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• W3012960536 abstract "Prostate cancer is a significant global health issue, and limitations to current patient management pathways often result in overtreatment or undertreatment. New ways to stratify patients are urgently needed. We conducted a feasibility study of such novel assessments, looking for associations between genomic changes and lymphocyte infiltration. An innovative workflow using an in-house targeted sequencing panel, immune cell profiling using an image analysis pipeline, RNA sequencing, and exome sequencing in select cases was tested. Gene fusions were profiled by RNA sequencing in 27 of 27 cases, and a significantly higher tumor-infiltrating lymphocyte (TIL) count was noted in tumors without a TMPRSS2:ERG fusion compared with those with the fusion (P = 0.01). Although this finding was not replicated in a larger validation set (n = 436) of The Cancer Genome Atlas images, there was a trend in the same direction. Differential expression analysis of TIL-high and TIL-low tumors revealed the enrichment of both innate and adaptive immune response pathways. Mutations in mismatch repair genes (MLH1 and MSH6 mutations in 1 of 27 cases) were identified. We describe a potential immune escape mechanism in TMPRSS2:ERG fusion-positive tumors. Detailed profiling, as shown herein, can provide novel insights into tumor biology. Likely differences with findings with other cohorts are related to methods used to define region of interest, but this warrants further study in a larger cohort. Prostate cancer is a significant global health issue, and limitations to current patient management pathways often result in overtreatment or undertreatment. New ways to stratify patients are urgently needed. We conducted a feasibility study of such novel assessments, looking for associations between genomic changes and lymphocyte infiltration. An innovative workflow using an in-house targeted sequencing panel, immune cell profiling using an image analysis pipeline, RNA sequencing, and exome sequencing in select cases was tested. Gene fusions were profiled by RNA sequencing in 27 of 27 cases, and a significantly higher tumor-infiltrating lymphocyte (TIL) count was noted in tumors without a TMPRSS2:ERG fusion compared with those with the fusion (P = 0.01). Although this finding was not replicated in a larger validation set (n = 436) of The Cancer Genome Atlas images, there was a trend in the same direction. Differential expression analysis of TIL-high and TIL-low tumors revealed the enrichment of both innate and adaptive immune response pathways. Mutations in mismatch repair genes (MLH1 and MSH6 mutations in 1 of 27 cases) were identified. We describe a potential immune escape mechanism in TMPRSS2:ERG fusion-positive tumors. Detailed profiling, as shown herein, can provide novel insights into tumor biology. Likely differences with findings with other cohorts are related to methods used to define region of interest, but this warrants further study in a larger cohort. Prostate cancer is a major global health issue and the second most common cause of cancer death in males in the developing world.1Siegel R. Ma J. Zou Z. Jemal A. Cancer statistics, 2014.CA Cancer J Clin. 2014; 64: 9-29Crossref PubMed Scopus (10980) Google Scholar Better methods for patient stratification are urgently needed. Our understanding of the molecular pathology of prostate cancer is evolving fast, with advances in sequencing methods and bioinformatics. The Cancer Genome Atlas performed detailed molecular analysis on 333 primary prostate carcinomas, and 74% of tumors fell into one of the seven subtypes defined by specific gene fusions (ERG, ETV1/4, FLI1) or mutations (SPOP, FOXA1, IDH1).2Cancer Genome Atlas Research NetworkThe molecular taxonomy of primary prostate cancer.Cell. 2015; 163: 1011-1025Abstract Full Text Full Text PDF PubMed Scopus (1840) Google Scholar Recent publications by the International Cancer Genome Consortium have identified new cancer genes, routes of progression, and drug targets, sometimes affected by mutations in noncoding regions of genes, including NEAT1 and FOXA1.3Wedge D.C. Gundem G. Mitchell T. Woodcock D.J. Martincorena I. Ghori M. et al.Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets.Nat Genet. 