Can stratification biomarkers address the heterogeneity of autism spectrum disorder?

The search for biomarkers for autism spectrum disorder (henceforth autism) has received a lot of attention due to their potential clinical relevance. The clinical and aetiological heterogeneity of autism suggests the presence of subgroups. The lack of identification of a valid diagnostic biomarker for autism, and the inconsistencies seen in studies assessing differences between autism and typically developing control groups, may be partially explained by the vast heterogeneity observed in autism. The focus now is to better understand the clinical and biological heterogeneity and identify stratification biomarkers, which are measures that describe subgroups of individuals with shared biology. Using stratification approaches to assess treatment within pre-defined subgroups could clarify who may benefit from different treatments and therapies, and ultimately lead to more effective individualised treatment plans.

Targeting the tumor microenvironment in B-cell lymphoma: challenges and opportunities

B-cell lymphoma is a group of hematological malignancies with high clinical and biological heterogeneity. The pathogenesis of B-cell lymphoma involves a complex interaction between tumor cells and the tumor microenvironment (TME), which is composed of stromal cells and extracellular matrix. Although the roles of the TME have not been fully elucidated, accumulating evidence implies that TME is closely relevant to the origination, invasion and metastasis of B-cell lymphoma. Explorations of the TME provide distinctive insights for cancer therapy. Here, we epitomize the recent advances of TME in B-cell lymphoma and discuss its function in tumor progression and immune escape. In addition, the potential clinical value of targeting TME in B-cell lymphoma is highlighted, which is expected to pave the way for novel therapeutic strategies.

A comparison of data integration methods for single-cell RNA sequencing of cancer samples

Tumours are routinely profiled with single-cell RNA sequencing (scRNA-seq) to characterize their diverse cellular ecosystems of malignant, immune, and stromal cell types. When combining data from multiple samples or studies, batch-specific technical variation can confound biological signals. However, scRNA-seq batch integration methods are often not designed for, or benchmarked, on datasets containing cancer cells. Here, we compare 5 data integration tools applied to 171,206 cells from 5 tumour scRNA-seq datasets. Based on our results, STACAS and fastMNN are the most suitable methods for integrating tumour datasets, demonstrating robust batch effect correction while preserving relevant biological variability in the malignant compartment. This comparison provides a framework for evaluating how well single-cell integration methods correct for technical variability while preserving biological heterogeneity of malignant and non-malignant cell populations.

New Insights From Single-Cell Sequencing Data: Synovial Fibroblasts and Synovial Macrophages in Rheumatoid Arthritis

Single-cell RNA sequencing (scRNA-seq) technology can analyze the transcriptome expression level of cells with high-throughput from the single cell level, fully show the heterogeneity of cells, and provide a new way for the study of multicellular biological heterogeneity. Synovitis is the pathological basis of rheumatoid arthritis (RA). Synovial fibroblasts (SFs) and synovial macrophages are the core target cells of RA, which results in the destruction of articular cartilage, as well as bone. Recent scRNA-seq technology has made breakthroughs in the differentiation and development of two types of synovial cells, identification of subsets, functional analysis, and new therapeutic targets, which will bring remarkable changes in RA treatment.

Quantitative single-cell proteomics as a tool to characterize cellular hierarchies

Large-scale single-cell analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems, but have largely been limited to RNA-based technologies. Here we present a comprehensive benchmarked experimental and computational workflow, which establishes global single-cell mass spectrometry-based proteomics as a tool for large-scale single-cell analyses. By exploiting a primary leukemia model system, we demonstrate both through pre-enrichment of cell populations and through a non-enriched unbiased approach that our workflow enables the exploration of cellular heterogeneity within this aberrant developmental hierarchy. Our approach is capable of consistently quantifying ~1000 proteins per cell across thousands of individual cells using limited instrument time. Furthermore, we develop a computational workflow (SCeptre) that effectively normalizes the data, integrates available FACS data and facilitates downstream analysis. The approach presented here lays a foundation for implementing global single-cell proteomics studies across the world.

Opportunities for metastatic triple negative breast cancer therapy

Metastatic triple negative breast cancer (mTNBC) is a difficult task for the chemotherapist in view of the disease aggressiveness, biological heterogeneity of the tumor, as well as the limit of therapy options. The approved modern drugs, such as immunotherapy and PARP inhibitors, have improved the treatment results in women with mTNBC. However, not all women are the candidates for this kind of therapy due to the lack of suitable points of application. In this context, high hopes are placed on the new treatment options currently being studied in clinical trials. The review summarizes data on advanced drugs that have demonstrated their efficacy in this multiplex group of women, but not yet registered at the territory of the Russian Federation Russian Federation, and will allow us to form an idea of the future algorithm of treatment of women with mTNBC.

Utilizing the Heterogeneity of Clinical Data for Model Refinement and Rule Discovery Through the Application of Genetic Algorithms to Calibrate a High-Dimensional Agent-Based Model of Systemic Inflammation

Introduction: Accounting for biological heterogeneity represents one of the greatest challenges in biomedical research. Dynamic computational and mathematical models can be used to enhance the study and understanding of biological systems, but traditional methods for calibration and validation commonly do not account for the heterogeneity of biological data, which may result in overfitting and brittleness of these models. Herein we propose a machine learning approach that utilizes genetic algorithms (GAs) to calibrate and refine an agent-based model (ABM) of acute systemic inflammation, with a focus on accounting for the heterogeneity seen in a clinical data set, thereby avoiding overfitting and increasing the robustness and potential generalizability of the underlying simulation model.Methods: Agent-based modeling is a frequently used modeling method for multi-scale mechanistic modeling. However, the same properties that make ABMs well suited to representing biological systems also present significant challenges with respect to their construction and calibration, particularly with respect to the selection of potential mechanistic rules and the large number of associated free parameters. We have proposed that machine learning approaches (such as GAs) can be used to more effectively and efficiently deal with rule selection and parameter space characterization; the current work applies GAs to the challenge of calibrating a complex ABM to a specific data set, while preserving biological heterogeneity reflected in the range and variance of the data. This project uses a GA to augment the rule-set for a previously validated ABM of acute systemic inflammation, the Innate Immune Response ABM (IIRABM) to clinical time series data of systemic cytokine levels from a population of burn patients. The genome for the GA is a vector generated from the IIRABM’s Model Rule Matrix (MRM), which is a matrix representation of not only the constants/parameters associated with the IIRABM’s cytokine interaction rules, but also the existence of rules themselves. Capturing heterogeneity is accomplished by a fitness function that incorporates the sample value range (“error bars”) of the clinical data.Results: The GA-enabled parameter space exploration resulted in a set of putative MRM rules and associated parameterizations which closely match the cytokine time course data used to design the fitness function. The number of non-zero elements in the MRM increases significantly as the model parameterizations evolve toward a fitness function minimum, transitioning from a sparse to a dense matrix. This results in a model structure that more closely resembles (at a superficial level) the structure of data generated by a standard differential gene expression experimental study.Conclusion: We present an HPC-enabled machine learning/evolutionary computing approach to calibrate a complex ABM to complex clinical data while preserving biological heterogeneity. The integration of machine learning, HPC, and multi-scale mechanistic modeling provides a pathway forward to more effectively representing the heterogeneity of clinical populations and their data.