Biological Heterogeneity Research Articles

Associations of accelerated biological aging and metabolic heterogeneity of obesity with rheumatoid arthritis: a prospective cohort study.

To evaluate the associations between biological aging, metabolic heterogeneity of obesity, and rheumatoid arthritis (RA). This prospective cohort study analyzed 268,184 individuals from the UK Biobank. Biological age was estimated using phenotypic age (PhenoAge), Klemera-Doubal methods (KDM-BA), and telomere length. We calculated KDM-BA acceleration and PhenoAge acceleration after subtracting the effect of chronological age by regression residual. The metabolic heterogeneity of obesity can be evaluated by four BMI metabolic phenotypes, namely metabolically unhealthy normal weight (MUNW), metabolically healthy normal weight (MHNW), metabolically unhealthy overweight/obesity (MUOO), and metabolically healthy overweight/obesity (MHOO). Cox models were employed to estimate the associations between biological aging, metabolic heterogeneity of obesity, and RA risk. A total of 2842 patients experienced RA during a mean follow-up time of 12.21years. A standard deviation (SD) increase in KDM-BA acceleration and PhenoAge acceleration was associated with an increased risk of RA by 13% (hazard ratio = 1.13; 95% CI, 1.09-1.17) and 39% (HR = 1.39; 95% CI, 1.34-1.44), respectively. A SD increase in telomere length was associated with a reduced risk of RA by 5% (HR = 0.95; 95% CI, 0.91-0.98). Compared to the MHNW group, the MUOO group was associated with a 51% increase in the risk of incident RA. In the joint effect analysis, compared to the MHNW + KDM-BA younger subgroup, the HR (95% CI) for RA was 1.68 (1.48, 1.90) in the MUOO + KDM-BA older subgroup. Accelerated biological aging may heighten the susceptibility to RA, particularly in individuals with obesity or metabolic dysfunction. Key Points •Accelerated biological aging increases the risk of developing RA. •Overweight/obese people with a healthy metabolism have a higher risk of RA than those with normal weight and healthy metabolism. •The BMI metabolic phenotype has a strong modifying effect on the association between KDM-BA/PhenoAge and RA risk.

When You Get to the Fork in the Road, Take It: The Challenges in Managing Patients With Advanced Prostate Cancer.

As is the case with most solid tumors, the heterogeneity of the disease biology of prostate cancer presents clinicians managing this disease with daily challenges. However, in contrast to other common cancers such as breast, lung, and colorectal cancers, there are unique challenges in prostate cancer management, including the variety of clinicians who manage aspects of the disease (urologists, medical oncologist, radiation oncologists) and the striking absence of prospective comparative data to inform the optimal sequence of systemic therapy in patients with metastatic castration-resistant disease. The purpose of this review is to attempt to assist practicing oncologists with sorting through the myriad of prostate cancer disease subsets and the challenges in making therapeutic decisions in multiple data-free zones given the absence of level 1 comparative clinical trials in the metastatic hormone-sensitive and castration-resistant states.

Clinico-Pathological Factors and AR-LBD Mutations in Early and Late Castration-Resistant Prostate Cancer.

Prostate cancer (PCa) is not well understood because of its enormous biological heterogeneity and unreliable progression. We conducted this retrospective analysis to examine the variables predicting early and late progression to castration-resistant PCa (CRPC) for better management of this disease. This single institutional retrospective study was conducted from January 2018 to January 2022. A total of 98 consecutive men meeting with the diagnosis of CRPC as per the inclusion criteria were included in the study and were stratified in four quartiles on the basis of time to CRPC (time to castration resistance [TTCR]) development. Early CRPC (1st quartile, TTCR = 6-12 months) and late CRPC (4th quartile, TTCR = 38-120 months) were then compared on the basis of different clinical, pathological and AR-LBD sequence to find the correlation with response duration. Median time to develop castration resistance was 25 ± 26.44 months. The mean age of the patients was 66.8 ± 9.20 years and median baseline PSA was calculated 100±685.06 ng/mL respectively. Higher Gleason score (≥7-10) was found to be significantly associated with early development of CRPC (p<0.001) and lower nadir PSA was significantly indicating late CRPC progression (p<0.005). No mutations were found in androgen receptor exon-5, 6, 7 except a homozygous mutation in the 7th intronic region, which is involved in splice variants formation playing noteworthy role in CRPC development. Time for metastatic PCa to CRPC ranges from 6-120 months revealing its heterogeneous nature. Early age presentation in the clinic and high initial PSA and high grade (GS>7) at diagnosis were positively associated with early CRPC while lower nadir PSA was correlated with late CRPC progression. No remarkable genomic mutations were discovered. Therefore, more data are needed and further research is required with large no. of patients to discover the predictive prognostic biomarkers for better patients' management.

