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Nagarkar R, Gopichand M, Pal SK, Gupta A, Saquib NM, Sagar G, Rao KVS, Siddiqui Z, Longkumer I. The High Sensitivity of the Multi-Cancer Detection Test ONCOVERYX-F Offers a Promising Platform for Ovarian Cancer Screening. Int J Womens Health 2024; 16:1-7. [PMID: 38193139 PMCID: PMC10771777 DOI: 10.2147/ijwh.s444258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 12/28/2023] [Indexed: 01/10/2024] Open
Abstract
We evaluated the potential relevance of our multi-cancer detection test, OncoVeryx-F, for ovarian cancer screening. For this, we compared its accuracy with that of CA125-based screening. We demonstrate here that, in contrast to CA125-based detection, OncoVeryx-F detected ovarian cancer with very high sensitivity and specificity. Importantly here, Stage I cancers too could be detected with an accuracy of >98%. Furthermore, again unlike CA 125, the detection accuracy of OncoVeryx-F remained comparable in both Caucasian and South Asian/Indian women. Thus, the robustness and accuracy of OncoVeryx-F, particularly for early-stage detection, underscores its potential utility for ovarian cancer screening.
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Affiliation(s)
- Rajnish Nagarkar
- Surgical Oncology, HCG Manavta Cancer Centre, Nashik, Maharashtra, India
| | | | | | - Ankur Gupta
- Research and Development Section, PredOmix Technologies Private Limited, Gurugram, India
- Research and Development Section, PredOmix Health Sciences Private Limited, Singapore
| | - Najmuddin Mohd Saquib
- Research and Development Section, PredOmix Technologies Private Limited, Gurugram, India
- Research and Development Section, PredOmix Health Sciences Private Limited, Singapore
| | - Ganga Sagar
- Research and Development Section, PredOmix Technologies Private Limited, Gurugram, India
| | - Kanury V S Rao
- Research and Development Section, PredOmix Technologies Private Limited, Gurugram, India
- Research and Development Section, PredOmix Health Sciences Private Limited, Singapore
| | - Zaved Siddiqui
- Research and Development Section, PredOmix Technologies Private Limited, Gurugram, India
- Research and Development Section, PredOmix Health Sciences Private Limited, Singapore
| | - Imliwati Longkumer
- Biochemistry, North East Cancer Hospital and Research Institute, Guwahati, Assam, India
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2
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Gupta A, Siddiqui Z, Sagar G, Rao KVS, Saquib N. A non-invasive method for concurrent detection of multiple early-stage cancers in women. Sci Rep 2023; 13:19083. [PMID: 37925521 PMCID: PMC10625604 DOI: 10.1038/s41598-023-46553-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 11/02/2023] [Indexed: 11/06/2023] Open
Abstract
Untargeted serum metabolomics was combined with machine learning-powered data analytics to develop a test for the concurrent detection of multiple cancers in women. A total of fifteen cancers were tested where the resulting metabolome data was sequentially analysed using two separate algorithms. The first algorithm successfully identified all the cancer-positive samples with an overall accuracy of > 99%. This result was particularly significant given that the samples tested were predominantly from early-stage cancers. Samples identified as cancer-positive were next analysed using a multi-class algorithm, which then enabled accurate discernment of the tissue of origin for the individual samples. Integration of serum metabolomics with appropriate data analytical tools, therefore, provides a powerful screening platform for early-stage cancers.
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Affiliation(s)
- Ankur Gupta
- PredOmix Health Sciences Private Limited, 10 Anson Road, #22-02 International Plaza, Singapore, 079903, Singapore
- PredOmix Technologies Private Limited, Tower B, SAS Tower, Medicity, Sector-38, Gurugram, 122002, India
| | - Zaved Siddiqui
- PredOmix Health Sciences Private Limited, 10 Anson Road, #22-02 International Plaza, Singapore, 079903, Singapore
- PredOmix Technologies Private Limited, Tower B, SAS Tower, Medicity, Sector-38, Gurugram, 122002, India
| | - Ganga Sagar
- PredOmix Technologies Private Limited, Tower B, SAS Tower, Medicity, Sector-38, Gurugram, 122002, India
| | - Kanury V S Rao
- PredOmix Health Sciences Private Limited, 10 Anson Road, #22-02 International Plaza, Singapore, 079903, Singapore
- PredOmix Technologies Private Limited, Tower B, SAS Tower, Medicity, Sector-38, Gurugram, 122002, India
| | - Najmuddin Saquib
- PredOmix Health Sciences Private Limited, 10 Anson Road, #22-02 International Plaza, Singapore, 079903, Singapore.
- PredOmix Technologies Private Limited, Tower B, SAS Tower, Medicity, Sector-38, Gurugram, 122002, India.
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Godlewski A, Czajkowski M, Mojsak P, Pienkowski T, Gosk W, Lyson T, Mariak Z, Reszec J, Kondraciuk M, Kaminski K, Kretowski M, Moniuszko M, Kretowski A, Ciborowski M. A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors. Sci Rep 2023; 13:11044. [PMID: 37422554 PMCID: PMC10329700 DOI: 10.1038/s41598-023-38243-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/05/2023] [Indexed: 07/10/2023] Open
Abstract
Metabolomics combined with machine learning methods (MLMs), is a powerful tool for searching novel diagnostic panels. This study was intended to use targeted plasma metabolomics and advanced MLMs to develop strategies for diagnosing brain tumors. Measurement of 188 metabolites was performed on plasma samples collected from 95 patients with gliomas (grade I-IV), 70 with meningioma, and 71 healthy individuals as a control group. Four predictive models to diagnose glioma were prepared using 10 MLMs and a conventional approach. Based on the cross-validation results of the created models, the F1-scores were calculated, then obtained values were compared. Subsequently, the best algorithm was applied to perform five comparisons involving gliomas, meningiomas, and controls. The best results were obtained using the newly developed hybrid evolutionary heterogeneous decision tree (EvoHDTree) algorithm, which was validated using Leave-One-Out Cross-Validation, resulting in an F1-score for all comparisons in the range of 0.476-0.948 and the area under the ROC curves ranging from 0.660 to 0.873. Brain tumor diagnostic panels were constructed with unique metabolites, which reduces the likelihood of misdiagnosis. This study proposes a novel interdisciplinary method for brain tumor diagnosis based on metabolomics and EvoHDTree, exhibiting significant predictive coefficients.
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Affiliation(s)
- Adrian Godlewski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Marcin Czajkowski
- Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland
| | - Patrycja Mojsak
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Tomasz Pienkowski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Wioleta Gosk
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Tomasz Lyson
- Department of Neurosurgery, Medical University of Bialystok, Białystok, Poland
| | - Zenon Mariak
- Department of Neurosurgery, Medical University of Bialystok, Białystok, Poland
| | - Joanna Reszec
- Department of Medical Pathomorphology, Medical University of Bialystok, Białystok, Poland
| | - Marcin Kondraciuk
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Białystok, Poland
| | - Karol Kaminski
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Białystok, Poland
| | - Marek Kretowski
- Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland
| | - Marcin Moniuszko
- Department of Regenerative Medicine and Immune Regulation, Medical University of Bialystok, Białystok, Poland
- Department of Allergology and Internal Medicine, Medical University of Bialystok, Białystok, Poland
| | - Adam Kretowski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Białystok, Poland
| | - Michal Ciborowski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland.
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Brito-Rocha T, Constâncio V, Henrique R, Jerónimo C. Shifting the Cancer Screening Paradigm: The Rising Potential of Blood-Based Multi-Cancer Early Detection Tests. Cells 2023; 12:cells12060935. [PMID: 36980276 PMCID: PMC10047029 DOI: 10.3390/cells12060935] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
Cancer remains a leading cause of death worldwide, partly owing to late detection which entails limited and often ineffective therapeutic options. Most cancers lack validated screening procedures, and the ones available disclose several drawbacks, leading to low patient compliance and unnecessary workups, adding up the costs to healthcare systems. Hence, there is a great need for innovative, accurate, and minimally invasive tools for early cancer detection. In recent years, multi-cancer early detection (MCED) tests emerged as a promising screening tool, combining molecular analysis of tumor-related markers present in body fluids with artificial intelligence to simultaneously detect a variety of cancers and further discriminate the underlying cancer type. Herein, we aim to provide a highlight of the variety of strategies currently under development concerning MCED, as well as the major factors which are preventing clinical implementation. Although MCED tests depict great potential for clinical application, large-scale clinical validation studies are still lacking.
