The authors have declared that no competing interests exist.
Conceived and designed the experiments: WSL DWC JC MB MJB AKS DVH RKR RF GJW. Performed the experiments: WSL MB LP HB EB AB GHH JE. Analyzed the data: DWC TI SS AC JA AK WT CL. Contributed reagents/materials/analysis tools: RP HH DL ECS JK. Wrote the paper: WSL. Data interpretation: WSL MB JE MD GW DH JK.
Pancreatic adenocarcinoma (PAC) is among the most lethal malignancies. While research has implicated multiple genes in disease pathogenesis, identification of therapeutic leads has been difficult and the majority of currently available therapies provide only marginal benefit. To address this issue, our goal was to genomically characterize individual PAC patients to understand the range of aberrations that are occurring in each tumor. Because our understanding of PAC tumorigenesis is limited, evaluation of separate cases may reveal aberrations, that are less common but may provide relevant information on the disease, or that may represent viable therapeutic targets for the patient. We used next generation sequencing to assess global somatic events across 3 PAC patients to characterize each patient and to identify potential targets. This study is the first to report whole genome sequencing (WGS) findings in paired tumor/normal samples collected from 3 separate PAC patients. We generated on average 132 billion mappable bases across all patients using WGS, and identified 142 somatic coding events including point mutations, insertion/deletions, and chromosomal copy number variants. We did not identify any significant somatic translocation events. We also performed RNA sequencing on 2 of these patients' tumors for which tumor RNA was available to evaluate expression changes that may be associated with somatic events, and generated over 100 million mapped reads for each patient. We further performed pathway analysis of all sequencing data to identify processes that may be the most heavily impacted from somatic and expression alterations. As expected, the KRAS signaling pathway was the most heavily impacted pathway (P<0.05), along with tumor-stroma interactions and tumor suppressive pathways. While sequencing of more patients is needed, the high resolution genomic and transcriptomic information we have acquired here provides valuable information on the molecular composition of PAC and helps to establish a foundation for improved therapeutic selection.
Pancreatic cancer is a malignant carcinoma that is currently the fourth leading cause of cancer-related deaths in the United States
Significant effort by a number of groups has led to the identification of genomic alterations in pancreatic cancer. Heavily implicated genes include
To carry out unbiased whole genome analyses in actual patients, we performed whole genome sequencing (WGS) of tumor biopsy DNA and matched normal DNA from blood from three separate PAC patients to identify somatic events in each patient's tumor. Our primary aim is to separately characterize each of these patients to evaluate the molecular background of each tumor. To understand the possible implications of identified genomic events and to evaluate transcriptional alterations in the tumor, we also performed RNA sequencing (RNAseq) for 2 of the patients for which RNA was available. Lastly, for patients 1 and 2, we performed comparative genomic hybridization (CGH) analyses to validate copy number changes identified through sequencing. The use of next generation sequencing (NGS) and the combined analysis of separate sets of data help to create a detailed picture of the disease in each patient and contribute to our understanding of the disease. We present here very detailed genomic characterizations of three separate PAC patients.
Detailed supplementary methods are described under Supporting Information (
All patients were treated on protocols approved by the Mayo Clinic Institutional Review Board (MCIRB) and the Western Institutional Review Board (WIRB). This study was conducted in accordance with the 1996 Declaration of Helsinki. Written informed consent was obtained from all patients.
For this study, patients had to be ≥18 years of age and provided signed informed consent. These patients included those with a pathologic or clinical diagnosis of a pancreatic malignant neoplasm, or who were undergoing a medically indicated procedure to obtain tissue or to resect their pancreatic tumor. Other eligibility criteria included: Karnofsky performance status (PS) ≥80%, life expectancy >3 months, baseline laboratory data indicating acceptable bone marrow reserve, liver, and renal function. Patients were allowed to participate on another clinical trial involving treatment prior to or during participation on this study. Main exclusion criteria included: symptomatic central nervous system (CNS) metastasis, untreated CNS metastases, known active infections requiring intravenous antimicrobial therapy, known HIV, HBV or HCV infection requiring antiviral therapy, pregnant or breast feeding women, or inaccessible tumor for biopsy.
Tumor samples were obtained under institutional review protocols and were preserved as fresh frozen. Normal DNA was obtained from peripheral blood mononuclear cells. Percent tumor cellularity of patient 1's biopsy (tumor content) was assessed as 60% tumor, patient 2 50% tumor, and patient 3 40–50% tumor. Direct visualization of samples collected from all three patients was obtained to estimate tumor content and extent of tissue heterogeneity by a board certified pathologist (GH).
