1
|
Zhang H, Yu X, Ye J, Li H, Hu J, Tan Y, Fang Y, Akbay E, Yu F, Weng C, Sankaran VG, Bachoo RM, Maher E, Minna J, Zhang A, Li B. Systematic investigation of mitochondrial transfer between cancer cells and T cells at single-cell resolution. Cancer Cell 2023; 41:1788-1802.e10. [PMID: 37816332 PMCID: PMC10568073 DOI: 10.1016/j.ccell.2023.09.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/27/2023] [Accepted: 09/05/2023] [Indexed: 10/12/2023]
Abstract
Mitochondria (MT) participate in most metabolic activities of mammalian cells. A near-unidirectional mitochondrial transfer from T cells to cancer cells was recently observed to "metabolically empower" cancer cells while "depleting immune cells," providing new insights into tumor-T cell interaction and immune evasion. Here, we leverage single-cell RNA-seq technology and introduce MERCI, a statistical deconvolution method for tracing and quantifying mitochondrial trafficking between cancer and T cells. Through rigorous benchmarking and validation, MERCI accurately predicts the recipient cells and their relative mitochondrial compositions. Application of MERCI to human cancer samples identifies a reproducible MT transfer phenotype, with its signature genes involved in cytoskeleton remodeling, energy production, and TNF-α signaling pathways. Moreover, MT transfer is associated with increased cell cycle activity and poor clinical outcome across different cancer types. In summary, MERCI enables systematic investigation of an understudied aspect of tumor-T cell interactions that may lead to the development of therapeutic opportunities.
Collapse
Affiliation(s)
- Hongyi Zhang
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA; Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Xuexin Yu
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA; Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jianfeng Ye
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Huiyu Li
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Jing Hu
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA; Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yuhao Tan
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Yan Fang
- Department of Molecular Biology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Esra Akbay
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Fulong Yu
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Chen Weng
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Vijay G Sankaran
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Robert M Bachoo
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Elizabeth Maher
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - John Minna
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Anli Zhang
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Bo Li
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA; Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| |
Collapse
|
2
|
El Aziz Ahmed EA, Ibrahim RA, Abdelsalam AK. A Comparative Analysis for Machine Learning-based Short-Term Load Forecasting Techniques. 2023 IEEE 6TH INTERNATIONAL ELECTRICAL AND ENERGY CONFERENCE (CIEEC) 2023. [DOI: 10.1109/cieec58067.2023.10165934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
|
3
|
Ma H, Ding F, Wang Y. A novel multi-innovation gradient support vector machine regression method. ISA TRANSACTIONS 2022; 130:343-359. [PMID: 35354538 DOI: 10.1016/j.isatra.2022.03.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 03/06/2022] [Accepted: 03/06/2022] [Indexed: 06/14/2023]
Abstract
For the regression problem of support vector machine, the solution processes of the most existing methods use offline datasets, which cannot be realized online. For this problem, this paper presents a new online approach to identify these unknown parameters contained in the support vector machine. A new cost function is constructed by substituting the error term into the standard cost function, which is different from the standard support vector machine, and the gradient descent approach is then used to minimize the newly created loss function, thus proposing a stochastic gradient support vector machine algorithm to estimate the unknown parameters based on the recursive identification methods. Furthermore, to advance the property of the stochastic gradient support vector machine algorithm, a moving data window is used to widen the scalar information into a fixed-length innovation vector, thereby increasing the amount of information used in the parameter estimation based on the multi-innovation identification theory. In addition, the forgetting factor is brought into the proposed algorithms, and the corresponding forgetting factor recursive algorithms are derived. These methods are recursive identification methods, which may be implemented online and are more efficient in terms of computing. Finally, utilizing the MatLab platform, the validity and usefulness of the explored methodologies are proven using several numerical simulation examples.
Collapse
Affiliation(s)
- Hao Ma
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China.
| | - Feng Ding
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China; School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, PR China.
| | - Yan Wang
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China.
| |
Collapse
|
6
|
Support vector regression-based QSAR models for prediction of antioxidant activity of phenolic compounds. Sci Rep 2021; 11:8806. [PMID: 33888843 PMCID: PMC8062522 DOI: 10.1038/s41598-021-88341-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 04/12/2021] [Indexed: 12/15/2022] Open
Abstract
The Support vector regression (SVR) was used to investigate quantitative structure-activity relationships (QSAR) of 75 phenolic compounds with Trolox-equivalent antioxidant capacity (TEAC). Geometric structures were optimized at the EF level of the MOPAC software program. Using Pearson correlation coefficient analysis, four molecular descriptors [n(OH), Cosmo Area (CA), Core-Core Repulsion (CCR) and Final Heat of Formation (FHF)] were selected as independent variables. The QSAR model was developed from the training set consisting of 57 compounds and then used the leave-one-out cross-validation (LOOCV) correlation coefficient to evaluate the prediction ability of the QSAR model. Used Artificial neural network (ANN) and multiple linear regression (MLR) for comparing. The RMSE (root mean square error) values of LOOCV in SVR, ANN and MLR models were 0.44, 0.46 and 0.54. The RMSE values of prediction of external 18 compounds were 0.41, 0.39 and 0.54 for SVR, ANN and MLR models, respectively. The obtained result indicated that the SVR models exhibited excellent predicting performance and competent for predicting the TEAC of phenolic compounds.
Collapse
|
7
|
Ye J, Yang Z, Li Z. Quadratic hyper-surface kernel-free least squares support vector regression. INTELL DATA ANAL 2021. [DOI: 10.3233/ida-205094] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We present a novel kernel-free regressor, called quadratic hyper-surface kernel-free least squares support vector regression (QLSSVR), for some regression problems. The task of this approach is to find a quadratic function as the regression function, which is obtained by solving a quadratic programming problem with the equality constraints. Basically, the new model just needs to solve a system of linear equations to achieve the optimal solution instead of solving a quadratic programming problem. Therefore, compared with the standard support vector regression, our approach is much efficient due to kernel-free and solving a set of linear equations. Numerical results illustrate that our approach has better performance than other existing regression approaches in terms of regression criterion and CPU time.
Collapse
Affiliation(s)
- Junyou Ye
- College of Mathematics and Systems Science, Xinjiang University, Urumuqi, China
| | - Zhixia Yang
- College of Mathematics and Systems Science, Xinjiang University, Urumuqi, China
- Institute of Mathematics and Physics, Xinjiang University, Urumqi, China
| | - Zhilin Li
- Department of Mathematics, North Carolina State University, Raleigh, NC, USA
| |
Collapse
|
9
|
Wang L, Ma Y, Chang X, Gao C, Qu Q, Chen X. Projection wavelet weighted twin support vector regression for OFDM system channel estimation. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09853-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
AbstractIn this paper, an efficient projection wavelet weighted twin support vector regression (PWWTSVR) based orthogonal frequency division multiplexing system (OFDM) system channel estimation algorithm is proposed. Most Channel estimation algorithms for OFDM systems are based on the linear assumption of channel model. In the proposed algorithm, the OFDM system channel is consumed to be nonlinear and fading in both time and frequency domains. The PWWTSVR utilizes pilot signals to estimate response of nonlinear wireless channel, which is the main work area of SVR. Projection axis in optimal objective function of PWWRSVR is sought to minimize the variance of the projected points due to the utilization of a priori information of training data. Different from traditional support vector regression algorithm, training samples in different positions in the proposed PWWTSVR model are given different penalty weights determined by the wavelet transform. The weights are applied to both the quadratic empirical risk term and the first-degree empirical risk term to reduce the influence of outliers. The final regressor can avoid the overfitting problem to a certain extent and yield great generalization ability for channel estimation. The results of numerical experiments show that the propose algorithm has better performance compared to the conventional pilot-aided channel estimation methods.
Collapse
|