There will always be variants of uncertain significance. Analysis of VUSs.

The ACMG/AMP guidelines include five categories of which variants of uncertain significance (VUSs) have received increasing attention. Recently, Fowler and Rehm claimed that all or most VUSs could be reclassified as pathogenic or benign within few years. To test this claim, we collected validated benign, pathogenic, VUSand conflicting variants from ClinVar and LOVD and investigated differences at gene, protein, structure, and variant levels. The gene and protein features included inheritance patterns, actionability, functional categories for housekeeping, essential, complete knockout, lethality and haploinsufficient proteins, Gene Ontology annotations, and protein network properties. Structural properties included the location at secondary structural elements, intrinsically disordered regions, transmembrane regions, repeats, conservation, and accessibility. Gene features were distributions of nucleotides, their groupings, codons, and location to CpG islands. The distributions of amino acids and their groups were investigated. VUSs did not markedly differ from other variants. The only major differences were the accessibility and conservation of pathogenic variants, and reduced ratio of repeat-locating variants in VUSs. Thus, all VUSs cannot be distinguished from other types of variants. They display one form of natural biological heterogeneity. Instead of concentrating on eradicating VUSs, the community would benefit from investigating and understanding factors that contribute to phenotypic heterogeneity.

Automated Microfluidics-Assisted Hydrogel-Based Wet-Spinning for the Biofabrication of Biomimetic Engineered Myotendinous Junction.

The muscle-tendon junction (MTJ) plays a pivotal role in efficiently converting the muscular contraction into a controlled skeletal movement through the tendon. Given its complex biomechanical intricacy, the biofabrication of such tissue interface represents a significant challenge in the field of musculoskeletal tissue engineering. Herein, a novel method to produce MTJ-like hydrogel yarns using a microfluidics-assisted 3D rotary wet-spinning strategy is developed. Optimization of flow rates, rotational speed, and delivery time of bioinks enables the production of highly compartmentalized scaffolds that recapitulate the muscle, tendon, and the transient MTJ-like region. Additionally, such biofabrication parameters are validated in terms of cellular response by promoting an optimal uniaxial alignment for both muscle and tendon precursor cells. By sequentially wet-spinning C2C12 myoblasts and NIH 3T3 fibroblasts, a gradient-patterned cellular arrangement mirroring the intrinsic biological heterogeneity of the MTJ is successfully obtained. The immunofluorescence assessment further reveals the localized expression of tissue-specific markers, including myosin heavy chain and collagen type I/III, which demonstrate muscle and tenogenic tissue maturation, respectively. Remarkably, the muscle-tendon transition zone exhibits finger-like projection of the multinucleated myotubes in the tenogenic compartment, epitomizing the MTJ signature architecture.

Cluster classification of a Brazilian gastric cancer cohort reveals remarkable populational differences in normal p53 rate.

