This article is mentioned in:

Introduction

Metastasis to distant organs is an acquired characteristic of cancer cells (1) and is a major cause of deaths from various human cancers. Metastatic progression from the primary tumor site involves multiple factors such as accumulation of genetic and epigenetic events and aquiring ability to colonize at the distant host organs (2). Although understanding the biology of primary tumors have been a main strategy to treat metastatic tumors, how these primary tumor cells evolve in the course of spreading to the mestatic organ is poorly understood.

Lung and colorectal cancer are the top and third most common cancers in the world, with over 1.6 million and 1.2 million new cases each year, respectively (3). Mutation status of EGFR, K-ras and EML4-ALK are important factors determining therapeutic regimens in non-small cell lung cancer (NSCLC) patients (4–5). Colon cancer is a genetically well-characterized human cancer. Since the multi-step carcinogenesis model was suggested, sequential genetic alterations such as mutations of APC, K-ras, Mismatch repair (MMRs), TP53 and loss of 18q have been reported in colorectal cancers (6,7). Genotype-phenotype correlations are also well-characterized in colorectal cancer. Notwithstanding these well-established genetic characteristics of colorectal cancer, the genetic mechanism causing metastasis to other organs is unclear. Liver and lung are the most common metastatic sites from colorectal cancer. Although metastasis to the lung from different primary tumors is a frequent event considering its dense connectivity with lymph nodes and blood vessels and its physical location, genetic alterations and underlying mechanisms would also affect its frequency and biological characteristics.

In this study, we describe a comprehensive genomic analysis of a metastatic colon cancer to the lung. Mutation analysis targeting whole exome is becoming prevalent and several papers describing somatic mutations at exome level have been published in primary lung and colorectal cancers (8–10). Nontheless, mutation or genetic information underlying metastatic colorectal cancer to the lung is very limited. To gain insights into genetic alterations associated with lung metastasis from colon cancer, we performed whole exome sequencing and genome-wide expression analysis in a patient with metastatic colon cancer to the lung. Among the identified mutation candidates, we chose 16 mutations for further validation using Ion Torrent PGM customized panel. Our pathway analyses identified that most of mutations were related with lung caner pathogenesis rather than a primary colon cancer, while gene expression signatures of the metastatic tumor were more correlated with the primary colon cancer. Undiscovered potential tumor supressor and oncogenes were also identified by combined exome sequencing and copy number analysis.

Materials and methods

Patient information

Samples were collected under the IRB approval (11-06107) granted by the CHR of the University of California San Francisco (UCSF) along with written informed consent from the patient. The tumor and matched normal lung tissue studied came from a 74-year-old Asian female never-smoker who had colorectal cancer 4 years prior to this incident. She visited the hospital for chest discomfort. A chest X-ray revealed a mass-like lesion in the right lung. Chest computed tomography (CT) scan showed a 2 cm sized tumor in the right upper lobe. A positron emission tomography (PET) scan showed hypermetabolic fluorodeoxyglucose uptake with no evidence of other metastasis. She was suspected to have a lung metastasis, because of her history of colon cancer. She underwent a wedge resection of the right upper lobe via thoracotomy and had an uneventful recovery after the operation. The pathologic examination confirmed the lesion was metastatic adenocarcinoma originating from the primary colon cancer. She lived for 1 1/2 years after the lung metastasis operation.

Whole exome sequencing

Whole exome sequencing was done on SOLiD 5500 (Life Technologies). Samples were prepared according to protocols suggested by the manufacturer. Fragment library preparation began with 3 μg DNA and sheared with the Covaris S220 system. Resulting DNA fragments were end repaired, size selected with Agencourt Ampure XP reagent, dA-tailed, adaptor and barcode ligated, and then amplified with 6 PCR cycles. The library was then quantified with Agilent Technologies High Sensitivity DNA chip. A total of 500 ng of DNA was used to capture the exome regions using TargetSeq Exome Enrichment kit, for about 72 h. Exome DNA was amplified and then quantified with the SOLiD Library TaqMan Quantitation kit. The final exome library was diluted to 500 pM, templated to beads, amplified through emulsion PCR and enriched using the SOLiD EZ Bead Emulsifier, Amplifier and Enricher. The resulting libraries were then loaded onto the Flow Chip with a total of 6 multiplexed samples in 6 lanes.

Ion Torrent AmpliSeq sequencing for validation

Sixteen mutation candidates from the exome sequencing were validated by deep sequencing (more than 1,000 x coverage) using the Ion Torrent PGM AmpliSeq Custom Panel for the selected targets. In brief, the 200 bp Standard DNA option was used for the AmpliSeq primer design. Sample DNA was diluted to a concentration of 10 ng/μl. A total of 1 μl of the diluted DNA was used for amplicon library preparation according to the Ion Torrent protocol. Target sequences were amplified using the custom primers, followed by a partial digestion of the primers. Adapters and barcodes were ligated to the amplicons and purified using Agencourt AMPure XP reagent. The library was quantified by qPCR using the Ion Library Quantitation kit according to the protocol. The libraries were combined, templated onto beads and run through emulsion PCR using the OneTouch2 and ES machines. Samples were loaded into the 318 chips and run on the Ion Torrent PGM.

Microarray gene expression analysis

We also measured the mRNA expression level of samples using Affymetrix GeneTitan Gene ST 1.1 array. The detailed protocol has been previously described (11). In short, total RNA was extracted from the matched tumor and normal tissues, amplified into cRNA, and then made into cDNA. The cDNA was then fragmented and labeled. The labeled cDNA was added into the hybridization cocktail. The samples were then put onto hybridization trays and loaded into the Affymetrix GeneTitan MC for hybridization, washing and scanning. The Log2-scale expression data were extracted using the built-in Robust Multi-array Average (RMA) algorithm in the Affymetrix software (12).

Data analysis: somatic mutation detections

For the whole exome sequencing performed by SOLiD 5500, the color space raw data (XSQ) were converted to sequences and aligned to reference human genome hg19 using LifeScope Genomic Analysis Software v2.5.1, creating a BAM file for each barcode in each lane. The paired-end targeted resequencing workflow of the software was used for the task, which also includes the DiBayes algorithm for Single Nucleotide Variation (SNV) detection. In the tumor tissue, 40,314 SNPs and 2,287 InDels were called, of which 37,456 and 1,990 were in concordance with dbSNP132 (92.91 and 80.03%, respectively). The transition/transversion ratio of the SNPs in the tumor was 2.60. In the adjacent normal tissue, 41,738 SNPs and 2,570 InDels were called, of which 39,069 and 2,096 were in concordance with dbSNP132 (93.61 and 81.56%, respectively). The transition/transversion ratio of the SNPs in the matched normal was 2.61.

BAM files for each barcode and each lane were merged into a single BAM file for every sample (the tumor and the matched normal) using Picard 1.87 (http://picard.sourceforge.net). We used Strelka (13) and MuTect (14) for small somatic mutation detections. Both algorithms adopt a Bayesian probability model comparing the tumor with its matched normal data, taken into consideration that tumor samples are often heterogeneous and impure. Default configurations were kept when using both software, e.g., prior probability of somatic mutation is 10−6. The mutation calls were output in a VCF (variant calling format) file. Each variant was annotated by ANNOVAR, which annotates the gene symbol, chromosome position and the type of variant (e.g., synonymous, missense, nonsense, etc.) (15).

Ion Torrent PGM generated fastq files were aligned to hg19 using the MAP2 alignment algorithm of Torrent Mapping Alignment Program (TMAP 3.4.0). The number of reference and novel allele counts for the 16 sites were tallied using the Samtools 0.1.18 mpileup function (16).

Copy number analysis

We used ExomeCNV, an R package, to conduct a copy number variation (CNV) analysis to the exome sequencing data (17). ExomeCNV infers copy number alteration based on the normalized Log2 ratio of the depth of coverage between the tumor and its matched normal tissue (17). The highest novel-to-reference (i.e., mutant to wild-type) allele ratio in our mutation data was about 1:2. If we assume that a mutation occurs in all tumor cells and none in normal cells, then in the tumor tissue, we expect a 50-50 ratio of novel/reference counts for heterozygous mutations. The extra reference counts would come from normal cell contamination, contributing for a total of 1/3 of the coverage. Hence, we estimated a normal contamination of 1/3 in this tumor sample. The estimated normal contamination value 0.3 was applied to the copy number analysis.

Copy number of the JAZF1 gene, identified as being amplified in whole exome sequencing data was chosen for further individual validation by TaqMan Copy Number assay (Life Technologies). Genomic DNAs from matched tumor and normal were amplified in ABI 7900HT according to the manufacturer’s instruction and analyzed by copy number analysis algorithm as previously described (18). Copy number of RNase P gene was simultaneously analyzed to use as an endogenous normalization contol. Additional genomic DNAs from 8 matched primary lung tumors and normal tissues were included.

Results

Identification of somatic mutations: a comparison of Strelka and MuTect

To retrieve somatic mutation calls from the whole exome sequencing data, Strelka (13) and MuTect (14) software was used. Strelka is a more stringent somatic mutation caller, identifying a total of 310 somatic variants including 135 non-synonymous variants. MuTect is more permissive, having identified a total of 7,341 somatic variants including 1,393 non-synonymous variants. Among the 135 identified non-synonymous candidate sites by Strelka, 100 of those non-synonymous variants were overlapped with MuTect’s calls (74.1% of concordance rate). Among the 310 total Strelka identified candidate sites, 221 of them were in common with MuTect’s calls (71.3% of the Strelka findings). On the other hand, well under 10% of MuTect calls were in common with Strelka. The mutation calls in common were much more likely to be true mutations, with an average novel allele (i.e., mutant) frequency of nearly 0.30, and novel allele counts of about 15. There was also no statistically significant difference between the variant and non-synonymous variants.

Pathway analysis using 71 high-confidence somatic mutations

Among 135 non-synonymous somatic mutation calls by Strelka, there were 70 mutation candidates with a somatic quality score (QSS) greater than 30, and a mutant allele frequency greater than 10% (Table I). We defined those 70 mutation calls as high-confidence mutations. In addition to those 70 high-confidence mutations, JAZF1 was a validated mutation with a QSS of 27 and mutant allele frequency of 25.2%, so a total of 71 genes were used for pathway analysis (Ingenuity Pathway Analysis, IPA) to get a general overview of their genetic pathways and functions (Table I). Perhaps unsurprisingly, due to the fact that those are somatic mutations from a metastatic colon cancer to the lung, the top two ‘Diseases and Disorders Networks’ were cancer and respiratory disease. The top 3 functional networks within Cancer were lung cancer (p=1.3×10−6, 35 genes), lung adenocarcinoma (p=5.4×10−6), and carcinoma in lung (p=7.7×10−6). Colon-related cancer network was not included in the top 10 networks (Table II).

Table I. Summary of 71 high-confidence mutations from whole exome sequencing.

Table I.

Summary of 71 high-confidence mutations from whole exome sequencing.

