News Article | May 10, 2017
No statistical methods were used to predetermine sample size. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment. The fresh-frozen tissue and blood samples analysed in the current study were obtained from Australian melanoma biospecimen banks, including the Melanoma Institute Australia (n = 160), Australasian Biospecimen Network-Oncology Cell Line Bank QIMR-Berghofer Institute of Medical Research (n = 15; all lines authenticated by DNA profiling and tested for mycoplasma contamination), Ludwig Institute for Cancer Research (n = 4) and Peter MacCallum Cancer Centre/Victorian (n = 4) biospecimen banks. All tissues and bloods form part of prospective collection of fresh-frozen samples accrued with written informed patient consent and institutional review board approval. Fresh surgical specimens were macro-dissected and tumour tissues were procured (with as little contaminating normal tissue as possible) and snap frozen in liquid nitrogen within 1 h of surgery. All samples were pathologically assessed before inclusion into the study, with samples requiring greater than 80% tumour content and less than 30% necrosis to be included. All samples were independently reviewed by melanoma pathologists (R.A.S., R.E.V.) to confirm the presence of melanoma and qualification of the above criteria. Samples requiring tumour enrichment underwent macrodissection or frozen tissue coring (Cryoxtract, Woburn, Massachusetts, USA) using a marked haematoxylin and eosin slide as a reference. The histopathology of all mucosal and acral samples was reviewed by R.A.S. to confirm the diagnosis. Acral melanomas were classified as occurring within acral skin of the palm of the hand, sole of the foot and under nail beds. The lack of hair follicles, thickened stratum corneum and clinical site was confirmed in all cases. Mucosal melanomas were defined as occurring in the mucosal membranes lining oral, respiratory, gastrointestinal and urogenital tracts. The haematoxylin and eosin slides of the primary melanomas were reviewed for all mucosal and acral samples, and any tumour that arose in the junction of the acral/mucosal and cutaneous skin was excluded. Occult/unknown primary melanomas were considered cutaneous, since their genome landscape is indistinguishable from that of melanomas arising in the skin40. Tumour DNA was extracted using DNeasy Blood and Tissue Kits (69506, Qiagen), according to the manufacturer’s instructions. Blood DNA was extracted from whole blood using Flexigene DNA Kits (51206, Qiagen). All samples were quantified using the NanoDrop (ND1000, Thermoscientific) and Qubit dsDNA HS Assay (Q32851, Life Technologies), and DNA size and quality were tested using gel electrophoresis. Samples with a concentration of less than 50 ng μl−1, or absence of a high molecular mass band in electrophoresis gels, were excluded from further analyses. WGS was performed on Illumina Hiseq 2000 instruments (Illumina, San Diego, California, USA) at three Australian sequencing facilities (Australian Genomic Research Facility, Ramaciotti Centre for Genomics, John Curtin School of Medical Research) and Macrogen (Geumcheon-gu, Seoul, South Korea). All facilities performed library construction using TruSeq DNA Sample Preparation kits (Illumina) according to the manufacturer’s instructions. The subsequent 100 base pair (bp) pair-end libraries were sequenced using Truseq SBS V3-HS kits to average depth 85× (range 43–219×) in the tumour sample and 64× (range 30–214×) in the matched normal. Sequence data were aligned to the GRCh37 assembly using multi-threaded BWA 0.6.2-mt, resulting in sorted lane level files in sequence alignment/mapping format which were compressed and converted to binary file (BAM) created by samtools 0.1.19. Sample-level merged BAMs, one each for matched germline and tumour samples, were produced by in-house tools and duplicate reads marked with Picard MarkDuplicates 1.97 (http://picard.sourceforge.net). Quality assessment and coverage estimation was performed by qProfiler and qCoverage (http://sourceforge.net/projects/adamajava). To test for the presence of sample or data swaps, all sequence data were assessed by qSignature for concordance at approximately 1.4 million polymorphic genomic positions including the genotyping array data where available. Somatic mutations and germline variants were detected using a dual calling strategy using qSNP41 and GATK42, and indels of 1–50 bp