Molekulare Tumordiagnostik als Triebfeder der Präzisionsonkologie

Die Weiterentwicklung molekularpathologischer Diagnostik ist relevant für jeden onkologischen Patienten, für prospektive klinische Studien und für die Generierung von Realwelt-Daten zur Medikamentenentwicklung. Die Verbesserung einer skalierbaren Molekulardiagnostik und die Entwicklung adaptiver Diagnostikstrategien ist notwendig, um die enorme Plastizität einer Krebserkrankung im zeitlichen Verlauf abbilden zu können.

Abstract

Molecular pathological diagnostics plays a central role in personalized oncology and requires multidisciplinary teamwork. It is just as relevant for the individual patient who is being treated with an approved therapy method or an individual treatment attempt as it is for prospective clinical studies that require the identification of specific therapeutic target structures or complex biomarkers for study inclusion. It is also of crucial importance for the generation of real-world data, which is becoming increasingly important for drug development. Future developments will be significantly shaped by improvements in scalable molecular diagnostics, in which increasingly complex and multi-layered data sets must be quickly converted into clinically useful information. One focus will be on the development of adaptive diagnostic strategies in order to be able to depict the enormous plasticity of a cancer disease over time.

Kernaussagen

• Die umfassenderen genomischen Analysen der Bioinformatik erhöhen die Komplexität und somit die Anforderungen an die bioinformatische Expertise.

• Die erfolgreiche Nutzung molekularer Diagnostik kann nur mit einem multidisziplinären Team bewerkstelligt werden.

• Mutationssignaturen bilden ein Mutationsporträt der Tumorentwicklung und können Aufschluss darüber geben, welche Mutationsprozesse im Laufe der Zeit wirkten.

• Um die Integration von Mutationssignaturen in die klinische Entscheidungsfindung weiter zu erhöhen, ist es wichtig, dass alternative Sequenzierungsansätze entwickelt und genutzt werden.

• Die arraybasierte Methylierungsanalyse wird aufgrund ihrer standardisierten Technologie sowie ihrer technischen Einfachheit und Robustheit bei der Verwendung von DNA aus formalinfixierten Tumorproben am häufigsten verwendet.

• RNA-Sequenzierung hat sich als wertvolles Werkzeug für den Nachweis von Genfusionen in der prädiktiven Tumordiagnostik erwiesen.

• Ein zukünftiges klinisch-diagnostisches Anwendungsfeld der umfassenden RNA-Sequenzierung kann die unterstützende diagnostische Einordnung von Karzinomen unbekannter Primärerkrankung (CUP) darstellen.

• Die Integration von umfassenden Proteomanalysen in der Molekularpathologie ist noch nicht weit verbreitet, bietet aber ein großes diagnostisches Potenzial.

• Die Generalisierbarkeit maschineller Lernmodelle und das Blackbox-Design von KI-Ansätzen sind herausfordernde Eigenschaften dieser neuen Technik und stellen mögliche Einschränkungen dar.

Schlüsselwörter

molekulare Tumordiagnostik, - Bioinformatik - Mutationssignaturen - Methylierungsarrays - künstliche Intelligenz

Keywords

molecular tumor diagnostics - bioinformatics - mutation signatures - methylation arrays - artificial intelligence