Tissue was disrupted and homogenized in Buffer RLT plus (Qiagen AllPrep DNA/RNA Mini Kit), using the Bullet Blender™, Next Advance, and transferred to a microcentrifuge tube containing Buffer RLT plus and 1.6 mm stainless steel beads (patient 1), or 0.9 mm–2.0 mm RNase free stainless steel beads (patients 2 and 3). Blood leukocytes (buffy coat) were isolated from whole blood by centrifugation at room temperature and resuspended in Buffer RLT plus. All samples were homogenized, centrifuged at full speed, and lysates were transferred to the Qiagen AllPrep DNA spin column. Genomic DNA was purified following the manufacturer's protocol. DNA was quantified using the Nanodrop spectrophotometer and quality was accessed from 260/280 and 260/230 absorbance ratios.
Tissue was disrupted and homogenized in Buffer RLT plus using the Bullet Blender, and transferred to a microcentrifuge tube containing Buffer RLT plus and 0.9 mm–2.0 mm RNAse free stainless steel beads. The tissue was homogenized in the Bullet Blender, and centrifuged at full speed. The supernatant was transferred to the QiagenAllPrep DNA spin column. 70% ethanol was added to the flow-through and the mixture was applied to an RNeasy spin column. Total RNA purification was conducted as directed by the AllPrep DNA/RNA Mini Handbook. FirstChoice normal human pancreatic RNA was purchased from Ambion (catalog#AM7954) and used as the RNAseq control. RNA was quantified using the Nanodrop spectrophotometer and quality was assessed using the Agilent Bioanalyzer.
3 µg of genomic DNA from each sample was fragmented to a target size of 300–350 base pairs (bp). Overhangs in the fragmented samples were repaired and adenine bases were ligated on. Diluted paired end Illumina adapters were then ligated onto the A-tailed products. Following ligation, samples were run on a 3% TAE gel to separate products. Ligation products at 300 bp and 350 bp were selected for each sample, isolated from gel punches, and purified. 2× Phusion High-Fidelity PCR Master Mix (Finnzymes; catalog#F-531L) was used to perform PCR to enrich for these products. Enriched PCR products were run on a 2% TAE gel and extracted. Products were quantified using Agilent's High Sensitivity DNA chip (catalog#5067-4626) on the Agilent 2100 Bioanalyzer (catalog#G2939AA).
All RNA samples were analyzed on the Agilent Bioanalyzer RNA 6000 Nano Chip to validate RNA integrity (RIN≥7.0). 10 ng of total RNA was used to generate whole transcriptome libraries for RNA sequencing. Using the Nugen Ovation RNA-Seq System (cat#7100-08), total RNA was used to generate double stranded cDNA, which was amplified using Nugen's SPIA linear amplification process. Amplified cDNA was input into Illumina's TruSeq DNA Sample Preparation Kit – Set A (cat#FC-121-1001) for library preparation. In summary, 1 µg of amplified cDNA was fragmented to a target insert size of 300 bp and end repaired. Samples were then adenylated and indexed paired end adapters were ligated. Ligation products were run on a 2% TAE gel and size selected at 400 bp. Ligation products were isolated from gel punches and purified. Cleaned ligation products were input into PCR to enrich for libraries. PCR products were cleaned and quantified using the Agilent Bioanalyzer.
Tumor and normal libraries were prepared for paired end sequencing. Clusters were generated using Illumina's cBot and HiSeq Paired End Cluster Generation Kits (catalog#PE-401-1001) and sequenced on Illumina's HiSeq 2000 using Illumina'sHiSeq Sequencing Kit (catalog#FC-401-1001).
Samples were run with the SurePrint G3 Human aCGH Microarray 1 M (Agilent Technologies, Palo Alto, CA). The digestion, labeling, and hybridization steps were performed as previously described with minor modifications
DNA content based flow assays were used to identify and purify proliferating 2N (G1) populations, 4N(G2/M), and aneuploid populations from the biopsy. The biopsy was minced in the presence of NST buffer and DAPI according to published protocols
Raw sequence data in the form of .bcl files were generated by the Illumina HiSeq 2000. These data were converted to .qseq files, which were used to generate .fastq files. Fastq files were validated to evaluate the distribution of quality scores and to ensure that quality scores do not drastically drop over each read. Validated fastq files were aligned to the human reference genome (build 36) using the Burrows-Wheeler Alignment (BWA) tool. Following alignment,.sai files were used to create .sam (sequence alignment map) files
This plot summarizes all significant genomic events that were identified in patient 1 using WGS. Copy number changes are shown in the inner circle plot with red marking amplifications and green marking deletions. SNVs are indicated with dark blue tick marks and indels are indicated with light blue tick marks.
This plot summarizes all significant genomic events that were identified in patient 2 using WGS. Copy number changes are shown in the inner circle plot with red marking amplifications and green marking deletions. SNVs are indicated with dark blue tick marks and indels are indicated with light blue tick marks.
This plot summarizes all significant genomic events that were identified in patient 3 using WGS. Copy number changes are shown in the inner circle plot with red marking amplifications and green marking deletions. SNVs are indicated with dark blue tick marks and indels are indicated with light blue tick marks.