in length were called with Pindel43. All mutations were submitted to the International Cancer Genome Consortium44 Data Coordination Centre. Mutations were annotated with gene consequence using Ensembl gene annotation with SnpEff. Somatic genes that were significantly mutated were identified using two approaches: MutSigCV and OncodriveFML 1.1 (ref. 22) using a threshold of q < 0.1. Significant non-coding elements were detected using OncodriveFML 1.1 (ref. 22). Somatic copy number and ploidy were determined using the TITAN tool45. Structural variants were identified using the qSV tool and chromosomes containing highly significant non-random distributions of breakpoints were identified as previously described46. Chromosomes identified to have clustering of breakpoints were inspected against criteria for chromothripsis47 and the BFB cumulative rearrangement model48. Chromosomes with high numbers of translocations were identified with a minimum threshold of ten translocation breakpoints per chromosome following manual review. Mutational signatures were predicted in each sample using a published framework1. Essentially, the substitution mutations across the whole genome in all cases were analysed in context of the flanking nucleotides (96 possible trinucleotide combinations). Identified signatures were compared with other validated signatures and the frequency of each signature per megabase was determined. Statistical significance of recurrent non-coding mutations was estimated using a permutation test. A null distribution of recurrence was estimated by randomly shuffling all mutations within each sample and recording the number of recurrent mutations within the regions of interest. To take into account not only the varying mutation burden but also the different mutation signatures, we restricted the random shuffling such that the mutation in the middle of a trinucleotide, ABC, was only shuffled to the same trinucleotide. Promoters and UTRs likely to play a role in tumorigenesis were identified with OncodriveFML22, a framework able to detect signals of positive selection in both the coding and the non-coding regions of the genome by measuring the bias towards the accumulation of functional mutations. The functional impact of mutations in gene promoters was assessed using the CADD (Combined Annotation Dependent Depletion)49, TFBS creation and TFBS disruption scores. The CADD scores measure the deleteriousness of mutations, and are calculated by integrating multiple annotations into a single metric by contrasting variants that survived natural selection with simulated mutations. The scores for TFBS creation (motif gain) and disruption (motif break) were computed by following the steps described in ref. 50. The score value indicates the difference between position weight matrix matching scores of the germline and mutant alleles. The 5′ UTRs were analysed using the TFBS disruption scores while 3′ UTRs were analysed using the CADD scores. The statistical significance of promoters and UTRs was derived by comparing the average functional impact score of the mutations in the element with the functional impact scores obtained by permuting 100,000-fold the observed mutations, maintaining their trinucleotide context. In addition, since the rate of somatic mutations in melanoma is highly increased at active TFBS23, OncodriveFML was adapted (version 1.1) to perform a strictly local permutation in windows of 51 bp (25 nucleotides at each side of the mutation). This variation in the background model of OncodriveFML allowed us to better estimate whether the mutations observed in tumours disrupted or created TFBS more than expected by chance. The statistical significance of promoters and UTRs mutated in at least two samples was adjusted with the Benjamini–Hochberg correction for multiple testing. We also used miRanda version 3.3a to predict whether the recurrent 3′ UTR mutations alter (disrupt or create) microRNA (miRNA) binding sites. The 50-base sequence surrounding each 3′ UTR was used as input to miRanda. miRNAs that were predicted to hit either the wild-type or mutant sequence (but not both) were considered potential targets and further filtered as follows. We required a hit to perfectly align against the seed region of the miRNA (nucleotides 2–8), that the mutation lay within the seed and that the predicted binding energy was higher (lower ΔG) in the non-hit than in the hit. To estimate telomere length, we counted telomere motifs in the whole gene data using the quantitative-PCR-validated qMotif tool (https://sourceforge.net/p/adamajava/wiki/qMotif) implemented in JAVA using the Picard library (version 1.110). qMotif is driven by a single plain-text configuration file in the ‘Windows INI-file’ style and the input is a WGS BAM file that has been duplicate-marked and coordinate-sorted. Essentially, qMotif uses a two-stage matching system where the first stage is a quick-but-strict string match and the second stage is a slower but more flexible regular expression match; only reads that pass stage 1 go on to the much slower match in stage 2. For telomere quantification, a string representing three concurrent repeats of the canonical telomere motif (TTAGGGTTAGGGTTAGGG) was used as the stage 1 match, and a simple pattern match for stage 2 which captured any read with two or more concurrent occurrences of the telomeric repeat with variation allowed in the first three bases. The relative tumour telomere length was then estimated by comparing the tumour read counts with the matched normal sample. Telomere length was validated by quantitative PCR51. Direct PCR amplification and Sanger sequencing were performed using the primers hTERT_F ACGAACGTGGCCAGCGGCAG and hTERT_R CTGGCGTCCCTGCACCCTGG, which amplify a 474 bp region of the TERT promoter52. PCR was done in a 13 μl volume containing 1 μl of 20 ng μl−1 gDNA, 1.25 μl of 10× MgCl , 2.5 μl betaine, 1.25 μl deoxynucleotides (2.5 mM), 1 μl of 10 μM primers and 0.25 μl of PFU Turbo (600250, Agilent). PCR reactions were performed under the following conditions: 95 °C for 5 min, followed by four cycles of 95 °C for 30 s, annealing at 66 °C for 1 min and polymerization at 72 °C for 1 min. This was followed by 4 more cycles with a lowered annealing temperature of 64 °C for 1 min, followed by 28 cycles with annealing temperatures of 62 °C. Subsequent PCR products were sequenced on an AbiPrism 3130xl Genetic Analyzer (Applied Biosystems) and data analysed using Sequence Scanner Software 2 (Applied Biosystems) with reference to the sequences from the NCBI gene database, TERT (chr5:1295071–1295521). Illumina TruSeq Custom Amplicon V1.5 was used to validate 20 recurrent non-coding point mutations in the promoter (n = 11), 3′ UTR (n = 3) and 5′ UTR (n = 6) regions of genes with frequent non-coding mutations in 164 of the 183 samples. Illumina DesignStudio (Illumina, San Diego, California, USA) was used to design 250 bp sequences of the target regions. Sequencing libraries were prepared using the TruSeq Custom Amplicon Library Preparation Guide and the TruSeq Custom Amplicon Index Kit, and sequenced on a MiSeq Illumina sequencer V2 (Illumina). Sequences were aligned to the reference genome (GRCh37/hg19) using BWA 0.6.2-mt. A pileup approach was used to determine the base count at each position of interest. A mutation was considered verified if the mutant allele frequency was >10%. Exome capture was performed on 1 μg of DNA extracted from tumour and normal blood using an Illumina TruSeq Exome Library Prep Kit. Libraries were sequenced (paired-end sequencing) on an Illumina HiSeq2000 platform with a minimum coverage of 61× (normal) and 59× (tumour). Exome sequence data were produced for 53 patients in the cohort and used to validate coding mutations detected by WGS. Total RNA was extracted from fresh-frozen tissue using a mirVana miRNA Isolation Kit (Applied Biosystems, AM1560). RNA quality and presence of a small RNA fraction were measured using the Agilent 2100 RNA 6000 Nano and small RNA kits. RNA sequencing was performed using 1 μg of total RNA, which was converted into messenger RNA libraries using an Illumina mRNA TruSeq kit. RNA sequencing was performed using 2 × 75 bp paired-end reads on an Illumina Hiseq2000. Small RNA sequencing was performed using 1 μg of total RNA, which was converted into a small RNA libraries, size selection range 145–160 bp (RNA of 18–33 nucleotides) using Illumina’s TruSeq Small RNA Library Preparation Kit and sequenced on an Illumina Hiseq2000 using 50 bp single-read sequencing with 1% control spiked in. RNA sequence reads were aligned to transcripts corresponding to ensemble 70 annotations using RSEM53. RSEM data were normalized using TMM (weighted trimmed mean of M-values) as implemented in the R package ‘edgeR’. For downstream analyses, normalized RSEM data were converted to counts per million. Genes with at least 5 counts per million in at least two samples were considered expressed. Total numbers of SNV/indel and structural variants were compared according to primary melanoma body site, categorized into abdomen, acral hand, acral foot, back, lower arm, lower leg, mucosal, neck, shoulder, thorax, upper arm, upper leg, and face and scalp. Any samples with an unknown primary site or occult classification were excluded from analysis. Heat maps were produced in Spotfire-Tibco (version 6.0, http://spotfire.tibco.com) on the basis of the combined total number of SNV and indels, or by structural variants. A two-colour heat map (red high, blue low) was produced and colours were overlaid onto an adapted SVG human body diagram that was created using Adobe Illustrator CS6. The frequency of clinically actionable mutations was assessed by annotating genomic variants using the IntOGen Cancer Drivers Actionability database (2014), which identifies mutations that may confer sensitivity to therapeutic agents54. The database was used to assign an activating or loss-of-function role to mutated genes. Loss of heterozygosity, silent mutations, deletions to activating genes or amplifications to loss-of-function genes were not included in the analysis. Additionally, visual inspection using the Integrative Genome Viewer (IGV, Broad) was used to identify only high-confidence structural rearrangement breakpoints with clustered supporting reads with both discordant read pair and soft clipping evidence. Structural variants with a high incidence of random non-clustered background signal surrounding the breakpoints along with low numbers of supporting non-duplicate reads were excluded from analysis for this figure (Extended Data Fig. 10). The proportion of tumours with a mutation to a particular actionable gene was calculated and classified on the basis of mutation type into (1) SNV/indel, (2) SNV/indel and structural variant, (3) structural variant or (4) copy number variation only. A hand-curated list of commonly mutated tumour suppressor genes and oncogenes was created and analysed for the frequency of mutation (Fig. 4a). Mutations were defined as SNV/indel, structural variant, copy number amplifications and copy number deletions. Loss of heterozygosity, silent mutations, RNA mutations, deletions to oncogenes or amplifications to tumour suppressor genes were not included in the figure. Structural variant breakpoints were subjected to manual inspection using the Integrative Genome Viewer (IGV, Broad) and only events confirmed as somatic and predicted to alter transcription processing were considered further. We then overlaid the alterations from Fig. 4a onto pathways defined by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) gene sets from MSigDB version 5.0. A pathway was considered altered in a given sample if at least one gene in the pathway contained an SNV/indel or structural variant. The pathways were then stratified according to cutaneous or non-cutaneous subtypes. A mutation file with sample identities and their mutated pathways was entered for analysis into the OncoPrinter tool (http://cbioportal.org). MAPK and PI3K pathway status was also assessed by multiplex-immunofluorescent staining for phosphorylated ERK and AKT (106/183). All immunohistochemical staining was performed on a Dako Autostainer Plus (Dako, Glostrup, Denmark) using a Dako Envision Flex detection kit (K8000, Dako) and OPAL 7-colour IHC Kit for visualization (NEL797B001KT, PerkinElmer). Consecutive staining rounds included p-AKT (1:100, NCL-L-Akt-Phos, Leica), p-ERK (1:1,600, CS4370, Cell Signalling) and SOX10 (1:800, ACI 3099A, Biocare). Multispectral quantitative image analysis was performed on a Vectra 3 slide scanner (PerkinElmer, USA). The captured multispectral images were analysed using the quantitative InForm image analysis software (PerkinElmer, USA). All somatic variants for this study have been deposited in the International Cancer Genome Consortium Data Coordination Centre and are publicly available at https://dcc.icgc.org. The BAM files have been deposited in the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/) with accession number EGAS00001001552. Tools used in this publication that were developed in-house are available from the SourceForge public code repository under the AdamaJava project (http://sourceforge.net/projects/adamajava/). Source data are provided or are available from the corresponding author upon reasonable request.
