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Tizhoosh H, Pantanowitz L. On image search in histopathology. J Pathol Inform 2024; 15:100375. [PMID: 38645985 PMCID: PMC11033156 DOI: 10.1016/j.jpi.2024.100375] [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: 02/15/2024] [Revised: 03/21/2024] [Accepted: 03/29/2024] [Indexed: 04/23/2024] Open
Abstract
Pathology images of histopathology can be acquired from camera-mounted microscopes or whole-slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent advancements in search technologies allow for implicit quantification of tissue morphology across diverse primary sites, facilitating comparisons, and enabling inferences about diagnosis, and potentially prognosis, and predictions for new patients when compared against a curated database of diagnosed and treated cases. In this article, we comprehensively review the latest developments in image search technologies for histopathology, offering a concise overview tailored for computational pathology researchers seeking effective, fast, and efficient image search methods in their work.
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Affiliation(s)
- H.R. Tizhoosh
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Liron Pantanowitz
- Department of Pathology, School of Medicine, University of Pittsburgh, PA, USA
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2
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Hoang DT, Dinstag G, Shulman ED, Hermida LC, Ben-Zvi DS, Elis E, Caley K, Sammut SJ, Sinha S, Sinha N, Dampier CH, Stossel C, Patil T, Rajan A, Lassoued W, Strauss J, Bailey S, Allen C, Redman J, Beker T, Jiang P, Golan T, Wilkinson S, Sowalsky AG, Pine SR, Caldas C, Gulley JL, Aldape K, Aharonov R, Stone EA, Ruppin E. A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics. NATURE CANCER 2024; 5:1305-1317. [PMID: 38961276 DOI: 10.1038/s43018-024-00793-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 06/06/2024] [Indexed: 07/05/2024]
Abstract
Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values. We show that DeepPT successfully predicts transcriptomics in all 16 The Cancer Genome Atlas cohorts tested and generalizes well to two independent datasets. ENLIGHT-DeepPT successfully predicts true responders in five independent patient cohorts involving four different treatments spanning six cancer types, with an overall odds ratio of 2.28 and a 39.5% increased response rate among predicted responders versus the baseline rate. Notably, its prediction accuracy, obtained without any training on the treatment data, is comparable to that achieved by directly predicting the response from the images, which requires specific training on the treatment evaluation cohorts.
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Affiliation(s)
- Danh-Tai Hoang
- Biological Data Science Institute, College of Science, Australian National University, Canberra, Australian Capital Territory, Australia.
| | | | - Eldad D Shulman
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Leandro C Hermida
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA, USA
- Tumor Microenvironment Center, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | - Katherine Caley
- Biological Data Science Institute, College of Science, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Stephen-John Sammut
- Breast Cancer Now Toby Robins Research Centre, Institute of Cancer Research, London, UK
- The Royal Marsden Hospital NHS Foundation Trust, London, UK
| | - Sanju Sinha
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Neelam Sinha
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Christopher H Dampier
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Chani Stossel
- Oncology Institute, Sheba Medical Center at Tel-Hashomer, Tel Aviv University, Tel Aviv, Israel
| | - Tejas Patil
- Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Arun Rajan
- Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Wiem Lassoued
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Julius Strauss
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Shania Bailey
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Clint Allen
- Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Jason Redman
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | | | - Peng Jiang
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Talia Golan
- Oncology Institute, Sheba Medical Center at Tel-Hashomer, Tel Aviv University, Tel Aviv, Israel
| | - Scott Wilkinson
- Laboratory of Genitourinary Cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Adam G Sowalsky
- Laboratory of Genitourinary Cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Sharon R Pine
- Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Carlos Caldas
- School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - James L Gulley
- Genitourinary Malignancy Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Kenneth Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | | | - Eric A Stone
- Biological Data Science Institute, College of Science, Australian National University, Canberra, Australian Capital Territory, Australia.
