Comparative molecular cell-of-origin classification of diffuse large B-cell lymphoma based on liquid and tissue biopsies

Ewan Hunter1, Ronald McCord2, Aroul Ramadass1, Jayne Green1, Jurjen W. Westra1, Kirsten Mundt3, Alexandre Akoulitchev1
1Oxford BioDynamics Plc, Oxford, UK
2Genentech, Inc., South San Francisco, USA
3Hoffmann La-Roche AG, Basel, Switzerland

Tóm tắt

Abstract Background Diffuse large B-cell lymphoma (DLBCL) is a heterogenous blood cancer, but can be broadly classified into two main subtypes, germinal center B-cell-like (GCB) and activated B-cell-like (ABC). GCB and ABC subtypes have very different clinical courses, with ABC having a much worse survival prognosis. It has been observed that patients with different subtypes also respond differently to therapeutic intervention, in fact, some have argued that ABC and GCB can be thought of as separate diseases altogether. Due to this variability in response to therapy, having an assay to determine DLBCL subtypes has important implications in guiding the clinical approach to the use of existing therapies, as well as in the development of new drugs. The current gold standard assay for subtyping DLBCL uses gene expression profiling on formalin fixed, paraffin embedded (FFPE) tissue to determine the “cell of origin” and thus disease subtype. However, this approach has some significant clinical limitations in that it 1) requires a biopsy 2) requires a complex, expensive and time-consuming analytical approach and 3) does not classify all DLBCL patients. Methods Here, we took an epigenomic approach and developed a blood-based chromosome conformation signature (CCS) for identifying DLBCL subtypes. An iterative approach using clinical samples from 118 DLBCL patients was taken to define a panel of six markers (DLBCL-CCS) to subtype the disease. The performance of the DLBCL-CCS was then compared to conventional gene expression profiling (GEX) from FFPE tissue. Results The DLBCL-CCS was accurate in classifying ABC and GCB in samples of known status, providing an identical call in 100% (60/60) samples in the discovery cohort used to develop the classifier. Also, in the assessment cohort the DLBCL-CCS was able to make a DLBCL subtype call in 100% (58/58) of samples with intermediate subtypes (Type III) as defined by GEX analysis. Most importantly, when these patients were followed longitudinally throughout the course of their disease, the EpiSwitch™ associated calls tracked better with the known patterns of survival rates for ABC and GCB subtypes. Conclusion This proof-of-concept study provides an initial indication that a simple, accurate, cost-effective and clinically adoptable blood-based diagnostic for identifying DLBCL subtypes is possible.

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Tài liệu tham khảo

Reddy A, et al. Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma. Cell [published online ahead of print: 2017]. https://doi.org/10.1016/j.cell.2017.09.027.

Xu P-P, et al. B-cell Function Gene Mutations in Diffuse Large B-cell Lymphoma: A Retrospective Cohort Study. EBioMed [published online ahead of print: 2017]. https://doi.org/10.1016/j.ebiom.2017.01.027.

Alizadeh AA, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature [published online ahead of print: 2000]. https://doi.org/10.1038/35000501.

Rosenwald A, et al. The Use of Molecular Profiling to Predict Survival after Chemotherapy for Diffuse Large-B-Cell Lymphoma. N Engl J Med. [published online ahead of print: 2002]. https://doi.org/10.1056/NEJMoa012914.

Wright G, et al. A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc Natl Acad Sci. [published online ahead of print: 2003]. https://doi.org/10.1073/pnas.1732008100.

Davis RE, Brown KD, Siebenlist U, Staudt LM. Constitutive nuclear factor kappaB activity is required for survival of activated B cell-like diffuse large B cell lymphoma cells. J Exp Med. [published online ahead of print: 2001]. https://doi.org/10.1084/jem.194.12.1861.

Feuerhake F, et al. NFkappaB activity, function, and target-gene signatures in primary mediastinal large B-cell lymphoma and diffuse large B-cell lymphoma subtypes. Blood [published online ahead of print: 2005]. https://doi.org/10.1182/blood-2004-12-4901.

Rimsza LM, et al. Accurate classification of diffuse large B-cell lymphoma into germinal center and activated B-cell subtypes using a nuclease protection assay on formalin-fixed, paraffin-embedded tissues. Clin. Cancer Res. [published online ahead of print: 2011]. https://doi.org/10.1158/1078-0432.CCR-10-2573.

Linton K, et al. Microarray gene expression analysis of fixed archival tissue permits molecular classification and identification of potential therapeutic targets in diffuse large B-cell lymphoma. J Mol Diagnostics JMD [published online ahead of print: 2012]. https://doi.org/10.1016/j.jmoldx.2012.01.008.

Barrans SL, et al. Whole genome expression profiling based on paraffin embedded tissue can be used to classify diffuse large B-cell lymphoma and predict clinical outcome. Br J Haematol. [published online ahead of print: 2012]. https://doi.org/10.1111/bjh.12045.

Care MA, et al. A Microarray Platform-Independent Classification Tool for Cell of Origin Class Allows Comparative Analysis of Gene Expression in Diffuse Large B-cell Lymphoma. PLoS One [published online ahead of print: 2013]. https://doi.org/10.1371/journal.pone.0055895.

Scott DW, et al. Determining cell-of-origin subtypes of diffuse large B-cell lymphoma using gene expression in formalin-fixed paraffin-embedded tissue. Blood [published online ahead of print: 2014]. https://doi.org/10.1182/blood-2013-11-536433.

Gutiérrez-García G, et al. Gene-expression profiling and not immunophenotypic algorithms predicts prognosis in patients with diffuse large B-cell lymphoma treated with immunochemotherapy. Blood [published online ahead of print: 2011]. https://doi.org/10.1182/blood-2010-12-322362.

Coutinho R, et al. Poor concordance among nine immunohistochemistry classifiers of cell-of-origin for diffuse large b-cell lymphoma: Implications for therapeutic strategies. Clin. Cancer Res. [published online ahead of print: 2013]. https://doi.org/10.1158/1078-0432.CCR-13-1482.

Meyer PN, et al. Immunohistochemical methods for predicting cell of origin and survival in patients with diffuse large B-cell lymphoma treated with rituximab. J Clin Oncol. [published online ahead of print: 2011]. https://doi.org/10.1200/JCO.2010.30.0368.

Choi WWL, et al. A new immunostain algorithm classifies diffuse large B-cell lymphoma into molecular subtypes with high accuracy. Clin. Cancer Res. [published online ahead of print: 2009]. https://doi.org/10.1158/1078-0432.CCR-09-0113.

Xue X, Zeng N, Gao Z, Du M-Q. Diffuse large B-cell lymphoma: sub-classification by massive parallel quantitative RT-PCR. Lab. Invest. [published online ahead of print: 2015]. https://doi.org/10.1038/labinvest.2014.136.

Boyan B, Giacomo C. Organization and function of the 3D genome. Nat Rev Genet. 2016;17:661–78.

Dekker J, Rippe K, Dekker M, Kleckner N. Capturing chromosome conformation. Science [published online ahead of print: 2002]. https://doi.org/10.1126/science.1067799.

Tordini F, Aldinucci M, Milanesi L, Liò P, Merelli I. The genome conformation as an integrator of multi-omic data: the example of damage spreading in cancer. Front Genet. 2016;7. https://doi.org/10.3389/fgene.2016.00194.

Cao F, et al. Super-enhancers and broad h3k4me3 domains form complex gene regulatory circuits involving chromatin interactions. Sci Rep. [published online ahead of print: 2017]. https://doi.org/10.1038/s41598-017-02257-3.

Bastonini E, et al. Chromatin barcodes as biomarkers for melanoma. Pigment Cell Melanoma Res. 2014;27(5):788–800.

Mukhopadhyay S, Ramadass AS, Akoulitchev A, Gordon S. Formation of distinct chromatin conformation signatures epigenetically regulate macrophage activation. Int. Immunopharmacol [published online ahead of print: 2014]. https://doi.org/10.1016/j.intimp.2013.10.024.

Jakub JW, et al. A pilot study of chromosomal aberrations and epigenetic changes in peripheral blood samples to identify patients with melanoma. Melanoma Res. [published online ahead of print: 2015]. https://doi.org/10.1097/CMR.0000000000000182.

Carini C, et al. Chromosome conformation signatures define predictive markers of inadequate response to methotrexate in early rheumatoid arthritis. J Transl Med. 2018;16(1). https://doi.org/10.1186/s12967-018-1387-9.

Crutchley JL, Wang XQD, Ferraiuolo MA, Dostie J. Chromatin conformation signatures: ideal human disease biomarkers? Biomark Med. [published online ahead of print: 2010]. https://doi.org/10.2217/bmm.10.68.

Seymour JF, et al. R-CHOP with or without bevacizumab in patients with previously untreated diffuse large B-cell lymphoma: Final MAIN study outcomes. Haematologica [published online ahead of print: 2014]. https://doi.org/10.3324/haematol.2013.100818.

Lenz G, et al. Stromal gene signatures in large-B-cell lymphomas. N Engl J. Med. [published online ahead of print: 2008]. https://doi.org/10.1056/NEJMoa0802885.

Pfeifer M, et al. Anti-CD22 and anti-CD79B antibody drug conjugates are active in different molecular diffuse large B-cell lymphoma subtypes. Leukemia [published online ahead of print: 2015]. https://doi.org/10.1038/leu.2015.48.

Salter M, et al. Epigenetic signatures and early detection of neurodegenerative diseases. In: The Lancet Neurology Conference, The Lancet Neurology Conference; 2016.

Salter M, Powell R, Back J, et al. Genomic architecture differences at the HTT locus associated with symptomatic and pre-symptomatic cases of Huntington’s disease in a pilot study. F1000Research. 2019;7:1757. https://doi.org/10.12688/f1000research.15828.3.

Szklarczyk D, et al. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. [published online ahead of print: 2017]. https://doi.org/10.1093/nar/gkw937.

Kanehisa M, Goto S. KEGG : Kyoto Encyclopedia of Genes and Genomes. Nucleic Acid Res. [published online ahead of print: 2000]. https://doi.org/10.1093/nar/28.1.27.

Ashburner M, et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000. https://doi.org/10.1038/75556.

Carbon S, et al. Expansion of the gene ontology knowledgebase and resources: The gene ontology consortium. Nucleic Acids Res. [published online ahead of print: 2017]. https://doi.org/10.1093/nar/gkw1108.

R Development Core Team R. R: A Language and Environment for Statistical Computing. 2011.

Pasqualucci L. The genetic basis of diffuse large B-cell lymphoma. Curr Opin Hematol. 2013. https://doi.org/10.1097/MOH.0b013e3283623d7f.

Pasqualucci L, et al. Nat. Genet. [published online ahead of print: 2011]. https://doi.org/10.1038/ng.892.

Roschewski M, Staudt LM, Wilson WH. Diffuse large B-cell lymphoma—treatment approaches in the molecular era. Nat Rev Clin Oncol. [published online ahead of print: 2013]. https://doi.org/10.1038/nrclinonc.2013.197.

Cerhan JR, et al. Genome-wide association study identifies multiple susceptibility loci for diffuse large B cell lymphoma. Nat Genet. [published online ahead of print: 2014]. https://doi.org/10.1038/ng.3105.

DLBCL GCB Gene List. http://atlasgeneticsoncology.org/Anomalies/DLBLGerminCenterID2147.html. cited.

Li S, Young KH, Medeiros LJ. Diffuse large B-cell lymphoma. Pathology. 2018. https://doi.org/10.1016/j.pathol.2017.09.006.

Hans CP, et al. Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray. Blood [published online ahead of print: 2004]. https://doi.org/10.1182/blood-2003-05-1545.

Barton S, Hawkes EA, Wotherspoon A, Cunningham D. Are We Ready To Stratify Treatment for Diffuse Large B-Cell Lymphoma Using Molecular Hallmarks? Oncologist [published online ahead of print: 2012]. https://doi.org/10.1634/theoncologist.2012-0218.

Read JA, et al. Evaluating cell-of-origin subtype methods for predicting diffuse large B-Cell lymphoma survival: A meta-analysis of gene expression profiling and immunohistochemistry algorithms. Clin. Lymphoma, Myeloma Leuk. [published online ahead of print: 2014]. https://doi.org/10.1016/j.clml.2014.05.002.

Davis RE, Brown KD, Siebenlist U, Staudt LM. Constitutive Nuclear Factor κB Activity Is Required for Survival of Activated B Cell–like Diffuse Large B Cell Lymphoma Cells. J Exp Med. [published online ahead of print: 2001]. https://doi.org/10.1084/jem.194.12.1861.

Compagno M, et al. Mutations of multiple genes cause deregulation of NF-B in diffuse large B-cell lymphoma. Nature [published online ahead of print: 2009]. https://doi.org/10.1038/nature07968.

