Skip to main content
Log in

Prädiktive Biomarker in der onkologischen Uropathologie

Predictive biomarkers in oncologic uropathology

  • Schwerpunkt: Präzisionsonkologie
  • Published:
Der Pathologe Aims and scope Submit manuscript

Zusammenfassung

Hintergrund

Urologische Tumoren zählen zu den häufigsten malignen Erkrankungen. In den letzten Jahren hat das Wissen um die molekularen Hintergründe und damit auch die potenzielle Zahl an prädiktiven Biomarkern deutlich zugenommen.

Ziel der Arbeit

Ziel der vorliegenden Arbeit ist es, eine Übersicht über aktuelle (molekulare) Entwicklungen und die damit verbundenen prädiktiven Biomarker in der urologischen Onkologie zu geben sowie einen Ausblick auf die kommenden Entwicklungen zu formulieren.

Material und Methoden

Es wurden die aktuelle Literatur und (Studien‑)Datenlage sowie eigene Erfahrungen berücksichtigt und zu Tumoren des ableitenden Harnsystems, der Niere und Prostata zusammengefasst.

Ergebnisse und Diskussion

Die molekularen Subtypen des muskelinvasiven Urothelkarzinoms der Harnblase (MIBC) zeigen eine prädiktive und prognostische Bedeutung mit klinikopathologischem Korrelat. Die Immuntherapie mit Checkpointinhibitoren (CPI) spielt besonders beim Urothelkarzinom, aber auch beim Nierenzellkarzinom und einer Subgruppe des Prostatakarzinoms eine Rolle. Bisher ist dabei lediglich die Erstlinientherapie des Urothelkarzinoms an die PD-L1-Expression gebunden (≥IC2/3, CPS ≥ 10). Weitere prädiktive Marker zur CPI-Therapie sind unter Evaluation, wobei die Wertigkeit der Tumormutationslast (TMB) noch nicht abschließend geklärt ist. Neben weiteren Subgruppen auch der Nierenzellkarzinome stellen Prostatakarzinome mit Veränderungen in den DNA-Reparaturmechanismen eine besondere klinische Gruppe mit speziellen Therapieoptionen dar (PARP-Inhibition, platinhaltige Chemotherapie). Die Vielzahl der potenziell therapierelevanten molekularen Veränderungen und damit verbundenen prädiktiven Marker macht differenzierte Genpanelanalysen sinnvoll, was in der urologischen Pathologie zu einer immer stärkeren Dynamik führt.

Abstract

Background

Tumors of the genitourinary system are common. In recent years, our understanding of their molecular background and therefore the number of potential predictive biomarkers has massively increased.

Objectives

The aim of the current work is to give an overview of recent (molecular) developments and predictive biomarkers in urologic oncology and to give a perspective of what might become relevant in the future of the field.

Material and methods

We considered the recent literature and study data and combined it with our own expertise in tumors of the urinary system, kidneys, and prostate.

Results and conclusions

The molecular subtypes of muscle-invasive urothelial bladder cancer (MIBC) hold a predictive and prognostic significance and correlate with clinicopathological features. Immune therapy with checkpoint inhibitors (CPI) has a major role in urothelial carcinoma (UC), but also in renal cell carcinoma and a subgroup of prostate cancers. The first-line use in UC is restricted to PD-L1-“positive” cases (≥IC2/3, CPS ≥ 10). Further predictive markers are currently under evaluation, while the predictive significance of tumor mutational burden (TMB) is under debate. In addition to a subgroup of renal cell carcinomas, a subgroup of prostate carcinomas with alterations in the DNA repair system might benefit from a customized therapy approach (PARP inhibitors, platin-containing chemotherapy). The multitude of potentially therapy-relevant molecular alterations and related predictive biomarkers calls for the implementation of sophisticated molecular analyses in daily routine. This will lead to an even more rapid dynamic in the field of genitourinary pathology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Abb. 1
Abb. 2
Abb. 3
Abb. 4

Literatur

  1. Abida W, Cheng ML, Armenia J et al (2018) Analysis of the prevalence of microsatellite instability in prostate cancer and response to immune checkpoint blockade. JAMA Oncol. https://doi.org/10.1001/jamaoncol.2018.5801

    Article  PubMed  Google Scholar 

  2. Agaimy A (2016) Succinate dehydrogenase (SDH)-deficient renal cell carcinoma. Pathologe 37:144–152

    Article  CAS  Google Scholar 

  3. Agaimy A, Hartmann A (2016) Hereditary renal tumors: more common than expected? Pathologe 37:134–143

    Article  CAS  Google Scholar 

  4. Aggen DH, Drake CG (2017) Biomarkers for immunotherapy in bladder cancer: a moving target. J Immunother Cancer 5:94

    Article  Google Scholar 

  5. Antonarakis ES, Lu C, Wang H et al (2014) AR-V7 and resistance to enzalutamide and abiraterone in prostate cancer. N Engl J Med 371:1028–1038

    Article  Google Scholar 

  6. Antoni S, Ferlay J, Soerjomataram I et al (2017) Bladder cancer incidence and mortality: a global overview and recent trends. Eur Urol 71:96–108

    Article  Google Scholar 

  7. Arvaniti E, Fricker KS, Moret M et al (2018) Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci Rep 8:12054

    Article  Google Scholar 

  8. AWMF (2017) Leitlinienprogramm Onkologie: Diagnostik, Therapie und Nachsorge des Nierenzellkarzinoms, Langversion 1.2. In: Deutsche Krebsgesellschaft, Deutsche Krebshilfe, AWMF

  9. Ayers M, Lunceford J, Nebozhyn M et al (2017) IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest 127:2930–2940

    Article  Google Scholar 

  10. Azad AA, Volik SV, Wyatt AW et al (2015) Androgen receptor gene aberrations in circulating cell-free DNA: biomarkers of therapeutic resistance in castration-resistant prostate cancer. Clin Cancer Res 21:2315–2324

    Article  CAS  Google Scholar 

  11. Barentsz JO, Weinreb JC, Verma S et al (2016) Synopsis of the PI-RADS v2 guidelines for multiparametric prostate magnetic resonance imaging and recommendations for use. Eur Urol 69:41–49

    Article  Google Scholar 

  12. Basu A, Yearley JH, Annamalai L et al (2019) Association of PD-L1, PD-L2, and immune response markers in matched renal clear cell carcinoma primary and metastatic tissue specimens. Am J Clin Pathol 151:217–225

    Article  Google Scholar 

  13. Becerra MF, Reznik E, Redzematovic A et al (2018) Comparative genomic profiling of matched primary and metastatic tumors in renal cell carcinoma. Eur Urol Focus 4:986–994

    Article  Google Scholar 

  14. Bellmunt J, De Wit R, Vaughn DJ et al (2017) Pembrolizumab as second-line therapy for advanced urothelial carcinoma. N Engl J Med 376:1015–1026

    Article  CAS  Google Scholar 

  15. Beltran H, Eng K, Mosquera JM et al (2015) Whole-exome sequencing of metastatic cancer and biomarkers of treatment response. JAMA Oncol 1:466–474

    Article  Google Scholar 

  16. Beltran H, Rickman DS, Park K et al (2011) Molecular characterization of neuroendocrine prostate cancer and identification of new drug targets. Cancer Discov 1:487–495

    Article  CAS  Google Scholar 

  17. Bertz S, Eckstein M, Stoehr R et al (2017) Urothelial bladder cancer: an update on molecular pathology with clinical implications. Eur Urol 16(Suppl):272–294

    Article  Google Scholar 

  18. Bertz S, Hartmann A, Knuchel-Clarke R et al (2016) Specific types of bladder cancer. Pathologe 37:40–51

    Article  CAS  Google Scholar 

  19. Beuselinck B, Job S, Becht E et al (2015) Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting. Clin Cancer Res 21:1329–1339

    Article  CAS  Google Scholar 

  20. Bihr S, Ohashi R, Moore AL et al (2019) Expression and mutation patterns of PBRM1, BAP1 and SETD2 mirror specific evolutionary subtypes in clear cell renal cell carcinoma. Neoplasia 21:247–256

    Article  CAS  Google Scholar 

  21. Cancer Genome Atlas Research Network (2015) The molecular taxonomy of primary prostate cancer. Cell 163:1011–1025

    Article  Google Scholar 

  22. Cheng HH, Pritchard CC, Boyd T et al (2016) Biallelic inactivation of BRCA2 in platinum-sensitive metastatic castration-resistant prostate cancer. Eur Urol 69:992–995

    Article  CAS  Google Scholar 

  23. Choi W, Porten S, Kim S et al (2014) Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy. Cancer Cell 25:152–165

    Article  CAS  Google Scholar 

  24. Dadhania V, Czerniak B, Guo CC (2015) Adenocarcinoma of the urinary bladder. Am J Clin Exp Urol 3:51–63

    PubMed  PubMed Central  Google Scholar 

  25. Eckstein M, Erben P, Kriegmair MC et al (2019) Performance of the Food and Drug Administration/EMA-approved programmed cell death ligand-1 assays in urothelial carcinoma with emphasis on therapy stratification for first-line use of atezolizumab and pembrolizumab. Eur J Cancer 106:234–243

    Article  CAS  Google Scholar 

  26. Franiel T, Rothke M (2017) PI-RADS 2.0 for Prostate MRI. Radiologe 57:665–678

    Article  CAS  Google Scholar 

  27. Gerlinger M, Horswell S, Larkin J et al (2014) Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat Genet 46:225–233

    Article  CAS  Google Scholar 

  28. Gerlinger M, Rowan AJ, Horswell S et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883–892

    Article  CAS  Google Scholar 

  29. Goodman AM, Kato S, Bazhenova L et al (2017) Tumor mutational burden as an independent predictor of response to immunotherapy in diverse cancers. Mol Cancer Ther 16:2598–2608

    Article  CAS  Google Scholar 

  30. Hadaschik B (2018) Paradigm shift in urology: prostate cancer diagnosis using MRI-targeted or standard transrectal ultrasonography-guided biopsy. Urologe 57:727–728

    Article  CAS  Google Scholar 

  31. Haroske G, Bürrig K‑F, Füzesi L et al (2018) Leitfaden Digitale Pathologie in der Diagnostik—Befunderstellung an digitalen Bildern. Bundesverband Deutscher Pathologen e. V. Version1.0, S 1–27

    Google Scholar 

  32. Johnson DC, Vukina J, Smith AB et al (2015) Preoperatively misclassified, surgically removed benign renal masses: a systematic review of surgical series and United States population level burden estimate. J Urol 193:30–35

    Article  Google Scholar 

  33. Kamoun A, De Reynies A, Allory Y et al (2018) The consensus molecular classification of muscle-invasive bladder cancer. bioRxiv:488460

    Book  Google Scholar 

  34. Kamoun A, De Reynies A, Allory Y et al (2018) Transcriptomic classifier for Muscle-Invasive Bladder Cancer. https://github.com/cit-bioinfo/consensusMIBC. Zugegriffen: 29. Januar 2019

    Google Scholar 

  35. Kasivisvanathan V, Rannikko AS, Borghi M et al (2018) MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med 378:1767–1777

    Article  Google Scholar 

  36. Kimura H, Matsui Y, Nakajima T et al (2017) 1144OPhase III randomized controlled trial of adjuvant chemoimmunotherapy in patients with resected primary lung cancer. Ann Oncol 28:v403–v427

    Google Scholar 

  37. Kobayashi K, Kondo K, Ikeda I et al (1997) Microsatellite instability occurs infrequently in sporadic renal cell carcinoma. Oncol Rep 4:941–944

    CAS  PubMed  Google Scholar 

  38. Lerner SP, Mcconkey DJ, Hoadley KA et al (2016) Bladder cancer molecular taxonomy: summary from a consensus meeting. Bladder Cancer 2:37–47

    Article  Google Scholar 

  39. Linder BJ, Boorjian SA, Cheville JC et al (2013) The impact of histological reclassification during pathology re-review—evidence of a Will Rogers effect in bladder cancer? J Urol 190:1692–1696

    Article  Google Scholar 

  40. Mariathasan S, Turley SJ, Nickles D et al (2018) TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554:544–548

    Article  CAS  Google Scholar 

  41. Martin JW, Carballido EM, Ahmed A et al (2016) Squamous cell carcinoma of the urinary bladder: systematic review of clinical characteristics and therapeutic approaches. Arab J Urol 14:183–191

    Article  Google Scholar 

  42. Mateo J, Carreira S, Sandhu S et al (2015) DNA-repair defects and Olaparib in metastatic prostate cancer. N Engl J Med 373:1697–1708

    Article  CAS  Google Scholar 

  43. Memorial Sloan Kettering Cancer Center (2019) cBioPortal for Cancer Genomics. www.cbioportal.org/ Zugegriffen: 29. Januar 2019

  44. Motzer RJ, Escudier B, Mcdermott DF et al (2015) Nivolumab versus everolimus in advanced renal-cell carcinoma. N Engl J Med 373:1803–1813

    Article  CAS  Google Scholar 

  45. Motzer RJ, Powles T, Atkins MB et al (2018) IMmotion151: a randomized phase III study of atezolizumab plus bevacizumab vs sunitinib in untreated metastatic renal cell carcinoma (mRCC). J Clin Oncol 36:578–578

    Article  Google Scholar 

  46. Nagpal K, Foote D, Liu Y et al (2018) Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer. CoRR arxiv.org/abs/1811.06497

    Google Scholar 

  47. Nakazawa M, Lu C, Chen Y et al (2015) Serial blood-based analysis of AR-V7 in men with advanced prostate cancer. Ann Oncol 26:1859–1865

    Article  CAS  Google Scholar 

  48. Paul-Ehrlich-Institut (2018) Rote-Hand-Brief: Keytruda (Pembrolizumab): Einschränkung des Anwendungsgebiets zur Behandlung des lokal fortgeschrittenen oder metastasierenden Urothelkarzinoms bei Erwachsenen, die nicht für eine Cisplatin-basierte Therapie geeignet sind. https://www.pei.de/DE/arzneimittelsicherheit-vigilanz/archiv-sicherheitsinformationen/2018/ablage2018/2018-07-11-rhb-keytruda-pembrolizumab.html . Zugegriffen: 29. Jan. 2019

    Google Scholar 

  49. Paul-Ehrlich-Institut (2018) Rote-Hand-Brief: Tecentriq (Atezolizumab): Einschränkung der Indikation zur Behandlung des lokal fortgeschrittenen oder metastasierten Urothelkarzinoms bei erwachsenen Patienten, die für eine cisplatinbasierte Chemotherapie ungeeignet sind. https://www.pei.de/DE/arzneimittelsicherheit-vigilanz/archiv-sicherheitsinformationen/2018/ablage2018/2018-07-09-rhb-tecentriq-atezolizumab.html. Zugegriffen: 29. Januar 2019

    Google Scholar 

  50. Pawlik TM, Keyomarsi K (2004) Role of cell cycle in mediating sensitivity to radiotherapy. Int J Radiat Oncol Biol Phys 59:928–942

    Article  Google Scholar 

  51. Pena-Llopis S, Vega-Rubin-De-Celis S, Liao A et al (2012) BAP1 loss defines a new class of renal cell carcinoma. Nat Genet 44:751–759

    Article  CAS  Google Scholar 

  52. Pietzak EJ, Zabor EC, Bagrodia A et al (2019) Genomic differences between „primary“ and „secondary“ muscle-invasive bladder cancer as a basis for disparate outcomes to cisplatin-based neoadjuvant chemotherapy. Eur Urol 75:231–239

    Article  Google Scholar 

  53. Plimack ER, Dunbrack RL, Brennan TA et al (2015) Defects in DNA repair genes predict response to neoadjuvant cisplatin-based chemotherapy in muscle-invasive bladder cancer. Eur Urol 68:959–967

    Article  CAS  Google Scholar 

  54. Rebouissou S, Bernard-Pierrot I, De Reynies A et al (2014) EGFR as a potential therapeutic target for a subset of muscle-invasive bladder cancers presenting a basal-like phenotype. Sci Transl Med 6:244ra291

    Article  Google Scholar 

  55. Reis H, Krafft U, Niedworok C et al (2018) Biomarkers in urachal cancer and adenocarcinomas in the bladder: a comprehensive review supplemented by own data. Dis Markers 2018:7308168

    Article  Google Scholar 

  56. Reis H, Szarvas T (2018) Urachal cancer—current concepts of a rare cancer (German version). Pathologe 39:291–300

    Article  CAS  Google Scholar 

  57. Reis H, Serrette RN, Gopalan A et al (2018) USCAP 2018 abstracts: genitourinary pathology (894–1126): 1053 PD-L1 expression in urothelial carcinoma with divergent differentiation, concordance among three antibodies. Mod Pathol 98:323–403

    Article  Google Scholar 

  58. Rinaldetti S, Rempel E, Worst TS et al (2018) Subclassification, survival prediction and drug target analyses of chemotherapy-naive muscle-invasive bladder cancer with a molecular screening. Oncotarget 9:25935–25945

    Article  Google Scholar 

  59. Rosenberg JE, Hoffman-Censits J, Powles T et al (2016) 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 387:1909–1920

    Article  CAS  Google Scholar 

  60. Salvi S, Casadio V, Conteduca V et al (2015) Circulating cell-free AR and CYP17A1 copy number variations may associate with outcome of metastatic castration-resistant prostate cancer patients treated with abiraterone. Br J Cancer 112:1717–1724

    Article  CAS  Google Scholar 

  61. Schildhaus HU (2018) Predictive value of PD-L1 diagnostics. Pathologe 39:498–519

    Article  Google Scholar 

  62. Schuler M, Nogova L, Heidenreich A et al (2017) 859PAnti-tumor activity of the pan-FGFR inhibitor rogaratinib in patients with advanced urothelial carcinomas selected based on tumor FGFR mRNA expression levels. Ann Oncol 28:v295. https://doi.org/10.1093/annonc/mdx371

    Article  Google Scholar 

  63. Seiler R, Erho AHDN et al (2017) Impact of molecular subtypes in muscle-invasive bladder cancer on predicting response and survival after neoadjuvant chemotherapy. Eur Urol 72:544–554

    Article  CAS  Google Scholar 

  64. Siefker-Radtke A, Necchi A, Park SH et al (2018) First results from the primary analysis population of the phase 2 study of erdafitinib (ERDA; JNJ-42756493) in patients (pts) with metastatic or unresectable urothelial carcinoma (mUC) and FGFR alterations (FGFRalt). J Clin Oncol 36(15_suppl):4503

    Article  Google Scholar 

  65. Sjodahl G, Eriksson P, Liedberg F et al (2017) Molecular classification of urothelial carcinoma: global mRNA classification versus tumour-cell phenotype classification. J Pathol 242:113–125

    Article  Google Scholar 

  66. Sjodahl G, Lauss M, Lovgren K et al (2012) A molecular taxonomy for urothelial carcinoma. Clin Cancer Res 18:3377–3386

    Article  Google Scholar 

  67. Szarvas T, Olah C, Reis H (2019) Neoadjuvant cisplatin-based chemotherapy in „primary“ and „secondary“ muscle-invasive bladder cancer—is it a surrogate for molecular subtypes? Transl Cancer Res. https://doi.org/10.21037/tcr.2019.01.05

    Article  Google Scholar 

  68. Tretiakova M, Fulton R, Kocherginsky M et al (2018) Concordance study of PD-L1 expression in primary and metastatic bladder carcinomas: comparison of four commonly used antibodies and RNA expression. Mod Pathol 31:623–632

    Article  CAS  Google Scholar 

  69. Turkbey B, Rosenkrantz AB, Haider MA et al (2019) Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol. https://doi.org/10.1016/j.eururo.2019.02.033

    Article  PubMed  Google Scholar 

  70. Turney A (2019) FDA approves first targeted therapy for metastatic bladder cancer. In: U.S. Food and Drug Administration. https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm635906.htm. Zugegriffen: 29. Januar 2019

    Google Scholar 

  71. Udager AM, Mcdaniel AS, Hovelson DH et al (2018) Frequent PD-L1 protein expression and molecular correlates in urinary bladder squamous cell carcinoma. Eur Urol 74:529–531

    Article  Google Scholar 

  72. Van Allen EM, Mouw KW, Kim P et al (2014) Somatic ERCC2 mutations correlate with cisplatin sensitivity in muscle-invasive urothelial carcinoma. Cancer Discov 4:1140–1153

    Article  Google Scholar 

  73. Voss MH, Reising A, Cheng Y et al (2018) Genomically annotated risk model for advanced renal-cell carcinoma: a retrospective cohort study. Lancet Oncol 19:1688–1698

    Article  Google Scholar 

  74. Yarchoan M, Johnson BA 3rd, Lutz ER et al (2017) Targeting neoantigens to augment antitumour immunity. Nat Rev Cancer 17:209–222

    Article  CAS  Google Scholar 

Download references

Funding

Förderung

Mit Unterstützung durch das ungarische Wissenschafts‑, Entwicklungs- und Innovationsministerium (NKFIH/FK 12443, NVKP_16-1-2016-004, ÙNKP-18-4-SE-66). T. Szarvas hat ein János-Bolyai-Forschungsstipendium von der ungarischen Akademie der Wissenschaften erhalten.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to H. Reis.

Ethics declarations

Interessenkonflikt

H. Reis: Honorare von Roche, Bristol-Myer Squibb. Forschungsunterstützung von Bristol-Myer Squibb. V. Grünwald: Berater für Pfizer, Novartis, Bristol-Myer Squibb, Ipsen, Eisai, EUSAPharm, MSD, Merck Serono, Lilly und Roche. Honorare von AstraZeneca, Pfizer, Novartis, Bristol-Myer Squibb, MSD, Ipsen, Eisai, Lilly und Roche. Aktien von AstraZeneca, Bristol-Myer Squibb und MSD. Forschungsunterstützung von Bristol-Myer Squibb, MSD, AstraZeneca, Pfizer und Novartis. T. Szarvas: Honorare von Sandoz.

Für diesen Beitrag wurden von den Autoren keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.

Additional information

Schwerpunktherausgeber

K. W. Schmid, Essen

H. A. Baba, Essen

H.-U. Schildhaus, Essen

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Reis, H., Szarvas, T. & Grünwald, V. Prädiktive Biomarker in der onkologischen Uropathologie. Pathologe 40, 264–275 (2019). https://doi.org/10.1007/s00292-019-0606-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00292-019-0606-3

Schlüsselwörter

Keywords

Navigation