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Whole genome sequencing reveals potential targets for therapy in patients with refractory KRASmutated metastatic colorectal cancer

  • Vijayalakshmi Shanmugam1,
  • Ramesh K Ramanathan1, 2,
  • Nicole A Lavender1,
  • Shripad Sinari1,
  • Manpreet Chadha2,
  • Winnie S Liang1,
  • Ahmet Kurdoglu1,
  • Tyler Izatt1,
  • Alexis Christoforides1,
  • Hollie Benson1,
  • Lori Phillips1,
  • Angela Baker1,
  • Christopher Murray1,
  • Galen Hostetter3,
  • Daniel D Von Hoff1,
  • David W Craig1 and
  • John D Carpten1Email author
BMC Medical Genomics20147:36

https://doi.org/10.1186/1755-8794-7-36

Received: 20 May 2013

Accepted: 29 May 2014

Published: 18 June 2014

Abstract

Background

The outcome of patients with metastatic colorectal carcinoma (mCRC) following first line therapy is poor, with median survival of less than one year. The purpose of this study was to identify candidate therapeutically targetable somatic events in mCRC patient samples by whole genome sequencing (WGS), so as to obtain targeted treatment strategies for individual patients.

Methods

Four patients were recruited, all of whom had received > 2 prior therapy regimens. Percutaneous needle biopsies of metastases were performed with whole blood collection for the extraction of constitutional DNA. One tumor was not included in this study as the quality of tumor tissue was not sufficient for further analysis. WGS was performed using Illumina paired end chemistry on HiSeq2000 sequencing systems, which yielded coverage of greater than 30X for all samples. NGS data were processed and analyzed to detect somatic genomic alterations including point mutations, indels, copy number alterations, translocations and rearrangements.

Results

All 3 tumor samples had KRAS mutations, while 2 tumors contained mutations in the APC gene and the PIK3CA gene. Although we did not identify a TCF7L2-VTI1A translocation, we did detect a TCF7L2 mutation in one tumor. Among the other interesting mutated genes was INPPL1, an important gene involved in PI3 kinase signaling. Functional studies demonstrated that inhibition of INPPL1 reduced growth of CRC cells, suggesting that INPPL1 may promote growth in CRC.

Conclusions

Our study further supports potential molecularly defined therapeutic contexts that might provide insights into treatment strategies for refractory mCRC. New insights into the role of INPPL1 in colon tumor cell growth have also been identified. Continued development of appropriate targeted agents towards specific events may be warranted to help improve outcomes in CRC.

Keywords

Metastatic colorectal cancer Whole genome sequencing KRAS mutations

Background

Colorectal cancer (CRC) is one of the most common cancers in the United States with an estimated 150,000 new cases and 50,000 deaths each year [1]. While early stage CRC (stage I and II) has a high cure rate after surgery, the recurrence rate is about 50% for stage III CRC after surgery alone and most patients with metastatic disease will ultimately succumb to their cancer [2]. Chemotherapy is the primary treatment for metastatic disease. Currently, there are roughly 5 classes of approved drugs for treating mCRC [3]. These agents include: (1) Fluoropyrimidines: 5-FU and capecitabine (2) Platinum derivative: oxaliplatin (3) Camptothecin derivative: irinotecan (4) EGFR inhibitors: cetuximab and panitumumab and (5) VEGF inhibitors: bevacizumab, afibercept and regorafenib. EGFR inhibitors represent “targeted agents” and their use is limited to about 60% of tumors, which have wild type KRAS genotype. These agents are given in combination, and ultimately patients with KRAS mutations run out of treatment options after 2–3 lines of therapy, with a commonly used sequence being a combination of 5-FU, oxaliplatin and bevacizumab (FOLFOX + bevacizumab) followed by 5-FU/irinotecan (FOLFIRI) with the addition of bevacizumab or afibercept, and the recently approved agent, regorafenib as a third line option in some patients [4].

A number of molecular targets and pathways have been described in CRC. Aberrations in chromosome instability and mismatch repair have been widely identified in a number of cases (85%) [5] with mutations in APC and MutL-homolog (MLH) genes. Mutations in TP53 have been found in about 50% of colorectal cancers globally [6] as p53 plays key roles in cell regulation, apoptosis, DNA repair and differentiation. KRAS mutations are also common in CRC, and occur at a frequency of ~ 40% [7]. Several other pathways which also trigger the malignant phenotype include the TGFβ signaling pathway mediated through downstream targets such as SMAD2 and SMAD4, and components of RAS/MAPK, JNK and PI3K/AKT pathways [8]. Studies of protein coding genomic sequences of colorectal cancers revealed that only a subset of these genes actually contribute to the process of carcinogenesis whereas a vast majority of them actually affect other cellular processes such as transcription, adhesion and invasion [9]. Sequencing studies of the mutational landscape of colorectal cancer revealed that the mutational spectrum is comprised of a limited number of frequently mutated genes and a large number of infrequently mutated genes [10]. Furthermore, a previous sequencing study of 9 colorectal cancers and matched normal tissues reported additional recurrent events, notably a VTI1A-TCF7L2 fusion gene present in ~ 3% of the patients [11].