2018; 50: 682-692Crossref PubMed Scopus (137) Google Scholar There is now greater understanding of the links between morphology and molecular alterations (specific morphomolecular correlations), molecular aberrations and inflammation, and the therapeutic implications of these associations. Immunotherapy is a treatment option for late-stage prostate cancer, and to optimize response to immunotherapy, it is often necessary to target oncogenic driver pathways in combination with immunotherapy.4Bryant G. Wang L. Mulholland D.J. Overcoming oncogenic mediated tumor immunity in prostate cancer.Int J Mol Sci. 2017; 18: 1542Crossref Scopus (29) Google Scholar Increasing evidence indicates the clinical utility of inflammatory infiltrates in determining cancer prognosis, with a widely accepted role for the immune system in controlling cancer growth and progression. In several different cancers, including prostate, colorectal, melanoma, and bladder, strong T-cell infiltration is associated with a favorable clinical outcome.5Fridman W.H. Pagès F. Sautès-Fridman C. Galon J. The immune contexture in human tumours: impact on clinical outcome.Nat Rev Cancer. 2012; 12: 298-306Crossref PubMed Scopus (3102) Google Scholar The literature detailing prostate cancer progression and inflammation is often conflicting. For example, a critical review showed radical prostatectomy cases with higher rates of biochemical progression to have higher levels of systemic inflammatory markers. In another study, higher-grade inflammation was statistically associated with risk of extraprostatic extension, positive margins, and seminal vesicle invasion,6Sciarra A. Gentilucci A. Salciccia S. Pierella F. Del Bianco F. Gentile V. Silvestri I. Cattarino S. Prognostic value of inflammation in prostate cancer progression and response to therapeutic: a critical review.J Inflamm (Lond). 2016; 13: 35Crossref PubMed Scopus (45) Google Scholar which is largely contradictory to the literature in other tumor types. Issues around accuracy of quantification of inflammatory cells, which in many of these studies will have been conducted by pathologist assessment, can be addressed with quantitative image analysis. Prostate cancer shows a high degree of heterogeneity at histologic and genetic levels, which poses a significant treatment challenge. As mutations and activation of oncogenic driver pathways accumulate with tumor progression, this may promote increases in immunosuppressive cells and exhaustion of immune effector cells in the tumor microenvironment.4Bryant G. Wang L. Mulholland D.J. Overcoming oncogenic mediated tumor immunity in prostate cancer.Int J Mol Sci. 2017; 18: 1542Crossref Scopus (29) Google Scholar A recent study showed two of the 25 sequenced prostate cancers to be mismatch repair (MMR) deficient (one related to Lynch syndrome, one sporadic) and showed high inflammatory infiltrates and loss of the two relevant MMR proteins on immunohistochemistry (IHC; MSH2 and MSH6).7Linch M. Goh G. Hiley C. Shanmugabavan Y. McGranahan N. Rowan A. Wong Y.N.S. King H. Furness A. Freeman A. Linares J. Akarca A. Herrero J. Rosenthal R. Harder N. Schmidt G. Wilson G.A. Birkbak N.J. Mitter R. Dentro S. Cathcart P. Arya M. Johnston E. Scott R. Hung M. Emberton M. Attard G. Szallasi Z. Punwani S. Quezada S.A. Marafioti T. Gerlinger M. Ahmed H.U. Swanton C. Intratumoural evolutionary landscape of high-risk prostate cancer: the PROGENY study of genomic and immune parameters.Ann Oncol. 2017; 28: 2472-2480Abstract Full Text Full Text PDF PubMed Scopus (41) Google Scholar A study describing prostate cancer in Lynch syndrome showed tumors to be generally high grade (Gleason scores 8 to 10) with mutations in MSH2, MLH1, and MSH6 and loss of the respective protein on IHC in 69% of tumors.8Dominguez-Valentin M. Joost P. Therkildsen C. Jonsson M. Rambech E. Nilbert M. Frequent mismatch-repair defects link prostate cancer to Lynch syndrome.BMC Urol. 2016; 16: 15Crossref PubMed Scopus (39) Google Scholar Microsatellite instability (MSI) and deficient MMR have been reported in prostate cancers to range from approximately 1% in primary up to 12% of metastatic cancers.9Hempelmann J.A. Lockwood C.M. Konnick E.Q. Schweizer M.T. Antonarakis E.S. Lotan T.L. Montgomery B. Nelson P.S. Klemfuss N. Salipante S.J. Pritchard C.C. Microsatellite instability in prostate cancer by PCR or next-generation sequencing.J Immunother Cancer. 2018; 6: 29Crossref PubMed Scopus (64) Google Scholar Other associations that have been found between genomics and inflammation include the loss of PTEN and immunosuppression in a dose-dependent manner and the loss or mutation of p53 may enhance the immunosuppressive microenvironment.4Bryant G. Wang L. Mulholland D.J. Overcoming oncogenic mediated tumor immunity in prostate cancer.Int J Mol Sci. 2017; 18: 1542Crossref Scopus (29) Google Scholar At fusion level, it has been postulated that TMPRSS2:ERG fusions generate chimeric amino acid sequences that are targetable by T cells.10Kalina J.L. Neilson D.S. Lin Y.-Y. Hamilton P.T. Comber A.P. Loy E.M.H. Sahinalp S.C. Collins C. Hach F. Lum J.J. Mutational analysis of gene fusions predicts novel MHC class I-restricted T cell epitopes & immune signatures in a subset of prostate cancer.Clin Cancer Res. 2017; 23: 7596-7607Crossref PubMed Scopus (11) Google Scholar Prediction algorithms have been used to identify potentially antigenic epitopes from TMPRSS2:ERG fusions.11Zhang J. Mardis E.R. Maher C.A. INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery.Bioinformatics. 2017; 33: 555-557PubMed Google Scholar Herein, we studied the relationship between immune cell infiltration and mutations in prostate cancer by using an integrative approach combining image analysis and DNA and RNA sequencing. This study involved digital image analysis (DIA) or next-generation sequencing of archival prostate cancer samples, as depicted in Figure 1. The study was undertaken under the Oxford Radcliffe Biobank Research Tissue Bank Research Ethics Committee approval (reference 09/H0606 5+5), and informed consent was obtained in all cases. The cohort included 27 unselected and sequential radical prostatectomy cases stored as formalin-fixed, paraffin-embedded (FFPE) blocks in the diagnostic archives of Oxford University Hospitals National Health Service Foundation Trust. The cases dated from 2014 to 2016, and sequencing was conducted in 2017 and 2018; thus, the range of age of blocks (samples) was between 1 and 4 years. Eighteen cases were Gleason grade group 2 [Gleason score 7 (3 + 4)], seven cases were grade group 3 [Gleason score 7 (4 + 3)], and one of these cases had tertiary pattern 5, and two cases were grade group 5 [Gleason score 9 (4 + 5)]. Eleven cases were prostate-confined tumor (stage pT2), 13 showed extraprostatic extension (pT3a), and two showed seminal vesicle invasion (pT3b). Tissue sections were subject to routine hematoxylin and eosin (H&E) staining, using a Tissue-Tek Prisma Autostainer (Sakura, Flemingweg, the Netherlands). Immunohistochemistry was performed using a Bond RX automatic stainer (Leica Biosystems, Wetzlar, Germany) using the IHC Protocol F program, following incubation of sections at 60°C for 10 minutes. This machine performed all steps, including deparaffinization, antigen retrieval and staining, counterstaining, and washing using Leica Biosystems reagents: Leica Bond Dewax Solution (AR9222), Wash Buffer (AR9590), epitope retrieval solution 1 (AR9961), epitope retrieval solution 2 (AR9640), enzyme 1 from the enzyme pretreatment kit (AR9551), and Bond Polymer Detection System (DS9800). Epitope retrieval conditions were dependent on the antibody manufacturer's recommendations (Table 1). Antibodies (Leica Biosystems; Cell Signaling Technology, Danvers, MA; Abcam, Cambridge, UK; Dako/Agilent Technologies, Santa Clara, CA) requiring dilution before use [anti–forkhead box protein P3 (FoxP3), anti–mismatch repair endonuclease PMS2 (PMS2), and anti–DNA mismatch repair protein MSH6 (MSH6)] were diluted to 1:100 using Bond Primary Antibody Diluent (Leica Biosystems; AR9352). For all antibodies, tonsil tissue sections were used as a positive control. After protocol completion, sections were dehydrated with serial 1-minute washes in 70%, 90%, and twice in 100% ethanol (Sigma Aldrich, St. Louis, MO; 652261). This was followed by 2 × 5 minutes histo-clear II (National Diagnostics, Atlanta, GA; HS-202) washes and mounting using omnimount (National Diagnostics; HS-110).Table 1A Summary of Antibodies Used for Immunohistochemistry and Specific Epitope Retrieval ConditionsPrimary antibodyTypeSupplierCatalog no.Antigen retrievalDilutionCD3 (LN10)MLeica BiosystemsPA055320 minutes ER2RTUCD4 (LB12)MLeica BiosystemsPA042720 minutes ER2RTUCD8 (4B11)MLeica BiosystemsPA018320 minutes ER2RTUCD20 (L26)MLeica BiosystemsPA020020 minutes ER1RTUPDL-1 [E1L3N(R)]MCell Signaling Technology13684S20 minutes ER21:200FOXP3 (236A/E7)MAbcamAb9604820 minutes ER11:100Granzyme B (11F1)MLeica BiosystemsPA029120 minutes ER2RTUCytokeratin 5 (XM26)MLeica BiosystemsPA046820 minutes ER2RTUMulticytokeratin (AE1/AE3)MLeica BiosystemsPA090910 minutes Enz 1RTUAndrogen receptorPAbcamab7427220 minutes ER11/200ERG (EP111)MDAKOM731420 minutes ER11/100PTEN (Y184)MAbcamab3219920 minutes ER11/100Ki-67 (antibody clone MIB-1)MDako20 minutes ER21/400AntibodyImmune cellCD3T cellCD4T helper cellCD8Cytotoxic T cellCD20B cellPDL-1PD1 ligandFOXP3T regulatory cellGranzyme BCytotoxic T cellEnz, enzyme; ER, epitope retrieval solution; ERG, transcriptional regulator ERG; FOXP3, forkhead box protein P3; M, monoclonal; P, polyclonal; PDL-1, programmed death ligand 1; PTEN, phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN; RTU, ready to use. Open table in a new tab Enz, enzyme; ER, epitope retrieval solution; ERG, transcriptional regulator ERG; FOXP3, forkhead box protein P3; M, monoclonal; P, polyclonal; PDL-1, programmed death ligand 1; PTEN, phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN; RTU, ready to use. Immunohistochemistry was performed on FFPE tissue with antibodies to CD3, CD4, CD8, CD20, granzyme B, FoxP3, multicytokeratin (MCK), cytokeratin 5 (CK5), DNA mismatch repair protein MSH2 (MSH2), MSH6, DNA mismatch repair protein Mlh1 (MLH1), and PMS2 on whole mount sections and phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN (PTEN), androgen receptor (AR), and transcriptional regulator ERG (ERG) on a tissue microarray (TMA). Full details of the staining protocols and primary antibodies are shown in Table 1. The Bond Polymer Refine Detection kit, which uses diaminobenzidine as the chromogen and hematoxylin as the counterstain, was used as the detection system on a Leica Bond Immunostainer (Leica Biosystems). Tonsil tissue was used as a positive control for all markers. Stained sections were scanned at 20× using a NanoZoomer 2.0 digital pathology slide scanner (Hamamatsu Photonics, Hamamatsu-City, Japan). Manual visual analysis was performed using the Hamamatsu NDP viewer (Hamamatsu Photonics; U12388-01). DIA was performed using Visiopharm's Integrator System platform version 2018.4.3.4480 (Hoersholm, Denmark), and image analysis protocols were implemented as analysis protocol packages (APPs). Several APPs were designed to quantify and calculate tumor-infiltrating lymphocyte (TIL) density on slides stained with CD3, CD4, CD8, CD20, FOXP3, granzyme B, and H&E. The image analysis process consists of the following steps: i) Image alignment/registration: The Tissuealign module was used to align three digitized serial sections: two slides stained with tumor markers CK5 and pan-cytokeratin (panCK) and another slide stained with a cancer biomarker (eg, CD3, CD4, CD20). The alignment was performed both on a large scale, and on a finer detailed level, to get the best possible match of the three tissue sections. The alignments were verified visually. ii) Detecting region of interest (ROI): For invasive tumor detection, a region of interest (tumor) detection APP was generated. The APP identified pan-cytokeratin (MCK)+ luminal epithelial cells and CK5+ basal cells. Basal cells are absent in prostate adenocarcinoma glands, but present in benign glands (or prostatic intraepithelial neoplasia). Differential staining was used to define regions of interest (MCK+ CK5+ regions = nontumor, and MCK+ CK5− regions = invasive tumor). In addition, the tumor region was also additionally divided into invasive margin and center of tumor using an APP built in house using the Erode post-processing function of the Visiopharm's Integrator System platform that decreases the tumor region by 120 pixels. The first auxiliary APP was run on the slide using threshold classification that identifies the tissue regions. The second auxiliary APP was run on the panCK slide using threshold classification that identifies the tumor regions, invasive margin, and center of tumor. The third auxiliary APP was run on the CK5 slide using threshold classification that identifies the benign regions. The ROIs were then superimposed on the aligned cancer biomarker slide to outline various regions for subsequent analysis, limited to the inside of the specific regions. The ROI detection APP operates at a low magnification, which enables outlining ROIs in a few seconds. Fifty patches (0.8 × 0.8 mm) that were generated from five representative images were used to select the optimal threshold. Supplemental Figure S1 shows the output of the ROI detection pipeline. iii) Immune cell quantification: hematoxylin/diaminobenzidine-diaminobenzidine color deconvolution band was used to detect positively stained cells on CD3, CD4, CD8, CD20, FOXP3, and granzyme B slides. H&E-hematoxylin color deconvolution band was used to detect positively stained lymphocytes on H&E slides. To enhance the stained cells, while suppressing the background variation, several preprocessing steps were included. The color deconvolution bands were inputted into a threshold classifier. Thresholding classification method defines a threshold for a given feature and assigns one class to all pixels with a feature value above or equal to that value, and another class for the rest. The classification rule was defined as:Class(Feature(X,Y))={A,Feature(x,y)≥TB,Otherwise(1) where T is the user-selected threshold (cutoff value), and A and B are the labels/classes to which the pixel is assigned. As post-processing steps, a method for cell separation that is based on shape and size was used, cell areas that were too small were removed, and finally unbiased counting frames were applied to avoid cells that were intersecting with neighboring tile boundaries and avoid them being counted twice (or more). The APPs operate at magnification ×10, which enables analyzing a whole slide image in 5 to 7 minutes. All immune marker results discussed are density, as calculated by number of cells detected/area (mm2). The output variables obtained from the APPs are shown in Table 1. Validation of APPs for the quantification of immune infiltrates was achieved by performing a comparison between the APP and manual cell counting of equivalent images by a pathologist (C.V.) and showed good concordance (Supplemental Table S1). TIL density was also calculated in The Cancer Genome Atlas data set (http://portal.gdc.cancer.gov/repository, last accessed August 25, 2019) using the Visiopharm H&E TIL APP. As IHC sections are not part of the data set, ROI was defined by manual pathologist (C.V.) annotation on the AIDA platform (https://imageannotation.nds.ox.ac.uk:8443/AIDA/, registration required, last accessed August 24, 2019). Most scoring was done with image analysis, as described above. For some of the stains [ki-67 (antibody clone MIB-1), PTEN, programmed death ligand 1 encoded by the CD274 gene (PDL-1), AR, and ERG], manual pathologist (C.V.) scoring was undertaken as the staining pattern was complex and needed some degree of pathologist interpretation. For PTEN and AR, H scoring was undertaken. This is a commonly used scoring system, using the following formula [1 × (% cells 1+) + 2 × (% cells 2+) + 3 × (% cells 3+)] with a final score of 0 to 300. For PTEN, in addition, guidance available in the study by Ferraldeschi et al12Ferraldeschi R. Nava Rodrigues D. Riisnaes R. Miranda S. Figueiredo I. Rescigno P. Ravi P. Pezaro C. Omlin A. Lorente D. Zafeiriou Z. Mateo J. Altavilla A. Sideris S. Bianchini D. Grist E. Thway K. Perez Lopez R. Tunariu N. Parker C. Dearnaley D. Reid A. Attard G. de Bono J. PTEN protein loss and clinical outcome from castration-resistant prostate cancer treated with abiraterone acetate.Eur Urol. 2015; 67: 795-802Abstract Full Text Full Text PDF PubMed Scopus (175) Google Scholar was used, in which an H score of <10 was considered negative and also cases were separately assessed for clonally negative areas with any negative areas being regarded overall as negative. No standard definition exists for PTEN positivity or loss by IHC and thus this previously described system was followed. In that study, they showed that all cases with a homozygous loss of PTEN had loss on IHC, heterozygous loss cases had H scores of (median) 0 to 80 (low/absent), and in heterozygous loss cases, there was commonly loss of IHC expression. PDL-1 was scored according to guidance previously published,13Gevensleben H. Dietrich D. Golletz C. Steiner S. Jung M. Thiesler T. Majores M. Stein J. Uhl B. Müller S. Ellinger J. Stephan C. Jung K. Brossart P. Kristiansen G. The immune checkpoint regulator PD-L1 is highly expressed in aggressive primary prostate cancer.Clin Cancer Res. 2016; 22: 1969-1977Crossref PubMed Scopus (137) Google Scholar where staining is scored as negative (0), weak (1), moderate (2), or strong (3) for specific membrane and cytoplasmic staining of epithelial tumor cells. ERG was assessed by a previously described method14Sung J.Y. Jeon H.G. Jeong B.C. Seo S.I. Jeon S.S. Lee H.M. Choi H.Y. Kang S.Y. Choi Y.L. Kwon G.Y. Correlation of ERG immunohistochemistry with molecular detection of TMPRSS2-ERG gene fusion.J Clin Pathol. 2016; 69: 586-592Crossref PubMed Scopus (13) Google Scholar of intensity only scoring. Cases are scored from 0 to 3 by visual assessment and then cases that are scored as 1 to 3 are considered positive (ERG-high) with only cases scored as 0 being considered negative (ERG-low). Tumor budding was defined as previously described in colorectal carcinoma studies as single cells or clusters of up to four or five cells at the invasive tumor front.15Zlobec I. Lugli A. Invasive front of colorectal cancer: dynamic interface of pro-/anti-tumor factors.World J Gastroenterol. 2009; 15: 5898-5906Crossref PubMed Scopus (89) Google Scholar Assessment was made using standard H&E slides, but if assessment was difficult, this was further facilitated by using panCK stains. The border of the tumor was also categorized as being a pushing (or expanding) or infiltrating border. A pushing tumor border was described as one with margins that were reasonably well circumscribed, whereas an infiltrative tumor border was described as one with dissection of normal tissue with loss of a clear boundary between tumor and host tissues15Zlobec I. Lugli A. Invasive front of colorectal cancer: dynamic interface of pro-/anti-tumor factors.World J Gastroenterol. 2009; 15: 5898-5906Crossref PubMed Scopus (89) Google Scholar (Supplemental Table S2). The presence or absence of an intraductal adenocarcinoma and, if present, percentage volume was assessed by a pathologist (C.V.). If it was unclear from the H&E section, the CK5 stain was referred to for clarification. The region of tissue to be used for DNA extraction was determined by pathologist (C.V.) assessment of H&E-stained slides, and tissue macrodissection was performed. DNA was extracted using the High Pure FFPET DNA Isolation kit (Roche, Basel, Switzerland; 06650767001). DNA was quantitated using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA; Q32854), and concentration ranged between 2.3 and 73.5 ng/μL. Template DNA (50 ng) was used for all samples except one (in which the concentration was <2.5 ng/μL). DNA single-molecule molecular inversion probe (smMIP) sequencing was performed as previously described.16Eijkelenboom A. Kamping E.J. Kastner-van Raaij A.W. Hendriks-Cornelissen S.J. Neveling K. Kuiper R.P. Hoischen A. Nelen M.R. Ligtenberg M.J.L. Tops B.B.J. Reliable next-generation sequencing of formalin-fixed, paraffin-embedded tissue using single molecule tags.J Mol Diagn. 2016; 18: 851-863Abstract Full Text Full Text PDF PubMed Scopus (81) Google Scholar Briefly, smMIPs were designed using MIPgen17Boyle E.A. O'Roak B.J. Martin B.K. Kumar A. Shendure J. MIPgen: optimized modeling and design of molecular inversion probes for targeted resequencing.Bioinformatics. 