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Translational Lung Cancer Research logoLink to Translational Lung Cancer Research
. 2020 Jun;9(3):670–681. doi: 10.21037/tlcr-19-401

Integrated analysis of optical mapping and whole-genome sequencing reveals intratumoral genetic heterogeneity in metastatic lung squamous cell carcinoma

Yizhou Peng 1,2, Chongze Yuan 1,2, Xiaoting Tao 1,2, Yue Zhao 1,2, Xingxin Yao 1,2, Lingdun Zhuge 1,2, Jianwei Huang 3, Qiang Zheng 2,4, Yue Zhang 3, Hui Hong 1,2, Haiquan Chen 1,2,, Yihua Sun 1,2,
PMCID: PMC7354123  PMID: 32676329

Abstract

Background

Intratumoral heterogeneity is a crucial factor to the outcome of patients and resistance to therapies, in which structural variants play an indispensable but undiscovered role.

Methods

We performed an integrated analysis of optical mapping and whole-genome sequencing on a primary tumor (PT) and matched metastases including lymph node metastasis (LNM) and tumor thrombus in the pulmonary vein (TPV). Single nucleotide variants, indels and structural variants were analyzed to reveal intratumoral genetic heterogeneity among tumor cells in different sites.

Results

Our results demonstrated there were less nonsynonymous somatic variants shared with PT in LNM than in TPV, while there were more structural variants shared with PT in LNM than in TPV. More private variants and its affected genes associated with tumorigenesis and progression were identified in TPV than in LNM. It should be noticed that optical mapping detected an average of 77.1% (74.5–78.5%) large structural variants (>5,000 bp) not detected by whole-genome sequencing and identified several structural variants private to metastases.

Conclusions

Our study does demonstrate structural variants, especially large structural variants play a crucial role in intratumoral genetic heterogeneity and optical mapping could make up for the deficiency of whole-genome sequencing to identify structural variants.

Keywords: Heterogeneity, lung squamous cell carcinoma (LUSC), metastasis, optical mapping, structural variants

Introduction

Lung cancer is the leading cause of cancer-related death worldwide (1). The two major histological types are non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) (2). Lung squamous cell carcinoma (LUSC), one of the common histological types of NSCLC, remains poor prognosis despite of development in therapeutic strategies (3-5). Meanwhile, intratumoral heterogeneity, which refers to heterogeneity among tumor cells of a single patient, is crucial for the clinical outcome of patients with lung cancer, impacting the curative effect of chemotherapy, radiotherapy and immunotherapy (6,7).

Next-generation sequencing (NGS), a method relying on short reads, has been performed on multiregional tumors to explore intratumoral genetic heterogeneity (ITGH) in NSCLC (8-10). Previous studies focused more on ITGH involving mutations that distinguish different tumor cells in a single or multiple primary NSCLC (7-9,11). A previous study explored the ITGH based on analysis of single nucleotide variants (SNVs) and copy number variants (CNVs) using whole-genome sequencing (WGS) on primary tumors, metastatic lymph nodes and tumor cells in the pleura (10). Because of the challenge in detecting technology, structural variants (SVs) increasingly appears to have an indispensable but undiscovered role in ITGH (12,13). However, ITGH which manifests uneven distribution of genetic alterations among lung tumor cells in primary tumor and associated metastases is not comprehensively characterized due to the lack of studies focusing on distant metastasis and SVs. Recently, optical mapping, a newly non-sequencing method, shed a light to dig large SVs (14,15).

In this study, we combined optical mapping and WGS to reveal the ITGH in various forms of SNVs, indels and SVs, especially large SVs (>5 kb) within primary tumor and associated metastases in a LUSC patient. We also compared SVs detected by optical mapping and those detected by WGS. Furthermore, after comparing the genes affected by variants with those associated with tumorigenesis and progression, we inferred the functional consequence of distinct genomic alterations among tumor cells within the primary site and paired metastatic sites.

Methods

Tissue collection

Surgical specimens of primary tumor (PT), lymph node metastases (LNM), tumor thrombus in the pulmonary vein (TPV) and adjacent normal lung tissue (at least 2cm away from tumor) were obtained from a patient who diagnosed with pathologically confirmed lung squamous cell carcinoma. This study was approved by the Committee for Ethical Review of Research. Informed consent was obtained.

Whole-genome sequencing

DNA extraction and sequencing: After fragmented by sonication to a size of 350 bp, genomic DNA fragments were end-polished, A-tailed, and ligated with adapter for Illumina sequencing. Then after further PCR amplification and purification, libraries were analyzed for size distribution by Agilent 2100 Bioanalyzer and quantified for concentration (2 nM) by flurogenic-quantitative PCR (Qubit 2.0). Then DNA libraries were sequenced on Illumina Novaseq 6000 sequencing platform with 30X sequencing depth. 150 bp paired-end reads were generated. Contaminated reads including adaptors, low quality reads and those with more “N” was extracted based on chastity score and quality score.

Variants detection and filtration: Paired-end reads in FastQ format were aligned to the reference human genome (UCSC Genome Browser, version hg19) by Burrows-Wheeler Aligner (BWA) (16). Subsequent BAM files were processed by SAMtools (17), Picard tool (http://picard.sourceforge.net/), and the Genome Analysis Toolkit (GATK) (18) to sort and remove duplication, local realignment, and base quality recalibration.

SNVs and indels detection: Mutect (19) was used to detect the somatic SNVs and indel with tumor-normal paired BAM files. ANNOVAR was used to further annotate for VCF (Variant Call Format) (20). Somatic SNVs were further filtered for analysis of mutational spectrum and signatures with the following criteria: SNVs which has no record in 1000 Genomes project, dbsnp or Berry4000 (Berry Genomics) were filtered (21,22).