Personalized CRC patient treatment based on characterizing the individual tumors by high throughput sequencing strategies has been attempted [12]. Most studies reporting sequencing data have been with panels of select genes, or with cell lines, patient derived xenografts, or primary tumors removed at surgery [13]. Our group has instituted a pilot program to sequence various solid tumors in patients with refractory solid tumors [14, 15]. In obtaining clinically relevant information that can be of use by the treating physician, tumor biopsies are obtained in patients with advanced solid tumors refractory to approved therapies [16, 17]. In our current study, we utilized next generation sequencing technologies (NGS) to identify potential biomarkers so as to identify treatment options for patients with mCRC.

Methods

Participants & samples

All patients signed an IRB approved consent form prior to participation at the Virginia G. Piper Cancer Center, Scottsdale Healthcare, Scottsdale, AZ. Fresh frozen tumor biopsy specimens were collected and quality assessed for tumor cellularity, necrosis, crush artifact, etc. A blood sample was also provided for the collection of constitutional genomic DNA. RNA and DNA were extracted from tumor biopsy specimens using the Qiagen All Prep kit (Qiagen, Germantown, MD). Germline DNA (Table 1) provides information regarding patients and samples.
Table 1

Patient clinical information

 

CLN2

CLN3

CLN4

Age at diagnosis

73

45

50

Gender

male

male

male

Ethnicity

Caucasian

Caucasian

Hispanic

Diagnosis

Colon adenocarcinoma

Rectal adenocarcinoma

Colon adenocarcinoma

Tumor cellularity

60%

50%

30%

Sequenced biopsy

Liver metastasis

Right gluteal mass

Right abdominal mass

Genomic DNA isolation

Fresh frozen tissue

Tissue from the needle biopsy was disrupted and homogenized in Buffer RLT plus, Qiagen AllPrep DNA/RNA Mini Kit, using the Bullet Blender™, Next Advance. Specifically, tissue was transferred to a microcentrifuge tube containing 600 μl of Buffer RLT plus, and stainless steel beads. The tissue was homogenized in the Bullet Blender at room temperature. The sample was centrifuged at full speed and the lysate was transferred to the Qiagen AllPrep DNA spin column. Genomic DNA purification was conducted as directed by the AllPrep DNA/RNA Mini Handbook, Qiagen. DNA was quantified using the Nanodrop spectrophotometer and quality was accessed from 260/280 and 260/230 absorbance ratios.

Blood

The QIAamp DNA Blood Maxi Kit, Qiagen, was used to isolate DNA from 8–10 ml of whole blood. The protocol was conducted as written. Specifically, the buffy coat layer was isolated from whole blood by centrifugation. The volume of buffy coat was brought up to 5–10 ml with PBS and treated with Qiagen protease at 70°C. 100% ethanol was added and the sample was applied to a QIAamp Maxi column and centrifuged. Samples were then washed with buffers AW1 and AW2 and eluted in 1000 μl of Buffer AE. The Qubit 2.0 Fluorometer, Invitrogen, and the Nanodrop spectrophotometer, Thermo Scientific, were used to assess DNA quality and concentration.

Sequencing data analysis

Illumina whole genome sequencing

DNA libraries were prepared using the NEBNext DNA Sample Prep Master Mix Set (New England Biolabs, Ipswich, MA). For each sample library preparation, 1 μg of high molecular weight genomic DNA was fragmented using the Covaris S2 system. Fragmented samples were end repaired using T4 DNA polymerase, DNA polymerase I Klenow fragment, and T4 polynucleotide kinase. Samples were next adenylated using Klenow fragment 3′-5′ exo minus enzyme, ligated with Illumina adapters, size selected at 350-450 bp, and PCR amplified using Phusion High-Fidelity PCR Master Mix w/HF buffer (New England Biolabs). The DNA libraries were clustered onto flowcells using Illumina’s cBot and HiSeq Paired End Cluster Generation kits as per manufacturer protocol (Illumina, San Diego, CA). NGS of CLN2 and CLN3 samples were carried out using the Illumina HiSeq 2000 platform using the v1.5 chemistry reagents and flowcells. The CLN4 sample was sequenced on the Illumina HiSeq 2000 platform using the v3 chemistry. The total length of each paired end sequencing run was 200 cycles.

Single nucleotide variant detection

Somatic single nucleotide variant calling was performed using SolSNP [18] and Mutation Walker. SolSNP, an individual sample variant detector (classifier) implemented in Java does the variant calling based on a modified Kolmogorov-Smirnov like statistic, which incorporates base quality scores. The algorithm is non-parametric and makes no assumptions on the nature of the data. It compares the discrete sampled distribution, the pileup on each strand, to the expected distributions (according to ploidy). In case of diploid genome, both strands need to provide evidence for the variation. Zero quality bases are trimmed off the pileup before the comparisons. An important aspect of SolSNP that reduces overcalling inherent to the K-S statistic algorithm is that filters are included to reduce false positive rates, of which both strands must provide evidence for the variation.

While making somatic calls, SolSNP’s high quality genotype call is made for all callable loci of the normal sample. To reduce false negatives, variant loci in tumor samples are called with the Variant Consensus mode. Variant loci in tumor samples that exhibit a high quality homozygous reference genotype in the normal sample are considered as somatic. To call somatic variants, SolSNP is augmented by a Python script.

Mutation Walker, an in house tool developed in Java, utilizes the variant discovery tools from Genome Analysis Toolkit (GATK) [19] as a framework. SNPs that were called using both tools were compiled and further examined. Two sets of thresholds, strict and lenient, were enabled to reduce the false negative rate. Data from each of these two sets were visually examined for false positives to generate a final filtered list of true SNVs, which were annotated with GENCODE using an internal annotation script.