Queiroz et al. showed that the application of cluster methodology for classifying gastric cancer is suitable and efficient within a Brazilian cohort, which is known for its population heterogeneity. The study highlighted the potential utilization of this method within public health services due to its low-cost, presenting a viable means to improve the diagnosis and prognosis of gastric cancer. Our Brazilian cohort with gastric cancer has a distinct distribution between mutated and normal p53. New genetic marker-based classifications improve gastric cancer diagnosis accuracy. Machine learning integration enhances predictive value in gastric cancer diagnosis. Molecular biomarkers complement clinical decisions, advancing personalized medicine. Gastric adenocarcinoma remains an aggressive disease with a poor prognosis, as evidenced by a 5-year survival rate of approximately 31%. The histological classifications already proposed do not accurately reflect the high biological heterogeneity of this neoplasm, particularly in diverse populations, and new classification systems using genetic markers have recently been proposed. Following these newly proposed models, we aimed to assess the cluster distribution in a Brazilian cohort. Furthermore, we evaluated whether the inclusion of other clinical and histological parameters could enhance the predictive value. We used a previously described four-immunohistochemistry/EBER-ISH marker to classify a cohort of 30 Brazilian patients with gastric adenocarcinoma into five different clusters and compared the distribution with other genetically diverse populations. Furthermore, we used artificial intelligence methods to evaluate whether other clinical and pathological parameters could improve the results of the methodology. Disclosing the genetic variability between populations, we observed a more balanced distribution of the aberrant/normal p53 ratio (0.6) between patients negative for the other markers tested, unlike previous studies with Asian and North American populations. In addition, decision tree analysis reinforced the efficiency of these new classifications, as the stratification accuracy was not altered with or without additional data. Our study underscores the importance of local research in characterizing diverse populations and highlights the complementary role of molecular biomarkers in personalized medicine for gastric adenocarcinoma, enhancing diagnostic accuracy and potentially improving survival rates.

Enhancing Predictive Accuracy for Recurrence-Free Survival in Head and Neck Tumor: A Comparative Study of Weighted Fusion Radiomic Analysis.

Despite advancements in oncology, predicting recurrence-free survival (RFS) in head and neck (H&N) cancer remains challenging due to the heterogeneity of tumor biology and treatment responses. This study aims to address the research gap in the prognostic efficacy of traditional clinical predictors versus advanced radiomics features and to explore the potential of weighted fusion techniques for enhancing RFS prediction. We utilized clinical data, radiomic features from CT and PET scans, and various weighted fusion algorithms to stratify patients into low- and high-risk groups for RFS. The predictive performance of each model was evaluated using Kaplan-Meier survival analysis, and the significance of differences in RFS rates was assessed using confidence interval (CI) tests. The weighted fusion model with a 90% emphasis on PET features significantly outperformed individual modalities, yielding the highest C-index. Additionally, the incorporation of contextual information by varying peritumoral radii did not substantially improve prediction accuracy. While the clinical model and the radiomics model, individually, did not achieve statistical significance in survival differentiation, the combined feature set showed improved performance. The integration of radiomic features with clinical data through weighted fusion algorithms enhances the predictive accuracy of RFS outcomes in head and neck cancer. Our findings suggest that the utilization of multi-modal data helps in developing more reliable predictive models and underscore the potential of PET imaging in refining prognostic assessments. This study propels the discussion forward, indicating a pivotal step toward the adoption of precision medicine in cancer care.

Human neural stem cell-derived artificial organelles to improve oxidative phosphorylation

Oxidative phosphorylation (OXPHOS) in the mitochondrial inner membrane is a therapeutic target in many diseases. Neural stem cells (NSCs) show progress in improving mitochondrial dysfunction in the central nervous system (CNS). However, translating neural stem cell-based therapies to the clinic is challenged by uncontrollable biological variability or heterogeneity, hindering uniform clinical safety and efficacy evaluations. We propose a systematic top-down design based on membrane self-assembly to develop neural stem cell-derived oxidative phosphorylating artificial organelles (SAOs) for targeting the central nervous system as an alternative to NSCs. We construct human conditionally immortal clone neural stem cells (iNSCs) as parent cells and use a streamlined closed operation system to prepare neural stem cell-derived highly homogenous oxidative phosphorylating artificial organelles. These artificial organelles act as biomimetic organelles to mimic respiration chain function and perform oxidative phosphorylation, thus improving ATP synthesis deficiency and rectifying excessive mitochondrial reactive oxygen species production. Conclusively, we provide a framework for a generalizable manufacturing procedure that opens promising prospects for disease treatment.

Thermal biology diversity of bee pollinators: Taxonomic, phylogenetic, and plant community‐level correlates

AbstractCommunity‐wide assembly of plant–pollinator systems depends on an intricate combination of biotic and abiotic factors, including heterogeneity among pollinators in thermal biology and responses to abiotic factors. Studies on the thermal biology of pollinators have mostly considered only one or a few species of plants or pollinators at a time, and the possible driving role of the diversity in thermal biology of pollinator asemblages at the plant community level remains largely unexplored. More specifically, it is unknown whether diversity in the thermal biology of bees, a major pollinator group worldwide, contributes to the assembly and maintenance of diverse bee communities; broadens the spectrum of possibilities available to bee‐pollinated plants; facilitates interspecific partitioning of ecological gradients across habitats, seasons, and time of day; and/or enhance plant pollination success through complementarity effects. The objectives of this study were to assess the diversity in thermal biology of the bee assemblage that pollinates plants in a Mediterranean montane area, evaluate its taxonomic and phylogenetic underpinnings, and elucidate whether there existed seasonal, daily, between‐habitat, or floral visitation correlates of bee thermal biology which could contribute to partition ecological gradients among plant and bee species. Thermal biology parameters were obtained in the laboratory (K, intrinsic warming constant) and the field (thoracic and ambient temperature at foraging site, Tth and Tair) on individual bees of a diverse sample (N = 204 bee species) comprising most bee pollinators of the regional plant community. Species‐specific thermal biology parameters were combined with quantitative field data on bee pollinators and flower visitation for the regional community of entomophilous plants (N = 292 plant species). Results revealed that the regional bee assemblage harbored considerable diversity in thermal biology features; that such diversity was mostly taxonomically, phylogenetically, and body‐size structured; and that the broad interspecific heterogeneity in thermal biology represented in the bee community as a whole eventually translated into daily, seasonal, among‐habitat, and flower visitation patterns at the plant community level. This lends support to the hypothesis that broad diversity in thermal biology of bees can enhance opportunities for bee coexistence, spatiotemporal partitioning of floral resources, and plant pollination success.

Joint trajectory inference for single-cell genomics using deep learning with a mixture prior

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE's efficacy in integrative analyses of multiomic datasets with continuous cell population structures.

VICTOR: Validation and inspection of cell type annotation through optimal regression

Single-cell RNA sequencing provides unprecedent opportunities to explore the heterogeneity and dynamics inherent in cellular biology. An essential step in the data analysis involves the automatic annotation of cells. Despite development of numerous tools for automated cell annotation, assessing the reliability of predicted annotations remains challenging, particularly for rare and unknown cell types. Here, we introduce VICTOR: Validation and inspection of cell type annotation through optimal regression. VICTOR aims to gauge the confidence of cell annotations by an elastic-net regularized regression with optimal thresholds. We demonstrated that VICTOR performed well in identifying inaccurate annotations, surpassing existing methods in diagnostic ability across various single-cell datasets, including within-platform, cross-platform, cross-studies, and cross-omics settings.

IBCL-541 A Comprehensive Single-Cell Multi-Omic Analysis to Depict the Biological Heterogeneity of Follicular and Large B-Cell Lymphomas

IBCL-541 A Comprehensive Single-Cell Multi-Omic Analysis to Depict the Biological Heterogeneity of Follicular and Large B-Cell Lymphomas

A Comprehensive Single-Cell Multi-Omic Analysis to Depict the Biological Heterogeneity of Follicular and Large B-Cell Lymphomas

A Comprehensive Single-Cell Multi-Omic Analysis to Depict the Biological Heterogeneity of Follicular and Large B-Cell Lymphomas

A Comprehensive Single-Cell Multi-Omic Analysis to Depict the Biological Heterogeneity of Follicular and Large B-Cell Lymphomas

A Comprehensive Single-Cell Multi-Omic Analysis to Depict the Biological Heterogeneity of Follicular and Large B-Cell Lymphomas

The emerging role of melflufen and peptide-conjugates in multiple myeloma.

The past two decades have witnessed an impressive expansion in the treatment landscape of multiple myeloma, leading to significant improvements in progression-free; as well as overall survival. However, almost all patients still experience multiple relapses during their disease course, with biological and cytogenetic heterogeneity affecting response to subsequent treatments. The purpose of this review is to provide a historical background regarding the role of alkylating agents and an updated data regarding the use of peptide-drug conjugates such as melflufen for patients with multiple myeloma. The combination of daratumumab-melflufen-dexamethasone evaluated in the LIGHTHOUSE study showed a statistically significant improvement in progression-free survival compared to single-agent daratumumab (not reached vs. 4.9 months respectively; P = 0.0032), with improvement in overall response rate to 59% vs. 30% respectively; P = 0.03. There have been an interest in developing and utilizing peptide-drug conjugates such as melflufen for treatment of patients with multiple myeloma, especially in the relapsed setting given historical results with alkylating agents, the use of which has been limited by dose-related toxicities in a disease that remains largely incurable. Single agent melflufen initially showed promising results especially in specific subgroups of heavily pretreated patients before the decision to suspend all clinical trials evaluating this agent after results from the OCEAN phase 3 trial. Subsequent reported analyses especially for melflufen-based combinations appear promising and suggest a potential use of peptide-drug conjugates provided optimal patient selection, as well as identification of the best companion agent.

Brain aging patterns in a large and diverse cohort of 49,482 individuals.

Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.

Histone deacetylase inhibitor and PD‑1 blockade synergistically inhibit B‑cell lymphoma progression in mice model by promoting T‑cell infiltration and apoptosis.

B‑cell lymphoma is difficult to cure because of its biological and clinical heterogeneity, and due to native chemoresistance. Immunotherapies that overcome cancer‑induced immune evasion have been the center of recent developments in oncology. This is emphasized by the accomplishment of various agents that disrupt programmed cell death protein 1 (PD‑1)‑mediated immune suppression in diverse tumors. However, while PD‑1 blockade has been effective in numerous malignancies, a significant proportion of cancers, including B‑cell lymphoma, show certain rates of primary resistance to these therapeutic strategies. Histone deacetylase inhibitors (HDACis) have exhibited anticancer activity though suppressing cell proliferation, inducing differentiation and triggering apoptosis. The present study aimed to explore a therapeutic strategy combining a HDACi (romidepsin) and PD‑1 blockade (BMS‑1) in B‑cell lymphoma, utilizing a constructed mouse model of B‑cell lymphoma. The IC50 of the two inhibitors was confirmed by MTT assay, and their inhibitory effects were revealed to be dose‑ and time‑dependent. The data demonstrated that the combined treatment of romidepsin and BMS‑1 synergistically inhibited the growth of B‑cell lymphoma. Furthermore, it was revealed that romidepsin and BMS‑1 synergistically triggered apoptosis in mouse B‑cell lymphoma. The synergistic effect of these agents was capable of activating tumor‑infiltrating lymphocytes, particularly CD3+CD4+ and CD3+CD8+ T cells. The results of the present study underscore the potential of HDAC inhibition in conjunction with PD‑1 blockade as a novel therapeutic approach for B‑cell lymphoma, highlighting the synergistic effects of these two mechanisms in enhancing antitumor immunity.