SNP (single nucleotide polymorphism) calling was performed using SolSNP (
RNAseq data was aligned against human reference genome (build 36) with TopHat 1.2; RNAseq reads were only aligned against the autosomes and sex chromosomes. Mitochondrial DNA and annotations were removed from the genome and annotation references prior to alignment. Cuffdiff was used to identify differentially expressed genes and isoforms. Differential analysis was performed on FPKM (Fragments Per Kilobase of transcript per Million fragments mapped) expression values calculated for gene and isoform. P-values were corrected for multiple testing using the Benjamini and Hochberg method. ChimeraScan
Integrative analysis of whole genome and transcriptomic data was performed using the Functional Ontology Enrichment Tool in MetaCore from GeneGo, Inc. (v6.8; Thomson Reuters Business, Philadelphia, PA). Pathway analysis specific to pancreatic cancer was performed using the MetaMiner (Oncology) Pancreatic Cancer Disease Module add-on. P-values associated with each analysis are calculated in MetaCore using a hypergeometric distribution.
Our study was performed on a set of fresh pancreatic tumor specimens and whole blood samples from three patients diagnosed with PAC. Clinical information is listed in
Patient 1 | Patient 2 | Patient 3 | |
Age at diagnosis (years) | 55 | 76 | 57 |
Gender | male | female | male |
Ethnicity | Caucasian | Caucasian | Caucasian |
Diagnosis | adenocarcinoma w/liver metastases | adenocarcinoma w/no metastasis | adenocarcinoma w/liver metastases |
Tumor stage | IV | IIB | IV |
Tumor grade | poorly differentiated | moderately differentiated | poorly differentiated |
Tumor content | 60% | 50% | 40–50% |
Sequenced biopsy | liver metastasis | primary tumor | liver metastasis |
Clinical status | Received treatment |
Did not receive treatment: no recurrence after 24 months | Received treatment |
Clinical benefit with FOLFOX (folinic acid, fluorouracil, oxaliplatin) systemic therapy for 24 weeks with 98% maximal serum CA19-9 reduction and partial metabolic response by EORTC PET criteria.
Transient clinical benefit with FOLFOX systemic therapy for 10 weeks with maximal serum CA19-9 reduction of 36% and RECIST (Response Evaluation Criteria in Solid Tumors) reduction of 21% in sum of largest diameters.
WGS metrics | Patient 1 | Patient 2 | Patient 3 | Normal human pancreas | |
Total amount of data generated (GB) | 271.75 | 315.80 | 420.3 | - | |
Q30 data generated (GB) | 207.80 | 237.67 | 352.7 | - | |
Average Total cluster densities (K/mm2) | 381.36 | 681.31 | 569.21 | - | |
Average PF cluster densities (K/mm2) | 348.80 | 521.76 | 466.84 | - | |
Average PF rate | 76.43 | 76.61 | 83.4 | - | |
Total number of reads | 2256848363 | 2767484751 | 2878046795 | - | |
Aligned Reads - Normal | 1052366015 | 1441444310 | 1271057635 | - | |
Aligned Reads - Tumor | 1204482348 | 1326040441 | 1606989160 | - | |
Aligned Bases - Normal | 96863052455 | 1.4991E+11 | 1.3219E+11 | - | |
Aligned Bases - Tumor | 1.11483E+11 | 1.37908E+11 | 1.67127E+11 | - | |
Average coverage depth - Normal | 31.31 | 48.46 | 42.73 | - | |
Average coverage depth - Tumor | 36.04 | 44.58 | 54.03 | - | |
Variant Analysis | BWA | BWA | BWA | - | |
Germline SNPs called | 2013281 | 2129857 | 3610297 | - | |
Transition/Transversion Ratio | 2.24 | 2.17 | 2.01 | - | |
dbSNP 129 rate | 87.59 | 87.65 | 87.29 | - | |
Non-synonymous germline variants | 504 | 10151 | 12830 | - | |
Somatic SNVs called (strict lists) | 20323 | 714 | 25 | - | |
False Positives (in dbSNP or 1000 Genomes) (strict lists) | 0.107 | 0.41 | 0.36 | - | |
Somatic indels called (CODING and UTR) | 8 | 5 | 3 | - | |
