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Developmental Deconvolution for Classification of Cancer Origin.

Enrico Moiso1,2, Alexander Farahani3, Hetal D Marble3

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Summary
This summary is machine-generated.

This study maps tumor developmental origins using single-cell organogenesis and transcriptomes. A deep learning classifier accurately predicts cancer type, aiding diagnosis, especially for cancers of unknown primary (CUP).

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Area of Science:

  • Developmental Biology
  • Computational Biology
  • Oncology

Background:

  • Cancer is a developmental disease, but a systematic atlas of tumor origins is missing.
  • Tumor classification is typically based on cell or tissue of origin.

Purpose of the Study:

  • To map the single-cell organogenesis of developmental trajectories to tumor transcriptomes.
  • To develop a deep learning classifier for predicting cancer origin.
  • To apply this tool to diagnose cancers of unknown primary (CUP).

Main Methods:

  • Mapped 56 developmental trajectories to over 10,000 tumor transcriptomes across 33 cancer types.
  • Deconvoluted tumor transcriptomes into signals for developmental trajectories.
  • Constructed a developmental multilayer perceptron (D-MLP) classifier.

Main Results:

  • The D-MLP classifier achieved high accuracy (ROC-AUC: 0.974 for top prediction), outperforming benchmarks.
  • Analyzed CUPs, revealing distinct groups based on developmental trajectories.
  • Successfully provided diagnoses for patient tumors with unknown primary.

Conclusions:

  • Established a comprehensive atlas of tumor developmental origins.
  • Developed a powerful tool for diagnostic pathology.
  • Demonstrated the utility of developmental classification for patient tumors.