Copy number analysis

For copy number analysis, a custom tool was developed based on a sliding window comparison of coverage for tumor/normal. This method has been adopted by Liang et al. for their analysis [16]. Copy number gains and losses were calculated from log2 difference in normalized physical coverage between germline and tumor samples across a sliding window of 2 kb, where physical coverage was incremented for the length of the insert between the read pairs for insert sizes less than 3 standard deviations of the mean. High repeat regions such as centromeres were defined as regions where the log2 normalized coverage exceeded 3 in the germline sample and were thus excluded. Regions where the coverage was zero were replaced by 1 so that homozygous deletions avoid infinite values and are generally capped at approximately −3.

Indel (Insertion/deletion) detection

For detecting somatic indels we employed a two-step strategy. In the first step we removed from the tumor sample bam, reads whose insert size lay outside a 50 bp - 500 bp interval from the tumor bam files. GATK [19] is then used to generate a list of potential small indels from this bam. A customized perl script, which uses the Bio-SamTools library from BioPerl [20], takes these indel positions and for each of the indels looks at the region in the normal sample consisting of 5 bp upstream from the start and 5 bp downstream from the end of the indel. An indel is determined to be somatic only if there was no indel detected in the region under consideration in the normal DNA.

Translocation detection

A series of customized perl scripts are employed in the detection of translocation. These scripts use SAMtools [21] internally to access the bam files. The algorithm consists of two steps. The first is to detect potential translocation in both tumor and normal samples. The second is a comparison of potential translocations in tumor to those detected in the normal sample to weed out potential false positives. The detection of a potential translocation is an exercise in outlier detection. We take a sliding window of 2kbp and count the discordant reads, whose mates align on a different chromosome. We use 2kbp, as it is close to the mean of the estimated insert size distribution, and gives the best resolution for the detection of an interchromosomal translocation. For each window we choose the highest hit to be the chromosome to which mates of most of the discordant reads mapped. For purposes of brevity, we call the subset of discordant reads whose mate maps to the highest hit in the window as the hit discordant reads. We compare the ratios of the hit discordant reads, to the total aligned reads, across all the windows to detect potential outliers. Outlier detection is performed under the assumption that the distribution, of the proportion of hit discordant reads in a 2 kb window aggregates across the chromosome, and will follow a normal distribution. We then compute the mean of this distribution and choose a cutoff of 3 standard deviations. The window with a proportion of hit discordant reads, higher than this cutoff contains the region of potential translocation. The actual region of translocation is then determined by the span of the hit discordant reads in the window. For somatic translocations, the normal and the tumor sample are called separately and regions of overlap are eliminated. These regions were further inspected visually to reduce false positives to arrive at the most confident list.

SIFT analysis

Single nucleotide variations identified from paired tumor normal analysis were analyzed using the SIFT algorithm. Non-synonymous SNPs in the coding region were checked to see if an amino acid substitution in the protein could actually be damaging by altering the function of the protein [22].

Cell culture conditions and treatments

X-MAN HCT116 cell line was purchased from Horizon Discovery Ltd. (Cambridge, UK) and cultured in McCoy’s 5A modified media with 10% FBS (Life Technologies, Grand Island, NY). HEK293 cells were obtained from the American Type Culture Collection (ATCC) and passaged in RPMI with 10% FBS and 100 units insulin (Sigma-Aldrich, St. Louis, MO). Inhibition of INPPL1 expression was achieved through the use of siRNA sequences (Qiagen, Valencia, CA). siRNA transfections were performed with Lipofectamine 2000 (Life Technologies) and pooled INPPL1 (FlexiTube GeneSolution GS3636) siRNA sequences [Qiagen]. Non-silencing negative control and GFP siRNA sequences were utilized as negative controls (Qiagen). AllStars Hs Cell Death and UBBs1 sequences served as positive controls to assess transfection efficiency (Qiagen). Cells were seeded in 384-well pates containing siRNA sequences (i.e., reverse transfection) and proliferation was measured via CyQUANT Direct Cell Proliferation Assay (Life Technologies). Changes in proliferation were assessed based on comparison of non-silencing siRNA controls to target siRNA. Data shown for siRNA experiments was generated using Excel version 14.3.5 (Microsoft Corporation, Redmond, WA). For siRNA data, differences in proliferation were evaluated by T Test and p values ≤ 0.05 were considered statistically significant.

Cell lysates and immunoblotting

Untreated as well as transfected whole cell lysates were prepared using the cOmplete Lysis-M kit (Roche Applied Science, Indianapolis, IN). Protein samples were prepared by combining lysates with NuPAGE Sample Reducing Agent, NuPAGE LDS Sample Buffer, and nuclease-free water (Life Technologies) and then boiling for 5 minutes at 100°C. Samples were loaded onto NuPAGE 4-12% Bis Tris Gels (Life Technologies), separated by SDS-PAGE, and then transferred to PVDF membranes. Proteins were blocked in 5% non-fat milk and incubated with the appropriate antibody in 5% BSA (Sigma-Aldrich). The following antibodies were used for Western blot analyses: AKT (#9272), pAKT Thr308 (#4056), pAKT Ser473 (#4058), CASP7 (#9492), Fox01 (#2880), pFox01 Ser256 (#9461), GSK-3β (#9315), pGSK-3β Ser9 (#9336), INNPL1 (#2839), p70 S6 Kinase (#2708), PDK1 (#5662), pPDK1 Ser241 (#3438), PI3K p85 (#4292), and GAPDH (#2118) [Cell Signaling, Danvers MA]; and pGSK-3β Tyr216 (#75745) [Abcam Inc., Cambridge, MA]. Protein bands were visualized using Pierce ECL Western Blotting Substrate (Thermo Fisher Scientific, Rockford, IL).

