Attention-based deep learning for analysis of pathology images and gene expression data in lung squamous premalignant lesions

Abstract Molecular and cellular alterations to the normal pseudostratified columnar bronchial epithelium results in the development of bronchial premalignant lesions representing a spectrum of histology from normal to hyperplasia, metaplasia, dysplasia (mild, moderate, and severe), carcinoma in situ and invasive carcinoma. Several studies have identified molecular alterations associated with lesion histology and progression. The broad and continuous spectrum of histologic and molecular changes makes reproducible stratification of lesions across multiple studies challenging. Here we propose a transformer-based framework that flexibly utilizes transcriptomic and histologic patterns to distinguish lesions with bronchial dysplasia or worse from normal, hyperplasia, and metaplasia. We leveraged H&E whole slide images (WSIs) of endobronchial biopsies and bulk gene expression data (GE) from previously published studies and on-going lung precancer atlas efforts obtained from patients as high-risk for lung cancer. Models trained using both WSIs and GE compared to a single data modality had higher performance. On an external testing dataset of WSIs, the area under the ROC curve (AUROC) of the model trained on WSIs plus GE was 0.761±0.015 compared to 0.690±0.027 for model trained on WSIs. On external testing datasets of GE, the AUROC of the model trained on WSIs plus GE was 0.890±0.023 versus 0.816±0.032 for a model trained on GE. Based on these results, we leveraged data across 4 studies to train a flexible fusion model that allows one or both data modalities to be used in training. The model achieved an AUROC of 0.809±0.036 on external testing WSIs data and 0.903±0.022 on external testing GE data. Despite model training on a binary label, model probabilities are associated with histologic grade and the model identifies gene expression alterations associated with bronchial dysplasia across multiple studies. This framework maps bronchial premalignant lesions that contain at least one data modality into a spectrum of disease. In the future, a framework trained on multiple data modalities may be useful in predicting premalignant disease severity, progression, and interception agent efficacy. Competing Interest Statement The authors have declared no competing interest. Funding Statement This work was supported by grants from the National Institutes of Health (National Cancer Institute R21-CA253498, National Cancer Institute U2C-CA233238, R01-HL159620, R01- AG062109, R01-AG083735, R01-NS142076 and National Center for Advancing Translational Sciences through BU-CTSI Grant Number 1UL1TR001430), Johnson & Johnson Enterprise Innovation, Inc., the American Association for Cancer Research under grant SU2C-AACR-DT23-17 and the American Heart Association (20SFRN35460031). This work was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through BU-CTSI Grant Number 1UL1TR001430. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The PCA Dataset 2 and DECAMP-1 data will be made public upon publication. The Lung PCA Consortium study was approved by the individual site IRBs for every participating site, which were centrally and annually reviewed for compliance by a multi-site office of research subject protection. All subjects were approached for and provided written informed consent to participate in the study in accordance with IRB regulations. Accrual, safety information, and adverse events are monitored by individual sites. The Detection of Early Lung Cancer Among Military Personnel (DECAMP) Consortium study was approved by the Human Research Protection Office of the Department of Defense and the individual site IRBs for every participating site. All subjects were approached for and provided written informed consent to participate in the study in accordance with IRB regulations. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present study are available upon reasonable request to the authors.