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A precision oncology approach to the pharmacological targeting of mechanistic dependencies in neuroendocrine tumors

Nature Geneticsvolume 50pages979989 (2018) | Download Citation

Abstract

We introduce and validate a new precision oncology framework for the systematic prioritization of drugs targeting mechanistic tumor dependencies in individual patients. Compounds are prioritized on the basis of their ability to invert the concerted activity of master regulator proteins that mechanistically regulate tumor cell state, as assessed from systematic drug perturbation assays. We validated the approach on a cohort of 212 gastroenteropancreatic neuroendocrine tumors (GEP-NETs), a rare malignancy originating in the pancreas and gastrointestinal tract. The analysis identified several master regulator proteins, including key regulators of neuroendocrine lineage progenitor state and immunoevasion, whose role as critical tumor dependencies was experimentally confirmed. Transcriptome analysis of GEP-NET-derived cells, perturbed with a library of 107 compounds, identified the HDAC class I inhibitor entinostat as a potent inhibitor of master regulator activity for 42% of metastatic GEP-NET patients, abrogating tumor growth in vivo. This approach may thus complement current efforts in precision oncology.

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Change history

  • 07 September 2018

    In the version of this article initially published, the Supplementary Note was omitted from the Supplementary Text and Figures PDF. The error has now been corrected.

References

  1. 1.

    Weinstein, I. B. Addiction to oncogenes–the Achilles heal of cancer. Science 297, 63–64 (2002).

  2. 2.

    Tannock, I. F. & Hickman, J. A. Limits to personalized cancer medicine. N. Engl. J. Med. 375, 1289–1294 (2016).

  3. 3.

    Commo, F. et al. Impact of centralization on aCGH-based genomic profiles for precision medicine in oncology. Ann. Oncol. 26, 582–588 (2015).

  4. 4.

    MacConaill, L. E. et al. Prospective enterprise-level molecular genotyping of a cohort of cancer patients. J. Mol. Diagn. 16, 660–672 (2014).

  5. 5.

    Jang, S. & Atkins, M. Which drug, and when, for patients with BRAF-mutant melanoma? Lancet Oncol. 14, e60–e69 (2013).

  6. 6.

    Davoli, A., Hocevar, B. A. & Brown, T. L. Progression and treatment of HER2-positive breast cancer. Cancer Chemother. Pharmacol. 65, 611–623 (2010).

  7. 7.

    Basu, A. et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154, 1151–1161 (2013).

  8. 8.

    Califano, A. & Alvarez, M. J. The recurrent architecture of tumour initiation, progression and drug sensitivity. Nat. Rev. Cancer 17, 116–130 (2017).

  9. 9.

    Piovan, E. et al. Direct reversal of glucocorticoid resistance by AKT inhibition in acute lymphoblastic leukemia. Cancer Cell 24, 766–776 (2013).

  10. 10.

    Compagno, M. et al. Mutations of multiple genes cause deregulation of NF-kappaB in diffuse large B-cell lymphoma. Nature 459, 717–721 (2009).

  11. 11.

    Bisikirska, B. et al. Elucidation and pharmacological targeting of novel molecular drivers of follicular lymphoma progression. Cancer Res. 76, 664–674 (2016).

  12. 12.

    Carro, M. S. et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature 463, 318–325 (2010).

  13. 13.

    Aytes, A. et al. Cross-species regulatory network analysis identifies a synergistic interaction between FOXM1 and CENPF that drives prostate cancer malignancy. Cancer Cell 25, 638–651 (2014).

  14. 14.

    Mitrofanova, A., Aytes, A., Shen, C., Abate-Shen, C. & Califano, A. A systems biology approach to predict drug response for human prostate cancer based on in vivo preclinical analyses of mouse models. Cell Rep. 12, 1–12 (2015).

  15. 15.

    Rajbhandari, P. et al. Cross-cohort analysis identifies a TEAD4-MYCN positive-feedback loop as the core regulatory element of high-risk neuroblastoma. Cancer Discov. 8, 582–599 (2018).

  16. 16.

    Rodriguez-Barrueco, R. et al. Inhibition of the autocrine IL-6-JAK2-STAT3-calprotectin axis as targeted therapy for HR−/HER2+ breast cancers. Genes Dev. 29, 1631–1648 (2015).

  17. 17.

    Luo, J., Solimini, N. L. & Elledge, S. J. Principles of cancer therapy: oncogene and non-oncogene addiction. Cell 136, 823–837 (2009).

  18. 18.

    Schreiber, S. L. et al. Towards patient-based cancer therapeutics. Nat. Biotechnol. 28, 904–906 (2010).

  19. 19.

    Lefebvre, C. et al. A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Mol. Syst. Biol. 6, 377 (2010).

  20. 20.

    Alvarez, M. J. et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat. Genet. 48, 838–847 (2016).

  21. 21.

    Walsh, L. A. et al. An integrated systems biology approach identifies TRIM25 as a key determinant of breast cancer metastasis. Cell Rep. 20, 1623–1640 (2017).

  22. 22.

    Oberg, K. & Eriksson, B. Endocrine tumours of the pancreas. Best Pract. Res. Clin. Gastroenterol. 19, 753–781 (2005).

  23. 23.

    Francis, J. M. et al. Somatic mutation of CDKN1B in small intestine neuroendocrine tumors. Nat. Genet. 45, 1483–1486 (2013).

  24. 24.

    Konishi, T. et al. Prognosis and risk factors of metastasis in colorectal carcinoids: results of a nationwide registry over 15 years. Gut 56, 863–868 (2007).

  25. 25.

    Diez, M., Teule, A. & Salazar, R. Gastroenteropancreatic neuroendocrine tumors: diagnosis and treatment. Ann. Gastroenterol. 26, 29–36 (2013).

  26. 26.

    Basso, K. et al. Reverse engineering of regulatory networks in human B cells. Nat. Genet. 37, 382–390 (2005).

  27. 27.

    Margolin, A. A. et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7, S7 (2006).

  28. 28.

    Basso, K. et al. Integrated biochemical and computational approach identifies BCL6 direct target genes controlling multiple pathways in normal germinal center B cells. Blood 115, 975–984 (2010).

  29. 29.

    Faith, J. J. et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 5, e8 (2007).

  30. 30.

    Cahan, P. et al. CellNet: network biology applied to stem cell engineering. Cell 158, 903–915 (2014).

  31. 31.

    Rosai, J. The origin of neuroendocrine tumors and the neural crest saga. Mod. Pathol. 24, S53–S57 (2011).

  32. 32.

    Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

  33. 33.

    Pfragner, R. et al. Establishment and characterization of three novel cell lines - P-STS, L-STS, H-STS - derived from a human metastatic midgut carcinoid. Anticancer Res. 29, 1951–1961 (2009).

  34. 34.

    Pfragner, R. et al. Establishment of a continuous cell line from a human carcinoid of the small intestine (KRJ-I). Int. J. Oncol. 8, 513–520 (1996).

  35. 35.

    Vijayvergia, N. et al. Molecular profiling of neuroendocrine malignancies to identify prognostic and therapeutic markers: a Fox Chase Cancer Center pilot study. Br. J. Cancer 115, 564–570 (2016).

  36. 36.

    Oberg, K. et al. A Delphic consensus assessment: imaging and biomarkers in gastroenteropancreatic neuroendocrine tumor disease management. Endocr. Connect. 5, 174–187 (2016).

  37. 37.

    Jiao, Y. et al. DAXX/ATRX, MEN1, and mTOR pathway genes are frequently altered in pancreatic neuroendocrine tumors. Science 331, 1199–1203 (2011).

  38. 38.

    Ezzat, K. et al. PepFect 14, a novel cell-penetrating peptide for oligonucleotide delivery in solution and as solid formulation. Nucleic Acids Res. 39, 5284–5298 (2011).

  39. 39.

    Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

  40. 40.

    Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

  41. 41.

    Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

  42. 42.

    Kaufman, L. & Rousseeuw, P. Partition Around Medoids (Program Pam) 68-125 (Wiley Online Library, 1990).

