High Throughput Genetic Profiling Using a Robust All-Molecular Diagnostic Approach Is Feasible to Specifically Identify a High Risk Group of Myeloma Patients

Introduction

Co-segregation of two or more adverse structural genetic aberrations in myeloma is associated with a particularly bad outcome and defines a molecular high risk subgroup of patients that is in urgent need of innovative treatment approaches (Boyd, Leukemia 2012). Interphase in situ fluorescence hybridization(iFISH) is the current clinical standard for detecting structural genetic aberrations in myeloma. However, iFISH is labor-intensive, slow and dependent on investigator expertise, which makes standardization difficult. There is an urgent need to develop a standardized and easily accessible all-molecular diagnostic test to enable the design of risk-stratified trials and, finally, risk-adapted precision medicine treatments for high risk patients.

Material and Methods

Bone marrow material from 1596 patients was received by a central laboratory for patients enrolled in the NCRI Myeloma XI trial (NCT01554852) at diagnosis from over 80 centers throughout the UK. Myeloma cells were purified to a purity of >98% (median across samples) using an AutoMACS (Miltenyi Biotech) system and DNA and RNA were extracted using AllPrep columns (QIAGEN). Recurrent translocations were predicted by gene expression using a sensitive and specific TC-classification based multiplex qRT-PCR assay on a standard TaqMan (Life Technologies) real-time cycler (Kaiser et al., Leukemia 2013). Myeloma specific copy number alterations were assayed using the sensitive and specific multiplex ligation-dependent probe amplification assay (MLPA P425; MRC Holland; Alpar et al, Gen Chrom Cancer 2013) on a standard thermocycler and a standard ABI 3730 capillary electrophoresis Genetic Analyzer. Analysis of qRT-PCR and MLPA results was performed on a desktop computers using standard software without need for bioinformatics expertise or infrastructure.

Results

Translocation status was successfully analyzed for 1201 cases and copy number aberrations were successfully analyzed for 1232 cases. Matched translocation and copy number aberration data was available for 1044 cases. Genetic lesions associated with an adverse prognosis were detected with the following frequencies among the 1044 cases: t(4;14): 13%; t(14;16): 4%; t(14;20): 1%; del(1p32): 9%; gain(1q): 27%; amp(1q): 8%; del(17p): 9%. Non-high risk recurrent IGH translocations as well as copy number aberrations were assayed through both tests as well.

Co-segregation analysis of all detected abnormalities using Fisher’s exact test, corrected for multiple testing, revealed co-occurrence more than expected by chance of the following lesions: t(4;14) and gain(1q): q=6.2x10^-4; t(4;14) and amp(1q): q=2.1x10^-7; del(1p32) and gain(1q): 1.1x10^-3. Statistically significant co-occurrence was also observed for del(12p) and del(17p): q=2.1x10^-5 as well as del(12p) and t(4;14): q=1.8x10^-5.

Survival data at the timepoint of analysis was available for 450 patients with a median follow-up of 25 months. Patients were classified as previously described (Boyd et al, Leukemia 2013) into molecular risk groups with standard risk defined by absence of adverse genetic lesions (n=224), intermediate risk with presence of one adverse genetic lesion (n=161) and high risk with presence of two adverse lesions (n=65). On Cox analysis, there was a significant difference in terms of PFS between these groups with a median PFS of 31.3 months (95% CI 28.5-35.2), 25.8 months (CI 22.1-27.6) and 16.2 months (CI 10.6-23.7) for groups with none, one, two or more genetic lesions, respectively. The 2-year OS was also significantly different between the groups with 84% (CI 79-89%) in standard risk, 78% (CI 71-85%) in intermediate risk and 65% (CI 53-78%) in high risk patients.

Conclusion

This all-molecular diagnostic approach for recurrent structural aberrations in myeloma offers a fast, robust and high throughput alternative to iFISH that can be run in any molecular diagnostic laboratory on standard equipment. The methods described here enable standardized and specific identification of a high risk subgroup of patients without the need for a bioinformatics infrastructure or expertise. The clinical applicability of this method makes it an ideal candidate method for prospective molecular risk-stratified clinical trials.