PubMed | University of Bristol, University of Cardiff, University of Warwick and QIMR
Type: | Journal: The Journal of biological chemistry | Year: 2016
T-cell cross-reactivity is essential for effective immune surveillance, but has also been implicated as a pathway to autoimmunity. Previous studies have demonstrated that T-cell receptors (TCRs) that focus on a minimal motif within the peptide are able to facilitate a high level of T-cell cross-reactivity. However, the structural database shows that most TCRs exhibit less focussed antigen binding involving contact with more peptide residues. To further explore the structural features that allow the clonally expressed TCR to functionally engage with multiple peptide-major histocompatibility complexes (pMHCs), we examined the ILA1 CD8+ T-cell clone that responds to a peptide sequence derived from human telomerase reverse transcriptase (hTERT). The ILA1 TCR contacted its pMHC with a broad peptide-binding footprint encompassing spatially distant peptide residues. Despite the lack of focused TCR-peptide binding , the ILA1 T-cell clone was still cross-reactive. Overall, the TCR-peptide contacts apparent in the structure correlated well with the level of degeneracy at different peptide positions. Thus, the ILA1 TCR was less tolerant of changes at peptide residues that were at, or adjacent to, key contact sites. This study provides new insights into the molecular mechanisms that control T-cell cross-reactivity, with important implications for pathogen surveillance, autoimmunity and transplant rejection.
News Article | November 7, 2016
BEIJING and LA JOLLA, Calif., Nov 7, 2016 /PRNewswire/ -- Yisheng Biopharma Co., Ltd. ("Yisheng Biopharma"), a biopharmaceutical company focusing on research, development, manufacturing, sales and marketing of vaccine products, today announced that it has entered into a collaboration with The Scripps Research Institute ("TSRI") to test a new generation of AIDS vaccine based on novel gp140 trimers and self-assembling nanoparticles designed by TSRI scientists and Toll-Like Receptor 3 (TLR3) agonist adjuvant technology ("PIKA") developed by the company. The cooperative research partnership represents a new opportunity for both organizations to create more effective and safe vaccine products against HIV infection. Under the terms of the agreement, the scientists at TSRI will evaluate the potential of PIKA adjuvant for AIDS vaccine candidates. The PIKA adjuvant is a proprietary technology developed by Yisheng Biopharma, named as part of "National Key Medicine Innovation" in 2013 by the National Ministry of Science and Technology of China. Most recently Yisheng completed Phase II and Phase I clinical studies of PIKA based rabies and hepatitis B vaccines, respectively, which exhibited promising efficacy and safety in human subjects. "TSRI has gained a great deal of expertise in AIDS research which lays the groundwork in revolutionizing vaccine design strategy," commented Assistant Professor Jiang Zhu at TSRI. "TLR-3 agonists such as PIKA adjuvant are known to enhance immune responses. When used together with highly optimized HIV vaccine immunogens, PIKA could activate innate immune signaling and induce a more robust immune response that confers protection against HIV infection. We look forward to working with researchers at Yisheng to develop a more powerful AIDS vaccine." "The partnership with TSRI is critical to our long-term vision in advancing vaccine development. We're thrilled to work alongside TSRI's world-class faculty and bring Yisheng's capabilities and focus to bear in creating new medicines into the future. We are very pleased to join forces with TSRI in exploring a more effective vaccine against AIDS, which remains a significant unmet medical need. Our PIKA adjuvant has exhibited broad potential in preclinical and clinical investigations against rabies, HIV, Hepatitis-B, influenza, tuberculosis, and other viruses, which could significantly change the clinical practice paradigms against many human and animal virus infections. We are looking forward to updating the progress on these fronts in due course," stated Mr. Yi Zhang, the Chairman of Yisheng Biopharma and the project leader of PIKA adjuvant technology. Mr. Zhang