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2023 Beijing Health Data Science Summit. HEALTH DATA SCIENCE 2024; 4:0112. [PMID: 38854991 PMCID: PMC11157085 DOI: 10.34133/hds.0112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/05/2023] [Indexed: 06/11/2024]
Abstract
The 5th annual Beijing Health Data Science Summit, organized by the National Institute of Health Data Science at Peking University, recently concluded with resounding success. This year, the summit aimed to foster collaboration among researchers, practitioners, and stakeholders in the field of health data science to advance the use of data for better health outcomes. One significant highlight of this year's summit was the introduction of the Abstract Competition, organized by Health Data Science, a Science Partner Journal, which focused on the use of cutting-edge data science methodologies, particularly the application of artificial intelligence in the healthcare scenarios. The competition provided a platform for researchers to showcase their groundbreaking work and innovations. In total, the summit received 61 abstract submissions. Following a rigorous evaluation process by the Abstract Review Committee, eight exceptional abstracts were selected to compete in the final round and give presentations in the Abstract Competition. The winners of the Abstract Competition are as follows:•First Prize: "Interpretable Machine Learning for Predicting Outcomes of Childhood Kawasaki Disease: Electronic Health Record Analysis" presented by researchers from the Chinese Academy of Medical Sciences, Peking Union Medical College, and Chongqing Medical University (presenter Yifan Duan).•Second Prize: "Survival Disparities among Mobility Patterns of Patients with Cancer: A Population-Based Study" presented by a team from Peking University (presenter Fengyu Wen).•Third Prize: "Deep Learning-Based Real-Time Predictive Model for the Development of Acute Stroke" presented by researchers from Beijing Tiantan Hospital (presenter Lan Lan). We extend our heartfelt gratitude to the esteemed panel of judges whose expertise and dedication ensured the fairness and quality of the competition. The judging panel included Jiebo Luo from the University of Rochester (chair), Shenda Hong from Peking University, Xiaozhong Liu from Worcester Polytechnic Institute, Liu Yang from Hong Kong Baptist University, Ma Jianzhu from Tsinghua University, Ting Ma from Harbin Institute of Technology, and Jian Tang from Mila-Quebec Artificial Intelligence Institute. We wish to convey our deep appreciation to Zixuan He and Haoyang Hong for their invaluable assistance in the meticulous planning and execution of the event. As the 2023 Beijing Health Data Science Summit comes to a close, we look forward to welcoming all participants to join us in 2024. Together, we will continue to advance the frontiers of health data science and work toward a healthier future for all.
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Ivanova M, Pescia C, Trapani D, Venetis K, Frascarelli C, Mane E, Cursano G, Sajjadi E, Scatena C, Cerbelli B, d’Amati G, Porta FM, Guerini-Rocco E, Criscitiello C, Curigliano G, Fusco N. Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence. Cancers (Basel) 2024; 16:1981. [PMID: 38893102 PMCID: PMC11171409 DOI: 10.3390/cancers16111981] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 05/13/2024] [Accepted: 05/17/2024] [Indexed: 06/21/2024] Open
Abstract
Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes.
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Affiliation(s)
- Mariia Ivanova
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Carlo Pescia
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Dario Trapani
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, 20141 Milan, Italy; (D.T.); (C.C.); (G.C.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Konstantinos Venetis
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Chiara Frascarelli
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Eltjona Mane
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Giulia Cursano
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Elham Sajjadi
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Cristian Scatena
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy;
| | - Bruna Cerbelli
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 00185 Rome, Italy;
| | - Giulia d’Amati
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, 00185 Rome, Italy;
| | - Francesca Maria Porta
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
| | - Elena Guerini-Rocco
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Carmen Criscitiello
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, 20141 Milan, Italy; (D.T.); (C.C.); (G.C.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology IRCCS, 20141 Milan, Italy; (D.T.); (C.C.); (G.C.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Nicola Fusco
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (M.I.); (C.P.); (K.V.); (C.F.); (E.M.); (G.C.); (E.S.); (F.M.P.); (E.G.-R.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
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Kim Y, Han Y, Hopper C, Lee J, Joo JI, Gong JR, Lee CK, Jang SH, Kang J, Kim T, Cho KH. A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations. CELL REPORTS METHODS 2024; 4:100773. [PMID: 38744288 PMCID: PMC11133856 DOI: 10.1016/j.crmeth.2024.100773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 03/19/2024] [Accepted: 04/19/2024] [Indexed: 05/16/2024]
Abstract
Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.
