Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets.

Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels to new cancer specimens from other studies or clinical trials. Here, we address this barrier by applying five different machine learning approaches to multi-omic data from 8,791 TCGA tumor samples comprising 106 subtypes from 26 different cancer cohorts to build models based upon small numbers of features that can classify new samples into previously defined TCGA molecular subtypes-a step toward molecular subtype application in the clinic.

Correction: Computational capabilities of a multicellular reservoir computing system.

[This corrects the article DOI: 10.1371/journal.pone.

A multiscale model of immune surveillance in micrometastases gives insights on cancer patient digital twins.

Metastasis is the leading cause of death in patients with cancer, driving considerable scientific and clinical interest in immunosurveillance of micrometastases. We investigated this process by creating a multiscale mathematical model to study the interactions between the immune system and the progression of micrometastases in general epithelial tissue. We analyzed the parameter space of the model using high-throughput computing resources to generate over 100,000 virtual patient trajectories.

Generating Biomedical Knowledge Graphs from Knowledge Bases, Registries, and Multiomic Data.

As large clinical and multiomics datasets and knowledge resources accumulate, they need to be transformed into computable and actionable information to support automated reasoning. These datasets range from laboratory experiment results to electronic health records (EHRs). Barriers to accessibility and sharing of such datasets include diversity of content, size and privacy.

Knowledge graphs facilitate prediction of drug response for acute myeloid leukemia.

Acute myeloid leukemia (AML) is a highly aggressive and heterogeneous disease, underscoring the need for improved therapeutic options and methods to optimally predict responses. With the wealth of available data resources, including clinical features, multiomics analysis, and drug screening from AML patients, development of drug response prediction models has become feasible. Knowledge graphs (KGs) embed the relationships between different entities or features, allowing for explanation of a wide breadth of drug sensitivity and resistance mechanisms.

Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia.

Purpose: The inherent genetic heterogeneity of acute myeloid leukemia (AML) has challenged the development of precise and effective therapies. The objective of this study was to elucidate the genomic basis of drug resistance or sensitivity, identify signatures for drug response prediction, and provide resources to the research community.

Experimental Design: We performed targeted sequencing, high-throughput drug screening, and single-cell genomic profiling on leukemia cell samples derived from patients with AML.

Toward mechanistic medical digital twins: some use cases in immunology.

A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins.

Forum on immune digital twins: a meeting report.

Medical digital twins are computational models of human biology relevant to a given medical condition, which are tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. In February 2023, an international group of experts convened for two days to discuss these challenges related to immune digital twins.

A framework towards digital twins for type 2 diabetes.

Introduction: A digital twin is a virtual representation of a patient's disease, facilitating real-time monitoring, analysis, and simulation. This enables the prediction of disease progression, optimization of care delivery, and improvement of outcomes.

Methods: Here, we introduce a digital twin framework for type 2 diabetes (T2D) that integrates machine learning with multiomic data, knowledge graphs, and mechanistic models.

Computational capabilities of a multicellular reservoir computing system.

The capacity of cells to process information is currently used to design cell-based tools for ecological, industrial, and biomedical applications such as detecting dangerous chemicals or for bioremediation. In most applications, individual cells are used as the information processing unit. However, single cell engineering is limited by the necessary molecular complexity and the accompanying metabolic burden of synthetic circuits.

Characterizing the Impact of Communication on Cellular and Collective Behavior Using a Three-Dimensional Multiscale Cellular Model.

Communication between cells enables the coordination that drives structural and functional complexity in biological systems. Both single and multicellular organisms have evolved diverse communication systems for a range of purposes, including synchronization of behavior, division of labor, and spatial organization. Synthetic systems are also increasingly being engineered to utilize cell-cell communication.

SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery.

Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, a new component of the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC), that provides an integrated framework of cloud-hosted data resources and curated workflows to enable facile prediction of SLIs. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the availability of multiple comparable prediction approaches.

Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation.

We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins.

Probabilistic boolean networks predict transcription factor targets to induce transdifferentiation.