|
|||||
Total amount of data generated (GB) | - | 25.7 | 22.4 | 14.8 | |
Q30 data generated (GB) | - | 21.1 | 18.4 | 12.2 | |
Average Total cluster densities (K/mm2) | - | 810.0 | 694.0 | 1063.0 | |
Average PF cluster densities (K/mm2) | - | 680.4 | 589.2 | 533.6 | |
Average PF rate | - | 84.0 | 84.9 | 50.2 | |
Total number of reads | - | 272694175 | 247382440 | 377376444 | |
Total mapped reads | - | 124914613 | 104693716 | 98290756 |
Aligned reads for both tumor and normal libraries were evaluated to identify genomic events including non-synonymous SNVs (nsSNVs), indels, and copy number variants (CNVs). Summaries of identified variants in each patient are shown in
Patient | Chr. | Location | Gene Name | Coding event | Alteration | Sequence Change | Effect |
1 | 13 | 31805365 |
|
Indel | deletion |
|
NMD |
1 | 1 | 86818484 |
|
Indel | deletion | CCTACA | no NMD |
1 | 1 | 52078651 |
|
Indel | deletion | TCT | no NMD |
1 | 9 | 124313206 |
|
Indel | insertion | T | NMD unknown; frameshift |
1 | 8 | 8272117 |
|
Indel | insertion | G | NMD; frameshift |
1 | 19 | 57578957 |
|
Indel | deletion | A | NMD; frameshift |
1 | 17 | 7518264 |
|
SNV | R248W | G/A | damaging |
1 | 12 | 25289551 |
|
SNV | G12V | C/A | damaging |
1 | 4 | 55642955 |
|
SNV | T1258M | G/A | damaging |
1 | 3 | 131766828 |
|
SNV | S321N | G/A | tolerated |
1 | 4 | 185938530 |
|
SNV | K143X | T/A | termination |
1 | 11 | 129794256 |
|
SNV | L288F | G/A | damaging |
1 | 11 | 94172689 |
|
SNV | R229X | C/T | termination |
1 | 4 | 114415228 |
|
SNV | G553R | G/A | damaging |
1 | 10 | 28312788 |
|
SNV | F270Y | A/T | tolerated |
1 | 17 | 44591549 |
|
SNV | T217M | C/T | damaging |
1 | 7 | 33976531 |
|
SNV | W123X | G/A | termination |
1 | 4 | 24419470 |
|
SNV | A336G | G/C | damaging |
1 | 17 | 42569603 |
|
SNV | H615Q | A/T | damaging |
1 | 16 | 79619379 |
|
SNV | A305P | G/C | damaging |
1 | 5 | 1387414 |
|
SNV | A294V | G/A | damaging |
1 | 5 | 156718707 |
|
SNV | R232M | G/T | tolerated |
1 | 1 | 55090513 |
|
SNV | C511F | C/A | damaging |
1 | 11 | 117156429 |
|
SNV | R118H | C/T | tolerated |
1 | 19 | 48702891 |
|
SNV | F239S | A/G | tolerated |
1 | 4 | 126592198 |
|
SNV | L1824S | T/C | tolerated |
1 | 2 | 169467273 |
|
SNV | C97X | C/A | termination |
1 | 4 | 144580767 |
|
SNV | P456Q | C/A | damaging |
1 | 4 | 90388090 |
|
SNV | R732L | C/A | damaging |
1 | 1 | 67628381 |
|
SNV | K676N | G/T | damaging |
1 | 15 | 72213782 |
|
SNV | R545H | G/A | damaging |
1 | X | 48707520 |
|
SNV | R158H | C/T | damaging |
1 | 6 | 24664901 |
|
SNV | D924N | C/T | tolerated |
1 | 19 | 59437727 |
|
SNV | E114D | C/A | tolerated |
1 | 4 | 88985483 |
|
SNV | I147F | A/T | damaging |
1 | 7 | 141354973 |
|
SNV | K109M | A/T | damaging |
1 | 12 | 47726693 |
|
SNV | L1462F | G/A | no prediction |
1 | 22 | 24494160 |
|
SNV | D95H | G/C | tolerated |
1 | 11 | 112610991 |
|
SNV | V8M | G/A | tolerated |
1 | 4 | 96980836 |
|
SNV | L171P | T/C | damaging |
1 | X | 24816075 |
|
SNV | R1360H | G/A | tolerated |
1 | 12 | 10926455 |
|
SNV | Q70H | C/A | damaging |
1 | 9 | 134965563 |
|
SNV | V773I | C/T | damaging |
1 | 7 | 5658630 |
|
SNV | H643P | T/G | tolerated |
1 | 8 | 10505716 |
|
SNV | P1101H | G/T | damaging |
1 | 11 | 57138427 |
|
SNV | P477T | C/A | tolerated |
1 | 4 | 975236 |
|
SNV | Q86K | G/T | damaging |
1 | 5 | 150648782 |
|
SNV | E89X | C/A | termination |
1 | 14 | 37748706 |
|
SNV | R121C | C/T | tolerated |
1 | 16 | 1068870 |
|
SNV | M1V | A/G | damaging |
1 | 9 | 129482338 |
|
SNV | V515I | G/A | tolerated |
1 | 3 | 33170461 |
|
SNV | L223V | G/C | tolerated |
1 | 16 | 19359304 |
|
SNV | G148V | G/T | damaging |