Results

Clinical history

Four patients were recruited for this study. In patient CLN1, the tumor sample was not considered adequate for NGS and results are not presented. Clinical information is provided in Table 1.

Patient CLN2 is a 73 year old Caucasian male first diagnosed with a T3N2 moderately differentiated adenocarcinoma of the ascending colon. He received adjuvant therapy with FOLFOX but had rapid recurrence of disease 5 months after initial surgery. The patient had subsequent therapy with irinotecan/bevacizumab and then gemcitabine, mitomycin and insulin potentiating therapy. At time of referral, there was metastatic disease in the pelvis and multiple omental lesions. A CT guided biopsy of a nodule in the right side of the abdomen was performed. The biopsy from this mass (60% tumor cellularity) was consistent with the colon primary.

Patient CLN3 is a 45 year old Caucasian male first diagnosed with a T2NO rectal cancer for which primary surgery was performed. The patient was found to have recurrent lung metastasis and a pelvic pre sacral mass 18 months after surgery. He also had a chronic draining fistula in the right buttock which developed soon after surgery. A hard mass developed in the right buttock and on biopsy was found to be another site of metastasis. Treatment for metastatic disease had included FOLFOX /bevacizumab, lung radiation and isolated pelvic chemotherapy perfusion. A biopsy of the right buttock mass was performed with pathology consistent with the rectal primary (50% tumor cellularity).

Patient CLN4 is a 50 year old Hispanic male diagnosed with hemicolectomy for a T3N1 right sided colon cancer. The patient was found to have synchronous liver metastasis at the same time. He had undergone prior therapy with FOLFOX, FOLFIRI/bevacizumab and yittum-90 therapy to the liver. The patient entered the study 3 years after first diagnosis of metastatic disease and had tumors in the liver and a soft tissue mass in the epigastric area of the abdomen. A biopsy from the abdominal mass was consistent with the colon primary (30% tumor cellularity).

Whole genome sequencing

For each patient tumor and germline DNA were sequenced to identify somatic alterations. WGS summary statistics are shown in Table 2. Aligned reads from both tumor and normal samples were evaluated for somatic events including non-synonymous single nucleotide variants (nsSNVs), indels, copy number variants (CNVs) and translocations. Circos plots in Additional files 1, 2 and 3 illustrate somatic events occurring in CLN2, CLN3 and CLN4 respectively [23].
Table 2

Whole genome sequencing

Participant code

Total reads sequenced

Total bases sequenced

Coverage

Number of germline variants

Percent dbSNP

Transition/transversion ratio

CLN2 Normal

1.9 billion

200 Gb

50X

2.3 million

88%

2.05

CLN2 Tumor

1.4 billion

146 Gb

42X

  

CLN3 Normal

2.0 billion

210 Gb

62X

3.4 million

88%

2.06

CLN3 Tumor

1.96 billion

204 Gb

40X

  

CLN4 Normal

0.97 billion

100 Gb

28X

3.3 million

88%

2.06

CLN4 Tumor

1.03 billion

107 Gb

31X

  
The complete lists of somatic SNVs detected in the three evaluable specimens are provided in Additional file 4: SNVs and Indels. All three tumors contained KRAS mutations. Among the cancer genes mutated in patient CLN2 were APC, KRAS, PIK3CA, SMAD4, MYST4, HUNK, INPPL1, TGFβ3, and TCF7L2. Cancer genes mutated in CLN3 included KRAS, INPP4B, PTPRE, CARD16 and LRP2. Cancer gene mutations in CLN4 were detected in APC, KRAS, PIK3CA, KDR and AURKC (Table 3). Some of the genes which were known to have non-synonymous mutations in colon cancer genes in prior studies were also seen in these samples. They include APC, KCNQ5, KIAA1409, KRAS, LRP2, PIK3CA, SMAD4, TCF7L2 and UHRF2[9].
Table 3

SNVs and indels in relevance to cancer

Sample

Chr

Gene name

Position

Coding event

Sequence change

Substitution

CLN2

3

CPB1

150045158

SNV

G/A

R231Q

 

6

ESR1

152423875

SNV

C/T

T431I

 

10

TCF7L2

114907771

SNV

G/A

G424E

 

11

INPPL1

71621016

SNV

A/G

E567G

 

12

KRAS

25289548

SNV

C/T

G13D

 

14

TGFβ3

75495381

SNV

G/T

Q381K

 

18

BCL2

59136317

SNV

C/A

W188L

 

18

PTPRM

8384547

SNV

G/A

V1415M

 

10

MYST4

76460457

SNV

C/T

R1957W

 

18

SMAD4

46858795

SNV

T/G

L540R

 

19

SHANK1

55864301

SNV

G/A

R910C

 

21

HUNK

32293208

SNV

G/A

R662Q

 

3

PIK3CA

180399422

SNV

G/A

NA

 

1

PTPRC

196985273

SNV

G/A

S852N

 

13

MLNR

48693327

Indel

cccgg−/−ccgcc

Insertion

 

2

SLC4A10

162427730

Indel

atcag−/−aaaa

Insertion

 

6

UTRN

145111149

Indel

AAAT-g-GGAAA

Frameshift

 

7

HNRNPA2B1

26202550

Indel

CAGAT-cctcc-TCTAA

Frameshift

CLN3

1

ARID1A

26973896

SNV

G/T

E1531

 

2

LRP2

169845248

SNV

T/C

N400S

 

3

MITF

70011193

SNV

C/G

S92C

 

4

INPP4B

143222686

SNV

C/A

E864

 

5

GPR98

89974441

SNV

G/T

V787L

 

7

CYLN2

73409559

SNV

C/A

S344Y

 

10

PTPRE

129758044

SNV

G/A

R369Q

 

12

KRAS

25289551

SNV

C/T

G12D

 

4

MAML3

26202550

Indel

AAAT-ctg-CTGCT

AA_Deletion

 

6

PGC

140871034

Indel

CCTGC-aga-AGAGC

AA_Deletion

CLN4

1

ARID4B

233412380

SNV

C/A

R826M

 

3

PIK3CA

180434779

SNV

A/G

H1048R

 

4

KDR

55655861

SNV

G/A

R946C

 

5

APC

112129944

SNV

G/T

G53V

 

5

APC

112201150

SNV

C/T

Q654

 

5

APC

112203580

SNV

G/T

E1464

 

5

APC

112205360

SNV

A/G

K2057R

 

6

PTCRA

42998820

SNV

G/T

V46F

 

12

KRAS

25289552

SNV

C/A

G12C

 

2

HOXD9

176696536

Indel

cagcc-/gcagc

Insertion

Non-synonymous SNVs found in the coding regions of genes were analyzed using the SIFT (Sorting Tolerant From Intolerant) algorithm to determine if such mutations may affect protein function [22]. Genes identified to have damaging effects on the protein product are indicated in Additional file 5: SIFT Predictions for SNVs and Indels. Of the 115 coding variants in CLN2, 61 were predicted to be damaging (53%); CLN3 had 90 coding variants of which 38 were damaging (42%); and CLN4 had 44 coding variants of which 20 were damaging (47%).

Copy number analysis

Copy number analysis was performed using a sliding window comparison of coverage between tumor and normal using our in-house custom tool. Regions of gain or loss in the tumor samples are outputted as log2 ratios in Additional file 6: CNVs. CLN2 had whole chromosome copy number gain of chromosome 13, and chromosome 8q. Significant genes deleted in CLN2 include TP53, BCL2, PIK3CA, SMAD2, SMAD3, SMAD4, APC2, DCC, TGFβ1, TCF3, TCF4 and TCF12. CLN3 exhibited whole chromosome copy number gain of chromosomes 1 – 5. A significant amplification occurred at 1pter and encompassed NOCL2, PLEKHN1, SDF4, UBE2J2, CENTB5, CPSF3L, MXRA8, ATAD3B, ATAD3A, SSU7A, SLC35E2, NADK, GNB1, GABRD, and PRKCZ. A focal amplicon at 12p contains KRAS, which is also mutated in this patient’s tumor. An interstitial deletion of about 16 Mb was seen in chromosome 13, which encompasses the RB1 locus. Somatic copy number analysis in CLN4 was confounded by low tumor cellularity (30%); however we were able to detect several events including whole chromosome gain of chromosome 13.

Furthermore, several genes containing SNVs also mapped within regions of copy number change. A list of these genes has been included in Table 4. Notably in CLN2, SMAD4 was deleted and harbored an L540R somatic mutation. Additionally, PTPRM was deleted and contained a V1415M nsSNV.
Table 4

Correlation of genes with SNVs and amplifications and deletions

Sample

Chr

Gene name

SNV location

CNV variant type

Change associated with variant

Start

End

Correlation

CLN2

8

ADAMDEC1

24312932

CNV-Loss

-1.098323504

24298000

24318700

-

 

18

BCL2

59136317

CNV-Loss

-1.142019005

58946900

59136800

-

 

17

C17orf39

17905985

CNV-Loss

-1.086327397

17886900

17909200

-

 

13

DNAJC3

95127539

CNV-Gain

1.034261883

95159600

95201700

+

 

18

DOK6

65495961

CNV-Loss

-1.080170349

65220300

65659400

-

 

19

EEF2

3929160

CNV-Loss

-1.10433666

3927700

3935600

-

 

13

MED4

47567121

CNV-gain

1.011225847

47549300

47562800

+

 

15

MORF4L1

76965549

CNV-Loss

-1.25642835

76953100

76976400

-

 

19

OR1M1

9065467

CNV-Loss

-1.185833041

9065000

9065800

-

 

13

PCDH17

57105203

CNV-Gain

1.158413612

57105000

57140500

+

 

8

PI15

75900191

CNV-Gain

1.077222314

75901700

75924000

+

 

15

PML

72124335

CNV-Loss

-1.130706684

72074500

72122000

-

 

18

PTPRM

8384547

CNV-Loss

-1.028666588

7637600

8387100

-

 

19

PVRL2

50083243

CNV-Loss

-1.070323897

50042300

50073700

-

 

19

SHANK1

55864301

CNV-Loss

-1.135111902

55859100

55907600

-

 

18

SMAD4

46858795

CNV-Loss

-1.077097357

46827500

46855600

-

 

15

THSD4

69844560

CNV-Loss

-1.12539172

69808500

69856700

-

 

8

VPS13B

100723477

CNV-Gain

1.06690452

100098000

100957000

+

 

19

XAB2

7594144

CNV-Loss

-1.039001256

7596500

7599800

-

 

19

ZNF235

49483380

CNV-Loss

-1.210566986

49486200

49499300

-

 

19

ZNF480

57510937

CNV-Loss

-1.12807401

57496100

57517900

-

 

19

ZNF83

57808787

CNV-Loss

-1.330073623

57808100

57809600

-

CLN3

4

ANKRD17

74175875

CNV-Gain

1.050578956

74176700

74257700

+

 

2

APOB

21086873

CNV-Gain

1.381146277

21078200

21119700

+

 

1

ARID1A

26973896

CNV-Gain

1.088551435

26707600

26977900

+

 

3

ATP13A5

194544518

CNV-Gain

1.074832063

194495900

194576400

+

 

2

CLEC4F

70897420

CNV-Gain

1.474467603

70890000

70900800

+

 

1

FNDC7

109063011

CNV-Gain

1.060116535

109071800

109085900

+

 

3

GADL1

30871204

CNV-Gain

1.083837201

30750200

30822400

+

 

4

GUCY1A3

156862650

CNV-Gain

1.051035883

156845700

156866700

+

 

4

INPP4B

143222686

CNV-Gain

1.071823665

143169400

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2

IWS1

127960697

CNV-Gain

1.157172502

127955100

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3

KCNMB2

180028679

CNV-Gain

1.036705549

180021600

180027700

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12

KRAS

25289551

CNV-Gain

1.035184393

25274800

25275900

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2

LRP2

169845248

CNV-Gain

1.274350136

169693500

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3

LSG1

195872054

CNV-Gain

1.046959963

195844100

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1

MACF1

39573148

CNV-Gain

1.075279401

39322700

39722600

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3

MITF

70011193

CNV-Gain

1.055910933

70070200

70096500

+

 

5

PAPD4

78951267

CNV-Gain

1.023426699

78969500

78969700

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2

PCBP1

70168678

CNV-Gain

1.147337373

70169200

70169400

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4

PDE5A

120666238

CNV-Gain

1.066043219

120655300

120742100

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1

PHTF1

114082912

CNV-Gain

1.075512922

114060600

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3

RPSA

39428561

CNV-Gain

1.099952073

39424200

39428600

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4

SH3BP2

2805228

CNV-Gain

1.208480128

2792500

2804000

+

 

5

SLC6A19

1265596

CNV-Gain

1.142273516

1257300

1274700

+

 

3

SMC4

161633513

CNV-Gain

1.02766824

161602500

161603100

+

 

2

SMYD5

73305580

CNV-Gain

1.59092836

73301300

73306500

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1

SPOCD1

32038250

CNV-Gain

1.187067318

32029900

32040400

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1

ST6GALNAC3

76650550

CNV-Gain

1.08116566

76355200

76864700

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1

TMEM39B

32340671

CNV-Gain

1.071050645

32318800

32339600

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2

VPS54

63993297

CNV-Gain

1.158423232

63974100

64064600

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2

WDR35

20045755

CNV-Gain

1.180181947

19976800

20052800

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5

WWC1

167824543

CNV-Gain

1.083413977

167656200

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CLN4

16

ABCC12

46688204

CNV-Gain

0.757287

46702800

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13

SPATA13

23761296

CNV-Gain

0.894588

23774800

23761296

+

Impact of INPPL1silencing on cell proliferation in HCT116 cells

The PI3 kinase pathway has been previously associated with colorectal cancer, where PIK3CA mutations occur in approximately 15% of colorectal tumors [10, 24]. Phosphatidyl-3,4,5-triphosphate (PI3,4,5P3), is a key phosphoinositide generated from PI3 kinase, which regulates PKB/AKT mediated cell survival and proliferation [25]. In our analysis, we identified a mutation in INPPL1 (inositol polyphosphate phosphatase-like 1), which encodes SHIP2, the phosphatase that plays an important role in the conversion of PI3,4,5P3 to PI3,4P2. The E567G mutation identified in the INPPL1gene was predicted to be damaging by SIFT, and validated by Sanger sequencing. Thus we investigated the impact of silencing INPPL1 by RNA inhibition on cell proliferation. For this study, we used small interfering RNA (siRNA). HCT116 cells were seeded into 384-well plates containing siRNA buffer (no transfection), control siRNA sequences, or INPPL1 siRNA sequences. At 72 hours post-transfection, cell proliferation was measured. Changes were measured based on differences between non-silencing control siRNA and INPPL1 siRNA sequences (Figure 1). At 72 hours post-transfection, we detected a 65% decrease in HCT116 cell proliferation, suggesting that INPPL1 may be required for CRC cell growth.
Figure 1

Effect of INPPL1 siRNA on cell proliferation in HCT116 cells. Changes in proliferation in HCT116 cells 72 hours post-transfection with pooled control or pooled INPPL1 sequences. Fluorescence data is shown for cells only (no siRNA sequences), negative controls (non-silencing siRNA sequences), INPPL1 siRNA, and positive controls (siRNA sequences targeting essential genes). The effects of INPPL1 siRNA are based on differences between average fluorescence signal generated by negative controls and INPPL1 siRNA sequences. Asterisks denotes statistical significance.

