Diagnosis

The use of genomics to enhance hematologic diagnosis was introduced following the identification of the disease-defining genetic event, the t(9;22) characteristic of CML. The use of this genetic diagnosis was expanded by the WHO classification of tumors of hematopoietic tissue,[1] which built diagnostic classifiers encompassing both histopathologic and genetic features (e.g., JAK2 mutations in polycythemia vera, the t(15;17) in acute promyelocytic leukemia, 5q-syndrome in myelodysplasia) and gene expression profiles in lymphoproliferative malignancies (e.g., germinal center versus nongerminal center subtype of diffuse large B-cell lymphoma).

Beyond the refinement of diagnostic approaches, the application of genomic analysis can allow the early detection of hematologic malignancies using blood samples. Blood draws are considered safe and are less complicated and less expensive than a tissue biopsy; because they can easily be done at multiple time points, they can allow repeated assessment of the tumor over time.[2] This approach used blood biopsies that are based on the analysis of circulating tumor cells (CTCs), circulating tumor DNA, cell-free DNA (cfDNA), and circulating microvesicles/exosomes/apoptotic bodies in the blood. This material can provide an accurate representation of the tumor acquired genetic changes simply by analyzing a vial of blood. An example is in angioimmunoblastic T-cell lymphoma where the G17V RHOA mutation in circulating DNA has been shown to be a useful diagnostic marker. [3] Genetics may also have a role in the generic work-up of cytopenia. Indeed, identifying genetic markers may help to discriminate various disease entities, some malignant or premalignant and some generally considered as benign (Fig. 1).

Fig1. EXAMPLE OF THE ROLE OF GENOMICS IN THE WORK-UP OF CYTOPENIA. Among the major causes of cytopenia, several disease entities can be identified. Despite clinical (usually depth of cytopenia, age), morphologic, and flow differences, molecular studies can help to differentiate between these similar entities. (Modified from Young NS. Aplastic anemia. N Engl J Med. 2018; 379:1643–1656.)

Precision Medicine and Molecularly Targeted Therapies

 The application of genomics has allowed us to subcategorize blood diseases based on their molecular features and as such to develop novel precision treatment strategies. These strategies may rely upon using a therapy that directly targets the mutation (e.g., a BRAF inhibitor in a patient with a BRAF V600E-mutated neoplasm in hairy cell leukemia) or inform therapeutic decisions that are less directly related (e.g., not using ibrutinib in the germinal center subtype of diffuse large B-cell lymphoma).

There are numerous examples of precision medicine in hematologic malignancies. In myeloid malignancies, the core-binding factor AML is defined by the presence of t(8;21)(q22;q22) or inv(16) (p13q22)/t(16;16)(p13;q22) that disrupts RUNX1 (previously CBFA/AML1) or CBFB transcription factor functions. These variants are associated with a favorable outcome with chemotherapy and therefore are generally not assigned to allotransplant in first complete remission. Nonetheless, they may co-occur with activating KIT mutations, in which case they are associated with an adverse prognosis and may potentially be treated with tyrosine kinase inhibitors (TKIs) such as dasatinib [4] in an attempt to overcome the adverse prognosis. In diffuse large B-cell lymphoma, building on the work of the Staudt group,[5] it has been possible to add COO subtypes to the mutation and refine the application of Bruton tyrosine kinase (BTK) inhibition (TKIs) (Fig. 2). The application of genomics in the clinic has led to a greater understanding of the complexities of multiple gene modifiers of outcome, including if an individual carries several driver mutations and which inhibitors should be targeted, as well as an appreciation of the statistical challenges of understanding such data.

Fig2. THE MOLECULAR DIAGNOSIS OF DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL). Gene expression subgroups first stratified DLBCL patients based on their cell of origin, whether germinal center B cell, activated B cell, or unclassified. By combining genetic events, this classification can be refined and four subgroups identified, characterized by mutational patterns and prognostic features termed N1 (for NOTCH1), MCD (for MYD88 and CD79B), BN2 (for BCL6 and NOTCH2), and EZB (for EZH2 and BCL2) (adapted from Schmitz et al.28); it can guide personalized treatment strategies with agents such as lenalidomide, ibrutinib, and tazemetostat.

Risk-Stratified Therapy

 It has been more than a decade since the first proof-of-principle studies were published demonstrating the possibility of using gene expression profiling to subclassify cancer. These studies raised the possibility that gene expression signatures might be implemented in the routine clinical setting. In myeloma, risk stratification has relied on iFISH analysis,[6] but it has been shown that risk scores based on gene expression signatures can outperform this strategy.[7] Currently, the gene expression based MyPRS test, based on a 70-gene signature, is approved for use in New York State but has not been widely taken up.[8] In chronic lymphocytic leukemia, risk stratification and appropriate selection of treatment rely upon the identification of the mutation status at the immunoglobulin genes, cytogenetic factors (del(13q), del(11q), trisomy 12, del(17p)), and mutations (TP53 mutation). Cases with loss of 17p and, more recently, mutation of TP53 are known to be chemoresistant and are treated differently with first line ibrutinib.[9] In acute myeloid leukemia, the identification of cytogenetic subgroups derived from metaphase cytogenetic analysis has been used for many years to determine risk status and to assign patients to receive allogeneic transplantation or not. This approach in acute leukemia has been further refined by the European Leukemia Network (ELN), who introduced the use of mutations such as biallelic CEBPA, monoallelic NPM1, RUNX1, ASXL1, or TP53 and internal tandem repeats at the FLT3 locus.[10]