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Affiliation(s)
- Tiago Brito-Rocha
- Cancer Biology and Epigenetics Group, Research Center (CI-IPOP)/RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO-Porto)/Porto Comprehensive Cancer Center Raquel Seruca (P.CCC), Rua Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal
- Master Program in Oncology, School of Medicine & Biomedical Sciences, University of Porto (ICBAS-UP), Rua Jorge Viterbo Ferreira 228, 4050-513 Porto, Portugal
| | - Vera Constâncio
- Cancer Biology and Epigenetics Group, Research Center (CI-IPOP)/RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO-Porto)/Porto Comprehensive Cancer Center Raquel Seruca (P.CCC), Rua Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal
- Doctoral Program in Biomedical Sciences, School of Medicine & Biomedical Sciences, University of Porto (ICBAS-UP), Rua Jorge Viterbo Ferreira 228, 4050-513 Porto, Portugal
| | - Rui Henrique
- Cancer Biology and Epigenetics Group, Research Center (CI-IPOP)/RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO-Porto)/Porto Comprehensive Cancer Center Raquel Seruca (P.CCC), Rua Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal
- Department of Pathology, Portuguese Oncology Institute of Porto (IPO-Porto), Rua Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal
- Department of Pathology and Molecular Immunology, School of Medicine & Biomedical Sciences, University of Porto (ICBAS-UP), Rua Jorge Viterbo Ferreira 228, 4050-513 Porto, Portugal
| | - Carmen Jerónimo
- Cancer Biology and Epigenetics Group, Research Center (CI-IPOP)/RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO-Porto)/Porto Comprehensive Cancer Center Raquel Seruca (P.CCC), Rua Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal
- Department of Pathology and Molecular Immunology, School of Medicine & Biomedical Sciences, University of Porto (ICBAS-UP), Rua Jorge Viterbo Ferreira 228, 4050-513 Porto, Portugal
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Liao J, Li X, Gan Y, Han S, Rong P, Wang W, Li W, Zhou L. Artificial intelligence assists precision medicine in cancer treatment. Front Oncol 2023; 12:998222. [PMID: 36686757 PMCID: PMC9846804 DOI: 10.3389/fonc.2022.998222] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/22/2022] [Indexed: 01/06/2023] Open
Abstract
Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.
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Affiliation(s)
- Jinzhuang Liao
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiaoying Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yu Gan
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Shuangze Han
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Pengfei Rong
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Wei Wang
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Wei Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Li Zhou
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,Department of Pathology, The Xiangya Hospital of Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
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Blood Plasma Metabolome Profiling at Different Stages of Renal Cell Carcinoma. Cancers (Basel) 2022; 15:cancers15010140. [PMID: 36612136 PMCID: PMC9818272 DOI: 10.3390/cancers15010140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 12/28/2022] Open
Abstract
Early diagnostics significantly improves the survival of patients with renal cell carcinoma (RCC), which is the prevailing type of adult kidney cancer. However, the absence of clinically obvious symptoms and effective screening strategies at the early stages result to disease progression and survival rate reducing. The study was focused on revealing of potential low molecular biomarkers for early-stage RCC. The untargeted direct injection mass spectrometry-based metabolite profiling of blood plasma samples from 51 non-cancer volunteers (control) and 78 patients with different RCC subtypes and stages (early stages of clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chrRCC) and advanced stages of ccRCC) was performed. Comparative analysis of the blood plasma metabolites between the control and cancer groups provided the detection of metabolites associated with different tumor stages. The designed model based on the revealed metabolites demonstrated high diagnostic power and accuracy. Overall, using the metabolomics approach the study revealed the metabolites demonstrating a high value for design of plasma-based test to improve early ccRCC diagnosis.
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