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Affiliation(s)
- Yunseong Kim
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Younghyun Han
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Corbin Hopper
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jonghoon Lee
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jae Il Joo
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jeong-Ryeol Gong
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Chun-Kyung Lee
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Seong-Hoon Jang
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Junsoo Kang
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Taeyoung Kim
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Kwang-Hyun Cho
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.
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Vasanthakumari P, Zhu Y, Brettin T, Partin A, Shukla M, Xia F, Narykov O, Weil MR, Stevens RL. A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening. Cancers (Basel) 2024; 16:530. [PMID: 38339281 PMCID: PMC10854925 DOI: 10.3390/cancers16030530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/12/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method.
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Affiliation(s)
- Priyanka Vasanthakumari
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (A.P.); (M.S.); (F.X.); (O.N.)
| | - Yitan Zhu
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (A.P.); (M.S.); (F.X.); (O.N.)
| | - Thomas Brettin
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (T.B.); (R.L.S.)
| | - Alexander Partin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (A.P.); (M.S.); (F.X.); (O.N.)
| | - Maulik Shukla
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (A.P.); (M.S.); (F.X.); (O.N.)
| | - Fangfang Xia
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (A.P.); (M.S.); (F.X.); (O.N.)
| | - Oleksandr Narykov
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA; (Y.Z.); (A.P.); (M.S.); (F.X.); (O.N.)
| | - Michael Ryan Weil
- Cancer Research Technology Program, Cancer Data Science Initiatives, Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA;
| | - Rick L. Stevens
- Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA; (T.B.); (R.L.S.)
- Department of Computer Science, The University of Chicago, Chicago, IL 60637, USA
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Ogunleye A, Piyawajanusorn C, Ghislat G, Ballester PJ. Large-Scale Machine Learning Analysis Reveals DNA Methylation and Gene Expression Response Signatures for Gemcitabine-Treated Pancreatic Cancer. HEALTH DATA SCIENCE 2024; 4:0108. [PMID: 38486621 PMCID: PMC10904073 DOI: 10.34133/hds.0108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 12/08/2023] [Indexed: 03/17/2024]
Abstract
Background: Gemcitabine is a first-line chemotherapy for pancreatic adenocarcinoma (PAAD), but many PAAD patients do not respond to gemcitabine-containing treatments. Being able to predict such nonresponders would hence permit the undelayed administration of more promising treatments while sparing gemcitabine life-threatening side effects for those patients. Unfortunately, the few predictors of PAAD patient response to this drug are weak, none of them exploiting yet the power of machine learning (ML). Methods: Here, we applied ML to predict the response of PAAD patients to gemcitabine from the molecular profiles of their tumors. More concretely, we collected diverse molecular profiles of PAAD patient tumors along with the corresponding clinical data (gemcitabine responses and clinical features) from the Genomic Data Commons resource. From systematically combining 8 tumor profiles with 16 classification algorithms, each of the resulting 128 ML models was evaluated by multiple 10-fold cross-validations. Results: Only 7 of these 128 models were predictive, which underlines the importance of carrying out such a large-scale analysis to avoid missing the most predictive models. These were here random forest using 4 selected mRNAs [0.44 Matthews correlation coefficient (MCC), 0.785 receiver operating characteristic-area under the curve (ROC-AUC)] and XGBoost combining 12 DNA methylation probes (0.32 MCC, 0.697 ROC-AUC). By contrast, the hENT1 marker obtained much worse random-level performance (practically 0 MCC, 0.5 ROC-AUC). Despite not being trained to predict prognosis (overall and progression-free survival), these ML models were also able to anticipate this patient outcome. Conclusions: We release these promising ML models so that they can be evaluated prospectively on other gemcitabine-treated PAAD patients.
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Affiliation(s)
- Adeolu Ogunleye
- Department of Organismal Biology,
Uppsala University, Uppsala, Sweden
| | | | - Ghita Ghislat
- Department of Life Sciences,
Imperial College London, London, UK
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6
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Dziubańska-Kusibab PJ, Nevedomskaya E, Haendler B. Preclinical Anticipation of On- and Off-Target Resistance Mechanisms to Anti-Cancer Drugs: A Systematic Review. Int J Mol Sci 2024; 25:705. [PMID: 38255778 PMCID: PMC10815614 DOI: 10.3390/ijms25020705] [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: 11/14/2023] [Revised: 12/22/2023] [Accepted: 12/28/2023] [Indexed: 01/24/2024] Open
Abstract
The advent of targeted therapies has led to tremendous improvements in treatment options and their outcomes in the field of oncology. Yet, many cancers outsmart precision drugs by developing on-target or off-target resistance mechanisms. Gaining the ability to resist treatment is the rule rather than the exception in tumors, and it remains a major healthcare challenge to achieve long-lasting remission in most cancer patients. Here, we discuss emerging strategies that take advantage of innovative high-throughput screening technologies to anticipate on- and off-target resistance mechanisms before they occur in treated cancer patients. We divide the methods into non-systematic approaches, such as random mutagenesis or long-term drug treatment, and systematic approaches, relying on the clustered regularly interspaced short palindromic repeats (CRISPR) system, saturated mutagenesis, or computational methods. All these new developments, especially genome-wide CRISPR-based screening platforms, have significantly accelerated the processes for identification of the mechanisms responsible for cancer drug resistance and opened up new avenues for future treatments.
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Affiliation(s)
| | | | - Bernard Haendler
- Research and Early Development Oncology, Pharmaceuticals, Bayer AG, Müllerstr. 178, 13353 Berlin, Germany; (P.J.D.-K.); (E.N.)
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Yin Y, Xie W, Xiong M, Gao Y, Liu Q, Han D, Ke G, Zhang XB. FINDER: A Fluidly Confined CRISPR-Based DNA Reporter on Living Cell Membranes for Rapid and Sensitive Cancer Cell Identification. Angew Chem Int Ed Engl 2023; 62:e202309837. [PMID: 37710395 DOI: 10.1002/anie.202309837] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 09/16/2023]
Abstract
The accurate, rapid, and sensitive identification of cancer cells in complex physiological environments is significant in biological studies, personalized medicine, and biomedical engineering. Inspired by the naturally confined enzymes on fluid cell membranes, a fluidly confined CRISPR-based DNA reporter (FINDER) was developed on living cell membranes, which was successfully applied for rapid and sensitive cancer cell identification in clinical blood samples. Benefiting from the spatial confinement effect for improved local concentration, and membrane fluidity for higher collision efficiency, the activity of CRISPR-Cas12a was, for the first time, found to be significantly enhanced on living cell membranes. This new phenomenon was then combined with multiple aptamer-based DNA logic gate for cell recognition, thus a FINDER system capable of accurate, rapid and sensitive cancer cell identification was constructed. The FINDER rapidly identified target cells in only 20 min, and achieved over 80 % recognition efficiency with only 0.1 % of target cells presented in clinical blood samples, indicating its potential application in biological studies, personalized medicine, and biomedical engineering.
