Integrated Multi-Level Omics to Characterize Bortezomib Resistance in Multiple Myeloma

Introduction

The use of proteasome inhibitors (PIs), such as bortezomib (BTZ), in multiple myeloma (MM) has markedly increased the survival of newly diagnosed patients. Although advancements in therapeutic regimens in the past decade have improved prognosis, we lack knowledge of the mechanisms that lead to drug resistance. To assess the contributors to BTZ-resistance, we integrated steady-state metabolomics, proteomics and gene expression from two naïve and BTZ-resistant cell line models. In addition, gene expression associated with ex vivo PI resistance has been analyzed. Potential predictive biomarkers of PI-resistance and novel targets for combination therapy will be investigated.

Methods

Parental cell lines, RPMI 8226 and U266, were acquired from ATCC. 8226-B25 and U266-PSR (kind gift from Dr. S. Grant) BTZ-resistant derivatives were selected from their respective parental naïve cell lines by chronic drug exposure. Untargeted metabolomics, activity-based protein profiling (ABPP), and expression proteomics data were acquired using liquid chromatography-mass spectrometry. Gene expression profiles of both cell lines and ex vivo patient specimens were derived from RNAseq. Metabolomics and proteomics data were normalized with iterative rank order normalization. Significantly different genes, proteins, and metabolites were integrated for pathway mapping and identification of biomarkers for PI resistance.

Results

Consistent with previous findings, kynurenine, a product of tryptophan catabolism, is significantly altered in both of our cell line models. In the 8226 and 8226-B25 pair, PI resistance was associated with increased kynurenine and positively correlated with TDO2 and IDO1 overexpression consistent with published literature (Li et al. Nature Medicine, 2019, 25, 850-60). As expected, PSMB2, a subunit of the proteasome, is overexpressed and has a higher activity in both 8226-B25 and U266-PSR in the ABPP and expression proteomics, and higher expression in 8226-B25 RNAseq data. PSMB2 is also overexpressed and significant in the RNAseq patient data, increasingly from newly diagnosed/pre-treatment to early relapse (p-value 2E-4) and late relapse (p-value 0.0052). In addition, CD38 is an enzyme responsible for conversion of NAD+ to nicotinamide and ADP-ribose. It has increased expression in MM cells and is significantly downregulated in ABPP (log₂ ratio -4.25, p-value 2E-13), expression proteomics (log₂ ratio -2.5), and RNAseq (log₂ ratio -2.6, p-value 5E-6) in the 8226-B25 BTZ-resistant cells. In the steady-state metabolomics of the 8226-B25 cells, ADP-ribose (log₂ ratio 4.11, p-value 2E-5) is the most upregulated known metabolite. This change suggests a downstream result of resistance within this interaction and a potential biomarker of PI resistance. However, gene expression of CD38 in patient samples was relatively unchanged. CD38 was not detected in the U266-PSR proteomics or RNAseq data and ADP-ribose (log₂ ratio -0.63, p-value 0.06) was not significantly altered, suggesting a different mechanism of resistance in this cell line.

Conclusions

Though common mechanisms of PI resistance were identified, our data clearly show that BTZ-resistance arises by heterogeneous means in the two cell line models, promoting the need for biomarkers that can determine resistance and predict response in individual patients (or cohorts). Decreased expression of CD38 in 8226-B25 could elucidate mechanisms of PI resistance and immune response evasion strategies of MM cells. Further investigation of CD38 expression as a BTZ-resistance biomarker could lead to improving combination therapies with monoclonal antibodies, such as daratumumab, and PIs in newly diagnosed MM patients by predicting response prior to treatment. Further examination of ADP-ribose metabolism may lead to the mechanism of synergy between PARP inhibitors and proteasome inhibitors. Ultimately, we plan to integrate and utilize these multi-omics approaches in patient specimens and improve MM patient care by identifying PI resistance biomarkers to predict patient response.