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Hu Q, Rizvi AA, Schau G, Ingale K, Muller Y, Baits R, Pretzer S, BenTaieb A, Gordhamer A, Nussenzveig R, Cole A, Leavitt MO, Jones RD, Joshi RP, Beaubier N, Stumpe MC, Nagpal K. Development and validation of a deep learning-based microsatellite instability predictor from prostate cancer whole-slide images. NPJ Precis Oncol 2024; 8:88. [PMID: 38594360 PMCID: PMC11004110 DOI: 10.1038/s41698-024-00560-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 02/22/2024] [Indexed: 04/11/2024] Open
Abstract
Microsatellite instability-high (MSI-H) is a tumor-agnostic biomarker for immune checkpoint inhibitor therapy. However, MSI status is not routinely tested in prostate cancer, in part due to low prevalence and assay cost. As such, prediction of MSI status from hematoxylin and eosin (H&E) stained whole-slide images (WSIs) could identify prostate cancer patients most likely to benefit from confirmatory testing to evaluate their eligibility for immunotherapy and need for Lynch syndrome testing. Prostate biopsies and surgical resections from prostate cancer patients referred to our institution were analyzed. MSI status was determined by next-generation sequencing. Patients sequenced before a cutoff date formed an algorithm development set (n = 4015, MSI-H 1.8%) and a paired validation set (n = 173, MSI-H 19.7%) that consisted of two serial sections from each sample, one stained and scanned internally and the other at an external site. Patients sequenced after the cutoff date formed a temporally independent validation set (n = 1350, MSI-H 2.3%). Attention-based multiple instance learning models were trained to predict MSI-H from H&E WSIs. The predictor achieved area under the receiver operating characteristic curve values of 0.78 (95% CI [0.69-0.86]), 0.72 (95% CI [0.63-0.81]), and 0.72 (95% CI [0.62-0.82]) on the internally prepared, externally prepared, and temporal validation sets, respectively, showing effective predictability and generalization to both external staining/scanning processes and temporally independent samples. While MSI-H status is significantly correlated with Gleason score, the model remained predictive within each Gleason score subgroup.
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Affiliation(s)
- Qiyuan Hu
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Abbas A Rizvi
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Geoffery Schau
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Kshitij Ingale
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Yoni Muller
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Rachel Baits
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Sebastian Pretzer
- Work done while at Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Aïcha BenTaieb
- Work done while at Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Abigail Gordhamer
- PathNet, Inc, 5100 Talley Rd Suite 300, Little Rock, AR, 72204, USA
- DDx Foundation, 2889 W Ashton Blvd. Suite 300, Lehi, UT, 84043, USA
| | - Roberto Nussenzveig
- PathNet, Inc, 5100 Talley Rd Suite 300, Little Rock, AR, 72204, USA
- DDx Foundation, 2889 W Ashton Blvd. Suite 300, Lehi, UT, 84043, USA
| | - Adam Cole
- PathNet, Inc, 5100 Talley Rd Suite 300, Little Rock, AR, 72204, USA
- DDx Foundation, 2889 W Ashton Blvd. Suite 300, Lehi, UT, 84043, USA
| | - Matthew O Leavitt
- PathNet, Inc, 5100 Talley Rd Suite 300, Little Rock, AR, 72204, USA
- DDx Foundation, 2889 W Ashton Blvd. Suite 300, Lehi, UT, 84043, USA
- Lumea, 2889 Ashton Blvd #300, Lehi, UT, 84043, USA
| | - Ryan D Jones
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Rohan P Joshi
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Nike Beaubier
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Martin C Stumpe
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA
| | - Kunal Nagpal
- Tempus AI, Inc, 600 W Chicago Ave #510, Chicago, IL, 60654, USA.
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Pankiw M, Brezden-Masley C, Charames GS. Comprehensive genomic profiling for oncological advancements by precision medicine. Med Oncol 2023; 41:1. [PMID: 37993657 DOI: 10.1007/s12032-023-02228-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 10/25/2023] [Indexed: 11/24/2023]
Abstract
Considerable advancements in next generation sequencing (NGS) techniques have sparked the use of comprehensive genomic profiling (CGP) as a guiding tool for precision-centered oncological treatments. The past two decades have seen the completion of the human genome project, and the consequential invention of NGS. High-throughput sequencing technologies support the discovery and commonplace use of individualized cancer treatments, specifically immune-centered checkpoint inhibitor therapies, and oncogene and tumor suppressor gene targeted therapies. Nevertheless, CGP is not commonly used in all clinical settings. This review investigates the clinically relevant applications of CGP. Studies published between the years 2000-2023 have shown substantial evidence of the benefits of integrating CGP into routine care practice, while also making important comparisons to current-standard oncological treatment strategies. Findings of a comprehensive genomic profile includes predictive, prognostic, and diagnostic biomarkers, together with somatic mutation identification which can indicate the efficacy of immunotherapies and molecularly guided therapies. This review highlights the importance of CGP in identifying driver mutations in tumors that subsequently can be effectively targeted with molecular therapeutics and lead to drug discovery, allowing for increased precision in treating tumors selectively based on their specific genetic mutations, thereby improving patient outcomes.
