Deep Genomic Signature for early metastasis prediction in prostate cancer
Open accessAbstract
For prostate cancer patients, timing and intensity of therapy are adjusted based on their prognosis. Clinical and pathological factors, and recently, gene expression-based signatures have been shown to predict metastatic prostate cancer. Previous studies used labelled datasets, i.e. those with information on the metastasis outcome, to discover gene signatures to predict metastasis. Due to steady progression of prostate cancer, datasets for this cancer have a limited number of labelled samples but more unlabelled samples. In addition to this issue, the high dimensionality of the gene expression data also poses a significant challenge to train a classifier and predict metastasis accurately. In this study, we aim to boost the prediction accuracy by utilizing both labelled and unlabelled datasets together. We propose Deep Genomic Signature (DGS), a method based on Denoising Auto-Encoders (DAEs) and transfer learning. DGS has the following steps: first, we train a DAE on a large unlabelled gene expression dataset to extract the most salient features of its samples. Then, we train another DAE on a small labelled dataset for a similar purpose. Since the labelled dataset is small, we employ a transfer learning approach and use the parameters learned from the first DAE in the second one. This approach enables us to train a large DAE on a small dataset. After training the second DAE, we obtain the list of genes with high weights by applying a standard deviation filter on the transferred and learned weights. Finally, we train an elastic net logistic regression model on the expression of the selected genes to predict metastasis. Because of the elastic net regularization, some of the selected genes have non-zero coefficients in the classifier which we consider as the DGS gene signature for metastasis. We apply DGS to six labelled and one large unlabelled prostate cancer datasets. Results on five validation datasets indicate that DGS outperforms state-of-the-art gene signatures (obtained from only labelled datasets) in terms of prediction accuracy. Survival analyses demonstrate the potential clinical utility of our gene signature that adds novel prognostic information to the well-established clinical factors and the state-of-the-art gene signatures. Finally, pathway analysis reveals that the DGS gene signature captures the hallmarks of prostate cancer metastasis. These results suggest that our method helps to identify a robust gene signature that may improve patient management.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Stacked Denoising Autoencoder allows discovery of gene interaction patterns and specific gene expression patterns by extracting features from RNA-Seq data.
“the dimension of RNA-Seq data can be reduced by techniques, such as Stacked Denoising Autoencoder (SDAE), which allows for the discovery of gene interaction patterns [154] and specific gene expression patterns [155] by extracting features from RNA-Seq data by a supervised learning classification model.”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [155]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649 · see citing works
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works
(2010) · Cited by 14,134 · see citing works
The Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807 · see citing works
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697 · see citing works
(2016) · Cited by 11,679 · see citing works
(2010) · Cited by 8,261 · see citing works
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works
Diagnosis of multiple cancer types by shrunken centroids of gene expression
Tibshirani, R. J., Hastie, T. J., Narasimhan, B., & Chu, G. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.082099299 · Cited by 2,938 · see citing works
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873 · see citing works
A molecular signature of metastasis in primary solid tumors
Ramaswamy, S., Ross, K. N., Lander, E. S., & Golub, T. R. (2002) · Nature Genetics · doi:10.1038/ng1060 · Cited by 2,448 · see citing works
Predicting effects of noncoding variants with deep learning–based sequence model
Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338 · see citing works
Actin, a Central Player in Cell Shape and Movement
Pollard, T. D., & Cooper, J. A. (2009) · Science · doi:10.1126/science.1175862 · Cited by 2,094 · see citing works
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710 · see citing works
Deep learning for computational biology
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473 · see citing works
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
Rifai, S., Vincent, P., Muller, X., Glorot, X., & Bengio, Y. (2011) · Cited by 1,189 · see citing works
Deep Learning of Representations for Unsupervised and Transfer Learning.
