Molecular Signature of MT1-MMP: Transactivation of the Downstream Universal Gene Network in Cancer
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Profiling Data
Compound Name DiscoveRx Gene Symbol Entrez Gene Percent Compound Symbol Control Concentration (nM) JNK-IN-8 AAK1 AAK1 69 1000 JNK-IN-8 ABL1(E255K)-phosphorylated ABL1 100 1000 JNK-IN-8 ABL1(F317I)-nonphosphorylated ABL1 87 1000 JNK-IN-8 ABL1(F317I)-phosphorylated ABL1 100 1000 JNK-IN-8 ABL1(F317L)-nonphosphorylated ABL1 65 1000 JNK-IN-8 ABL1(F317L)-phosphorylated ABL1 61 1000 JNK-IN-8 ABL1(H396P)-nonphosphorylated ABL1 42 1000 JNK-IN-8 ABL1(H396P)-phosphorylated ABL1 60 1000 JNK-IN-8 ABL1(M351T)-phosphorylated ABL1 81 1000 JNK-IN-8 ABL1(Q252H)-nonphosphorylated ABL1 100 1000 JNK-IN-8 ABL1(Q252H)-phosphorylated ABL1 56 1000 JNK-IN-8 ABL1(T315I)-nonphosphorylated ABL1 100 1000 JNK-IN-8 ABL1(T315I)-phosphorylated ABL1 92 1000 JNK-IN-8 ABL1(Y253F)-phosphorylated ABL1 71 1000 JNK-IN-8 ABL1-nonphosphorylated ABL1 97 1000 JNK-IN-8 ABL1-phosphorylated ABL1 100 1000 JNK-IN-8 ABL2 ABL2 97 1000 JNK-IN-8 ACVR1 ACVR1 100 1000 JNK-IN-8 ACVR1B ACVR1B 88 1000 JNK-IN-8 ACVR2A ACVR2A 100 1000 JNK-IN-8 ACVR2B ACVR2B 100 1000 JNK-IN-8 ACVRL1 ACVRL1 96 1000 JNK-IN-8 ADCK3 CABC1 100 1000 JNK-IN-8 ADCK4 ADCK4 93 1000 JNK-IN-8 AKT1 AKT1 100 1000 JNK-IN-8 AKT2 AKT2 100 1000 JNK-IN-8 AKT3 AKT3 100 1000 JNK-IN-8 ALK ALK 85 1000 JNK-IN-8 AMPK-alpha1 PRKAA1 100 1000 JNK-IN-8 AMPK-alpha2 PRKAA2 84 1000 JNK-IN-8 ANKK1 ANKK1 75 1000 JNK-IN-8 ARK5 NUAK1 100 1000 JNK-IN-8 ASK1 MAP3K5 100 1000 JNK-IN-8 ASK2 MAP3K6 93 1000 JNK-IN-8 AURKA AURKA 100 1000 JNK-IN-8 AURKA AURKA 84 1000 JNK-IN-8 AURKB AURKB 83 1000 JNK-IN-8 AURKB AURKB 96 1000 JNK-IN-8 AURKC AURKC 95 1000 JNK-IN-8 -
HEY1 Functions Are Regulated by Its Phosphorylation at Ser-68
Biosci. Rep. (2016) / 36 / art:e00343 / doi 10.1042/BSR20160123 HEY1 functions are regulated by its phosphorylation at Ser-68 Irene Lopez-Mateo*,´ Amaia Arruabarrena-Aristorena†, Cristina Artaza-Irigaray‡, Juan A. Lopez´ §, Enrique Calvo§ and Borja Belandia*1 *Department of Cancer Biology, Instituto de Investigaciones Biomedicas´ Alberto Sols, CSIC-UAM, Arturo Duperier 4, 28029 Madrid, Spain †Proteomics Unit, CIC bioGUNE, Bizkaia Technology Park, 48160 Derio, Spain ‡Division´ de Inmunolog´ıa, Centro de Investigacion´ Biomedica´ de Occidente – IMSS, Sierra Mojada 800, 44340 Guadalajara, Jalisco, Mexico §Unidad de Proteomica, Centro Nacional de Investigaciones Cardiovasculares, CNIC, 28029 Madrid, Spain Synopsis HEY1 (hairy/enhancer-of-split related with YRPW motif 1) is a member of the basic helix–loop–helix-orange (bHLH-O) family of transcription repressors that mediate Notch signalling. HEY1 acts as a positive regulator of the tumour suppressor p53 via still unknown mechanisms. A MALDI-TOF/TOF MS analysis has uncovered a novel HEY1 regulatory phosphorylation event at Ser-68. Strikingly, this single phosphorylation event controls HEY1 stability and function: simulation of HEY1 Ser-68 phosphorylation increases HEY1 protein stability but inhibits its ability to enhance p53 transcriptional activity. Unlike wild-type HEY1, expression of the phosphomimetic mutant HEY1-S68D failed to induce p53-dependent cell cycle arrest and it did not sensitize U2OS cells to p53-activating chemotherapeutic drugs. We have identified two related kinases, STK38 (serine/threonine kinase 38) and STK38L (serine/threonine kinase 38 like), which interact with and phosphorylate HEY1 at Ser-68. HEY1 is phosphorylated at Ser-68 during mitosis and it accumulates in the centrosomes of mitotic cells, suggesting a possible integration of HEY1-dependent signalling in centrosome function. -
