Patients with chromosome 13q deletion or normal cytogenetics represent the majority of chronic lymphocytic leukaemia (CLL) cases, yet have relatively few driver mutations. To better understand their genomic landscape, here we perform whole-genome sequencing on a cohort of patients enriched with these cytogenetic characteristics. Mutations in known CLL drivers are seen in only 33% of this cohort, and associated with normal cytogenetics and unmutated IGHV. The most commonly mutated gene in our cohort, IGLL5, shows a mutational pattern suggestive of activation-induced cytidine deaminase (AID) activity. Unsupervised analysis of mutational signatures demonstrates the activities of canonical AID (c-AID), leading to clustered mutations near active transcriptional start sites; non-canonical AID (nc-AID), leading to genome-wide non-clustered mutations, and an ageing signature responsible for most mutations. Using mutation clonality to infer time of onset, we find that while ageing and c-AID activities are ongoing, nc-AID-associated mutations likely occur earlier in tumour evolution.

Fidelity of DNA replication is maintained using polymerase proofreading and the mismatch repair pathway. Tumors with loss of function of either mechanism have elevated mutation rates with characteristic mutational signatures. Here we report that tumors with concurrent loss of both polymerase proofreading and mismatch repair function have mutational patterns that are not a simple sum of the signatures of the individual alterations, but correspond to distinct, previously unexplained signatures: COSMIC database signatures 14 and 20. We then demonstrate that in all five cases in which the chronological order of events could be determined, polymerase epsilon proofreading alterations precede the defect in mismatch repair. Overall, we illustrate that multiple distinct mutational signatures can result from different combinations of a smaller number of mutational processes (of either damage or repair), which can influence the interpretation and discovery of mutational signatures.

required to delineate dose-response-associated RNA profiles in radiation-induced EVs, and to define tumor-and tissue-specific biodosimetric signatures. Conclusion: EVs have tremendous potential as biomarkers and regulators of disease. NanoFACS offers a unique platform for the analysis and separation of tumor/tissue-specific and treatment-associated EV subsets. Future studies will investigate whether nanoFACS can be used to identify EV subsets associated with tumor growth, anti-tumor immune responses, and resistance to radiation. By using nanoFACS to detect and isolate tumor-derived EVs from peripheral blood, we plan to investigate whether EV subset signatures correlate with treatment responses and can be used to guide adaptive therapies.