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Affiliation(s)
- Maya Pankiw
- Department of Medicine, Mount Sinai Hospital, Toronto, ON, Canada
- Department of Pathology and Lab Medicine, Mount Sinai Hospital, Toronto, ON, Canada
| | - Christine Brezden-Masley
- Department of Medicine, Mount Sinai Hospital, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - George S Charames
- Department of Pathology and Lab Medicine, Mount Sinai Hospital, Toronto, ON, Canada.
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
- Department of Lab Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.
- Mount Sinai Services, Toronto, ON, Canada.
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Dos Santos GA, Chatsirisupachai K, Avelar RA, de Magalhães JP. Transcriptomic analysis reveals a tissue-specific loss of identity during ageing and cancer. BMC Genomics 2023; 24:644. [PMID: 37884865 PMCID: PMC10604446 DOI: 10.1186/s12864-023-09756-w] [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] [Received: 02/20/2023] [Accepted: 10/20/2023] [Indexed: 10/28/2023] Open
Abstract
INTRODUCTION Understanding changes in cell identity in cancer and ageing is of great importance. In this work, we analyzed how gene expression changes in human tissues are associated with tissue specificity during cancer and ageing using transcriptome data from TCGA and GTEx. RESULTS We found significant downregulation of tissue-specific genes during ageing in 40% of the tissues analyzed, which suggests loss of tissue identity with age. For most cancer types, we have noted a consistent pattern of downregulation in genes that are specific to the tissue from which the tumor originated. Moreover, we observed in cancer an activation of genes not usually expressed in the tissue of origin as well as an upregulation of genes specific to other tissues. These patterns in cancer were associated with patient survival. The age of the patient, however, did not influence these patterns. CONCLUSION We identified loss of cellular identity in 40% of the tissues analysed during human ageing, and a clear pattern in cancer, where during tumorigenesis cells express genes specific to other organs while suppressing the expression of genes from their original tissue. The loss of cellular identity observed in cancer is associated with prognosis and is not influenced by age, suggesting that it is a crucial stage in carcinogenesis.
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Affiliation(s)
- Gabriel Arantes Dos Santos
- Laboratory of Medical Investigation (LIM55), Urology Department, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
- Genomics of Ageing and Rejuvenation Lab, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, B15 2WB, UK
| | - Kasit Chatsirisupachai
- Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, L7 8TX, UK
| | - Roberto A Avelar
- Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, L7 8TX, UK
| | - João Pedro de Magalhães
- Genomics of Ageing and Rejuvenation Lab, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, B15 2WB, UK.
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Dong Z, Zhang H, Chen Y, Payne PRO, Li F. Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling. Cancers (Basel) 2023; 15:4210. [PMID: 37686486 PMCID: PMC10486573 DOI: 10.3390/cancers15174210] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 09/10/2023] Open
Abstract
Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations, the major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusions of AI models untransparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in real-world human-AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and the mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, a self-attention-based node and edge pool (henceforth SANEpool), that can compute the attention score (importance) of genes and connections based on the genomic features and topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. Experiments on various well-adopted drug-synergy-prediction datasets demonstrate that (1) the SANEpool model has superior predictive ability to generate accurate synergy score prediction, and (2) the sub-molecular networks detected by the SANEpool are self-explainable and salient for identifying synergistic drug combinations.
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Affiliation(s)
- Zehao Dong
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; (Z.D.); (Y.C.)
| | - Heming Zhang
- Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; (H.Z.); (P.R.O.P.)
| | - Yixin Chen
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; (Z.D.); (Y.C.)
| | - Philip R. O. Payne
- Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; (H.Z.); (P.R.O.P.)
| | - Fuhai Li
- Institute for Informatics, Data Science, and Biostatistics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; (H.Z.); (P.R.O.P.)
- Department of Pediatrics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA
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Yang MR, Wu YW. A Cross-Validated Feature Selection (CVFS) approach for extracting the most parsimonious feature sets and discovering potential antimicrobial resistance (AMR) biomarkers. Comput Struct Biotechnol J 2022; 21:769-779. [PMID: 36698972 PMCID: PMC9842539 DOI: 10.1016/j.csbj.2022.12.046] [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: 08/06/2022] [Revised: 12/27/2022] [Accepted: 12/27/2022] [Indexed: 12/29/2022] Open
Abstract
Understanding genes and their underlying mechanisms is critical in deciphering how antimicrobial-resistant (AMR) bacteria withstand detrimental effects of antibiotic drugs. At the same time the genes related to AMR phenotypes may also serve as biomarkers for predicting whether a microbial strain is resistant to certain antibiotic drugs. We developed a Cross-Validated Feature Selection (CVFS) approach for robustly selecting the most parsimonious gene sets for predicting AMR activities from bacterial pan-genomes. The core idea behind the CVFS approach is interrogating features among non-overlapping sub-parts of the datasets to ensure the representativeness of the features. By randomly splitting the dataset into disjoint sub-parts, conducting feature selection within each sub-part, and intersecting the features shared by all sub-parts, the CVFS approach is able to achieve the goal of extracting the most representative features for yielding satisfactory AMR activity prediction accuracy. By testing this idea on bacterial pan-genome datasets, we showed that this approach was able to extract the most succinct feature sets that predicted AMR activities very well, indicating the potential of these genes as AMR biomarkers. The functional analysis demonstrated that the CVFS approach was able to extract both known AMR genes and novel ones, suggesting the capabilities of the algorithm in selecting relevant features and highlighting the potential of the novel genes in expanding the antimicrobial resistance gene databases.
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Affiliation(s)
- Ming-Ren Yang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, ROC,Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan, ROC
| | - Yu-Wei Wu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan, ROC,Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan, ROC,TMU Research Center for Digestive Medicine, Taipei Medical University, Taipei 110, Taiwan, ROC,Correspondence to: Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250, Wuxing St., Sinyi Distr., Taipei 110, Taiwan, ROC.
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6
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A narrative review of cancer molecular diagnostics: past, present, and future. JOURNAL OF BIO-X RESEARCH 2022. [DOI: 10.1097/jbr.0000000000000136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
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Bosch DE, Yeh MM, Salipante SJ, Jacobson A, Cohen SA, Konnick EQ, Paulson VA. Isolated MLH1 Loss by Immunohistochemistry Because of Benign Germline MLH1 Polymorphisms. JCO Precis Oncol 2022; 6:e2200227. [PMID: 36044719 PMCID: PMC9489174 DOI: 10.1200/po.22.00227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Mismatch repair (MMR) immunohistochemistry (IHC) is frequently used to inform prognosis, select (immuno-)therapy, and identify patients for heritable cancer syndrome testing. However, false-negative and false-positive MMR IHC interpretations have been described.
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Affiliation(s)
- Dustin E Bosch
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA.,Department of Pathology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA
| | - Matthew M Yeh
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA
| | - Stephen J Salipante
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA
| | - Angela Jacobson
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA
| | - Stacey A Cohen
- Division of Medical Oncology, Department of Medicine, University of Washington School of Medicine, Seattle, WA.,Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Eric Q Konnick
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA
| | - Vera A Paulson
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA
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Using pan-TRK and RET Immunohistochemistry for the Detection of Fusion Types of Salivary Gland Secretory Carcinoma. Appl Immunohistochem Mol Morphol 2021; 30:264-272. [DOI: 10.1097/pai.0000000000001003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 11/21/2021] [Indexed: 11/26/2022]
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9
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Zhang W, Yao J, Zhong M, Zhang Y, Guo X, Wang HY. A Brief Overview and Update on Major Molecular Genomic Alterations in Solid, Bone and Soft Tissue Tumors, Hematopoietic As Well As Lymphoid Malignancies. Arch Pathol Lab Med 2021; 145:1358-1366. [PMID: 34270703 DOI: 10.5858/arpa.2021-0077-ra] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/26/2021] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Recent advances in comprehensive genomic profiling by next-generation sequencing have uncovered the genomic alterations at the molecular level for many types of tumors; as such, numerous small specific molecules that target these alterations have been developed and widely used in the management of these cancers. OBJECTIVE.— To provide a concise molecular genomic update in solid, bone and soft tissue tumors, hematopoietic as well as lymphoid malignancies; discuss its clinical applications; and familiarize practicing pathologists with the emerging cancer biomarkers and their diagnostic utilities. DATA SOURCES.— This review is based on the National Comprehensive Cancer Network guidelines and peer-reviewed English literature. CONCLUSIONS.— Tumor-specific biomarkers and molecular/genomic alterations, including pan-cancer markers, have been significantly expanded in the past decade thanks to large-scale high-throughput technologies and will continue to emerge in the future. These biomarkers can be of great value in diagnosis, prognosis, and/or targeted therapy/treatment. Familiarization with these emerging and ever-changing tumor biomarkers will undoubtedly aid pathologists in making accurate and state-of-the-art diagnoses and enable them to be more actively involved in the care of cancer patients.
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Affiliation(s)
- Wei Zhang
- From the Department of Pathology and Laboratory Medicine, University of Wisconsin, Madison (W. Zhang).,W. Zhang and Yao are co-first authors.,W. Zhang and H.-Y. Wang are co-senior authors and supervised this manuscript equally
| | - Jinjuan Yao
- The Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York (Yao).,W. Zhang and Yao are co-first authors
| | - Minghao Zhong
- The Department of Pathology, Yale University School of Medicine, New Haven, Connecticut (Zhong)
| | - Yaxia Zhang
- The Department of Pathology and Laboratory Medicine, Hospital for Special Surgery, New York, New York (Y. Zhang).,The Department of Pathology and Laboratory Medicine, Weill Cornell College of Medicine, New York, New York (Y. Zhang)
| | - Xiaoling Guo
- The Department of Pathology, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, New York (Guo)
| | - Huan-You Wang
- The Department of Pathology, University of California San Diego, La Jolla (Wang).,W. Zhang and H.-Y. Wang are co-senior authors and supervised this manuscript equally
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