finally commented, "We are grateful to our research collaborators worldwide for their continuous support of PIKA adjuvant technology and vaccine development over the years, including The Pasteur Institute, the US NIH, the United States Army Medical Research Institute of Infectious Diseases, Chinese Center For Disease Control And Prevention (China CDC), China National Institutes For Food and Drug Control, DSO National Laboratories Singapore, Chinese Academy of Sciences, Australia QIMR, Sun Yat-Sen University of China, Aeras Pharmaceutical of the US, and Academy of Military Sciences of China." PIKA adjuvant technology is a proprietary technology developed in-house at Yisheng Biopharma. The adjuvant is a double- stranded RNA, which acts as a toll-like receptor-3 (TLR-3) ligand to the activation of the innate immune cells, such as dendritic cells, macrophages and NK cells. PIKA adjuvant is formulated as a component of vaccine candidates. The Scripps Research Institute (TSRI) is one of the world's largest independent, not-for-profit organizations focusing on research in the biomedical sciences. TSRI is internationally recognized for its contributions to science and health, including its role in laying the foundation for new treatments for cancer, rheumatoid arthritis, hemophilia, and other diseases. An institution that evolved from the Scripps Metabolic Clinic founded by philanthropist Ellen Browning Scripps in 1924, the institute now employs more than 2,500 people on its campuses in La Jolla, CA, and Jupiter, FL, where its renowned scientists - including two Nobel laureates and 20 members of the National Academy of Science, Engineering or Medicine - work toward their next discoveries. The institute's graduate program, which awards PhD degrees in biology and chemistry, ranks among the top ten of its kind in the nation. For more information, see www.scripps.edu. About Yisheng Biopharma Co., Ltd. Yisheng Biopharma Co., Ltd. is a biopharmaceutical company headquartered in Beijing, China, focusing on the research, development, manufacturing and sales and marketing of immunological and vaccine products, with approximately 1,000 employees in China, the USA and Singapore. For more information, see www.yishengbio.com
PubMed | QIMR
Type: Journal Article | Journal: Journal of clinical oncology : official journal of the American Society of Clinical Oncology | Year: 2016
9683 Background: More than 100 variables have been described to predict prognosis in breast cancer but only maximum tumour size (T), histological grade, nodal status, ER and HER-2 are routinely incorporated into clinical practice. In this study both classic histological features and novel biomarkers using immunohistochemistry (IHC) were evaluated to assess overall survival.Formalin fixed paraffin embedded tissue was collected from 414 breast cancer patients with invasive ductal carcinoma. 247 patients were node negative. Median follow-up was 110 months (Range 1-148 months). Routine histological parameters were assessed and a number of antigens including ER, PR, HER-2, p53, Ki67 and microvessel density were evaluated using IHC. ER, PR, HER-2 and p53 were assessed semiquantitatively using the Quickscore method. Microvessel density was counted over a 200x field and adjusted to mmWith univariate analysis, grade (p <0.001), nodal status (p <0.001), tumour volume (p <0.01), HER-2 (p = 0.006), Ki67 (p <0.001), mitotic rate (p <0.001) and T (p <0.001) were statistically significant. For node negative patients grade (p = 0.02), tumour volume (p = 0.03) and mitotic rate (p = 0.009) were significant. Using multivariate analysis, mitotic rate (p = 0.0005) and tumour volume (p <0.001) predicted nodal involvement, and grade (p = 0.002), nodal status (p = 0.001) and T (p <0.001) predicted overall survival.This analysis validates the current staging system. Additional IHC markers failed to predict overall survival when standard histological features were utilised. Mitotic rate and tumour volume were independent predictors of nodal metastases. Tumour volume estimation may be superior to T in predicting nodal metastases and requires further evaluation to determine its potential role in staging. No significant financial relationships to disclose.