Transient Differentiation-State Plasticity Occurs during Acute Lymphoblastic Leukemia Initiation

Leukemia is characterized by oncogenic lesions that result in a block of differentiation, whereas phenotypic plasticity is retained. A better understanding of how these two phenomena arise during leukemogenesis in humans could help inform diagnosis and treatment strategies. Here, we leveraged the well-defined differentiation states during T-cell development to pinpoint the initiation of T-cell acute lymphoblastic leukemia (T-ALL), an aggressive form of childhood leukemia, and study the emergence of phenotypic plasticity. Single-cell whole genome sequencing of leukemic blasts was combined with multiparameter flow cytometry to couple cell identity and clonal lineages. Irrespective of genetic events, leukemia-initiating cells altered their phenotypes by differentiation and dedifferentiation. The construction of the phylogenies of individual leukemias using somatic mutations revealed that phenotypic diversity is reflected by the clonal structure of cancer. The analysis also indicated that the acquired phenotypes are heritable and stable. Together, these results demonstrate a transient period of plasticity during leukemia initiation, where phenotypic switches seem unidirectional. Significance: A method merging multicolor flow cytometry with single-cell whole genome sequencing to couple cell identity with clonal lineages uncovers differentiation-state plasticity in leukemia, reconciling blocked differentiation with phenotypic plasticity in cancer

Chromothripsis is a common mechanism driving genomic rearrangements in primary and metastatic colorectal cancer

ABSTRACT: BACKGROUND: Structural rearrangements form a major class of somatic variation in cancer genomes. Local chromosome shattering, termed chromothripsis, is a mechanism proposed to be the cause of clustered chromosomal rearrangements and was recently described to occur in a small percentage of tumors. The significance of these clusters for tumor development or metastatic spread is largely unclear. RESULTS: We used genome-wide long mate-pair sequencing and SNP array profiling to reveal that chromothripsis is a widespread phenomenon in primary colorectal cancer and metastases. We find large and small chromothripsis events in nearly every colorectal tumor sample and show that several breakpoints of chromothripsis clusters and isolated rearrangements affect cancer genes, including NOTCH2, EXO1 and MLL3. We complemented the structural variation studies by sequencing the coding regions of a cancer exome in all colorectal tumor samples and found somatic mutations in 24 genes, including APC, KRAS, SMAD4 and PIK3CA. A pairwise comparison of somatic variations in primary and metastatic samples indicated that many chromothripsis clusters, isolated rearrangements and point mutations are exclusively present in either the primary tumor or the metastasis and may affect cancer genes in a lesion-specific manner. CONCLUSIONS: We conclude that chromothripsis is a prevalent mechanism driving structural rearrangements in colorectal cancer and show that a complex interplay between point mutations, simple copy number changes and chromothripsis events drive colorectal tumor development and metastasis.

MutationalPatterns: the one stop shop for the analysis of mutational processes

BACKGROUND: The collective of somatic mutations in a genome represents a record of mutational processes that have been operative in a cell. These processes can be investigated by extracting relevant mutational patterns from sequencing data. RESULTS: Here, we present the next version of MutationalPatterns, an R/Bioconductor package, which allows in-depth mutational analysis of catalogues of single and double base substitutions as well as small insertions and deletions. Major features of the package include the possibility to perform regional mutation spectra analyses and the possibility to detect strand asymmetry phenomena, such as lesion segregation. On top of this, the package also contains functions to determine how likely it is that a signature can cause damaging mutations (i.e., mutations that affect protein function). This updated package supports stricter signature refitting on known signatures in order to prevent overfitting. Using simulated mutation matrices containing varied signature contributions, we showed that reliable refitting can be achieved even when only 50 mutations are present