An official website of the United States government
Abstract BackgroundThe recent development of high-throughput sequencing has created a large collection of multi-omics data, which enables researchers to better investigate cancer molecular profiles and cancer taxonomy based on molecular subtypes. Integrating multi-omics data has been proven to be effective for building more precise classification models. Most current multi-omics integrative models use either an early fusion in the form of concatenation or late fusion with a separate feature extractor for each omic, which are mainly based on deep neural networks. Due to the nature of biological systems, graphs are a better structural representation of bio-medical data. Although few graph neural network (GNN) based multi-omics integrative methods have been proposed, they suffer from three common disadvantages. One is most of them use only one type of connection, either inter-omics or intra-omic connection; second, they only consider one kind of GNN layer, either graph convolution network (GCN) or graph attention network (GAT); and third, most of these methods have not been tested on a more complex classification task, such as cancer molecular subtypes. ResultsIn this study, we propose a novel end-to-end multi-omics GNN framework for accurate and robust cancer subtype classification. The proposed model utilizes multi-omics data in the form of heterogeneous multi-layer graphs, which combine both inter-omics and intra-omic connections from established biological knowledge. The proposed model incorporates learned graph features and global genome features for accurate classification. We tested the proposed model on the Cancer Genome Atlas (TCGA) Pan-cancer dataset and TCGA breast invasive carcinoma (BRCA) dataset for molecular subtype and cancer subtype classification, respectively. The proposed model shows superior performance compared to four current state-of-the-art baseline models in terms of accuracy, F1 score, precision, and recall. The comparative analysis of GAT-based models and GCN-based models reveals that GAT-based models are preferred for smaller graphs with less information and GCN-based models are preferred for larger graphs with extra information.
more »
« less
Noninvasive detection of cancer spread to lymph nodes: A review of molecular imaging principles and protocols
Identification of cancer spread to tumor‐draining lymph nodes offers critical information for guiding treatment in many cancer types. Current clinical methods of nodal staging are invasive and can have substantial negative side effects. Molecular imaging protocols have long been proposed as a less invasive means of nodal staging, having the potential to enable highly sensitive and specific evaluations. This review article summarizes the current status and future perspectives for molecular targeted nodal staging.
more »
« less
- Award ID(s):
- 1653627
- PAR ID:
- 10074791
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Journal of Surgical Oncology
- Volume:
- 118
- Issue:
- 2
- ISSN:
- 0022-4790
- Page Range / eLocation ID:
- p. 301-314
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Early detection of cancer can significantly reduce mortality and save lives. However, the current cancer diagnosis is highly dependent on costly, complex, and invasive procedures. Thus, a great deal of effort has been devoted to exploring new technologies based on liquid biopsy. Since liquid biopsy relies on detection of circulating biomarkers from biofluids, it is critical to isolate highly purified cancer‐related biomarkers, including circulating tumor cells (CTCs), cell‐free nucleic acids (cell‐free DNA and cell‐free RNA), small extracellular vesicles (exosomes), and proteins. The current clinical purification techniques are facing a number of drawbacks including low purity, long processing time, high cost, and difficulties in standardization. Here, we review a promising solution, on‐chip electrokinetic‐based methods, that have the advantage of small sample volume requirement, minimal damage to the biomarkers, rapid, and label‐free criteria. We have also discussed the existing challenges of current on‐chip electrokinetic technologies and suggested potential solutions that may be worthy of future studies.more » « less
-
Abstract Metabolic magnetic resonance imaging (MRI) using hyperpolarized (HP) pyruvate is becoming a non‐invasive technique for diagnosing, staging, and monitoring response to treatment in cancer and other diseases. The clinically established method for producing HP pyruvate, dissolution dynamic nuclear polarization, however, is rather complex and slow. Signal Amplification By Reversible Exchange (SABRE) is an ultra‐fast and low‐cost method based on fast chemical exchange. Here, for the first time, we demonstrate not only in vivo utility, but also metabolic MRI with SABRE. We present a novel routine to produce aqueous HP [1‐13C]pyruvate‐d3for injection in 6 minutes. The injected solution was sterile, non‐toxic, pH neutral and contained ≈30 mM [1‐13C]pyruvate‐d3polarized to ≈11 % (residual 250 mM methanol and 20 μM catalyst). It was obtained by rapid solvent evaporation and metal filtering, which we detail in this manuscript. This achievement makes HP pyruvate MRI available to a wide biomedical community for fast metabolic imaging of living organisms.more » « less
-
Summary Cancer is a heterogeneous disease. Finite mixture of regression (FMR)—as an important heterogeneity analysis technique when an outcome variable is present—has been extensively employed in cancer research, revealing important differences in the associations between a cancer outcome/phenotype and covariates. Cancer FMR analysis has been based on clinical, demographic, and omics variables. A relatively recent and alternative source of data comes from histopathological images. Histopathological images have been long used for cancer diagnosis and staging. Recently, it has been shown that high-dimensional histopathological image features, which are extracted using automated digital image processing pipelines, are effective for modeling cancer outcomes/phenotypes. Histopathological imaging–environment interaction analysis has been further developed to expand the scope of cancer modeling and histopathological imaging-based analysis. Motivated by the significance of cancer FMR analysis and a still strong demand for more effective methods, in this article, we take the natural next step and conduct cancer FMR analysis based on models that incorporate low-dimensional clinical/demographic/environmental variables, high-dimensional imaging features, as well as their interactions. Complementary to many of the existing studies, we develop a Bayesian approach for accommodating high dimensionality, screening out noises, identifying signals, and respecting the “main effects, interactions” variable selection hierarchy. An effective computational algorithm is developed, and simulation shows advantageous performance of the proposed approach. The analysis of The Cancer Genome Atlas data on lung squamous cell cancer leads to interesting findings different from the alternative approaches.more » « less
-
Abstract BackgroundThe biofouling marine tube worm,Hydroides elegans, is an indirect developing polychaete with significance as a model organism for questions in developmental biology and the evolution of host‐microbe interactions. However, a complete description of the life cycle from fertilization through sexual maturity remains scattered in the literature, and lacks standardization. Results and discussionHere, we present a unified staging scheme synthesizing the major morphological changes that occur during the entire life cycle of the animal. These data represent a complete record of the life cycle, and serve as a foundation for connecting molecular changes with morphology. ConclusionsThe present synthesis and associated staging scheme are especially timely as this system gains traction within research communities. Characterizing theHydroideslife cycle is essential for investigating the molecular mechanisms that drive major developmental transitions, like metamorphosis, in response to bacteria.more » « less