Response-Adapted Therapy and Minimal Residual Disease Monitoring

Combination chemotherapeutic regimens have been a great success in the management of hematologic malignancies, leading to deep and durable responses, including cures, in some settings. The ability to monitor response and to adjust therapy based upon the level of response opens the potential for response-adapted therapeutic approaches. This response-adapted approach relies upon the development of sensitive testing strategies able to detect and monitor tumor cells below the level of clinical detection and has been termed mini mal residual disease (MRD) monitoring. Classically, flow cytometry has been used, but it is restricted by sample requirements, disease type, and technical limitations. Other approaches have been developed based on molecular approaches based either on PCR or NGS.

Response-adapted therapy was developed initially in CML. The initial approach to detect response was cytogenetics but lacked sensitivity, as did iFISH. Quantitative reverse transcription PCR (QRT PCR) was able to detect the Bcr-Abl RNA fusion gene down to a level of 1 tumor cell in 10⁶ normal cells and provided an excellent tool to monitor therapy in patients undergoing treatment with TKIs. In this setting the achievement of MRD negativity is one of the critical clinical end points. More recently, this end point has been used to design MRD-driven TKI discontinuation trials (e.g., the STIM study).[11] In this trial, 38% of patients remained in treatment-free remission at 60 months, without molecular recurrence. Patients eligible for discontinuation had to achieve MRD negative as measured by QRT-PCR that was maintained for at least 2 years. Across TKI discontinuation trials, treatment-free remission rates after maintaining deep molecular response for at least 1 year ranged from 40% to 60%.[12]

At around the same time as monitoring of CML was being developed, in childhood acute lymphoblastic leukemia high remission rates and cures were being achieved. Despite this high cure rate, a substantial proportion of cases relapsed, which was addressed by the application of MRD monitoring. A sensitive clonality-based test using rearrangement of the immunoglobulin gene Ig loci was developed for application in lymphoid tumors. Applying this approach in ALL showed that the failure to fully eradicate the disease to a sensitivity level of one tumor cell in a million normal cells at a prespecified time point during treatment was associated with high rates of relapse, allowing the potential to modify the therapy early on in therapy.

The early technical approach to clonality detection relied on Southern blotting and was very time consuming but has now been replaced by NGS of T-cell and B-cell receptor genes. This sequencing approach targets a limited number of genomic regions that are involved in V(D)J recombination of the T-cell and B-cell receptors, thus allowing identification of monoclonal B and T cells, which define the malignant tumor cells. Because these regions are sequenced to great “depth,” malignant clones can be detected even if they occur with a frequency of only 1 in 10⁵ to 10⁶.

One of the approved indications for this MRD detection with NGS-based clonality testing is multiple myeloma, the therapy of which has been transformed over the past 15 years with the advent of many new therapeutic agents. In younger patients after autologous stem cell transplantation, a meta-analysis has provided strong evidence for improved outcomes in patients achieving MRD-negative responses. However, there remains debate around the optimum level of sensitivity, with the optimum level being one tumor cell in 106 normal cells. There is also debate about the optimum testing strategy to be used, either flow cytometry or DNA-based clonality assays based on NGS.[13] These debates will be resolved as the approach goes through evaluation by the FDA for application as a legitimate trial end point.

Pharmacogenomics

 Pharmacogenomics aims to apply genome variants that reflect drug behavior, typically via alterations in drugs’ pharmacokinetics (absorption, distribution, elimination, metabolism) or via accentuation of drugs’ pharmacodynamics (modifying the pharmacologic effects of a drug target). Classical examples of pharmacogenomics approaches in hematology include methylene tetrahydrofolate reductase (MTHFR) genotypes that affect the safety and efficacy of 6-mercaptopurine and methotrexate therapies[14] in leukemia and lymphoma. Similarly, a nonsynonymous SNP in the OCT2 gene (rs316019), the organic cat ion transporter, in lymphoma or myeloma has been associated with reduced cisplatin-induced nephrotoxicity.[15,16]

To understand interpatient responses to drugs is pressing in oncology, where anticancer agents have narrow therapeutic indices and severe side effects. Pharmacogenomic approaches are also being used to determine the safety and efficacy of novel, targeted treatments, not only by analyzing the presence of a target tumor biomarker such as ALK fusions for crizotinib or IDH2 mutations for enasidenib but also by determining their safety profile. For instance, belinostat, a histone deacetylase inhibitor drug approved in T-cell lymphoma, is predominantly metabolized by UGT1A1, which is polymorphic and requires genotype-based dose adjustment to normalize belinostat exposure, allowing for a better, more tolerable therapeutic experience.[17]

References
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1- Swerdlow SH, Campo E, Pileri SA, et al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood. 2016;127(20):2375–2390. https://doi.org/10.1182/ blood-2016-01-643569.

