Jochen Singer

Reconstructing the evolution of tumors is a key aspect towards the identification of appropriate cancer therapies. The task is challenging because tumors evolve as heterogeneous cell populations. Single-cell sequencing holds the promise of resolving the heterogeneity of tumors; however, it has its own challenges including elevated error rates, allelic drop-out, and uneven coverage. Here, we develop a new approach to mutation detection in individual tumor cells by leveraging the evolutionary relationship among cells. Our method, called SCIΦ, jointly calls mutations in individual cells and estimates the tumor phylogeny among these cells. Employing a Markov Chain Monte Carlo scheme enables us to reliably call mutations in each single cell even in experiments with high drop-out rates and missing data. We show that SCIΦ outperforms existing methods on simulated data and applied it to different real-world datasets, namely a whole exome breast cancer as well as a panel acute lymphoblastic leukemia dataset.

MOTIVATION: Next-generation sequencing technologies produce unprecedented amounts of data, leading to completely new research fields. One of these is metagenomics, the study of large-size DNA samples containing a multitude of diverse organisms. A key problem in metagenomics is to functionally and taxonomically classify the sequenced DNA, to which end the well-known BLAST program is usually used. But BLAST has dramatic resource requirements at metagenomic scales of data, imposing a high financial or technical burden on the researcher. Multiple attempts have been made to overcome these limitations and present a viable alternative to BLAST. RESULTS: In this work we present Lambda, our own alternative for BLAST in the context of sequence classification. In our tests, Lambda often outperforms the best tools at reproducing BLAST's results and is the fastest compared with the current state of the art at comparable levels of sensitivity. AVAILABILITY AND IMPLEMENTATION: Lambda was implemented in the SeqAn open-source C++ library for sequence analysis and is publicly available for download at http://www.seqan.de/projects/lambda. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Clonal hematopoiesis (CH) is associated with age and an increased risk of myeloid malignancies, cardiovascular risk, and all-cause mortality. We tested for CH in a setting where hematopoietic stem cells (HSCs) of the same individual are exposed to different degrees of proliferative stress and environments, ie, in long-term survivors of allogeneic hematopoietic stem cell transplantation (allo-HSCT) and their respective related donors (n = 42 donor-recipient pairs). With a median follow-up time since allo-HSCT of 16 years (range, 10-32 years), we found a total of 35 mutations in 23 out of 84 (27.4%) study participants. Ten out of 42 donors (23.8%) and 13 out of 42 recipients (31%) had CH. CH was associated with older donor and recipient age. We identified 5 cases of donor-engrafted CH, with 1 case progressing into myelodysplastic syndrome in both donor and recipient. Four out of 5 cases showed increased clone size in recipients compared with donors. We further characterized the hematopoietic system in individuals with CH as follows: (1) CH was consistently present in myeloid cells but varied in penetrance in B and T cells; (2) colony-forming units (CFUs) revealed clonal evolution or multiple independent clones in individuals with multiple CH mutations; and (3) telomere shortening determined in granulocytes suggested ∼20 years of added proliferative history of HSCs in recipients compared with their donors, with telomere length in CH vs non-CH CFUs showing varying patterns. This study provides insight into the long-term behavior of the same human HSCs and respective CH development under different proliferative conditions.

Molecular profiling of tumor biopsies plays an increasingly important role not only in cancer research, but also in the clinical management of cancer patients. Multi-omics approaches hold the promise of improving diagnostics, prognostics and personalized treatment. To deliver on this promise of precision oncology, appropriate bioinformatics methods for managing, integrating and analyzing large and complex data are necessary. Here, we discuss the specific requirements of bioinformatics methods and software that arise in the setting of clinical oncology, owing to a stricter regulatory environment and the need for rapid, highly reproducible and robust procedures. We describe the workflow of a molecular tumor board and the specific bioinformatics support that it requires, from the primary analysis of raw molecular profiling data to the automatic generation of a clinical report and its delivery to decision-making clinical oncologists. Such workflows have to various degrees been implemented in many clinical trials, as well as in molecular tumor boards at specialized cancer centers and university hospitals worldwide. We review these and more recent efforts to include other high-dimensional multi-omics patient profiles into the tumor board, as well as the state of clinical decision support software to translate molecular findings into treatment recommendations.