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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

Supplementary Table from Developmental Deconvolution for Classification of Cancer Origin

Cancer is a disease manifesting in abrogation of developmental programs, and malignancies are named based on their 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 use this map to deconvolute individual tumors into their constituent developmental trajectories. Based on these deconvoluted developmental programs, we construct a Developmental Multilayer Perceptron (D-MLP) classifier that outputs cancer origin. The D-MLP classifier (ROC-AUC: 0.974 for top prediction) outperforms classification based on expression of either oncogenes or highly variable genes. We analyze tumors from patients with cancer of unknown primary (CUP), selecting the most difficult cases where extensive multimodal workup yielded no definitive tumor type. D-MLP revealed insights into developmental origins and diagnosis for most patient tumors. Our results provide a map of tumor developmental origins, provide a tool for diagnostic pathology, and suggest developmental classification may be a useful approach for otherwise unclassified patient tumors.

Cancer is a disease manifesting in abrogation of developmental programs, and malignancies are namedbased on their 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 use this map to deconvolute individual tumors into their constituent developmental components. Based on these deconvoluted developmental programs, we construct a Developmental Machine Learning Perceptron (D-MLP) classifier that outputs cancer origin. The D-MLP classifier (ROC-AUC: 0.974 for top prediction) outperforms classification based on expression of either oncogenes or highly variable genes. We analyze tumors from patients with cancer of unknown primary (CUP), selecting the most difficult cases where extensive multimodal workup yielded no definitive tumor type. D-MLP revealed insights into developmental origins and diagnosis for most patient tumors. Our results provide a map of tumor developmental origins, provide a tool for diagnostic pathology, and suggest developmental classification may be a useful approach for otherwise unclassified patient tumors. Citation Format: Enrico Moiso, Alexander Farahani, Hetal Marble, Austin Hendricks, Samuel Mildrum, Stuart Levine, Jochen Lennerz, Salil Garg. Developmental deconvolution for classification of cancer origin [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4100.

Engineered model of the human gut-liver-brain axis can be used to study the link between the microbiome and Parkinson’s disease., Slow progress in the fight against neurodegenerative diseases (NDs) motivates an urgent need for highly controlled in vitro systems to investigate organ-organ– and organ-immune–specific interactions relevant for disease pathophysiology. Of particular interest is the gut/microbiome-liver-brain axis for parsing out how genetic and environmental factors contribute to NDs. We have developed a mesofluidic platform technology to study gut-liver-cerebral interactions in the context of Parkinson’s disease (PD). It connects microphysiological systems (MPSs) of the primary human gut and liver with a human induced pluripotent stem cell–derived cerebral MPS in a systemically circulated common culture medium containing CD4+ regulatory T and T helper 17 cells. We demonstrate this approach using a patient-derived cerebral MPS carrying the PD-causing A53T mutation, gaining two important findings: (i) that systemic interaction enhances features of in vivo–like behavior of cerebral MPSs, and (ii) that microbiome-associated short-chain fatty acids increase expression of pathology-associated pathways in PD.

Advances in next-generation sequencing technologies have allowed RNA sequencing to become an increasingly time efficient, cost-effective, and accessible tool for genomic research. We present here an automated and miniaturized workflow for RNA library preparation that minimizes reagent usage and processing time required per sample to generate Illumina compatible libraries for sequencing. The reduced-volume libraries show similar behavior to full-scale libraries with comparable numbers of genes detected and reproducible clustering of samples.