2-  Crowley E, Di Nicolantonio F, Loupakis F, Bardelli A. Liquid biopsy: monitoring cancer-genetics in the blood. Nat Rev Clin Oncol. 2013;10(8):472–484. https://doi.org/10.1038/nrclinonc.2013.110.

3- Nakamoto-Matsubara R, Sakata-Yanagimoto M, Nguyen T, et al. G17V Rhoa mutation in circulating DNA is a useful marker for diagnosis of AITL and AITL-related lymphoma. Blood. 2015;126(23). https://doi. org/10.1182/blood.V126.23.1447.1447. 1447–1447.

4- Boissel N, Renneville A, Leguay T, et al. Dasatinib in high-risk core binding factor acute myeloid leukemia in first complete remission: a French Acute Myeloid Leukemia Intergroup trial. Haematologica. 2015;100(6):780–785. https://doi.org/10.3324/haematol.2014.114884.

5- Phelan JD, Young RM, Webster DE, et al. A multiprotein supercomplex controlling oncogenic signalling in lymphoma. Nature. 2018;560(7718):387–391. https://doi.org/10.1038/s41586-018-0290-0.

6- Boyd KD, Ross FM, Chiecchio L, et al. A novel prognostic model in myeloma based on co-segregating adverse FISH lesions and the ISS: analysis of patients treated in the MRC Myeloma IX trial. Leukemia. 2012;26(2):349–355. https://doi.org/10.1038/leu.2011.204.

7- Shaughnessy JD, Zhan F, Burington BE, et al. A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. Blood. 2007;109(6):2276–2284. https:// doi.org/10.1182/blood-2006-07-038430.

8- van Laar R, Flinchum R, Brown N, et al. Translating a gene expression signature for multiple myeloma prognosis into a robust high-throughput assay for clinical use. BMC Med Genomics. 2014;7:25. https://doi. org/10.1186/1755-8794-7-25.

9- Edelmann J, Gribben JG. Managing patients with TP53-deficient chronic lymphocytic leukemia. J Oncol Pract. 2017;13(6):371–377. https://doi. org/10.1200/JOP.2017.023291.

10- Döhner H, Estey E, Grimwade D, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017;129(4):424–447. https://doi.org/10.1182/ blood-2016-08-733196.

11- Etienne G, Guilhot J, Rea D, et al. Long-term follow-up of the French Stop Imatinib (STIM1) study in patients with chronic myeloid leukemia. J Clin Oncol. 2017;35(3):298–305. https://doi.org/10.1200/JCO.2016.68.2914.

12- Saussele S, Richter J, Guilhot J, et al. Discontinuation of tyrosine kinase inhibitor therapy in chronic myeloid leukaemia (EURO-SKI): a prespecified interim analysis of a prospective, multicentre, non-randomised, trial. Lancet Oncol. 2018;19(6):747–757. https://doi.org/10.1016/S1470 2045(18)30192-X.

13-  Della Starza I, Chiaretti S, De Propris MS, et al. Minimal residual disease in acute lymphoblastic leukemia: technical and clinical advances. Front Oncol. 2019;9:726. https://doi.org/10.3389/fonc.2019.00726.

14- Gervasini G, Vagace JM. Impact of genetic polymorphisms on chemotherapy toxicity in childhood acute lymphoblastic leukemia. Front Genet. 2012;3:249. https://doi.org/10.3389/fgene.2012.00249.

15- Filipski KK, Mathijssen RH, Mikkelsen TS, Schinkel AH, Sparreboom A. Contribution of organic cation transporter 2 (OCT2) to cisplatin-induced nephrotoxicity. Clin Pharmacol Ther. 2009;86(4):396–402. https://doi. org/10.1038/clpt.2009.139.

16- Ciarimboli G, Deuster D, Knief A, et al. Organic cation transporter 2 mediates cisplatin-induced oto- and nephrotoxicity and is a target for protective interventions. Am J Pathol. 2010;176(3):1169–1180. https://doi. org/10.2353/ajpath.2010.090610.

17- Peer CJ, Goey AKL, Sissung TM, et al. UGT1A1 genotype-dependent dose adjustment of belinostat in patients with advanced cancers using population pharmacokinetic modeling and simulation. J Clin Pharmacol. 2016;56(4):450–460. https://doi.org/10.1002/jcph.627.

مواضيع ذات صلة

Involvement of the U1 snRNP Particle in Splicing

البادئ (Promoter)

تضاعف الدنا

The Discovery and Assay of RNA Splicing

Termination

Polymerase Elongation Rate

فائدة القفازات

الحلزون المزدوج للدنا

تركيب الدنا

Melted DNA Under RNA Polymerase

Measurement of Binding and Initiation Rates

Structural Variation