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Affiliation(s)
- Yao Yin
- Molecular Sciences and Biomedicine Laboratory, State Key Laboratory for Chemo / Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Wei Xie
- Molecular Sciences and Biomedicine Laboratory, State Key Laboratory for Chemo / Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Mengyi Xiong
- Molecular Sciences and Biomedicine Laboratory, State Key Laboratory for Chemo / Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Yingying Gao
- Molecular Sciences and Biomedicine Laboratory, State Key Laboratory for Chemo / Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Qin Liu
- Molecular Sciences and Biomedicine Laboratory, State Key Laboratory for Chemo / Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Da Han
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
- Institute of Molecular Medicine and Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Guoliang Ke
- Molecular Sciences and Biomedicine Laboratory, State Key Laboratory for Chemo / Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Xiao-Bing Zhang
- Molecular Sciences and Biomedicine Laboratory, State Key Laboratory for Chemo / Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, Hunan, 410082, China
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Shin SY, Centenera MM, Hodgson JT, Nguyen EV, Butler LM, Daly RJ, Nguyen LK. A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer. Front Mol Biosci 2023; 10:1094321. [PMID: 36743211 PMCID: PMC9892654 DOI: 10.3389/fmolb.2023.1094321] [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: 11/10/2022] [Accepted: 01/06/2023] [Indexed: 01/20/2023] Open
Abstract
Precision medicine has emerged as an important paradigm in oncology, driven by the significant heterogeneity of individual patients' tumour. A key prerequisite for effective implementation of precision oncology is the development of companion biomarkers that can predict response to anti-cancer therapies and guide patient selection for clinical trials and/or treatment. However, reliable predictive biomarkers are currently lacking for many anti-cancer therapies, hampering their clinical application. Here, we developed a novel machine learning-based framework to derive predictive multi-gene biomarker panels and associated expression signatures that accurately predict cancer drug sensitivity. We demonstrated the power of the approach by applying it to identify response biomarker panels for an Hsp90-based therapy in prostate cancer, using proteomic data profiled from prostate cancer patient-derived explants. Our approach employs a rational feature section strategy to maximise model performance, and innovatively utilizes Boolean algebra methods to derive specific expression signatures of the marker proteins. Given suitable data for model training, the approach is also applicable to other cancer drug agents in different tumour settings.
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Affiliation(s)
- Sung-Young Shin
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia,Cancer Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia,*Correspondence: Sung-Young Shin, ; Lan K. Nguyen,
| | - Margaret M. Centenera
- South Australian Immunogenomics Cancer Institute and Freemasons Foundation Centre for Men’s Health, University of Adelaide, Adelaide, SA, Australia,South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Joshua T. Hodgson
- South Australian Immunogenomics Cancer Institute and Freemasons Foundation Centre for Men’s Health, University of Adelaide, Adelaide, SA, Australia,South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Elizabeth V. Nguyen
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia,Cancer Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Lisa M. Butler
- South Australian Immunogenomics Cancer Institute and Freemasons Foundation Centre for Men’s Health, University of Adelaide, Adelaide, SA, Australia,South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Roger J. Daly
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia,Cancer Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Lan K. Nguyen
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia,Cancer Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia,*Correspondence: Sung-Young Shin, ; Lan K. Nguyen,
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9
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Cheng X, Dai C, Wen Y, Wang X, Bo X, He S, Peng S. NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data. BMC Med 2022; 20:368. [PMID: 36244991 PMCID: PMC9575288 DOI: 10.1186/s12916-022-02549-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 09/01/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Considering the heterogeneity of tumors, it is a key issue in precision medicine to predict the drug response of each individual. The accumulation of various types of drug informatics and multi-omics data facilitates the development of efficient models for drug response prediction. However, the selection of high-quality data sources and the design of suitable methods remain a challenge. METHODS In this paper, we design NeRD, a multidimensional data integration model based on the PRISM drug response database, to predict the cellular response of drugs. Four feature extractors, including drug structure extractor (DSE), molecular fingerprint extractor (MFE), miRNA expression extractor (mEE), and copy number extractor (CNE), are designed for different types and dimensions of data. A fully connected network is used to fuse all features and make predictions. RESULTS Experimental results demonstrate the effective integration of the global and local structural features of drugs, as well as the features of cell lines from different omics data. For all metrics tested on the PRISM database, NeRD surpassed previous approaches. We also verified that NeRD has strong reliability in the prediction results of new samples. Moreover, unlike other algorithms, when the amount of training data was reduced, NeRD maintained stable performance. CONCLUSIONS NeRD's feature fusion provides a new idea for drug response prediction, which is of great significance for precise cancer treatment.
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Affiliation(s)
- Xiaoxiao Cheng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Chong Dai
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China.,Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China
| | - Yuqi Wen
- Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China
| | - Xiaoqi Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xiaochen Bo
- Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China.
| | - Song He
- Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China.
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China. .,The State Key Laboratory of Chemo/Biosensing and Chemometrics, Hunan University, Changsha, China.
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10
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On the Frontiers of Breast Cancer Diagnosis and Treatment: Current and Future Directions in a Rapidly Changing Field. Medicina (B Aires) 2022; 58:medicina58081026. [PMID: 36013493 PMCID: PMC9415763 DOI: 10.3390/medicina58081026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 07/28/2022] [Indexed: 11/26/2022] Open
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