Phertilizer: Growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors

CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling

Low-pass single-cell DNA sequencing technologies and algorithmic advancements have enabled haplotype-specific copy number calling on thousands of cells within tumors. However, measurement uncertainty may result in spurious CNAs inconsistent with realistic evolutionary constraints. We introduce evolution-aware copy number calling via deep reinforcement learning (CNRein). Our simulations demonstrate CNRein infers more accurate copy-number profiles and better recapitulates ground truth clonal structure than existing methods. On sequencing data of breast and ovarian cancer, CNRein produces more parsimonious solutions than existing methods while maintaining agreement with single-nucleotide variants. Additionally, CNRein shows consistency on a breast cancer patient sequenced with distinct low-pass technologies.

Prognostic factors and outcomes in pediatric acute myeloid leukemia: a comprehensive bibliometric analysis of global research trends

Background

Pediatric AML prognosis research has advanced significantly, yet gaps in understanding genetic and molecular interactions persist. Despite improved outcomes, relapse/refractory cases and personalized treatment integration remain critical clinical challenges.

Objective

To analyze the global research landscape on pediatric AML prognosis, highlight influential components and collaborations, and identify major potential research trends.

Methods

Publications on pediatric AML prognosis research from 1999 to 2023 were retrieved from the Clarivate Analytics Web of Science Core Collection (WoSCC) database. Bibliometric analysis was conducted using CiteSpace and VOSviewer to identify leading countries, prominent institutions, high-impact journals, key research categories, influential authors, and emerging research topics.

Results

The bibliometric analysis encompassed 924 publications, with St. Jude Children's Research Hospital emerging as the most prolific institution. The United States leads globally in terms of countries, institutions, journals, and authors. Todd A. Alonzo ranks highest in publication volume, while U. Creutzig leads in citations. The top research categories were Oncology, Hematology, and Pediatrics. Key research topics included genomics, transcriptomics, epigenomics, targeted therapies, immune therapy, and integrative diagnostic approaches.

Conclusion

This bibliometric analysis highlights significant advancements in pediatric AML prognosis over the past 25 years, driven by the integration of genetic markers, immunological insights, transcriptomics, and epigenomics, which have collectively transformed risk stratification and treatment strategies. Overcoming challenges, such as discovering new therapeutic targets and enhancing treatment combinations, will depend on global collaboration and advanced technologies to propel the field forward.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Isotype-aware inference of B cell clonal lineage trees from single-cell sequencing data

Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the micro-evolutionary processes of B cells during an adaptive immune response, capturing features of somatic hypermutation (SHM) and class switch recombination (CSR). Existing phylogenetic approaches for reconstructing B cell evolution have primarily focused on the SHM process alone. Here, we present tree inference of B cell clonal lineages (TRIBAL), an algorithm designed to optimally reconstruct the evolutionary history of B cell clonal lineages undergoing both SHM and CSR from scRNA-seq data. Through simulations, we demonstrate that TRIBAL produces more comprehensive and accurate B cell lineage trees compared to existing methods. Using real-world datasets, TRIBAL successfully recapitulates expected biological trends in a model affinity maturation system while reconstructing evolutionary histories with more parsimonious class switching than state-of-the-art methods. Thus, TRIBAL significantly improves B cell lineage tracing, useful for modeling vaccine responses, disease progression, and the identification of therapeutic antibodies.

Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data

Single-cell DNA sequencing enables the construction of evolutionary trees that can reveal how tumors gain mutations and grow. Different whole-genome amplification procedures render genomic materials of different characteristics, often suitable for the detection of either single-nucleotide variation or copy number aberration, but not ideally for both. Consequently, this hinders the inference of a comprehensive phylogenetic tree and limits opportunities to investigate the interplay of SNVs and CNAs. Existing methods such as SCARLET and COMPASS require that the SNVs and CNAs are detected from the same sets of cells, which is technically challenging. Here we present a novel computational tool, SCsnvcna, that places SNVs on a tree inferred from CNA signals, whereas the sets of cells rendering the SNVs and CNAs are independent, offering a more practical solution in terms of the technical challenges. SCsnvcna is a Bayesian probabilistic model using both the genotype constraints on the tree and the cellular prevalence to search the optimal solution. Comprehensive simulations and comparison with seven state-of-the-art methods show that SCsnvcna is robust and accurate in a variety of circumstances. Particularly, SCsnvcna most frequently produces the lowest error rates, with ability to scale to a wide range of numerical values for leaf nodes in the tree, SNVs, and SNV cells. The application of SCsnvcna to two published colorectal cancer data sets shows highly consistent placement of SNV cells and SNVs with the original study while also supporting a refined placement of ATP7B, illustrating SCsnvcna's value in analyzing complex multitumor samples.