The genomic landscape of hypodiploid acute lymphoblastic leukemia

Abstract

The genetic basis of hypodiploid acute lymphoblastic leukemia (ALL), a subtype of ALL characterized by aneuploidy and poor outcome, is unknown. Genomic profiling of 124 hypodiploid ALL cases, including whole genome and exome sequencing of 40 cases, identified two subtypes that differ in severity of aneuploidy, transcriptional profile and submicroscopic genetic alterations. Near haploid cases with 24–31 chromosomes harbor alterations targeting receptor tyrosine kinase- and Ras signaling (71%) and the lymphoid transcription factor IKZF3 (AIOLOS; 13%). In contrast, low hypodiploid ALL with 32–39 chromosomes are characterized by TP53 alterations (91.2%) which are commonly present in non-tumor cells, and alterations of IKZF2 (HELIOS; 53%) and RB1 (41%). Both near haploid and low hypodiploid tumors exhibit activation of Ras- and PI3K signaling pathways, and are sensitive to PI3K inhibitors, indicating that these drugs should be explored as a new therapeutic strategy for this aggressive form of leukemia.

The landscape of genomic alterations across childhood cancers

“…External data were downloaded from the European Genome-Phenome Archive (EGA; https://www.ebi.ac.uk/ega/home) using the accession numbers EGAD00001000085, EGAD00001000135, EGAD00001000159, EGAD00001000160, EGAD00001000161, EGAD00001000162, EGAD00001000163, EGAD00001000164, EGAD00001000165, EGAD00001000259, EGAD00001000260, EGAD00001000261, EGAD00001000268, and EGAD00001000269 [49][50][51][52][53][54][55][56][57][58][59][60][61][62] ; internal datasets are related to previous PMIDs 27748748, 27479119, 26923874, 25670083, 25253770, 24972766, 24553142, 25135868, 26632267, 26179511, 24651015, 28726821, 23817572, 25962120, 26294725 17,19,44,63-74 (Supplementary Note 1).…”

“…Ninety-five per cent of the patients were under 18 years of age (or age unspecified but confirmed age group paediatric), but available data were included for patients up to 25 years, as these were considered relevant for cancer types that typically peak at a young age. All centres have approved data access and informed consent had been obtained from all patients.External data were downloaded from the European Genome-Phenome Archive (EGA; https://www.ebi.ac.uk/ega/home) using the accession numbers EGAD00001000085, EGAD00001000135, EGAD00001000159, EGAD00001000160, EGAD00001000161, EGAD00001000162, EGAD00001000163, EGAD00001000164, EGAD00001000165, EGAD00001000259, EGAD00001000260, EGAD00001000261, EGAD00001000268, and EGAD00001000269 [49][50][51][52][53][54][55][56][57][58][59][60][61][62] ; internal datasets are related to previous PMIDs 27748748, 27479119, 26923874, 25670083, 25253770, 24972766, 24553142, 25135868, 26632267, 26179511, 24651015, 28726821, 23817572, 25962120, 26294725 17,19,44,63-74 (Supplementary Note 1).The final cohort included 914 individual patients of no more than 25 years of age including primary tumours for 879 patients with 47 matched relapsed tumours, and an additional 35 independent relapsed tumours ( Supplementary Tables 1, 2). Deep-sequencing (~ 30× ) whole-genome data (WGS) were available for 547 samples with matched control, whole-exome sequencing (WES) for 414, and low-coverage whole-genome sequencing (lcWGS) for an additional 54 germline and 186 tumour samples.…”

Ex vivo drug response profiling detects recurrent sensitivity patterns in drug-resistant acute lymphoblastic leukemia

“…1 Progress in ALL genomics 2 provides unprecedented insight into potentially actionable targets, such as activating mutations in tyrosine kinases, 3 RAS, 4 or interleukin-7R. 5 Recurrent features such as MLL-AF4 rearrangements, the TCF3-HLF fusion, 6 and hypodiploid karyotypes 4 define rare subgroups with highly drug-resistant disease.…”

“…1 Progress in ALL genomics 2 provides unprecedented insight into potentially actionable targets, such as activating mutations in tyrosine kinases, 3 RAS, 4 or interleukin-7R. 5 Recurrent features such as MLL-AF4 rearrangements, the TCF3-HLF fusion, 6 and hypodiploid karyotypes 4 define rare subgroups with highly drug-resistant disease. However, a majority of patients who may benefit from innovative therapies are still identified on the basis of the persistence of minimal residual disease (MRD) 1,7,8 or failure of remission induction therapy.…”

Redefining ALL classification: toward detecting high-risk ALL and implementing precision medicine

“…Over the last decade, studies utilizing microarray analysis of gene expression, DNA copy-number alterations, and next-generation sequencing have provided major insights into the pathogenesis and clinical behavior of ALL. [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] Most ALL genomes harbor sequence and structural DNA alterations involving coding genes, as well as alterations of noncoding elements such as noncoding RNAs 23 and enhancer elements. 24,25 Here, we consider results from these studies in several categories: (1) identification of new subtypes of ALL that lack recurring gross chromosomal alterations; (2) characterization of the constellations of genetic alterations that define each ALL subtype; (3) the relationship between genetic alterations, clonal heterogeneity, and relapse; (4) identification of inherited genetic variants and mutations linked to ALL susceptibility and outcome; (5) and translating new discoveries to improved diagnostic, prognostic, and precision medicine approaches.…”