Ngo VN, et al. Oncogenically active MYD88 mutations in human lymphoma. Nature [published online ahead of print: 2011]. https://doi.org/10.1038/nature09671.

Liu Z, et al. Identification of Hub Genes and Key Pathways Associated with Two Subtypes of Diffuse Large B-Cell Lymphoma Based on Gene Expression Profiling via Integrated Bioinformatics. Biomed Res Int. [published online ahead of print: 2018]. https://doi.org/10.1155/2018/3574534.

Paul J, et al. Simultaneous Inhibition of PI3Kδ and PI3Kα Induces ABC-DLBCL Regression by Blocking BCR-Dependent and -Independent Activation of NF-κB and AKT. Cancer Cell [published online ahead of print: 2017]. https://doi.org/10.1016/j.ccell.2016.12.003.

Zhang M, et al. RelA NF-kB subunit activation as a therapeutic target in diffuse large B-cell lymphoma. Aging (Albany). [published online ahead of print: 2016]. https://doi.org/10.18632/aging.101121.

Sun X, et al. DCZ3301, a novel cytotoxic agent, inhibits proliferation in diffuse large B-cell lymphoma via the STAT3 pathway. Cell Death Dis. [published online ahead of print: 2017]. https://doi.org/10.1038/cddis.2017.472.

Toader D, et al. Discovery of small molecule inhibitors of MAP3K7 (TAK1) that induce apoptosis of distinct subtypes of B-cell lymphoma cells. Am Assoc Cancer Res Annu Meet. 2008;68(9):1292.

Duncan R, Carpenter B, Main LC, Telfer C, Murray GI. Characterisation and protein expression profiling of annexins in colorectal cancer. Br J Cancer [published online ahead of print: 2008]. https://doi.org/10.1038/sj.bjc.6604128.

Song J, Shih I-M, Chan DW, Zhang Z. Suppression of Annexin A11 in Ovarian Cancer: Implications in Chemoresistance. Neoplasia [published online ahead of print: 2009]. https://doi.org/10.1593/neo.09286.

Hua K, et al. Downregulation of Annexin A11 (ANXA11) Inhibits Cell Proliferation, Invasion, and Migration via the AKT/GSK-3β Pathway in Gastric Cancer. Med Sci Monit. [published online ahead of print: 2018]. https://doi.org/10.12659/MSM.905372.

Chapuy B, et al. Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat Med [published online ahead of print: 2018]. https://doi.org/10.1038/s41591-018-0016-8.

Feinberg AP. The key role of epigenetics in human disease prevention and mitigation. N Engl J Med. 2018;378(14):1323–34.

Akinleye A, Rasool Z. Immune checkpoint inhibitors of PD-L1 as cancer therapeutics. J Hematol Oncol. 2019. https://doi.org/10.1186/s13045-019-0779-5.

Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer. 2019. https://doi.org/10.1038/s41568-019-0116-x.

Shah P, et al. Development and validation of baseline predictive biomarkers for response to avelumab in second-line (2L) non-small cell lung cancer (NSCLC) using EpiSwitchTM epigenetic profiling. J. Immunother. Cancer. 2019;7(282):78.

Shah P, et al. Development and validation of baseline predictive biomarkers for response to immuno-checkpoint treatments in the context of multi-line and multi-therapy cohorts using EpiSwitch epigenetic profiling. J Immunother Cancer. 2019;7(282):78–9. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6833189/.

Lu S, et al. Comparison of biomarker modalities for predicting response to PD-1/PD-L1 checkpoint blockade: a systematic review and meta-analysis. JAMA Oncol. 2019. https://doi.org/10.1001/jamaoncol.2019.1549.

Crutchley JL, Wang XQD, Ferraiuolo M. A, Dostie J. chromatin conformation signatures: ideal human disease biomarkers?. Biomark. Med. 2010;4(4):611–29.

Diaz Blanco N, et al. Chromatin conformation analysis of primary patient tissue using a low input Hi-C method. bioRxiv [published online ahead of print: 2018]. https://doi.org/10.1101/372789.

Nowakowski G, Chiappella A, Whitzig T, Gascoyne RD, Zhang L. Feasibility of real-time cell-of-origin subtype identification by gene expression profile in the phase 3 trial of lenalidomide plus R-CHOP vs placebo plus R-CHOP in patients with untreated ABC-type diffuse large B-cell lymphoma (ROBUST). J Clin Oncol. 2016;34(15):7538.