  43. 43.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

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Acknowledgements

We acknowledge the Falconwood Foundation for its generous support of research on neuroendocrine tumors, and the molecular pathology shared resources of the Herbert Irving Medical Center for tumor banking management/processing and histology support. This work was also supported by the National Cancer Institute (NCI) Cancer Target Discovery and Development Program (U01CA217858), an NCI Outstanding Investigator Award (R35CA197745) for A.C., the NCI Research Centers for Cancer Systems Biology Consortium (1U54CA209997), NIH instrumentation grants (S10OD012351 and S10OD021764), the NIH grant for the Biobank and Translational Research Core Facility at Cedars-Sinai (G20 RR030860), NCI 3P50 CA095103 and SPORE in GI cancer for K.W. and C.S., and support from the Swedish Cancer Foundation for J.R. and U.L.

Author information

Author notes

  1. These authors contributed equally: Mariano J. Alvarez, Prem S. Subramaniam.

Affiliations

  1. Department of Systems Biology, Columbia University, New York, NY, USA

    • Mariano J. Alvarez
    • , Prem S. Subramaniam
    • , Adina Grunn
    • , Mahalaxmi Aburi
    • , Elena V. Komissarova
    •  & Andrea Califano
  2. DarwinHealth Inc, New York, NY, USA

    • Mariano J. Alvarez
  3. Memorial Sloan Kettering Cancer Center, New York, NY, USA

    • Laura H. Tang
    • , Lisa Bodei
    •  & Diane Reidy-Lagunes
  4. Institute for Systems Genetics, New York University Langone Medical Center, New York, NY, USA

    • Gabrielle Rieckhof
  5. Department of Urology, Columbia University, New York, NY, USA

    • Elizabeth A. Hagan
  6. Division of Pathology, European Institute of Oncology, Milan, Italy

    • Lisa Bodei
    •  & Massimo Barberis
  7. Broad Institute of Harvard and MIT, Cambridge, MA, USA

    • Paul A. Clemons
    •  & Stuart Schreiber
  8. Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    • Filemon S. Dela Cruz
    • , Daniel Diolaiti
    • , Allison R. Rainey
    •  & Andrew L. Kung
  9. Cedars-Sinai Medical Center, Los Angeles, CA, USA

    • Deepti Dhall
    • , Xiaopu Yuan
    •  & Beatrice S. Knudsen
  10. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    • Douglas A. Fraker
    • , Virginia LiVolsi
    • , Robert Roses
    • , Anil Rustgi
    •  & David C. Metz
  11. PsychoGenics Inc., Tarrytown, NY, USA

    • Afshin Ghavami
    •  & Emer Leahy
  12. Department of General and Visceral Surgery, Zentralklinik, Bad Berka, Germany

    • Daniel Kaemmerer
    •  & Merten Hommann
  13. Sulzberger Columbia Genome Center, Columbia University, New York, NY, USA

    • Charles Karan
    • , Hai Li
    •  & Ronald B. Realubit
  14. Wren Laboratories, Branford, CT, USA

    • Mark Kidd
  15. Division of Hematology Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

    • Kyoung M. Kim
    • , Hee C. Kim
    • , Jeeyun Lee
    •  & Young S. Park
  16. Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA

    • Lakshmi P. Kunju
    •  & Arul M. Chinnaiyan
  17. Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA

    • Lakshmi P. Kunju
    •  & Arul M. Chinnaiyan
  18. Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA

    • Lakshmi P. Kunju
    •  & Arul M. Chinnaiyan
  19. Department of Neurochemistry, the Arrhenius Laboratories for Nat. Sci., Stockholm University, Stockholm, Sweden

    • Ülo Langel
    •  & Jakob Regberg
  20. Laboratory of Molecular Biotechnology, Institute of Technology, University of Tartu, Tartu, Estonia

    • Ülo Langel
  21. Falconwood Foundation, New York, NY, USA

    • Zhong Li
    •  & Tony Detre
  22. Institute of Pathophysiology and Immunology, Medical University of Graz, Graz, Austria

    • Roswitha Pfragner
  23. Department of Pathology, Columbia University, New York, NY, USA

    • Helen Remotti
    •  & Antonia R. Sepulveda
  24. Department of Pathology, University Health Network, University of Toronto, Toronto, Canada

    • Stefano Serra
    •  & Shereen Ezzat
  25. Department of Pathology, Vanderbilt University Medical Center, Nashville, TN, USA

    • Chanjuan Shi
    •  & Kay Washington
  26. Division of Colon and Rectal Surgery, State University of New York, Stony Brook, NY, USA

    • Roberto Bergamaschi
  27. Howard Hughes Medical Institute, University of Michigan Medical School, Ann Arbor, MI, USA

    • Arul M. Chinnaiyan
  28. Department of Urology, University of Michigan Medical School, Ann Arbor, MI, USA

    • Arul M. Chinnaiyan
  29. Imperial College London, London, UK

    • Andrea Frilling
  30. Medical Oncology, National Center for Tumor Diseases Heidelberg, University Medical Center Heidelberg, Heidelberg, Germany

    • Dirk Jaeger
  31. Mount Sinai School of Medicine, New York, NY, USA

    • Michelle K. Kim
  32. Department of Surgery, New York-Presbyterian Hospital, Weill Cornell Medicine, New York, NY, USA

    • Jeffrey W. Milsom
  33. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    • Stuart Schreiber
  34. Department of Internal Medicine, Division of Gastroenterology, Charite, Universitätsmedizin Berlin, Berlin, Germany

    • Bertram Wiedenmann
  35. Emeritus Professor Gastrointestinal Surgery, School of Medicine, Yale University, New Haven, Connecticut, USA

    • Irvin Modlin
  36. Department of Biomedical Informatics, Columbia University, New York, NY, USA

    • Andrea Califano
  37. Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA

    • Andrea Califano
  38. J.P. Sulzberger Columbia Genome Center, Columbia University, New York, NY, USA

    • Andrea Califano
  39. Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA

    • Andrea Califano

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Contributions

A.C., I.M. and M.J.A. conceived the study and wrote the manuscript. A.C. and I.M. assembled and coordinated the consortium activities. M.J.A. and A.C. conceptualized and designed the algorithms and the experiments; M.J.A. developed the algorithms and analyzed the data. P.S.S. designed and performed the experimental assays, analyzed the resulting data and wrote the manuscript. L.H.T. assessed GEP-NET sample quality, tumor purity and tumor pathology. A. Grunn and E.V.K. performed sample preparation, RNA isolation and immunohistochemistry assays, and managed the sample repository; T.D., G.R., M.A., E.A.H. and Z.L. coordinated the study logistics and sample procurement across participating institutions; P.A.C. and S. Schreiber conceived and performed the differential drug response curve assays and analyzed the data; C.K., R.B.R. and H.L. performed the RNA-Seq profiling following drug perturbation assays. F.S.D.C., D. Diolaiti, A.R.R. and A.L.K. performed in vivo experiments and analyzed the data; R.P., I.M. and M.K. contributed GEP-NET-derived cell lines; L.B., D. Dhall, D.A.F., A. Ghavami, D.K., M.K., K.M.K, H.C.K., L.P.K., U.L., J.L., V.L.V., H.R., J.R., R.R., A.R., A.R.S., S. Serra, C.S., X.Y., M.B., R.B., A.M.C., S.E., A.F., M.H., D.J., M.K.K., B.S.K., E.L., D.C.M., J.W.M., Y.S.P., D.R.-L., K.W. and B.W. contributed fresh-frozen GEP-NET samples.

Competing interests

M.J.A. is Chief Scientific Officer and equity holder at DarwinHealth, Inc., a company that has licensed some of the algorithms used in this manuscript from Columbia University. A.C. is founder and equity holder of DarwinHealth Inc. Columbia University is also an equity holder in DarwinHealth Inc.

Corresponding authors

Correspondence to Irvin Modlin or Andrea Califano.

Integrated supplementary information

  1. Supplementary Figure 1 Analysis of inter-sample variation in the GEP-NET expression dataset and reliability analysis for different interactomes as models for GEP-NET.

    a, Violin plot showing the probability density for the distribution of mean squared error (MSE) computed between all closest sample pairs or the GEP-NET and 33 tumor types profiled by TCGA. The median value is indicated by a horizontal line. The number of samples for each tumor type is indicated on top of the figure. b, Bar plot showing the integrated network score computed as the area over the |NES| cumulative probability (Supplementary Fig. 3b,c). NES was computed by VIPER for 212 GEP-NET samples and all the regulatory proteins represented in the 29 evaluated interactomes (indicated inside the bars; Supplementary Table 3). When the network model is not representative of tissue-specific regulation, the master regulator analysis produces very few and barely significant results10. Here our GEP-NET interactome produced the strongest enrichment for 212 GEP-NET signatures when compared to 28 additional interactomes (Supplementary Table 3), indicating that GEP-NET is the best interactome, among all 29 tested ones, as a model for GEP-NET context-specific transcriptional regulation. c,d, Probability density for the functional conservation score of each regulon, expressed as z score (null model standard deviation units), between the GEP-NET interactome and interactomes assembled based only on metastases (MET; n = 4,340), primary tumors (Primary; n = 4,711), pancreas NETs (P-NET; n = 4,621) and small intestine NETs (SI-NET; n = 4,254) samples (shown by the filled histograms). Conservation of protein activity signatures computed from two disjoint subsets of each regulon (empty histograms) are shown as a reference point for the maximum achievable scores and as an indication of regulon robustness. The regulon functional conservation score was computed as the correlation between the VIPER-inferred activity signature for each protein across all NET tumors, as inferred from the GEP-NET interactome regulon, and regulons were assembled from specific subsets of samples, as previously described10. 99%, 98.9%, 97.2% and 94.5% of the GEP-NET interactome regulons were significantly conserved (FDR < 0.05) in the MET-, primary tumors–, P-NET- and SI-NET-specific interactomes, respectively, while the distribution for the functional conservation scores closely followed that of the maximal achievable conservation.

  2. Supplementary Figure 2 Unsupervised analysis and cluster reliability for 212 GEP-NET samples.

    a, Scatterplots showing the first five principal components, capturing 33% of the variance for 212 GEP-NET expression profiles. The tissue of origin is indicated by different colors. Primary tumors are shown with circles, while METs are shown with triangles. b, Two-dimensional tSNE projection for the expression data. Different colors indicate the different tissue of origin. c, Two-dimensional tSNE projection of the VIPER-inferred protein activity for 212 GEP-NET samples. The color of the symbols indicates tissue of origin, and their shape indicates status as primary tumors (circles) or METs (triangles). The color of the clouds indicates cluster membership according to Fig. 1b. d, Integrated reliability score for different cluster structures (different number of clusters) for the consensus cluster of 212 GEP-NET expression profiles (red) or VIPER-inferred protein activity profiles (blue). e, Probability density plot for cluster reliability estimated from the expression profiles and VIPER-inferred protein activity profiles for 212 GEP-NET samples (see g). f, Integrated reliability score for the complete cluster structure computed as the area over the cumulative probability curve. g, Cluster reliability score for 212 GEP-NET expression and VIPER-inferred protein activity profiles after consensus clustering in five clusters. The horizontal black line indicates the threshold for FDR < 0.01. h,i, Cluster reliability (h) and silhouette score (i) for each sample from the four-cluster structure based on expression and the five-cluster structure based on VIPER-inferred protein activity data. j, Cluster membership for the H-STS xenograft model. Shown is enrichment of the samples from each of the five clusters on the distance to the xenograft model based on the correlation between protein activity signatures. Enrichment significance is shown as –log10 (P value) by the bar plot (one-tailed aREA test).

