The challenge is broken down into three Crunches, ordered by increasing complexity.

Crunch 1 – Oct 28 to Feb 9 – Predict gene expression in spatial transcriptomics data from matched pathology images

Crunchers will build a model to predict the expression of 460 genes in held-out patches of colon tissue using H&E pathology images and Xenium spatial transcriptomics training data. Hematoxylin and Eosin (H&E) images provide insight into cell organization, while Xenium data add information on gene expression and cellular pathways of disease.

Crunch 2 – Nov 18 to Mar 21 – Predicting Unseen Genes

In this phase, participants will predict the expression of all protein-coding genes, including those that were not measured in the spatial training data, using single-cell RNA-seq data as support. This Crunch focuses on leveraging cell transcriptional profiles to enhance the predictive model’s ability to infer the expression of unknown genes in spatial contexts.

Crunch 3 – Dec 9 to Apr 18 (submission deadline) / Apr 30 (peer review deadline) – Identifying Gene Markers for Pre-cancerous Regions

Participants will rank genes by their ability to distinguish between dysplasia (pre-cancerous) regions and noncancerous tissue in IBD patients, increasing our ability to detect cancer early. The final gene panel will be chosen based on participant performance in Crunch 2 and on peer review of participants' methods taking place after the submission deadline. The gene panel will be experimentally validated in a new colon tissue with dysplasia, and all participants' ranked gene lists will be scored.

The best-performing models will be experimentally tested to validate their ability to predict cancer risk, which could lead to early detection and improved treatment options. The best models will be publish in an official publication from Broad Institute.

Participant Output Requirements

For each Crunch, participants must submit predictions in CSV format. Each submission must adhere to the provided log1p-normalization standards.

Outputs will be evaluated using:

Mean Squared Error (Crunch 1). To avoid overfitting Crunch will score all submitted models on a private dataset during two Checkpoints.
Spearman’s Correlation (Crunch 2)
Accuracy and Diversity Metrics (Crunch 3)

Evaluation Criteria

Performance will be evaluated through:

Accuracy in gene expression prediction (Crunch 1 & 2)
Gene panel design for distinguishing between noncancerous and dysplasia regions (Crunch 3)
Diversity of selected gene programs in Crunch 3, with extra emphasis on identifying unique biological pathways
Peer review of methods to select dysplasia gene panel in Crunch 3