Deep Multiomics Profiling of Brain Tumors Identifies Signaling Networks
ARTICLE https://doi.org/10.1038/s41467-019-11661-4 OPEN Deep multiomics profiling of brain tumors identifies signaling networks downstream of cancer driver genes Hong Wang 1,2,3, Alexander K. Diaz3,4, Timothy I. Shaw2,5, Yuxin Li1,2,4, Mingming Niu1,4, Ji-Hoon Cho2, Barbara S. Paugh4, Yang Zhang6, Jeffrey Sifford1,4, Bing Bai1,4,10, Zhiping Wu1,4, Haiyan Tan2, Suiping Zhou2, Laura D. Hover4, Heather S. Tillman 7, Abbas Shirinifard8, Suresh Thiagarajan9, Andras Sablauer 8, Vishwajeeth Pagala2, Anthony A. High2, Xusheng Wang 2, Chunliang Li 6, Suzanne J. Baker4 & Junmin Peng 1,2,4 1234567890():,; High throughput omics approaches provide an unprecedented opportunity for dissecting molecular mechanisms in cancer biology. Here we present deep profiling of whole proteome, phosphoproteome and transcriptome in two high-grade glioma (HGG) mouse models driven by mutated RTK oncogenes, PDGFRA and NTRK1, analyzing 13,860 proteins and 30,431 phosphosites by mass spectrometry. Systems biology approaches identify numerous master regulators, including 41 kinases and 23 transcription factors. Pathway activity computation and mouse survival indicate the NTRK1 mutation induces a higher activation of AKT down- stream targets including MYC and JUN, drives a positive feedback loop to up-regulate multiple other RTKs, and confers higher oncogenic potency than the PDGFRA mutation. A mini-gRNA library CRISPR-Cas9 validation screening shows 56% of tested master regulators are important for the viability of NTRK-driven HGG cells, including TFs (Myc and Jun) and metabolic kinases (AMPKa1 and AMPKa2), confirming the validity of the multiomics inte- grative approaches, and providing novel tumor vulnerabilities. -
Ufmylation Inhibits the Proinflammatory Capacity of Interferon-Γ–Activated Macrophages
UFMylation inhibits the proinflammatory capacity of interferon-γ–activated macrophages Dale R. Balcea,1,2, Ya-Ting Wanga,b, Michael R. McAllasterb,1, Bria F. Dunlapa, Anthony Orvedahlc, Barry L. Hykes Jra, Lindsay Droita, Scott A. Handleya, Craig B. Wilend,e, John G. Doenchf, Robert C. Orchardg, Christina L. Stallingsb, and Herbert W. Virgina,1,2 aDepartment of Pathology and Immunology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110; bDepartment of Molecular Microbiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110; cDepartment of Pediatrics, Washington University School of Medicine in St. Louis, St. Louis, MO 63110; dDepartment of Laboratory Medicine, Yale School of Medicine, New Haven, CT 06510; eDepartment of Immunobiology, Yale School of Medicine, New Haven, CT 06510; fBroad Institute of MIT and Harvard, Cambridge, MA 02142; and gDepartment of Immunology and Microbiology, University of Texas Southwestern Medical Center, Dallas, TX 75390 Contributed by Herbert W. Virgin, November 19, 2020 (sent for review June 15, 2020; reviewed by Masaaki Komatsu and Hong Zhang) Macrophages activated with interferon-γ (IFN-γ) in combination in regulating this aspect of cellular immunity. Thus, understanding with other proinflammatory stimuli, such as lipopolysaccharide pathways that positively and negatively regulate IFN-γ–dependent or tumor necrosis factor-α (TNF-α), respond with transcriptional macrophage activation is an important priority. and cellular changes that enhance clearance of intracellular path- Here we performed a genome-wide CRISPR screen to identify ogens at the risk of damaging tissues. IFN-γ effects must therefore proteins that negatively regulate IFN-γ responses in macrophages. -
