Abstract PO-023: Spatial genomics coupled with machine learning to identify p53-driven molecular signatures that are predictive of lung adenocarcinoma progression
Authors: Lockhart John H. , Lee Kyubum , Ackerman Hayley D. , Abdulah Mahmoud , Davis Andrew , Montey Nicole , Boyle Theresa , Saller James , Keske Aysenur , Hanggi Kay , Stringfield Olya , Tan Aik Choon , Flores Elsa R. 
Published: December 2020

Abstract
P53 is frequently mutated in a wide variety of tumors; yet, the regulation and expression of downstream targets during tumor progression and response to therapy is unknown. Here, we use Kras/p53-driven lung adenocarcinoma in the mouse as a model system to identify p53-driven molecular signatures that predict lung adenocarcinoma progression. Using these tumors, we have developed a digital pathology tool using machine learning to grade lung adenocarcinomas. To do this, we analyzed hematoxylin and eosin (H&E) from mouse models of lung adenocarcinoma with Kras (KrasG12D/+) and in combination with p53 mutations (KrasG12D/+; p53R172H/+ and KrasG12D/+; p53R270H/+) and loss of TAp73 (KrasG12D/+; TAp73Δtd/Δtd). After grading, slides were divided into approximately 100,000 patches with dimensions of 224 × 224 pixels (113 × 113 µm). A convolutional neural network (CNN) model based on ResNet18 was trained to classify normal lung tissue, normal airways, and the different grades (1–4) of lung adenocarcinoma using 16,000 patches of each class. The resulting classification maps were used for analyses of tumor burden and progression. Adjacent tissue sections used for immunohistochemistry were co-registered to mapped H&E sections to investigate correlation between protein expression and tumor grade. Our CNN demonstrated a strong correspondence with human pathologists on our holdout dataset (81% agreement). Because our CNN can assign different grades to different regions within an image patch, we also uncovered a high degree of intratumor heterogeneity that was missed by human pathologists who assigned grades to entire tumors in a homogeneous manner. Both the pathologists and the CNN reported a significant increase in the tumor burden of compound mutant mice (KrasG12D/+; p53R172H/+, KrasG12D/+; p53R270H/+, and KrasG12D/+; TAp73Δtd/Δtd) compared to KrasG12D/+ mice. In addition, compound mutant mice were noted to have a greater proportion of high-grade (3-4) tumors by both approaches. In conclusion, our CNN demonstrates a high degree of agreement with human pathologists. Furthermore, this computational approach drastically increases the resolution of tumor grading in our mouse models and can detect regions of different grades within a single tumor. We are currently using single cell transcriptome analysis to define p53-molecular signatures that predict lung adenocarcinoma progression and grade. Future work will expand this tool into a multidimensional digital pathology pipeline that can accelerate current investigations and reveal new therapeutic targets and prognostic markers.
Citation Format: John H. Lockhart, Kyubum Lee, Hayley D. Ackerman, Mahmoud Abdulah, Andrew Davis, Nicole Montey, Theresa Boyle, James Saller, Aysenur Keske, Kay Hanggi, Olya Stringfield, Aik Choon Tan, Elsa R. Flores. Spatial genomics coupled with machine learning to identify p53-driven molecular signatures that are predictive of lung adenocarcinoma progression [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-023.