No abstract

The landscape of genomic alterations across childhood cancers a list of authors and affiliations appears at the end of the paper. OPENPan-cancer analyses that examine commonalities and differences among various cancer types have emerged as a powerful way to obtain novel insights into cancer biology. Here we present a comprehensive analysis of genetic alterations in a pan-cancer cohort including 961 tumours from children, adolescents, and young adults, comprising 24 distinct molecular types of cancer. Using a standardized workflow, we identified marked differences in terms of mutation frequency and significantly mutated genes in comparison to previously analysed adult cancers. Genetic alterations in 149 putative cancer driver genes separate the tumours into two classes: small mutation and structural/copy-number variant (correlating with germline variants). Structural variants, hyperdiploidy, and chromothripsis are linked to TP53 mutation status and mutational signatures. Our data suggest that 7-8% of the children in this cohort carry an unambiguous predisposing germline variant and that nearly 50% of paediatric neoplasms harbour a potentially druggable event, which is highly relevant for the design of future clinical trials.Cure rates for childhood cancers have increased to about 80% in recent decades, but cancer is still the leading cause of death by disease in the developed world among children over one year of age 1,2 . Furthermore, many children who survive cancer suffer from long-term sequelae of surgery, cytotoxic chemotherapy, and radiotherapy, including mental disabilities, organ toxicities, and secondary cancers 3 . A crucial step in developing more specific and less damaging therapies is the unravelling of the complete genetic repertoire of paediatric malignancies, which differ from adult malignancies in terms of their histopathological entities and molecular subtypes 4 . Over the past few years, many entityspecific sequencing efforts have been launched, but the few paediatric pan-cancer studies thus far have focused only on mutation frequencies, germline predisposition, and alterations in epigenetic regulators [4][5][6] .We have carried out a broad exploration of cancers in children, adolescents, and young adults, by incorporating small mutations and copy-number or structural variants on somatic and germline levels, and by identifying putative cancer genes and comparing them to those previously reported in adult cancers by The Cancer Genome Atlas (TCGA) 7 . We have also examined mutational signatures and potential drug targets. The compendium of genetic alterations presented here is available to the scientific community at http://www.pedpancan.com.This integrative analysis includes 24 types of cancer and covers all major childhood cancer entities, many of which occur exclusively in children 8 (Fig. 1, Supplementary Table 1). Ninety-five per cent of the patients in this study were diagnosed during childhood or adolescence (aged 18 years or younger) and 5% as young adults (up to 25 years) (Extended Data ...

The pan-cancer analysis of whole genomes The expansion of whole-genome sequencing studies from individual ICGC and TCGA working groups presented the opportunity to undertake a meta-analysis of genomic features across tumour types. To achieve this, the PCAWG Consortium was established. A Technical Working Group implemented the informatics analyses by aggregating the raw sequencing data from different working groups that studied individual tumour types, aligning the sequences to the human genome and delivering a set of high-quality somatic mutation calls for downstream analysis (Extended Data Fig. 1). Given the recent meta-analysis

Multi‐omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi‐Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi‐omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy‐chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single‐cell multi‐omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.

Chronic lymphocytic leukaemia (CLL) has several unique features that distinguish it from other cancers. Most CLL tumour cells are inert and arrested in G0/G1 of the cell cycle and there is only a small proliferative compartment; however, the progressive accumulation of malignant cells will ultimately lead to symptomatic disease. Pathogenic mechanisms have been elucidated that involve multiple external (for example, microenvironmental stimuli and antigenic drive) and internal (genetic and epigenetic) events that are crucial in the transformation, progression and evolution of CLL. Our growing understanding of CLL biology is allowing the translation of targets and biological classifiers into clinical practice.

To assess the frequency of TP53 alterations and their correlation with other genetic changes and outcome in acute myeloid leukemia with complex karyotype (CK-AML), we performed integrative analysis using TP53 mutational screening and array-based genomic profiling in 234 CKAMLs. TP53 mutations were found in 141 of 234 (60%) and TP53 losses were identified in 94 of 234 (40%) CK-AMLs; in total, 164 of 234 (70%) cases had TP53 alterations. TP53-altered CK-AML were characterized by a higher degree of genomic complexity (aberrations per case, 14.30 vs 6.16; P < .0001) and by a higher frequency of specific copy number alterations, such as ؊5/5q؊, ؊7/7q؊, ؊16/ 16q؊, ؊18/18q؊, ؉1/؉1p, and ؉11/؉11q/ amp11q13ϳ25; among CK-AMLs, TP53-altered more frequently exhibited a monosomal karyotype (MK). Patients with TP53 alterations were older and had significantly lower complete remission rates, inferior event-free, relapse-free, and overall survival. In multivariable analysis for overall survival, TP53 alterations, white blood cell counts, and age were the only significant factors. In conclusion, TP53 is the most frequently known altered gene in CK-AML. TP53 alterations are associated with older age, genomic complexity, specific DNA copy number alterations, MK, and dismal outcome. In multivariable analysis, TP53 alteration is the most important prognostic factor in CK-AML, outweighing all other variables, including the MK category. (Blood. 2012;119(9):2114-2121)

CLL with TP53 mutation carries a poor prognosis regardless of the presence of 17p deletion when treated with F-based chemotherapy. Thus, TP53 mutation analysis should be incorporated into the evaluation of patients with CLL before treatment initiation. Patients with TP53 mutation should be considered for alternative treatment approaches.

Key Points• Independent prognostic impact of biological markers, notably TP53 and SF3B1 mutations, in CLL patients requiring therapy.• NOTCH1 mutation as a predictive factor for reduced benefit from the addition of rituximab to FC chemotherapy.Mutations in TP53, NOTCH1, and SF3B1 were analyzed in the CLL8 study evaluating firstline therapy with fludarabine and cyclophosphamide (FC) or FC with rituximab (FCR) among patients with untreated chronic lymphocytic leukemia (CLL). TP53, NOTCH1, and SF3B1 were mutated in 11.5%, 10.0%, and 18.4% of patients, respectively. NOTCH1 mut and SF3B1 mut virtually showed mutual exclusivity (0.6% concurrence), but TP53 mut was frequently found in NOTCH1 mut (16.1%) and in SF3B1 mut (14.0%) patients. There were few significant associations with clinical and laboratory characteristics, but genetic markers had a strong influence on response and survival. In multivariable analyses, an independent prognostic impact was found for FCR, thymidine kinase (TK) ‡10 U/L, unmutated IGHV, 11q deletion, 17p deletion, TP53 mut , and SF3B1 mut on progression-free survival; and for FCR, age ‡65 years, Eastern Cooperative Oncology Group performance status ‡1, b2-microglobulin ‡3.5 mg/L, TK ‡10 U/L, unmutated IGHV, 17p deletion, and TP53 mut on overall survival. Notably, predictive marker analysis identified an interaction of NOTCH1 mutational status and treatment in that rituximab failed to improve response and survival in patients with NOTCH1 mut . In conclusion, TP53 and SF3B1 mutations appear among the strongest prognostic markers in CLL patients receiving current-standard first-line therapy. NOTCH1 mut was identified as a predictive marker for decreased benefit from the addition of rituximab to FC. This study is registered at www.clinicaltrials.gov as #NCT00281918. (Blood. 2014;123(21):3247-3254)