Publication History

Article published online:
01 September 2023

• Literatur

• 1 Leichsenring J, Kazdal D, Ploeger C. et al. [From panel diagnostics to comprehensive genomic analysis: Infobesity or empowerment?]. Pathol 2019; 40: 235-242
• 2 National Human Genome Research Institute (NHGRI). The Cost of Sequencing a Human Genome. 2021 Accessed July 03, 2023 at: https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost
• 3 Ewing B, Green P. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res 1998; 8: 186-194 (PMID: 9521922)
• 4 Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinforma Oxf Engl 2009; 25: 1754-1760
• 5 Nurk S, Koren S, Rhie A. et al. The complete sequence of a human genome. Science 2022; 376: 44-53
• 6 Bartha Á, Győrffy B. Comprehensive Outline of Whole Exome Sequencing Data Analysis Tools Available in Clinical Oncology. Cancers 2019; 11: 1725 DOI: 10.3390/cancers11111725. (PMID: 31690036)
• 7 Garcia-Prieto CA, Martínez-Jiménez F, Valencia A. et al. Detection of oncogenic and clinically actionable mutations in cancer genomes critically depends on variant calling tools. Bioinformatics 2022; 38: 3181-3191 DOI: 10.1093/bioinformatics/btac306. (PMID: 35512388)
• 8 Menzel M, Ossowski S, Kral S. et al. Towards standardized whole exome sequencing (WES) for cancer patients: lessons from a multicentric pilot study (submitted).
• 9 Finotello F, Rieder D, Hackl H. et al. Next-generation computational tools for interrogating cancer immunity. Nat Rev Genet 2019; 20: 724-746 DOI: 10.1038/s41576-019-0166-7. (PMID: 31515541)
• 10 Koh G, Degasperi A, Zou X. et al. Mutational signatures: emerging concepts, caveats and clinical applications. Nat Rev Cancer 2021; 21: 619-637 DOI: 10.1038/s41568-021-00377-7. (PMID: 34316057)
• 11 Alexandrov LB, Nik-Zainal S, Wedge DC. et al. Signatures of mutational processes in human cancer. Nature 2013; 500: 415-421 DOI: 10.1038/nature12477. (PMID: 23945592)
• 12 COSMIC, the Catalogue Of Somatic Mutations In Cancer. Mutational Signatures. Accessed July 03, 2023 at: https://cancer.sanger.ac.uk/signatures/
• 13 Niu B, Ye K, Zhang Q. et al. MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. Bioinformatics 2014; 30: 1015-1016 DOI: 10.1093/bioinformatics/btt755. (PMID: 24371154)
• 14 Salipante SJ, Scroggins SM, Hampel HL. et al. Microsatellite instability detection by next generation sequencing. Clin Chem 2014; 60: 1192-1199 DOI: 10.1373/clinchem.2014.223677. (PMID: 24987110)
• 15 Huang MN, McPherson JR, Cutcutache I. et al. MSIseq: Software for Assessing Microsatellite Instability from Catalogs of Somatic Mutations. Sci Rep 2015; 5: 13321
• 16 Jia P, Yang X, Guo L. et al. MSIsensor-pro: Fast, Accurate, and Matched-normal-sample-free Detection of Microsatellite Instability. Genomics Proteomics Bioinformatics 2020; 18: 65-71 DOI: 10.1016/j.gpb.2020.02.001. (PMID: 32171661)
• 17 Davies H, Glodzik D, Morganella S. et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat Med 2017; 23: 517-525 DOI: 10.1038/nm.4292. (PMID: 28288110)
• 18 Nguyen L, Martens WM, Van Hoeck J. et al. Pan-cancer landscape of homologous recombination deficiency. Nat Commun 2020; 11: 5584
• 19 Razavi P, Li BT, Brown DN. et al. High-intensity sequencing reveals the sources of plasma circulating cell-free DNA variants. Nat Med 2019; 25: 1928-1937 DOI: 10.1038/s41591-019-0652-7. (PMID: 31768066)
• 20 Perner J, Abbas S, Nowicki-Osuch K. et al. The mutREAD method detects mutational signatures from low quantities of cancer DNA. Nat Commun 2020; 11: 3166
• 21 Wan JCM, Stephens D, Luo L. et al. Genome-wide mutational signatures in low-coverage whole genome sequencing of cell-free DNA. Nat Commun 2022; 13: 4953 DOI: 10.1038/s41467-022-32598-1. (PMID: 35999207)
• 22 Capper D, Jones DTW, Sill M. et al. DNA methylation-based classification of central nervous system tumours. Nature 2018; 55: 469-474 DOI: 10.1038/nature26000. (PMID: 29539639)
• 23 Capper D, Stichel D, Sahm F. et al. Practical implementation of DNA methylation and copy-number-based CNS tumor diagnostics: the Heidelberg experience. Acta Neuropathol (Berl. ) 2018; 136: 181-210
• 24 Koelsche C, Schrimpf D, Stichel D. et al. Sarcoma classification by DNA methylation profiling. Nat Commun 2021; 12: 498 DOI: 10.1038/s41467-020-20603-4. (PMID: 33479225)
• 25 Neumann O, Ball M, Lehmann U. et al. Genes and pathways: FGFR2 translocations and gene fusion analysis. Pathol Heidelb Ger 2022; 43: 384-386 DOI: 10.1007/s00292-022-01080-6. (PMID: 35925311)
• 26 Perou CM, Sørlie T, Eisen MB. et al. Molecular portraits of human breast tumours. Nature 2000; 406: 747-752 DOI: 10.1038/35021093. (PMID: 10963602)
• 27 Sørlie T, Perou CM, Tibshirani R. et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 2001; 98: 10869-10874
• 28 van’t Veer LJ, Dai H, van de Vijver MJ. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415: 530-536
• 29 Paik S, Shak S, Tang G. et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004; 351: 2817-2826 DOI: 10.1056/NEJMoa041588. (PMID: 15591335)
• 30 Parker JS, Mullins M, Cheang MC. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 2009; 27 (08) 1160-1167 DOI: 10.1200/JCO.2008.18.1370. (PMID: 19204204)
• 31 Krämer A, Bochtler T, Pauli C. et al. Cancer of unknown primary: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol 2023; 34 (03) 228-246 DOI: 10.1016/j.annonc.2022.11.013. (PMID: 36563965)
• 32 Erlander MG, Ma XJ, Kesty NC. et al. Performance and clinical evaluation of the 92-gene real-time PCR assay for tumor classification. J Mol Diagn 2011; 13: 493-503 DOI: 10.1016/j.jmoldx.2011.04.004. (PMID: 21708287)
• 33 Kurahashi I, Fujita Y, Arao T. et al. A microarray-based gene expression analysis to identify diagnostic biomarkers for unknown primary cancer. PloS One 2013; 8: e63249 DOI: 10.1371/journal.pone.0063249. (PMID: 23671674)
• 34 Hayashi H, Takiguchi Y, Minami H. et al. Site-Specific and Targeted Therapy Based on Molecular Profiling by Next-Generation Sequencing for Cancer of Unknown Primary Site: A Nonrandomized Phase 2 Clinical Trial. JAMA Oncol 2020; 6: 1931-1938 DOI: 10.1001/jamaoncol.2020.4643. (PMID: 33057591)
• 35 Zhao Y, Pan Z, Namburi S. et al. CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence. EBioMedicine 2020; 61: 103030 DOI: 10.1016/j.ebiom.2020.103030. (PMID: 33039710)
• 36 Vibert J, Pierron G, Benoist C. et al. Identification of Tissue of Origin and Guided Therapeutic Applications in Cancers of Unknown Primary Using Deep Learning and RNA Sequencing (TransCUPtomics). J Mol Diagn 2021; 23: 1380-1392 DOI: 10.1016/j.jmoldx.2021.07.009. (PMID: 34325056)
• 37 Zhang J, Bajari R, Andric D. et al. The International Cancer Genome Consortium Data Portal. Nat Biotechnol 2019; 37: 367-369 DOI: 10.1038/s41587-019-0055-9. (PMID: 30877282)
• 38 Hainsworth JD, Rubin MS, Spigel DR. et al. Molecular gene expression profiling to predict the tissue of origin and direct site-specific therapy in patients with carcinoma of unknown primary site: a prospective trial of the Sarah Cannon research institute. J Clin Oncol 2013; 31 (02) 217-223
• 39 Fizazi K, Maillard A, Penel N. et al. LBA15_PR – A phase III trial of empiric chemotherapy with cisplatin and gemcitabine or systemic treatment tailored by molecular gene expression analysis in patients with carcinomas of an unknown primary (CUP) site (GEFCAPI 04). Annals of Oncology 2019; 30 (Suppl. 05) v851-v934 DOI: 10.1093/annonc/mdz394.
• 40 Hayashi H, Kurata T, Takiguchi Y. et al. Randomized Phase II Trial Comparing Site-Specific Treatment Based on Gene Expression Profiling With Carboplatin and Paclitaxel for Patients With Cancer of Unknown Primary Site. J Clin Oncol 2019; 37 (07) 570-579
• 41 Macklin A, Khan S, Kislinger T. Recent advances in mass spectrometry based clinical proteomics: applications to cancer research. Clin Proteomics 2020; 17: 17 DOI: 10.1186/s12014-020-09283-w. (PMID: 32489335)
• 42 Hristova VA, Chan DW. Cancer biomarker discovery and translation: proteomics and beyond. Expert Rev Proteomics 2019; 16: 93-103 DOI: 10.1080/14789450.2019.1559062. (PMID: 30556752)
• 43 Kitata RB, Yang JC, Chen YJ. Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape. Mass Spectrom Rev 2022; e21781 DOI: 10.1002/mas.21781. (PMID: 35645145)
• 44 Ding Z, Wang N, Ji N. et al. Proteomics technologies for cancer liquid biopsies. Mol Cancer 2022; 21: 53 DOI: 10.1186/s12943-022-01526-8. (PMID: 35168611)
• 45 Lesur A, Dittmar G. The clinical potential of prm-PASEF mass spectrometry. Expert Rev Proteomics 2021; 18: 75-82 DOI: 10.1080/14789450.2021.1908895. (PMID: 33874828)
• 46 Mund A, Brunner AD, Mann M. Unbiased spatial proteomics with single-cell resolution in tissues. Mol Cell 2022; 82: 2335-2349 DOI: 10.1016/j.molcel.2022.05.022. (PMID: 35714588)
• 47 Kelly RT. Single-cell Proteomics: Progress and Prospects. Mol Cell Proteomics 2020; 19: 1739-1748 DOI: 10.1074/mcp.R120.002234. (PMID: 32847821)
• 48 Umer HM, Audain E, Zhu Y. et al. Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides. Bioinformatics 2022; 38: 1470-1472 DOI: 10.1093/bioinformatics/btab838. (PMID: 34904638)
• 49 Keyl P, Bockmayr M, Heim D. et al. Patient-level proteomic network prediction by explainable artificial intelligence. NPJ Precis Oncol 2022; 6: 35 DOI: 10.1038/s41698-022-00278-4. (PMID: 35672443)
• 50 Keyl P, Bischoff P, Dernbach G. et al. Single-cell gene regulatory network prediction by explainable AI. Nucleic Acids Res 2023; 51 (04) e20 DOI: 10.1093/nar/gkac1212. (PMID: 36629274)
• 51 Jurmeister P, Glöß S, Roller R. et al. DNA methylation-based classification of sinonasal tumors. Nat Commun 2022; 13: 7148 DOI: 10.1038/s41467-022-34815-3. (PMID: 36443295)
• 52 Jurmeister P, Bockmayr M, Seegerer P. et al. Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases. Sci Transl Med 2019; 11 (509) eaaw8513
• 53 Stenzinger A, Alber M, Allgäuer M. et al. Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling. Semin. Cancer Biol 2022; 84: 129-143
• 54 Lapuschkin S, Wäldchen S, Binder A. et al. Unmasking Clever Hans predictors and assessing what machines really learn. Nat Commun 2019; 10: 1096