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
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Şenbabaoğlu Y, Prabhakar V, Khormali A, Eastham J, Liu E, Warner E, Nabet B, Srivastava M, Ballinger M, Liu K. MOSBY enables multi-omic inference and spatial biomarker discovery from whole slide images. Sci Rep 2024; 14:18271. [PMID: 39107505 PMCID: PMC11303705 DOI: 10.1038/s41598-024-69198-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 08/01/2024] [Indexed: 08/10/2024] Open
Abstract
The utility of deep neural nets has been demonstrated for mapping hematoxylin-and-eosin (H&E) stained image features to expression of individual genes. However, these models have not been employed to discover clinically relevant spatial biomarkers. Here we develop MOSBY (Multi-Omic translation of whole slide images for Spatial Biomarker discoverY) that leverages contrastive self-supervised pretraining to extract improved H&E whole slide images features, learns a mapping between image and bulk omic profiles (RNA, DNA, and protein), and utilizes tile-level information to discover spatial biomarkers. We validate MOSBY gene and gene set predictions with spatial transcriptomic and serially-sectioned CD8 IHC image data. We demonstrate that MOSBY-inferred colocalization features have survival-predictive power orthogonal to gene expression, and enable concordance indices highly competitive with survival-trained multimodal networks. We identify and validate (1) an ER stress-associated colocalization feature as a chemotherapy-specific risk factor in lung adenocarcinoma, and (2) the colocalization of T effector cell vs cysteine signatures as a negative prognostic factor in multiple cancer indications. The discovery of clinically relevant biologically interpretable spatial biomarkers showcases the utility of the model in unraveling novel insights in cancer biology as well as informing clinical decision-making.
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Affiliation(s)
| | | | | | - Jeff Eastham
- Genentech, Inc., South San Francisco, CA, 94080, USA
| | - Evan Liu
- Genentech, Inc., South San Francisco, CA, 94080, USA
| | - Elisa Warner
- Genentech, Inc., South San Francisco, CA, 94080, USA
| | - Barzin Nabet
- Genentech, Inc., South San Francisco, CA, 94080, USA
| | | | | | - Kai Liu
- Genentech, Inc., South San Francisco, CA, 94080, USA.
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Miyahira AK, Kamran SC, Jamaspishvili T, Marshall CH, Maxwell KN, Parolia A, Zorko NA, Pienta KJ, Soule HR. Disrupting prostate cancer research: Challenge accepted; report from the 2023 Coffey-Holden Prostate Cancer Academy Meeting. Prostate 2024; 84:993-1015. [PMID: 38682886 DOI: 10.1002/pros.24721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 04/16/2024] [Indexed: 05/01/2024]
Abstract
INTRODUCTION The 2023 Coffey-Holden Prostate Cancer Academy (CHPCA) Meeting, themed "Disrupting Prostate Cancer Research: Challenge Accepted," was convened at the University of California, Los Angeles, Luskin Conference Center, in Los Angeles, CA, from June 22 to 25, 2023. METHODS The 2023 marked the 10th Annual CHPCA Meeting, a discussion-oriented scientific think-tank conference convened annually by the Prostate Cancer Foundation, which centers on innovative and emerging research topics deemed pivotal for advancing critical unmet needs in prostate cancer research and clinical care. The 2023 CHPCA Meeting was attended by 81 academic investigators and included 40 talks across 8 sessions. RESULTS The central topic areas covered at the meeting included: targeting transcription factor neo-enhancesomes in cancer, AR as a pro-differentiation and oncogenic transcription factor, why few are cured with androgen deprivation therapy and how to change dogma to cure metastatic prostate cancer without castration, reducing prostate cancer morbidity and mortality with genetics, opportunities for radiation to enhance therapeutic benefit in oligometastatic prostate cancer, novel immunotherapeutic approaches, and the new era of artificial intelligence-driven precision medicine. DISCUSSION This article provides an overview of the scientific presentations delivered at the 2023 CHPCA Meeting, such that this knowledge can help in facilitating the advancement of prostate cancer research worldwide.