We developed a computational approach to find the best intervention to achieve transcription factor (TF) mediated transdifferentiation. We construct probabilistic Boolean networks (PBNs) from single-cell RNA sequencing data of two different cell states to model hematopoietic transcription factors cross-talk. This was achieved by a "sampled network" approach, which enabled us to construct large networks.

Evolution of biomarker research in autoimmunity conditions for health professionals and clinical practice.

Medical abzymology has made a great contribution to the development of general autoimmunity theory: it has put the autoantibodies (Ab) as the key brick of the theory to the level of physiological functionality by providing such Ab with the ability to catalyze and mediate direct and independent cytotoxic effect on cellular and molecular targets. Natural catalytic autoantibodies (abzymes) while being a pool of canonical Abs and possessing catalytic activity belong to the new group of physiologically active substances whose features and properties are evolutionary consolidated in one functionally active biomolecule. Therefore, further studies on Ab-mediated autoAg degradation and other targeted Ab-mediated proteolysis may provide biomarkers of newer generations and thus a supplementary tool for assessing the disease progression and predicting disability of the patients and persons at risks.

Synthetic lethal kinases in Ras/p53 mutant squamous cell carcinoma.

The oncogene Ras and the tumor suppressor gene p53 are frequently co-mutated in human cancer and mutations in Ras and p53 can cooperate to generate a more malignant cell state. To discover novel druggable targets for cancers carrying co-mutations in Ras and p53, we performed arrayed, kinome focused siRNA and oncology drug phenotypic screening utilizing a set of syngeneic Ras mutant squamous cell carcinoma (SCC) cell lines that also carried co-mutations in selected p53 pathway genes. These cell lines were derived from SCCs from carcinogen-treated inbred mice which harbored germline deletions or mutations in Trp53, p19, Atm, or Prkdc.

A functional module states framework reveals transcriptional states for drug and target prediction.

Cells are complex systems in which many functions are performed by different genetically defined and encoded functional modules. To systematically understand how these modules respond to drug or genetic perturbations, we develop a functional module states framework. Using this framework, we (1) define the drug-induced transcriptional state space for breast cancer cell lines using large public gene expression datasets and reveal that the transcriptional states are associated with drug concentration and drug targets, (2) identify potential targetable vulnerabilities through integrative analysis of transcriptional states after drug treatment and gene knockdown-associated cancer dependency, and (3) use functional module states to predict transcriptional state-dependent drug sensitivity and build prediction models for drug response.

Patient-Specific Cell Communication Networks Associate With Disease Progression in Cancer.

The maintenance and function of tissues in health and disease depends on cell-cell communication. This work shows how high-level features, representing cell-cell communication, can be defined and used to associate certain signaling "axes" with clinical outcomes. We generated a scaffold of cell-cell interactions and defined a probabilistic method for creating per-patient weighted graphs based on gene expression and cell deconvolution results.

NCI Imaging Data Commons.

The National Cancer Institute (NCI) Cancer Research Data Commons (CRDC) aims to establish a national cloud-based data science infrastructure. Imaging Data Commons (IDC) is a new component of CRDC supported by the Cancer Moonshot. The goal of IDC is to enable a broad spectrum of cancer researchers, with and without imaging expertise, to easily access and explore the value of deidentified imaging data and to support integrated analyses with nonimaging data.

A CRISPR/Cas9-Engineered -Deficient Human Gastric Cancer Organoid Model Reveals Essential and Nonessential Modes of Oncogenic Transformation.

Mutations in rank among the most common molecular aberrations in human cancer. However, oncogenic consequences of mutation in human cells remain poorly defined due to lack of forward genetic models. Here, CRISPR/Cas9-mediated knockout (KO) in primary human gastric organoids induced morphologic dysplasia, tumorigenicity, and mucinous differentiation.

Discovery of driver non-coding splice-site-creating mutations in cancer.

Non-coding mutations can create splice sites, however the true extent of how such somatic non-coding mutations affect RNA splicing are largely unexplored. Here we use the MiSplice pipeline to analyze 783 cancer cases with WGS data and 9494 cases with WES data, discovering 562 non-coding mutations that lead to splicing alterations. Notably, most of these mutations create new exons.

A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma.

Background: Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue-level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment.