Clustering Adult ACUTE Lymphoblastic Leukemia (ALL) Philadelphia Negative (Ph-) By Whole Exome Sequencing (WES) Analysis
Authors: Ferrari Anna , Ghelli Luserna Di Rora Andrea , Do Valle Italo , Garonzi Marianna , Robustelli Valentina , Hernandez-Rivas Jesus Maria , Santoro Alessandra , Pospisilova Sarka , Haferlach Torsten , Padella Antonella , Simonetti Giorgia , Papayannidis Cristina , Abbenante Maria Chiara , Sazzini Marco , Ferrarini Alberto , Schira Julien , Delledonne Massimo , Castellani Gastone , Remondini Daniel , Martinelli Giovanni 
Published: October 2019

Abstract
Introduction: Adult ALL represents a biologically and clinically heterogeneous group. Incidence and cure rates differ among children and adults. In adults, ALL is less common and generally carries a worse prognosis with shorter long-term survival probability. Although the remarkable progress made in the treatment of ALL in children and, with less efficacy, in adults, several ALL subtypes continue to have a poor prognosis.
Aims: focus our attention on adult Ph-negative ALL pts using whole exome experiments to discover novel insights into the mechanisms involved in leukemogenesis and to develop genetic models that accurately define novel ALL subtypes based on the genomic profile of individual patients (pts).
Patients and Methods: we performed the WES analysis of 72 samples of B-cell precursor ALL acute lymphoblastic leukemia (B-ALL) cases using the Illumina Hiseq2000 platform. All were adult patients (18-79 years) and were negative for Philadelphia chromosome (BCR-ABL) translocation and negative for the recurrent known molecular rearrangements (E2A-PBX, TEL, AML1-MLL-AF4). Peripheral blood and/or bone marrow samples were collected from adult B-ALL at the time of diagnosis and/or at the time of relapse. Matched samples of primary tumour (peripheral blood or bone marrow) and germline DNA from buccal swab or peripheral blood at the remission time were analyzed.
MuTect and GATK tools to call mutations (Single Nucleotide Variants=SNVs and/or INDELs) were used and we selected variants with a minor allele frequency (MAF) lower than 0.05 and filtered using dbSNP142.
Results: The WES analysis of the 41 Ph negative cases identified 735 point mutations and 25 mutations that occur in splicing sites in 651 genes. The average number of somatic coding mutations was 17 per case (range 1-47). 38 genes were recurrently mutated with 11 genes mutated in at least 3 cases: PAX5, PRDM12, JAK2, TTN, TP53, PTPN11, PKHD1L1, CUL3, PIEZO2, TACC2, RBBP6. The first two genes present more point mutations, in 5 pts and in 4 pts respectively. Some mutations in genes like PAX5, JAK2, TP53, PTPN11 were deeply described in acute lymphoblastic leukemic; PKHD1L1 was described mutated in one case of T-cell large granular lymphocyte leukemia; PRDM12 disruption was described in an aggressive CML case;TACC2 expression in infant ALL was described as predictor of outcome andtranscription factor and RBBP6 expression was differentially expressed in leukemic cells that overexpressed Gfi-1B gene. The alterations in the remaining genes were not previously described in ALL and/or leukemia.
Using KEGG database we mapped the 651 mutated genes to detect the mostly represented pathways. The following resulted significantly enriched (p=0.0004 to p=0.006): Jak-STAT signaling pathway (11 genes), Cell Cycle (13), Dilated Cardiomyophaty (9), Hypertrophic cardiomyophaty (8), Axon Guidance (9), Calcium Signaling pathways (10), Huntington's disease (10), Wnt signaling pathway (9), Metabolic pathways (30), Pacreat Secretion (7). Preliminar analysis lead considering both SNVs and INDELs, detected totally 956 gene variations. Again the pathways mainly significantly (p=8.05e-05 to p=0.0076) affected are the Jak-STAT signaling pathway (14) and the Cell Cycle (13). Also Huntington's disease (14), Dilated Cardiomyophaty (10), Hypertrophic cardiomyophaty (9), Wnt signaling pathway (12), Metabolic pathways (40 genes), Calcium Signaling pathways (12), Metabolic pathways (40), TGF-Beta signaling pathway (8), Pacreat Secretion (8) were alterated.
Prediction of protein interactions, using STRING database, generated a network with the genes mutated in more than 5 patients. Then, the nodes were clustered with K-means identifying 4 groups that contain several of our analysis variations (Fig.1).
Conclusions: Point mutations are the prevalent mechanism identified in our pts cohort (75.5%). INDELs are less represented (21.5%). Altogether the identified mutations may help cluster Ph- ALL pts. Analysis of SNVs confirmed mutations in important genes known to be involved in leukemogenesis. Relevant alterations affect crucial pathways as cell cycle and Jak-STAT signaling which may be effectively targeted by currently available JAK inhibitors. Supported by: ELN, AIL, AIRC, PRIN, progetto Regione-Università 2010-12 (L. Bolondi), FP7 NGS-PTL project.

Disclosures
Haferlach: MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Martinelli:AMGEN: Consultancy; Ariad: Consultancy; Pfizer: Consultancy; ROCHE: Consultancy; MSD: Consultancy; BMS: Consultancy, Speakers Bureau; Novartis: Consultancy, Speakers Bureau.