1 | 3 | 113263420 |
|
SNV | K343N | G/T | tolerated |
1 | 1 | 173638901 |
|
SNV | A325E | G/T | tolerated |
1 | 19 | 59634093 |
|
SNV | P346L | C/T | tolerated |
1 | 18 | 72721138 |
|
SNV | V354L | G/T | tolerated |
1 | 16 | 4755940 |
|
SNV | E14G | T/C | tolerated |
2 | 4 | 88756318 |
|
Indel | deletion | GACAGCAGC | no NMD; frameshift |
2 | 2 | 153184312 |
|
Indel | insertion | CCA | no NMD |
2 | 2 | 233420470 |
|
Indel | deletion | ACA | NMD; frameshift |
2 | 8 | 89150850 |
|
Indel | insertion | A | NMD unknown; frameshift |
2 | 12 | 25289551 |
|
SNV | G12V | C/A | damaging |
2 | 19 | 2242562 |
|
SNV | G72S | C/T | tolerated |
2 | 17 | 1508349 |
|
SNV | F1818C | A/C | damaging |
2 | 21 | 43397525 |
|
SNV | S34F | G/A | damaging |
3 | 19 | 51042923 |
|
Indel | deletion | GA | no NMD |
3 | 12 | 25289552 |
|
SNV | G12R | C/G | damaging |
3 | 17 | 47065916 |
|
SNV | R295H | C/T | damaging |
3 | 1 | 156418469 |
|
SNV | A118T | G/A | tolerated |
3 | 16 | 65505678 |
|
SNV | L241P | A/G | tolerated |
3 | 10 | 135290149 |
|
SNV | T30S | T/A | tolerated |
3 | 19 | 55006332 |
|
SNV | L198F | G/A | possibly damaging |
3 | 7 | 142361153 |
|
SNV | A313T | C/T | tolerated |
3 | 14 | 23954072 |
|
SNV | A1093T | G/A | damaging |
3 | 14 | 76650235 |
|
SNV | R341C | C/T | damaging |
3 | 1 | 70277766 |
|
SNV | A1191V | C/T | damaging |
3 | 7 | 4251904 |
|
SNV | A2108T | G/A | tolerated |
3 | 1 | 12300948 |
|
SNV | E2461K | G/A | tolerated |
3 | 19 | 58772525 |
|
SNV | E300A | A/C | damaging |
Effects were determined using SIFT/Polyphen-2.
NMD = nonsense mediated decay.
Patient | Chromosome | CNV |
Physical Position (Mb) | Patient | Chromosome | CNV1 | Physical Position (Mb) |
1 | 1p | Loss | 0.8–29.0 | 1 | 13q | Focal Loss | 18.6–20.7 |
1 | 1q | Focal Gain | 143.7–144.0 | 1 | 13q | Loss | 25.2–87.2 |
1 | 20p | Loss | 0.2–18.8 | 1 | 13q | Focal Loss | 111.7–114.2 |
1 | 21p | Focal Gain | 9.9 | 1 | 14q | Loss | 41.4–73.3 |
1 | 21q | Loss | 13.9–46.9 | 1 | 15q | Focal Gain | 19.3 |
1 | 22q | Focal Loss | 15.4–16.7 | 1 | 16p | Focal Loss | 0.5–1.3 |
1 | 2p | Loss | 17.6–63.3 | 1 | 16q | Focal Gain | 69.7 |
1 | 2q | Loss | 189.0–242.5 | 1 | 17p | Loss | 0.06–21.2 |
1 | 3p | Loss | 38.5–77.2 | 1 | 18p | Loss | 3.2–10.7 |
1 | 3q | Gain | 162.1–175.5 | 1 | 18q | Focal Loss | 71.0–76.0 |
1 | 4p | Loss | 0.3–20.7 | 1 | 19p | Loss | 0.2–24.1 |
1 | 4q | Loss | 184.0–189.4 | 1 | 19q | Loss | 34.3–59.4 |
1 | 5q | Loss | 52.9–133.8 | 2 | 1p | Loss | 53.3–115.0 |
1 | 5q | Focal Loss | 69.3–70.4 | 2 | 1q | Loss | 177.8–198.4 |
1 | 5q | Focal Loss | 118.3–119.0 | 2 | 3q | Focal Gain | 106.7–107.0 |
1 | 6p | Focal Loss | 32.1–32.1 | 2 | 5p | Focal Gain | 1.3 |
1 | 6q | Loss | 57.1–134.6 | 2 | 5p | Gain | 31.5–50.8 |
1 | 6q | Loss | 154.4–170.8 | 2 | 8q | Focal Gain | 131.2–135.7 |
1 | 6q | Focal Loss | 157.6–158.0 | 2 | 15q | Focal Gain | 19.8–19.9 |
1 | 6q | Focal Loss | 167.9–168.0 | 2 | 17p | Focal Loss | 0.09 |
1 | 7p | Loss | 0.5–6.0 | 2 | 18p | Gain | 9.1–14.2 |
1 | 7q | Focal Loss | 74.1 | 3 | 1p | Focal Loss | 1.1–3.6 |
1 | 8p | Focal Loss | 21.9–30.1 | 3 | 1p/q | Gain | 120.0–143.7 |
1 | 8q | Gain | 100.8–146.3 | 3 | 3q | Focal Loss | 121.8–121.9 |
1 | 9p | Loss | 0.3–27.5 | 3 | 4p | Focal Loss | 1.7–3.4 |
1 | 9p | Focal Loss | 19.7–22.0 | 3 | 4q | Focal Loss | 69.1 |
1 | 10p | Loss | 0.2–22.4 | 3 | 5p | Focal Gain | 32.4 |
1 | 10q | Loss | 67.6–135.3 | 3 | 9q | Focal Loss | 136.3–138.4 |
1 | 11p | Loss | 0.2–36.3 | 3 | 12p | Focal Gain | 23.9–26.4 |
1 | 12q | Loss | 60.5–132.3 | 3 | 18q | Focal Loss | 74.8–75.3 |
Focal gains/losses are defined as CNVs occurring across regions that are < = 5 Mb.