Bengio, Y. (2011) · Cited by 885 · see citing works
Cuzick, J. M., Swanson, G. P., Fisher, G., Brothman, A. R., Berney, D. M., Reid, J., Mesher, D., Speights, V. O., Stankiewicz, E., Foster, C. S., et al. (2011) · The Lancet Oncology · doi:10.1016/s1470-2045(10)70295-3 · Cited by 792 · see citing works
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
Oliver, A., Odena, A., Raffel, C., Cubuk, E. D., & Goodfellow, I. J. (2018) · arXiv · doi:10.48550/arxiv.1804.09170 · Cited by 772 · see citing works
Erho, N., Crisan, A., Vergara, I. A., Mitra, A. P., Ghadessi, M., Buerki, C., Bergstralh, E. J., Kollmeyer, T. M., Fink, S. R., Haddad, Z., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066855 · Cited by 646 · see citing works
Self-citationRegulation of cancer cell motility through actin reorganization
Yamazaki, D., Kurisu, S., & Takenawa, T. (2005) · Cancer Science · doi:10.1111/j.1349-7006.2005.00062.x · Cited by 614 · see citing works
Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., & Zhavoronkov, A. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00346 · Cited by 587 · see citing works
Gene Expression Signature to Improve Prognosis Prediction of Stage II and III Colorectal Cancer
Salazar, R., Roepman, P., Capellá, G., Moreno, V. R., Simón, I., Dreezen, C., López‐Doriga, A., Santos, C., Marijnen, C. A. M., Westerga, J., et al. (2010) · Journal of Clinical Oncology · doi:10.1200/jco.2010.30.1077 · Cited by 538 · see citing works
Penalized Survival Models and Frailty
Therneau, T. M., Grambsch, P. M., & Pankratz, V. S. (2003) · Journal of Computational and Graphical Statistics · doi:10.1198/1061860031365 · Cited by 499 · see citing works
Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives
Bengio, Y., Courville, A. C., & Vincent, P. (2012) · Cited by 455 · see citing works
Gene expression inference with deep learning
Chen, Y., Li, Y., Narayan, R., Subramanian, A., & Xie, X. S. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw074 · Cited by 431 · see citing works
The reactive stroma microenvironment and prostate cancer progression
Barron, D. A., & Rowley, D. R. (2012) · Endocrine Related Cancer · doi:10.1530/erc-12-0085 · Cited by 331 · see citing works
A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION
Danaee, P., Ghaeini, R., & Hendrix, D. A. (2016) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813207813_0022 · Cited by 305 · see citing works
Prensner, J. R., Zhao, S. G., Erho, N., Schipper, M. J., Iyer, M. K., Dhanasekaran, S. M., Magi‐Galluzzi, C., Mehra, R., Sahu, A., Siddiqui, J., et al. (2014) · The Lancet Oncology · doi:10.1016/s1470-2045(14)71113-1 · Cited by 264 · see citing works
Self-citationWay, G. P., & Greene, C. S. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0008 · Cited by 256 · see citing works
Spratt, D. E., Yousefi, K., Deheshi, S., Ross, A. E., Den, R. B., Schaeffer, E. M., Trock, B. J., Zhang, J., Glass, A. G., Dicker, A. P., et al. (2017) · Journal of Clinical Oncology · doi:10.1200/jco.2016.70.2811 · Cited by 248 · see citing works
Self-citationRoss, A. E., Johnson, M. H., Yousefi, K., Davicioni, E., Netto, G. J., Marchionni, L., Fedor, H. L., Glavaris, S. A., Choeurng, V., Buerki, C., et al. (2015) · European Urology · doi:10.1016/j.eururo.2015.05.042 · Cited by 244 · see citing works
Self-citationA single-sample microarray normalization method to facilitate personalized-medicine workflows
Piccolo, S., Sun, Y., Campbell, J. D., Lenburg, M. E., Bild, A. H., & Johnson, W. E. (2012) · Genomics · doi:10.1016/j.ygeno.2012.08.003 · Cited by 244 · see citing works
Klein, E. A., Yousefi, K., Haddad, Z., Choeurng, V., Buerki, C., Stephenson, A. J., Li, J., Kattan, M. W., Magi‐Galluzzi, C., & Davicioni, E. (2014) · European Urology · doi:10.1016/j.eururo.2014.10.036 · Cited by 186 · see citing works
Self-citationNakagawa, T., Kollmeyer, T. M., Morlan, B. W., Anderson, S. K., Bergstralh, E. J., Davis, B. J., Asmann, Y. W., Klee, G. G., Ballman, K. V., & Jenkins, R. B. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002318 · Cited by 179 · see citing works
Rørth, P., Szabó, K., & Texidó, G. (2000) · Molecular Cell · doi:10.1016/s1097-2765(05)00008-0 · Cited by 165 · see citing works
mRNA Expression Signature of Gleason Grade Predicts Lethal Prostate Cancer
Penney, K. L., Sinnott, J. A., Fall, K., Yudi Pawitan, Y., Hoshida, Y., Kraft, P., Rider, J. R., Fiorentino, M., Perner, S., Finn, S. P., et al. (2011) · Journal of Clinical Oncology · doi:10.1200/jco.2010.32.6421 · Cited by 157 · see citing works
Tan, J., Ung, M. H., Cheng, C., & Greene, C. S. (2014) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814644730_0014 · Cited by 152 · see citing works
Den, R. B., Feng, F. Y.-C., Showalter, T. N., Mishra, M. V., Trabulsi, E. J., Lallas, C. D., Gomella, L. G., Kelly, W. K., Birbe, R., McCue, P. A., et al. (2014) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2014.04.052 · Cited by 149 · see citing works