Impact of INPPL1siRNA on protein expression of downstream signaling targets of PI3K -Western analysis

Since SHIP2 plays a role in PI3K/AKT signaling, INPPL1 siRNA transfected and untransfected HCT116 cell lysates were used for Western blots and probed with antibodies to several downstream PI3K/AKT pathway targets as illustrated in Figure 2. HEK293 cell lysates were used as a positive control in our western blots, due to the ease of maintenance, abundance of proteins, and known expression of multiple proteins. As expected, a reduction in INPPL1 protein levels was observed at 24, 48 and 72 hours post transfection. INPPL1 siRNA treated HCT116 cells showed no expression of PI3K as early as 24 hours post transfection. Hence we looked at the protein levels of a series of downstream signaling targets of PI3K. For PDK1, both pPDK1 (Ser 241) and PDK1 antibodies were used. In the untransfected cell lines, pPDK and PDK1 were expressed. Similar to PI3K, INPPL1 siRNA treated cells showed no expression of either the pPDK1 or PDK1 at 24 and 48 hours. AKT phosphorylation was detected at T308 and S473 in HEK293 cells, however, only phosphor-T308 was detected in HCT116 cells. Total AKT was detected at high levels in HCT116 cells. INPPL1 siRNA transfected cells showed a marked reduction in the phosphorylation of AKT as seen in 24 and 48 hours post transfection. Moreover, INPPL1 siRNA inhibited total AKT protein expression at 48 hours in HCT116 cells, with expression returning at 72 hours post transfection. INPPL1 siRNA transfection also led to reduced p70 S6 kinase in the HCT116 cells within 24 hours post transfection. Phosphorylated FOX01 (Ser256) was detected in untransfected cells, but was significantly diminished in HCT116 cells transfected with INPPL1 siRNA. Another downstream effector of AKT is GSK-3β, which upon phosphorylation by AKT at Ser9 becomes inactivated leading to increased cell cycle and β-catenin signaling. INPPL1 siRNA led to decreased total GSK-3β at 24 hours in HCT116 parental cell lines. Interestingly, INPPL1 knockdown in HCT116 cells led to increased phospho-(Ser9) GSK-3β in HCT116 cells at 48 hours. Finally, Caspase 7 activation, which is an indicator of apoptosis, also increased as indicated by the cleaved 35 kDa band in INPPL1 siRNA transfected HCT116 cells. These results support inactivation of the PI3K/AKT pathway upon INPPL1 knockdown, suggesting a growth promoting role for INPPL1 in colon cancer.
Figure 2

Western blots. Basal protein expression of HEK293 and HCT116 cells as well as changes in expression in HCT116 cells 24, 48, and 72 hours post-INPPL1 siRNA transfection. For post-transfection samples, cell lysates were treated with pooled INPPL1 siRNA sequences and harvested at each time point.

Discussion

It is now established that the key mediators of the cancer cell phenotype are single base mutations, copy number alterations, translocations/rearrangements and epigenetic modifications of genomic DNA. Only now with the advent of NGS and bioinformatics capabilities can the entire human genome be interrogated for these changes. Importantly, with the development of targeted therapies, somatic genome analysis of tumors can shed light on possible therapeutically relevant events that might help inform treatment recommendations for advanced cancers.

In our small sample size, all three tumors had mutations in KRAS. KRAS mutations have been observed in 33% of the CRCs and are crucial for the early progression of adenoma to carcinoma in these tumors. Activating KRAS mutations in CRC tumor samples are an early event in the progression of colon carcinoma and the only predictive molecular marker useful for treatment decision as EGFR directed therapy is ineffective in the context of concomitant KRAS mutation [26]. However, even in the presence of KRAS wild type, therapy with either cetuximab or panitumumab is only effective in about 30% of cases suggesting that there are other molecular determinants of resistance. Additional genes harboring somatic mutations that have been characterized as cancer genes in our study include, APC, TGFβ3, SMAD4, BCL2, INPPL1, INPP4B, PIK3CA, PTPRE and KDR. Mutations in the APC gene have been identified in sporadic cancers and play a role in the WNT/β-catenin signaling pathway. Loss of APC function leads to β-catenin accumulation and binding to TCF/LEF transcription factors thereby activating MYC and cyclin D1 leading to the transformation of the colon epithelium [27]. Thus APC mutations generally play a role in the initiation of colorectal cancers. APC also regulates cell proliferation of RAS induced ERK activation playing an important role in colorectal tumor suppression [28]. Colorectal tumors are known to have mismatch repair mutations that can increase the mutation rate in these tumors. However, one cannot also rule out the possibility that some of the mutations identified in these advanced cancers are not the result of previously administered chemo or radiation therapies.

Some notable CNVs were seen in these patients. CLN2 had amplification in the MYC oncogene. A number of known tumor suppressor genes were deleted in CLN2 such as TP53, SMAD2, SMAD3, SMAD4, BCL2 and TCF4. SMAD4 alterations occur in over 50% of CRC and are believed to occur later in the course of disease [29]. SMAD4 acts as a tumor suppressor to inhibit β-catenin [30] and targets the TGFβ signaling pathway to control epithelial cell growth [31]. Tumor CLN2 contained SMAD4 deletion and a concomitant SMAD4 mutation. Deletion of BCL2 leads to an increase in the relapse of stage II colon cancers and could be a likely biomarker for therapeutic decisions [32]. Importantly, CLN2 also contained a somatic mutation in the BCL2 gene. TCF4 has been found to be mutated in variety of tumor types such as renal cell carcinoma, gastric carcinoma and breast cancer [33, 34]. TCF4 mutations have been reported in primary CRCs and its loss induces cell proliferation suggesting a possible role as a tumor suppressor [3537]. CLN3 has a copy number gain encompassing the VEGFA locus, found in 3% of the cases and linked to higher tumor grade and vascular invasion and being an aggressive subgroup [38]. Copy number gain was also noted in KDR (VEGFR-2), a mediator of angiogenesis and its expression correlated with poor outcome in non-small cell lung carcinoma [3942]. CLN4 had amplification of EGFR, CDK8, and MIRH1. EGFR mutations in the extracellular domain with gene amplifications are common in glioblastomas [43] and mutations in the tyrosine kinase domain with increased copy numbers are seen in lung carcinomas. However, amplification of EGFR seems to be an uncommon event in colorectal cancer [44, 45]. CDK8, a cyclin dependent kinase amplified in CLN4, plays a major role in cell proliferation and PI3K inhibitors can be used in clinical trials for CDK8[46]. With regards to structural events in colon cancer, gene fusions of TCF7L2 with VTI1A have previously been observed in 3% of the colon cancers [11]. We did not detect this translocation event in any of our tumors due to our sample size, however we did detected a novel TCF7L2 (G424E) mutation in one of our tumors, suggesting that this gene can be perturbed by multiple mechanisms.

In our three CRC cases that underwent successful NGS, mutations were detected in genes and pathways that could possibly be therapeutically targetable. The PI3K pathway is recognized to be critical in cellular transformation, cell proliferation, adhesion, survival, and motility of cancer cells. In support of our observations, studies have shown that PIK3CA is mutated in up to 30% of CRC as well as other tumors such as breast, ovarian, and liver cancer [47] typically leading to activation of the PI3K/AKT/mTOR signaling pathway. Activated PI3K leads to an increase in the phosphoinositides PI3,4,5P3 and PI3,4P2 which in turn leads to phosphorylation of AKT. PI3,4,5P3 serves as a substrate for SHIP2 where it is converted to PI3,4P2. Both PI3,4,5P3 and PI3,4P2 have been shown to activate AKT phosphorylation [48]. However studies have shown that PI3,4P2 is more efficient than PI3,4,5P3 in binding to the PH domain of AKT, phosphorylating S473 and leading to membrane activation [25, 49]. In vitro studies using phospholipid vesicles with PI3,4P2 alone were sufficient to activate AKT[50]. INPPL1, encodes SHIP2, the inositol phosphatase that converts PI3,4,5P3 to PI3,4P2. SHIP2 is also a negative regulator of insulin signaling, plays an important role in EGF receptor turnover, and also has been reported to negatively regulate the PI3K pathway [51]. INPPL1 has been shown to act as either a tumor suppressor or an oncogene in different tumor types. Furthermore, SHIP2 expression has been associated with metastasis in breast cancer [52]. INPPL1 mutations have been previously reported and occur in ~4% of colon tumors [53]. The INPPL1 E567G mutation discovered in tumor CLN2 resides within the catalytic inositol polyphosphate 5-phosphatase domain that is critical to inositol phosphatase activity, and is predicted to be damaging by SIFT. Thus we performed additional functional genomic and mechanistic studies using RNAi to better understand the role of INPPL1 in colon cancer cell growth and signaling. We used the HCT116 cell line model, which also harbors PIK3CA and KRAS mutations, similar to the patient containing the INPPL1 mutation. As knockdown of INPPL1 led to growth suppression, we hypothesize that this mutation may lead to a gain of function of INPPL1 thereby playing an oncogenic role in colon cancer as indicated by our in vitro studies. Upon INPPL1 knockdown, we observed significant negative changes in phopho-signaling of key effectors of the PI3K/AKT pathway that suggest INPPL1 might promote growth in colon cancer. This is an important insight as SHIP2 converts PI3,4,5P3 to PI3,4P2, which has been shown to directly activate AKT independent of PI3,4,5P3. Studies by Fuhler et al. also show that treatment of multiple myeloma cells with SHIP1/2 inhibitors causes cell arrest in the G2/M phase and induction of apoptosis via Caspase activation [54]. Therefore, additional studies of the role of SHIP2 in CRC are warranted, as this could provide alternative ways to approach inhibition of the PI3K/AKT axis as a means of treatment for a subset of colon cancer tumors.

Conclusion

This study provides insights into the mutational landscape of metastatic recurrent colorectal cancer. KRAS being the most frequently mutated in human cancers with ~30% in colorectal cancers are the hallmarks in all these tumor samples. Several inhibitors for the downstream signaling targets of RAS such as RAF and MEK have not been very successful. PI3K inhibitors have been used in phase II clinical trials but have also not been very promising. There remains an urgent need to develop KRAS inhibitors to enhance treatment options in mCRC patients with KRAS mutations. Using an in vitro model with a colon cancer cell line, we have identified that an effective way of activating AKT signaling could be through PI3,4P2 and INPPL1. The inhibition of INPPL1 may have a very significant role in cell proliferation and survival in colon cancer. And although specific recurrent mutations exist, our study further highlights therapeutically relevant contexts within metastatic colon tumors that might lead to new and improved ways to manage these difficult to treat tumors.

Declarations

Acknowledgements

This work was funded by the Stardust Foundation (Scottsdale, AZ), and The Bernice E. Holland Foundation (Colorado Springs, CO).

Authors’ Affiliations

(1)
Translational Genomics Research Institute (TGen)
(2)
Virginia G Piper Cancer Center
(3)
Van Andel Research Institute (VARI)

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  55. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/7/36/prepub

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© Shanmugam et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

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