SVs detection, filtration and classification: Manta was applied for SVs detection (23), SVs were reported as INS (insertion), DEL (deletion), DUP (duplication), INV (inversion), and BND (further identified as inter-chromosomal translocation). Somatic SVs in PT, LNM and TPV were identified with the data of adjacent normal lung sample as control. ANNOVAR was applied for annotation (20). SVs were filtered if: SVs <50 bp; mapped to the mitochondrial genome or chromosome Y; overlapped with gap region, telomere, centromere or low complexity regions; with MinQUAL, MinGQ, Ploidy, MaxDepth, MaxMQ0Frac and NoPairSupport in VCF FILTER fields; and supported by <2 split reads (SR).

Optical mapping

DNA preparation: High Molecular Weight (HMW) DNA were extracted using Bionano Prep Animal Tissue DNA Isolation Fibrous Tissue Protocol (https://bionanogenomics.com/support-page/animal-tissue-dna-isolation-kit/) from the tissue of frozen PT, LNM and TPV. Firstly, approximately 10 mg of tissue were fixed, disrupted with a rotor-stator, embedded in 2% agarose, and digested with proteinase K and RNase. After multiple stabilization and recovery followed by digestion with Agarase (Thermo Fisher) enzyme, HMW DNA were released, cleaned by drop dialysis and homogenized. HMW DNA were quantitated using Qubit dsDNA BR Assay Kit.

Direct labeling: HMW DNA were extracted using Bionano Prep Direct Label and Stain (DLS) Protocol (https://bionanogenomics.com/support-page/dna-labeling-kit-dls/). Firstly, 750 ng HMW DNA were nicked by DLE-1 enzyme, recovered, labled with fluorophore and stained. Then labled and stained DNA were quantitated using modified Qubit dsDNA HS (High Sensitivity) Assay Kit. Each labeled sample was added to a BioNano Saphyr Chip (Bionano Genomics) and run on the Bionano Saphyr instrument, targeting 100× human genome coverage. The raw data were filtered by Bionano Access (v1.2.1) with the following criteria: molecule length >150 kb with average label density of 10–25/100 kb.

SVs detection and filtration: De novo assembly of long molecules into genome map and SVs detection by comparing with Hg19 were performed with software Bionano Solve (version 3.2.1). SVs were annotated by Enliven (Berry Genomics). Then SVs were filtered if: for translocation and inversion, (I) confidence value <0.9, (II) breakpoints were located in the chromosome fragile site, (III) breakpoints were located in the segmental region of the chromosome, (IV) breakpoints were within these previously identified SVs (24); For insertion and deletion, (I) confidence value <0.9, (II) length of variation <5 kb, (III) breakpoints were in the gap region of reference genome.

Comparison of SVs from optical mapping and WGS

WGS provide SVs breakpoints (start and end) with base pair resolution, while optical mapping provides only the nearest labeling site to the interval of SVs. We determined whether SVs from optical mapping overlap with SVs from WGS with the following criteria: (I) Deletions, insertions and duplications detected by WGS must overlap with the interval of SVs detected by optical mapping. (II) The breakpoints of Inversions detected by WGS must lie within 500 kb to the interval of SVs detected by optical mapping.

Comparison of SVs from WGS among PT, LNM and TPV

Somatic SVs from WGS in PT, LNM and TPV were classified as shared SVs or private SVs among tumors with the following criteria: SVs has the same breakpoints (start and end), consistent type with SVs in another tumor were identified as identical and classified as shared SVs.

Comparison of SVs from optical mapping among PT, LNM and TPV

SVs from optical mapping in PT, LNM and TPV were classified as shared or private SVs among tumors with the following criteria: SVs have overlapped interval, consistent type with SVs in another tumor were identified as shared SVs. We further filtered the shared SVs in all tumors due to the shared somatic SVs and germline SVs could not be distinguished.

Identification of genes affected by SVs

For variants from WGS, we inferred a gene affected by variants if (I) a protein coding gene is annotated with an exon-annotated deletion, insertion and duplication; (II) the breakpoint (start or end) of inversion or inter-chromosome translocation lies within one or more exon of the genes; (III) the genes carried an nonsynonymous variants (nonsynonymous SNVs or frameshifting indels).

For SVs from optical mapping, we inferred a gene affected by variants if the gene was annotated with an exon-annotated SVs.

Functional consequence analysis

For genes affected by variants, we inferred whether these genes are associated with tumorigenesis and progression based on data of lung cancer driver genes (25-27), pan-cancer driver genes (28), COSMIC (https://cancer.sanger.ac.uk/census) (29), DNA repair genes (30) and hallmark genes of epithelial-mesenchymal transition (EMT) (31-38). Based on the data of The Human Protein Atlas (www.proteinatlas.org) (39-41), we further examined whether RNA expression of these genes correlate with the outcome of lung cancer and its protein expression and classified them as unprognostic, prognostic favorable and prognostic unfavorable genes.

KEGG enrichment

Genes only affected by variants in LNM and TPV were used to KEGG enrichment analysis by The Database for Annotation, Visualization and Integrated Discovery (DIVID) (42) and KOBAS 3.0 (http://kobas.cbi.pku.edu.cn/index.php).

Statistical analysis

We used R (version 3.3.3, version 3.6.1) software. “SomaticSignatures”, “ggplot2”, “ggrepel”, “ggthemes” were used in the analyses (43,44).

Results

Patients’ characterization

A 50-year-old East Asian male with 20 pack year history of smoking for 20 years, was diagnosed with lung squamous cell carcinoma with histopathological confirmation (Figure 1). Before systematic treatment, primary tumor (PT) located in the left upper lobe of lung, metastasis of left lower paratracheal (4L) lymph node (LNM) and tumor thrombus of the left Superior pulmonary vein (TPV) were sampled by surgical section. Furthermore, there is no reported family history of lung cancer. No significant difference in Tumor grade heterogeneity among tumor cells in primary and metastatic sites were identified by hematoxylin and eosin staining (Figure 1C, Figure S1).