Journal of Oncology Research and Therapy Deutsch AL and Maitra R
Journal of Oncology Research and Therapy Deutsch AL and Maitra R. J Oncol Res Ther 4: 077. Review Article DOI: 10.29011/2574-710X.000077 miR-196a Silencing of HoxD8: A Mechanism of BRAFi Resistance Aryeh Leib Deutsch, Radhashree Maitra* Department of Biology, Yeshiva University, New York, USA *Corresponding author: Radhashree Maitra, Department of Biology, Yeshiva University, New York, USA. Email: radhashree.mai- [email protected] Citation: Deutsch AL, Maitra R (2019) miR-196a Silencing of HoxD8: A Mechanism of BRAFi Resistance. J Oncol Res Ther 4: 077. DOI: 10.29011/2574-710X.000077 Received Date: 11 February, 2019; Accepted Date: 25 February, 2019; Published Date: 05 March, 2019 Abstract Colorectal Cancer (CRC) is the third most common cancer found in the United States. Similar to many cancers, CRC com- monly arises from mutations of the genes found along the Mitogen Activated Protein Kinase (MAPK) pathway. Cancer treatments using inhibitors of the mutated proteins along the pathway have become widely used. Despite the overall effectiveness of these treatments, it is always possible that a pathway of resistance may develop, causing the cancer to become unresponsive to treat- ment. Previous literature has implicated the mutation of HoxD8, a homeobox gene responsible for embryo development and limb segmentation, as a possible pathway of resistance, though the details of this pathway are unknown. This review seeks to propose such a mechanism. It was discovered that miR‐196a, a type of miRNA, is known to silence HoxD8, which in turn causes the increased expression of STK38. This can either activate the rest of the MAPK or stabilize MYC, affecting the cell’s morphology and allowing cancerous cells to continuously proliferate. -
PRODUCTS and SERVICES Target List
PRODUCTS AND SERVICES Target list Kinase Products P.1-11 Kinase Products Biochemical Assays P.12 "QuickScout Screening Assist™ Kits" Kinase Protein Assay Kits P.13 "QuickScout Custom Profiling & Panel Profiling Series" Targets P.14 "QuickScout Custom Profiling Series" Preincubation Targets Cell-Based Assays P.15 NanoBRET™ TE Intracellular Kinase Cell-Based Assay Service Targets P.16 Tyrosine Kinase Ba/F3 Cell-Based Assay Service Targets P.17 Kinase HEK293 Cell-Based Assay Service ~ClariCELL™ ~ Targets P.18 Detection of Protein-Protein Interactions ~ProbeX™~ Stable Cell Lines Crystallization Services P.19 FastLane™ Structures ~Premium~ P.20-21 FastLane™ Structures ~Standard~ Kinase Products For details of products, please see "PRODUCTS AND SERVICES" on page 1~3. Tyrosine Kinases Note: Please contact us for availability or further information. Information may be changed without notice. Expression Protein Kinase Tag Carna Product Name Catalog No. Construct Sequence Accession Number Tag Location System HIS ABL(ABL1) 08-001 Full-length 2-1130 NP_005148.2 N-terminal His Insect (sf21) ABL(ABL1) BTN BTN-ABL(ABL1) 08-401-20N Full-length 2-1130 NP_005148.2 N-terminal DYKDDDDK Insect (sf21) ABL(ABL1) [E255K] HIS ABL(ABL1)[E255K] 08-094 Full-length 2-1130 NP_005148.2 N-terminal His Insect (sf21) HIS ABL(ABL1)[T315I] 08-093 Full-length 2-1130 NP_005148.2 N-terminal His Insect (sf21) ABL(ABL1) [T315I] BTN BTN-ABL(ABL1)[T315I] 08-493-20N Full-length 2-1130 NP_005148.2 N-terminal DYKDDDDK Insect (sf21) ACK(TNK2) GST ACK(TNK2) 08-196 Catalytic domain -