COMSE: analysis of single-cell RNA-seq data using community detection-based feature selection

BackgroundSingle-cell RNA sequencing enables studying cells individually, yet high gene dimensions and low cell numbers challenge analysis. And only a subset of the genes detected are involved in the biological processes underlying cell-type specific functions.ResultIn this study, we present COMSE, an unsupervised feature selection framework using community detection to capture informative genes from scRNA-seq data. COMSE identified homogenous cell substates with high resolution, as demonstrated by distinguishing different cell cycle stages. Evaluations based on real and simulated scRNA-seq datasets showed COMSE outperformed methods even with high dropout rates in cell clustering assignment. We also demonstrate that by identifying communities of genes associated with batch effects, COMSE parses signals reflecting biological difference from noise arising due to differences in sequencing protocols, thereby enabling integrated analysis of scRNA-seq datasets of different sources.ConclusionsCOMSE provides an efficient unsupervised framework that selects highly informative genes in scRNA-seq data improving cell sub-states identification and cell clustering. It identifies gene subsets that reveal biological and technical heterogeneity, supporting applications like batch effect correction and pathway analysis. It also provides robust results for bulk RNA-seq data analysis.

Transport cocktails for cancer therapeutics

Beyond biological cell heterogeneity, evidenced by different resistances to therapeutics, “delivery heterogeneity” crucially limits treatment efficacy for advanced solid tumors: variations in therapeutic drug delivery to different tumor areas (perivascular, perinecrotic) leading to nonuniform drug concentrations/doses and to unsuccessful treatment (cancer cell kill). Short-range (40–80 µm), high energy (1–5 MeV) α particles successfully address the biological heterogeneity: the double-strand DNA breaks they cause make them impervious to cell resistance mechanisms. Multiresponsive nanocarriers and/or engineered antibody-drug-conjugates are elegant approaches to delivering such α-particle emitters. Delivery heterogeneity, however, remains a challenge in established (i.e., large, vascularized) tumors. Remarkably, delivery properties enabling efficacy at the cell scale (targeting selectivity, affinity, cell drug uptake) may act against spatial delivery uniformity at the tumor scale (binding-site barrier effect). We have previously demonstrated, in different mouse models, that spatial delivery uniformity, key to the effective killing of solid tumors, can be achieved utilizing combinations of different, distinct delivery carriers of the same emitter, but with different, complementary delivery properties, “leaving no cancer cell behind.” We build first principles reaction-transport models (quantitatively informed by experiments) that explain the “geographically complementary” behaviors of such carrier cocktails, and help optimally design these cocktails and their delivery protocols. Published by the American Physical Society 2024

Malignant Rhabdoid Tumor and Related Pediatric Tumors: Multimodality Imaging Review with Pathologic Correlation.

Malignant rhabdoid tumors (MRTs) are rare but lethal solid neoplasms that overwhelmingly affect infants and young children. While the central nervous system is the most common site of occurrence, tumors can develop at other sites, including the kidneys and soft tissues throughout the body. The anatomic site of involvement dictates tumor nomenclature and nosology. While the clinical and imaging manifestations of MRTs and other more common entities may overlap, there are some site-specific distinctive imaging characteristics. Irrespective of the site of occurrence, somatic and germline mutations in SMARCB1, and rarely in SMARCA4, underlie the entire spectrum of rhabdoid tumors. MRTs have a simple and remarkably stable genome but can demonstrate considerable molecular and biologic heterogeneity. Related neoplasms encompass an expanding category of phenotypically dissimilar (nonrhabdoid tumors driven by SMARC-related alterations) entities. US, CT, MRI, and fluorodeoxyglucose PET/CT or PET/MRI facilitate diagnosis, initial staging, and follow-up, thus informing therapeutic decision making. Multifocal synchronous or metachronous rhabdoid tumors occur predominantly in the context of underlying rhabdoid tumor predisposition syndromes (RTPSs). These autosomal dominant disorders are driven in most cases by pathogenic variants in SMARCB1 (RTPS type 1) and rarely by pathogenic variants in SMARCA4 (RTPS type 2). Genetic testing and counseling are imperative in RTPS. Guidelines for imaging surveillance in cases of RTPS are based on age at diagnosis. ©RSNA, 2024 Supplemental material is available for this article.