variation research and has shown excellent capabilities in ITH research. For example, in the TRACERx (TRAcking Cancer Evolution through therapy (Rx)) lung study, multi-region sampling of lung cancer tissues from 100 early stage non-small cell lung cancer (NSCLC) patients using multi-region whole-exome sequencing (MRS) revealed ubiquitous ITH in patients and copy number variation (CNV). ITH was associated with prognosis, which provides a reference for subsequent cancer genome research10. Elucidating the heterogeneity of SCLC could help better our understanding of disease management. A recent study found that chemotherapy caused increased ITH, leading to the development of multiple mechanisms of drug resistance in ED SCLC11. However, the ITH of LD SCLC patients without chemotherapy remains unknown due to a lack of tumor samples.

In this study, we aim to provide the intratumoral genetic heterogeneity landscape of surgically resected SCLC, by analyzing the whole-exome sequencing data of 120 samples from 40 patients with SCLC. We characterize their mutational burden, heterogeneity, evolution, and potential biomarkers. Considerable intratumoral genetic heterogeneity is present among SCLC. We further identify several heterogeneity-related prognostic biomarkers.

Results

Patients’ characteristics

We included 40 surgically resected SCLC patients in this study, among them, 6 were diagnosed with combined SCLC (C-SCLC). Most SCLCs (34/40) were pure SCLC (P-SCLC). Table 1 shows the patients’ clinical characteristics. The median age was 62 years old. Most patients were male (35, 87.5%) and had a history of smoking (31, 77.5%). All patients underwent surgery, with a median tumor size of 22.5 mm. About 65% of patients received further treatment after surgery. Fifteen patients (15, 38%) died after a median follow-up time of 22.82 months.

Table 1 Clinical characterization of our SCLC cohort.

Mutation landscape of 40 SCLC patients using multiple-regional sequencing

We subjected 120 formalin-fixed paraffin-embedded (FFPE) SCLC samples (3 regions per patient) to MRS. In total, 33,153 non-silent somatic mutations were identified with an average 252× sequencing depth (Supplementary Data 1). We found an average of 340 mutations (range 33–1552) from multi-region for each patient. The median multi-region based tumor mutation burden (TMB) of SCLC was similar with single-region based TMB in our cohort and The Cancer Genome Atlas (TCGA) cohort (Supplementary Fig. 1a, Mann–Whitney–Wilcoxon test, both p > 0.05). There was a positive correlation between TMB and tumor neoantigen burden (TNB) (Spearman’s correlation coefficient, r = 0.59, p < 0.001; Supplementary Fig. 1b). The most frequent mutant genes were TP53 (88%) and RB1 (72%), which were clonal mutations; while LRP1B (22%), PCLO (15%), and KMT2D (15%) were subclonal mutations (Fig. 1a, Supplementary Fig. 2c, Supplementary Data 2). The C > T and C > A transversions were enriched in these patients (Supplementary Fig. 1c, d). The age-associated, BRCA1/2-associated, tobacco-associated, and aflatoxin-associated signatures were also major mutational signatures in these patients (Fig. 1a). The age-associated, aflatoxin-associated, and DNA repair-associated signatures were the top signatures in the branch, while the age-associated and smoking-associated signatures were major ones in the trunk (Supplementary Fig. 1e, f).

Fig. 1: Mutational spectrum of SCLC.

a Mutational landscape of SCLC (n = 40). Mutated gene frequency >15% involved in previously reported significant mutated genes in SCLC are shown for each region of the individual patient. Upper, TMB count; middle, heatmap for driver mutations; lower, mutational signatures. b Counts in clonal and subclonal mutations for each patient (n = 40). c Percentage of subclonal mutations for each patient (n = 40). SCLC small cell lung cancer, P-SCLC pure small cell lung cancer, C-SCLC combined small cell lung cancer, SNVs single-nucleotide variants, CDS coding sequence.

Non-silent mutation distribution showed ITH in patients with SCLC varied significantly (Fig. 1b). Percentages ranged from 17 to 100% (Fig. 1c). We found a medium mutational heterogeneity (0.30, quartile 0.12–0.56) in our SCLC cohort, and the SNV ITH of P-SCLC and C-SCLC were not significantly different with NSCLC of TRACERx study (p = 0.065 and p = 0.32)10 (Fig. 1c and Supplementary Fig. 2b). We also showed the distribution of mutations in ten common oncogenic signaling pathways12 (Supplementary Fig. 2g) and identified that mutations in the TP53 and RTK-Ras-ERK signaling pathways were predominantly clonal mutations.

Intratumoral heterogeneity in CNV

SCLC exhibited somatic arm-level CNV alterations including amplification at chromosomes 1, 12, 18, 19, 20, 3q, 5p, 6p, and 8q, and deletions at chromosomes 4, 10, 3p, 5q, 13q, 15q, 16q, 17q, 21p, and 11q (Fig. 2a, Supplementary Data 35). Significantly amplified regions included 1p34.2 (HEYL), 1q21.3 (APH1A), 2p24.3 (MYCN), 3q29 (PIK3CA), 5p13.2 (IL7R), 6p22.3 (E2F3), 8q24.21 (MYC), and 9p24.1 (CD274, PDCD1LG2) as well as deleted regions 3p12.1, 4q13.2, 5q35.3, 9q21.11(CBWD3), 10q23.31 (PTEN), 13q14.2 (RB1), 14q11.2, 15q25.3 (NTRK3), 19p12 (ZNF429), and 22q11.1 (Fig. 2b, c). Using CNV ITH, a median of 0.485 (range 0.02–0.99 per sector) was found in SCLC (Fig. 2d). Among them, IL7R, PIK3CA, SETDB1, TERT, SEPT9, MYC, CEBPA, and CD274 genes were amplified as frequently recurring clonal genes, while the clonal depleted genes like CBWD3, RB1, and PTEN were identified in our patients (Supplementary Fig. 2e).

Fig. 2: Copy number alterations in our cohort.

a Arm level CNVs identified by GISTIC2.0 in SCLC (n = 40). False discovery rate (FDR) corrected pvalue represents significant changes from Benjamini–Hochberg testing. b The genome chromosome plots depict significant cytobands identified by GISTIC2.0. c The significant somatic focal CNVs of pure SCLC and combined lung cancer are shown for each region of the individual patient. Cytobands with genes involved in cosmic drivers and those that occurred in at least 50% of patients are shown. d Counts in the trunk and branch of CNVs for each patient; Percentage of branch CNVs for each patient (n = 40). SCLC small cell lung cancer, Amp amplification, Del deletion, CNVs copy number variations.

Clonal evolution and pathway enrichment

We also constructed phylogenetic trees based on somatic mutations detected in multiple regions. Figure 3a shows the phylogenetic tree for each patient according to their disease stage. In particular, TP53, EGFR, and CREBBP mutations were common early clonal events involved in the evolution of SCLC (Fig. 3b), while RB1 and other mutations were late clonal events. Generally, among clonal and subclonal mutations, passenger mutations were proportionally higher than driver mutations (oncogene and TSG, Fig. 4e).

Fig. 3: Phylogenetic trees and evolution in SCLC.
figure3

a Phylogenetic trees for each patient (n = 40) stratified according to stages. b The evolution mode in all patients (n = 40). P-SCLC pure small cell lung cancer, C-SCLC combined small cell lung cancer, pre-GD pre-genome doubling.

Fig. 4: The ITH and clinicopathological characteristics of SCLC.
figure4

The comparison of a SNV ITH, b CNV ITH, c TMB, d average TMB per cluster between pure SCLC (n = 34) and combined lung cancer (n = 6), EGFR mutant (n = 7), and wild type (n = 33), as well as smoking (n = 31) and nonsmoking (n = 8) subgroups. p Value from two-sided Mann–Whitney U test. Boxplots are represented by a centerline, median; box limits, the 25th and 75th percentiles; whiskers extend represent the lower and upper values within 1.5 * inter-quartile range. e, f The proportion of driver genes, passenger genes, and other genes in the trunk and branch. p Value from two-sided Fisher’s exact test. SNV single-nucleotide variant, CNV copy number variation, ITH intratumoral heterogeneity, P-SCLC pure small cell lung cancer, C-SCLC combined small cell lung cancer, TMB tumor mutation burden, TSG tumor suppressor gene.

Correlation between genetic alterations and clinical characterization

No significant relationship was observed between ITH and other clinical variables, including pathology, smoking history, EGFR mutation status, and tumor stage (Fig. 4a, b, Supplementary Fig. 2d). Among the EGFR mutations, three patients carried non-classic EGFR mutations (p.G652W, p.E114Q, p.Q701L|p.R108K; Supplementary Data 6) and four had classic mutations (p.L858R and EX19del). Classic EGFR mutations were found in two (5.9%, 2/34) P-SCLC and two (33%, 2/6) C-SCLC patients, respectively. In our cohort, we found that all EGFR mutations co-occurred with TP53 inactivation and RB1 inactivation (mutation and/or loss) (Supplementary Data 6). The TP53/RB1/EGFR mutations were independent of clinical (tumor stage and tumor size), and genomic features (TMB, ITH, and WGD) in SCLC (Supplementary Fig. 6a). Intriguingly, EGFR/RB1/TP53-mutant patients exhibited higher ploidy than those with wild-type (p = 0.017). And WGD occurred in all of the EGFR/RB1/TP53mutant patients (Supplementary Fig. 6a). Besides, these mutations were not associated with disease-free survival (DFS) or OS in the absence or presence of treatment after surgery (Supplementary Fig. 6b, c).

Supplementary Fig. 3 and Fig. 5a show the basic clinicopathological information in this cohort. Patients with P-SCLC/C-SCLC, smoker/non-smoker, EGFR mutant/wild type had similar levels of ITH, TMB, and mutation clusters, and they exhibited no discrepancy in their gene signature and mutation landscape (Fig. 4, Supplementary Fig. 4b, c). Remarkably, a higher TMB/cluster correlated with better DFS using univariate analysis, while the SNV ITH was correlated to OS (Fig. 5b, c). However, no significant correlation was observed among DFS or OS and TMB, mutation cluster, or tumor stage (Fig. 5b, c, Supplementary Fig. 6d, e). In a multivariate analysis adjusted for age, tumor size, tumor stage, and smoking status, only TMB/cluster were associated with better DFS, and SNV ITH is also linked to worse OS of SCLC (Fig. 5d, e).

Fig. 5: The relationship between heterogeneity and clinical characterization in SCLC.

a A heatmap displaying the clinical information and genomic features for each patient (n = 40). The Kaplan–Meier plot depicts the estimation of disease-free survival (b) and overall survival (c) with parameters including SNV ITH, CNV ITH, mutation cluster, and TMB per cluster. The p value and hazard ratio were determined using the two-sided log-rank test. The forest plot showing multiple covariate Cox regression analysis of disease-free survival (d) and overall survival (e) by subgroups including age, smoking, tumor size, stage, and ITH in SCLC. A two-sided, unpaired, Wilcoxon rank test was performed for the statistical comparison among subgroups. WGD, whole-genome duplication; GII genome instability index, MSI microsatellite instability, SNV single-nucleotide variant, CNV copy number variation, ITH intratumoral heterogeneity, P-SCLC pure small cell lung cancer, C-SCLC combined small cell lung cancer, TMB tumor mutation burden, HR hazard ratio, CI confidence interval.

All the cases with recurrence received systemic chemotherapy in our cohort. No ITH discrepancies were observed in patients according to the recurrence status and systemic chemotherapy (Supplementary Fig. 6f). ITH and TMB/cluster were not associated with survival outcomes in the recurrent cases (p > 0.05, n = 11, Supplementary Fig. 6g). Cases that received systemic chemotherapy had a superior overall outcome (Supplementary Fig. 6g), suggesting the favorable role of chemotherapy after surgery in the treatment of SCLC.

Discussion

Many SCLC patients are sensitive to initial treatment, but all patients inevitably face the dilemma of chemoresistance. It has been speculated that ITH is common in treatment-naive SCLC, with many drug-resistant subclones13. Yet, because of the lack of available tumor samples, this gap is still vacant in the field of SCLC research. Moreover, research in the field has mainly utilized traditional genomic sequencing of a single site which is unable to capture the full genomic landscape14. Whereas MRS is superior in evaluating the ITH of SCLC. Therefore, we performed MRS in a cohort of surgery resected SCLC patients. There was widespread ITH in SNV and CNV in SCLC, with a medium ITH score among different patients. Such universal ITH indicates a complex genomic landscape of SCLC even at the early stage and illustrates the dilemma of current treatment, such as rapid disease progression and relapse with refractory disease.

For the somatic mutations, TP53 and RB1 had the highest mutation frequency15. This corresponds with current research. Previous single-region sequencing revealed extensive common cancer-specific genomic alterations in SCLC, such as TP53 and RB116,17,18. They are also the most common clonal mutations identified in the MRS data, namely, somatic genetic alterations of TP53 and CREBBP, which were almost exclusively early clonal events. Most of the patients in our cohort carried subclonal mutations, including LRP1B, KMT2D, and PCLO, which appeared randomly in different regions. The same phenomenon occurred in the CNV events, however, not all CNV events existed in every tissue from the same sample. This highlights the limitations of single-region sequencing and emphasizes the advantages of MRS for better understanding the genomic landscape in precision medicine.

