Accuracy of Answers to Cell Lineage Questions Depends on Single-Cell Genomics Data Quality and Quantity.

Advances in single-cell (SC) genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells, as determined by phylogenetic analysis of the somatic mutations harbored by each cell. Theoretically, complete and accurate knowledge of the genome of each cell of an individual can produce an extremely accurate cell lineage tree of that individual. However, the reality of SC genomics is that such complete and accurate knowledge would be wanting, in quality and in quantity, for the foreseeable future. In this paper we offer a framework for systematically exploring the feasibility of answering cell lineage questions based on SC somatic mutational analysis, as a function of SC genomics data quality and quantity. We take into consideration the current limitations of SC genomics in terms of mutation data quality, most notably amplification bias and allele dropouts (ADO), as well as cost, which puts practical limits on mutation data quantity obtained from each cell as well as on cell sample density. We do so by generating in silico cell lineage trees using a dedicated formal language, eSTG, and show how the ability to answer correctly a cell lineage question depends on the quality and quantity of the SC mutation data. The presented framework can serve as a baseline for the potential of current SC genomics to unravel cell lineage dynamics, as well as the potential contributions of future advancement, both biochemical and computational, for the task

Identification of metastasis founder cells in breast cancer by cell lineage tracing

Background: Metastatic dissemination often occurs at early stages of breast cancer progression implying that disseminated cancer cells (DCCs) evolve outside the primary tumor in a process of selection and adaptation. To identify metastasis founder cells in humans we strived to construct cell lineage trees by longitudinally tracking genetic changes in cancer cells isolated from patients at various disease stages. Methods: We used sample triplets comprising primary tumor, bone marrow DCCs isolated at presumably curative surgery and CTCs/DCCs at a further time point before and after progressing into metastasis. To prepare samples for single cell lineage tree analysis we developed robust methods for isolation of single cells of high DNA quality from all tissues, including - (i) Isolation of single cells from flash frozen tumor tissue; (ii) Laser capture microdissection to isolate DCCs from diagnostic cytospins; (iii) Isolation of blood CTCs from CellSearch cartridges. In addition, we isolated CD3+ T cells, CD68+ macrophages and oral epithelial cells to serve as outgroups for cell lineage tree analysis. Cell lineage tree reconstructions are based on short tandem repeats (STRs) mutations reflecting cell divisions which determine cellular descent by tracing random mutational events. Around 12000 STR loci are sequenced after target enrichment using a patient-generic panel of duplex molecular inversion probes. Results: Detection, isolation and whole genome amplification from all sample sources could be successfully established. Results of STR based lineage tree analysis showed a significantly separate clustering of advanced primary tumor cells from metastatic cells. Interestingly these metastatic cells have detectable early DCC ancestors. Conclusions: The data allows reconstructing the genomic make-up of metastases founder cells from the available phylogenetic trees. Longitudinal analysis of systemic breast cancer evolution may provide insights for diagnostic monitoring and inform the development of novel adjuvant therapies

Monitoring genotypic and phenotypic progression of systemic melanoma by cell lineage tree analysis and for molecular disease staging

A recent study on metastatic seeding in melanomas showed that lymphatic dissemination occurs very early. Disseminated cancer cells (DCCs) of melanoma patients can leave the primary tumor (PT) in a genomically immature state, then evolve within the lymph nodes (LNs) and adapt to the ectopic site until they start to proliferate and form metastasis. Since the PT and the metastasis are often genetically disparate, the focus for treating metastases should be on the DCCs. The molecular characterization of the DCCs that left the PT at an early stage could reveal new therapeutic targets against metastasis. After routine LN removal in melanoma patients, staining of LNs against the tumour marker MCSP identified two different phenotypes: small MCSP-positive and large MCSP-positive DCCs. While the small phenotype appears mostly in LNs with a low DCCD (DCC-density; number of DCCs per million mononuclear cells), the large phenotype could be found in LNs with a higher DCCD. Furthermore, we also observed LNs with both small and large DCCs, that had a medium DCCD. Based on these findings we hypothesized that small MCSP-DCCs are precursors of large MCSP-DCCs and represent very early DCCs. In addition, we wanted to have a closer look at the two most common BRAF mutations in malignant melanoma and its association with the DCCD of the LNs. We hypothesized that acquisition of BRAF mutations marks the transition from pre-colonizing DCCs to colonizing DCCs and hence a significant progression step in systemic cancer development. The hypothesis if small MCSP-positive DCCs are the precursors of large MCSP-positive DCCs should be investigated with the help of a cell lineage tree reconstruction based on short tandem repeats (STRs). To study the incidence of the BRAF mutations we established an allele-specific PCR with a blocking reagent (ASB-PCR) for DCCs. The cell lineage tree reconstruction of patient MM15-127 resulted in three distinct clusters of DCCs. Two of the clusters were found in close proximity to the PT, while one DCC cluster was closer to the metastatic tumour cells than the PT. Both small and large MCSP-positive DCCs were found in the two clusters close to the PT. The cluster closer to the metastatic tumour cells only contained large MCSP-positive DCCs. Retrospective testing of 80 DCCs with the established ASB-PCR resulted in the correct identification of wild type and mutant DCCs in 98% and 96% of the samples, respectively. From patient MM16-423, DCCs were isolated from the sentinel lymph node (SLN) and the non-SLNs and tested for BRAF mutations by the ASB-PCR. While the PT and the DCCs isolated from the SLN at primary diagnosis were wild type, the DCCs isolated from non-SLNs after LN relapse harboured a BRAF mutation. Testing a cohort of 150 malignant melanoma patients for BRAF mutations in DCCs, showed that 19.8% patients with a pathologically negative LN and 59.4% with a pathologically positive LN harboured a mutation. However, studying the incidence of the BRAF mutation depending on the DCCD, we found out that there is a large increase of the BRAF mutation from 14.9% in LNs with a DCCD>1≤10 to 62.5% in LNs with a DCCD>10≤30. Based on the result of the cell lineage tree reconstruction of patient MM15-127 our hypothesis that small MCSP-positive DCCs are the precursors of large MCSP-positive DCCs could neither be confirmed nor rejected. The resolution of the cell lineage tree is no yet good enough 8 to provide such accurate insights. However, three distinct clusters of DCCs were identified which could be an indication that DCCs disseminated at different time points. The ASB-PCR of DCCs from patient MM16-423 showed that BRAF mutations were acquired outside of the PT at a later time point of disease progression, when metastases were detected in the non-SLN. However, 62.5% of patients with a DCCD>10≤30 harboured a BRAF mutation, indicating that the BRAF mutation could be acquired early before colonisation of the DCCs