Figure 1.

Figure 1

Clinical and histological diagnostic results of a patient with LUSC. (A) Schematic diagram of the primary tumors (PT) and lymph node metastases (LNM) and tumor thrombus in pulmonary vein (TPV). (B) Preoperative enhanced computerized tomography (enhanced-CT) scanning showed the PT (upper), LNM (middle) and TPV (lower). (C) Postoperative paraffin section and hematoxylin and eosin (H&E) staining image based on 400× magnification. Tumor cells in PT, LNM and TPV were moderately or poorly differentiated. PT, primary tumor; LNM, lymph node metastases; TPV, tumor thrombus in pulmonary vein.

Figure S1.

Figure S1

Postoperative paraffin section and hematoxylin and eosin (H&E) staining image for PT (A and B), LNM (C) and TPV (D) based on 40–100× magnification. PT, primary tumor; LNM, lymph node metastases; TPV, tumor thrombus in pulmonary vein.

ITGH in the form of SNVs and indels

To gain an insight into alterations of different mutational characteristics between the primary tumor and the metastases, we performed WGS on PT, LNM, TPV and adjacent normal lung tissue at an average depth of 30X.

A total of 268 nonsynonymous somatic variants (including nonsynonymous SNVs and frameshifting indels) in 252 genes were identified in at least one tumor (Table S1), and 14.2% (38) of these variants were shared between PT and either one of the two metastases (Figure 2 and Figure 3A). Among them, 3 mutations were common in all tumors, while compared with LNM (5), a larger number of mutations (36) in TPV were shared with PT. 17, 15 and 195 mutations were uniquely seen in PT, LNM and TPV, respectively. Specifically, nonsynonymous SNV in TP53 which is one of the most commonly mutated gene in LUCC (45) were only detected in TPV. We further analyzed the mutation spectrum of SNVs (Figure 3A,B,C), trying to identify significant discordance between LNM and TPV. To be specific, we identified that TPV and PT both displayed a predominance of cytosine-adenine (C > A) nucleotide transversions which implied a correlation with tobacco exposure (46), consistent with the long-term smoking history of this patient. Meanwhile, the LNM exhibited a distinct preponderance of guanine-adenine (G > A) and adenine-guanine (A > G). Moreover, the detailed analysis of mutational signature showed S1 and S2 were extracted (Figure 3D). Compared with the previously known mutational signatures shown in COSMIC (29), S1 had the most similarity with signature 4 likely due to direct damage by mutagens in tobacco, and S2 exhibits the thymine-cytosine (T > C) as same as the signature 5 increased in many cancer types due to tobacco smoking (Figure 3E). Primary tumor and metastasis shared identical mutational signatures, but the proportion is different (Figure 3F). These results demonstrated patient have primary tumor and metastasis in different sites has high ITGH in the form of SNVs and indels.

Table S1. Somatic nonsynonymous SNVs and indels detected in PT, LNM and TPV.

Start End Ref Alt Exonicfunc Sample Gene
8399673 8399673 C A Stopgain PT, TPV SLC45A1
13183833 13183833 C T Nonsynonymous SNV PT, LNM, TPV HNRNPCL2
33385852 33385852 C T Nonsynonymous SNV PT AQP7
79403883 79403883 T C Nonsynonymous SNV PT, TPV ADGRL4
33385863 33385863 G T Nonsynonymous SNV PT AQP7;AQP7
146057344 146057344 T C Nonsynonymous SNV PT, LNM NBPF11
144061414 144061414 G A Nonsynonymous SNV PT ARHGEF5
242121845 242121845 G T Nonsynonymous SNV PT, TPV BECN2
69034420 69034420 G T Nonsynonymous SNV PT, TPV ARHGAP25
84822875 84822875 C G Nonsynonymous SNV PT, TPV DNAH6
88478308 88478308 G A Nonsynonymous SNV PT, TPV THNSL2
98127921 98127921 T C Nonsynonymous SNV PT, LNM ANKRD36B
143713839 143713839 A T Nonsynonymous SNV PT, TPV KYNU
40523437 40523437 C G Nonsynonymous SNV PT, TPV ZNF619
42956494 42956494 G T Nonsynonymous SNV PT, TPV ZNF662
1201932 1201932 G T Nonsynonymous SNV PT, TPV SLC6A19
38972028 38972028 C G Nonsynonymous SNV PT, TPV RICTOR
75427978 75427978 G A Nonsynonymous SNV PT, TPV SV2C
26056229 26056229 C A Nonsynonymous SNV PT, TPV HIST1H1C
32713598 32713598 T C Nonsynonymous SNV PT, TPV HLA-DQA2
34949727 34949727 G C Nonsynonymous SNV PT, TPV ANKS1A
51656112 51656112 T G Nonsynonymous SNV PT, TPV PKHD1
143269952 143269952 A T Nonsynonymous SNV PT CTAGE15
48545953 48545953 C T Nonsynonymous SNV PT, TPV ABCA13
161487805 161487805 T C Nonsynonymous SNV PT FCGR2A
82934997 82934997 T C Nonsynonymous SNV PT GOLGA6L10
118922882 118922882 A C Nonsynonymous SNV PT HYOU1
45994014 45994014 C T Nonsynonymous SNV PT KRTAP10-4
150269712 150269712 G A Nonsynonymous SNV PT, TPV GIMAP4
100642828 100642828 C T Nonsynonymous SNV PT MUC12
100643427 100643427 G A Nonsynonymous SNV PT MUC12
70918964 70918964 G A Nonsynonymous SNV PT, TPV FOXD4L3
90502176 90502176 C A Nonsynonymous SNV PT, TPV SPATA31E1
107266990 107266990 G A Stopgain PT, TPV OR13F1
112189256 112189256 C T Stopgain PT, TPV PTPN3
4967678 4967678 G A Nonsynonymous SNV PT, LNM, TPV OR51A4
145326106 145326106 A T Nonsynonymous SNV PT NBPF10
248616705 248616711 TGCTGCG Frameshift deletion PT OR2T2
78591144 78591144 A G Nonsynonymous SNV PT, TPV NAV3
24523931 24523931 G C Nonsynonymous SNV PT, TPV CARMIL3
6797520 6797520 G C Nonsynonymous SNV PT RSPH10B;RSPH10B2
68475842 68475842 T G Nonsynonymous SNV PT TESMIN
50830413 50830413 C G Stopgain PT, TPV CYLD
60050130 60050130 T A Nonsynonymous SNV PT, TPV MED13
72341009 72341009 G T Nonsynonymous SNV PT, TPV KIF19
18534948 18534948 G C Nonsynonymous SNV PT, TPV ROCK1
3150255 3150255 G C Nonsynonymous SNV PT, TPV GNA15
1306817 1306817 G A Nonsynonymous SNV PT TPSD1
39111054 39111054 C G Nonsynonymous SNV PT, TPV EIF3K
40399430 40399430 T C Nonsynonymous SNV PT, TPV FCGBP
55100038 55100038 C A Nonsynonymous SNV PT, TPV FAM209A
32647032 32647032 A C Nonsynonymous SNV PT TXLNA
16277757 16277757 C T Nonsynonymous SNV PT, LNM, TPV POTEH
10472843 10472843 T G Nonsynonymous SNV PT TYK2
104379506 104379506 TT Frameshift insertion PT, TPV TDG;TDG
12942047 12942047 C T Nonsynonymous SNV LNM PRAMEF4
145302775 145302775 T G Nonsynonymous SNV LNM NBPF10
195509939 195509939 G T Nonsynonymous SNV LNM MUC4
195509941 195509941 A C Nonsynonymous SNV LNM MUC4
140574103 140574103 T G Nonsynonymous SNV LNM PCDHB10
56499000 56499000 A G Nonsynonymous SNV LNM DST
74159167 74159167 G C Nonsynonymous SNV LNM, TPV GTF2I
100644127 100644127 C T Nonsynonymous SNV LNM MUC12
100644211 100644211 C T Nonsynonymous SNV LNM, TPV MUC12
100644793 100644793 C T Nonsynonymous SNV LNM MUC12
128471007 128471007 T G Nonsynonymous SNV LNM FLNC
135440222 135440222 C T Nonsynonymous SNV LNM FRG2B
89819380 89819380 A G Nonsynonymous SNV LNM UBTFL1
74363307 74363307 C T Nonsynonymous SNV LNM, TPV GOLGA6A
54745682 54745682 C T Nonsynonymous SNV LNM LILRA6;LILRB3
56274086 56274086 G A Nonsynonymous SNV LNM RFPL4A
24579049 24579049 G A Nonsynonymous SNV LNM SUSD2
23653975 23653975 - CCGG Frameshift insertion LNM BCR
2523380 2523380 G T Nonsynonymous SNV TPV MMEL1
55545264 55545264 C T Nonsynonymous SNV TPV USP24
91403621 91403621 C G Nonsynonymous SNV TPV ZNF644
108771623 108771623 C A Nonsynonymous SNV TPV NBPF4
117158857 117158857 C T Nonsynonymous SNV TPV IGSF3
145356733 145356733 C G Nonsynonymous SNV TPV NBPF19
156531719 156531719 C T Nonsynonymous SNV TPV IQGAP3
157514189 157514189 C T Nonsynonymous SNV TPV FCRL5
179562624 179562624 G A Nonsynonymous SNV TPV TDRD5
204438869 204438869 C A Nonsynonymous SNV TPV PIK3C2B
214184949 214184949 G T Nonsynonymous SNV TPV PROX1
247769320 247769320 G A Nonsynonymous SNV TPV OR2G3
248737734 248737734 G A Nonsynonymous SNV TPV OR2T34
11337731 11337731 T A Nonsynonymous SNV TPV ROCK2
71795319 71795319 G C Nonsynonymous SNV TPV DYSF
108487966 108487966 A G Nonsynonymous SNV TPV RGPD4
121729586 121729586 G T Nonsynonymous SNV TPV GLI2
128364989 128364989 G T Nonsynonymous SNV TPV MYO7B
128615641 128615641 C T Nonsynonymous SNV TPV POLR2D
141946102 141946102 C A Nonsynonymous SNV TPV LRP1B
178098960 178098960 C G Nonsynonymous SNV TPV NFE2L2
179398041 179398041 T C Nonsynonymous SNV TPV TTN
179456813 179456813 G T Nonsynonymous SNV TPV TTN
196599665 196599665 G T Nonsynonymous SNV TPV SLC39A10
225422494 225422494 T C Nonsynonymous SNV TPV CUL3
228137779 228137779 G T Nonsynonymous SNV TPV COL4A3
238672406 238672406 G T Nonsynonymous SNV TPV LRRFIP1
4829646 4829646 C T Stopgain TPV ITPR1
12458381 12458381 G A Nonsynonymous SNV TPV PPARG
37670790 37670790 G A Nonsynonymous SNV TPV ITGA9
49721811 49721811 C T Nonsynonymous SNV TPV MST1
121350823 121350823 C T Nonsynonymous SNV TPV HCLS1
165547837 165547837 C A Nonsynonymous SNV TPV BCHE
169565951 169565951 C A Nonsynonymous SNV TPV LRRC31
193028470 193028470 G C Nonsynonymous SNV TPV ATP13A5
194118528 194118528 G T Nonsynonymous SNV TPV GP5
1231985 1231985 C A Stopgain TPV CTBP1
1920144 1920144 A G Nonsynonymous SNV TPV NSD2
98902467 98902467 T G Nonsynonymous SNV TPV STPG2
118005739 118005739 T A Nonsynonymous SNV TPV TRAM1L1
123236706 123236706 C G Nonsynonymous SNV TPV KIAA1109
162577500 162577500 A T Nonsynonymous SNV TPV FSTL5
177071237 177071237 A T Nonsynonymous SNV TPV WDR17
187549886 187549886 T A Nonsynonymous SNV TPV FAT1
24505347 24505347 C G Nonsynonymous SNV TPV CDH10
41911175 41911175 T C Nonsynonymous SNV TPV C5orf51
75858298 75858298 T A Nonsynonymous SNV TPV IQGAP2
90024685 90024685 C A Nonsynonymous SNV TPV ADGRV1
113740318 113740318 A G Nonsynonymous SNV TPV KCNN2
114860009 114860009 C T Nonsynonymous SNV TPV FEM1C
131007333 131007333 C T Nonsynonymous SNV TPV FNIP1
131931309 131931309 C T Stopgain TPV RAD50
140307748 140307748 C A Nonsynonymous SNV TPV PCDHAC1
140554795 140554795 C G Nonsynonymous SNV TPV PCDHB7
27222843 27222843 G T Nonsynonymous SNV TPV PRSS16
32713784 32713784 C A Nonsynonymous SNV TPV HLA-DQA2
41899529 41899529 G C Nonsynonymous SNV TPV BYSL
64422909 64422909 A C Nonsynonymous SNV TPV PHF3
66005999 66005999 G C Nonsynonymous SNV TPV EYS
90402365 90402365 C A Nonsynonymous SNV TPV MDN1
126196041 126196041 A T Nonsynonymous SNV TPV NCOA7
136599115 136599115 C A Nonsynonymous SNV TPV BCLAF1
150343262 150343262 T C Nonsynonymous SNV TPV RAET1L
152614857 152614857 C T Nonsynonymous SNV TPV SYNE1
158538843 158538843 G T Nonsynonymous SNV TPV SERAC1
168708765 168708765 C G Nonsynonymous SNV TPV DACT2
7622874 7622874 G C Nonsynonymous SNV TPV MIOS
29915496 29915496 T A Nonsynonymous SNV TPV WIPF3
37951827 37951827 G T Nonsynonymous SNV TPV SFRP4
39379482 39379482 C A Nonsynonymous SNV TPV POU6F2
49815575 49815575 G A Nonsynonymous SNV TPV VWC2
107720188 107720188 A G Nonsynonymous SNV TPV LAMB4
128478472 128478472 T A Nonsynonymous SNV TPV FLNC
140051918 140051918 T C Nonsynonymous SNV TPV SLC37A3
140179090 140179090 C A Nonsynonymous SNV TPV MKRN1
150778698 150778698 G T Nonsynonymous SNV TPV TMUB1
150835349 150835349 G T Nonsynonymous SNV TPV AGAP3
151856028 151856028 G T Nonsynonymous SNV TPV KMT2C
154863275 154863275 G T Nonsynonymous SNV TPV HTR5A
24324457 24324457 A C Nonsynonymous SNV TPV ADAM7
70591803 70591803 G T Nonsynonymous SNV TPV SLCO5A1
92988192 92988192 C G Nonsynonymous SNV TPV RUNX1T1
107715182 107715182 G A Nonsynonymous SNV TPV OXR1
113275870 113275870 A T Stopgain TPV CSMD3
145193975 145193975 G A Nonsynonymous SNV TPV HGH1
21187197 21187197 G T Nonsynonymous SNV TPV IFNA4
21974676 21974676 C T Nonsynonymous SNV TPV CDKN2A;CDKN2A
27558545 27558545 C T Nonsynonymous SNV TPV C9orf72
69423770 69423770 C T Nonsynonymous SNV TPV ANKRD20A4
85597659 85597659 G A Nonsynonymous SNV TPV RASEF
23622026 23622026 T C Nonsynonymous SNV TPV C10orf67
28030395 28030395 T G Nonsynonymous SNV TPV MKX
68526048 68526048 G T Nonsynonymous SNV TPV CTNNA3
86133479 86133479 G C Nonsynonymous SNV TPV CCSER2
93702292 93702292 G A Nonsynonymous SNV TPV BTAF1
116247751 116247751 C T Nonsynonymous SNV TPV ABLIM1
116605214 116605214 G A Nonsynonymous SNV TPV FAM160B1
134942632 134942632 C A Nonsynonymous SNV TPV ADGRA1
4929407 4929407 C A Nonsynonymous SNV TPV OR51A7
5068137 5068137 G A Nonsynonymous SNV TPV OR52J3
6291913 6291913 G C Nonsynonymous SNV TPV CCKBR
6341448 6341448 G T Nonsynonymous SNV TPV CAVIN3
44296961 44296961 G C Stopgain TPV ALX4
64084615 64084615 C A Nonsynonymous SNV TPV TRMT112
64877317 64877317 G A Stopgain TPV VPS51
68845988 68845988 G C Nonsynonymous SNV TPV TPCN2
68846022 68846022 G C Nonsynonymous SNV TPV TPCN2
68846223 68846223 G C Nonsynonymous SNV TPV TPCN2
70118395 70118395 G C Nonsynonymous SNV TPV PPFIA1
100211220 100211220 A G Nonsynonymous SNV TPV CNTN5
120329909 120329909 G T Nonsynonymous SNV TPV ARHGEF12
2711117 2711117 T C Nonsynonymous SNV TPV CACNA1C
3788238 3788238 G C Nonsynonymous SNV TPV CRACR2A
15747894 15747894 G T Nonsynonymous SNV TPV PTPRO
88482957 88482957 T A Nonsynonymous SNV TPV CEP290
122745983 122745983 C A Nonsynonymous SNV TPV VPS33A
128899361 128899361 G T Nonsynonymous SNV TPV TMEM132C
21563012 21563012 C A Nonsynonymous SNV TPV LATS2
24243249 24243249 C G Nonsynonymous SNV TPV TNFRSF19
32757165 32757165 A T Nonsynonymous SNV TPV FRY
33017514 33017514 C A Nonsynonymous SNV TPV N4BP2L2
33247368 33247368 C G Nonsynonymous SNV TPV PDS5B
35683531 35683531 T A Stopgain TPV NBEA
61103338 61103338 G T Nonsynonymous SNV TPV TDRD3
107822979 107822979 T G Nonsynonymous SNV TPV FAM155A
19553478 19553478 G A Nonsynonymous SNV TPV POTEG
22138850 22138850 A G Nonsynonymous SNV TPV OR4E1
79432646 79432646 T A Nonsynonymous SNV TPV NRXN3
93581417 93581417 C A Nonsynonymous SNV TPV ITPK1
95582849 95582849 C T Nonsynonymous SNV TPV DICER1
23811612 23811612 C T Nonsynonymous SNV TPV MKRN3
24922008 24922008 A T Nonsynonymous SNV TPV NPAP1
33941414 33941414 G A Nonsynonymous SNV TPV RYR3
33954985 33954985 C A Nonsynonymous SNV TPV RYR3
42289384 42289384 C T Nonsynonymous SNV TPV PLA2G4E
43572000 43572000 C A Stopgain TPV TGM7
76136822 76136822 G T Nonsynonymous SNV TPV UBE2Q2
93015599 93015599 A G Nonsynonymous SNV TPV C15orf32
94841718 94841718 A G Nonsynonymous SNV TPV MCTP2
23711953 23711953 C T Nonsynonymous SNV TPV ERN2
51172691 51172691 C T Nonsynonymous SNV TPV SALL1
74419248 74419248 C G Nonsynonymous SNV TPV NPIPB15
3101635 3101635 A T Nonsynonymous SNV TPV OR1A2
4720319 4720319 G A Nonsynonymous SNV TPV PLD2
6381356 6381356 G A Nonsynonymous SNV TPV PITPNM3
7574003 7574003 G A Stopgain TPV TP53
12620686 12620686 A T Nonsynonymous SNV TPV MYOCD
18539842 18539842 C T Nonsynonymous SNV TPV TBC1D28
28782467 28782467 T C Nonsynonymous SNV TPV CPD
29123323 29123323 G A Nonsynonymous SNV TPV CRLF3
32953362 32953362 G A Nonsynonymous SNV TPV TMEM132E
47121429 47121429 T G Nonsynonymous SNV TPV IGF2BP1
47121430 47121430 T G Nonsynonymous SNV TPV IGF2BP1
48542697 48542697 A C Nonsynonymous SNV TPV CHAD
71281726 71281726 C T Nonsynonymous SNV TPV CDC42EP4
11610531 11610531 G A Nonsynonymous SNV TPV SLC35G4
19395677 19395677 A T Nonsynonymous SNV TPV MIB1
2115396 2115396 T A Nonsynonymous SNV TPV AP3D1
3623954 3623954 T C Nonsynonymous SNV TPV CACTIN
9086220 9086220 C G Nonsynonymous SNV TPV MUC16
10469852 10469852 A T Nonsynonymous SNV TPV TYK2;TYK2
12739889 12739889 A G Nonsynonymous SNV TPV ZNF791
15756539 15756539 C T Nonsynonymous SNV TPV CYP4F3
18375446 18375446 C A Nonsynonymous SNV TPV KIAA1683
22941567 22941567 A G Nonsynonymous SNV TPV ZNF99
23040922 23040922 C G Stopgain TPV ZNF723
47870310 47870310 A G Nonsynonymous SNV TPV DHX34
51984886 51984886 C A Nonsynonymous SNV TPV CEACAM18
54515274 54515274 C A Nonsynonymous SNV TPV CACNG6
57293327 57293327 A G Nonsynonymous SNV TPV ZIM2
21330036 21330036 A G Nonsynonymous SNV TPV XRN2
25655939 25655939 C T Nonsynonymous SNV TPV ZNF337
50286574 50286574 C T Nonsynonymous SNV TPV ATP9A
55206742 55206742 T C Nonsynonymous SNV TPV TFAP2C
37618419 37618419 T C Nonsynonymous SNV TPV DOPEY2
45953704 45953704 C G Nonsynonymous SNV TPV TSPEAR
45993666 45993666 A G Nonsynonymous SNV TPV KRTAP10-4
47320917 47320917 G A Nonsynonymous SNV TPV PCBP3
19883067 19883067 T G Nonsynonymous SNV TPV TXNRD2
29957800 29957800 T C Nonsynonymous SNV TPV NIPSNAP1
50518810 50518810 A G Nonsynonymous SNV TPV MLC1
50704016 50704016 G A Nonsynonymous SNV TPV MAPK11
31792183 31792183 C A Nonsynonymous SNV TPV DMD
32382707 32382707 C A Nonsynonymous SNV TPV DMD
35938079 35938079 G C Nonsynonymous SNV TPV CFAP47
110491848 110491848 C A Nonsynonymous SNV TPV CAPN6
148577938 148577938 C A Nonsynonymous SNV TPV IDS
157803028 157803028 C Frameshift deletion TPV CD5L
171627269 171627269 A Frameshift insertion TPV ERICH2
6574049 6574052 TACT Frameshift deletion TPV VAMP1
63970153 63970153 T Frameshift insertion TPV HERC1
63970155 63970155 AACT Frameshift insertion TPV HERC1
38969124 38969124 C Frameshift deletion TPV RYR1
58570657 58570657 C Frameshift deletion TPV ZNF135
19420859 19420868 TCATTCCCAT Frameshift deletion TPV MRPL40

PT, primary tumor; LNM, lymph node metastases; TPV, tumor thrombus in pulmonary vein.

Figure 2.

Figure 2

Exonic somatic variants identified in PT, LNM and TPV. The exonic somatic variants were classified as shared or private variants. Red color represent genes contain different variants among different tumors. PT, primary tumor; LNM, lymph node metastases; TPV, tumor thrombus in pulmonary vein.

Figure 3.

Figure 3

Intratumoral genetic heterogeneity in form of SNVs and indels. (A) The number of exonic somatic variants (SNVs and indels) and nonsynonymous somatic variants in each of tumors. (B) The mutation spectrum of SNVs in PT, LNM and TPV. (C) Mutational signatures of all tumor sample. (D) Two mutational signatures (S1, S2) extracted from all tumors. (E) Cluster analysis of S1, S2 and 30 COSMIC mutational signature based on the cosine similarity. (F) The proportion of S1 and S2 in PT, LNM and TPV. PT, primary tumor; LNM, lymph node metastases; TPV, tumor thrombus in pulmonary vein.

Comparison of structural variants detected by WGS and optical mapping

We utilized WGS data and performed optical mapping on PT, LNM and TPV at 100X coverage. SVs were called and filtered as presented in Figure 4. There were a mean of 3,617 SVs detected by WGS (3,907, 3,580, and 3,365 in PT, LNM, and TPV, respectively), of which deletions were most commonly detected type of SV (Figure S2). While SVs detected by optical mapping was 1,026 on average (979, 1,118, 980 in PT, LNM, TPV, respectively), Insertions account for the most (Figure S2).

Figure 4.

Figure 4

Workflow for detection of structural variants. The workflow for extracting structural variants from a combination of whole-genome sequencing and optical mapping. Detail explanation seen in Methods.

Figure S2.

Figure S2

The proportions of different types of SVs detected by whole-genome sequencing (left) or optical mapping (right) in PT (upper), LNM (middle) and TPV (lower). PT, primary tumor; LNM, lymph node metastases; TPV, tumor thrombus in pulmonary vein.

By comparing the SVs detected by WGS and optical mapping, we observed an average of 22.9 percent of SVs detected by optical mapping overlapped with those detected by WGS (25.1%, 21.4% and 22.2% in PT, LNM and TPV, respectively) (Figure 5A,B), of which the deletions had similar size (the median size was 6,452 bp, 6,191 bp in optical mapping and WGS) (Figure 5C, Figure S3). The median size of non-overlapping SVs in optical mapping was distinct from the non-overlapping ones detected by WGS (8,875 bp, 143 bp in optical mapping and WGS respectively) (Figure 5C, Figure S3). Specifically, Optical mapping is more capable of detecting large SVs (>5,000 bp) (Figure 5D). Generally, WGS can detect SVs at a high resolution of base but has many limitations: it depends on a short-read sequencing technique, needs a reference genome, and challenges of computational and bioinformatics algorithms exist. In contrast, optical mapping detects large and complex SVs using high molecular weight (HMW) DNA which are longer, ranging from 0.1 to 2Mb. The results suggested that the combination of WGS and optical mapping used for detecting SVs allows to a more comprehensive understanding of structural variants among tumor cells within different sites and demonstrated optical mapping is more sensitive for detection of large SVs.

Figure 5.

Figure 5

Comparison of structural variants detected by WGS and optical mapping. (A) The number of structural variants detected by whole-genome sequencing and optical mapping. (B) The number of different types of structural variants detected by whole-genome sequencing and optical mapping in TPV. (C) Size distribution of deletions in TPV. (D) The number of large structural variants (>5,000 bp) detected by whole-genome sequencing and optical mapping in TPV. TPV, tumor thrombus in pulmonary vein.

Figure S3.

Figure S3

The number of different types of structural variants detected by whole-genome sequencing and optical mapping in PT (A) and LNM (C), of which size distribution of deletions in PT (B) and LNM (D). PT, primary tumor; LNM, lymph node metastases.

ITGH in the form of SVs

We did an comparison among PT, LNM and TPV based on SVs detected by WGS and SVs detected by optical mapping, identifying a greater amount of private SVs in TPV (126 from WGS, 83 from optical mapping) than in either PT (4 from WGS, 75 from optical mapping) or LNM (4 from WGS, 118 from optical mapping) (Figure 6A), consistent with the results of SNVs and indels analysis. There was no overlap between private SVs identified by WGS and private SVs identified by optical mapping in each of tumors except TPV (7 private SVs from optical mapping overlapped with 6 private SVs from WGS). Smaller number of SVs in TPV (17 from WGS, 23 from optical mapping) overlapped with SVs of PT than those in LNM (105 from optical mapping). Specifically, 52 SVs from optical mapping undetected in PT were shared between LNM and TPV.

Figure 6.

Figure 6

Intratumoral genetic heterogeneity in form of structural variants. (A) Overlap of structural variants detected by whole-genome sequencing (upper) and optical mapping (lower) among PT, LNM and TPV. (B) Genes associated with tumorigenesis and progression affected by structural variants detected by whole-genome sequencing and optical mapping in PT, LNM and TPV. (C) Genes associated with prognosis of lung cancer affected by structural variants detected by whole-genome sequencing and optical mapping. (Red dotted line represents P value >0.05) (D) KEGG enrichment of genes only affected by metastases-specific structural variants. (Red dotted line represents adjusted P value >0.05). PT, primary tumor; LNM, lymph node metastases; TPV, tumor thrombus in pulmonary vein.

We further explored whether these SVs overlap with genes previously associated with tumorigenesis and progression (Figure 6B). Several private SVs of TPV detected by either WGS or optical mapping were associated with DNA repair genes including APEX2, FANCA, FANCB and RAD9A suggesting that mutations in DNA repair genes may play a role in progression of metastatic lung cancer by generating chromosomal instability. We also identified several EMT associated genes including BASP1, LAMA2, SAT1, SERPINH1 and TIMP1 were affected by SVs only detected in TPV. Completely different with TPV, only CSMD3, a frequently mutated gene in LUSC (47,48) was affected by private SVs of LNM. Loss of CSMD3 was reported to be associated with the proliferation of airway epithelial cells (47) and mutations in CSMD3 is associated with a better prognosis in patients with LUSC (48). Compared with the gene expression and survival data in The Human Protein Atlas (HPA) (39-41), we also identified 21 other genes affected by SVs previously unrecognized as tumor associated genes, of which expression was significantly associated with the prognosis of lung cancer patients (Figure 6C).

Furthermore, to comprehensively understand the functional consequence of genomic alterations only found in tumor cells in metastatic sites, we performed a KEGG enrichment analysis based on genes only affected by SNVs, indels and SVs in metastases (Figure 6D). Specifically, genes involved in the PI3K-Akt pathway which has an important role in tumorigenesis and progression (49), were significantly affected by variants in TPV.

Discussion

SNVs and CNVs detected by next-generation sequencing in multiregional tumors has improved our understanding of ITGH (8-10,46,50), while studies focusing on the analysis of ITGH in the form of SVs among tumor cells in primary and different metastatic sites are limited. Previous studies detected SVs through WGS (51,52). WGS, relying on sequencing by synthesis, is based on short reads. The DNA molecules are fragmented to countless reads and amplified by polymerase chain reaction (PCR), to meet the requirement of the high-throughput. And then we detect the SVs based on the read-pair or SR. That is, WGS detects the SVs on the basis of incomplete structure of DNA, which may miss some SVs in specific locations of chromosome or those with large size (53). In contrast, the integrity of DNA molecular is crucial for optical mapping to detect the SVs, with specific site labeled HMW DNA and nano-channel imaging system, optical mapping could de novo identify SVs without the bias of PCR amplification. Therefore, optical mapping and WGS could complement mutually.

To our knowledge, our study is the first study applying WGS and optical mapping to multiregional samples of a LUSC patient, aiming to compressively investigate the intratumoral heterogeneity within one patient. We do observe a significant difference in the variants burden between primary tumor and metastases and between metastases in different sites. Like SNVs and indels, SVs play an indispensable role in heterogeneity. Combination of WGS and optical mapping allows us to gain a more comprehensive understanding of structural variants, especially large SVs. Compared with the analysis of SVs detected by WGS, optical mapping were more informative in identifying private SVs for ITGH.

Variants shared between primary tumor and metastases indicate that mutations in primary tumor subclones with metastatic potential accumulated before metastasizing. Among them, mutations shared between TPV and PT which affect genes associated with tumorigenesis and progression, may enable tumor cells in the primary site to metastasize and live in hemato-microenvironment. Tumor cells harbor mutations identified both in PT and TPV may have more capability to metastasize and settle down in lymph node.

Meanwhile, private variants detected in different groups of tumors suggest genetic mutations occurred both before and after metastasis. Mutations unique to LNM or TPV indicate an interaction between tumor cells and microenvironment in metastatic sites. Private variants in TPV, especially those affected genes associated with DNA repair and epithelial-mesenchymal transition (EMT), are much more frequently identified than in PT or LNM. This suggests that tumor cells in hemato-microenvironment bear a higher degree of chromosomal instability and has more potential to act as a metastases relay station between primary tumor and metastases of distant organs, previously observed by Ferronika et al. (54).

It should be noted that the major limitation of our study is that analysis only based on one individual. The main reason is that most LUSC patients received surgery are at early stage and non-metastatic. In clinical practice, metastatic lymph node and tumor thrombus collected from the same patient in this study is rare to obtain by surgical resection. And biopsy sampling of multiple metastatic regions has not been widely accepted due to the potential risks for the prognosis of patients (55). Additionally, previous studies confirmed that analysis in a small number of cases even in one patient could reveal ITGH (6,10,15).

Notwithstanding its limitation, our results do demonstrate the ability of optical mapping in detection of large SVs to make up the deficiency of WGS and reveal that SVs are as crucial in describing ITGH as SNVs and indels.

Supplementary

The article’s supplementary files as

tlcr-09-03-670-coif.pdf (256.7KB, pdf)
DOI: 10.21037/tlcr-19-401

Acknowledgments

We thank the patient to provide the samples for this study; Litao Han and Ben Ma for advice to manuscript. We also thank Lili Tan for excellent technical assistance; Hainan Cheng for bioinformatics analysis.

Funding: This work was supported by Ministry of Science and Technology of the People’s Republic of China (2017YFA0505500; 2016YFA0501800), Science and Technology Commission of Shanghai Municipality (19XD1401300).

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Fudan University Shanghai Cancer Center Institutional Review Board (No. 090977-1) and written informed consent was obtained from all patients.

Footnotes

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tlcr-19-401). The authors have no conflicts of interest to declare.

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tlcr-09-03-670-coif.pdf (256.7KB, pdf)
DOI: 10.21037/tlcr-19-401

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