Targeted MS Quantitation Assays for Signal Transduction Protein Pathways
Targeted MS quantitation assays for signal transduction protein pathways Paul Haney R&D Platform Manager Thermo Scientific Protein Research Products Rockford, IL Why Targeted Quantitative Proteomics via Mass Spec? . Measure protein abundance and protein isoforms (e.g. splice variants, PTMs) without the need for antibodies . Avoid immuno-based cross- reactivity during multiplexing. Validate relative quantitation data from discovery proteomic experiments 2 How Do You Select Peptides for Targeted MS Assays? List of Proteins Spectral library Discovery data repositories In silico prediction Pinpoint 1.2 Hypothesis Experimental List of target peptides and transitions 3 How Do You Select Peptides for Targeted MS Assays? List of Proteins Spectral library Discovery data repositories In silico prediction Pinpoint 1.2 Hypothesis Experimental Validation Assay results List of target peptides and transitions 4 Tools for Target Peptide Identification and Scheduling . Active-site probes for enzyme subclass enrichment . Rapid recombinant heavy protein expression using human cell-free extracts . Peptide retention time calibration mixture for chromatography QC and targeted method 100 80 60 acquisition scheduling Intensity 40 20 0 5 10 15 20 25 30 5 Tools for Target Peptide Identification and Scheduling . Active-site probes for enzyme subclass enrichment . Rapid recombinant heavy protein expression using human cell-free extracts • Stergachis, A. & MacCoss, M. (2011) Nature Methods (submitted) . Peptide retention time calibration mixture for 100 80 chromatography -
Defining the KRAS-Regulated Kinome in KRAS-Mutant Pancreatic Cancer
bioRxiv preprint doi: https://doi.org/10.1101/2021.04.27.441678; this version posted April 30, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Submitted Manuscript: Confidential Defining the KRAS-regulated kinome in KRAS-mutant pancreatic cancer J. Nathaniel Diehl1, Jennifer E. Klomp2, Kayla R. Snare2, Devon R. Blake3, Priya S. Hibshman2,4, Zane D. Kaiser2, Thomas S.K. Gilbert3,5, Elisa Baldelli6, Mariaelena Pierobon6, Björn Papke2, Runying Yang2, Richard G. Hodge2, Naim U. Rashid2,7, Emanuel F. Petricoin III6, Laura E. Herring3,5, Lee M. Graves2,3,5, Adrienne D. Cox2,3,4,8, Channing J. Der1,2,3,4* 1Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; 2Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; 3Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; 4Cell Biology and Physiology Curriculum, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; 5UNC Michael Hooker Proteomics Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; 6Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, Virginia, USA; 7Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; 8Department of Radiation Oncology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. *Corresponding author: Email: [email protected] Running title: Defining the KRAS-regulated kinome 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.27.441678; this version posted April 30, 2021. -
Dema and Faust Et Al., Suppl. Material 2020.02.03
