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Al-Tashi Q, Saad MB, Muneer A, Qureshi R, Mirjalili S, Sheshadri A, Le X, Vokes NI, Zhang J, Wu J. Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review. Int J Mol Sci 2023; 24:7781. [PMID: 37175487 PMCID: PMC10178491 DOI: 10.3390/ijms24097781] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/10/2023] [Accepted: 04/19/2023] [Indexed: 05/15/2023] Open
Abstract
The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.
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Affiliation(s)
- Qasem Al-Tashi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Maliazurina B. Saad
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Amgad Muneer
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rizwan Qureshi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006, Australia
- Yonsei Frontier Lab, Yonsei University, Seoul 03722, Republic of Korea
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
| | - Ajay Sheshadri
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xiuning Le
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Natalie I. Vokes
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jia Wu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Ohe C, Yoshida T, Amin MB, Uno R, Atsumi N, Yasukochi Y, Ikeda J, Nakamoto T, Noda Y, Kinoshita H, Tsuta K, Higasa K. Deep learning-based predictions of clear and eosinophilic phenotypes in clear cell renal cell carcinoma. Hum Pathol 2023; 131:68-78. [PMID: 36372298 DOI: 10.1016/j.humpath.2022.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022]
Abstract
We have recently shown that histological phenotypes focusing on clear and eosinophilic cytoplasm in clear cell renal cell carcinoma (ccRCC) correlated with prognosis and the response to angiogenesis inhibition and checkpoint blockade. This study aims to objectively show the diagnostic utility of clear or eosinophilic phenotypes of ccRCC by developing an artificial intelligence (AI) model using the TCGA-ccRCC dataset and to demonstrate if the clear or eosinophilic predicted phenotypes correlate with pathological factors and gene signatures associated with angiogenesis and cancer immunity. Before the development of the AI model, histological evaluation using hematoxylin and eosin whole-slide images of the TCGA-ccRCC cohort (n = 435) was performed by a urologic pathologist. The AI model was developed as follows. First, the highest-grade area on each whole slide image was captured for image processing. Second, the selected regions were cropped into tiles. Third, the AI model was trained using transfer learning on a deep convolutional neural network, and clear or eosinophilic predictions were scaled as AI scores. Next, we verified the AI model using a validation cohort (n = 95). Finally, we evaluated the accuracy of the prognostic predictions of the AI model and revealed that the AI model detected clear and eosinophilic phenotypes with high accuracy. The AI model stratified the patients' outcomes, and the predicted eosinophilic phenotypes correlated with adverse clinicopathological characteristics and high immune-related gene signatures. In conclusion, the AI-based histologic subclassification accurately predicted clear or eosinophilic phenotypes of ccRCC, allowing for consistently reproducible stratification for prognostic and therapeutic stratification.
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Affiliation(s)
- Chisato Ohe
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan.
| | - Takashi Yoshida
- Department of Urology and Andrology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Mahul B Amin
- Department of Pathology and Laboratory Medicine, University of Tennessee Health Sciences Center, 930 Madison Avenue, Memphis, TN 38163, USA; Department of Urology, University of Southern California, 1441 Eastlake Avenue, Los Angeles, CA 90033, USA
| | - Rena Uno
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan; Department of Pathology, Hyogo Cancer Center, Akashi, Hyogo 673-8558, Japan
| | - Naho Atsumi
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Yoshiki Yasukochi
- Department of Genome Analysis, Institute of Biomedical Science, Kansai Medical University, Hirakata, Osaka 573-1191, Japan
| | - Junichi Ikeda
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan; Department of Urology and Andrology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Takahiro Nakamoto
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan; Department of Urology and Andrology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Yuri Noda
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Hidefumi Kinoshita
- Department of Urology and Andrology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Koji Tsuta
- Department of Pathology, Kansai Medical University, 2-3-1 Shin-machi, Hirakata, Osaka 573-1191, Japan
| | - Koichiro Higasa
- Department of Genome Analysis, Institute of Biomedical Science, Kansai Medical University, Hirakata, Osaka 573-1191, Japan
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Lou Y, Ye M, Xu C, Tao F. Insight into the physiological and pathological roles of USP44, a potential tumor target (Review). Oncol Lett 2022; 24:455. [PMID: 36380875 PMCID: PMC9650596 DOI: 10.3892/ol.2022.13575] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 10/06/2022] [Indexed: 11/24/2022] Open
Abstract
Ubiquitin-specific peptidase 44 (USP44) is a member of the ubiquitin-specific proteases (USPs) family and its functions in various biological processes have been gradually elucidated in recent years. USP44 targets multiple downstream factors and regulates multiple mechanisms through its deubiquitination activity. Ubiquitination is, in essence, a process in which a single ubiquitin molecule or a multiubiquitin chain binds to a substrate protein to form an isopeptide bond. Deubiquitination is the catalyzing of the isopeptide bonds between ubiquitin and substrate proteins through deubiquitylating enzymes. These two processes serve an important role in the regulation of the expression, conformation, localization and function of substrate proteins by regulating their binding to ubiquitin. Based on existing research, this paper summarized the current state of knowledge about USP44. The physiological roles of USP44 in various cellular events and its pathophysiological roles in different cancer types are evaluated and the therapeutic potential of USP44 for cancer treatment is evaluated.
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Affiliation(s)
- Yuming Lou
- Department of Breast and Thyroid Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang 321000, P.R. China,Department of Stomach and Intestine Surgery, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, Zhejiang 312000, P.R. China
| | - Minfeng Ye
- Department of Stomach and Intestine Surgery, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, Zhejiang 312000, P.R. China
| | - Chaoyang Xu
- Department of Breast and Thyroid Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang 321000, P.R. China,Department of Stomach and Intestine Surgery, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, Zhejiang 312000, P.R. China,Correspondence to: Dr Chaoyang Xu, Department of Breast and Thyroid Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, 365 Renmin East Road, Jinhua, Zhejiang 321000, P.R. China, E-mail:
| | - Feng Tao
- Department of Stomach and Intestine Surgery, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, Zhejiang 312000, P.R. China,Professor Feng Tao, Department of Stomach and Intestine Surgery, Shaoxing Hospital, Zhejiang University School of Medicine, 568 Zhongxing North Road, Shaoxing, Zhejiang 312000, P.R. China, E-mail:
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Jiang J, Chan L, Nadkarni GN. The promise of artificial intelligence for kidney pathophysiology. Curr Opin Nephrol Hypertens 2022; 31:380-386. [PMID: 35703218 PMCID: PMC10309072 DOI: 10.1097/mnh.0000000000000808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
PURPOSE OF REVIEW We seek to determine recent advances in kidney pathophysiology that have been enabled or enhanced by artificial intelligence. We describe some of the challenges in the field as well as future directions. RECENT FINDINGS We first provide an overview of artificial intelligence terminologies and methodologies. We then describe the use of artificial intelligence in kidney diseases to discover risk factors from clinical data for disease progression, annotate whole slide imaging and decipher multiomics data. We delineate key examples of risk stratification and prognostication in acute kidney injury (AKI) and chronic kidney disease (CKD). We contextualize these applications in kidney disease oncology, one of the subfields to benefit demonstrably from artificial intelligence using all if these approaches. We conclude by elucidating technical challenges and ethical considerations and briefly considering future directions. SUMMARY The integration of clinical data, patient derived data, histology and proteomics and genomics can enhance the work of clinicians in providing more accurate diagnoses and elevating understanding of disease progression. Implementation research needs to be performed to translate these algorithms to the clinical setting.
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Affiliation(s)
- Joy Jiang
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lili Chan
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N. Nadkarni
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Chen H, Zhao L, Liu J, Zhou H, Wang X, Fang X, Xia X. Bioinformatic Analyzes of the Association Between Upregulated Expression of JUN Gene via APOBEC-Induced FLG Gene Mutation and Prognosis of Cervical Cancer. Front Med (Lausanne) 2022; 9:815450. [PMID: 35510248 PMCID: PMC9058067 DOI: 10.3389/fmed.2022.815450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 03/15/2022] [Indexed: 02/01/2023] Open
Abstract
Globally, cervical cancer (CC) is the most common malignant tumor of the female reproductive system and its incidence is only second after breast cancer. Although screening and advanced treatment strategies have improved the rates of survival, some patients with CC still die due to metastasis and drug resistance. It is considered that cancer is driven by somatic mutations, such as single nucleotide, small insertions/deletions, copy number, and structural variations, as well as epigenetic changes. Previous studies have shown that cervical intraepithelial neoplasia is associated with copy number variants (CNVs) and/or mutations in cancer-related genes. Further, CC is also related to genetic mutations. The present study analyzed the data on somatic mutations of cervical squamous cell carcinoma (CESC) in the Cancer Genome Atlas database. It was evident that the Apolipoprotein B mRNA editing enzyme-catalyzed polypeptide-like (APOBEC)-related mutation of the FLG gene can upregulate the expression of the JUN gene and ultimately lead to poor prognosis for patients with CC. Therefore, the findings of the current study provide a new direction for future treatment of CC.
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Affiliation(s)
- Huan Chen
- Department of Obstetrics and Gynecology, The Second XIANGYA Hospital of Central South University, Changsha, China
| | - Liyun Zhao
- Department of Obstetrics and Gynecology, The Second XIANGYA Hospital of Central South University, Changsha, China
| | - Jiaqiang Liu
- Laboratory Medicine Center, Zhu Zhou Hospital Affiliated to Xiangya School of Medicine, Central South University (CSU), Zhuzhou, China
| | - Housheng Zhou
- Department of Obstetrics and Gynecology, Zhu Zhou Hospital Affiliated to Xiangya School of Medicine, CSU, Zhuzhou, China
| | - Xi Wang
- Department of Obstetrics and Gynecology, The Second XIANGYA Hospital of Central South University, Changsha, China
| | - Xiaoling Fang
- Department of Obstetrics and Gynecology, The Second XIANGYA Hospital of Central South University, Changsha, China
| | - Xiaomeng Xia
- Department of Obstetrics and Gynecology, The Second XIANGYA Hospital of Central South University, Changsha, China
- *Correspondence: Xiaomeng Xia
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Lee M, Wei S, Anaokar J, Uzzo R, Kutikov A. Kidney cancer management 3.0: can artificial intelligence make us better? Curr Opin Urol 2021; 31:409-415. [PMID: 33882560 DOI: 10.1097/mou.0000000000000881] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence holds tremendous potential for disrupting clinical medicine. Here we review the current role of artificial intelligence in the kidney cancer space. RECENT FINDINGS Machine learning and deep learning algorithms have been developed using information extracted from radiomic, histopathologic, and genomic datasets of patients with renal masses. SUMMARY Although artificial intelligence applications in medicine are still in their infancy, they already hold immediate promise to improve accuracy of renal mass characterization, grade, and prognostication. As algorithms become more robust and generalizable, artificial intelligence is poised to significantly disrupt kidney cancer care.
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Affiliation(s)
| | | | - Jordan Anaokar
- Department of Diagnostic Imaging, Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA
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Giulietti M, Cecati M, Sabanovic B, Scirè A, Cimadamore A, Santoni M, Montironi R, Piva F. The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors. Diagnostics (Basel) 2021; 11:206. [PMID: 33573278 PMCID: PMC7912267 DOI: 10.3390/diagnostics11020206] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 01/22/2021] [Accepted: 01/26/2021] [Indexed: 02/07/2023] Open
Abstract
The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.
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Affiliation(s)
- Matteo Giulietti
- Department of Specialistic Clinical & Odontostomatological Sciences, Polytechnic University of Marche, 60126 Ancona, Italy; (M.G.); (M.C.); (B.S.)
| | - Monia Cecati
- Department of Specialistic Clinical & Odontostomatological Sciences, Polytechnic University of Marche, 60126 Ancona, Italy; (M.G.); (M.C.); (B.S.)
| | - Berina Sabanovic
- Department of Specialistic Clinical & Odontostomatological Sciences, Polytechnic University of Marche, 60126 Ancona, Italy; (M.G.); (M.C.); (B.S.)
| | - Andrea Scirè
- Department of Life and Environmental Sciences, Polytechnic University of Marche, 60126 Ancona, Italy;
| | - Alessia Cimadamore
- Section of Pathological Anatomy, Polytechnic University of Marche, United Hospitals, 60126 Ancona, Italy; (A.C.); (R.M.)
| | - Matteo Santoni
- Oncology Unit, Macerata Hospital, 62012 Macerata, Italy;
| | - Rodolfo Montironi
- Section of Pathological Anatomy, Polytechnic University of Marche, United Hospitals, 60126 Ancona, Italy; (A.C.); (R.M.)
| | - Francesco Piva
- Department of Specialistic Clinical & Odontostomatological Sciences, Polytechnic University of Marche, 60126 Ancona, Italy; (M.G.); (M.C.); (B.S.)
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