Activin receptor-like kinase 1 (ALK1) is a Transforming Growth Factor-β (TGF-β) receptor type I, mainly expressed in endothelial cells that plays a pivotal role in vascular remodelling and angiogenesis. Mutations in the ALK1 gene (ACVRL1) give rise to Hereditary Haemorrhagic Telangiectasia, a dominant autosomal vascular dysplasia caused by a haploinsufficiency mechanism. In spite of its patho-physiological relevance, little is known about the transcriptional regulation of ACVRL1. Here, we have studied the different origins of ACVRL1 transcription, we have analyzed in silico its 5'-proximal promoter sequence and we have characterized the role of Sp1 in the transcriptional regulation of ACVRL1. We have performed a 5'Rapid Amplification of cDNA Ends (5'RACE) of ACVRL1 transcripts, finding two new transcriptional origins, upstream of the one previously described, that give rise to a new exon undiscovered to date. The 5'-proximal promoter region of ACVRL1 (-1,035/+210) was analyzed in silico, finding that it lacks TATA/CAAT boxes, but contains a remarkably high number of GC-rich Sp1 consensus sites. In cells lacking Sp1, ACVRL1 promoter reporters did not present any significant transcriptional activity, whereas increasing concentrations of Sp1 triggered a dose-dependent stimulation of its transcription. Moreover, silencing Sp1 in HEK293T cells resulted in a marked decrease of ACVRL1 transcriptional activity. Chromatin immunoprecipitation assays demonstrated multiple Sp1 binding sites along the proximal promoter region of ACVRL1 in endothelial cells. Furthermore, demethylation of CpG islands, led to an increase in ACVRL1 transcription, whereas in vitro hypermethylation resulted in the abolishment of Sp1-dependent transcriptional activation of ACVRL1. Our results describe two new transcriptional start sites in ACVRL1 gene, and indicate that Sp1 is a key regulator of ACVRL1 transcription, providing new insights into the molecular mechanisms that contribute to the expression of ACVRL1 gene. Moreover, our data show that the methylation status of CpG islands markedly modulates the Sp1 regulation of ACVRL1 gene transcriptional activity.
Adoptive transfer of tumor-infiltrating lymphocytes (TIL) has shown remarkable results in melanoma, but only modest clinical benefits in other cancers, even after TIL have been genetically modified to improve their tumor homing, cytotoxic potential or overcome cell exhaustion. The required
Abstract Cytotoxic CD8+ T lymphocytes (CTLs) are key players of adaptive anti-tumor immunity based on their ability to specifically recognize and destroy tumor cells. Many cancer immunotherapies rely on unleashing CTL function. However, tumors can evade killing through strategies which are not yet fully elucidated. To provide deeper insight into tumor evasion mechanisms in an antigen-dependent manner, we established a human co-culture system composed of tumor and primary immune cells. Using this system, we systematically investigated intrinsic regulators of tumor resistance by conducting a complementary CRISPR screen approach. By harnessing CRISPR activation (CRISPRa) and CRISPR knockout (KO) technology in parallel, we investigated gene gain-of-function as well as loss-of-function across genes with annotated function. CRISPRa and CRISPR KO screens uncovered 186 and 704 hits respectively, with 60 gene hits overlapping between both. These data confirmed the role of interferon-γ (IFN-γ), tumor necrosis factor α (TNF-α) and autophagy pathways and uncovered new genes implicated in tumor resistance to killing. Notably, we discovered that ILKAP encoding the integrin-linked kinase-associated serine/threonine phosphatase 2C, a gene previously unknown to play a role in antigen specific CTL-mediated killing, mediate tumor resistance independently from regulating antigen presentation, IFN-γ or TNF-α responsiveness. Moreover, our work describes the contrasting role of soluble and membrane-bound ICAM-1 in regulating tumor cell killing. The deficiency of membrane-bound ICAM-1 (mICAM-1) or the overexpression of soluble ICAM-1 (sICAM-1) induced resistance to CTL killing, whereas PD-L1 overexpression had no impact. These results highlight the essential role of ICAM-1 at the immunological synapse between tumor and CTL and the antagonist function of sICAM-1.
