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supplementary Table S1 to S3; Supplementary Figure S1 to S9

The ectonucleotidases CD39 and CD73 catalyze extracellular ATP to immunosuppressive adenosine, and as such, represent potential cancer targets. We investigated biological impacts of CD39 and CD73 in pancreatic ductal adenocarcinoma (PDAC) by studying clinical samples and experimental mouse tumors. Stromal CD39 and tumoral CD73 expression significantly associated with worse survival in human PDAC samples and abolished the favorable prognostic impact associated with the presence of tumor-infiltrating CD8+ T cells. In mouse transplanted KPC tumors, both CD39 and CD73 on myeloid cells, as well as CD73 on tumor cells, promoted polarization of infiltrating myeloid cells towards an M2-like phenotype, which enhanced tumor growth. CD39 on tumor-specific CD8+ T cells and pancreatic stellate cells also suppressed IFNγ production by T cells. Although therapeutic inhibition of CD39 or CD73 alone significantly delayed tumor growth in vivo, targeting of both ectonucleotidases exhibited markedly superior antitumor activity. CD73 expression on human and mouse PDAC tumor cells also protected against DNA damage induced by gemcitabine and irradiation. Accordingly, large-scale pharmacogenomic analyses of human PDAC cell lines revealed significant associations between CD73 expression and gemcitabine chemoresistance. Strikingly, increased DNA damage in CD73-deficient tumor cells associated with activation of the cGAS–STING pathway. Moreover, cGAS expression in mouse KPC tumor cells was required for antitumor activity of the CD73 inhibitor AB680 in vivo. Our study, thus, illuminates molecular mechanisms whereby CD73 and CD39 seemingly cooperate to promote PDAC progression.

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.

Purpose:Despite the established role of EGFR tyrosine kinase inhibitors (TKIs) in EGFR-mutated NSCLC, drug resistance inevitably ensues, with a paucity of treatment options especially in EGFRT790M-negative resistance.Experimental Design:We performed whole-exome and transcriptome analysis of 59 patients with first- and second-generation EGFR TKI-resistant metastatic EGFR-mutated NSCLC to characterize and compare molecular alterations mediating resistance in T790M-positive (T790M+) and -negative (T790M−) disease.Results:Transcriptomic analysis revealed ubiquitous loss of adenocarcinoma lineage gene expression in T790M− tumors, orthogonally validated using multiplex IHC. There was enrichment of genomic features such as TP53 alterations, 3q chromosomal amplifications, whole-genome doubling and nonaging mutational signatures in T790M− tumors. Almost half of resistant tumors were further classified as immunehot, with clinical outcomes conditional on immune cell-infiltration state and T790M status. Finally, using a Bayesian statistical approach, we explored how T790M− and T790M+ disease might be predicted using comprehensive genomic and transcriptomic profiles of treatment-naïve patients.Conclusions:Our results illustrate the interplay between genetic alterations, cell lineage plasticity, and immune microenvironment in shaping divergent TKI resistance and outcome trajectories in EGFR-mutated NSCLC. Genomic and transcriptomic profiling may facilitate the design of bespoke therapeutic approaches tailored to a tumor's adaptive potential.

Supplementary Data from Integrative Profiling of T790M-Negative EGFR-Mutated NSCLC Reveals Pervasive Lineage Transition and Therapeutic Opportunities

Supplementary results and methods

The ectonucleotidases CD39 and CD73 catalyze extracellular ATP to immunosuppressive adenosine, and as such, represent potential cancer targets. We investigated biological impacts of CD39 and CD73 in pancreatic ductal adenocarcinoma (PDAC) by studying clinical samples and experimental mouse tumors. Stromal CD39 and tumoral CD73 expression significantly associated with worse survival in human PDAC samples and abolished the favorable prognostic impact associated with the presence of tumor-infiltrating CD8+ T cells. In mouse transplanted KPC tumors, both CD39 and CD73 on myeloid cells, as well as CD73 on tumor cells, promoted polarization of infiltrating myeloid cells towards an M2-like phenotype, which enhanced tumor growth. CD39 on tumor-specific CD8+ T cells and pancreatic stellate cells also suppressed IFNγ production by T cells. Although therapeutic inhibition of CD39 or CD73 alone significantly delayed tumor growth in vivo, targeting of both ectonucleotidases exhibited markedly superior antitumor activity. CD73 expression on human and mouse PDAC tumor cells also protected against DNA damage induced by gemcitabine and irradiation. Accordingly, large-scale pharmacogenomic analyses of human PDAC cell lines revealed significant associations between CD73 expression and gemcitabine chemoresistance. Strikingly, increased DNA damage in CD73-deficient tumor cells associated with activation of the cGAS–STING pathway. Moreover, cGAS expression in mouse KPC tumor cells was required for antitumor activity of the CD73 inhibitor AB680 in vivo. Our study, thus, illuminates molecular mechanisms whereby CD73 and CD39 seemingly cooperate to promote PDAC progression.

Oncogene-induced senescence provides a barrier against malignant transformation. However, it can also promote cancer through the secretion of a plethora of factors released by senescent cells, called the senescence associated secretory phenotype (SASP). We have previously shown that in proliferating cells, nuclear lncRNA MIR31HG inhibits p16/CDKN2A expression through interaction with polycomb repressor complexes and that during BRAF-induced senescence, MIR31HG is overexpressed and translocates to the cytoplasm. Here, we show that MIR31HG regulates the expression and secretion of a subset of SASP components during BRAF-induced senescence. The SASP secreted from senescent cells depleted for MIR31HG fails to induce paracrine invasion without affecting the growth inhibitory effect. Mechanistically, MIR31HG interacts with YBX1 facilitating its phosphorylation at serine 102 (p-YBX1S102) by the kinase RSK. p-YBX1S102 induces IL1A translation which activates the transcription of the other SASP mRNAs. Our results suggest a dual role for MIR31HG in senescence depending on its localization and points to the lncRNA as a potential therapeutic target in the treatment of senescence-related pathologies., Senescence-associated secretory phenotype (SASP) involves secretion of factors such as pro-inflammatory cytokines. Here the authors show that MIR31HG regulates the expression and secretion of a subset of SASP components that induce paracrine invasion, through interaction with YBX1 and induction of IL1A translation.

Background Overexpression of epidermal growth factor receptor (EGFR), and downstream pathway activation appears to be a common oncogenic driver in the majority of head and neck squamous cell cancers (HNSCCs); yet targeting EGFR for the treatment of HNSCC has met with limited success. Apart from the anti-EGFR antibody cetuximab, no small molecule EGFR/tyrosine kinase inhibitors (TKIs) have progressed to routine clinical use. The aim of this study was to determine factors contributing to the lack of response to TKIs and identify alternative therapeutic vulnerabilities. Methods Genomic and transcriptomic sequencing, high-throughput compound screens, overexpression and siRNA knockdown, western blot, in vivo xenograft studies. Findings We derived three pairs of isogenic gefitinib (TKI)-sensitive and resistant patient-derived HNSCC cell lines. Genomic sequencing of gefitinib-resistant cell lines identified a lack of activating and resistance-associated EGFR mutations. Instead, transcriptomic sequencing showed upregulated EMT gene signature in the gefitinib-resistant cells with a corresponding increase in their migratory phenotype. Additionally, the resistant cell displayed reduced growth rate. Surprisingly, while gefitinib-resistant cells were independent of EGFR for survival, they nonetheless displayed activation of downstream ERK and AKT signalling. High-throughput screening (HTS) of druggable, small molecule libraries revealed that the gefitinib-resistant cells were particularly sensitive to inhibitors of genes involved in cell cycle and mitosis, such as Aurora kinase inhibitors (AKIs), cyclin-dependent kinase (CDK) inhibitors, and microtubule inhibitors. Notably our results showed that in the EGFR inhibited state, Aurora kinases are essential for cell survival. Interpretation Our study demonstrates that in the absence of activating EGFR mutations, HNSCCs may gain resistance to gefitinib through decreased cell proliferation, which makes them exceptionally vulnerable to cell-cycle inhibitors. Funding Agency for Science, Technology, and Research (A*STAR), National Medical Research Council (NMRC), and the National Institutes of Health (NIH)/National Cancer Institute (NCI).

Tumors are heterogeneous cellular environments with entwined metabolic dependencies. Here, we use 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 identify metabolic genes and processes recurrently altered in cancer cells across tumor types, highlighting pan-cancer upregulation of deoxythymidine triphosphate (dTTP) production. In contrast, the tryptophan catabolism rate-limiting enzymes IDO1 and TDO2 are highly overexpressed in stroma, raising the hypothesis that kynurenine-mediated suppression of antitumor immunity may be predominantly constrained by the stroma. Oxidative phosphorylation is 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.

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.

