Supplementary Materials and Methods




Download 25.54 Kb.
NameSupplementary Materials and Methods
A typeDocumentation
Supplementary Information
Supplementary Materials and Methods

Patients and samples
Eighteen formalin-fixed paraffin embedded (FFPE) tumor tissues were obtained at the time of diagnosis from the following four centers between 01/01/2005 and 12/31/2016: (1) The First Affiliated Hospital of Zhengzhou University, (2) Hospital of Xiping City, (3) Cancer Hospital of Anyang, and (4) The First Affiliated Hospital of China Three Gorges University. Among the 18 SPTCL cases, 10 cases were from our cancer center, while the remaining 8 cases were from other cancer centers. We therefore chose the 10 cases of SPTCL from our cancer center for whole-exome sequencing, and then validated these findings in the remaining 8 SPTCL cases by targeted sequencing. The study was conducted in accordance with the Declaration of Helsinki. Signed informed consent was obtained from the patients. All cases were reviewed and interpreted independently by three experienced pathologists from our hospital, and the diagnoses were made according to the current World Health Organization classification criteria. The demographics, clinical and immunohistological features of the patient cohort are summarized in Supplementary Table 1. This study was conducted under the Institutional Review Board approval from the First Affiliated Hospital of Zhengzhou University.
Immunohistochemistry and TCR gene rearrangement assays

The FFPE sections were immunostained using the Dako EnVision™ Flex+ System (K8012; Dako, Glostrup, Denmark) as previously described (Fan et al, 2013). Deparaffinization and epitope unmasking were carried out in a PT-Link using an EnVision™ Flex target retrieval solution (Dako, Carpinteria, CA, USA). The sections were treated with 0.3% hydrogen peroxide (H2O2) for 5 min to block endogenous peroxidase. Sections were incubated overnight at 4°C with the following antibodies: (1) CD2 (AB75; 1:40; Leica, Buffalo Grove, IL), (2) CD3 (polyclonal; 1:25; Dako), (3) CD4 (1F6; 1:150; Novocastra, Newcastle upon Tyne, UK), (4) CD5 (Clone 4C7; 1:50; Dako), (5) CD7 (Clone CBC.37; 1:50; Dako), (6) CD8 (Clone C8/144B; 1:50; Dako), (7) CD20 (L26; 1:200; Dako), (8) CD79a (Clone JCB117; 1:50; Dako), (9) CD30 (BerH2; 1:100; Dako), (10) CD56 (Clone 123C3; 1:100; Dako), (11) TIA1 (1:200; Biocare, Birmingham, UK), (12) granzyme B (Clone GrB-7; 1:50; Dako), (13) βF1 (8A3; 1:20; Endogen, Rockford, IL), (14) Ki-67 (Clone MIB-1; 1:50; Dako), (15) phospho-Akt (Ser473) (D9E; 1:100; Cell Signaling Technology, Beverly, MA) and (16) phospho-4E-BP1 (Thr37/46) (236B4; 1:100; Cell Signaling Technology). The specimens were subsequently treated with EnVision™ Flex linker mouse or rabbit (15 min), EnVision™ Flex/HRP enzyme (30 min), and 3′3-diaminobenzidine tetrahydrochloride (10 min). The samples were counterstained with hematoxylin, dehydrated and mounted on a Richard-Allan Scientific Cyto seal XYL (Thermo Scientific, Waltham, MA, USA). The sample series included appropriate positive and negative controls. Phospho-AKT1 and phospho-4E-BP1 expression levels were scored semi-quantitatively based on the percentage of positive cells utilizing the following scale: +, <25%; ++, 25–49%; +++, 50–74%; and ++++, 75–100%.

TCR gene rearrangement assays were performed on DNA extracted from paraffin-embedded tissue using the IdentiClone TCRB, TCRG, and TCRD Gene Clonality Assays (InVivoScribe Technologies, San Diego, CA). The PCR products were separated by capillary electrophoresis with an automated sequencing system (ABI 3500, Applied Biosystems, Foster City, CA) and analyzed with GeneScan software (Applied Biosystems).
DNA extraction

Genomic DNA from whole blood and FFPE samples was extracted using the Blood & Cell Culture DNA Kit (Qiagen, Hilden, Germany) and the QIAamp DNA FFPE Tissue Kit (Qiagen), respectively. The quality and yield of purified DNA were assessed by fluorimetry (Qubit, Invitrogen), Nanodrop 1000 spectrophotometry (Thermo Scientific, Wilmington, DE, USA) and gel electrophoresis.
WES and bioinformatics analysis

