Code for Transcriptomics-guided Slide Representation Learning in Computational Pathology

Welcome to the official GitHub repository of our CVPR 2024 paper, "Transcriptomics-guided Slide Representation Learning in Computational Pathology". This project was developed by the Mahmood Lab at Harvard Medical School and Brigham and Women's Hospital.

[arXiv]

Highlights

This work introduces the first method for slide representation learning using multimodal pretraining. Slide representation learning defines a new class of self-supervised methods that aim at extracting information-rich embeddings of histology whole-slide images without using explicit supervision (such as cancer subtype or cancer grade). In this work, we introduce TANGLE, a method for Slide + Expression (S+E) pretraining. Conceptually, this method follows the CLIP principle widely employed in Vision-Language models. Here, we align the slide with its corresponding gene expression profile. The resulting slide encoder embeds the underlying molecular landscape of the tissue and can, as such, be used for various downstream tasks. In this work, we focus on morphological subtyping of breast and lung cancer and morphological lesion detection in pre-clinical drug safety studies.

Code

This repository contains the implementation of TANGLE, with step-by-step instructions to pretrain a TANGLE model on the TCGA-BRCA (Invasive breast cancer) cohort. The resulting models is evaluated using linear probing (logistic regression) on BCNC for ER/PR/HER2 status prediction. While TANGLE paper uses CTransPath and ResNet50-IN patch encoders, we provide instructions and checkpoints when using the UNI patch encoder (Chen et al., Nature Medicine, 2024).

In addition, we provide a script and checkpoint for pan-cancer TANGLE pretraining that aggregates all TCGA cohorts into a unified training. This model was not part of TANGLE and is referred to as TANGLEv2. More information will follow.

Installation

# Clone repo
git clone https://github.com/mahmoodlab/TANGLE
cd TANGLE

# Create conda env
conda create -n tangle
conda activate tangle
pip install -r requirements.txt

Preprocessing

This code assumes that you have already preprocessed the data, using (1) tissue segmentation, (2) patch coordinate extraction, and (3) patch encoding. In the original paper, we train TANGLE using 2 patch encoder: a ResNet50 features (pretrained on ImageNet) and CTransPath. Here, we use the recently proposed UNI model that leads to better downstream performance.

• Tissue segmentation, patching, and ResNet50 patch embedding extraction can be done using the CLAM toolbox.
• CTransPath patch embedding extraction can be done using their official implementation.
• UNI patch embedding extraction can be done following instruction here.

Preprocessing the corresponding gene expression profile can be done in several ways. For the TCGA cohorts, we used normalized RNA sequencing data available here.

Training on TCGA-BRCA

To simplify reproducing results, we provide a link to a Drive that includes (1) TCGA-BRCA UNI patch embeddings, and (2) the corresponding expression profiles as pt files. These need to be downloaded and moved to the base directory in a new dir called data. The folder structure should look like:

data
|__brca
   |____uni_features
        |__tcga_features
   |____rna

To train Tangle (and baselines), use:

# Train Tangle
source scripts/launch_tangle_training.sh

Training on all TCGA cohorts (Tanglev2)

Tanglev2 training assumes you have TCGA cohorts organized in the following format

tcga
|__brca
   |____uni_features
        |__slide_id_0.pt
        |__slide_id_1.pt
   |____molecular_data
        |____normed
          |__slide_id_0.pt
          |__slide_id_1.pt
.
.
.
|__ucec
   |____uni_features
        |__slide_id_0.pt
        |__slide_id_1.pt
   |____molecular_data
        |____normed
          |__slide_id_0.pt
          |__slide_id_1.pt

To train Tanglev2 on all TCGA cohorts, use:

# Train Tangle
source scripts/launch_tanglev2_training.sh

Note that due to storage constraints, we cannot provide pre-extracted patch embeddings and gene expression data.

Evaluate TANGLE (BRCA-trained) and TANGLEv2 (pancancer-trained) on BCNB molecular status prediction

We provide a link to a Drive that includes (1) 4 pretrained checkpoints for Tangle-PanCancer, Tangle-BRCA, Tangle-Rec and Intra, and (2) pre-extracted BCNC slide embeddings and evaluation. In addition, we provide two scripts for downstream evaluation on BCNB.

To run few-shot evaluation:

# Extract slide embeddings 
python extract_slide_embeddings_from_checkpoint.py --pretrained <PATH_TO_PRETRAINED_MODEL>
python run_linear_probing.py

AUC performance of these models is:

		k=1			k=10			k=25
	ER	PR	HER2	ER	PR	HER2	ER	PR	HER2
Tangle (BRCA)	0.681	0.579	0.514	0.826	0.752	0.651	0.847	0.77	0.664
Tanglev2 (Pancancer)	0.637	0.587	0.53	0.791	0.72	0.63	0.817	0.755	0.67
Tangle-Rec	0.693	0.57	0.505	0.811	0.735	0.603	0.82	0.755	0.651
Intra	0.56	0.516	0.496	0.692	0.636	0.571	0.737	0.678	0.625

Additional TANGLEv2 evaluation

AUC performance of TANGLEv2 on internal breast and lung cohorts for morphological and molecular subtyping.

	k=1	k=10	k=all
MGB Breast subtyping	0.661	0.891	0.975
MGB Breast ER	0.604	0.661	0.715
MGB Breast PR	0.645	0.638	0.651
MGB Breast HER2	0.509	0.59	0.685
MGB Lung subtyping	0.778	0.928	0.978
MGB Lung TTF-1	0.633	0.761	0.822
MGB Lung P40	0.609	0.74	0.837
MGB Lung P63	0.595	0.638	0.755
MGB Lung napsina	0.621	0.771	0.779
MGB Lung CDX-2	0.60	0.535	0.612
MGB Lung CD5-6	0.628	0.794	0.889

Balanced accuracy performance of TANGLEv2 on eBrains and BRACS.

	k=1	k=10	k=all
eBrains fine-grained	0.309	0.647	0.672
eBrains coarse	0.43	0.778	0.842
BRACS fine-grained	0.172	0.266	0.344
BRACS coarse	0.389	0.536	0.583

Issues

• The preferred mode of communication is via GitHub issues.
• If GitHub issues are inappropriate, email gjaume@bwh.harvard.edu (and cc avaidya@mit.edu).
• Immediate response to minor issues may not be available.

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{jaume2024transcriptomics,
  title={Transcriptomics-guided Slide Representation Learning in Computational Pathology},
  author={Jaume, Guillaume and Oldenburg, Lukas and Vaidya, Anurag Jayant and Chen, Richard J. and Williamson, Drew FK and Peeters, Thomas and Song, Andrew H. and Mahmood, Faisal},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2024}
}