GRCh38 · COSMIC v92


This section shows a general overview of information for the selected study (COSU identifier) or publication (COSP identifier). Studies may have been performed by the Sanger Institute Cancer Genome Project, or imported from the ICGC/TCGA. You can see more information on the help pages.

Therapy-induced mutations drive the genomic landscape of relapsed acute lymphoblastic leukemia.
Paper ID
Li B, Brady SW, Ma X, Shen S, Zhang Y, Li Y, Szlachta K, Dong L, Liu Y, Yang F, Wang N, Flasch DA, Myers MA, Mulder HL, Ding L, Liu Y, Tian L, Hagiwara K, Xu K, Zhou X, Sioson E, Wang T, Yang L, Zhao J, Zhang H, Shao Y, Sun H, Sun L, Cai J, Sun HY, Lin TN, Du L, Li H, Rusch M, Edmonson MN, Easton J, Zhu X, Zhang J, Cheng C, Raphael BR, Tang J, Downing JR, Alexandrov LB, Zhou BS, Pui CH, Yang JJ and Zhang J
Key Laboratory of Pediatric Hematology and Oncology Ministry of Health.
Blood, 2019
ISSN: 1528-0020
PMID: 31697823 (view at PubMed or Europe PMC)
To study the mechanisms of relapse in acute lymphoblastic leukemia (ALL), we performed whole-genome sequencing of 103 diagnosis-relapse-germline trios and ultra-deep sequencing of 208 serial samples in 16 patients. Relapse-specific somatic alterations were enriched in 12 genes (NR3C1, NR3C2, TP53, NT5C2, FPGS, CREBBP, MSH2, MSH6, PMS2, WHSC1, PRPS1, and PRPS2) involved in drug response. Their prevalence was 17% in very early relapse (<9 months from diagnosis), 65% in early relapse (9-36 months), and 32% in late relapse (>36 months) groups. Convergent evolution, where multiple subclones harbor mutations in the same drug resistance gene, was observed in six relapses and confirmed by single-cell sequencing in one case. Mathematical modeling and mutational signature analysis indicated that early relapse resistance acquisition was frequently two-step process where a persistent clone survived initial therapy, and later acquired bona fide resistance mutations during therapy. In contrast, very early relapses arose from pre-existing resistant clone(s). Two novel relapse-specific mutational signatures, one of which was caused by thiopurine treatment based on in vitro drug exposure experiments, were identified in early and late relapses but were absent from 2,540 pan-cancer diagnosis samples and 129 non-ALL relapses. The novel signatures were detected in 27% of relapsed ALLs and were responsible for 46% of acquired resistance mutations in NT5C2, PRPS1, NR3C1, and TP53. These results suggest that chemotherapy-induced drug resistance mutations facilitate a subset of pediatric ALL relapses.
Paper Status
Genes Analysed
Mutated Samples
Total No. of Samples

Mutation Matrix

This section shows the correlation plot between the top 20 genes and samples. There is more information in our help pages.


This table shows genes with mutations in the selected study/paper [more details]
Genes Mutated Samples
This table shows genes without mutations in the selected study/paper [more details]

Table Information


This is a whole exome/systematic screen paper and the negatives for this paper should be inferred.


This tab shows genes with mutations in the selected study/paper [more details]

Genes Samples CDS Mutation AA Mutation

This tab shows non coding variant in the selected study/paper [more details]

Sample ID Sample Name ID NCV Annotation Zygosity Chromosome Genome start Genome stop Genome version Strand WT seq Mut seq FATHMM-MKL

This tab shows the gene expression and copy number variation data for this study [more details]

Table Information


The table currently shows only high value (numeric) copy number data. Copy number segments are excluded if the total copy number and minor allele values are unknown.

Click here to include all copy number data. For more detailed information about copy number data and gain/loss definitions click here.

Sample Gene Expression Expr Level (Z-Score)

Over Expressed; Z-Score > 2.0

Under Expressed; Z-Score < -2.0

Normal; Z-Score within the range -2.0 to 2.0

CN Type Minor Allele Copy Number CN Segment Posn. Average Ploidy

1. N/A represents cases where the average ploidy value is not available( mostly ICGC samples). For some TCGA samples where the minor allele information is not available the average ploidy value could not be calculated.

2. For TCGA samples, the ASCAT algorithm was used to calculate the average ploidy.

3. For CGP samples, the PICNIC algorithm was used to calculate the average ploidy.


This table lists the samples in the selected study which have low/high methylation for each gene. [more details]

No data

This tab shows the fusion mutations observed in this sample [more details]

Gene Sample Name Id Sample(COSS) CDS Mutation Somatic status Zygosity Validated Type


This table shows mutated samples in the selected study/paper.

Sample Name Mutation Count

This table shows samples without mutations in the selected study/paper.

Non-Mutant Samples Sample Id (COSS)