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Affiliation(s)
- Andrea K Miyahira
- Science Department, Prostate Cancer Foundation, Santa Monica, California, USA
| | - Sophia C Kamran
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tamara Jamaspishvili
- Department of Pathology and Laboratory Medicine, SUNY Upstate Medical University, Syracuse, New York, USA
| | - Catherine H Marshall
- Department of Oncology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kara N Maxwell
- Department of Medicine-Hematology/Oncology and Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Medicine Service, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA
| | - Abhijit Parolia
- Department of Pathology, Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan, USA
| | - Nicholas A Zorko
- Division of Hematology, Oncology and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, Minnesota, USA
- University of Minnesota Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, USA
| | - Kenneth J Pienta
- The James Buchanan Brady Urological Institute, The Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Howard R Soule
- Science Department, Prostate Cancer Foundation, Santa Monica, California, USA
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Jaume G, Peeters T, Song AH, Pettit R, Williamson DFK, Oldenburg L, Vaidya A, de Brot S, Chen RJ, Thiran JP, Le LP, Gerber G, Mahmood F. AI-driven Discovery of Morphomolecular Signatures in Toxicology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.19.604355. [PMID: 39091765 PMCID: PMC11291055 DOI: 10.1101/2024.07.19.604355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Early identification of drug toxicity is essential yet challenging in drug development. At the preclinical stage, toxicity is assessed with histopathological examination of tissue sections from animal models to detect morphological lesions. To complement this analysis, toxicogenomics is increasingly employed to understand the mechanism of action of the compound and ultimately identify lesion-specific safety biomarkers for which in vitro assays can be designed. However, existing works that aim to identify morphological correlates of expression changes rely on qualitative or semi-quantitative morphological characterization and remain limited in scale or morphological diversity. Artificial intelligence (AI) offers a promising approach for quantitatively modeling this relationship at an unprecedented scale. Here, we introduce GEESE, an AI model designed to impute morphomolecular signatures in toxicology data. Our model was trained to predict 1,536 gene targets on a cohort of 8,231 hematoxylin and eosin-stained liver sections from Rattus norvegicus across 127 preclinical toxicity studies. The model, evaluated on 2,002 tissue sections from 29 held-out studies, can yield pseudo-spatially resolved gene expression maps, which we correlate with six key drug-induced liver injuries (DILI). From the resulting 25 million lesion-expression pairs, we established quantitative relations between up and downregulated genes and lesions. Validation of these signatures against toxicogenomic databases, pathway enrichment analyses, and human hepatocyte cell lines asserted their relevance. Overall, our study introduces new methods for characterizing toxicity at an unprecedented scale and granularity, paving the way for AI-driven discovery of toxicity biomarkers.
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Affiliation(s)
- Guillaume Jaume
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA
| | - Thomas Peeters
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Signal Processing Laboratory, EPFL, Lausanne, Switzerland
| | - Andrew H. Song
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA
| | - Rowland Pettit
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Drew F. K. Williamson
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Pathology & Laboratory Medicine, Emory University School of Medicine, Atlanta, GA
| | - Lukas Oldenburg
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Anurag Vaidya
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA
| | - Simone de Brot
- Institute of Animal Pathology, Vetsuisse, University of Bern, Switzerland
- COMPATH, Institute of Animal Pathology, University of Bern, Switzerland
- Bern Center for Precision Medicine, University of Bern, Switzerland
| | - Richard J. Chen
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA
| | | | - Long Phi Le
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Harvard Data Science Initiative, Harvard University, Cambridge, MA
| | - Georg Gerber
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Health Sciences and Technology, Harvard-MIT, Cambridge, MA
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA
- Harvard Data Science Initiative, Harvard University, Cambridge, MA
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Hoang DT, Shulman ED, Turakulov R, Abdullaev Z, Singh O, Campagnolo EM, Lalchungnunga H, Stone EA, Nasrallah MP, Ruppin E, Aldape K. Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning. Nat Med 2024; 30:1952-1961. [PMID: 38760587 DOI: 10.1038/s41591-024-02995-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 04/11/2024] [Indexed: 05/19/2024]
Abstract
Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal treatment. DNA methylation profiles, which capture the methylation status of thousands of individual CpG sites, are state-of-the-art data-driven means to enhance diagnostic accuracy but are also time consuming and not widely available. Here, to address these limitations, we developed Deep lEarning from histoPathoLOgy and methYlation (DEPLOY), a deep learning model that classifies CNS tumors to ten major categories from histopathology. DEPLOY integrates three distinct components: the first classifies CNS tumors directly from slide images ('direct model'), the second initially generates predictions for DNA methylation beta values, which are subsequently used for tumor classification ('indirect model'), and the third classifies tumor types directly from routinely available patient demographics. First, we find that DEPLOY accurately predicts beta values from histopathology images. Second, using a ten-class model trained on an internal dataset of 1,796 patients, we predict the tumor categories in three independent external test datasets including 2,156 patients, achieving an overall accuracy of 95% and balanced accuracy of 91% on samples that are predicted with high confidence. These results showcase the potential future use of DEPLOY to assist pathologists in diagnosing CNS tumors within a clinically relevant short time frame.
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Affiliation(s)
- Danh-Tai Hoang
- Biological Data Science Institute, College of Science, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Eldad D Shulman
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Rust Turakulov
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Zied Abdullaev
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Omkar Singh
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Emma M Campagnolo
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - H Lalchungnunga
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Eric A Stone
- Biological Data Science Institute, College of Science, Australian National University, Canberra, Australian Capital Territory, Australia
| | - MacLean P Nasrallah
- Division of Neuropathology, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
| | - Kenneth Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
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Jasti J, Zhong H, Panwar V, Jarmale V, Miyata J, Carrillo D, Christie A, Rakheja D, Modrusan Z, Kadel EE, Beig N, Huseni M, Brugarolas J, Kapur P, Rajaram S. Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response in Renal Cancer Clinical Trial. ARXIV 2024:arXiv:2405.18327v1. [PMID: 38855551 PMCID: PMC11160863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Background Predictive biomarkers of treatment response are lacking for metastatic clearcell renal cell carcinoma (ccRCC), a tumor type that is treated with angiogenesis inhibitors, immune checkpoint inhibitors, mTOR inhibitors and a HIF2 inhibitor. The Angioscore, an RNA-based quantification of angiogenesis, is arguably the best candidate to predict anti-angiogenic (AA) response. However, the clinical adoption of transcriptomic assays faces several challenges including standardization, time delay, and high cost. Further, ccRCC tumors are highly heterogenous, and sampling multiple areas for sequencing is impractical. Approach Here we present a novel deep learning (DL) approach to predict the Angioscore from ubiquitous histopathology slides. In order to overcome the lack of interpretability, one of the biggest limitations of typical DL models, our model produces a visual vascular network which is the basis of the model's prediction. To test its reliability, we applied this model to multiple cohorts including a clinical trial dataset. Results Our model accurately predicts the RNA-based Angioscore on multiple independent cohorts (spearman correlations of 0.77 and 0.73). Further, the predictions help unravel meaningful biology such as association of angiogenesis with grade, stage, and driver mutation status. Finally, we find our model is able to predict response to AA therapy, in both a real-world cohort and the IMmotion150 clinical trial. The predictive power of our model vastly exceeds that of CD31, a marker of vasculature, and nearly rivals the performance (c-index 0.66 vs 0.67) of the ground truth RNA-based Angioscore at a fraction of the cost. Conclusion By providing a robust yet interpretable prediction of the Angioscore from histopathology slides alone, our approach offers insights into angiogenesis biology and AA treatment response.
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Affiliation(s)
- Jay Jasti
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Hua Zhong
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Vandana Panwar
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Vipul Jarmale
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jeffrey Miyata
- Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Deyssy Carrillo
- Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Alana Christie
- Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
- O'Donnell School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas
| | - Dinesh Rakheja
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | | | - Niha Beig
- Genentech, South San Francisco, CA, USA
| | | | - James Brugarolas
- Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Internal Medicine (Hematology-Oncology), University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Payal Kapur
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Satwik Rajaram
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Murchan P, Baird AM, Ó Broin P, Sheils O, Finn SP. Surrogate Biomarker Prediction from Whole-Slide Images for Evaluating Overall Survival in Lung Adenocarcinoma. Diagnostics (Basel) 2024; 14:462. [PMID: 38472935 DOI: 10.3390/diagnostics14050462] [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: 01/23/2024] [Revised: 02/14/2024] [Accepted: 02/16/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Recent advances in computational pathology have shown potential in predicting biomarkers from haematoxylin and eosin (H&E) whole-slide images (WSI). However, predicting the outcome directly from WSIs remains a substantial challenge. In this study, we aimed to investigate how gene expression, predicted from WSIs, could be used to evaluate overall survival (OS) in patients with lung adenocarcinoma (LUAD). METHODS Differentially expressed genes (DEGs) were identified from The Cancer Genome Atlas (TCGA)-LUAD cohort. Cox regression analysis was performed on DEGs to identify the gene prognostics of OS. Attention-based multiple instance learning (AMIL) models were trained to predict the expression of identified prognostic genes from WSIs using the TCGA-LUAD dataset. Models were externally validated in the Clinical Proteomic Tumour Analysis Consortium (CPTAC)-LUAD dataset. The prognostic value of predicted gene expression values was then compared to the true gene expression measurements. RESULTS The expression of 239 prognostic genes could be predicted in TCGA-LUAD with cross-validated Pearson's R > 0.4. Predicted gene expression demonstrated prognostic performance, attaining a cross-validated concordance index of up to 0.615 in TCGA-LUAD through Cox regression. In total, 36 genes had predicted expression in the external validation cohort that was prognostic of OS. CONCLUSIONS Gene expression predicted from WSIs is an effective method of evaluating OS in patients with LUAD. These results may open up new avenues of cost- and time-efficient prognosis assessment in LUAD treatment.
