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Qiu Y, Liu L, Yang H, Chen H, Deng Q, Xiao D, Lin Y, Zhu C, Li W, Shao D, Jiang W, Wu K, He J. Integrating Histologic and Genomic Characteristics to Predict Tumor Mutation Burden of Early-Stage Non-Small-Cell Lung Cancer. Front Oncol 2021; 10:608989. [PMID: 33996530 PMCID: PMC8121003 DOI: 10.3389/fonc.2020.608989] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 12/29/2020] [Indexed: 12/15/2022] Open
Abstract
Tumor mutation burden (TMB) serves as an effective biomarker predicting efficacy of mono-immunotherapy for non-small cell lung cancer (NSCLC). Establishing a precise TMB predicting model is essential to select which populations are likely to respond to immunotherapy or prognosis and to maximize the benefits of treatment. In this study, available Formalin-fixed paraffin embedded tumor tissues were collected from 499 patients with NSCLC. Targeted sequencing of 636 cancer related genes was performed, and TMB was calculated. Distribution of TMB was significantly (p < 0.001) correlated with sex, clinical features (pathological/histological subtype, pathological stage, lymph node metastasis, and lympho-vascular invasion). It was also significantly (p < 0.001) associated with mutations in genes like TP53, EGFR, PIK3CA, KRAS, EPHA3, TSHZ3, FAT3, NAV3, KEAP1, NFE2L2, PTPRD, LRRK2, STK11, NF1, KMT2D, and GRIN2A. No significant correlations were found between TMB and age, neuro-invasion (p = 0.125), and tumor location (p = 0.696). Patients with KRAS p.G12 mutations and FAT3 missense mutations were associated (p < 0.001) with TMB. TP53 mutations also influence TMB distribution (P < 0.001). TMB was reversely related to EGFR mutations (P < 0.001) but did not differ by mutation types. According to multivariate logistic regression model, genomic parameters could effectively construct model predicting TMB, which may be improved by introducing clinical information. Our study demonstrates that genomic together with clinical features yielded a better reliable model predicting TMB-high status. A simplified model consisting of less than 20 genes and couples of clinical parameters were sought to be useful to provide TMB status with less cost and waiting time.
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Affiliation(s)
- Yuan Qiu
- National Clinical Research Center of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- State Key Laboratory of Respiratory Diseases, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Liping Liu
- National Clinical Research Center of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- State Key Laboratory of Respiratory Diseases, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- The Translational Medicine Laboratory, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Haihong Yang
- National Clinical Research Center of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- State Key Laboratory of Respiratory Diseases, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Hanzhang Chen
- National Clinical Research Center of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- State Key Laboratory of Respiratory Diseases, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qiuhua Deng
- National Clinical Research Center of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- State Key Laboratory of Respiratory Diseases, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Dakai Xiao
- National Clinical Research Center of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- State Key Laboratory of Respiratory Diseases, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yongping Lin
- National Clinical Research Center of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- State Key Laboratory of Respiratory Diseases, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | | | - Weiwei Li
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
| | - Di Shao
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
| | | | - Kui Wu
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
- BGI-Shenzhen, Shenzhen, China
- China National GeneBank, BGI-Shenzhen, Shenzhen, China
| | - Jianxing He
- National Clinical Research Center of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- State Key Laboratory of Respiratory Diseases, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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Lim HG, Lee OJ, Shung KK, Kim JT, Kim HH. Classification of Breast Cancer Cells Using the Integration of High-Frequency Single-Beam Acoustic Tweezers and Convolutional Neural Networks. Cancers (Basel) 2020; 12:cancers12051212. [PMID: 32408544 PMCID: PMC7281163 DOI: 10.3390/cancers12051212] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 05/06/2020] [Accepted: 05/09/2020] [Indexed: 12/20/2022] Open
Abstract
Single-beam acoustic tweezers (SBAT) is a widely used trapping technique to manipulate microscopic particles or cells. Recently, the characterization of a single cancer cell using high-frequency (>30 MHz) SBAT has been reported to determine its invasiveness and metastatic potential. Investigation of cell elasticity and invasiveness is based on the deformability of cells under SBAT’s radiation forces, and in general, more physically deformed cells exhibit higher levels of invasiveness and therefore higher metastatic potential. However, previous imaging analysis to determine substantial differences in cell deformation, where the SBAT is turned ON or OFF, relies on the subjective observation that may vary and requires follow-up evaluations from experts. In this study, we propose an automatic and reliable cancer cell classification method based on SBAT and a convolutional neural network (CNN), which provides objective and accurate quantitative measurement results. We used a custom-designed 50 MHz SBAT transducer to obtain a series of images of deformed human breast cancer cells. CNN-based classification methods with data augmentation applied to collected images determined and validated the metastatic potential of cancer cells. As a result, with the selected optimizers, precision, and recall of the model were found to be greater than 0.95, which highly validates the classification performance of our integrated method. CNN-guided cancer cell deformation analysis using SBAT may be a promising alternative to current histological image analysis, and this pretrained model will significantly reduce the evaluation time for a larger population of cells.
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Affiliation(s)
- Hae Gyun Lim
- Future IT Innovation Laboratory, Pohang University of Science and Technology, Pohang 37673, Korea; (H.G.L.); (O.-J.L.)
| | - O-Joun Lee
- Future IT Innovation Laboratory, Pohang University of Science and Technology, Pohang 37673, Korea; (H.G.L.); (O.-J.L.)
| | - K. Kirk Shung
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA;
| | - Jin-Taek Kim
- Department of Creative IT Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
- Correspondence: (J.-T.K.); (H.H.K.); Tel.: +82-54-279-8853 (J.-T.K.); +82-54-279-8864 (H.H.K.)
| | - Hyung Ham Kim
- Department of Creative IT Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
- Correspondence: (J.-T.K.); (H.H.K.); Tel.: +82-54-279-8853 (J.-T.K.); +82-54-279-8864 (H.H.K.)
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Moitra D, Mandal RK. Prediction of Non-small Cell Lung Cancer Histology by a Deep Ensemble of Convolutional and Bidirectional Recurrent Neural Network. J Digit Imaging 2020; 33:895-902. [PMID: 32333132 DOI: 10.1007/s10278-020-00337-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Histology subtype prediction is a major task for grading non-small cell lung cancer (NSCLC) tumors. Invasive methods such as biopsy often lack in tumor sample, and as a result radiologists or oncologists find it difficult to detect proper histology of NSCLC tumors. The non-invasive methods such as machine learning may play a useful role to predict NSCLC histology by using medical image biomarkers. Few attempts have so far been made to predict NSCLC histology by considering all the major subtypes. The present study aimed to develop a more accurate deep learning model by clubbing convolutional and bidirectional recurrent neural networks. The NSCLC Radiogenomics dataset having 211 subjects was used in the study. Ten best models found during experimentation were averaged to form an ensemble. The model ensemble was executed with 10-fold repeated stratified cross-validation, and the results got were tested with metrics like accuracy, recall, precision, F1-score, Cohen's kappa, and ROC-AUC score. The accuracy of the ensemble model showed considerable improvement over the best model found with the single model. The proposed model may help significantly in the automated prognosis of NSCLC and other types of cancers.
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