Developmental Deconvolution for Classification of Cancer Origin

Abstract

Abstract Cancer is partly a developmental disease, with malignancies named based on cell or tissue of origin. However, a systematic atlas of tumor origins is lacking. Here we map the single-cell organogenesis of 56 developmental trajectories to the transcriptomes of over 10,000 tumors across 33 cancer types. We deconvolute tumor transcriptomes into signals for individual developmental trajectories. Using these signals as inputs, we construct a developmental multilayer perceptron (D-MLP) classifier that outputs cancer origin. D-MLP (ROC-AUC: 0.974 for top prediction) outperforms benchmark classifiers. We analyze tumors from patients with cancer of unknown primary (CUP), selecting the most difficult cases in which extensive multimodal workup yielded no definitive tumor type. Interestingly, CUPs form groups distinguished by developmental trajectories, and classification reveals diagnosis for patient tumors. Our results provide an atlas of tumor developmental origins, provide a tool for diagnostic pathology, and suggest developmental classification may be a useful approach for patient tumors. Significance: Here we map the developmental trajectories of tumors. We deconvolute tumor transcriptomes into signals for mammalian developmental programs and use this information to construct a deep learning classifier that outputs tumor type. We apply the classifier to CUP and reveal the developmental origins of patient tumors. See related commentary by Wang, p. 2498. This article is highlighted in the In This Issue feature, p. 2483