Senescent cells secrete cytokines, chemokines and growth factors collectively known as the senescence-associated secretory phenotype (SASP) which can reinforce senescence and activate the immune response. However, it can also negatively impact neighbouring tissues facilitating tumor progression. We have previously shown that in proliferating cells the nuclear long non-coding RNA (lncRNA) MIR31HG inhibits p16/CDKN2A expression through interaction with polycomb repressor complexes (PRC1/2) and that during BRAF-induced senescence MIR31HG is overexpressed and translocates to the cytoplasm. Here, we show that MIR31HG regulates the expression of a subset of SASP components during BRAF-induced senescence. The SASP secreted from senescent cells depleted for MIR31HG fails to induce paracrine invasion without affecting the growth inhibitory effect. Mechanistically, MIR31HG interacts with YBX1 facilitating its phosphorylation at serine 102 (p-YBX1S102) by the kinase RSK. p-YBX1S102 induces IL1A translation which acts as upstream regulator inducing the transcription of the other SASP mRNAs. Our results suggest a dual role for MIR31HG in senescence depending on its cellular localization and points to the lncRNA as a potential therapeutic target in the treatment of senescence-related pathologies.

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.

Epithelial to mesenchymal transition (EMT) has implications in tumor progression and metastasis. Metabolic alterations have been described in cancer development but studies focused on the metabolic re-wiring that takes place during EMT are still limited. We performed metabolomics profiling of a breast epithelial cell line and its EMT derived mesenchymal phenotype to create genome-scale metabolic models descriptive of both cell lines. Glycolysis and OXPHOS were higher in the epithelial phenotype while amino acid anaplerosis and fatty acid oxidation fueled the mesenchymal phenotype. Through comparative bioinformatics analysis, PPAR-γ1, PPAR- γ2 and AP-1 were found to be the most influential transcription factors associated with metabolic re-wiring. In silico gene essentiality analysis predicts that the LAT1 neutral amino acid transporter is essential for mesenchymal cell survival. Our results define metabolic traits that distinguish an EMT derived mesenchymal cell line from its epithelial progenitor and may have implications in cancer progression and metastasis. Furthermore, the tools presented here can aid in identifying critical metabolic nodes that may serve as therapeutic targets aiming to prevent EMT and inhibit metastatic dissemination., Financial support for this work was provided by the Icelandic Research Council (Grants number: 130591-051, 130816-051 and 152358-051).

Biological network models offer a framework for understanding disease by describing the relationships between the mechanisms involved in the regulation of biological processes. Crowdsourcing can efficiently gather feedback from a wide audience with varying expertise. In the Network Verification Challenge, scientists verified and enhanced a set of 46 biological networks relevant to lung and chronic obstructive pulmonary disease. The networks were built using Biological Expression Language and contain detailed information for each node and edge, including supporting evidence from the literature. Network scoring of public transcriptomics data inferred perturbation of a subset of mechanisms and networks that matched the measured outcomes. These results, based on a computable network approach, can be used to identify novel mechanisms activated in disease, quantitatively compare different treatments and time points, and allow for assessment of data with low signal. These networks are periodically verified by the crowd to maintain an up-to-date suite of networks for toxicology and drug discovery applications.

To access publisher's full text version of this article click on the hyperlink at the bottom of the page Gluconate is a commonly encountered nutrient, which is degraded by the enzyme gluconokinase to generate 6-phosphogluconate. Here we used isothermal titration calorimetry to study the properties of this reaction. ΔH, KM and kcat are reported along with substrate binding data. We propose that the reaction follows a ternary complex mechanism, with ATP binding first. The reaction is inhibited by gluconate, as it binds to an Enzyme-ADP complex forming a dead-end complex. The study exemplifies that ITC can be used to determine mechanisms of enzyme catalyzed reactions, for which it is currently not commonly applied. info:eu-repo/grantAgreement/EC/FP7/232816 RANNIS grant number: 130591-053.

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend., Author Summary The epidermal growth factor receptor (EGFR) signaling cascade is one of the key signaling pathways that are involved in the induction of Epithelial Mesenchymal Transition (EMT) and tumor metastasis. These signaling cascades often affect metabolic fate in tumor cells and control their progression. Here we demonstrate a method to build a mathematical model of the EGFR signaling cascade and use it to study signaling in EMT and how signaling affects metabolism. The model was used to obtain a list of potential signaling and metabolic targets of EMT. These targets may aid in the understanding of the molecular mechanisms that underlie EMT and metastasis. Our results further highlight the heterogeneity of cell models used to study EMT and support the idea of cell specific anti-cancer interventions.