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• The genomic landscape of pediatric tumors is distinct from adult tumors due to low mutational burden, and relatively few significantly mutated genes. • Unlike adult solid tumor that are predominantly carcinomas, pediatric solid tumors are histologically diverse and include carcinomas, embryonal tumors, gonadal tumors, brain tumors, leukemias and lymphomas, and sarcomas. • Despite advances in detection and treatment of childhood, over 1,900 pediatric patients in the US succumb to disease each year, and survivors often face lifelong side effects from toxic chemo and/or radiotherapy treatments. • Recent studies suggest that genomic alterations may help guide treatment decisions and clinical trial selection. • We describe a dataset of 1,215 pediatric tumors (ages 0-18) comprised of sarcomas, extracranial embryonal tumors, brain tumors, hematologic malignancies, carcinomas, and gonadal tumors. • This collection contains multiple rare entities that have not been profiled previously in large numbers. • We describe the discovery of novel fusions, point mutations, and the spectrum of therapeutic targets across multiple tumor types. • Collectively, this dataset represents the largest group of genomically profiled pediatric tumors to date, and can be used as a resource for discovery of novel alterations and validation of findings from other studies. FFPE TUMOR SAMPLE A Genomic DNA Sequencing Library Hybridization Capture Biotinylated DNA Baits SEQUENCING LIBRARY PREPARATION B BASE SUBSTITUTIONS Bayesian algorithm SHORT INSERTIONS/DELETIONS Local assembly COPY NUMBER ALTERATIONS Comparison with process- matched normal control GENE FUSIONS Analysis of chimeric read pairs ANALYSIS PIPELINE C CLINICAL REPORT D DNA EXTRACTION SEQUENCING ILLUMINA HISEQ ANALYSIS & INTERPRETATION 1) DNA/RNA extraction 3) Analysis pipeline Pre-Analytic Process (Pre-Sequencing) Post-Analytic Process (Post-Sequencing) 2) LC, Hybrid Capture 4) Clinical report Illumina HiSeq Sample characteris.cs Juliann Chmielecki 1 , Mark Bailey 1 , Jie He 1 , Julia Elvin 1 , Jo-Anne Vergillio 1 , Shakti Ramkissoon 1 , James Suh 1 , Garrett M. Frampton 1 , Siraj Ali 1 , Jeffrey S. Ross 1,2 , Vincent A. Miller 1 , Philip J. Stephens 1 , Doron Lipson 1 1 Foundation Medicine, Inc., Cambridge, MA; 2 Albany Medical College, Albany, NY Genomic profiling of 1,215 diverse pediatric cancers iden.fies novel discoveries across tumors Conclusions Introduc.on Materials and Methods Distribution of samples across FoundationOne® assays For samples assayed on Founda/onOne, DNA was adaptorligated and hybrid capture was performed for all coding exons of 182 (v1), 287 (v2), 323 (v3), or 395 (v5) cancerrelated genes plus select introns from 14 (v1), 19 (v2), 24 (v3), or 31 (v5) genes frequently rearranged in cancer; samples assayed on Founda/onOne Heme (v4) underwent DNAbased hybrid capture for all coding regions of 465 genes plus select introns from 31 genes frequently rearranged in cancer. For samples in which RNA was available, targeted RNAseq was performed for rearrangement analysis in 333 genes. • We describe genomic profiles from 1,215 pediatric tumors represen/ng sarcomas, extracranial embryonal tumors, brain tumors, hematologic malignancies, carcinomas, and gonadal tumors. • The discovery poten/al of this dataset was demonstrated by the iden/fica/on of six novel kinase fusions involving ALK, BRAF, and NTRK3 and two novel transcrip/on fusions involving PAX3 and PAX5. • In silico analysis of recurrent variants of unknown significance (VUSes) iden/fied four altera/ons in three genes with poten/al func/onal significance. • A dataset this large challenges the paradigm of “diseasespecific” altera/ons; we iden/fied ALK and NTRK1 fusions in diseases other than the tumor types in which they were reported originally. • Publicly available genomic data from pediatric tumors can help accelerate discoveries of novel therapeu/c targets, validate oncogenic mechanisms, guide treatment decisions, and design appropriate clinical trials for children with cancer. • Prospec/ve iden/fica/on of clinically relevant genomic altera/ons can have poten/ally significant impact on considera/on of treatment op/ons and clinical trial selec/on Founda.on Medicine Pipeline Computa.onal screening of genomic variants Variants different from reference genome (hg19) Dele/ons/trunca/ons and known deleterious muta/ons in tumor suppressor genes Removal of benign germline variants (dbSNP v142) Soma/c altera/ons in COSMIC v62 Likely oncogenic altera/ons Addi/onal filtering through ExAC and internal algorithms Variants of unknown significance (VUSes) Detec.on Filtering Classifica.on Assay Genes (DNA) Genes (RNA) Total Sample # v1 182 0 53 v2 287 0 182 v3 323 0 58 v4 (DNA only) 465 0 67 v4 (DNA+RNA) 465 333 542 v5 395 0 313 MYCN ALK ATRX CDKN2A RPTOR TP53 NRAS MDM2 ARID1A CDK6 CDK4 NF1 CDKN2B FGFR1 BRAF HGF BRCA2 KRAS MET SMARCA4 0 5 10 15 20 25 30 Percent of patients Neuroblastoma T P 5 3 NF1 FOXO1 MYC MDM2 BRD4 AKT2 MYCN CCNE FGF14 IRS2 CDKN2A ICK ARID1A MYST3 PAX3 FLT3 BCOR CDK4 CDK8 0 5 10 15 20 25 Percent of patients Rhabdomyosarcoma PTEN MYCN TP53 PTCH1 RPTOR AKT3 MLL2 TSC1 ERBB2 SUFU HGF CDK6 LZTR1 STK11 EPHA3 MSH6 CTNNB1 CDKN2A SMARCA4 MET Medulloblastoma 0 2 4 6 8 10 12 14 16 Percent of patients substitutions/indels amplification homozygous deletions rearrangements truncations A. B. C. D. E. 0 5 10 15 20 25 30 Percent of patients CDKN2A NRAS CDKN2B ETV6 KRAS TP53 NOTCH1 CREBBP PHF6 MLL JAK2 PAX5 FBXW7 RB1 CRLF2 PIK3CA RUNX1 PTPN11 FLT3 WT1 ALL NRAS RUNX1 MLL FLT3 WT1 CDKN2A TP53 KRAS NF1 CDKN2B PTPN11 CREBBP CEBPA ASXL1 KIT CD36 SETBP1 NPM1 ETV6 KDM5A 0 5 10 15 20 25 AML Percent of patients A. B. C. D. E. Neuroblastoma Astrocytoma Astrocytoma Ganglioglioma Solitary fibrous tumor Rhabdomyosarcoma ALL Discovery of novel fusions and iden.fica.on of known fusions in other diseases Publicly available browsable data: hNps://pediatricdata.founda.onmedicine.com Create login credentials Browse prevalence of tumors/genes/alterations Investigate co-occurrence and mutual exclusivity trends Download figures and tables Comparison to published datasets Discovery of novel poten.ally oncogenic muta.ons in MLL3, PRSS1, and DKC1 SQSTM1 exons 1(2 NTRK1 exons 10(17 STRN exons 1(3 ALK exons 20(29 EML4 exons 1(13 ALK exons 20(29 EML4 exons 1(2 ALK exons 20(29 STRN exons 1(3 ALK exons 20(29 Thyroid His5ocy5c neoplasm Ganglioglioma Fibrosarcoma Kidney carcinoma A. B. C. D. PAX3 exons 1(7 NCOA1 exons 12(12 Rhabdomyosarcoma MLL3 A293V MLL3 CDKN2B MYCN CDKN2A ALK PIK3CA NOTCH1 SS18 DNMT3A PHF6 EWSR1 Synovial ALL Neuroblastoma DSRC PNET MLL3 P309L MLL3 TP53 NF1 RB1 TET2 RET ATR BRAF H3F3A PTCH1 Astrocytoma Glioblastoma Neuroblastoma Neuroblastoma Glioblastoma PRSS1 G191R PRSS1 RPTOR MYCN CCND2 ARID1A STK11 AKT3 AURKB GATA4 Medulloblastoma Wilms tumor Medulloblastoma Glioblastoma 0 1000 2000 3000 4000 4911 aa PHD-like Zn-binding domain PHD finger SET domain PHD-like Zn finger domain F/Y rich N-terminus F/Y rich C-terminus A293V P309L DKC1 CDKN2A TP53 ALK NOTCH1 CDKN2B WT1 CEBPA LRRK2 ETV6 SMARCA4 NRAS PTEN BRCA2 RICTOR NF2 EED MYC RUNX1 TSC2 KDM5A Neuroblastoma Other AML AML Bone sarcoma ALL ALL Neuroblastoma DKC1 K505 indels 0 100 200 247 aa G191R Trypsin 0 100 200 300 400 514 aa K505_A506insK DKCLD domain TruB pseudouridylate synthase tRNA pseudouridylate synthase B PUA KMT2C/MLL3 PRSS1 DKC1 Distribu.on of sample types within the pediatric data cohort. Samples were grouped into one of six major categories (led panel). Each major category was subsequently divided into mul/ple subcategories that contained detailed informa/on about the tumor diagnosis, with the excep/on of gonadal tumors. Extracranial embryonal tumors contained 3 subtypes (led panel, top right), brain tumors contained 9 subtypes (led panel, middle led), sarcomas contained 16 subtypes (led panel, middle right), heme malignancies contained 8 subtypes (led panel, boeom led), and carcinomas contained 14 subtypes (led panel, boeom right). Gonadal tumors were composed en/rely of this tumor type (not shown). The sample cohort contained 1,215 tumors from 49 unique subtypes (right panel). Each subtype contained at least 5 samples. Long tail distribu.ons across the 5 most common diseases. The top 20 altered genes in (A) neuroblastoma, (B) ALL, (C) AML, (D) rhabdomyosarcoma, and (E) medulloblastoma. Types of altera/ons are colorcoded using the key to the right. Tables (right) compare the frequencies of altera/ons between the Founda/on Medicine cohort and published series. Compared to other large published datasets, few significant differences were observed in the frequencies of altera/ons in most genes that were deemed biologically significant in these disease types. The two excep/ons were a decreased rate of FLT3 ITD muta/ons (5.3% versus 16.5%, p=0.0472) in AML and an increased rate of TP53 muta/ons (20% versus 5.3%, p=0.0132) in rhabdomyosarcomas within the Founda/on Medicine samples. Novel kinase and transcrip.on factor fusions. Novel kinase fusions in ALK (A, led), BRAF (B, led), and NTRK3 (C, led) have similar breakpoints to known fusions involving these genes. Novel transcrip/on factor fusions involving PAX3 (D, led) and PAX5(E, led) were also iden/fied. Kinase fusions involving NTRK1 (A, right) and ALK (B and C, right) were iden/fied in addi/onal tumors from where they were described originally. We also confirmed a second occurrence of the rare PAX3NCOA1 fusion in a rhabdomyosaroma (D, right). Diagrams are not drawn to scale. Discovery of poten.ally oncogenic altera.ons in MLL3/KMT2C, PRSS1, and DKC1. Tileplots (led top) showing the distribu/on of cooccurring altera/ons in samples with poten/ally novel oncogenic altera/ons in MLL3/KMT2C, PRSS1, and DKC1. . Each sample is represented by a ver/cal column. Gene altera/ons are represented in horizontal rows. Colors correspond to the type of altera/on (see legend). Recurrent VUS point muta/ons (n > 3) were evaluated further using Muta/onAssessor, an in silico analysis tool that predicts func/onal impact of base subs/tu/ons based on evolu/onary conserva/on altera/ons (led boeom). Results were not available (NA) for DKC1 altera/ons as only base subsitu/ons can be evaluated by this tool. Muta/onMapper was used to visualize muta/ons (right panel). Muta.onAssessor Analysis Results Abstract: LB178 Gene mutation Refseq Func. Impact KMT2C/MLL3 p.A293V NP_733751 medium KMT2C/MLL3 p.P309L NP_733751 medium PRSS1 p.G191R NP_002760 medium DKC1 p.K505del p.K505_A506insKK p.K505_A506insK NP_001354 NA Extracranial embryonal 22.4% Sarcoma 26.6% Carcinoma 9.8% Heme 19.5% Brain 20.4% Gonadal tumors 1.4% Glioma 11.5% Ependymoma 10.7% PNET 5.1% ATRT 5.1% Meningioma 3.1% Glioblastoma 23.3% Astrocytoma 26.5% Medulloblastoma 12.6% Ganglioglioma 2% AML 31.5% ALL 36.5% Lymphoma 8.3% MDS/MPN 7.1% MM 2.1% Leukemia (nos) 5.8% Histiocytic neoplasm 5.4% MLL 3.3% Neuroblastoma 83.0% Wilms tumor 10.5% Hepatoblastoma 6.5% Kidney 9.9% Gyn 9.1% Thyroid 7.4% Upper GI 7.4% Lower GI 6.6% HCC 5.8% Other 5.8% FLO 5.0% Adrenal 5.0% Panc/biliary 4.1% Lung 9.9% Neuroendocrine 9.9% Head & Neck 12.4% Unknown 1.7% Bone sarcoma 19.1% RMS 20.0% Ewing 11.8% Soft tissue (nos) 11.2% Soft tissue assorted 6.7% Fibromatosis 5.5% DSRC 4.2% Unknown 3.6% MPNST 3.3% Synovial 3.0% Hemangioendothelioma 2.1% IMTs 2.1% Fibrosarcoma 2.1% Hemangioma 1.8% ASPS 1.8% Angiosarcoma 1.5% All Tumors Extracranial embryonal Brain Sarcoma Heme Carcinoma
