This repository is under review for potential modification in compliance with Administration directives.

This repository is developed by Sage Bionetworks to host and share resources related to cancer research, and remains fully operational. We continue to maintain and accept cancer-related data and resources throughout this review process.

Welcome to the Cancer Complexity Knowledge Portal

Discover and explore resources about cancer, metastasis, tumor-immune microenvironment, drug resistance, tumor heterogeneity, software analysis tools, tumor progression

The NCI Division of Cancer Biology supports multiple research programs composed of interdisciplinary communities of scientists who aim to integrate approaches, data, and tools to address important questions in basic and translational cancer research. Discover and download datasets, publications, and other resources generated by these programs.

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What is the Cancer Complexity Knowledge Portal?

The Cancer Complexity Knowledge Portal (CCKP) integrates robust, curated datasets, publications, grant information, and other resources, to streamline your ability to identify relevant data, refine analyses, and share insights with collaborators. With the CCKP, you can more efficiently connect the dots across multiple studies, advancing our understanding of cancer biology and accelerating innovations in prevention, diagnosis, and treatment.

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Browse data, publications, and tools.

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Search, filter, and refine to find the right data.

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Download datasets, publications, and tools.

What Resources are Available?

Looking for funding or collaboration? Find active cancer research programs, connect with leading investigators, and explore opportunities to advance your your work.

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Need high quality cancer datasets? Access available datasets from cancer specimens and model systems hosted across various repositories in one spot.

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Building or refining analytical tools? Or looking for an appropriate tool for your analysis? Leverage investigator developed software, models, or algorithms to explore cancer complexity and extract deeper insights.

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Seeking the latest research breakthroughs? Browse peer reviewed studies filtered by disease, experiment, or research theme to stay informed or guide your next steps.

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Interested in learning more about cancer research or running your own training event? Explore existing resources and adapt templates to meet your training goals for different audiences and education levels.

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What Research Themes are Scientists Currently Focusing On?

What's New

August 2026

New Dataset: Visualizing Programmed Lineage Trajectories at Cancer Initiation

Screening and treating early breast lesions (DCIS) hasn't done much to lower breast cancer deaths, a sign that some tumors are already headed down a dangerous path before doctors can catch them. This dataset captures one such fork in the road. Using a cell-tracking technique, researchers followed tumor lineages in mice with different HER2 …

August 2026

Cancer Complexity Toolkit Feature: dynamo-release

dynamo-release answers a hard question in single-cell cancer biology: not just what state a cell is in, but where it's headed. This open-source Python package infers RNA velocity from conventional or metabolic-labeling scRNA-seq, builds a continuous vector field of expression dynamics, and uses it to run in silico perturbations and predict cell fate. It's …

July 2026

New Dataset: A Targetable Secreted Neural Protein Drives Pancreatic Cancer Metastatic Colonization

Pancreatic cancer kills about 90% of patients, mostly because it spreads to other organs. This dataset helps explain how. Researchers found that aggressive pancreatic cancer cells make more of a protein called NPTX1, which acts through a receptor called AMIGO2 to help tumors take hold in the liver. To see what changes as cells …

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The Cancer Resource Information Ecosystem

This Portal was developed bySage Bionetworksand is part of theSage Platform.

Use of this Portal is governed by theTerms and Conditions.

Research and Portal development was supported by the NCI at the NIH (U24CA209923, U24CA274494)