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Tumors are heterogeneous cellular environments with entwined metabolic dependencies. Here, we used a tumor transcriptome deconvolution approach to profile the metabolic states of cancer and non-cancer (stromal) cells in bulk tumors of 20 solid tumor types. We identified metabolic genes and processes recurrently altered in cancer cells across tumor types, including pan-cancer upregulation of deoxythymidine triphosphate (dTTP) production. In contrast, the tryptophan catabolism rate limiting enzymes, IDO1 and TDO2, were highly overexpressed in stroma, suggesting that kynurenine-mediated suppression of antitumor immunity is predominantly constrained by the stroma. Oxidative phosphorylation was unexpectedly the most upregulated metabolic process in cancer cells compared to both stromal cells and a large atlas of cancer cell lines, suggesting that the Warburg effect may be less pronounced in cancer cells in vivo. Overall, our analysis highlights fundamental differences in metabolic states of cancer and stromal cells inside tumors and establishes a pan-cancer resource to interrogate tumor metabolism.

Signaling between cancer and nonmalignant (stromal) cells in the tumor microenvironment (TME) is a key to tumor progression. Here, we deconvoluted bulk tumor transcriptomes to infer cross-talk between ligands and receptors on cancer and stromal cells in the TME of 20 solid tumor types. This approach recovered known transcriptional hallmarks of cancer and stromal cells and was concordant with single-cell, in situ hybridization and IHC data. Inferred autocrine cancer cell interactions varied between tissues but often converged on Ephrin, BMP, and FGFR-signaling pathways. Analysis of immune checkpoints nominated interactions with high levels of cancer-to-immune cross-talk across distinct tumor types. Strikingly, PD-L1 was found to be highly expressed in stromal rather than cancer cells. Overall, our study presents a new resource for hypothesis generation and exploration of cross-talk in the TME. Significance: This study provides deconvoluted bulk tumor transcriptomes across multiple cancer types to infer cross-talk in the tumor microenvironment.

Molecular features associated with risk of metachronous metastases (MM) in clear cell renal cell carcinoma (ccRCC) are poorly defined. Here, we performed systematic genomic and molecular analysis of 192 ccRCC primary tumors with extended clinical follow-up for development of MM. Whole-exome sequencing analysis revealed no association between somatic mutations and development of MM. However, chromosomal deletions at 1p33 were more prevalent in tumors that later progressed with MM. We inferred immune cell infiltrates and found that depletion of regulatory T cells and M1 macrophages were associated with development of MM. Next, we used bulk tumor transcriptome deconvolution to estimate cancer (malignant) and stromal (non-malignant) cell gene expression differences between MM and indolent primary tumors. Downregulation of the Crumbs complex component PATJ (1p31) in cancer cells of MM tumors suggested loss of apicobasal cell polarity as a risk factor in metastatic progression. We also identified downregulation of fatty acid degradation in cancer cells of MM tumors. Overall, our results nominate novel genomic and molecular features underpinning metachronous metastasis in ccRCC. Citation Format: Marjan Mojtabavi Naeini, Neha Rohatgi, Umesh Ghoshdastider, Ari A. Hakimi, Anders Jacobsen Skanderup. Genomic and molecular features of metachronous metastasis in clear cell renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB241.

Signaling between cancer and nonmalignant (stromal) cells in the tumor microenvironment (TME) is key to tumorigenesis yet challenging to decipher from tumor transcriptomes. Here, we report an unbiased, data-driven approach to deconvolute bulk tumor transcriptomes and predict crosstalk between ligands and receptors on cancer and stromal cells in the TME of 20 solid tumor types. Our approach recovers known transcriptional hallmarks of cancer and stromal cells and is concordant with single-cell and immunohistochemistry data, underlining its robustness. Pan-cancer analysis reveals previously unrecognized features of cancer-stromal crosstalk. We find that autocrine cancer cell cross-talk varied between tissues but often converged on known cancer signaling pathways. In contrast, many stromal cross-talk interactions were highly conserved across tumor types. Interestingly, the immune checkpoint ligand PD-L1 was overexpressed in stromal rather than cancer cells across all tumor types. Moreover, we predicted and experimentally validated aberrant ligand and receptor expression in cancer cells of basal and luminal breast cancer, respectively. Collectively, our findings validate a data-driven method for tumor transcriptome deconvolution and establishes a new resource for hypothesis generation and downstream functional interrogation of the TME in tumorigenesis and disease progression.

Tumors are heterogeneous cellular environments with entwined metabolic dependencies. However, it is challenging to differentially profile metabolism in cancer and infiltrating non-malignant cells inside patient tumors. Here, we used a tumor transcriptome deconvolution approach to profile the metabolic states of cancer and non-cancer (stromal) cells in bulk tumors of 20 solid tumor types. This analysis highlighted metabolic genes and processes recurrently upregulated in cancer cells across tumor types, including the mitochondrial dicarboxylate carrier SLC25A10. Intriguingly, the tryptophan-degrading dioxygenase, IDO1, was highly recurrently overexpressed in stroma compared to cancer cells and normal tissue, suggesting that tryptophan-mediated suppression of antitumor immune response is predominantly constrained by the stroma. Oxidative phosphorylation was unexpectedly the most upregulated metabolic process in cancer cells compared to both stromal cells and a large atlas of cancer cell lines, suggesting that the warburg effect may be less pronounced in cancer cells in vivo. Overall, our analysis highlights fundamental differences in metabolic states of cancer and stromal cells inside tumors and establishes a pan-cancer resource to interrogate tumor metabolism. Citation Format: Neha Rohatgi, Umesh Ghoshdastider, Probhonjon Baruah, Anders Jacobsen Skanderup. Pan-cancer metabolic profiling of the tumor microenvironment [abstract]. In: Abstracts: AACR Special Virtual Conference on Epigenetics and Metabolism; October 15-16, 2020; 2020 Oct 15-16. Philadelphia (PA): AACR; Cancer Res 2020;80(23 Suppl):Abstract nr PO-055.

Signaling between cancer and nonmalignant (stromal) cells in the tumor microenvironment (TME) is key to tumorigenesis, yet challenging to decipher from tumor transcriptomes. Here, we report an unbiased, data-driven approach to deconvolute bulk tumor transcriptomes and predict crosstalk between ligands and receptors on cancer and stromal cells in the TME of 20 solid tumor types. Our approach recovers known transcriptional hallmarks of cancer and stromal cells and is concordant with single-cell and immunohistochemistry data, underlining its robustness. Pan-cancer analysis reveals previously unrecognized features of cancer-stromal crosstalk. We find that autocrine cancer cell crosstalk varied between tissues but often converged on known cancer signaling pathways. In contrast, many stromal crosstalk interactions were highly conserved across tumor types. Interestingly, the immune checkpoint ligand PD-L1 was overexpressed in stromal rather than cancer cells across all tumor types. Moreover, we predicted and experimentally validated aberrant ligand and receptor expression in cancer cells of basal and luminal breast cancer, respectively. Collectively, our findings validate a data-driven method for tumor transcriptome deconvolution and establish a new resource for hypothesis generation and downstream functional interrogation of the TME in tumorigenesis and disease progression. Citation Format: Umesh Ghoshdastider, Marjan Naeini, Neha Rohatgi, Sundar Solai, Tin Nguyen, Egor Revkov, Anders Skanderup. Data-driven inference of crosstalk in the tumor microenvironment [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 32.