Gene	Chr	Exon	Amino acid change	Nucleotide change	QSS	Frequency	Copy no.	Exp-T	Exp-N
EIF4G3	chr1	exon9	I335V	c.1003A>G	118	0.34	2	6.81	6.66
C1orf173	chr1	exon12	G689R	c.2065G>A	140	0.32	2	2.05	2.3
BCAN	chr1	exon6	T287M	c.860C>T	31	0.3	2	2.29	2.5
SLC9C2	chr1	exon14	K552R	c.1655A>G	129	0.51	2	0	0
KLHL12	chr1	exon9	R416Q	c.1247G>A	52	0.3	2	5.58	5.29
ZNF512	chr2	exon14	R507W	c.1519C>T	39	0.35	1	5.17	5.47
THADA	chr2	exon36	V1752M	c.5254G>A	37	0.3	2	6.22	6.53
C2orf78	chr2	exon3	P860T	c.2578C>A	35	0.28	2	2.83	2.41
LONRF2	chr2	exon12	R743W	c.2227C>T	34	0.38	2	2.4	3.38
LRP1B	chr2	exon75	E3802V	c.11405A>T	35	0.3	3	2.21	2.1
SCN2A	chr2	exon27	R1902C	c.5704C>T	96	0.28	2	1.87	1.85
SCN7A	chr2	exon6	I194R	c.581C>G	68	0.29	2	3.36	7.28
SCRN3	chr2	exon8	L389F	c.1165C>T	108	0.31	2	4.89	5.14
COL4A3	chr2	exon24	A507S	c.1519G>T	44	0.31	2	3.21	5.16
CNTN4	chr3	exon17	G620R	c.1858G>A	158	0.76	2	2.28	3.29
ITIH4	chr3	exon6	R214W	c.640C>T	53	0.33	2	3	3.53
MYLK	chr3	exon12	A524T	c.1570G>A	81	0.33	2	5.08	7.64
CPNE4	chr3	exon9	K269T	c.806A>C	80	0.32	3	2.47	2.28
EPHB1	chr3	exon11	R691Q	c.2072G>A	145	0.45	2	3.34	2.93
RBPJ	chr4	exon12	E478K	c.1432G>A	61	0.24	2	6.62	6.78
GRID2	chr4	exon11	T601M	c.1802C>T	120	0.46	2	2.01	2.11
ADCY2	chr5	exon17	T714A	c.2140A>G	98	0.47	2	3.34	3.33
RAD17	chr5	exon6	M200T	c.599T>C	72	0.26	2	4.79	4.91
APC	chr5	exon16	Q767X	c.2299C>T	39	0.31	2	4.57	5.02
SEMA6A	chr5	exon3	H44R	c.131A>G	79	0.2	2	3.94	5.8
ATP10B	chr5	exon26	E1360K	c.4078G>A	46	0.31	2	5.93	3.3
SERPINB6	chr6	exon7	P239L	c.716C>T	50	0.45	2	6.9	7.2
ROS1	chr6	exon40	F2103S	c.6308T>C	37	0.35	2	6.47	6.65
TMEM244	chr6	exon5	L126M	c.376T>A	94	0.27	3	0	0
SOSTDC1	chr7	exon1	I31S	c.92T>G	47	0.29	3	1.97	3.53
DNAH11	chr7	exon68	R3663H	c.10988G>A	35	0.28	3	2.64	2.95
JAZF1	chr7	exon3	R71Q	c.212G>A	27	0.19	3	4.95	5.56
CNOT7	chr8	exon6	R220W	c.658C>T	58	0.64	1	7.47	7.64
PDE7A	chr8	exon8	L228M	c.682C>A	51	0.27	3	3.75	4.59
ENY2	chr8	exon4	D65G	c.194A>G	42	0.23	3	7.4	6.71
SLC24A2	chr9	exon1	I124N	c.371T>A	34	0.27	2	2.5	2.47
RORB	chr9	exon4	V146I	c.436G>A	40	0.46	2	2.67	2.92
TSC1	chr9	exon5	L120F	c.358C>T	33	0.32	2	5.34	5.69
ZNF438	chr10	exon7	Y582C	c.1745A>G	96	0.34	2	3.52	4.17
TDRD1	chr10	exon20	T933M	c.2798C>T	42	0.33	2	2.22	2.24
TTC40	chr10	exon9	A311V	c.932C>T	54	0.45	2	0	0
OR52K2	chr11	exon1	Y254X	c.762C>A	108	0.3	2	2.19	2.15
ARHGEF17	chr11	exon19	V1903M	c.5707G>A	30	0.19	2	4.52	5.76
OR10G9	chr11	exon1	V7M	c.19G>A	115	0.29	2	2.29	2.17
CHD4	chr12	exon22	G1098D	c.3293G>A	40	0.25	2	7.8	7.44
LRP6	chr12	exon13	T933M	c.2798C>T	44	0.35	2	6.42	7.02
KRAS	chr12	exon2	G12A	c.35G>C	57	0.24	2	6.15	5.89
KRT71	chr12	exon5	R305C	c.913C>T	60	0.18	2	3.41	3.05
HSP90B1	chr12	exon10	R414H	c.1241G>A	219	0.35	2	10.1	9.68
MYCBP2	chr13	exon6	Y377H	c.1129T>C	112	0.29	3	6.46	6.96
OR4M1	chr14	exon1	C141F	c.422G>T	189	0.24	1	1.75	1.8
SAMD4A	chr14	exon2	S112F	c.335C>T	93	0.52	1	4.17	5.77
GNB5	chr15	exon5	C159Y	c.476G>A	105	0.58	2	4.32	4.67
VWA9	chr15	exon10	R379C	c.1135C>T	150	0.63	2	0	0
ADCY9	chr16	exon10	S948L	c.2843C>T	49	0.2	2	4.94	6.48
SRCAP	chr16	exon16	G813D	c.2438G>A	69	0.26	2	6.06	5.99
ADAMTS18	chr16	exon3	S154P	c.460T>C	31	0.17	2	2.45	3.54
SLC5A10	chr17	exon5	R127H	c.380G>A	63	0.42	1	3.86	3.43
MYOM1	chr18	exon30	K1393N	c.4179A>T	41	0.39	1	2.45	2.68
ARMC6	chr19	exon5	R277C	c.829C>T	36	0.32	2	5.45	4.83
RYR1	chr19	exon1	A4T	c.10G>A	36	0.42	2	2.75	2.95
ATP1A3	chr19	exon9	A379T	c.1135G>A	114	0.35	1	3.39	2.65
ZNF285	chr19	exon4	P455Q	c.1364C>A	36	0.12	2	2.49	2.51
MYBPC2	chr19	exon12	D415G	c.1244A>G	30	0.19	2	3.27	2.41
ACTR5	chr20	exon4	R313W	c.937C>T	75	0.44	3	5.22	4.2
TMPRSS15	chr21	exon22	R871X	c.2611C>T	84	0.34	1	1.51	1.61
DEPDC5	chr22	exon28	T864M	c. 2591C>T	58	0.3	2	4.99	5.04
GPKOW	chrX	exon2	E68D	c.204A>C	54	0.24	2	6.69	5.76
TENM1	chrX	exon21	A1242V	c.3725C>T	51	0.2	2	0	0
SLITRK4	chrX	exon2	S34X	c.101C>A	64	0.27	2	3.02	2.92
HDAC6	chrX	exon11	F.S.	c.918_919del	31	0.32	2	5.31	4.93

[i] Chr, chromosome; QSS, the Strelka reported somatic quality score in Phred scale; Exp-T, mRNA expression of the tumor; Exp-N, mRNA expression of the normal tissue; F.S., frameshift mutation.

Table II. Ingenuity pathway analysis (IPA) of the high-confidence mutations.

Table II.

Ingenuity pathway analysis (IPA) of the high-confidence mutations.

Rank	Functions annotation	P-value	Genes
1	Lung cancer	1.3×10−6	ADCY2, ADCY9, APC, ATP1A3, BCAN, C1orf173, C2orf78, CHD4, CNTN4, DEPDC5, DNAH11, GPKOW, GRID2, HDAC6, HSP90B1, KRAS, LRP1B, MYBPC2, MYCBP2, MYLK, OR10G9, RORB, RYR1, SAMD4A, SCN2A, SCN7A, SEMA6A, SLC24A2, SLC9C2, SRCAP, TENM1, THADA, TSC1, ZNF285, ZNF438
Rank	Ingenuity canonical pathways	P-value	Genes
1	Phospholipase C signaling	2.2×10−4	HDAC6, ADCY9, ADCY2, GNB5, ARHGEF17, KRAS
2	Molecular mechanisms of cancer	2.5×10−4	ADCY9, ADCY2, LRP6, RBPJ, ARHGEF17, KRAS, APC
3	Colorectal cancer metastasis signaling	2.6×10−4	ADCY9, ADCY2, LRP6, GNB5, KRAS, APC

The top 3 IPA Canonical Networks were ‘Phospholipase C Signaling’ (a total of 263 genes in this network, 6 of which are our somatic mutation submissions, or 6/263, p=2.2×10−4), ‘Molecular Mechanisms of Cancer’ (7/381, p=2.5×10−4), and ‘Colorectal Cancer Metastasis Signaling’ (6/262, p=2.6×10−4) (Table II). Knowing this tumor was a metastatic colon cancer, the Colorectal Cancer Metastasis Signaling network was of particular interest. The 6 genes (with somatic mutations in our sample) involved in Colorectal Cancer Metastasis Signaling were ADCY2, ADCY9, APC, GNB5, K-ras and LRP6. Aside from APC and K-ras which are known cancer markers, ADCY2, ADCY9, GNB5 and LRP6 also appear in Phospholipase C Signaling and Molecular Mechanisms of Cancer networks, indicating their potential role in colon cancer metastasis to the lung.

Mutation validation by targeted high coverage deep sequencing

We selected 16 candidates for deep sequencing validation by Ion Torrent PGM. Out of 16 candidates, 9 were either kinases or genes identified in the COSMIC (Catalog of Somatic Mutations in Cancer) Cancer Gene Census (http://cancer.sanger.ac.uk/cancergenome/projects/census/). For the remaining 7, all but 1 mutation calls were identified by both Strelka and MuTect.

Out of 16 mutation candidates, 15 were sucessfully amplified by targeted ampliseq panel (Ion Torrent PGM) and 13 out of 15 (87%) were validated as true mutations (Table III). For the validated mutations, the mutant allele counts ranged from several hundreds to over a thousand in the tumor, and less than 10 in the matched normal tissue (average coverage >2,000X). The 13 validated mutations include two mutations in APC and K-ras, well-known genes frequently mutated in colon cancers, three nonsense mutations in OR52K2, TMPRSS15, and SLITRK4, one frameshift deletion in HDAC6, and seven missense mutations in EIF4G3, MYLK, EPHB1, ROS1, JAZF1, TSC1 and C1orf173.

Table III. The mutations validated by customized ultra-high coverage sequencing.

Table III.

The mutations validated by customized ultra-high coverage sequencing.

Gene symbol	Nucleotide change	Amino acid change	Mut/wild counts exome		Mut/wild counts PGM		Microarray expression		QSS	CNV
			T	N	T	N	T	N
EIF4G3	c.1003A>G	I335V	30/58	0/108	778/1,963	5/2,536	6.81	6.66	118	2
C1orf173	c.2065G>A	G689R	36/76	1/144	833/1,430	7/2,063	2.05	2.30	140	2
MYLK	c.1570G>A	A524T	22/44	0/72	869/1,652	6/2,579	5.08	7.64	81	2
EPHB1	c.2072G>A	R691Q	35/43	0/91	976/1,714	2/2,507	3.34	2.93	145	2
APC	c.2299C>T	Q767X	8/18	0/37	580/1,294	3/1,622	4.57	5.02	39	2
ROS1	c.6308T>C	F2103S	8/15	0/32	492/1,071	0/1,386	6.47	6.65	37	2
JAZF1	c.212G>A	R71Q	6/26	0/39	587/1,738	7/1,894	4.95	5.56	27	3
TSC1	c.358T>T	L120F	8/17	0/30	504/983	2/1,302	5.34	5.69	33	2
OR52K2	c.762C>A	Y254X	27/64	1/129	828/1,765	5/2,420	2.19	2.15	108	2
KRAS	c.35G>C	G12A	12/37	0/83	536/1,019	2/1,588	6.15	5.89	57	2
TMPRSS15	c.2611C>T	R871X	16/31	1/93	353/1,115	2/1,962	1.51	1.61	84	1
SLITRK4	c.101C>A	S34X	22/61	1/96	1,102/2,656	8/3,236	3.02	2.92	64	2
HDAC6	c.918_919del	F.S.	7/15	0/33	864/1,731	2/2,340	5.31	4.93	31	2

[i] Exome, exome data; PGM, customized validation sequencing result using the Ion Torrent PGM; Mut/wild counts, mutation to wild-type counts in tumor and matched normal tissue; T, tumor; N, normal tissue; QSS, the Strelka reported somatic quality score in Phred scale; CNV, copy number variation; F.S., frameshift mutation.

Copy number analysis of genes mutated in metastatic colon cancer to the lung

We performed a copy number analysis using whole exome sequencing data in order to have additional information on the genes mutated in metastastic colon cancer to the lung. The Copy Number Variation (CNV) calls for the 71 high-confidence and 13 validated mutation genes are shown in Tables I and III, respectively. Among the genes mutated and either amplified or deleted, copy number of JAZF1 harboring a missense mutation and being amplified in a metastatic colon tumor was further validated by TaqMan copy number assay. In accordance with the CNV information from whole exome sequencing data, the validation assay confirmed that JAZF1 was highly amplified in a metastastic colon cancer to the lung (Fig. 1).

Figure 1. A validated somatic mutation and copy number of JAZF1 from the exome sequencing in a metastatic colon cancer to the lung. (A) A missense mutation of JAZF1 (c.212G>A or R71Q) is shown by Integrative Genomics Viewer (IGV) (reverse strand). Six out of 26 total coverage were the mutant call in tumor and none out of 39 were the mutant call in normal. (B) Validation of copy number analysis of JAZF1 by TaqMan Copy Number assay. Copy number of genomic DNA from a metastatic colon tumor to the lung (223T, white bar) showed high amplification of the JAZF1 gene (copy number, 4.3). Additional DNA samples from matched primary lung tumor and normal tissues were simutaneously analyzed (black bars).

Genes differentially expressed in metastatic colon cancer to the lung and their network analysis

Of more than 20,000 genes in the Gene ST 1.1 array, 3,231 genes showed 2-fold differential expression between the metastatic tumor and the matched normal tissues. There were 786 genes with at least a 4-fold difference, and 247 genes with at least an 8-fold difference (Table IV). We entered these genes in 3 separate IPA analyses (analysis with the 3,231, 786 and 247 genes, respectively). In each case, the top ‘Disease and Disorder Network’ was Cancer category. Interestingly, unlike the networks of mutation signatures earlier described, top-ranked expression signatures of this metastatic tumor, in each analysis, was Gastrointestinal Disease followed by Respiratory Disease in the Disease and Disorder Network. Within the Cancer network, colorectal cancer, intestinal cancer and digestive tumor all rank above lung cancer. This analysis suggested that expression profiles of metastatic colon cancer to the lung represents more their primary tumor site (colon) retaining its initial characteristics of tumor development.

Table IV. A total of 247 genes with over 8-fold differential expression between normal and metastatic colon cancer to the lung.

Table IV.

A total of 247 genes with over 8-fold differential expression between normal and metastatic colon cancer to the lung.

No.	Probe set ID	Gene	N	T	T-N
1	7969288	OLFM4	3.2	11.3	8.1
2	8029086	CEACAM5	2.9	10.7	7.8
3	8151795	CDH17	2.0	9.5	7.6
4	8135033	MUC12	3.0	10.2	7.2
5	7979658	GPX2	2.7	9.6	7.0
6	8173979	NOX1	2.4	9.2	6.9
7	8090180	MUC13	2.2	9.0	6.8
8	8053654	FABP1	1.8	8.5	6.7
9	8114964	SPINK1	2.3	9.0	6.7
10	8036591	LGALS4	3.5	10.0	6.6
11	7951297	MMP12	2.1	8.5	6.4
12	8135031	MUC12	3.8	10.0	6.2
13	8151730	CALB1	1.8	8.0	6.1
14	7944164	TMPRSS4	2.6	8.5	5.8
15	8015124	KRT20	2.6	8.1	5.6
16	8048319	VIL1	2.6	8.2	5.5
17	8173869	POF1B	2.6	8.1	5.5
18	8135048	MUC17	2.4	7.8	5.4
19	7928766	C10orf99	2.7	8.1	5.4
20	8037053	CEACAM7	2.1	7.5	5.4
21	8026490	UCA1	2.3	7.7	5.4
22	8142171	SLC26A3	1.5	6.8	5.3
23	7984001	GCNT3	3.1	8.3	5.2
24	7961413	C12orf36	1.8	6.8	5.0
25	7915472	SLC2A1	5.3	10.3	5.0
26	8063590	PCK1	2.2	7.2	4.9
27	8062823	HNF4A	3.0	7.9	4.9
28	8132270	NPSR1	2.6	7.4	4.9
29	8120088	MEP1A	2.2	7.0	4.8
30	8178070	LY6G6D	2.5	7.3	4.8
31	8179309	LY6G6D	2.5	7.3	4.8
32	7934898	ANKRD22	2.8	7.6	4.7
33	8092726	CLDN1	3.9	8.6	4.7
34	7962212	PKP2	3.7	8.4	4.7
35	8162884	ALDOB	2.2	6.8	4.7
36	8070567	TFF3	4.7	9.3	4.7
37	7983239	CKMT1A	3.5	8.2	4.6
38	7983256	CKMT1A	3.5	8.2	4.6
39	8038683	KLK6	2.3	7.0	4.6
40	7901175	TSPAN1	4.8	9.4	4.6
41	7918694	BCL2L15	1.9	6.5	4.6
42	8064904	FERMT1	2.2	6.8	4.6
43	7961455	GUCY2C	2.1	6.6	4.5
44	8081488	HHLA2	2.5	7.0	4.5
45	8115623	ATP10B	3.5	8.0	4.5
46	8015133	KRT23	2.8	7.2	4.5
47	8061579	TPX2	2.8	7.3	4.4
48	8171449	ACE2	3.3	7.7	4.4
49	7956229	SLC39A5	2.6	7.0	4.4
50	7922029	GPA33	4.1	8.5	4.4
51	8175747	MAGEA3	2.3	6.7	4.4
52	8048864	CCL20	2.0	6.4	4.3
53	8177222	CD24	4.6	8.9	4.3
54	8099476	PROM1	2.4	6.6	4.3
55	8124527	HIST1H1B	4.5	8.8	4.3
56	8140367	CCL24	3.4	7.7	4.2
57	8058091	SATB2	4.0	8.2	4.2
58	8122202	MYB	3.1	7.3	4.2
59	7912659	AGMAT	2.7	6.9	4.2
60	7970810	SLC7A1	4.7	8.9	4.2
61	7997962	DPEP1	5.6	9.8	4.2
62	8016994	RNF43	3.8	7.9	4.1
63	7945169	TMEM45B	5.3	9.4	4.1
64	8140140	CLDN3	4.3	8.4	4.1
65	7964927	TSPAN8	6.2	10.3	4.1
66	8020762	DSG3	1.8	5.9	4.1
67	8006865	PPP1R1B	3.9	7.9	4.1
68	8001082	SLC6A8	4.0	8.0	4.0
69	7995292	SLC6A8	4.3	8.3	4.0
70	8158167	LCN2	3.0	7.0	4.0
71	7905918	EFNA3	2.7	6.7	4.0
72	8170580	CSAG2	2.2	6.2	4.0
73	8175710	CSAG2	2.2	6.2	4.0
74	8166723	XK	2.3	6.3	4.0
75	8139640	DDC	2.9	6.8	4.0
76	7898809	EPHB2	4.7	8.7	4.0
77	7946903	USH1C	2.4	6.4	3.9
78	8132031	PRR15	4.3	8.2	3.9
79	7983650	SLC27A2	2.2	6.1	3.9
80	8088425	FAM3D	3.4	7.3	3.9
81	8037205	CEACAM1	3.4	7.3	3.9
82	8151032	GGH	4.4	8.3	3.9
83	8120335	FAM83B	2.4	6.3	3.8
84	7970727	CDX2	3.4	7.2	3.8
85	8135037	MUC12	1.9	5.7	3.8
86	7937612	MUC5B	2.8	6.6	3.8
87	7950534	WNT11	3.3	7.1	3.8
88	8096301	SPP1	6.7	10.4	3.8
89	7985213	CHRNA5	2.1	5.9	3.7
90	8067167	AURKA	2.3	6.1	3.7
91	8068684	FAM3B	3.7	7.4	3.7
92	7957140	LGR5	2.8	6.5	3.7
93	8136709	LOC93432	1.9	5.6	3.7
94	8118242	LY6G6D	2.3	6.0	3.7
95	8136801	TRY6	2.6	6.4	3.7
96	8083146	PLS1	4.3	8.0	3.7
97	8136807	PRSS2	3.0	6.7	3.7
98	8141328	CYP3A5	3.4	7.1	3.7
99	7994109	PLK1	3.4	7.0	3.7
100	8031999	PPAP2C	4.4	8.0	3.7
101	7988350	DUOX2	2.4	6.0	3.6
102	7937016	CLRN3	2.3	6.0	3.6
103	7964316	MYO1A	2.3	6.0	3.6
104	8081925	NR1I2	1.9	5.6	3.6
105	8009517	SOX9	4.9	8.6	3.6
106	7903565	GPSM2	4.7	8.3	3.6
107	8155083	CA9	4.1	7.7	3.6
108	8097017	UGT8	2.3	5.9	3.6
109	8146986	HNF4G	1.8	5.4	3.6
110	8040374	FAM84A	3.8	7.4	3.6
111	8059525	TM4SF20	1.7	5.3	3.6
112	8168146	KIF4A	2.5	6.0	3.6
113	8137271	ABP1	2.4	5.9	3.5
114	7962183	AK3L1	3.6	7.1	3.5
115	7919055	HMGCS2	3.1	6.6	3.5
116	8030094	FUT2	2.6	6.1	3.5
117	7930593	C10orf81	2.0	5.5	3.5
118	8093950	S100P	3.1	6.6	3.5
119	8015016	TNS4	2.4	5.9	3.5
120	7901748	FGGY	4.7	8.2	3.5
121	7933640	A1CF	2.0	5.5	3.5
122	7923347	LAD1	4.1	7.6	3.5
123	8154848	PRSS3	2.6	6.1	3.5
124	8062766	MYBL2	3.3	6.7	3.5
125	8124707	TRIM31	2.3	5.7	3.4
126	8117426	HIST1H2BH	5.1	8.6	3.4
127	8072587	SLC5A1	3.5	6.9	3.4
128	7945204	ST14	5.9	9.3	3.4
129	7937020	MKI67	3.1	6.5	3.4
130	8092000	TERC	5.5	8.9	3.4
131	8124394	HIST1H2BB	2.7	6.1	3.4
132	8179617	TRIM31	2.5	5.9	3.4
133	8117594	HIST1H2BM	4.9	8.3	3.4
134	7983478	C15orf48	3.6	7.0	3.4
135	8178330	TRIM31	2.1	5.5	3.4
136	8138381	AGR2	4.9	8.2	3.3
137	8041853	EPCAM	7.4	10.8	3.3
138	8016476	HOXB9	2.8	6.1	3.3
139	8066258	SNORA71A	6.4	9.7	3.3
140	7993815	ANKS4B	2.3	5.6	3.3
141	8086607	LTF	5.1	8.4	3.3
142	8014974	TOP2A	3.9	7.2	3.3
143	8018774	ST6GALNAC1	2.8	6.0	3.3
144	7936144	COL17A1	3.1	6.4	3.3
145	8135915	C7orf68	3.9	7.1	3.3
146	7927998	HKDC1	2.8	6.1	3.2
147	8106689	CKMT2	3.8	7.1	3.2
148	8066260	SNORA71C	7.0	10.2	3.2
149	8028991	CYP2S1	4.9	8.1	3.2
150	8132318	ANLN	3.1	6.3	3.2
151	8115455	HAVCR1	2.9	6.1	3.2
152	8098439	EPCAM	7.1	10.3	3.2
153	7983393	SORD	4.1	7.4	3.2
154	8035083	CYP4F2	2.3	5.5	3.2
155	8171161	ARSE	4.0	7.2	3.2
156	8117395	HIST1H2BF	3.7	6.8	3.2
157	7996819	CDH3	4.0	7.1	3.1
158	7941401	OVOL1	2.5	5.6	3.1
159	7989501	CA12	4.4	7.5	3.1
160	8015806	ETV4	3.6	6.7	3.1
161	7945321	GLB1L2	3.6	6.7	3.1
162	8167973	HEPH	4.1	7.2	3.1
163	8046488	CDCA7	3.0	6.1	3.1
164	8120838	TTK	2.2	5.3	3.1
165	8102523	FABP2	1.4	4.5	3.1
166	7969544	NDFIP2	5.4	8.5	3.1
167	8109629	FABP6	3.9	7.0	3.1
168	7969428	UCHL3	6.1	9.2	3.1
169	7969414	KLF5	6.3	9.3	3.1
170	8013536	NOS2	3.2	6.3	3.1
171	8062728	SGK2	2.5	5.6	3.1
172	7929334	CEP55	3.1	6.1	3.1
173	8170553	MAGEA6	2.8	5.9	3.1
174	8003204	GINS2	4.0	7.0	3.1
175	8108629	VTRNA1-2	2.6	5.7	3.0
176	7914592	TMEM54	5.4	8.4	3.0
177	7918394	EPS8L3	2.9	6.0	3.0
178	7983969	CCNB2	3.2	6.3	3.0
179	7928770	CDHR1	2.7	5.7	3.0
180	8016387	PRR15L	4.6	7.6	3.0
181	8138749	HOXA9	2.6	5.6	3.0
182	8107769	SLC12A2	6.6	9.6	3.0
183	8170992	SNORA56	2.2	5.2	3.0
184	7907271	FMO2	8.7	5.7	−3.0
185	8175016	APLN	6.1	3.1	−3.0
186	8007420	AOC3	7.9	4.9	−3.0
187	8055323	NCKAP5	6.9	3.9	−3.0
188	7903227	PALMD	7.7	4.6	−3.0
189	7917850	ARHGAP29	7.6	4.6	−3.0
190	8017964	ABCA6	6.9	3.9	−3.0
191	8036151	HSPB6	6.5	3.5	−3.0
192	7974902	RHOJ	8.5	5.5	−3.0
193	8041644	PLEKHH2	7.3	4.2	−3.1
194	8007701	HIGD1B	7.6	4.5	−3.1
195	8152297	ANGPT1	6.4	3.2	−3.1
196	8155734	FAM189A2	7.2	4.0	−3.1
197	8125341	AGER	10.6	7.5	−3.1
198	8012475	MYH10	8.4	5.3	−3.1
199	8017885	ABCA8	6.0	2.9	−3.1
200	8101675	ABCG2	6.5	3.3	−3.1
201	8179967	AGER	10.5	7.3	−3.1
202	8135594	CAV1	10.7	7.5	−3.1
203	8097080	SYNPO2	6.7	3.5	−3.2
204	8178771	AGER	10.6	7.4	−3.2
205	8101957	EMCN	7.7	4.5	−3.2
206	8134257	GNG11	9.0	5.8	−3.2
207	8146794	PREX2	7.6	4.4	−3.2
208	8082597	COL6A6	6.7	3.5	−3.2
209	8092970	APOD	7.8	4.6	−3.2
210	7917182	ELTD1	8.0	4.8	−3.2
211	7968789	C13orf15	9.5	6.3	−3.2
212	8057506	FRZB	6.8	3.6	−3.2
213	8069676	ADAMTS1	8.0	4.7	−3.3
214	8055952	NR4A2	8.3	5.0	−3.3
215	8094301	SLIT2	8.5	5.2	−3.3
216	7934979	ANKRD1	7.7	4.4	−3.3
217	8170119	FHL1	8.4	5.1	−3.3
218	7980908	FBLN5	9.0	5.7	−3.3
219	8175531	CDR1	9.5	6.1	−3.3
220	7960464	VWF	8.8	5.4	−3.4
221	7923034	B3GALT2	6.2	2.8	−3.4
222	8174513	CHRDL1	7.6	4.2	−3.4
223	8111677	LIFR	8.3	4.8	−3.4
224	8091402	TM4SF18	8.3	4.9	−3.4
225	8089145	ABI3BP	9.0	5.6	−3.4
226	7933855	RTKN2	8.6	5.2	−3.5
227	7964722	WIF1	7.6	4.1	−3.5
228	7946579	LYVE1	8.6	5.1	−3.5
229	8151532	FABP4	7.4	3.9	−3.5
230	8052753	GKN2	6.4	2.9	−3.5
231	8105084	C7	10.2	6.6	−3.5
232	8152522	ENPP2	10.4	6.9	−3.6
233	8162373	OGN	6.6	3.0	−3.6
234	8029693	FOSB	9.1	5.5	−3.6
235	7922130	DPT	8.3	4.7	−3.6
236	8013341	MFAP4	8.6	5.0	−3.7
237	7908312	PRG4	8.3	4.6	−3.7
238	8132092	INMT	10.0	6.2	−3.7
239	7971150	LHFP	9.5	5.8	−3.7
240	8155754	MAMDC2	7.4	3.6	−3.8
241	8101881	ADH1B	8.1	4.3	−3.8
242	8163202	SVEP1	7.1	3.3	−3.8
243	8056518	SCN7A	7.2	3.4	−3.9
244	8171427	FIGF	8.2	4.2	−4.0
245	7921690	ITLN1	7.7	3.6	−4.0
246	8096415	MMRN1	8.6	4.3	−4.3
247	8149071	ANGPT2	8.0	3.7	−4.3

[i] N, mRNA expression of the normal tissue (log₂ scale); T, mRNA expression of the tumor tissue (log₂ scale); T-N, differential expression between tumor and normal tissues.

Discussion

In this study, a comprehesive genomic analysis was performed on a patient with metastatic colon tumor to the lung. Whole exome sequencing and genome-wide expression analysis revealed characteristics of both somatic mutations and genes differentially expressed aquired during primary tumor progression to the metastasis. Exome analysis covers over 38 million base pairs. In exome screening, we found hundreds of mutation candidates, with varying confidence scores and allele frequencies. Thus, it is critical to know by experimentation what cutoff level gives a high-confidence threshold. To achieve this, we needed orthogonal method different from the original platform, to eliminate the possibility of false positives created by any platform specific artifacts. Thus, customized deep-sequencing panel was further designed and this allowed us to get validated mutations both in a quantitative and qualitiative way.

We did pathway analyses using both mutation and gene expression information from the same metastatic tumor. Interestingly, mutation data more closely reflected a characteristic of lung cancer (metastasized site) while gene expression data showed signatures related with colorectal cancer (primary cancer). There was only one colorectal cancer-related network by mutations. Six genes (ADCY2, ADCY9, APC, GNB5, K-ras and LRP6) were identified to be associated with colorectal cancer metastasis. ADCY2, ADCY9, GNB5 and LRP6 were also involved in Phospholipase C Signaling. It was reported that Phospholipase C ɛ (PLCE1) inhibited proliferation of colorectal cancer (19). Reduced expression of PLCD1 and PLCE1 was also reported in colorectal cancer biopsies and cell lines (20). Thus, it seems that genes involved in Phospholipase C Signaling are playing an important role in colorectal cancer progression and metastasis.

While the mutation signatures of the metastatic tumor were more closely related with lung cancer, differentially expressed genes with a significant change seemed to be more associated with primary colon cancer. The most significant networks by gene expression analysis were gastrointestinal and colorectal cancer groups (8.78×10−26<p<1.37×10−20). This suggests that metastatic tumors still preserved its original mRNA gene expression pattern while tumor cells acquired new mutations in a metastatic site. Out of 13 validated mutations, only APC seemed to be clearly related with colon cancer development. We could not investigate further due to unavailability of the primary tumor samples whether the identified K-ras mutation came from its primary colon cancer or metastatic tumor to the lung.

In addition to mutation and gene expression analysis, we also performed copy number analysis from the exome data. Copy number alteration is a common genetic variation in the human genome (21). Traditionally, it has been difficult to estimate the copy number of a gene based on the regional coverage in exome sequencing, because different exome regions have different capture efficiencies, leading to different base coverage in different chromosome regions. However, the ExomeCNV model (17) assumes that the same region of two different samples, due to their similarity (nearly identical) in sequences, have the same capture efficiency. However, the results would be different if the tumor tissue is mixed with normal cells. The specificity for CNV calls inevitably decreases if the tumor tissue is contaminated with a large portion of normal cells. In our case, we have relatively low normal tissue contamination rate, 0.3. Validation assay of the JAZF1 gene using TaqMan copy number assay showed a good correlation between the exome copy number analysis and Taqman data. This suggests that whole exome sequencing data can be used to generate mutation as well as copy number information.

We identified a missense mutation at codon 71 (R71Q) in JAZF1, its gain-of-copy being validated. JAZF1 encoding a nuclear protein with three zinc finger domains is a well-characterized genetic susceptibility gene for type II diabetes (22–23) and lupus erythematosus (24). Various types of fusions such as JAZF1-SUZ12, JAZF1-JJAZ1 and JAZF1-PHF1 have been reported in ESS (endometrial stromal sarcoma) (25). Although variations of JAZF1 have been involved in many diseases, mutations in the JAZF1 gene have barely been described in the literature. Our copy number analysis showed that this gene was amplified in the tumor. In lung cancer, EML4-ALK oncogenic fusion was first identified in 2007 (26), and its inhibitor targeting MET-ALK (crizotinib) was approved for treatment of patients with EML4-ALK. Based on the high frequency of JAZF1 fusions in another type of cancer and our amplification data in a metastatic tumor to the lung, JAZF1 may function as an oncogene leading to lung metastasis from colon cancer. Further functional studies are required to validate roles of JAZF1 in tumor progression and a lung metastasis from colon cancer.

TMPRSS15 (transmembrane protease, serine 15) had a nonsense mutation (R871X). TMPRSS15 is an enteropeptidase or an enterokinase activating pancreatic trypsin for releasing digestive enzyme (27). Interestingly, nonsense and frameshift mutations of TMPRSS15 (enteropeptidase/enterokinase) were found in families with congenital enteropeptidase deficiency (27). One of the reported nonsense mutations occurred at codon 857 (R857X), which is close to the nonsense mutation site (R871X) of our patient. This suggests that the region containing codons 857 and 871 may be susceptible for stop-causing mutation.

ACTR5 [ARP5 actin-related protein 5 homolog (yeast)] is another noteworthy gene (Table I). There are scarce data on the function of this gene in human cancer. In our data, ACTR5 has a missense mutation and an amplification. The mRNA expression was also higher by more than 2-fold in tumor (log2 value = 5.22) than normal (log2 value = 4.2). Although no information is available for this gene in either lung or colon cancers at this time, a novel missense mutation that is highly expressed and amplified suggests that this gene could also be important in either colon or lung cancer development and progression.

Like ALK and JAZF1, a rearrangement or a fusion is common in ROS1 ranging from 1 to 3% of NSCLC (28,29). It was recenlty reported that a secondary missense mutation at codon 2032 (G2032R) of ROS1 was involved in a resistance to crizotinib in a lung cancer patient with CD74-ROS1 (30). Our mutation (F2103S) is located near the identified G2032R and the L2026 gatekeeper residue of ROS1. Moreover, based on the crystal structure model by Awad et al (30), the F2103 seems to be a critical residue for crizotinib binding. We confirmed that our patient did not receive crizotinib treament. Thus, it will be meaningful to investigate the characteristics of the ROS1 mutations in lung or colon cancer patients with and without crizotinib treatment.

Taken together, we performed whole exome analysis in addition to the copy number and genome-wide expression analysis in a metastatic tumor. Pathway analyses of the genomic information identified different enrichment of mutation and gene expression levels in a metastatic colon cancer to the lung. Furthermore, ultra-high coverage NGS sequencing (>1,000X) confirmed the accuracy of exome sequencing, and have led to potentially novel cancer-associated genes which may lead to a metastasis from a primary organ.

Acknowledgements

This study was supported by the Barbara Isackson Lung Cancer Research Fund (D.J.), the Eileen D. Ludwig Endowed Fund for Thoracic Oncology Research (D.J.), Uniting Against Lung Cancer (UALC) (I.-J.K.), and Mesothelioma Applied Research Foundation (MARF) (I.-J.K.).

References