To identify somatic genomic variants associated with SPTCL, we performed WES on 10 tumor samples and 3 matched whole blood samples. Genomic DNA (1-1.5 µg) was fragmented with a Covaris ultrasonicator targeting peak sizes ranging from 180-280 bp. The fragment ends were blunted and 5′ phosphorylated with T4 polynucleotide kinase, T4 DNA polymerase, and Klenow Large Fragment (New England BioLabs). The 3′ ends were A-tailed using Klenow Exo-Minus (New England BioLabs), and the fragments were ligated to Illumina paired-end adaptors. Ligation products were purified with Agencourt AMPure XP beads and enriched by PCR using the Illumina PCR primers InPE1.0 and InPE2.0 and PCR primer indices. Pooled, indexed libraries were captured using the Agilent SureSelect Human All Exon 50 Mb kit (Agilent Technologies) according to the manufacturer’s protocol and sequenced on an Illumina HiSeq 2500 instrument.

Sequencing reads were aligned to the human reference genome (hg19, downloaded from the UCSC Genome Browser http://genome.ucsc.edu/) using Burrows-Wheeler Aligner (BWA) (Li & Durbin, 2010) version 0.5.8 with default parameter settings. SAMtools (Li et al, 2009) was used to convert the SAM files into BAM files and to pile up after local alignment. Picard was used to remove PCR duplications. Then, insertion or deletion (InDel) realignment and base quality score recalibration were performed with the Gnome Analysis Toolkit (GATK) (DePristo et al, 2011) version 2.6.5. For paired tumor-normal tissue samples, somatic single nucleotide variations (SNV) and somatic InDels were called by MuTect (Cibulskis et al, 2013) and Strelka (Saunders et al, 2012), respectively. For the seven non-paired tumor samples with no matched germline DNA, probable somatic variants were detected by SomVarIUS (Smith et al, 2016) with the default settings. Variants were further filtered to remove variants that were present in dbSNP v135 (http://www.ncbi.nlm.nih.gov/SNP/), the 1000 Genomes project (http://browser.1000genomes.org), or an in-house database containing germline variants identified in approximately 500 Chinese exomes but not in the Catalogue of Somatic Mutations in Cancer (Forbes et al, 2015) (COSMIC, v64, http://cancer.sanger.ac.uk/cosmic/). These variants were also visually inspected with the Integrative Genomics Viewer (Robinson et al, 2011) (IGV, https://www.broadinstitute.org/igv/) to exclude probable sequencing artifacts. Gene mutation annotation of the identified variants was carried out using ANNOVAR (Wang et al, 2010). The impact of the SNVs on protein function was predicted by PolyPhen-2 (Adzhubei et al, 2010), SIFT (Kumar et al, 2009) or MutationTaster (Schwarz et al, 2014).

For pathway analysis, we used the PathScan algorithm (Wendl et al, 2011) (http://genome.wustl.edu/software/pathscan) to identify significant pathways enriched for somatic mutations in annotated KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www.genome.jp/kegg/) pathways. p<0.01 and an FDR q-value <0.1 were considered statistically significant.

Whole-exome sequencing data were deposited into the NCBI Sequence Read Archive under Accession Code SRP078777.
Targeted sequencing

We designed a custom panel of 560 genes (Supplementary Table 6) using the SureDesign Tool (Agilent Technologies). Sequencing libraries were prepared from DNA extracted from 8 paired tumor and normal samples using the SureSelect XT2 Target Enrichment System for the Illumina Multiplexed Sequencing Platform (Illumina) according to the manufacturer’s instructions. Target-enriched libraries were then sequenced on an Illumina HiSeq 2500 sequencing platform. A bioinformatics analysis was performed as described for the exome sequencing analysis. All candidate variants were manually inspected in IGV to exclude false positives.
Sanger sequencing

Selected SNVs detected by WES were validated by Sanger sequencing. Primers specific to the regions of interest were designed and synthesized by Sangon Biotech (Shanghai, China, Supplementary Table 3). PCR was performed using standard procedures followed by direct sequencing on an ABI 3730xl automatic sequencer (PE Applied Biosystems, Foster City, CA).
Cell viability assay in primary SPTCL cells

Primary SPTCL samples were harvested from patient #SP1. Single-cell suspensions were prepared, and the tumor cells were enriched and purified with CD8+ and CD56- MicroBeads (Miltenyi Biotec, CA, USA). The purity of the isolated CD8+CD56- cell populations was evaluated by flow cytometry after labeling with CD8-FITC, CD56-PE and CD3-APC (BD Biosciences, San Jose, CA, USA). The purified primary SPTCL cells were cultured in RPMI 1640 (Invitrogen, California, USA) supplemented with 10% human serum (Sigma-Aldrich, St Louis, MO, USA), 2 mM L-glutamine (Invitrogen), 100 U/ml penicillin (Invitrogen), 100 µg/ml streptomycin (Invitrogen) and 500 IU/ml interleukin-2 (PeproTech, Rocky Hill, NJ, USA). Ten thousand cells were seeded in 96-well plates in three technical replicates and treated with the mTOR and/or PI3K inhibitors rapamycin (#S1039; Selleck Chemicals), everolimus (#S1120; Selleck Chemicals), or apitolisib (#S2696; Selleck Chemicals), the NF-κB inhibitor BAY 11-7082 (#S2913; Selleck Chemicals) and the HDAC inhibitor romidepsin (#S3020; Selleck Chemicals) for 72 h at the indicated concentrations. Cell viability was measured with a Cell Counting Kit-8 Assay (Dojindo, Kumamoto, Japan). The efficacies of the inhibitors were further evaluated by assessing their effects on downstream targets, including phospho-p70S6K, phospho-4E-BP1 and phospho-AKT1, by performing western blot analysis. All these experiments were repeated at least twice with each inhibitor.
Western blot analysis

Western blot analysis was performed as described previously (Li et al, 2013). Briefly, cells were lysed in cold lysis buffer (Tris-HCl pH 7.5, 1 mM EDTA, 150 mM NaCl, 0.5% Triton X-100, 1 mM MgCl2, phosphatase inhibitor mixture [1 mM Na3VO4, 1 mM NaF and 1 mM β-glycerophosphate], and a protease inhibitor mixture [1 mM PMSF, 1 µg/ml leupeptin, 2 µg/ml aprotinin and 1 µg/ml pepstatin A]). The cell lysates were clarified by centrifugation at 10000×g for 20 min. Proteins (10–25 μg) were resolved by SDS-PAGE and transferred onto nitrocellulose membranes (Amersham Biosciences, Piscataway, NJ, USA). The membranes were blocked in TBS-T buffer (20 mM Tris-HCl, pH 7.5, 150 mM NaCl and 0.05% Tween-20) containing 5% (w/v) non-fat milk at room temperature for 1 h and then probed at 4°C overnight with antibodies to detect Akt (pan) (C67E7), phospho-Akt (Ser473) (D9E), phospho-4E-BP1 (Thr37/46) (236B4), and phospho-p70 S6 Kinase (Thr389) (108D2) from Cell Signaling Technology (Boston, MA, USA); 4E-BP1 (R-113, sc-6936) and p70 S6 kinase (C-18, sc-230) from Santa Cruz Biotech (Santa Cruz, CA, USA); and β-actin (Clone: 2D4H5) from ProteinTech (Chicago, IL, USA). Detection was carried out with the SuperSignal West Femto Maximum Sensitivity Substrate Trial Kit (Pierce, Rockford, IL, USA). The band images were digitally captured and quantified with a FluorChem FC2 imaging system (Alpha Innotech, San Leandro, CA, USA).
Statistical analysis

