Spatial-context-aware RNA-sequence prediction from head and neck cancer histopathology images

Sharma, Shreya; Ragothaman, Srikanth; Vahadane, Abhishek; Mandal, D. K.; Majumdar, Shantanu

doi:10.1109/embc46164.2021.9630754

Cited by 3 publications

(2 citation statements)

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“…This comprehensive approach not only represents a cost-efficient and scalable means of predicting tumor molecular phenotypes but also offers insights into intratumor variability and clinical applications, making it a valuable tool for cancer research and diagnostics. This paper presents a novel deep learning-based approach for predicting RNA-sequence expression (RNA-seq) from Hematoxylin and Eosin whole-slide images (H&E WSI) in head and neck cancer patients [27]. The study addresses the challenge of efficiently leveraging histopathological images to infer molecular profiles, which can be invaluable for targeted cancer therapies.…”

Section: Spatial Transcriptomics Has Emerged As a Powerful Tool To An...mentioning

confidence: 99%

“…This paper presents a novel deep learning-based approach for predicting RNA-sequence expression (RNA-seq) from Hematoxylin and Eosin whole-slide images (H&E WSI) in head and neck cancer patients [27]. The study addresses the challenge of efficiently leveraging histopathological images to infer molecular profiles, which can be invaluable for targeted cancer therapies.…”

Section: Related Workmentioning

confidence: 99%

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Integrating cellular graph embeddings with tumor morphological features to predict in-silico spatial transcriptomics from H&E images

Prabhakar,

Warner,

Liu

2023

Preprint

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Spatial transcriptomics allows precise RNA abundance measurement at high spatial resolution, linking cellular morphology with gene expression. We present a novel deep learning algorithm predicting local gene expression from histopathology images. Our approach employs a graph isomorphism neural network capturing cell-to-cell interactions in the tumor microenvironment and a Vision Transformer (CTransPath) for obtaining the tumor morphological features. Using a dataset of 30,612 spatially resolved gene expression profiles matched with histopathology images from 23 breast cancer patients, we identify 250 genes, including established breast cancer biomarkers, at a 100 µm resolution. Additionally, we co-train our algorithm on spatial spot-level transcriptomics from 10x Visium breast cancer data along with another variant of our algorithm on TCGA-BRCA bulk RNA Seq. data, yielding mutual benefits and enhancing predictive accuracy on both these datasets. This work enables image-based screening for molecular biomarkers with spatial variation, promising breakthroughs in cancer research and diagnostics.

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Section: Spatial Transcriptomics Has Emerged As a Powerful Tool To An...mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Integrating cellular graph embeddings with tumor morphological features to predict in-silico spatial transcriptomics from H&E images

Prabhakar,

Warner,

Liu

2023

Preprint

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Deep Frame-Point Sequence Consistent Network for Handwriting Trajectory Recovery

Xiong,

Dai,

Meng

2023

2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)

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A Novel High-Dimensional Kernel Joint Non-Negative Matrix Factorization With Multimodal Information for Lung Cancer Study

Shi,

Jin,

Deng

et al. 2024

IEEE J. Biomed. Health Inform.

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Spatial-context-aware RNA-sequence prediction from head and neck cancer histopathology images

Cited by 3 publications

References 10 publications

Integrating cellular graph embeddings with tumor morphological features to predict in-silico spatial transcriptomics from H&E images

Integrating cellular graph embeddings with tumor morphological features to predict in-silico spatial transcriptomics from H&E images

Deep Frame-Point Sequence Consistent Network for Handwriting Trajectory Recovery

A Novel High-Dimensional Kernel Joint Non-Negative Matrix Factorization With Multimodal Information for Lung Cancer Study

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