Biology subjects

Niida, A.

Publications and source records attributed to Niida, A..

5 recordsLinked to original sources

bioRxiv2021

Combined landscape of single-nucleotide variants and copy-number alterations in clonal hematopoiesis

Implicated in the development of hematological malignancies (HM) and cardiovascular mortality, clonal hematopoiesis (CH) in apparently healthy individuals has been investigated by detecting either single-nucleotide variants and indels (SNVs/indels) or copy number alterations (CNAs), but not both. Here by combining targeted sequencing of 23 CH-related genes and array-based CNA detection of blood-derived DNA, we have delineated the landscape of CH-related SNVs/indels and CNAs in a general population of 11,234 individuals, including 672 with subsequent HM development. Both CH-related lesions significantly co-occurred, which combined, affected blood count, hypertension, and the mortality from HM and cardiovascular diseases depending on the total number of both lesions, highlighting the importance of detecting both lesions in the evaluation of CH.

cancer biology↗

bioRxiv2020

APC mutant cells exploit compensatory chromosome alterations to restore tumour cell fitness

Cancer cells tolerate copy number alterations (CNAs) of genomic regions that are lethal to non-cancer cells. Certain CNAs are preferentially associated with specific cancer types and lineages, but the mechanisms underlying the emergence and selection of specific CNAs remain unclear. Adenomatous polyposis coli (APC) mutations induce mitotic errors, but their impact on tumor evolution remains elusive. We investigated APC function in cultured cells and tumors and found that its loss led to {beta}-catenin accumulation at centrosomes, suppressing its maturation through inhibition of key centrosome regulators, including Aurora kinase A (AURKA) that promotes tumor growth. These defects collectively reduced cellular fitness, leading to impaired mitotic fidelity and delayed cell cycle progression. However, in APC-mutant tumors, AURKA activity was maintained, at least in part, through the amplification of chromosomes harboring AURKA and its activator genes, yet this alone was insufficient to fully restore proliferation: aberrant chromosomal reorganization also emerged and contributed to the adaptive fitness of APC-mutant cells. Such a process of adaptive CNA selection provides a framework for understanding how specific CNAs are selected to counteract disadvantages imposed by genetic alterations during tumor progression, providing one key insight into how specific CNAs are selected in this context.

cell biology↗

bioRxiv2019

A multifunctional R package for identification of tumor-specific neoantigens

It is known that some mutated peptides, such as those resulting from missense mutations and frameshift insertions, can bind to the major histocompatibility complex and be presented to antitumor T-cells on the surface of a tumor cell. These peptides are termed neoantigen and it is important to understand this process for cancer immunotherapy. Here, we introduce an R package that can predict a list of potential neoantigens from a variety of mutations, which include not only somatic point mutations but insertions, deletions, and structural variants. Beyond the existing applications, this package is capable of attaching and reflecting several additional information, e.g., wild-type binding capability, allele specific RNA expression levels, single nucleotide polymorphism information, and combinations of mutations to filter out infeasible peptides as neoantigen. AvailabilityThe R package is available at http://github/hase62/Neoantimon.

bioinformatics↗

bioRxiv2019

A unified simulation model for understanding the diversity of cancer evolution

Because cancer evolution underlies the therapeutic difficulties of cancer, it is clinically important to understand the evolutionary dynamics of cancer. Thus far, a number of evolutionary processes have been proposed to be working in cancer evolution. However, there exists no simulation model that can describe the different evolutionary processes in a unified manner. In this study, we constructed a unified simulation model for describing the different evolutionary processes and performed sensitivity analysis on the model to determine the conditions in which cancer growth is driven by each of the different evolutionary processes. Our sensitivity analysis has successfully provided a series of novel insights into the evolutionary dynamics of cancer. For example, we found that, while a high neutral mutation rate shapes neutral intratumor heterogeneity (ITH) characterized by a fractal-like pattern, a stem cell hierarchy can also contribute to shaping neutral ITH by apparently increasing the mutation rate. Although It has been reported that the evolutionary principle shaping ITH shifts from selection to accumulation of neutral mutations during colorectal tumorigenesis, our simulation revealed the possibility that this evolutionary shift is triggered by drastic evolutionary events that occur in a a short time and confer a marked fitness increase on one or a few cells. This result helps us understand that each process works not separately but simultaneously and continuously as a series of phases of cancer evolution. Collectively, this study serves as a basis to understand in greater depth the diversity of cancer evolution.

cancer biology↗

bioRxiv2019

Sensitivity analysis of agent-based simulation utilizing massively parallel computation and interactive data visualization

An essential step in the analysis of agent-based simulation is sensitivity analysis, which namely examines the dependency of parameter values on simulation results. Although a number of approaches have been proposed for sensitivity analysis, they still have limitations in exhaustivity and interpretability. In this study, we propose a novel methodology for sensitivity analysis of agent-based simulation, MASSIVE (Massively parallel Agent-based Simulations and Subsequent Interactive Visualization-based Exploration). MASSIVE takes a unique paradigm, which is completely different from those of sensitivity analysis methods developed so far, By combining massively parallel computation and interactive data visualization, MASSIVE enables us to inspect a broad parameter space intuitively. We demonstrated the utility of MASSIVE by its application to cancer evolution simulation, which successfully identified conditions that generate heterogeneous tumors. We believe that our approach would be a de facto standard for sensitivity analysis of agent-based simulation in an era of ever-growing computational technology. All the result form our MASSIVE analysis is available at https://www.hgc.jp/~niiyan/massive.

bioinformatics↗