Self-citationTan, J., Hammond, J. H., Hogan, D. A., & Greene, C. S. (2016) · mSystems · doi:10.1128/msystems.00025-15 · Cited by 124 · see citing works
Tyekucheva, S., Bowden, M., Bango, C., Giunchi, F., Huang, Y., Zhou, C. W., Bondi, A., Lis, R. T., Hemelrijck, M. V., Andrén, O., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00460-4 · Cited by 120 · see citing works
Remote electronic examinations: student experiences
Thomas, P., Price, B. A., Paine, C. B., et al. (2002) · British Journal of Educational Technology · doi:10.1111/1467-8535.00290 · Cited by 105 · see citing works
Randomized smoothing for (parallel) stochastic optimization
Duchi, J. C., Bartlett, P. L., & Wainwright, M. J. (2012) · doi:10.1109/cdc.2012.6426698 · Cited by 97 · see citing works
Nonparametric Convergence Assessment for MCMC Model Selection
Brooks, S. P., Giudici, P., & Philippe, A. (2003) · Journal of Computational and Graphical Statistics · doi:10.1198/1061860031347 · Cited by 89 · see citing works
Tan, J., Doing, G., Lewis, K. A., Price, C. E., Chen, K., Cady, K. C., Perchuk, B., Laub, M. T., Hogan, D. A., & Greene, C. S. (2017) · Cell Systems · doi:10.1016/j.cels.2017.06.003 · Cited by 89 · see citing works
Stromal Gene Expression is Predictive for Metastatic Primary Prostate Cancer
Mo, F., Lin, D., Takhar, M. M., Ramnarine, V. R., Dong, X., Bell, R. H., Volik, S. V., Wang, K., Xue, H., Wang, Y., et al. (2017) · European Urology · doi:10.1016/j.eururo.2017.02.038 · Cited by 76 · see citing works
Self-citationKarnes, R. J., Choeurng, V., Ross, A. E., et al. (2017) · European Urology · doi:10.1016/j.eururo.2017.03.036 · Cited by 70 · see citing works
Metwally, A. A., Yu, P. S., Reiman, D. J., Dai, Y. D., Finn, P. W., & Perkins, D. L. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006693 · Cited by 59 · see citing works
Chen, W.-Y., Tsai, Y.-C., Siu, M. K., Yeh, H.-L., Chen, C.-L., Yin, J., Huang, J., & Liu, Y.-N. (2017) · Oncogene · doi:10.1038/onc.2017.226 · Cited by 46 · see citing works
Self-citationChemotherapy for small-cell lung cancer
Kalemkerian, G. P. (2013) · The Lancet Oncology · doi:10.1016/s1470-2045(13)70555-2 · Cited by 43 · see citing works
Modern Methods for Delineating Metagenomic Complexity
Afshinnekoo, E., Meydan, C., Chowdhury, S., et al. (2015) · Cell Systems · doi:10.1016/j.cels.2015.07.007 · Cited by 26 · see citing works
Magnetic Resonance Imaging Before Prostate Biopsy: Time to Talk
Padhani, A. R., Petralia, G., & Sanguedolce, F. (2015) · European Urology · doi:10.1016/j.eururo.2015.07.036 · Cited by 19 · see citing works
Thomashauer, R. J. (2002) · Nature Genetics · doi:10.1038/ng0103-1 · Cited by 13 · see citing works
Justman, Q. A. (2017) · Cell Systems · doi:10.1016/j.cels.2017.07.001 · Cited by 11 · see citing works
Cetuximab in NSCLC: another trial needed
Perrone, F. (2010) · The Lancet Oncology · doi:10.1016/s1470-2045(10)70289-8 · Cited by 6 · see citing works
Trosman, J. R., Gerhart, J. I., Garcia, S. F., et al. (2017) · Journal of Clinical Oncology · doi:10.1200/jco.2017.35.8_suppl.27 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Wanling Xie, Meredith M. Regan, Marc Buyse, Susan Halabi, Philip W. Kantoff, Oliver Sartor, Howard Soule, Noel W. Clarke, Laurence Collette, James J. Dignam, Karim Fizazi, Wendy R. Paruleker, Howard M. Sandler, Matthew R. Sydes, Bertrand Tombal, Scott G. Williams, Christopher J. Sweeney, and on behalf of the ICECaP Working Group. Metastasis-free survival is a strong surrogate of overall survival in localized prostate cancer. Journal of Clinical Oncology, 35(27):3097-3104, 2017. PMID: 28796587.
- Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? In Advances in neural information processing systems, pages 3320-3328, 2014.
Cited by
Sorted by citations
Wu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193 · see citing works
- Authors
- Sharifi-Noghabi, H., Liu, Y., Erho, N., Shrestha, R., Alshalalfa, M., Davicioni, E., Collins, C. C., & Ester, M.
- Affiliations
- Simon Fraser University (CA)
- University of British Columbia (CA)
- Corresponding author
- Ester, Martin
- ORCID
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2018-03-04
- OpenAlex
- W2790387744
- Cited by
- 11
- References
- 56
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 2 · 2021: 2 · 2020: 2 · 2019: 2 · 2018: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nd
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- Funders
- Cleveland Clinic
- Johns Hopkins University
- Thomas Jefferson University
- Terry Fox Foundation (201012TFF)
- Canadian Institutes of Health Research (153073, 153073_1)
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Prostate Cancer Treatment and Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- prostate cancer, metastasis prediction, gene expression signatures, denoising autoencoder, transfer learning, unlabeled data, elastic net logistic regression, feature selection, prognostic factors, survival analysis, pathway analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