Supplementary Materials Cyclin-dependent kinase 18 controls trafficking of aquaporin-2 and its abundance through ubiquitin ligase STUB1, which functions as an AKAP Dema Alessandro1,2¶, Dörte Faust1¶, Katina Lazarow3, Marc Wippich3, Martin Neuenschwander3, Kerstin Zühlke1, Andrea Geelhaar1, Tamara Pallien1, Eileen Hallscheidt1, Jenny Eichhorst3, Burkhard Wiesner3, Hana Černecká1, Oliver Popp1, Philipp Mertins1, Gunnar Dittmar1, Jens Peter von Kries3, Enno Klussmann1,4* ¶These authors contributed equally to this work 1Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert- Rössle-Strasse 10, 13125 Berlin, Germany 2current address: University of California, San Francisco, 513 Parnassus Avenue, CA 94122 USA 3Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), Robert-Rössle-Strasse 10, 13125 Berlin, Germany 4DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Oudenarder Strasse 16, 13347 Berlin, Germany *Corresponding author Enno Klussmann Max Delbrück Center for Molecular Medicine Berlin in the Helmholtz Association (MDC) Robert-Rössle-Str. 10, 13125 Berlin Germany Tel. +49-30-9406 2596 FAX +49-30-9406 2593 E-mail: [email protected] 1 Content 1. CELL-BASED SCREENING BY AUTOMATED IMMUNOFLUORESCENCE MICROSCOPY 3 1.1 Screening plates 3 1.2 Image analysis using CellProfiler 17 1.4 Identification of siRNA affecting cell viability 18 1.7 Hits 18 2. SUPPLEMENTARY TABLE S4, FIGURES S2-S4 20 2 1. Cell-based screening by automated immunofluorescence microscopy 1.1 Screening plates Table S1. Genes targeted with the Mouse Protein Kinases siRNA sub-library. Genes are sorted by plate and well. Accessions refer to National Center for Biotechnology Information (NCBI, BLA) entries. The siRNAs were arranged on three 384-well microtitre platres. -
Conséquences De L'exposition De L'insecte Modèle Aedes Aegypti Aux
THÈSE Pour obtenir le grade de DOCTEUR DE L’UNIVERSITÉ GRENOBLE ALPES Spécialité : Biodiversité, Écologie, Environnement Arrêté ministériel : 7 août 2006 Présentée par Sophie PRUD’HOMME Thèse dirigée par Stéphane REYNAUD et codirigée par Jean-Philippe DAVID préparée au sein du Laboratoire d’Écologie Alpine dans l'École Doctorale Chimie et Sciences du Vivant Conséquences de l’exposition de l’insecte modèle Aedes aegypti aux polluants des eaux de surface : Des mécanismes moléculaires à la dynamique des populations Thèse soutenue publiquement le 18/12/2015 devant le jury composé de : Mme Claire VOURC’H Professeure à l’Université Grenoble Alpes, Présidente Mme Gaelle LE GOFF Chargée de recherche à l’INRA de Sophia Antipolis, Rapporteur M. Alain GEFFARD Professeur à l’Université de Reims, Rapporteur M. Jean LAROCHE Professeur à l’Université de Brest, Examinateur M. Vincent LAUDET Professeur à l’Université Pierre et Marie Curie - Examinateur M. Fabien PIERRON Chargé de recherche au CNRS, UMR5805, Bordeaux - Examinateur M. Stéphane REYNAUD Maitre de conférences à l’Université Grenoble Alpes – Directeur de thèse M. Jean-Philippe DAVID Chargé de recherche au CNRS, UMR5553, Grenoble – Co-directeur de thèse « Si les moustiques étaient des abeilles, ils ramèneraient du sang à la ruche, et la reine ferait du boudin » Philippe Geluck SOMMAIRE Liste des abréviations .................................................................................................................................................... 7 Liste des tableaux .......................................................................................................................................................... -
Mitochondrial Clearance by the STK38 Kinase Supports Oncogenic Ras-Induced Cell Transformation
www.impactjournals.com/oncotarget/ Oncotarget, Vol. 7, No. 28 Research Paper Mitochondrial clearance by the STK38 kinase supports oncogenic Ras-induced cell transformation Audrey Bettoun1, Carine Joffre1,5,*, Giulia Zago1,*, Didier Surdez1, David Vallerand1, Ramazan Gundogdu2, Ahmad A.D. Sharif2, Marta Gomez2, Ilaria Cascone1, Brigitte Meunier1, Michael A. White3, Patrice Codogno4, Maria Carla Parrini1, Jacques H. Camonis1, Alexander Hergovich2 1Institut Curie, Inserm U830, Paris Sciences et Lettres University Paris, 75248, France 2University College London, Cancer Institute, London, WC1E 6BT, United Kingdom 3University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA 4Inserm U1151-CNRS UMR 8253, Institut Necker Enfants-Malades, Paris, 75993, France 5Present address: Cancer Research Center of Toulouse, UMR1037, Toulouse, 31100, France *These authors have contributed equally to this work Correspondence to: Alexander Hergovich, email: [email protected] Jacques H. Camonis, email: [email protected] Keywords: Ras GTPase, STK38, selective autophagy, mitophagy, cellular transformation Received: December 08, 2015 Accepted: May 12, 2016 Published: June 7, 2016 ABSTRACT Oncogenic Ras signalling occurs frequently in many human cancers. However, no effective targeted therapies are currently available to treat patients suffering from Ras-driven tumours. Therefore, it is imperative to identify downstream effectors of Ras signalling that potentially represent promising new therapeutic options. Particularly, considering that autophagy inhibition can impair the survival of Ras-transformed cells in tissue culture and mouse models, an understanding of factors regulating the balance between autophagy and apoptosis in Ras-transformed human cells is needed. Here, we report critical roles of the STK38 protein kinase in oncogenic Ras transformation. STK38 knockdown impaired anoikis resistance, anchorage-independent soft agar growth, and in vivo xenograft growth of Ras-transformed human cells. -
Kinome Expression Profiling to Target New Therapeutic Avenues in Multiple Myeloma
Plasma Cell DIsorders SUPPLEMENTARY APPENDIX Kinome expression profiling to target new therapeutic avenues in multiple myeloma Hugues de Boussac, 1 Angélique Bruyer, 1 Michel Jourdan, 1 Anke Maes, 2 Nicolas Robert, 3 Claire Gourzones, 1 Laure Vincent, 4 Anja Seckinger, 5,6 Guillaume Cartron, 4,7,8 Dirk Hose, 5,6 Elke De Bruyne, 2 Alboukadel Kassambara, 1 Philippe Pasero 1 and Jérôme Moreaux 1,3,8 1IGH, CNRS, Université de Montpellier, Montpellier, France; 2Department of Hematology and Immunology, Myeloma Center Brussels, Vrije Universiteit Brussel, Brussels, Belgium; 3CHU Montpellier, Laboratory for Monitoring Innovative Therapies, Department of Biologi - cal Hematology, Montpellier, France; 4CHU Montpellier, Department of Clinical Hematology, Montpellier, France; 5Medizinische Klinik und Poliklinik V, Universitätsklinikum Heidelberg, Heidelberg, Germany; 6Nationales Centrum für Tumorerkrankungen, Heidelberg , Ger - many; 7Université de Montpellier, UMR CNRS 5235, Montpellier, France and 8 Université de Montpellier, UFR de Médecine, Montpel - lier, France ©2020 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol. 2018.208306 Received: October 5, 2018. Accepted: July 5, 2019. Pre-published: July 9, 2019. Correspondence: JEROME MOREAUX - [email protected] Supplementary experiment procedures Kinome Index A list of 661 genes of kinases or kinases related have been extracted from literature9, and challenged in the HM cohort for OS prognostic values The prognostic value of each of the genes was computed using maximally selected rank test from R package MaxStat. After Benjamini Hochberg multiple testing correction a list of 104 significant prognostic genes has been extracted. This second list has then been challenged for similar prognosis value in the UAMS-TT2 validation cohort.