Early identification of resistant cancer cells is currently a major challenge, as their expansion leads to refractoriness. To capture the dynamics of these cells, we made a comprehensive analysis of disease progression and treatment response in a chronic lymphocytic leukemia (CLL) patient using a combination of single-cell and bulk genomic methods. At diagnosis, the patient presented with unfavorable genetic markers, including notch receptor 1 (NOTCH1) mutation and loss(11q). The initial and subsequent treatment lines did not lead to a durable response and the patient developed refractory disease. Refractory CLL cells featured substantial dysregulation in B-cell phenotypic markers such as human leukocyte antigen (HLA) genes, immunoglobulin (IG) genes, CD19 molecule (CD19), membrane spanning 4-domains A1 (MS4A1; previously known as CD20), CD79a molecule (CD79A) and paired box 5 (PAX5), indicating B-cell de-differentiation and disease transformation. We described the clonal evolution and characterized in detail two cell populations that emerged during the refractory disease phase, differing in the presence of high genomic complexity. In addition, we successfully tracked the cells with high genomic complexity back to the time before treatment, where they formed a rare subpopulation. We have confirmed that single-cell RNA sequencing enables the characterization of refractory cells and the monitoring of their development over time.

• Keywords
• CLL, clonal evolution, rare subpopulation, refractoriness, single‐cell RNA sequencing,
• MeSH
• Single-Cell Analysis * methods   MeSH
• Drug Resistance, Neoplasm genetics   MeSH
• Leukemia, Lymphocytic, Chronic, B-Cell * genetics   pathology   MeSH
• Humans MeSH
• Sequence Analysis, RNA MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Publication type
• Journal Article MeSH
• Case Reports MeSH

Patients with chronic lymphocytic leukemia (CLL) bearing TP53 mutations experience chemorefractory disease and are therefore candidates for targeted therapy. However, the significance of low-burden TP53 mutations with <10% variant allele frequency (VAF) remains a matter for debate. Herein, we describe clonal evolution scenarios of low-burden TP53 mutations, the clinical impact of which we analyzed in a "real-world" CLL cohort. TP53 status was assessed by targeted next-generation sequencing (NGS) in 511 patients entering first-line treatment with chemo- and/or immunotherapy and 159 patients in relapse before treatment with targeted agents. Within the pretherapy cohort, 16% of patients carried low-burden TP53 mutations (0.1% to 10% VAF). Although their presence did not significantly shorten event-free survival after first-line therapy, it affected overall survival (OS). In a subgroup with TP53 mutations of 1% to 10% VAF, the impact on OS was observed only in patients with unmutated IGHV who had not received targeted therapy, as patients benefited from switching to targeted agents, regardless of initial TP53 mutational status. Analysis of the clonal evolution of low-burden TP53 mutations showed that the highest expansion rates were associated with fludarabine, cyclophosphamide, and rituximab regimen in both first- and second-line treatments (median VAF increase, 14.8× and 11.8×, respectively) in contrast to treatment with less intense treatment regimens (1.6×) and no treatment (0.8×). In the relapse cohort, 33% of patients carried low-burden TP53 mutations, which did not expand significantly upon targeted treatment (median VAF change, 1×). Sporadic cases of TP53 mutations' clonal shifts were connected with the development of resistance-associated mutations. Altogether, our data support the incorporation of low-burden TP53 variants in clinical decision making.

• MeSH
• Leukemia, Lymphocytic, Chronic, B-Cell genetics   therapy   MeSH
• Adult MeSH
• Immunotherapy MeSH
• Kaplan-Meier Estimate MeSH
• Clonal Evolution * drug effects   MeSH
• Middle Aged MeSH
• Humans MeSH
• Mutation drug effects   MeSH
• Tumor Cells, Cultured MeSH
• Tumor Suppressor Protein p53 genetics   MeSH
• Antineoplastic Combined Chemotherapy Protocols therapeutic use   MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Tumor Suppressor Protein p53 MeSH
• TP53 protein, human MeSH Browser

• MeSH
• Chromosome Aberrations * MeSH
• Leukemia, Lymphocytic, Chronic, B-Cell genetics   pathology   therapy   MeSH
• Combined Modality Therapy MeSH
• Humans MeSH
• Longitudinal Studies MeSH
• Mutation * MeSH
• Biomarkers, Tumor genetics   MeSH
• Follow-Up Studies MeSH
• Prognosis MeSH
• Exome Sequencing MeSH
• Case-Control Studies MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, N.I.H., Extramural MeSH
• Names of Substances
• Biomarkers, Tumor MeSH

BACKGROUND: High-throughput bioinformatics analyses of next generation sequencing (NGS) data often require challenging pipeline optimization. The key problem is choosing appropriate tools and selecting the best parameters for optimal precision and recall. RESULTS: Here we introduce ToTem, a tool for automated pipeline optimization. ToTem is a stand-alone web application with a comprehensive graphical user interface (GUI). ToTem is written in Java and PHP with an underlying connection to a MySQL database. Its primary role is to automatically generate, execute and benchmark different variant calling pipeline settings. Our tool allows an analysis to be started from any level of the process and with the possibility of plugging almost any tool or code. To prevent an over-fitting of pipeline parameters, ToTem ensures the reproducibility of these by using cross validation techniques that penalize the final precision, recall and F-measure. The results are interpreted as interactive graphs and tables allowing an optimal pipeline to be selected, based on the user's priorities. Using ToTem, we were able to optimize somatic variant calling from ultra-deep targeted gene sequencing (TGS) data and germline variant detection in whole genome sequencing (WGS) data. CONCLUSIONS: ToTem is a tool for automated pipeline optimization which is freely available as a web application at https://totem.software .

• Keywords
• Benchmarking, Next generation sequencing, Parameter optimization, Variant calling,
• MeSH
• Reproducibility of Results MeSH
• Software MeSH
• Computational Biology methods   MeSH
• High-Throughput Nucleotide Sequencing methods   MeSH
• Research Design MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH