Skip to main content
Top
Published in: Journal of Translational Medicine 1/2022

Open Access 01-12-2022 | Checkpoint Inhibitors | Research

Translation of the 27-gene immuno-oncology test (IO score) to predict outcomes in immune checkpoint inhibitor treated metastatic urothelial cancer patients

Authors: Robert S. Seitz, Michael E. Hurwitz, Tyler J. Nielsen, Daniel B. Bailey, Matthew G. Varga, Brian Z. Ring, Carrie F. Metts, Brock L. Schweitzer, Kimberly McGregor, Douglas T. Ross

Published in: Journal of Translational Medicine | Issue 1/2022

Login to get access

Abstract

Background

The IO Score is a 27-gene immuno-oncology (IO) classifier that has previously predicted benefit to immune checkpoint inhibitor (ICI) therapy in triple negative breast cancer (TNBC) and non-small cell lung cancer (NSCLC). It generates both a continuous score and a binary result using a defined threshold that is conserved between breast and lung. Herein, we aimed to evaluate the IO Score’s binary threshold in ICI-naïve TCGA bladder cancer patients (TCGA-BLCA) and assess its clinical utility in metastatic urothelial cancer (mUC) using the IMvigor210 clinical trial treated with the ICI, atezolizumab.

Methods

We identified a list of tumor immune microenvironment (TIME) related genes expressed across the TCGA breast, lung squamous and lung adenocarcinoma cohorts (TCGA-BRCA, TCGA-LUSQ, and TCGA-LUAD, 939 genes total) and then examined the expression of these 939 genes in TCGA-BLCA, to identify patients as having high inflammatory gene expression. Using this as a test of classification, we assessed the previously established threshold of IO Score. We then evaluated the IO Score with this threshold in the IMvigor210 cohort for its association with overall survival (OS).

Results

In TCGA-BLCA, IO Score positive patients had a strong concordance with high inflammatory gene expression (p < 0.0001). Given this concordance, we applied the IO Score to the ICI treated IMvigor210 patients. IO Score positive patients (40%) had a significant Cox proportional hazard ratio (HR) of 0.59 (95% CI 0.45–0.78 p < 0.001) for OS and improved median OS (15.6 versus 7.5 months) compared to IO Score negative patients. The IO Score remained significant in bivariate models combined with all other clinical factors and biomarkers, including PD-L1 protein expression and tumor mutational burden.

Conclusion

The IMvigor210 results demonstrate the potential for the IO Score as a clinically useful biomarker in mUC. As this is the third tumor type assessed using the same algorithm and threshold, the IO Score may be a promising candidate as a tissue agnostic marker of ICI clinical benefit. The concordance between IO Score and inflammatory gene expression suggests that the classifier is capturing common features of the TIME across cancer types.
Appendix
Available only for authorised users
Literature
1.
go back to reference Doroshow DB, Bhalla S, Beasley MB, Sholl LM, Kerr KM, Gnjatic S, et al. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nat Rev Clin Oncol. 2021;18(6):345–62.PubMedCrossRef Doroshow DB, Bhalla S, Beasley MB, Sholl LM, Kerr KM, Gnjatic S, et al. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nat Rev Clin Oncol. 2021;18(6):345–62.PubMedCrossRef
2.
go back to reference Lantuejoul S, Sound-Tsao M, Cooper WA, Girard N, Hirsch FR, Roden AC, et al. PD-L1 testing for lung cancer in 2019: perspective from the IASLC pathology committee. J Thorac Oncol. 2020;15(4):499–519.PubMedCrossRef Lantuejoul S, Sound-Tsao M, Cooper WA, Girard N, Hirsch FR, Roden AC, et al. PD-L1 testing for lung cancer in 2019: perspective from the IASLC pathology committee. J Thorac Oncol. 2020;15(4):499–519.PubMedCrossRef
3.
go back to reference McGrail DJ, Pilié PG, Rashid NU, Voorwerk L, Slagter M, Kok M, et al. High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types. Ann Oncol. 2021;32(5):661–72.PubMedCrossRef McGrail DJ, Pilié PG, Rashid NU, Voorwerk L, Slagter M, Kok M, et al. High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types. Ann Oncol. 2021;32(5):661–72.PubMedCrossRef
4.
go back to reference Petitprez F, Meylan M, de Reyniès A, Sautès-Fridman C, Fridman WH. The tumor microenvironment in the response to immune checkpoint blockade therapies. Front Immunol. 2020;11:784.PubMedPubMedCentralCrossRef Petitprez F, Meylan M, de Reyniès A, Sautès-Fridman C, Fridman WH. The tumor microenvironment in the response to immune checkpoint blockade therapies. Front Immunol. 2020;11:784.PubMedPubMedCentralCrossRef
5.
go back to reference Zhou C, Liu Q, Xiang Y, Gou X, Li W. Role of the tumor immune microenvironment in tumor immunotherapy (Review). Oncol Lett. 2022;23(2):53.PubMedCrossRef Zhou C, Liu Q, Xiang Y, Gou X, Li W. Role of the tumor immune microenvironment in tumor immunotherapy (Review). Oncol Lett. 2022;23(2):53.PubMedCrossRef
6.
go back to reference Tang T, Huang X, Zhang G, Hong Z, Bai X, Liang T. Advantages of targeting the tumor immune microenvironment over blocking immune checkpoint in cancer immunotherapy. Signal Transduct Target Ther. 2021;6(1):72.PubMedPubMedCentralCrossRef Tang T, Huang X, Zhang G, Hong Z, Bai X, Liang T. Advantages of targeting the tumor immune microenvironment over blocking immune checkpoint in cancer immunotherapy. Signal Transduct Target Ther. 2021;6(1):72.PubMedPubMedCentralCrossRef
7.
go back to reference Balar AV, Galsky MD, Rosenberg JE, Powles T, Petrylak DP, Bellmunt J, et al. Atezolizumab as first-line treatment in cisplatin-ineligible patients with locally advanced and metastatic urothelial carcinoma: a single-arm, multicentre, phase 2 trial. Lancet. 2017;389(10064):67–76.PubMedCrossRef Balar AV, Galsky MD, Rosenberg JE, Powles T, Petrylak DP, Bellmunt J, et al. Atezolizumab as first-line treatment in cisplatin-ineligible patients with locally advanced and metastatic urothelial carcinoma: a single-arm, multicentre, phase 2 trial. Lancet. 2017;389(10064):67–76.PubMedCrossRef
8.
go back to reference Rosenberg JE, Hoffman-Censits J, Powles T, van der Heijden MS, Balar AV, Necchi A, et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet. 2016;387(10031):1909–20.PubMedPubMedCentralCrossRef Rosenberg JE, Hoffman-Censits J, Powles T, van der Heijden MS, Balar AV, Necchi A, et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet. 2016;387(10031):1909–20.PubMedPubMedCentralCrossRef
9.
go back to reference Powles T, Durán I, van der Heijden MS, Loriot Y, Vogelzang NJ, De Giorgi U, et al. Atezolizumab versus chemotherapy in patients with platinum-treated locally advanced or metastatic urothelial carcinoma (IMvigor211): a multicentre, open-label, phase 3 randomised controlled trial. Lancet. 2018;391(10122):748–57.PubMedCrossRef Powles T, Durán I, van der Heijden MS, Loriot Y, Vogelzang NJ, De Giorgi U, et al. Atezolizumab versus chemotherapy in patients with platinum-treated locally advanced or metastatic urothelial carcinoma (IMvigor211): a multicentre, open-label, phase 3 randomised controlled trial. Lancet. 2018;391(10122):748–57.PubMedCrossRef
10.
go back to reference Mariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang Y, et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature. 2018;554(7693):544–8.PubMedPubMedCentralCrossRef Mariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang Y, et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature. 2018;554(7693):544–8.PubMedPubMedCentralCrossRef
11.
go back to reference Bianchini GD, Dugo M, Huang C, Egle D, Bermejo B, Seitz RS, Nielsen TJJ, Zamagni C, Thill M, Anton A, Russo S, Ciruelos EM, Schweitzer BL, Greil R, Semiglazov V, Gyorffy B, Valagussa P, Viale G, Callari M, Gianni L. Predictive value of gene-expression profiles (GEPs) and their dynamics during therapy in the NeoTRIPaPDL1 trial. Ann Oncol. 2021;32:S1283–346.CrossRef Bianchini GD, Dugo M, Huang C, Egle D, Bermejo B, Seitz RS, Nielsen TJJ, Zamagni C, Thill M, Anton A, Russo S, Ciruelos EM, Schweitzer BL, Greil R, Semiglazov V, Gyorffy B, Valagussa P, Viale G, Callari M, Gianni L. Predictive value of gene-expression profiles (GEPs) and their dynamics during therapy in the NeoTRIPaPDL1 trial. Ann Oncol. 2021;32:S1283–346.CrossRef
12.
go back to reference Dugo MH, Chiun-Sheng; Egle, Daniel; Berñejo, Bego a; Zamagni, Claudio; Seitz , Robert S.; Nielsen, Tyler J.; Thill, Marc; Anton, Antonio; Russo, Stefania; Ciruelos, Eva Maria; Schweitzer, Brock L.; Ross, Douglas T.; Galbardi, Barbara; Greil, Richard; Semiglazov, Vladimir; Gyorffy, Balazs; Colleoni, Marco; Kelly, Catherine; Mariani, Gabriella; Lucia Del Mastro; Valagussa, Pinuccia; Viale, Giuseppe; Callari, Maurizio; Gianni, Luca; Bianchini, Giampaolo. editor Predictive value of RT-qPCR 27-gene IO score and comparison with RNA-Seq IO score in the NeoTRIPaPDL1 trial. San Antonio Breast Cancer Symposium. San Antonio, Texas: AACR; 2021. Dugo MH, Chiun-Sheng; Egle, Daniel; Berñejo, Bego a; Zamagni, Claudio; Seitz , Robert S.; Nielsen, Tyler J.; Thill, Marc; Anton, Antonio; Russo, Stefania; Ciruelos, Eva Maria; Schweitzer, Brock L.; Ross, Douglas T.; Galbardi, Barbara; Greil, Richard; Semiglazov, Vladimir; Gyorffy, Balazs; Colleoni, Marco; Kelly, Catherine; Mariani, Gabriella; Lucia Del Mastro; Valagussa, Pinuccia; Viale, Giuseppe; Callari, Maurizio; Gianni, Luca; Bianchini, Giampaolo. editor Predictive value of RT-qPCR 27-gene IO score and comparison with RNA-Seq IO score in the NeoTRIPaPDL1 trial. San Antonio Breast Cancer Symposium. San Antonio, Texas: AACR; 2021.
13.
go back to reference Iwase T, Blenman KRM, Li X, Reisenbichler E, Seitz R, Hout D, et al. A novel immunomodulatory 27-gene signature to predict response to neoadjuvant immunochemotherapy for primary triple-negative breast cancer. Cancers (Basel). 2021;13(19):4839.CrossRef Iwase T, Blenman KRM, Li X, Reisenbichler E, Seitz R, Hout D, et al. A novel immunomodulatory 27-gene signature to predict response to neoadjuvant immunochemotherapy for primary triple-negative breast cancer. Cancers (Basel). 2021;13(19):4839.CrossRef
14.
go back to reference Nielsen TJ, Ring BZ, Seitz RS, Hout DR, Schweitzer BL. A novel immuno-oncology algorithm measuring tumor microenvironment to predict response to immunotherapies. Heliyon. 2021;7(3):e06438.PubMedPubMedCentralCrossRef Nielsen TJ, Ring BZ, Seitz RS, Hout DR, Schweitzer BL. A novel immuno-oncology algorithm measuring tumor microenvironment to predict response to immunotherapies. Heliyon. 2021;7(3):e06438.PubMedPubMedCentralCrossRef
15.
go back to reference Ranganath H, Jain AL, Smith JR, Ryder J, Chaudry A, Miller E, et al. Association of a novel 27-gene immuno-oncology assay with efficacy of immune checkpoint inhibitors in advanced non-small cell lung cancer. BMC Cancer. 2022;22(1):407.PubMedPubMedCentralCrossRef Ranganath H, Jain AL, Smith JR, Ryder J, Chaudry A, Miller E, et al. Association of a novel 27-gene immuno-oncology assay with efficacy of immune checkpoint inhibitors in advanced non-small cell lung cancer. BMC Cancer. 2022;22(1):407.PubMedPubMedCentralCrossRef
16.
go back to reference Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011;121(7):2750–67.PubMedPubMedCentralCrossRef Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011;121(7):2750–67.PubMedPubMedCentralCrossRef
17.
go back to reference Lehmann BD, Jovanović B, Chen X, Estrada MV, Johnson KN, Shyr Y, et al. Refinement of triple-negative breast cancer molecular subtypes: implications for neoadjuvant chemotherapy selection. PLoS ONE. 2016;11(6):e0157368.PubMedPubMedCentralCrossRef Lehmann BD, Jovanović B, Chen X, Estrada MV, Johnson KN, Shyr Y, et al. Refinement of triple-negative breast cancer molecular subtypes: implications for neoadjuvant chemotherapy selection. PLoS ONE. 2016;11(6):e0157368.PubMedPubMedCentralCrossRef
18.
go back to reference Ring BZ, Hout DR, Morris SW, Lawrence K, Schweitzer BL, Bailey DB, et al. Generation of an algorithm based on minimal gene sets to clinically subtype triple negative breast cancer patients. BMC Cancer. 2016;16:143.PubMedPubMedCentralCrossRef Ring BZ, Hout DR, Morris SW, Lawrence K, Schweitzer BL, Bailey DB, et al. Generation of an algorithm based on minimal gene sets to clinically subtype triple negative breast cancer patients. BMC Cancer. 2016;16:143.PubMedPubMedCentralCrossRef
19.
go back to reference Masuda H, Harano K, Miura S, Wang Y, Hirota Y, Harada O, et al. Changes in triple-negative breast cancer molecular subtypes in patients without pathologic complete response after neoadjuvant systemic chemotherapy. JCO Precis Oncol. 2022;6:e2000368.PubMedPubMedCentralCrossRef Masuda H, Harano K, Miura S, Wang Y, Hirota Y, Harada O, et al. Changes in triple-negative breast cancer molecular subtypes in patients without pathologic complete response after neoadjuvant systemic chemotherapy. JCO Precis Oncol. 2022;6:e2000368.PubMedPubMedCentralCrossRef
21.
go back to reference Kannan A, Hertweck KL, Philley JV, Wells RB, Dasgupta S. Genetic mutation and exosome signature of human papilloma virus associated oropharyngeal cancer. Sci Rep. 2017;7:46102.PubMedPubMedCentralCrossRef Kannan A, Hertweck KL, Philley JV, Wells RB, Dasgupta S. Genetic mutation and exosome signature of human papilloma virus associated oropharyngeal cancer. Sci Rep. 2017;7:46102.PubMedPubMedCentralCrossRef
22.
go back to reference Romeo E, Caserta CA, Rumio C, Marcucci F. The vicious cross-talk between tumor cells with an EMT phenotype and cells of the immune system. Cells. 2019;8(5):460.PubMedCentralCrossRef Romeo E, Caserta CA, Rumio C, Marcucci F. The vicious cross-talk between tumor cells with an EMT phenotype and cells of the immune system. Cells. 2019;8(5):460.PubMedCentralCrossRef
23.
go back to reference Xiao Y, Yu D. Tumor microenvironment as a therapeutic target in cancer. Pharmacol Ther. 2021;221:107753.PubMedCrossRef Xiao Y, Yu D. Tumor microenvironment as a therapeutic target in cancer. Pharmacol Ther. 2021;221:107753.PubMedCrossRef
24.
go back to reference Chen XH, Liu ZC, Zhang G, Wei W, Wang XX, Wang H, et al. TGF-β and EGF induced HLA-I downregulation is associated with epithelial-mesenchymal transition (EMT) through upregulation of snail in prostate cancer cells. Mol Immunol. 2015;65(1):34–42.PubMedCrossRef Chen XH, Liu ZC, Zhang G, Wei W, Wang XX, Wang H, et al. TGF-β and EGF induced HLA-I downregulation is associated with epithelial-mesenchymal transition (EMT) through upregulation of snail in prostate cancer cells. Mol Immunol. 2015;65(1):34–42.PubMedCrossRef
25.
go back to reference Team RC. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2020. Team RC. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2020.
26.
go back to reference Team R. RStudio: integrated development environment for R. Boston: RStudio, PBC; 2021. Team R. RStudio: integrated development environment for R. Boston: RStudio, PBC; 2021.
28.
go back to reference Morgan M, Davis Sean. Genomic Data Commons: NIH/NCI Genomic Data Commons Access. 2020. Morgan M, Davis Sean. Genomic Data Commons: NIH/NCI Genomic Data Commons Access. 2020.
29.
go back to reference Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016;44(8):e71.PubMedCrossRef Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016;44(8):e71.PubMedCrossRef