Whole transcriptome sequencing was performed for patients 2 and 3 and normal human pancreatic RNA (
Overall, in patient 2, 1,841 genes showed significant expression changes (q<0.05, corrected for multiple testing), whereas in patient 3, 1,939 genes showed significant changes. From these two analyses, 877 common genes/isoforms were identified as showing significant expression changes. Genes demonstrating both CNVs and significant expression changes (in patients 2 and 3) are listed in
Well-established genes implicated in PAC include
The R248 SNV identified in
Copy number gains encompassing
Somatic CNV losses identified using WGS also encompassed
We further performed aCGH analysis on patient 1's tumor and validated all CNVs described here (
Many patients who are treated with gemcitabine and 5-FU based treatments often fail and are thus interested in and positioned to try additional agents that might offer benefit. Knowledge of the specific mutations in a patient's cancer may indicate targetable drivers and an oncologist and physician may decide to empirically treat the tumor based off the hypothesis that targeting the mutant may offer benefit. Our WGS findings thus provide insight into potential therapeutic options as well as patients' responses to treatments. For patient 1, based off the deletion and copy number loss identified for
Patient 2 did not harbor any events in
Aside from
In patient 2, we identified 9 regions, covering 114 genes that demonstrate copy number alterations (
Copy number validation was performed using flow sorted aCGH which involves flow sorting nuclei from the tumor biopsy to identify aneuploid populations. The sorted aneuploid population is then separately analyzed using aCGH. Using this analysis, we validated CNV gains identified using WGS in
1,841 genes demonstrating significant expression changes (q<0.05, corrected for multiple testing) in the tumor were identified. COSMIC genes demonstrating significant expression changes are listed in
Patient | Gene | ln (fold change) | q-value (corrected) | Patient | Gene | ln (fold change) | q-value (corrected) |
2 |
|
−4.37 | 2.92E-03 | 3 |
|
5.51 | 5.50E-03 |
2 |
|
−3.84 | 2.25E-02 | 3 |
|
4.42 | 4.52E-02 |
2 |
|
−3.19 | 4.89E-02 | 3 |
|
−5.32 | 3.12E-05 |
2 |
|
−5.68 | 5.53E-06 | 3 |
|
4.43 | 4.40E-03 |
2 |
|
4.42 | 4.77E-03 | 3 |
|
3.93 | 1.40E-02 |
2 |
|
4.25 | 1.05E-02 | 3 |
|
6.64 | 2.06E-07 |
2 |
|
5.86 | 1.67E-05 | 3 |
|
5.22 | 2.58E-04 |
2 |
|
3.48 | 2.31E-02 | 3 |
|
−3.38 | 1.93E-02 |
2 |
|
4.54 | 1.74E-03 | 3 |
|
5.94 | 1.10E-03 |
2 |
|
3.42 | 3.88E-02 | 3 |
|
3.94 | 4.65E-03 |
2 |
|
3.75 | 1.79E-02 | 3 |
|
−3.27 | 4.73E-02 |
2 |
|
3.96 | 1.39E-02 | 3 |
|
−3.26 | 1.87E-02 |
2 |
|
−5.98 | 4.53E-08 | 3 |
|
−4.05 | 2.27E-03 |
2 |
|
−5.50 | 1.37E-05 | 3 |
|
−2.98 | 4.97E-02 |
2 |
|
−4.56 | 1.17E-02 | 3 |
|
4.23 | 5.35E-03 |
2 |
|
−6.44 | 4.06E-02 | 3 |
|
−2.82 | 4.28E-02 |
2 |
|
−4.82 | 3.02E-03 | 3 |
|
−3.21 | 1.67E-02 |
2 |
|
−4.94 | 2.17E-03 | 3 |
|
−4.38 | 2.09E-03 |
2 |
|
−3.23 | 3.85E-02 | 3 |
|
4.35 | 1.61E-03 |
2 |
|
3.89 | 1.16E-02 | 3 |
|
3.47 | 3.65E-02 |
2 |
|
−3.81 | 5.77E-03 | 3 |
|
−3.22 | 2.03E-02 |
2 |
|
3.33 | 1.60E-03 | 3 |
|
4.12 | 6.01E-03 |
2 |
|
−4.86 | 1.40E-04 | 3 |
|
2.99 | 4.69E-02 |
2 |
|
−3.84 | 5.91E-03 | 3 |
|
−3.13 | 1.86E-02 |
2 |
|
3.45 | 4.62E-02 | 3 |
|
3.08 | 3.75E-02 |
2 |
|
3.46 | 1.77E-02 | 3 |
|
4.05 | 3.35E-03 |
2 |
|
−3.47 | 6.01E-03 | 3 |
|
−3.10 | 3.29E-02 |
2 |
|
5.71 | 2.73E-05 | 3 |
|
4.12 | 7.57E-03 |
2 |
|
−3.23 | 2.54E-02 | 3 |
|
4.41 | 5.20E-03 |
2 |
|
−3.43 | 1.17E-02 | 3 |
|
3.28 | 3.40E-02 |
2 |
|
4.90 | 1.42E-03 | 3 |
|
3.84 | 2.57E-02 |
2 |
|
3.82 | 3.95E-02 | 3 |
|
4.44 | 2.54E-02 |
2 |
|
4.36 | 3.80E-03 | 3 |
|
6.92 | 2.04E-07 |
2 |
|
3.66 | 2.19E-02 | 3 |
|
6.49 | 5.05E-07 |
2 |
|
4.57 | 1.85E-03 | 3 |
|
4.77 | 3.84E-04 |
2 |
|
3.43 | 1.76E-02 | 3 |
|
3.61 | 8.25E-03 |
2 |
|
3.92 | 6.18E-03 | 3 |
|
8.99 | 2.97E-04 |
2 |
|
4.04 | 6.85E-03 | 3 |
|
8.30 | 1.24E-03 |
2 |
|
3.57 | 2.75E-02 | 3 |
|
3.99 | 8.54E-03 |
2 |
|
3.87 | 1.21E-02 | ||||
2 |
|
4.71 | 6.57E-04 | ||||
2 |
|
9.15 | 2.05E-04 | ||||
2 |
|
4.24 | 5.10E-03 | ||||
2 |
|
4.24 | 3.63E-03 | ||||
2 |
|
−5.56 | 1.57E-05 |
Selected genes are genes that are reported in COSMIC.
RNAseq was performed on patients 2 and 3.
Transcriptomic analysis led to the identification of significantly altered expression of genes that have been previously implicated in cancer. Significantly up-regulated genes in the tumor include
Down-regulated genes include
Of the fusion transcripts identified in patient 2, 2 genes that were identified as part of fusions also demonstrated statistically significant expression changes (q-value<0.05, corrected;
Following resection of the tumor, patient 2 was treated with chemoradiation followed by gemcitabine and erlotinib, and at 16 months post-resection, has not experienced a recurrence. The absence of somatic events affecting DNA repair genes and genes including
Patient 3 did not harbor any events in
Overall, CNV analysis of patient 3 led to the identification of 10 regions, covering 34 genes that demonstrated CNV alterations (COSMIC genes falling within these regions are listed in
In patient 3, 1,939 genes were found to demonstrate significant expression changes (q<0.05, corrected for multiple testing) in the tumor. Selected genes are listed in
Similar to patient 2, significant up-regulated expression was identified for
In patient 3, we detected putative fusion transcripts supported by the identification of reads spanning the transcript breakpoint (
We also identified
Prior to biopsy, patient 3 was first treated with TH-302, an investigational drug that activates nitroazole under hypoxic conditions plus gemcitabine as part of a phase I clinical trial. He had transient clinical benefit at first but progressed and was then treated with gemcitabine and nab-paclitaxel, but the disease continued to progress. Our identification of a copy number gain in and increased expression of
While the goal of this study is to perform patient-specific analyses, we also performed pathway analysis across all patients to evaluate affected biological processes. This type of analysis is preceded by Jones
Using GeneGo's Metaminer Pancreatic Cancer Disease module, we evaluated the extent to which 21 annotated pancreatic cancer pathways are affected in the three patients (
Whole genome and RNAseq data were integrated and analyzed using GeneGo's Metaminer Pancreatic Cancer Disease module to identify pathways that may be affected by mutations and/or significant expression changes (q-value<0.05, corrected). The top pathways (minimum mapping p-value across all WGS and RNAseq datasets <0.05) are summarized based off of GeneGo maps. Breakdown of affected pathways in each patient are shown in
Genomic events and expression changes were also analyzed across the entire GeneGo pathway database in order to perform an unbiased global analysis and to identify processes that may not be captured in the Pancreatic Cancer Disease module. The top map categories that were identified as demonstrating the largest number of alterations include prostatic neoplasms, hepatocellular carcinoma, and pancreatic neoplasms (
As expected, processes in the top pathways (minimum mapping p-value<0.05) identified through NGS analyses overlap with the 12 core signaling pathways previously reported by Jones
Although patient 1's tumor demonstrated the highest number of mutations, RNAseq data from patients 2 and 3 showed widespread pathway overlap with these genomic events. Patient 1 also uniquely harbors mutations that affect DNA repair pathways with respect to mismatch and nucleotide excision repair, DNA damage-induced responses, and BRCA1 as a transcription regulator. The larger number of identified genomic events in patient 1 may be associated with alterations in genes involved in DNA repair pathways and likely represents passenger mutations. Although tumors from patients 2 and 3 demonstrated fewer mutations, RNAseq data from these two patients suggest that common pathways are affected across all 3 patients.
