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Targetable Alterations in Adult Patients with Soft-Tissue Sarcomas

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Supplementary Online Content Lucchesi C, Khalifa E, Laizet Y, et al. Genomic landscape of adult soft-tissue sarcomas: moving toward personalized medicine [published online May 3, 2018] . JAMA Oncol. doi:10.1001/jamaoncol.2018.0723 eMethods. eTable 1. Actionable alterations in a series of 584 STS eTable 2. Clinical outcome of STS patients treated with targeted therapies eFigure 1. Proportion of genetic alterations according to genetic class of STS eFigure 2. Distribution of the number of alterations eFigure 3. Lollipop-style mutation diagrams for frequently altered genes eFigure 4. Distribution of actionable alterations in the 10 most frequent histological subtypes of STS eFigure 5. Co-occurrence and mutual exclusivity of genetic alterations in STS eFigure 6. Objective responses to targeted therapies in STS eFigure 7. Design of the MULTISARC study This supplementary material has been provided by the authors to give readers additional information about their work. © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 SUPPLEMENTARY METHODS Bioinformatics Analysis Percentage of Sarcoma samples covered by the different NGS assays of AACR Genie The GENIE data guide (referenced in the Material and Methods: http://www.aacr.org/Documents/GENIEDataGuide.pdf) describes the characteristics of NGS assays used in the study. 584 adults Soft tissue Sarcoma have been sequenced with differently-sized NGS assays whose cumulative sample distribution is reported below. It shows that 493 samples (84%, n=584) are covered by the 4 large-panel NGS assays of the MSK and DFCI Institutes, which include from 300 to 451 genes. © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 Data mining of the oncoKB data portal Targetable alterations from the oncoKB portal (http://oncokb.org) were selected via a manual process curated and supervised by our Clinical Department. The alterations and their annotations were extracted in a text table containing the gene symbol, the exact or generic alteration details and the oncoKB therapeutic classification. Then the GENIE data tables data_clinical.txt (patient annotation data), data_CNA.txt (copy number), data_fusions.txt (translocations) and data_mutations_extended.txt (snv/indel) (source https://www.synapse.org/#!Synapse:syn7844527) were filtered to include only the patient covered by the study and automatically mined for each of the following alteration scenarios: a) Explicit mutations, for which both gene and exact genetic alteration nomenclature was known (e.g. BRAF V600E): the columns <Hugo_Symbol> and <HGVSp_Short> of GENIE data_mutations_extended.txt table was looked for and the exact match of gene and alteration was reported b) Functional mutations, for which the gene was known and but only the mutation type: the columns <Hugo_Symbol> and <Consequence> of GENIE data_mutations_extended.txt table were looked for the code “missense_variant” for oncogenic mutations and the code list "frameshift_variant","frameshift_variant,splice_region_variant" for truncating mutations., "inframe_deletion,splice_region_variant","missense_variant,splice_region_variant","protein _altering_variant","splice_acceptor_variant","splice_acceptor_variant,coding_sequence_vari ant","splice_acceptor_variant,coding_sequence_variant,intron_variant","splice_acceptor_va riant,intron_variant","splice_donor_variant","splice_donor_variant,coding_sequence_varian t","splice_donor_variant,coding_sequence_variant,intron_variant","splice_region_variant,int ron_variant","splice_region_variant,synonymous_variant","start_lost","stop_gained","stop_ gained,protein_altering_variant","stop_gained,splice_region_variant","stop_lost" for inactivating mutations. c) Genomic amplifications (e.g. CDK4 amplifications) and homozygous deletions (e.g. SMARCB1 deletion): the GENIE data_CNA.txt table was looked for couples patient/gene whose CAN value was +2 for “amplifications” or -2 for homozygous deletion. d) Explicit gene fusions (e.g. KIAA1549-BRAF fusion) : the GENIE data_fusions.txt table was looked for matches of gene1 – gene2 as well as gene2-gene1 gene fusions e) Gene fusions with only one partner known (e.g. FGFR2 fusions): the GENIE data_fusions.txt table was looked for matches of gene1 – geneX where geneX was a generic partner When the match was positive a count of 1 was reported for the alteration in the patient. All data analysis have been performed using basic R and Dplyr libraries. © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 Supplementary Table 1: Actionable alterations in a series of 584 STS Supplementary Table 1. Actionable alterations in a series of 584 STS Targetable Genomic sarcomes Complex genomics sarcomas Translocation Other Total Alteration sarcomas - related Standard care biomarker predictive of response to drugs not approved for management of sarcoma patients PDGFRA fusion 1 0 0 1 ALK fusions 0 0 0 0 BRAF mutation (V600D, 0 0 0 0 V660E, V600G, V600K, V600M,V600R) BRCA1 truncating 2 0 0 2 mutations BRCA2 truncating 2 0 0 2 mutations CDK4 amplification 8 0 76 84 KIT mutation 3 0 0 3 MET amplification 4 1 1 6 MET mutation 4 0 0 4 PDGFRA mutation 1 0 0 1 RET fusion 0 0 0 0 TSC1 truncating mutations 1 0 0 1 TSC2 truncating mutations 3 0 0 3 Alterations associated with compelling clinical evidence of predictive value but neither biomarker nor drug are standard of care AKT1 mutation (E17K) 0 0 0 0 ALK mutation (L1196M, 0 0 0 0 L1196Q) ARAF mutation (S214A, 0 0 0 0 S214C) BRAF fusion 0 0 0 0 BRAF mutation (K601E, 0 0 0 0 L597Q, L597R, L597S, L597V) ERBB2 amplification 0 0 0 0 ERBB2 oncogenic 5 1 2 8 mutations © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 ERCC2 truncating 1 1 0 2 mutations ESR1 oncogenic mutations 2 0 0 2 FGFR1 amplification 3 1 3 7 FGFR2 fusion 0 1 0 1 FGFR3 fusion 0 0 0 0 FGFR3 mutation 2 1 0 3 FLT3 mutation 0 0 0 0 (Y572_Y630ins) IDH1 mutation (R132C, 0 0 0 0 R132G, R132H, R132Q, R132S) IDH2 mutation (R140Q, 0 0 0 0 R172G, R172K, R172M, R172S) JAK2 (PCM1-JAK2 Fusion) 0 0 0 0 MAP2K1 oncogenic 0 0 0 0 mutations MDM2 amplification 16 1 84 101 MET mutations 0 0 0 0 (963_D1010splice, 981_1028splice, X1006_splice, X1007_splice, X1008_splice, X1009_splice, X1010_splice, X963_splice, D1010H, D1010N, D1010Y) MTOR mutation (E2014K) 0 0 0 0 NRAS oncogenic mutations 8 0 0 8 NTRK1 fusions 0 0 0 0 NTRK2 fusions 0 0 0 0 NTRK3 fusions 0 0 0 0 PIK3CA oncogenic 10 5 0 15 mutations PTCH1 truncating 1 0 0 1 mutations ROS1 mutation (D2033N) 0 0 0 0 Alterations associated with compelling biological evidence of predictive value but neither biomarker nor drug are standard of care ALK mutation (R1275Q) 0 0 0 0 ATM mutation (N2875K, 0 0 0 0 R3008C) ATM truncating mutation 5 0 0 5 BRAF mutation (D594E, 0 0 0 0 D594N, G466V, G469A, G469V, G596C, L597Q, L597V) © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 BRAF (KIAA1549-BRAF 0 0 0 0 Fusion) CDKN2A truncating 7 0 0 7 mutations EGFR mutations 0 0 0 0 (729_761del, 729_761ins, L858R, 762_823ins, G719A, L861R, S768I) ERBB2 mutations 0 0 0 0 (E770_K831indel, E770_K831ins) ESR1 mutations (D538G, 0 0 0 0 Y537S) EZH2 oncogenic mutations 2 0 0 2 FGFR1 (BCR-FGFR1 Fusion) 0 0 0 0 FGFR3 mutations (G370C, 0 0 0 0 G380R, K650E, K650M, K650N, K650Q, K650R, K650T, R248C, S249C, S371C, Y373C) IDH1 mutations (R132C, 0 0 0 0 R132G, R132H, R132Q, R132S) KRAS oncogenic mutations 4 1 0 5 MTOR mutations (C1483F, 0 0 0 0 F1888L, L2230V, S2215F, T1977K) NF1 truncating mutations 13 1 1 15 PTEN truncating mutations 7 1 0 8 RAF1 mutation (S257L) 0 0 0 0 TSC1 deletions 0 0 0 0 TSC2 deletions 3 0 0 3 PTCH1 deletions 0 0 0 0 KDR (VEFGR2) oncogenic 7 0 1 8 mutations KDR (VEFGR2) 6 0 2 8 amplifications ATM truncating mutations 0 0 0 0 ATM deletions 1 0 0 1 CDKN2A deletions 21 2 3 26 SMARCB1 truncating 0 0 1 1 mutations SMARCB1 deletions 0 1 1 2 MAP2K1 amplifications 0 0 0 0 VHL oncogenic mutations 1 0 0 1 VHL deletions 0 0 0 0 ARAF oncogenic mutations 1 0 0 1 NF2 truncating mutations 3 0 1 4 © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 NF2 deletions 2 1 0 3 FGF3 amplifications 0 0 1 1 FGF4 amplifications 0 0 1 1 PTEN deletions 9 2 0 11 RICTOR amplifications 2 0 2 4 NF1 deletions 4 0 0 4 Targetable mutation 95 11 6 112 counts Targetable copy number 79 9 174 262 alterations Targetable translocation 1 1 0 2 Number of patients (with 131 19 89 239 at least 1 druggable (40%) (13%) (82%) (41%) alteration) © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 Supplementary Table 2: Clinical outcome of STS patients treated with targeted therapies © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 Supplementary Figure 1: Proportion of genetic alterations according to genetic class of STS © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 Supplementary Figure 2: Distribution of the number of alterations © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 Supplementary Figure 3. Lollipop-style mutation diagrams for frequently altered genes © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 Supplementary Figure 4: Incidence of the 20 most frequent targetable alterations in a series of 584 STS. © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 Supplementary Figure 5: Co-occurrence and mutual exclusivity of genetic alterations in STS © 2018 American Medical Association.
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  • Correlation Between DNA Ploidy, Metaphase High-Resolution Comparative Genomic Hybridization Results and Clinical Outcome of Syno

    Correlation Between DNA Ploidy, Metaphase High-Resolution Comparative Genomic Hybridization Results and Clinical Outcome of Syno

    Balogh et al. Diagnostic Pathology 2011, 6:107 http://www.diagnosticpathology.org/content/6/1/107 RESEARCH Open Access Correlation between DNA ploidy, metaphase high-resolution comparative genomic hybridization results and clinical outcome of synovial sarcoma Zsófia Balogh1†, Zsuzsanna Szemlaky1†, Miklós Szendrői2, Imre Antal2, Zsuzsanna Pápai3, László Fónyad1, Gergő Papp1, Yi C Changchien1 and Zoltán Sápi1* Abstract Background: Although synovial sarcoma is the 3rd most commonly occurring mesenchymal tumor in young adults, usually with a highly aggressive clinical course; remarkable differences can be seen regarding the clinical outcome. According to comparative genomic hybridization (CGH) data published in the literature, the simple and complex karyotypes show a correlation between the prognosis and clinical outcome. In addition, the connection between DNA ploidy and clinical course is controversial. The aim of this study was using a fine-tuning interpretation of our DNA ploidy results and to compare these with metaphase high-resolution CGH (HR-CGH) results. Methods: DNA ploidy was determined on Feulgen-stained smears in 56 synovial sarcoma cases by image cytometry; follow up was available in 46 cases (average: 78 months). In 9 cases HR-CGH analysis was also available. Results: 10 cases were found DNA-aneuploid, 46 were DNA-diploid by image cytometry. With fine-tuning of the diploid cases according to the 5c exceeding events (single cell aneuploidy), 33 cases were so called “simple-diploid” (without 5c exceeding events) and 13 cases were “complex-diploid"; containing 5c exceeding events (any number). Aneuploid tumors contained large numbers of genetic alterations with the sum gain of at least 2 chromosomes (A-, B- or C-group) detected by HR-CGH.