Stratification of patients for targeted and immune-based therapies requires extensive genomic profiling that enables sensitive detection of clinically relevant variants and interrogation of biomarkers, such as tumor mutational burden (TMB) and microsatellite instability (MSI). Detection of single and multiple nucleotide variants, copy number variants, MSI, and TMB was evaluated using a commercially available next-generation sequencing panel containing 523 cancer-related genes (1.94 megabases). Analysis of formalin-fixed, paraffin-embedded tissue sections and cytologic material from 45 tumor samples showed that all previously known MSI-positive samples (n = 7), amplifications (n = 9), and pathogenic variants (n = 59) could be detected. TMB and MSI scores showed high intralaboratory and interlaboratory reproducibility (eight samples tested in 11 laboratories). For reliable TMB analysis, 20 ng DNA was shown to be sufficient, even for relatively poor-quality samples. A minimum of 20% neoplastic cells was required to minimize variations in TMB values induced by chromosomal instability or tumor heterogeneity. Subsequent analysis of 58 consecutive lung cancer samples in a diagnostic setting was successful and revealed sufficient somatic mutations to generate mutational signatures in 14 cases. In conclusion, the 523-gene assay can be applied for evaluation of multiple DNA-based biomarkers relevant for treatment selection. Stratification of patients for targeted and immune-based therapies requires extensive genomic profiling that enables sensitive detection of clinically relevant variants and interrogation of biomarkers, such as tumor mutational burden (TMB) and microsatellite instability (MSI). Detection of single and multiple nucleotide variants, copy number variants, MSI, and TMB was evaluated using a commercially available next-generation sequencing panel containing 523 cancer-related genes (1.94 megabases). Analysis of formalin-fixed, paraffin-embedded tissue sections and cytologic material from 45 tumor samples showed that all previously known MSI-positive samples (n = 7), amplifications (n = 9), and pathogenic variants (n = 59) could be detected. TMB and MSI scores showed high intralaboratory and interlaboratory reproducibility (eight samples tested in 11 laboratories). For reliable TMB analysis, 20 ng DNA was shown to be sufficient, even for relatively poor-quality samples. A minimum of 20% neoplastic cells was required to minimize variations in TMB values induced by chromosomal instability or tumor heterogeneity. Subsequent analysis of 58 consecutive lung cancer samples in a diagnostic setting was successful and revealed sufficient somatic mutations to generate mutational signatures in 14 cases. In conclusion, the 523-gene assay can be applied for evaluation of multiple DNA-based biomarkers relevant for treatment selection. With the growing number and improved efficacy of targeted therapies, and the introduction of immunotherapy, it has become increasingly evident that comprehensive tumor profiling is needed. Drugs targeting specific mutated genes or activated pathways are clinically available for several indications, and many more targeted therapies are currently being evaluated in clinical trials. In addition, in the past decade, many improvements have been made in the field of immunotherapy. Currently approved immunotherapies target the immune checkpoint proteins cytotoxic T-lymphocyte-associated protein 4 and/or programmed cell death protein 1 (PD-1) or its ligand, programmed cell death 1 ligand 1 (PD-L1). Although the introduction of immunotherapy has benefitted many patients, more than half of the patients show no clear evidence of response.1Borghaei H. Paz-Ares L. Horn L. Spigel D.R. Steins M. Ready N.E. Chow L.Q. Vokes E.E. Felip E. Holgado E. Barlesi F. Kohlhaufl M. Arrieta O. 