All data were expressed as the mean ± s.e.m. Comparisons between and among groups were performed with Student’s t-test and ANOVA, respectively. Fisher’s exact test was used to analyze the differential expression of phospho-AKT1 and phospho-4E-BP1 in the SPTCL tumor samples with or without PI3K/AKT/mTOR pathway mutations. Statistical analysis was carried out using GraphPad Prism software version 5.0 (GraphPad Software Inc., La Jolla, CA) or the online statistics calculator VassarStats (www.vassarstats.net). p<0.05 was considered significant.
Supplementary References
Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR (2010) A method and server for predicting damaging missense mutations. Nat Methods 7: 248-249
Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Gabriel S, Meyerson M, Lander ES, Getz G (2013) Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol 31: 213-219
DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43: 491-498
Fan H, Yuan Y, Wang J, Zhou F, Zhang M, Giercksky KE, Nesland JM, Suo Z (2013) CD117 expression in operable oesophageal squamous cell carcinomas predicts worse clinical outcome. Histopathology 62: 1028-1037
Forbes SA, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H, Ding M, Bamford S, Cole C, Ward S, Kok CY, Jia M, De T, Teague JW, Stratton MR, McDermott U, Campbell PJ (2015) COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res 43: D805-811
Kumar P, Henikoff S, Ng PC (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 4: 1073-1081
Li H, Durbin R (2010) Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26: 589-595
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Subgroup GPDP (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25: 2078-2079
Li Z, Tian T, Lv F, Chang Y, Wang X, Zhang L, Li X, Li L, Ma W, Wu J, Zhang M (2013) Six1 promotes proliferation of pancreatic cancer cells via upregulation of cyclin D1 expression. PloS one 8: e59203
Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP (2011) Integrative genomics viewer. Nat Biotechnol 29: 24-26
Saunders CT, Wong WS, Swamy S, Becq J, Murray LJ, Cheetham RK (2012) Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28: 1811-1817
Schwarz JM, Cooper DN, Schuelke M, Seelow D (2014) MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods 11: 361-362
Smith KS, Yadav VK, Pei S, Pollyea DA, Jordan CT, De S (2016) SomVarIUS: somatic variant identification from unpaired tissue samples. Bioinformatics 32: 808-813
Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38: e164
Wendl MC, Wallis JW, Lin L, Kandoth C, Mardis ER, Wilson RK, Ding L (2011) PathScan: a tool for discerning mutational significance in groups of putative cancer genes. Bioinformatics 27: 1595-1602

Legends to supplementary tables and figures

Fig S1. Whole-exome sequencing of 10 SPTCL cases. (A) The number and type of somatic non-synonymous mutations. (B) The spectrum of nucleotide substitutions identified in each case. SNV, single nucleotide variant.

Fig S2. Immunohistochemical study of phospho-AKT1 and phospho-4E-BP1 in SPTCL tumor samples from mutated (n=8) or wild type (n=10) PI3K/AKT/mTOR pathway samples. Expression levels of (A) phospho-AKT1 and (B) phospho-4E-BP1 were scored semi-quantitatively based on the percentage of positive cells according to the following scale: +, <25%; ++, 25–49%; +++, 50–74%; and ++++, 75–100%. Scale bars are 50 µm. p values were calculated with a two-sided Fisher’s exact test.

Table S1. Demographics, clinical and immunohistological features of 18 subjects with SPTCL.

Table S2. Summary of whole-exome sequencing data of the 10 subjects with SPTCL.

Table S3. List of Sanger sequencing primers used for validation.

Table S4. List of somatic non-synonymous mutations affecting the known driver genes.

Table S5. Significantly enriched KEGG pathways according to the known driver genes with somatic non-synonymous mutations.

Table S6. List of 560 selected genes for the targeted sequencing.

Table S7. Summary of targeted sequencing data of the 8 subjects with SPTCL.

Table S8. Somatic non-synonymous mutations identified by targeted sequencing of the 8 subjects with SPTCL.




Share in:

Related:

Supplementary Materials and Methods iconSupplementary Materials and Methods and Supplementary Tables

Supplementary Materials and Methods iconSupplementary materials and methods

Supplementary Materials and Methods iconSupplementary Materials and Methods

Supplementary Materials and Methods iconSupplementary Materials and Methods

Supplementary Materials and Methods iconSupplementary Materials and Methods

Supplementary Materials and Methods iconSupplementary Materials and Methods

Supplementary Materials and Methods iconSupplementary Materials and methods

Supplementary Materials and Methods iconAdditional File 3 Supplementary Materials and Methods

Supplementary Materials and Methods iconSupplementary Materials and Methods, Tables, and Figure Legends

Supplementary Materials and Methods iconSupplementary Methods

Supplementary Materials and Methods iconSupplementary Methods

Supplementary Materials and Methods iconSupplementary Methods

Supplementary Materials and Methods iconSupplementary Methods

Supplementary Materials and Methods iconSupplementary 1: Material and methods

Supplementary Materials and Methods iconSupplementary information – Methods

Supplementary Materials and Methods iconText S1: Supplementary materials

Supplementary Materials and Methods iconRiba, et al. Supplementary Materials

Supplementary Materials and Methods iconDetailed materials and methods

Supplementary Materials and Methods iconSupporting Information Materials and methods

Supplementary Materials and Methods iconSupporting Information-Materials and Methods




manual




When copying material provide a link © 2017
contacts
manual-guide.com
search