EGFR mutations are a rare occurrence in either de novo SCLC or in cases of transformed EGFR-mutant (EGFR-mt) adenocarcinoma19. In our study, the frequency of classic EGFRmutations in P-SCLC was 5.9%. These data were comparable with previous reports of 2.6% in Taiwanese and 2.0% in a Chinese cohort19,20. Our EGFR-mutant SCLC patients did not receive EGFR-TKI therapy, and EGFR mutation status is not associated with recurrence after surgery (Supplementary Fig. 3d). An EGFR mutation is considered an early clonal event in our analysis (Fig. 3b). However, a lower driver dominant EGFR score did not support its role as a driver gene in SCLC, which is distinct from common NSCLC (Supplementary Fig. 4a). In other words, an EGFR mutation was not a predominant driver gene in SCLC. Currently, there is no targeted therapy in EGFR-mutant SCLC. The majority of de novo EGFR-mt SCLC are resistant to EGFR-TKI therapy, compared with EGFR-mt NSCLC21, which may be due to focusing much more on the driver gene “EGFR” and neglecting of passenger mutations’ effect. EGFR passenger mutations may also collaborate synergistically with driver mutations to trigger tumorigenesis in SCLC. Previous researchers have shown that EGFR/RB1/TP53 are key events that transform NSCLC to SCLC after EGFR-TKI treatment22,23. In our treatment-naive SCLC cohort, we also found that all EGFR mutations co-occurred with TP53 and RB1mutations. EGFR/RB1/TP53 mutant patients had WGD events and exhibited higher ploidy than those with wild-type (Supplementary Fig. 6a). Yet, the TP53/RB1/EGFR mutations were independent of clinicopathologic features and not associated with prognosis. Based on the tumor evolutionary algorithm model proposed by Swanton et al.10, we conferred that TP53and EGFR mutations were early events in the evolution of SCLC, while the RB1 mutation and loss occurred later, indirectly suggesting a key role of RB1 inactivation in SCLC evolution. However, this hypothesis needs validation in further studies.

We sought to explore the relationship between ITH scores and clinicopathological features. We were particularly interested in the six patients with C-SCLC in this study cohort. Comprehensive research showed that this group of patients behaved much in the same way as P-SCLC patients, both in terms of mutation distribution, ITH, TMB, mutation clusters, and gene signatures. This condition is also present in patients with EGFR mutations and those with a history of smoking. Among diagnosed SCLC patients, most patients have a history of smoking. We paid special attention to the evolutionary tree of non-smoking SCLC patients and found there was no obvious difference compared with smoker patients (Supplementary Fig. 5). To some extent, the intratumoral heterogeneity of the SCLC genome is independent of common clinicopathological features, such as pathological types, smoking history, and driver gene mutation status, but there is still a relatively uniform moderate level of intratumoral heterogeneity. A previous study reported widespread ITH in chemotherapy-treated SCLC and found that it may lead to poor treatment response and prognosis. We observed the same performance of SNV ITH in treatment-naïve LD SCLC patients. Multivariable COX analysis supported the independent prognostic role of SNV ITH for OS. We turned our perspective to another tumor heterogeneity assessment algorithm, TMB per cluster, which seems to be another potential prognosis biomarker. We found that more TMB per cluster is linked to early disease recurrence and progression. It indicated complex mutations inside the tumor may lead to the failure of anti-cancer treatment. Further research on its relationship with treatment sensitivity and resistance is needed.

Although our study presents several findings, there are several limitations. First, our results would have been more reliable with more patients from other centers. Related to our limited sample, we did not perform dynamic genome monitoring for each patient. We also did not provide a better understanding of the tumor microenvironment of SCLC. In addition, we should notice that the presence of technical noise in sequencing data is common, and genuine intratumor genetic heterogeneity is hard to distinguish from these sequencing artifacts24. It may lead to the overestimation of ITH. Therefore, we used two mutation calling algorithms and strict criteria to filtering out these private artifacts, and to minimize the impact of artifacts25,26. Due to the unavailability of the samples, we could not validate our results in the same sample. Nevertheless, further studies with high depth sequencing are required to accurately quantifying ITH.

We demonstrated the ITH landscape of surgically resected SCLC. Despite a moderate mutation burden, SCLC showed a medium intratumoral heterogeneity with high SNV and CNV ITH at the early stage, which may explain the difficult treatment dilemma faced by SCLC patients.


Article classification: Biological abstract
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