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Postow M.A. Efficacy and safety outcomes in patients with advanced melanoma who discontinued treatment with nivolumab and ipilimumab because of adverse events: a pooled analysis of randomized phase II and III trials.J Clin Oncol. 2017; 35: 3807-3814Crossref PubMed Scopus (225) Google Scholar PD-L1 expression is an approved predictive biomarker for immunotherapy,9Yu H. Boyle T.A. Zhou C. Rimm D.L. Hirsch F.R. PD-L1 expression in lung cancer.J Thorac Oncol. 2016; 11: 964-975Abstract Full Text Full Text PDF PubMed Scopus (143) Google Scholar but its analysis by immunohistochemistry has several significant challenges, including the typical interobserver variability in scoring and the use of different antibodies and different staining platforms.10Buttner R. Gosney J.R. Skov B.G. Adam J. Motoi N. Bloom K.J. Dietel M. Longshore J.W. Lopez-Rios F. Penault-Llorca F. Viale G. Wotherspoon A.C. Kerr K.M. Tsao M.S. 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PD-L1 expression as a predictive biomarker in cancer immunotherapy.Mol Cancer Ther. 2015; 14: 847-856Crossref PubMed Scopus (1069) Google Scholar,14Shukuya T. Carbone D.P. Predictive markers for the efficacy of anti-PD-1/PD-L1 antibodies in lung cancer.J Thorac Oncol. 2016; 11: 976-988Abstract Full Text Full Text PDF PubMed Scopus (136) Google Scholar The first Food and Drug Administration–approved tumor type–agnostic biomarker for immunotherapy is microsatellite instability (MSI). Microsatellite instability is caused by inactivation of one of the mismatch repair genes, which results in an inability to correct DNA replication errors and leads to a high amount of neopeptides that may serve as targets for the immune system.15Ward R. Meagher A. Tomlinson I. O'Connor T. Norrie M. Wu R. Hawkins N. Microsatellite instability and the clinicopathological features of sporadic colorectal cancer.Gut. 2001; 48: 821-829Crossref PubMed Scopus (289) Google Scholar,16Boussios S. Ozturk M.A. Moschetta M. Karathanasi A. Zakynthinakis-Kyriakou N. Katsanos K.H. Christodoulou D.K. Pavlidis N. The developing story of predictive biomarkers in colorectal cancer.J Pers Med. 2019; 9: E12Crossref PubMed Scopus (52) Google Scholar The incidence of MSI varies among cancers, but is overall rare in most cancer types. More recently, several studies have shown that tumor mutational burden [TMB; number of mutations/megabase (mut/Mb)] correlates with clinical outcome and the effectiveness of immune checkpoint inhibitor immunotherapies17Hellmann M.D. Rizvi N.A. Goldman J.W. Gettinger S.N. Borghaei H. Brahmer J.R. Ready N.E. Gerber D.E. Chow L.Q. Juergens R.A. Shepherd F.A. Laurie S.A. Geese W.J. Agrawal S. Young T.C. Li X. Antonia S.J. Nivolumab plus ipilimumab as first-line treatment for advanced non-small-cell lung cancer (CheckMate 012): results of an open-label, phase 1, multicohort study.Lancet Oncol. 2017; 18: 31-41Abstract Full Text Full Text PDF PubMed Scopus (564) Google Scholar, 18Carbone D.P. Reck M. Paz-Ares L. Creelan B. Horn L. Steins M. Felip E. van den Heuvel M.M. Ciuleanu T.E. Badin F. Ready N. Hiltermann T.J.N. Nair S. Juergens R. Peters S. Minenza E. Wrangle J.M. Rodriguez-Abreu D. Borghaei H. Blumenschein Jr., G.R. Villaruz L.C. Havel L. Krejci J. Corral Jaime J. Chang H. Geese W.J. Bhagavatheeswaran P. Chen A.C. Socinski M.A. CheckMate I. First-line nivolumab in stage IV or recurrent non-small-cell lung cancer.N Engl J Med. 2017; 376: 2415-2426Crossref PubMed Scopus (1319) Google Scholar, 19Hellmann M.D. Ciuleanu T.E. Pluzanski A. Lee J.S. Otterson G.A. Audigier-Valette C. Minenza E. Linardou H. Burgers S. Salman P. Borghaei H. Ramalingam S.S. Brahmer J. Reck M. O'Byrne K.J. Geese W.J. Green G. Chang H. Szustakowski J. Bhagavatheeswaran P. Healey D. Fu Y. Nathan F. Paz-Ares L. Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden.N Engl J Med. 2018; 378: 2093-2104Crossref PubMed Scopus (1497) Google Scholar, 20Hellmann M.D. Nathanson T. Rizvi H. Creelan B.C. Sanchez-Vega F. Ahuja A. Ni A. Novik J.B. Mangarin L.M.B. Abu-Akeel M. Liu C. Sauter J.L. Rekhtman N. Chang E. Callahan M.K. Chaft J.E. Voss M.H. Tenet M. Li X.M. Covello K. Renninger A. Vitazka P. Geese W.J. Borghaei H. Rudin C.M. Antonia S.J. Swanton C. Hammerbacher J. Merghoub T. McGranahan N. Snyder A. Wolchok J.D. Genomic features of response to combination immunotherapy in patients with advanced non-small-cell lung cancer.Cancer Cell. 2018; 33: 843-852.e844Abstract Full Text Full Text PDF PubMed Scopus (420) Google Scholar, 21Heeke S. Benzaquen J. Long-Mira E. Audelan B. Lespinet V. Bordone O. Lalvee S. Zahaf K. Poudenx M. Humbert O. Montaudie H. Dugourd P.M. Chassang M. Passeron T. Delingette H. Marquette C.H. Hofman V. Stenzinger A. Ilie M. Hofman P. In-house implementation of tumor mutational burden testing to predict durable clinical benefit in non-small cell lung cancer and melanoma patients.Cancers (Basel). 2019; 11: E1271Crossref PubMed Scopus (14) Google Scholar, 22Alborelli I. Leonards K. Rothschild S.I. Leuenberger L.P. Savic Prince S. Mertz K.D. Poechtrager S. Buess M. Zippelius A. Laubli H. Haegele J. Tolnay M. Bubendorf L. Quagliata L. Jermann P. Tumor mutational burden assessed by targeted NGS predicts clinical benefit from immune checkpoint inhibitors in non-small cell lung cancer.J Pathol. 2020; 250: 19-29Crossref PubMed Scopus (41) Google Scholar and thus may serve as a novel biomarker. Similar to MSI, the TMB value is thought to correlate with the efficacy of immunotherapy, because mutations result in slightly modified proteins that can be recognized as neoantigens by the immune system.23Rizvi N.A. Hellmann M.D. Snyder A. Kvistborg P. Makarov V. Havel J.J. Lee W. Yuan J. Wong P. Ho T.S. Miller M.L. Rekhtman N. Moreira A.L. Ibrahim F. Bruggeman C. Gasmi B. Zappasodi R. Maeda Y. Sander C. Garon E.B. Merghoub T. Wolchok J.D. Schumacher T.N. Chan T.A. Cancer immunology: mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer.Science. 2015; 348: 124-128Crossref PubMed Scopus (4486) Google Scholar,24Schumacher T.N. Schreiber R.D. Neoantigens in cancer immunotherapy.Science. 2015; 348: 69-74Crossref PubMed Scopus (2403) Google Scholar The TMB threshold for successful immunotherapy may differ per tumor type.25Samstein R.M. Lee C.H. Shoushtari A.N. Hellmann M.D. Shen R. Janjigian Y.Y. et al.Tumor mutational load predicts survival after immunotherapy across multiple cancer types.Nat Genet. 2019; 51: 202-206Crossref PubMed Scopus (936) Google Scholar For lung cancer, the threshold was set at 10 mut/Mb in the CheckMate 568.19Hellmann M.D. Ciuleanu T.E. Pluzanski A. Lee J.S. Otterson G.A. Audigier-Valette C. Minenza E. Linardou H. Burgers S. Salman P. Borghaei H. Ramalingam S.S. Brahmer J. Reck M. O'Byrne K.J. Geese W.J. Green G. Chang H. Szustakowski J. Bhagavatheeswaran P. Healey D. Fu Y. Nathan F. Paz-Ares L. Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden.N Engl J Med. 2018; 378: 2093-2104Crossref PubMed Scopus (1497) Google Scholar Several factors are associated with an increased TMB.26Alexandrov L.B. Nik-Zainal S. Wedge D.C. Aparicio S.A. Behjati S. Biankin A.V. et al.Signatures of mutational processes in human cancer.Nature. 2013; 500: 415-421Crossref PubMed Scopus (5072) Google Scholar For example, environmental factors, like UV light and tobacco use, can be responsible for an increased mutational load in melanoma and lung cancer, respectively.27Alexandrov L.B. Ju Y.S. Haase K. Van Loo P. Martincorena I. Nik-Zainal S. Totoki Y. Fujimoto A. Nakagawa H. Shibata T. Campbell P.J. Vineis P. Phillips D.H. Stratton M.R. 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Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden.Genome Med. 2017; 9: 34Crossref PubMed Scopus (1178) Google Scholar Also, MSI and mutations in exonuclease domains of DNA polymerase ε (POLE) and DNA polymerase δ 1 (POLD1), involved in DNA repair, can lead to a high TMB.29Chalmers Z.R. Connelly C.F. Fabrizio D. Gay L. Ali S.M. Ennis R. Schrock A. Campbell B. Shlien A. Chmielecki J. Huang F. He Y. Sun J. Tabori U. Kennedy M. Lieber D.S. Roels S. White J. Otto G.A. Ross J.S. Garraway L. Miller V.A. Stephens P.J. Frampton G.M. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden.Genome Med. 2017; 9: 34Crossref PubMed Scopus (1178) Google Scholar, 30Mensenkamp A.R. Vogelaar I.P. van Zelst-Stams W.A. Goossens M. Ouchene H. Hendriks-Cornelissen S.J. Kwint M.P. Hoogerbrugge N. Nagtegaal I.D. Ligtenberg M.J. 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Tumor clonality and resistance mechanisms in EGFR mutation-positive non-small-cell lung cancer: implications for therapeutic sequencing.Future Oncol. 2019; 15: 637-652Crossref PubMed Scopus (32) Google Scholar,34Manzano J.L. Layos L. Buges C. de Los Llanos Gil M. Vila L. Martinez-Balibrea E. Martinez-Cardus A. Resistant mechanisms to BRAF inhibitors in melanoma.Ann Transl Med. 2016; 4: 237Crossref PubMed Scopus (118) Google Scholar and immunotherapy35Kalbasi A. Ribas A. Tumour-intrinsic resistance to immune checkpoint blockade.Nat Rev Immunol. 2020; 20: 25-39Crossref PubMed Scopus (218) Google Scholar becomes increasingly important. Because the amount of DNA available for genetic analysis is often limited, it is preferred to simultaneously assess all major genetic biomarkers and drug targets important for targeted as well as immune-based therapies, in one single assay. The current article evaluates TruSight Oncology 500 (TSO500; Illumina, San Diego, CA), a next-generation sequencing (NGS) panel containing 523 cancer-related genes (1.94 Mb) that can be used to assess pathogenic single-nucleotide variants (SNVs) and multiple-nucleotide variants, copy number variants (CNVs), MSI, and TMB. TSO500 was selected because it is a hybrid capture–based assay evaluating >1.2 Mb of coding sequence that uses unique molecular identifiers (UMIs) to enable sensitive mutation detection and to reduce the background noise that is caused by deamination artifacts in formalin-fixed materials. Detection of mutations and CNVs and the intralaboratory and interlaboratory reproducibility of TMB and MSI analyses were evaluated using different amounts and sources of input material and different neoplastic cell percentages. Finally, to evaluate the applicability of the TSO500 assay in clinical practice, a consecutive series of 58 lung cancer samples was analyzed. For samples with >30 somatic SNVs, mutational signature analysis was performed. To evaluate the performance of the TSO500 DNA assay, 45 tumor samples were selected from our routine clinical practice on the basis of known genetic defects (including samples with splice site mutations and large deletions or duplications in relevant genes), amplifications (low and high level), and MSI status. This set of tumor samples consists of samples from different tumor types: lung (n = 16), colon (n = 7), melanoma (n = 6), bladder (n = 4), endometrium (n = 2), ovary (n = 2), prostate (n = 2), gastrointestinal stromal tumors (n = 2), glioblastoma (n = 1), larynx (n = 1), pancreas (n = 1), and salivary gland (n = 1). Mean patient age was 66 years (range, 40 to 96 years). In addition, 11 normal tissue samples were used in this evaluation phase. The mean age of these individuals was 67 years (range, 49 to 72 years). One of the control samples was a peripheral blood sample, and the other 10 samples were healthy tissue from colon (n = 3), lung (n = 3), lymph node (n = 3), and prostate (n = 1). After implementation of the assay in routine diagnostics, 58 consecutive lung cancer samples were analyzed. Most samples (n = 87) were formalin-fixed, paraffin-embedded (FPPE) tissue samples. In addition, one blood sample (normal control), one cerebrospinal fluid sample, one fresh frozen tissue sample, and 24 cytologic materials (16 embedded in agar, six Giemsa-stained tissue slides, and two Papanicolaou-stained slides) were used. More information about the samples used for the different analyses can be found in Figure 1 and Supplemental Table S1. The study was conducted in accordance with the institutional guidelines and regulations from Radboud University Medical Center (Nijmegen, the Netherlands; Commissie Mensgebonden Onderzoek 2018-4758). Genomic DNA was isolated from tissue sections (generally 6 × 10 μm) using 5% Chelex-100 and 400 μg proteinase K, followed by purification using the QIAamp DNA Micro Kit (Qiagen, Venlo, the Netherlands). DNA concentrations were measured using the Qubit Broad Range kit (Thermo Fisher Scientific, Waltham, MA). Subsequently, 40 ng DNA was used as input for the library preparation. A DNA integrity number (DIN), a measure for the size of the DNA fragments and consequently the DNA quality, was determined using the Genomic DNA ScreenTape (Agilent Technologies, Santa Clara, CA) on an Agilent 2200 TapeStation system (Agilent Technologies). During the verification phase, it was observed that a more accurate DNA input amount could be obtained when the DNA was first diluted to 10 ng/μL using 0.1× Tris-EDTA, after which the concentration was remeasured using the Qubit High Sensitivity kit (Thermo Fisher Scientific). In addition, purification using the QIAamp DNA Micro Kit turned out not to be required. These two modifications were applied before analyzing 58 consecutive lung cancer samples in routine diagnostics. Library preparation was performed using the hybrid capture–based TSO500 library preparation kit following the manufacturer's protocol. The TSO500 assay contains probes for 523 genes (Supplemental Table S2) and makes use of UMIs to analyze the number of individual DNA molecules sequenced at every position (unique coverage). Briefly, DNA was fragmented using a Covaris S2 (Covaris, Woburn, MA) to generate DNA fragments of 90 to 250 bp, with a target peak at approximately 180 bp. Samples next underwent end repair and A-tailing, before ligation of UMIs and amplification to add index sequences for sample multiplexing. Two hybridization/capture steps were performed. For the first hybridization, a pool of oligonucleotides specific to the 523 genes targeted by the TSO500 was hybridized to the prepared DNA libraries overnight. Next, streptavidin magnetic beads were used to capture probes hybridized to the DNA regions of interest. A second hybridization using the same probe set was performed to ensure high specificity of the captured regions. Next, the enriched libraries were amplified by PCR before purification using sample purification beads. Libraries were quantified and then normalized to ensure a uniform library representation. Finally, the libraries were pooled, denatured, and diluted to the appropriate loading concentration. Libraries were sequenced on a NextSeq 500 (Illumina), with eight to 10 libraries sequenced per run (NextSeq high output). The sequence data were processed and analyzed by the TruSight Oncology 500 Local App version 1.3 (Illumina). UMIs are used in the analysis to determine the unique coverage at each position and to reduce the background noise that is caused by sequencing and deamination artifacts in formalin-fixed material. The software produces a report including total and nonsynonymous mutations per Mb scores for TMB, the number and percentage of unstable sites for MSI, and quality parameters like median unique exon coverage and insert size (Supplemental Table S1). Moreover, coverage tables and a variant call file for single- and multiple-nucleotide variants, including number and percentage of variant alleles, are provided. For TMB estimation, variants are classified as somatic or germline by using bioinformatical approaches, which make use of various databases [including Catalogue of Somatic Mutations in Cancer (COSMIC), The Genome Aggregation Database (gnomAD), and 1000 genomes project] and take into account the variant allele frequencies (VAFs) of surrounding germline variants. For TMB calculation, somatic SNVs with a VAF >5% are included. Hotspot mutations are excluded to avoid overestimation of TMB, because the gene panel is biased toward frequently mutated genomic regions (cancer-related genes). An in-house developed user interface was used for variant annotation, variant filtering, and data visualization.36Neveling K. Feenstra I. Gilissen C. Hoefsloot L.H. Kamsteeg E.J. Mensenkamp A.R. Rodenburg R.J. Yntema H.G. Spruijt L. Vermeer S. Rinne T. van Gassen K.L. Bodmer D. Lugtenberg D. de Reuver R. Buijsman W. Derks R.C. Wieskamp N. van den Heuvel B. Ligtenberg M.J. Kremer H. Koolen D.A. van de Warrenburg B.P. Cremers F.P. Marcelis C.L. Smeitink J.A. Wortmann S.B. van Zelst-Stams W.A. Veltman J.A. Brunner H.G. Scheffer H. Nelen M.R. A post-hoc comparison of the utility of sanger sequencing and exome sequencing for the diagnosis of heterogeneous diseases.Hum Mutat. 2013; 34: 1721-1726Crossref PubMed Scopus (220) Google Scholar Variants were filtered by excluding the following: i) variants not overlapping with exons and splice site regions (−8/+8), ii) synonymous variants, unless located in a splice site region, and iii) variants present with a frequency >0.1% in the control population represented in The Exome Aggregation Consortium (ExAC) version 0.2. For tailored reporting purposes, virtual gene panels were defined per tumor type in close collaboration with the clinicians. For lung cancer, a panel of 15 genes was composed (Supplemental Table S3). After filtering, all remaining variants in the gene panel were manually inspected and curated. A laboratory-developed bioinformatic pipeline was used to analyze the unique coverage and the presence of amplifications, as described previously.37Eijkelenboom A. Tops B.B.J. van den Berg A. van den Brule A.J.C. Dinjens W.N.M. Dubbink H.J. Ter Elst A. Geurts-Giele W.R.R. Groenen P. Groenendijk F.H. Heideman D.A.M. Huibers M.M.H. Huijsmans C.J.J. Jeuken J.W.M. van Kempen L.C. Korpershoek E. Kroeze L.I. de Leng WWJ van Noesel C.J.M. Speel E.M. Vogel M.J. van Wezel T. Nederlof P.M. Schuuring E. Ligtenberg M.J.L. Recommendations for the clinical interpretation and reporting of copy number gains using gene panel NGS analysis in routine diagnostics.Virchows Arch. 2019; 474: 673-680Crossref PubMed Scopus (8) Google Scholar For analyses of gene amplifications, the coverage of each region in the tumor sample was normalized using the median sequencing depth of all regions in the sample. Subsequently, a relative coverage score was calculated by dividing the normalized coverage of each region through the mean normalized coverage of this same region in a set of 11 normal control samples. By analyzing formalin-fixed, paraffin-embedded and cytologic samples from our routine clinical practice, the performance of the TSO500 assay for TMB measurement, MSI analysis, variant calling, and CNV detection was determined. POLE-mutated and MSI-positive samples were used as positive controls, whereas normal tissue samples were used as negative controls for TMB analysis. Mutation detection, CNV analysis, and MSI analysis were evaluated by comparing the TSO500 results with results from our routine diagnostic tests. For mutation detection and CNV analysis, single-molecule molecular inversion probe (smMIP)–based NGS is used,37Eijkelenboom A. Tops B.B.J. van den Berg A. van den Brule A.J.C. Dinjens W.N.M. Dubbink H.J. Ter Elst A. Geurts-Giele W.R.R. Groenen P. Groenendijk F.H. Heideman D.A.M. Huibers M.M.H. Huijsmans C.J.J. Jeuken J.W.M. van Kempen L.C. Korpershoek E. Kroeze L.I. de Leng WWJ van Noesel C.J.M. Speel E.M. Vogel M.J. van Wezel T. Nederlof P.M. Schuuring E. Ligtenberg M.J.L. Recommendations for the clinical interpretation and reporting of copy number gains using gene panel NGS analysis in routine diagnostics.Virchows Arch. 2019; 474: 673-680Crossref PubMed Scopus (8) Google Scholar, 38Eijkelenboom 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. Tops B.B. 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 (64) Google Scholar, 39Steeghs E.M.P. Kroeze L.I. Tops B.B.J. van Kempen L.C. ter Elst A. Kastner-van Raaij A.W.M. Hendriks-Cornelissen S.J.B. Hermsen M.J.W. Jansen E.A.M. Nederlof P.M. Schuuring E. Ligtenberg M.J.L. Eijkelenboom A. Comprehensive routine diagnostic screening to identify predictive mutations, gene amplifications, and microsatellite instability in FFPE tumor material.BMC Cancer. 2020; 20: 291Crossref PubMed Scopus (10) Google Scholar and for MSI detection, genescan fragment length analyses of five mononucleotide markers (pentaplex PCR), immunohistochemistry of the mismatch repair genes, and/or evaluation of 55 microsatellite markers by smMIP-based NGS using mSINGS software version 3.4 are used.39Steeghs E.M.P. Kroeze L.I. Tops B.B.J. van Kempen L.C. ter Elst A. Kastner-van Raaij A.W.M. Hendriks-Cornelissen S.J.B. Hermsen M.J.W. Jansen E.A.M. Nederlof P.M. Schuuring E. Ligtenberg M.J.L. Eijkelenboom
Abstract Siglec-15 is a conserved sialic acid-binding Ig-like lectin expressed on osteoclast progenitors, which plays an important role in osteoclast development and function. It is also expressed by tumor-associated macrophages and by some tumors, where it is thought to contribute to the immunosuppressive microenvironment. It was shown previously that engagement of macrophage-expressed Siglec-15 with tumor cells expressing its ligand, sialyl Tn (sTn), triggered production of TGF-β. In the present study, we have further investigated the interaction between Siglec-15 and sTn on tumor cells and its functional consequences. Based on binding assays with lung and breast cancer cell lines and glycan-modified cells, we failed to see evidence for recognition of sTn by Siglec-15. However, using a microarray of diverse, structurally defined glycans, we show that Siglec-15 binds with higher avidity to sialylated glycans other than sTn or related antigen sequences. In addition, we were unable to demonstrate enhanced TGF-β secretion following co-culture of Siglec-15-expressing monocytic cell lines with tumor cells expressing sTn or following Siglec-15 cross-linking with monoclonal antibodies. However, we did observe activation of the SYK/MAPK signaling pathway following antibody cross-linking of Siglec-15 that may modulate the functional activity of macrophages.
Abstract Interleukin-11 (IL11) has been associated with tumorigenesis in a wide variety of tumors, including lung cancer, and it has been proposed as a diagnostic biomarker for lung adenocarcinoma. However, it is still not clear how IL11 affects the tumorigenesis. It is possible that, as other cytokines, it has a dual role in the tumor cell and tumoral microenvironment. Thus, the inhibition of IL11 could be an interesting therapeutic option to test in these patients. First, we confirmed the pro-tumorigenic effect of IL11 in patients and mouse models of lung adenocarcinoma (cancer cell lines xenografts, patient derived xenografts; PDXs and genetically engineered mouse models; GEMMs). Later, we knocked-down the expression of IL11 or its specific receptor IL11RA in adenocarcinoma cell lines in order to analyze their tumorigenic properties in vitro and in vivo. We confirmed that fibroblasts are a relevant source of IL11, so we knocked-down IL11 expression in order to analyze how it affects the fibroblasts´ properties, including the secretion of other pro-tumorigenic cytokines and growth factors. We reported that an increased expression of IL11 correlates with a poorer survival in lung adenocarcinoma patients and that IL11 stimulation increases the proliferation rates in xenografts, PDXs and GEMMs. On the contrary, IL11 or IL11RA knockdown in adenocarcinoma cell lines reduces their pro-tumorigenic properties in vitro and in vivo. Finally, the silencing of IL11 in fibroblasts reduces their proliferation, migration and secretion of pro-tumorigenic cytokines and growth factors. In conclusion, we propose that IL11 plays a direct pro-oncogenic role in lung adenocarcinoma tumoral cell and an indirect role in tumoral microenvironment. The genetic ablation of IL11 has an anti-tumoral effect, so it could be interesting to develop a pharmacological tool which neutralizes the IL11-IL11RA signaling to test as a therapeutic strategy in preclinical models. Thus, IL11 could represent a potential therapeutic target that deserves to be more exhaustively studied in the clinical settings. Citation Format: Laura Ojeda, Cristina Cirauqui, Sonia Molina-Pinelo, Eva M Garrido-Martín, Javier Ramos-Paradas, Patricia Yagüe, Alba Santos, Nuria Carrizo, Ana B. Enguita, Maria Teresa Muñoz, Jose Luis Solorzano, Luis Paz-Ares, Irene Ferrer. Interleukin-11 could be a novel therapeutic target for lung adenocarcinoma patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5247.
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