2014; 30: 2670-2672Crossref PubMed Scopus (106) Google Scholar and were synthesized by Integrated DNA Technologies (Coralville, IA). Initially, a capture reaction was performed whereby smMIPs anneal to target sequences (50 ng of template DNA), and gap filling occurs using the intervening target DNA as a template. Exonuclease treatment is then used to remove all linear DNA, leaving circularized smMIPs. PCR was performed using internal primer site amplification of the probes, and sequencing is followed by consensus generation for the genes of interest. Sequencing was performed at a depth of 2000×, to give an effective depth >150× after deduplication. Depth of coverage per probe is shown in Supplemental Figure S2. The MIP sequencing panel consisted of 3189 MIPs, covering coding exons or hot spots in 23 genes (AR, ARID1A, CDK12, CHD1, CTNNB1, FOXA1, IDH1, KDM6A, MED12, MLH1, MSH2, MSH6, NCOR1, NCOR2, NKX3-1, PIK3CA, PIK3CB, PIK3R1, PTEN, RB1, SPOP, TP53, ZFHX3) and spanning approximately 78 kbp on both strands with an approximate coverage of approximately 95%. Genes were selected on the basis of a literature review, which included commonly reported prostate cancer driver genes.18Gonzalez-Perez A. Perez-Llamas C. Deu-Pons J. Tamborero D. Schroeder M.P. Jene-Sanz A. Santos A. Lopez-Bigas N. IntOGen-mutations identifies cancer drivers across tumor types.Nat Methods. 2013; 10: 1081-1082Crossref PubMed Scopus (365) Google Scholar PIK3CA and TP53 are potential targets of investigational drugs, and CTNNB1, IDH1, NCOR1, NCOR2, and PIK3R1 are potential targets being investigated chemically.3Wedge D.C. Gundem G. Mitchell T. Woodcock D.J. Martincorena I. Ghori M. et al.Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets.Nat Genet. 2018; 50: 682-692Crossref PubMed Scopus (137) Google Scholar Whole exome sequencing was performed in selected samples using the TruSeq DNA Exome kit (Illumina, San Diego, CA; 20020614) with the following modifications: A total of 100 to 300 ng DNA was used for the library preparation, depending on sample quality, pre-enrichment amplification was performed for 12 cycles, and 500 ng amplified DNA was taken forward for enrichment. Samples were multiplexed and run on a NextSeq 500 sequencer (Illumina) as paired end at 75-bp read length, at a minimum average read depth of 70× for tumor samples and 35× for matched normal (after filtering PCR duplicates). For targeted deep sequencing, samples were first deduplicated on the basis of the unique molecular index sequence. Somatic variants [single-nucleotide variants (SNVs) and insertions/deletions (indels)] were called using Lofreq∗.19Wilm A. Aw P.P.K. Bertrand D. Yeo G.H.T. Ong S.H. Wong C.H. Khor C.C. Petric R. Hibberd M.L. Nagarajan N. LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets.Nucleic Acids Res. 2012; 40: 11189-11201Crossref PubMed Scopus (672) Google Scholar C>T and G>A mutations occurring only on one strand of the template, with a wild-type allele on the complementary strand, were considered to be formalin-fixation or PCR artifacts, and hence excluded from the list of true mutations. For whole exome sequencing, somatic variant calling was performed according to the Genome Analysis Toolkit (GATK) version 4.0.6.020McKenna A. Hanna M. Banks E. Sivachenko A. Cibulskis K. Kernytsky A. Garimella K. Altshuler D. Gabriel S. Daly M. DePristo M.A. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.Genome Res. 2010; 20: 1297-1303Crossref PubMed Scopus (14763) Google Scholar best practices workflow and further filtered using custom filters (tumor somatic variant log odds raio > 10, strand odds ratio < 2). Briefly, this involved filtering of PCR duplicates, recalibration of base quality, and somatic mutation calling with matched normals using Mutect2.21Cibulskis K. Lawrence M.S. Carter S.L. Sivachenko A. Jaffe D. Sougnez C. Gabriel S. Meyerson M. Lander E.S. Getz G. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples.Nat Biotechnol. 2013; 31: 213-219Crossref PubMed Scopus (2892) Google Scholar In both the above analyses, variants were annotated using the Variant Effects Predictor version 91.3.22McLaren W. Gil L. Hunt S.E. Riat H.S. Ritchie G.R.S. Thormann A. Flicek P. Cunningham F. The ensembl variant effect predictor.Genome Biol. 2016; 17: 122Crossref PubMed Scopus (2890) Google Scholar RNA was extracted from pathologist-marked (C.V.) unstained formalin-fixed, paraffin-embedded radical pr" @default.
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• W3012960536 title "Detailed Molecular and Immune Marker Profiling of Archival Prostate Cancer Samples Reveals an Inverse Association between TMPRSS2:ERG Fusion Status and Immune Cell Infiltration" @default.
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• W3012960536 doi "https://doi.org/10.1016/j.jmoldx.2020.02.012" @default.
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