Pathway analysis of WGS and RNAseq data allows us to understand which tumorigenic processes are present across the three patients. However, it is also important to recognize several caveats including: (1) mutations that are detected in larger genes (such as
Due to the lack of effectiveness of current treatments for PAC patients, we are tasked with improving our understanding of genomic aberrations and processes that drive PAC tumorigenesis, tumor progression, and malignancy in order to identify and develop efficacious treatments. Our approach involves individually characterizing patients to fully understand the range of molecular events associated with this disease. In this study, we report our findings of 3 individual genomic characterizations of tumors collected from 3 separate patients. In 2 of the 3 patients, we additionally performed RNAseq on the same whole genome sequenced biopsies to identify significant expression changes and fusion transcripts that may be associated with tumorigenesis and that may be linked to the genomic events identified from WGS. With this patient-specific characterization, we identified potentially actionable therapeutic targets and contribute our findings to the research and clinical communities. Using this approach, we also detected aberrations that have not been previously reported in PAC, but may represent viable targets in other patients who also carry the same alteration. While further studies are needed to determine which aberrations are passenger and driver mutations, these results contribute valuable information to our understanding of the disease.
The utility of RNAseq data is clear when considering our analyses of patients 2 and 3, compared to patient 1. While WGS allowed us to identify non-synonymous mutations and copy number changes in patient 1, expression data provides more information on likely affected biological processes. As needle biopsies are most commonly performed, analyses are typically limited by the availability of tumor biopsy tissue. This limitation thereby obstructs proteomic analyses. However, by layering in RNAseq data, we acquire a more detailed picture of potentially tumorigenic events in individual patients. By evaluating these changes, our aim is to demonstrate the utility of using NGS to understand what molecular events are occurring in the tumors of separate patients and to move towards a more detailed understanding of the spectrum of aberrations that occur in this disease. In doing so, this information may point to additional therapeutic options that clinicians may consider during therapeutic selection. Furthermore, identification of targets that fall outside of FDA-approved pharmaceuticals or clinical trials serves to provide novel and relevant areas of research for drug development. One caveat here is that such analyses are dependent on the quality and tumor content of the biopsies that are collected. The percentage of tumor cells in the 3 analyzed patients' biopsies ranged from 40% to 60%, average mapped coverages ranged from 31× to 54× using WGS, and using RNAseq, over 100 million mapped reads were achieved in each of patients 2 and 3. We show that an average tumor content of approximately 50% is sufficient for NGS analysis of tumor biopsies. Under circumstances whereby only biopsies with lower tumor contents are available, NGS analyses may prove to be difficult, particularly for the identification of heterozygous mutations, and otherwise will require an increase in coverage and an increase in the number of reads needed to identify pertinent genomic events and changes in gene expression. A second caveat in this study is that mutations that were not present in the original tumor may arise while patients are undergoing therapy and potentially hinder the efficacy of the treatment. While our understanding of the details surrounding such events is limited, additional sequencing of patients at different time points before, during, and after treatments, will allow us to begin to understand the contribution of these aberrations to the disease.
Given our findings, the advantages of whole genome and transcriptome NGS in cancer patients are threefold—(1) foremost is our ability to survey the entire genome and transcriptome in order to detect abnormalities that may be missed using currently available cancer testing panels, (2) the identification of expression changes that may be associated with genomic events or that point to putative drug targets, and (3) the annotation of PAC genomes that provide insight into the molecular and cellular events involved in tumorigenesis. The utility of NGS has also been demonstrated in other sequencing studies that have used this technology to evaluate genomic rearrangements in pancreatic cancer
Detailed methods are listed here.
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We would like to thank the patients and their families for contributing to this study.