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Affiliation(s)
- Pierre Murchan
- Department of Histopathology and Morbid Anatomy, Trinity Translational Medicine Institute, Trinity College Dublin, D08 W9RT Dublin, Ireland
- The SFI Centre for Research Training in Genomics Data Science, University of Galway, H91 CF50 Galway, Ireland
- Trinity St. James's Cancer Institute (TSJCI), St. James's Hospital, D08 RX0X Dublin, Ireland
| | - Anne-Marie Baird
- Trinity St. James's Cancer Institute (TSJCI), St. James's Hospital, D08 RX0X Dublin, Ireland
- School of Medicine, Trinity Translational Medicine Institute, Trinity College Dublin, D02 A440 Dublin, Ireland
| | - Pilib Ó Broin
- School of Mathematical & Statistical Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Orla Sheils
- Department of Histopathology and Morbid Anatomy, Trinity Translational Medicine Institute, Trinity College Dublin, D08 W9RT Dublin, Ireland
- Trinity St. James's Cancer Institute (TSJCI), St. James's Hospital, D08 RX0X Dublin, Ireland
| | - Stephen P Finn
- Department of Histopathology and Morbid Anatomy, Trinity Translational Medicine Institute, Trinity College Dublin, D08 W9RT Dublin, Ireland
- Trinity St. James's Cancer Institute (TSJCI), St. James's Hospital, D08 RX0X Dublin, Ireland
- Department of Histopathology, St. James's Hospital, James's Street, D08 X4RX Dublin, Ireland
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9
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Shafique A, Gonzalez R, Pantanowitz L, Tan PH, Machado A, Cree IA, Tizhoosh HR. A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks. Mod Pathol 2024; 37:100381. [PMID: 37939901 PMCID: PMC10891482 DOI: 10.1016/j.modpat.2023.100381] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/26/2023] [Accepted: 10/31/2023] [Indexed: 11/10/2023]
Abstract
Breast cancer is one of the most common cancers affecting women worldwide. It includes a group of malignant neoplasms with a variety of biological, clinical, and histopathologic characteristics. There are more than 35 different histologic forms of breast lesions that can be classified and diagnosed histologically according to cell morphology, growth, and architecture patterns. Recently, deep learning, in the field of artificial intelligence, has drawn a lot of attention for the computerized representation of medical images. Searchable digital atlases can provide pathologists with patch-matching tools, allowing them to search among evidently diagnosed and treated archival cases, a technology that may be regarded as computational second opinion. In this study, we indexed and analyzed the World Health Organization breast taxonomy (Classification of Tumors fifth ed.) spanning 35 tumor types. We visualized all tumor types using deep features extracted from a state-of-the-art deep-learning model, pretrained on millions of diagnostic histopathology images from the Cancer Genome Atlas repository. Furthermore, we tested the concept of a digital "atlas" as a reference for search and matching with rare test cases. The patch similarity search within the World Health Organization breast taxonomy data reached >88% accuracy when validating through "majority vote" and >91% accuracy when validating using top n tumor types. These results show for the first time that complex relationships among common and rare breast lesions can be investigated using an indexed digital archive.
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Affiliation(s)
- Abubakr Shafique
- Rhazes Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota; Kimia Lab, University of Waterloo, Waterloo, Ontario, Canada
| | - Ricardo Gonzalez
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Puay Hoon Tan
- Women's Imaging Centre, Luma Medical Centre, Singapore
| | - Alberto Machado
- WHO Classification of Tumours Group, International Agency for Research on Cancer, Lyon, France
| | - Ian A Cree
- WHO Classification of Tumours Group, International Agency for Research on Cancer, Lyon, France
| | - Hamid R Tizhoosh
- Rhazes Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota; Kimia Lab, University of Waterloo, Waterloo, Ontario, Canada.
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10
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Zheng Y, Pizurica M, Carrillo-Perez F, Noor H, Yao W, Wohlfart C, Marchal K, Vladimirova A, Gevaert O. Digital profiling of cancer transcriptomes from histology images with grouped vision attention. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.28.560068. [PMID: 37808782 PMCID: PMC10557714 DOI: 10.1101/2023.09.28.560068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we develop SEQUOIA, a transformer model to predict cancer transcriptomes from whole-slide histology images. To enable the full potential of transformers, we first pre-train the model using data from 1,802 normal tissues. Then, we fine-tune and evaluate the model in 4,331 tumor samples across nine cancer types. The prediction performance is assessed at individual gene levels and pathway levels through Pearson correlation analysis and root mean square error. The generalization capacity is validated across two independent cohorts comprising 1,305 tumors. In predicting the expression levels of 25,749 genes, the highest performance is observed in cancers from breast, kidney and lung, where SEQUOIA accurately predicts the expression of 11,069, 10,086 and 8,759 genes, respectively. The accurately predicted genes are associated with the regulation of inflammatory response, cell cycles and metabolisms. While the model is trained at the tissue level, we showcase its potential in predicting spatial gene expression patterns using spatial transcriptomics datasets. Leveraging the prediction performance, we develop a digital gene expression signature that predicts the risk of recurrence in breast cancer. SEQUOIA deciphers clinically relevant gene expression patterns from histology images, opening avenues for improved cancer management and personalized therapies.
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Affiliation(s)
- Yuanning Zheng
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA
| | - Marija Pizurica
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA
- Internet technology and Data science Lab (IDLab), Ghent University, Technologiepark-Zwijnaarde 126, Ghent, 9052, Gent, Belgium
| | - Francisco Carrillo-Perez
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA
| | - Humaira Noor
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA
| | - Wei Yao
- Roche Information Solutions, Roche Diagnostics Corporation, Santa Clara, California, USA
| | | | - Kathleen Marchal
- Internet technology and Data science Lab (IDLab), Ghent University, Technologiepark-Zwijnaarde 126, Ghent, 9052, Gent, Belgium
| | - Antoaneta Vladimirova
- Roche Information Solutions, Roche Diagnostics Corporation, Santa Clara, California, USA
| | - Olivier Gevaert
- Department of Medicine, Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, 94305, USA
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, USA
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11
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Dawood M, Eastwood M, Jahanifar M, Young L, Ben-Hur A, Branson K, Jones L, Rajpoot N, Minhas FUAA. Cross-linking breast tumor transcriptomic states and tissue histology. Cell Rep Med 2023; 4:101313. [PMID: 38118424 PMCID: PMC10783602 DOI: 10.1016/j.xcrm.2023.101313] [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: 04/20/2023] [Revised: 09/08/2023] [Accepted: 11/14/2023] [Indexed: 12/22/2023]
Abstract
Identification of the gene expression state of a cancer patient from routine pathology imaging and characterization of its phenotypic effects have significant clinical and therapeutic implications. However, prediction of expression of individual genes from whole slide images (WSIs) is challenging due to co-dependent or correlated expression of multiple genes. Here, we use a purely data-driven approach to first identify groups of genes with co-dependent expression and then predict their status from WSIs using a bespoke graph neural network. These gene groups allow us to capture the gene expression state of a patient with a small number of binary variables that are biologically meaningful and carry histopathological insights for clinical and therapeutic use cases. Prediction of gene expression state based on these gene groups allows associating histological phenotypes (cellular composition, mitotic counts, grading, etc.) with underlying gene expression patterns and opens avenues for gaining biological insights from routine pathology imaging directly.
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Affiliation(s)
- Muhammad Dawood
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK.
| | - Mark Eastwood
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK
| | | | - Lawrence Young
- Warwick Medical School, University of Warwick, Coventry, UK; Cancer Research Centre, University of Warwick, Coventry, UK
| | - Asa Ben-Hur
- Department of Computer Science, Colorado State University, Fort Collins, CO, USA
| | - Kim Branson
- Artificial Intelligence & Machine Learning, GlaxoSmithKline, San Francisco, CA, USA
| | - Louise Jones
- Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Nasir Rajpoot
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK; The Alan Turing Institute, London, UK
| | - Fayyaz Ul Amir Afsar Minhas
- Tissue Image Analytics Centre, University of Warwick, Coventry, UK; Cancer Research Centre, University of Warwick, Coventry, UK.
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12
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Tavolara TE, Su Z, Gurcan MN, Niazi MKK. One label is all you need: Interpretable AI-enhanced histopathology for oncology. Semin Cancer Biol 2023; 97:70-85. [PMID: 37832751 DOI: 10.1016/j.semcancer.2023.09.006] [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: 10/24/2022] [Revised: 09/06/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.
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Affiliation(s)
- Thomas E Tavolara
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Ziyu Su
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Metin N Gurcan
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - M Khalid Khan Niazi
- Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
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13
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Waqas A, Bui MM, Glassy EF, El Naqa I, Borkowski P, Borkowski AA, Rasool G. Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models. J Transl Med 2023; 103:100255. [PMID: 37757969 DOI: 10.1016/j.labinv.2023.100255] [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: 04/14/2023] [Revised: 09/06/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023] Open
Abstract
Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows pathologists to view and analyze microscopic images on a computer monitor, enabling computational pathology. By leveraging artificial intelligence (AI) and machine learning (ML), computational pathology has emerged as a promising field in recent years. Recently, task-specific AI/ML (eg, convolutional neural networks) has risen to the forefront, achieving above-human performance in many image-processing and computer vision tasks. The performance of task-specific AI/ML models depends on the availability of many annotated training datasets, which presents a rate-limiting factor for AI/ML development in pathology. Task-specific AI/ML models cannot benefit from multimodal data and lack generalization, eg, the AI models often struggle to generalize to new datasets or unseen variations in image acquisition, staining techniques, or tissue types. The 2020s are witnessing the rise of foundation models and generative AI. A foundation model is a large AI model trained using sizable data, which is later adapted (or fine-tuned) to perform different tasks using a modest amount of task-specific annotated data. These AI models provide in-context learning, can self-correct mistakes, and promptly adjust to user feedback. In this review, we provide a brief overview of recent advances in computational pathology enabled by task-specific AI, their challenges and limitations, and then introduce various foundation models. We propose to create a pathology-specific generative AI based on multimodal foundation models and present its potentially transformative role in digital pathology. We describe different use cases, delineating how it could serve as an expert companion of pathologists and help them efficiently and objectively perform routine laboratory tasks, including quantifying image analysis, generating pathology reports, diagnosis, and prognosis. We also outline the potential role that foundation models and generative AI can play in standardizing the pathology laboratory workflow, education, and training.
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Affiliation(s)
- Asim Waqas
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; Department of Electrical Engineering, University of South Florida, Tampa, Florida.
| | - Marilyn M Bui
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; Department of Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; University of South Florida, Morsani College of Medicine, Tampa, Florida
| | - Eric F Glassy
- Affiliated Pathologists Medical Group, Inc., Rancho Dominguez, California
| | - Issam El Naqa
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Piotr Borkowski
- Quest Diagnostics/Ameripath, Tampa, Florida; Center of Excellence for Digital and AI-Empowered Pathology, Quest Diagnostics, Tampa, Florida
| | - Andrew A Borkowski
- University of South Florida, Morsani College of Medicine, Tampa, Florida; James A. Haley Veterans' Hospital, Tampa, Florida; National Artificial Intelligence Institute, Washington, District of Columbia
| | - Ghulam Rasool
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; Department of Electrical Engineering, University of South Florida, Tampa, Florida; University of South Florida, Morsani College of Medicine, Tampa, Florida; Department of Neuro-Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
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14
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Atabansi CC, Nie J, Liu H, Song Q, Yan L, Zhou X. A survey of Transformer applications for histopathological image analysis: New developments and future directions. Biomed Eng Online 2023; 22:96. [PMID: 37749595 PMCID: PMC10518923 DOI: 10.1186/s12938-023-01157-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 09/15/2023] [Indexed: 09/27/2023] Open
Abstract
Transformers have been widely used in many computer vision challenges and have shown the capability of producing better results than convolutional neural networks (CNNs). Taking advantage of capturing long-range contextual information and learning more complex relations in the image data, Transformers have been used and applied to histopathological image processing tasks. In this survey, we make an effort to present a thorough analysis of the uses of Transformers in histopathological image analysis, covering several topics, from the newly built Transformer models to unresolved challenges. To be more precise, we first begin by outlining the fundamental principles of the attention mechanism included in Transformer models and other key frameworks. Second, we analyze Transformer-based applications in the histopathological imaging domain and provide a thorough evaluation of more than 100 research publications across different downstream tasks to cover the most recent innovations, including survival analysis and prediction, segmentation, classification, detection, and representation. Within this survey work, we also compare the performance of CNN-based techniques to Transformers based on recently published papers, highlight major challenges, and provide interesting future research directions. Despite the outstanding performance of the Transformer-based architectures in a number of papers reviewed in this survey, we anticipate that further improvements and exploration of Transformers in the histopathological imaging domain are still required in the future. We hope that this survey paper will give readers in this field of study a thorough understanding of Transformer-based techniques in histopathological image analysis, and an up-to-date paper list summary will be provided at https://github.com/S-domain/Survey-Paper .
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Affiliation(s)
| | - Jing Nie
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China.
| | - Haijun Liu
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China
| | - Qianqian Song
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China
| | - Lingfeng Yan
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China
| | - Xichuan Zhou
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China.
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15
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Hoang DT, Dinstag G, Hermida LC, Ben-Zvi DS, Elis E, Caley K, Sammut SJ, Sinha S, Sinha N, Dampier CH, Stossel C, Patil T, Rajan A, Lassoued W, Strauss J, Bailey S, Allen C, Redman J, Beker T, Jiang P, Golan T, Wilkinson S, Sowalsky AG, Pine SR, Caldas C, Gulley JL, Aldape K, Aharonov R, Stone EA, Ruppin E. Prediction of cancer treatment response from histopathology images through imputed transcriptomics. RESEARCH SQUARE 2023:rs.3.rs-3193270. [PMID: 37790315 PMCID: PMC10543028 DOI: 10.21203/rs.3.rs-3193270/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Our key contribution is showing that ENLIGHT-DeepPT successfully predicts true responders in five independent patients' cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, which needs to be trained and tested on the same cohort. ENLIGHT-DeepPT future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries.
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Affiliation(s)
- Danh-Tai Hoang
- Biological Data Science Institute, College of Science, Australian National University, Canberra, ACT, Australia
| | | | - Leandro C. Hermida
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA, USA
- Tumor Microenvironment Center, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | - Katherine Caley
- Biological Data Science Institute, College of Science, Australian National University, Canberra, ACT, Australia
| | - Stephen-John Sammut
- Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, United Kingdom
- The Royal Marsden Hospital NHS Foundation Trust, London, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Sanju Sinha
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Neelam Sinha
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Christopher H. Dampier
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Chani Stossel
- Oncology Institute, Sheba Medical Center at Tel-Hashomer, Tel Aviv University, Tel Aviv, Israel
| | - Tejas Patil
- Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Arun Rajan
- Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Wiem Lassoued
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Julius Strauss
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Shania Bailey
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Clint Allen
- Surgical Oncology Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Jason Redman
- Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | | | - Peng Jiang
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Talia Golan
- Oncology Institute, Sheba Medical Center at Tel-Hashomer, Tel Aviv University, Tel Aviv, Israel
| | - Scott Wilkinson
- Laboratory of Genitourinary Cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Adam G. Sowalsky
- Laboratory of Genitourinary Cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Sharon R. Pine
- Division of Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Carlos Caldas
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - James L. Gulley
- Genitourinary Malignancy Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Kenneth Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | | | - Eric A. Stone
- Biological Data Science Institute, College of Science, Australian National University, Canberra, ACT, Australia
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
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