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SOSTOS

Novel AI-based discoveries of biomarkers and new drug options for significantly life-prolonging outcomes

Our Cutting-Edge Technology
Cancer Treatment Optimization Solutions (CATOS)

Sostos LLC is a provider of cutting-edge artificial intelligence (AI)/big data technologies and health analytics services for precision medicine. CATOS products enable personalized cancer care throughout the patient journey from cancer screening, liquid biopsies, diagnosis, prognosis, treatment selection, to new drug options for patients with failed prior therapy. Our AI technology/software is proven effective in satellite system reliability assurance and precision oncology. Using our AI technology, our developed 7-gene lung cancer assay (CATOS-LU) for prognosis and prediction of therapeutic benefits has been validated in more than 1,600 patients including randomized phase III clinical trials. Using our AI/big data software, we have identified 16 therapeutic compounds as new or repositioning drugs for improving cancer patient survival outcomes. CATOS software can effectively improve cancer theranostics by efficiently analyzing multi-omics profiles of patient single cells, liquid biopsies, biopsies, and bulk tumors. Our technologies have been funded by the NSF, NASA, NIH, CDC, and CPSC. 

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Sponsors

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Academic Publications

Recent publications

  • Ye, Q., Raese, R. A., Luo, D., Feng, J., Xin, W., Dong, C., ... & Guo, N. L. (2023). MicroRNA-Based Discovery of Biomarkers, Therapeutic Targets, and Repositioning Drugs for Breast Cancer. Cells, 12(14), 1917.

  • Ye, Q., Wang, J., Ducatman, B., Raese, R. A., Rogers, J. L., Wan, Y. W., ... & Guo, N. L. (2023). Expression-Based Diagnosis, Treatment Selection, and Drug Development for Breast Cancer. International Journal of Molecular Sciences, 24(13), 10561.

  • Ye, Q., Raese, R., Luo, D., Cao, S., Wan, Y. W., Qian, Y., & Guo, N. L. (2023). MicroRNA, mRNA, and Proteomics Biomarkers and Therapeutic Targets for Improving Lung Cancer Treatment Outcomes. Cancers, 15(8), 2294.

  • Ye, Q., & Guo, N. L. (2022). Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets. Cells, 12(1), 101.

  • Ye, Q., & Guo, N. L. (2022). Hub Genes in Non-Small Cell Lung Cancer Regulatory Networks. Biomolecules, 12(12), 1782.

  • Ye, Q., Hickey, J., Summers, K., Falatovich, B., Gencheva, M., Eubank, T. D., ... & Guo, N. L. (2022). Multi-Omics Immune Interaction Networks in Lung Cancer Tumorigenesis, Proliferation, and Survival. International Journal of Molecular Sciences, 23(23), 14978.

  • Majumder, N., Deepak, V., Hadique, S., Aesoph, D., Velayutham, M., Ye, Q., ... & Hussain, S. (2022). Redox Imbalance in COVID-19 Pathophysiology. Redox Biology, 56, 102465.

  • Ye, Q., & Guo, N. L. (2022). Single B Cell Gene Co-Expression Networks Implicated in Prognosis, Proliferation, and Therapeutic Responses in Non-Small Cell Lung Cancer Bulk Tumors. Cancers, 14(13), 3123.

  • Ye, Q., Falatovich, B., Singh, S., Ivanov, A. V., Eubank, T. D., & Guo, N. L. (2021). A Multi-Omics Network of a Seven-Gene Prognostic Signature for Non-Small Cell Lung Cancer. International Journal of Molecular Sciences, 23(1), 219.

  • Ye, Q., Singh, S., Qian, P. R., & Guo, N. L. (2021). Immune-Omics Networks of CD27, PD1, and PDL1 in Non-Small Cell Lung Cancer. Cancers, 13(17), 4296.

  • Ye, Q., Putila, J., Raese, R., Dong, C., Qian, Y., Dowlati, A., & Guo, N. L. (2021). Identification of Prognostic and Chemopredictive MicroRNAs for Non-Small-Cell Lung Cancer by Integrating SEER-Medicare Data. International Journal of Molecular Sciences, 22(14), 7658.

  • Ye, Q., Mohamed, R., Dakhlallah, D., Gencheva, M., Hu, G., Pearce, M. C., ... & Guo, N. L. (2021). Molecular Analysis of ZNF71 KRAB in Non-Small-Cell Lung Cancer. International Journal of Molecular Sciences, 22(7), 3752.

  • Guo, N. L., Bello, D., Ye, Q., Tagett, R., Chanetsa, L., Singh, D., ... & Demokritou, P. (2020). Pilot Deep RNA Sequencing of Worker Blood Samples from Singapore Printing Industry for Occupational Risk Assessment. NanoImpact, 19, 100248.

  • Snyder-Talkington, B. N., Dong, C., Singh, S., Raese, R., Qian, Y., Porter, D. W., ... & Guo, N. L. (2019). Multi-Walled Carbon Nanotube-Induced Gene Expression Biomarkers for Medical and Occupational Surveillance. International Journal of Molecular Sciences, 20(11), 2635.

  • Guo, N. L., Poh, T. Y., Pirela, S., Farcas, M. T., Chotirmall, S. H., Tham, W. K., ... & Demokritou, P. (2019). Integrated Transcriptomics, Metabolomics, and Lipidomics Profiling in Rat Lung, Blood, and Serum for Assessment of Laser Printer-Emitted Nanoparticle Inhalation Exposure-Induced Disease Risks. International Journal of Molecular Sciences, 20(24), 6348.

  • Snyder-Talkington, B. N., Dong, C., Castranova, V., Qian, Y., & Guo, N. L. (2019). Differential Gene Regulation in Human Small Airway Epithelial Cells Grown in Monoculture versus Coculture with Human Microvascular Endothelial Cells Following Multiwalled Carbon Nanotube Exposure. Toxicology Reports, 6, 482-488.

  • Guo, N. L., Dowlati, A., Raese, R. A., Dong, C., Chen, G., Beer, D. G., ... & Qian, Y. (2018). A Predictive 7-Gene Assay and Prognostic Protein Biomarkers for Non-Small Cell Lung Cancer. EBioMedicine, 32, 102-110.

Our AI technology for satellite system reliability assurance

  • Ma, Y., Guo, L., & Cukic, B. (2007). A Statistical Framework for the Prediction of Fault-Proneness. In Advances in Machine Learning Applications in Software Engineering (pp. 237-263). IGI Global.

  • Guo, L., Mukhopadhyay, S., & Cukic, B. (2004, June). Does Your Result Checker Really Check?. In International Conference on Dependable Systems and Networks, 2004 (pp. 399-404). IEEE.

  • Guo, L., Cukic, B., & Singh, H. (2003, October). Predicting Fault Prone Modules by the Dempster-Shafer Belief Networks. In 18th IEEE International Conference on Automated Software Engineering, 2003. Proceedings. (pp. 249-252). IEEE.

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