  3. Supplementary Figure 3 Metastatic progression master regulators selected for validation.

    a, Conservation of the top 25 most activated and top 25 most inactivated master regulators between 66 NET liver metastases. b, Optimal number of clusters based on the regulators of metastatic progression for 66 liver metastases. c, Enrichment for the targets of all significant metastasis master regulators, including transcripts that according to the regulatory model are induced by the master regulator (indicated by red vertical lines) and represented (blue vertical lines). The x axis indicates the genome-wide expression signature (GES) for the patient 0 metastasis (genes are sorted from the most downregulated to the left to the ones most upregulated to the right) and the H-STS cell line GES. Statistical significance is shown as Bonferroni’s corrected P value (two-tailed aREA test). d, Effect of each individual shRNA hairpin (indicated with different colors) on the expression level of the targeted gene as compared to the effect of non-targeting shRNA control. Three replicates were performed per hairpin, and at least two hairpins were assayed per gene. P values were estimated by one-tailed ANOVA. e,f, Effect of master regulator silencing on the viability of KRJ-1 (e) and NCI-H716 (f). Three replicates were performed per hairpin. At least two hairpins were used per gene (indicated in different colors). P values were estimated by one-tailed ANOVA.

  4. Supplementary Figure 4 Selection of appropriate models.

    a, Probability density for the GEP-NET interactome network score computed for each cell line as the area over the |NES| cumulative probability. The H-STS and KRJ-1 cell lines are in the 4.2th and 3.5th percentile, respectively, of 923 evaluated cell lines. b, Histogram for the number of METs whose master regulators were conserved in each cell line at Bonferroni’s corrected P value < 0.01 (one-tailed aREA test). The H-STS and KRJ-1 cell lines are in the 2.4th and 3.6th percentile, respectively, of 923 evaluated cell lines. c, GEP-NET interactome network score and number of master regulator–matched METs at Bonferroni’s corrected P value < 0.01 (one-tailed aREA test) for each of the 923 cell lines. d, Top ten cell lines sorted by the sum of the ranks for the GEP-NET interactome network score and the number of master regulator–matched METs. e, H-STS cell line master regulators are recapitulated by its xenograft model. The figure shows enrichment of the H-STS cell master regulators on the protein activity signature of the H-STS xenograft computed by GSEA. The normalized enrichment score and P value were estimated by two-tailed aREA analysis.

  5. Supplementary Figure 5 Results of OncoTreat analysis.

    The heat map shows enrichment of the master regulators of each tumor and the H-STS xenograft model on the protein activity signature elicited by each drug perturbation on H-STS cells. Enrichment strength is shown as –log10 (P value), estimated by one-tailed aREA analysis, and is indicated by the numbers. Metastases showing significant similarity, at the master regulator level, to the H-STS xenograft model were included in the left-most heat map (see Fig. 2a). The remaining metastases are shown in the right-most heat map. The enrichment plot to the left shows enrichment of the patient 0 master regulators recapitulated by the xenograft model, on each drug perturbation protein activity signature.

  6. Supplementary Figure 6 Flow cytometry gating strategy.

    This representative schema describes the gating for H-STS cells double stained for CD80 and CD86. To set the negative population in step 1, live H-STS cells stained with appropriate isotype controls were gated from an SSC-H versus FSC-H scatterplot (step 1, R1, left panel). A quadrant gate ‘QG1’ was applied to the ‘R1’ population to define and locate the negative staining population in the lower left quadrant (step 1, right panel). This gate was then applied to the live cell ‘R1’ gate of subsequent samples that were positively stained with CD80 and CD86 antibodies as in step 2a; for example, H-STS cells treated with DMSO as indicated here are gated in step 3a, to segregate CD80- and CD86-positive populations. Similarly, samples treated with entinostat were gated by applying the QG1 gate to the corresponding live gate R1 as in step 3b.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–6 and Supplementary Note

  2. Reporting Summary

  3. Supplementary Table 1

    Members of the International NET Consortium

  4. Supplementary Table 2

    GEP-NET samples used for this study

  5. Supplementary Table 3

    List of interactomes used in the comparative analysis

  6. Supplementary Table 4

    GEP-NET metastasis MRegs

  7. Supplementary Table 5

    MRegs selected for validation

  8. Supplementary Table 6

    Compounds used to generate perturbational profiles

  9. Supplementary Table 7

    List of antibodies used for flow cytometry

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https://doi.org/10.1038/s41588-018-0138-4