30.
go back to reference Mounir M, Lucchetta M, Silva TC, Olsen C, Bontempi G, Chen X, et al. New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx. PLoS Comput Biol. 2019;15(3):e1006701.PubMedPubMedCentralCrossRef Mounir M, Lucchetta M, Silva TC, Olsen C, Bontempi G, Chen X, et al. New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx. PLoS Comput Biol. 2019;15(3):e1006701.PubMedPubMedCentralCrossRef
31.
go back to reference Silva TC, Colaprico A, Olsen C, D’Angelo F, Bontempi G, Ceccarelli M, et al. TCGA workflow: analyze cancer genomics and epigenomics data using Bioconductor packages. F1000Res. 2016;5:1542.PubMedPubMedCentralCrossRef Silva TC, Colaprico A, Olsen C, D’Angelo F, Bontempi G, Ceccarelli M, et al. TCGA workflow: analyze cancer genomics and epigenomics data using Bioconductor packages. F1000Res. 2016;5:1542.PubMedPubMedCentralCrossRef
35.
go back to reference Venables WN, Ripley BD. Modern applied statistics with S. 4th ed. New York: Springer; 2002.CrossRef Venables WN, Ripley BD. Modern applied statistics with S. 4th ed. New York: Springer; 2002.CrossRef
36.
go back to reference Habibzadeh F, Habibzadeh P, Yadollahie M. On determining the most appropriate test cut-off value: the case of tests with continuous results. Biochem Med (Zagreb). 2016;26(3):297–307.CrossRef Habibzadeh F, Habibzadeh P, Yadollahie M. On determining the most appropriate test cut-off value: the case of tests with continuous results. Biochem Med (Zagreb). 2016;26(3):297–307.CrossRef
37.
go back to reference Sjödahl G, Eriksson P, Liedberg F, Höglund M. Molecular classification of urothelial carcinoma: global mRNA classification versus tumour-cell phenotype classification. J Pathol. 2017;242(1):113–25.PubMedPubMedCentralCrossRef Sjödahl G, Eriksson P, Liedberg F, Höglund M. Molecular classification of urothelial carcinoma: global mRNA classification versus tumour-cell phenotype classification. J Pathol. 2017;242(1):113–25.PubMedPubMedCentralCrossRef
38.
go back to reference Sjödahl G, Lauss M, Lövgren K, Chebil G, Gudjonsson S, Veerla S, et al. A molecular taxonomy for urothelial carcinoma. Clin Cancer Res. 2012;18(12):3377–86.PubMedCrossRef Sjödahl G, Lauss M, Lövgren K, Chebil G, Gudjonsson S, Veerla S, et al. A molecular taxonomy for urothelial carcinoma. Clin Cancer Res. 2012;18(12):3377–86.PubMedCrossRef
39.
go back to reference Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27(8):1160–7.PubMedPubMedCentralCrossRef Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27(8):1160–7.PubMedPubMedCentralCrossRef
40.
go back to reference Yoon J, Kim M, Posadas EM, Freedland SJ, Liu Y, Davicioni E, et al. A comparative study of PCS and PAM50 prostate cancer classification schemes. Prostate Cancer Prostatic Dis. 2021;24(3):733–42.PubMedPubMedCentralCrossRef Yoon J, Kim M, Posadas EM, Freedland SJ, Liu Y, Davicioni E, et al. A comparative study of PCS and PAM50 prostate cancer classification schemes. Prostate Cancer Prostatic Dis. 2021;24(3):733–42.PubMedPubMedCentralCrossRef
41.
go back to reference Zhao SG, Chang SL, Erho N, Yu M, Lehrer J, Alshalalfa M, et al. Associations of luminal and basal subtyping of prostate cancer with prognosis and response to androgen deprivation therapy. JAMA Oncol. 2017;3(12):1663–72.PubMedPubMedCentralCrossRef Zhao SG, Chang SL, Erho N, Yu M, Lehrer J, Alshalalfa M, et al. Associations of luminal and basal subtyping of prostate cancer with prognosis and response to androgen deprivation therapy. JAMA Oncol. 2017;3(12):1663–72.PubMedPubMedCentralCrossRef
Metadata
Title
Translation of the 27-gene immuno-oncology test (IO score) to predict outcomes in immune checkpoint inhibitor treated metastatic urothelial cancer patients
Authors
Robert S. Seitz
Michael E. Hurwitz
Tyler J. Nielsen
Daniel B. Bailey
Matthew G. Varga
Brian Z. Ring
Carrie F. Metts
Brock L. Schweitzer
Kimberly McGregor
Douglas T. Ross
Publication date
01-12-2022
Publisher
BioMed Central
Published in
Journal of Translational Medicine / Issue 1/2022
Electronic ISSN: 1479-5876
DOI
https://doi.org/10.1186/s12967-022-03563-9

Other articles of this Issue 1/2022

Journal of Translational Medicine 1/2022 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine