Aminopeptidase expression in multiple myeloma associates with disease progression and sensitivity to melflufen

cancers

Article

Aminopeptidase Expression in Multiple Myeloma Associates

with Disease Progression and Sensitivity to Melﬂufen

Juho J. Miettinen ^1,†, Romika Kumari ^1,†, Gunnhildur Asta Traustadottir ², Maiju-Emilia Huppunen ¹,

Philipp Sergeev ¹, Muntasir M. Majumder ¹, Alexander Schepsky ², Thorarinn Gudjonsson ², Juha Lievonen ³,

Despina Bazou ⁴, Paul Dowling ⁵, Peter O‘Gorman ⁴, Ana Slipicevic ⁶, Pekka Anttila ³, Raija Silvennoinen ³

,

Nina N. Nupponen ⁶, Fredrik Lehmann ⁶and Caroline A. Heckman ^1,

*

1

Institute for Molecular Medicine Finland-FIMM, HiLIFE–Helsinki Institute of Life Science, iCAN Digital

Precision Cancer Medicine Flagship, University of Helsinki, 00290 Helsinki, Finland;

juho.miettinen@helsinki.ﬁ (J.J.M.); romika.kumari@helsinki.ﬁ (R.K.);

maiju-emilia.huppunen@helsinki.ﬁ (M.-E.H.); philipp.sergeev@helsinki.ﬁ (P.S.);

muntasir.mamun@helsinki.ﬁ (M.M.M.)

Stem Cell Research Unit, Biomedical Center, University of Iceland, 101 Reykjavik, Iceland;

gutra@hi.is (G.A.T.); schepsky@hi.is (A.S.); tgudjons@hi.is (T.G.)

Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center,

00290 Helsinki, Finland; juha.lievonen@hus.ﬁ (J.L.); pekka.anttila@hus.ﬁ (P.A.);

raija.silvennoinen@helsinki.ﬁ (R.S.)

2

3

4

5

6

Department of Hematology, Mater Misericordiae University Hospital, D07 Dublin, Ireland;

despina.bazou@ucd.ie (D.B.); pogorman@mirtireland.com (P.O.)

Department of Biology, Maynooth University, National University of Ireland,

W23 F2H6 Maynooth, Co. Kildare, Ireland; paul.dowling@mu.ie

Oncopeptides AB, 111 53 Stockholm, Sweden; ana.slipicevic@oncopeptides.com (A.S.);

nina.nupponen@oncopeptides.com (N.N.N.); fredrik.lehmann@oncopeptides.com (F.L.)

Correspondence: caroline.heckman@helsinki.ﬁ; Tel.: +358-50-415-6769

These authors equally contributed to this work.

ꢀꢁꢂꢀꢃꢄꢅꢆꢇ

ꢀꢁꢂꢃꢄꢅꢆ

Citation: Miettinen, J.J.; Kumari, R.;

Traustadottir, G.A.; Huppunen, M.-E.;

Sergeev, P.; Majumder, M.M.;

Schepsky, A.; Gudjonsson, T.;

Lievonen, J.; Bazou, D.; et al.

Aminopeptidase Expression in

Multiple Myeloma Associates with

Disease Progression and Sensitivity to

Melﬂufen. Cancers 2021, 13, 1527.

https://doi.org/10.3390/cancers

13071527

*

†

Simple Summary: The aims of this study were to investigate aminopeptidase expression in multiple

myeloma and to identify the aminopeptidases responsible for the activation of the peptide–drug

conjugate melﬂufen in multiple myeloma. We observed a differential expression of aminopeptidases

between relapsed/refractory and newly diagnosed multiple myeloma patients. A higher expression

of the aminopeptidase genes XPNPEP1, RNPEP, DPP3, and BLMH in multiple myeloma plasma

cells was associated with shorter patient overall survival. The peptide–drug conjugate melﬂufen

was particularly active towards plasma cells from relapsed/refractory multiple myeloma patients.

Melﬂufen could be hydrolyzed to its active form by the aminopeptidases LAP3, LTA4H, RNPEP,

and ANPEP, all of which are expressed in multiple myeloma. These results indicate critical roles for

aminopeptidases in disease progression and the activity of melﬂufen in multiple myeloma.

Academic Editor: Enrique M. Ocio

Received: 8 January 2021

Accepted: 20 March 2021

Published: 26 March 2021

Abstract: Multiple myeloma (MM) is characterized by extensive immunoglobulin production leading

to an excessive load on protein homeostasis in tumor cells. Aminopeptidases contribute to proteolysis

by catalyzing the hydrolysis of amino acids from proteins or peptides and function downstream

of the ubiquitin–proteasome pathway. Notably, aminopeptidases can be utilized in the delivery of

antibody and peptide-conjugated drugs, such as melﬂufen, currently in clinical trials. We analyzed

the expression of 39 aminopeptidase genes in MM samples from 122 patients treated at Finnish

cancer centers and 892 patients from the CoMMpass database. Based on ranked abundance, LAP3,

ERAP2, METAP2, TTP2, and DPP7 were highly expressed in MM. ERAP2, XPNPEP1, DPP3, RNPEP,

and CTSV were differentially expressed between relapsed/refractory and newly diagnosed MM

samples (p < 0.05). Sensitivity to melﬂufen was detected ex vivo in 11/15 MM patient samples,

and high sensitivity was observed, especially in relapsed/refractory samples. Survival analysis

revealed that high expression of XPNPEP1, RNPEP, DPP3, and BLMH (p < 0.05) was associated

with shorter overall survival. Hydrolysis analysis demonstrated that melﬂufen is a substrate for

aminopeptidases LAP3, LTA4H, RNPEP, and ANPEP. The sensitivity of MM cell lines to melﬂufen

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Cancers 2021, 13, 1527. https://doi.org/10.3390/cancers13071527

https://ww w .mdpi.com/journal/cancers

Cancers 2021, 13, 1527

2 of 21

was reduced by aminopeptidase inhibitors. These results indicate critical roles of aminopeptidases in

disease progression and the activity of melﬂufen in MM.

Keywords: multiple myeloma; aminopeptidase; gene expression; melﬂufen

1. Introduction

Multiple myeloma (MM) is a highly heterogeneous disease with complex genetic

alterations. Advanced molecular proﬁling of MM has revealed novel therapeutic targets

that may aid in drug development. Nevertheless, MM is still incurable for the majority

of patients, and new therapeutic approaches are required to improve patient survival.

One of the hallmarks of MM is the excessive production of abnormal immunoglobulins,

which makes the myeloma cells heavily dependent on the ubiquitin–proteasome system

to maintain protein homeostasis and availability of free amino acids. In general, human

tumors are highly dependent on free amino acids for their growth, with disruption of

protein turnover shown to induce apoptotic cascades in MM as well as in acute myeloid

leukemia (AML) [1]. Due to this dependency, inhibition of the ubiquitin–proteasome

system is currently the standard of care and a widely accepted strategy in the treatment

of both newly diagnosed (NDMM) and relapsed/refractory multiple myeloma (RRMM)

patients [2].

Aminopeptidases are an important group of metalloenzymes that catalyze the hy-

drolysis of terminal amino acid residues from proteins or peptides and operate as the

last step downstream of the ubiquitin–proteasome system. Consequently, they are impli-

cated in several cellular functions, including development, differentiation, cell cycle, DNA

repair, signal transduction, and programmed cell death [

aminopeptidases have been identiﬁed in the human genome, and their activity in the

human bone marrow (BM) and blood cells has been described as early as the 1960s [ ].

3]. Around 40 genes encoding

4,5

Aminopeptidases are involved in many human disorders, including autoimmune diseases

and cancer. For example, aminopeptidase N (ANPEP alias CD13) is abnormally expressed

in AML, B-cell acute lymphoblastic leukemia, and chronic lymphoblastic leukemia, where

it may be used as a diagnostic or prognostic marker [

subset of Waldenström macroglobulinemia, MM, and plasma cell leukemias [

expression in samples from MM patients has been shown to correlate with shorter overall

survival [11 12]. Recently, endoplasmic reticulum aminopeptidase 2 (ERAP2) expression

6–

8]. ANPEP is also expressed in a

9,

10]. ANPEP

,

has been associated with a better outcome in patients with bladder cancer treated with

immunotherapies [13]. Dipeptidyl peptidase 8 (DPP8) has recently been described as a

novel target for MM and AML therapeutics [14,15].

Even though aminopeptidases have conserved structures and their role in diseases

is well recognized, there are relatively few therapeutic molecules directly targeting these

enzymes. Bestatin was the ﬁrst aminopeptidase inhibitor used in clinical settings, while

more recently, a new-generation inhibitor, tosedostat (CHR-2797), entered in clinical trials

for AML and myelodysplastic syndromes [16,17]. An alternative approach to directly

targeting aminopeptidases is to exploit their mode of action in cells. This is based on

observations that aminopeptidase expression may differ between normal and malignant

cells or even among different subcellular compartments within a cell. The effects of

antibody–drug conjugates or peptide–drug conjugates, such as melphalan ﬂufenamide

(hereinafter named melﬂufen), are facilitated by protease/peptidase hydrolysis, which

leads to the release of a highly toxic payload inside the target cells [18,19]. Aminopeptidases

have been shown to be one of the peptidase groups facilitating melﬂufen activation in

cells [19]. Melﬂufen has been shown to have antitumor activity against MM cell lines and

primary MM cells [20,21].

To further understand the role of aminopeptidases in MM and disease progression,

we investigated the expression of aminopeptidase gene family members in a large cohort

Cancers 2021, 13, 1527

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of samples from MM patients and assessed differences in expression between NDMM and

RRMM. Furthermore, we determined whether speciﬁc aminopeptidases are associated

with poor prognosis in patients with MM. Using BM cells from MM patients, we observed

remarkable ex vivo sensitivity of MM plasma cells to melﬂufen, particularly in samples from

RRMM patients. We were also able to demonstrate that melﬂufen is a substrate of leucine

aminopeptidase 3 (LAP3), leukotriene A4 hydrolase (LTA4H), arginyl aminopeptidase

(RNPEP), and ANPEP, which we also found to be expressed in MM.

2. Materials and Methods

2.1. Sample Collection

Patient samples were collected after informed consent and using protocols approved

by an ethical committee of the Helsinki University Hospital (permit numbers 239/13/03/00/

2010, 303/13/03/01/2011) in accordance with the Declaration of Helsinki. In total, 178 (NDMM,

n = 57; RRMM, n = 121) different patient samples were collected from 140 different MM

patients (Table 1 , Online Supplementary Table S1). Bone marrow mononuclear cells (BM-

MNCs) were isolated from BM aspirates by Ficoll-Paque gradient centrifugation (GE

Healthcare, Little Chalfont, Buckinghamshire, UK). For RNA and exome sequencing,

BM CD138+ plasma cells were enriched by immunomagnetic bead selection (StemCell

Technologies, Vancouver, BC, Canada).

Table 1. MM patient and Disease Characteristics and Treatment History in the FIMM Dataset.

Patient and disease characteristics by disease stage ^a

NDMM (n = 57)

65 (46–84)

27/30

RRMM (n = 83)

63 (26–81)

31/52

Total (n = 140)

64 (26–84)

58/82

Age at diagnosis, years, median (range)

Sex, female/male, n

Cytogenetics, n (%)

t(11;14)

15 (26.3)

9 (15.8)

2 (3.5)

0

16 (19.3)

19 (22.9)

2 (2.4)

31 (22.1)

28 (20.0)

4 (2.9)

t(4;14)

t(14;16)

t(14;20)

2 (2.4)

2 (1.4)

del(17p)

del(13q)/-13

1q gain

5 (8.8)

39 (68.4)

18 (31.6)

2 (3.5)

20 (24.1)

42 (50.6)

46 (55.4)

0

25 (17.9)

81 (57.9)

64 (45.7)

2 (1.4)

No abnormalities found

International Staging System, n (%)

1

2

3

11 (19.3)

27 (47.4)

11 (19.3)

8 (14.0)

19 (22.9)

23 (27.7)

16 (19.3)

25 (30.1)

30 (21.4)

50 (35.7)

27 (19.3)

33 (23.6)

Not available

Treatment history of relapsed/refractory patients (n = 83)

Prior treatment, n (%)

Alkylating agents (MEL, CPM)

Bortezomib

Exposed

63 (75.9)

44 (53.0)

31 (37.3)

Refractory

16 (19.3)

28 (33.7)

34 (41.0)

Not exposed

4 (4.8)

11 (13.3)

18 (21.7)

IMiDs

a

If a patient provided both NDMM and RRMM samples, this patient was included in the NDMM group. If a patient provided

samples at multiple relapse stages and the diagnosis sample was missing, then data from the ﬁrst relapse were included in the table.

FIMM: Institute for Molecular Medicine Finland; NDMM: newly diagnosed multiple myeloma; RRMM: relapsed/refractory multiple

myeloma; MEL: melphalan; CPM: cyclophosphamide; IMiDs: immunomodulatory drugs.

2.2. RNA Sequencing and Analysis

RNA was extracted from CD138+ plasma cells using the AllPrep^®DNA/RNA/miRNA

Universal or miRNeasy kits (Qiagen, Hilden, Germany). RNA integrity was measured on

an Agilent Bioanalyzer 2100 instrument (Agilent, Santa Clara, CA, USA); only samples

with RNA integrity

≥

7 were used for sequencing. Illumina-compatible RNA sequenc-

Cancers 2021, 13, 1527

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ing libraries were prepared using ScriptSeq^TMtechnology and sequenced on Illumina

HiSeq^®1500 or 2500 instruments (Illumina, San Diego, CA, USA). After preprocessing,

ﬁltered reads were aligned to the GRCh38 human reference genome using the STAR aligner

tool [22]. Gene read counts were normalized using the reads per kilobase of transcript

per million mapped reads (RPKM) method. In total, 39 annotated aminopeptidase genes

(Online Supplementary Table S2) were identiﬁed in the human genome (assembly GRCh38)

utilizing the Ensembl release 99 and NCBI databases by using the search term “aminopepti-

dase” and further conﬁrming the molecular function (gene ontology) of identiﬁed genes. A

cutoff value >1 RPKM was used to ﬁlter the expressed aminopeptidase genes. The DESeq2

tool was used to identify differentially expressed genes in samples from newly diagnosed

multiple myeloma (NDMM) vs. relapsed/refractory multiple myeloma (RRMM) [23].

The association of aminopeptidase gene expression with survival outcome was es-

timated by Kaplan–Meier analysis; the analysis was performed using expression-based

grouping of the samples, whereby samples were grouped into “high” ( median expres-

≥

sion) and “low” (<median expression) expression groups. The signiﬁcance of the difference

between the two groups (high vs. low expression) was deduced using a Mantel–Cox

logrank test.

2.3. Data Validation Using the CoMMpass Dataset

To validate our results, clinical, gene expression, and genomic variant data (somatic

mutation and copy number variation (CNVs)) were obtained from the Multiple Myeloma

Research Foundation (MMRF) Relating Clinical Outcomes in MM to Personal Assess-

ment of Genetic Proﬁle (CoMMpass) study (both https://research.themmrf.org and

ww w .themmrf.org, were accessed on 5 February 2018).

The MMRF CoMMpass gene expression dataset included 892 samples in total (87%

baseline/diagnosis, 12% progressive disease, and 1% missing): 875 bone marrow samples

and 17 peripheral blood samples. The MMRF CoMMpass samples were obtained at

baseline/diagnosis (n = 780), or from patients with progressive disease (n = 81), stable

disease (n = 12), partial response (n = 7), and very good partial response (n = 4) to treatment;

for 8 patients, disease information was missing. A total of 1044 samples (bone marrow,

n = 1021; peripheral blood, n = 23) were used for CNV analysis; samples corresponded

to baseline (n = 877), progressive disease (n = 90), partial response (n = 17), stable disease

(n = 14), very good partial response (n = 9), complete response (n = 4), and stringent

complete response (n = 1); information was missing for 32 samples. A total of 1164 samples

(bone marrow, n = 1140; peripheral blood, n = 24) were included in the somatic mutation

dataset; samples corresponded to baseline (n = 946), progressive disease (n = 122), stable

disease (n = 20), partial response (n = 20), very good partial response (n = 16), and complete

response (n = 6); information was missing for 34 samples.

2.4. Exome Sequencing and Cytogenetics

The DNeasy^®Blood & Tissue kit or AllPrep^®DNA/RNA/miRNA Universal kit (Qia-

gen) was used to isolate genomic DNA from skin biopsies and CD138+ cells. The SeqCap^®

EZ MedExome kit (Roche NimbleGen, Madison, WI), SureSelect Clinical Research Exome

kit, or SureSelect Human All Exon V5 kit (Agilent Technologies, Santa Clara, CA) was

used for exome capture. Sequencing was performed on a HiSeq^®1500 or 2500 instrument.

VarScan2 somatic algorithm was implemented for calling somatic mutations [24], and

mutation annotations were performed using SnpEff 4.04 [25]. Gene CNVs were identiﬁed

using the CopyCat tool (https://github.com/chrisamiller/copycat, accessed on 19 Septem-

ber 2014). Cytogenetic data were generated using routine diagnostic ﬂuorescence in situ

hybridization technology following European Myeloma Network 2012 guidelines [26].

2.5. Liquid Chromatography–Tandem Mass Spectrometry-Based Proteomics

BM-MNC CD138+ cells from 23 MM patient samples were lysed in RIPA buffer (Cell

Signaling Technology, Danvers, MA, USA), and proteins digested. An amount of 500 ng

Cancers 2021, 13, 1527

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of each digested whole-cell protein lysate was loaded onto a Q Exactive (Thermo Fisher

Scientiﬁc, Hemel Hempstead, UK) high-resolution accurate mass spectrometer connected

to a Dionex Ultimate 3000 (RSLCnano) chromatography system (Thermo Fisher Scientiﬁc,

Hemel Hempstead, UK). Peptides were separated using a 2% to 40% gradient of acetonitrile

on a Biobasic C18 Picofrit column (Thermo Fisher Scientiﬁc, Hemel Hempstead, UK)

(100 mm length, 75 mm internal diameter) for over 65 min at a ﬂow rate of 250 nL/min. Data

were acquired with the mass spectrometer (MS) operating in automatic data-dependent

switching mode. A full MS scan at 140,000 resolution and a range of 300–1700 m/z was

followed by an MS/MS scan at 17,500 resolution and a range of 200–2000 m/z, selecting

the 10 most intense ions prior to MS/MS.

2.6. Proteomics Data Analysis

Protein identiﬁcation and label-free quantiﬁcation normalization of MS/MS data were

performed using MaxQuant v1.5.2.8 (http://ww w .maxquant.org, accessed on 20 May

2015). The Andromeda search algorithm incorporated in the MaxQuant software was used

to correlate MS/MS data against the Homo sapiens UniProt reference proteome database

(release 2016_11) and a contaminant sequence set provided by MaxQuant. Perseus v.1.5.6.0

(www.maxquant.org/, accessed on 1 June 2016) was used for data analysis, processing,

and visualization. Normalized label-free quantiﬁcation intensity values were used as the

quantitative measurement of protein abundance for subsequent analysis. The data matrix

was ﬁltered for the removal of contaminants and peptides identiﬁed by site. Label-free

quantiﬁcation intensity values were log2 transformed.

2.7. Flow Cytometry-Based Drug Sensitivity Testing

For flow cytometry (FC)-based drug testing, selinexor, 4-hydroperoxycyclophosphamide,

and bortezomib (Online Supplementary Table S3) were dispensed into 96-well V-bottom

plates (Thermo Fisher Scientiﬁc, Carlsbad, CA, USA) using an acoustic liquid handling

device, Echo 550 (Labcyte, Sunnyvale, CA, USA). Melﬂufen and melphalan were manually

pipetted into the 96-well V-bottom plate wells. Drug plate layout and concentrations are

presented in Online Supplementary Figure S1. Viably frozen BM-MNCs were thawed,

freezing solution washed away, cell pellet suspended in conditioned medium (RPMI

1640 medium supplemented with 10% fetal bovine serum (FBS), 2 mM L-glutamine, peni-

cillin (100 U/mL), streptomycin (100 µg/mL), and 25% conditioned medium from the HS-5

human bone marrow stromal cell line), DNase I (Promega, Madison, WI, USA) treated

for 60 min, and cultured in conditioned medium overnight. Cells were ﬁltered through

a 70

plated in parallel on pre-drugged 96-well plates (100,000 viable cells/well in 100

viability was poor (<50%), the Dead Cell Removal kit (Miltenyi Biotec, Auburn, CA, USA)

was used prior to plating the cells. Stock solutions (10,000 M) of melﬂufen (Recipharm

AB, Stockholm, Sweden) and melphalan (Sigma-Aldrich, St. Louis, MO, USA) in DMSO,

which had been prepared in advance and stored at

80^◦C, were thawed and diluted in

µm cell strainer (Fisher Scientiﬁc, Pittsburg, PA), cell viability measured, and cells

µL). If cell

µ

−

conditioned medium to desired concentrations shortly before the drug testing to minimize

autohydrolysis of melﬂufen and melphalan (exchange of Cl with OH at the N-mustard part

of the molecules). Melﬂufen and melphalan were added to the 96-well plates by manually

pipetting only after the cells had been plated, just prior to the start of the drug sensitivity

test. The cells were incubated with the drugs for 72 h at 37 ^◦C and 5% CO₂. Following

72 h incubation with the drugs, the cells were centrifuged (500

plates, and media discarded by inverting the plate. The cells were suspended in 25

×

g, 6 min) in the 96-well

L of

µ

antibody mix containing staining buffer (5% FBS in Dulbecco’s phosphate-buffered saline)

and the following two antibodies (BD Biosciences; Santa Jose, CA, USA): CD138 (BV605,

clone MI15, dilution 1:100) and CD38 (BV786, clone HIT2, dilution 1:100). The cells were

stained for 30 min at room temperature in the dark and subsequently washed with 100

staining buffer followed by centrifugation (500 g, 6 min) and supernatant removal by

inverting the plate. Apoptotic and dead cells were discriminated by 7-aminoactinomycin

µL

×

Cancers 2021, 13, 1527

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D (7-AAD) and PE Annexin V (BD Biosciences) staining with both dyes diluted 1:50 in

25 µL annexin V binding buffer. The plates were incubated for 10 min at room temper-

ature before FC analysis. FC analysis was performed using the IntelliCyt iQue Screener

PLUS instrument (Sartorius, Goettingen, Germany). MM patient sample BM-MNCs were

acquired for analysis from each 96-well plate well by the iQue Screener PLUS instrument

using a 17 s sip time per well and a pump speed of 32 revolutions per minute, resulting

in 35 min reading time for a full 96-well plate. ForeCyt software (Sartorius) was used

to gate cells and acquire population counts. Analysis was done from viable singlet cells,

and the detailed gating strategy is illustrated in Online Supplementary Figure S2. The

cell count of a well was normalized to its six adjacent DMSO controls, from which the

highest and lowest values were excluded and normalized values were used for calculating

cell viability percentages for each tested drug concentration, and dose-response curves

drawn. Half-maximal effective concentration (EC50) values were calculated based on the

dose-response curves.

2.8. Cell Lines and Viability Assay

RPMI-8226 (ATCC^®CCL-155 ) and MM.1S (ATCC^®CRL-2974

™ ™) cell lines were

purchased from the American Type Culture Collection (ATCC; Wesel, Germany). Both cell

lines were cultured in RPMI-1640 medium (Gibco, Thermo Fisher Scientiﬁc, Waltham, MA,

USA), supplemented with 10% heat-inactivated fetal bovine serum (FBS; Gibco, Thermo

Fisher Scientiﬁc; Waltham, MA, USA), 100 U/mL penicillin, and 100 µg/mL streptomycin

(Gibco, Thermo Fisher Scientiﬁc, Waltham, MA, USA) at 37 ^◦C in a humidiﬁed incubator

with 5% CO₂.

The resazurin-based PrestoBlue cell viability reagent (Thermo Fisher Scientific, Waltham,

MA, USA) was used to assess the viability of RPMI-8226 and MM.1S. Cells were treated

for 48 h with complete growth medium supplemented with 3.75, 7.5, 15, 30, or 60

tosedostat. Then, RPMI-8226 and MM.1S cells were pretreated with DMSO (control) or

the metalloaminopeptidase inhibitors bestatin (10 M) and tosedostat (10 M) for 1 h, and

then either left without additional treatment or treated with three different concentrations

(1, 3, and 5 M) of melﬂufen/melphalan for 15 min, after which the medium was replaced.

µM

µ

Cells were cultured in 96-well culture plates at a density of 20,000 cells/well (250 cells/µL),

incubated for 48 h at 37 ^◦C in a humidiﬁed incubator with 5% CO₂, after PrestoBlue was

◦

added (1/10 th of the total volume). After 2 h incubation at 37 C in a humidiﬁed incubator

with 5% CO₂, absorbance was read at 570 nm with a reference wavelength of 595 nm.

Each experimental setup was performed three times independently with at least three

replicates, and values were normalized to untreated control (mean

±

standard deviation

(SD), n = 3). Statistical signiﬁcance was assessed with one- or two-way analysis of variance

(ANOVA) followed by Dunnett’s or Tukey’s multiple comparison tests using GraphPad

Prism (* p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, **** p ≤ 0.0001).

2.9. Hydrolysis Assay

Frozen solutions containing the aminopeptidases LAP3 (TP309052) (OriGene, Rockville,

MD, USA), DPP3 (8087), DPP7 (3438-SE), LTA4H (4008-ZN), RNPEP (8089-ZN), XNPEP1

(2970-ZN), and ANPEP (3815-ZN) (R&D Systems, Minneapolis, MN, USA) were thawed

and diluted with their incubation buffers (Online Supplementary Table S4) to the desired

concentrations. A stock solution (1000 µM) of the test substrate melﬂufen was prepared in

DMSO and further diluted in incubation buffer to the desired concentration shortly before

the experiment to minimize autohydrolysis of melﬂufen. Incubation buffer and substrates

were mixed and incubated with aminopeptidases for 2 h at a substrate-to-enzyme ratio of

5000:1. Control incubations were performed without aminopeptidases. All incubations

◦

were performed in duplicate at 37 C. The incubation was stopped by adding ice-cold

acetonitrile. The samples were centrifuged and diluted with eluent A (water + formic acid,

1000 + 1, v + v). The samples were subjected to liquid chromatography–high-resolution

mass spectrometry analysis. The analysis system consisted of a binary high-performance

Cancers 2021, 13, 1527

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◦

liquid chromatography (HPLC) pump (1290 Series, Agilent), a column oven at 40 C

(1260 series, Agilent), an autosampler (HTC PAL, CTC Analytics, Zwingen, Switzerland),

and an Orbitrap mass spectrometer with electrospray ionization (Q Exactive, Thermo Sci-

entiﬁc). A Phenomenex Luna 3

µ

C18(2) HPLC column (50

×

2 mm, 3 µm) was used with a

C18 4 2 mm guard column (Phenomenex, Torrance, CA), and a binary gradient of eluents

×

A and B (acetonitrile + formic acid, 1000 + 1, v + v) was applied for separation. Mass spec-

tra were recorded in positive ionization mode at a resolution of 35,000 in FullScan mode.

The MS data were evaluated for (M + M)⁺traces of melﬂufen, monohydroxy melphalan

ﬂufenamide, dihydroxy melphalan ﬂufenamide, and 4-F-PheOEt, with peaks integrated

using LCquan (Thermo Scientiﬁc).

3. Results

3.1. Differential Expression of Aminopeptidase Genes in MM

To determine relative mRNA expression patterns of aminopeptidases in MM cells,

we performed RNAseq analysis on 122 MM patient-derived samples (NDMM = 41,

RRMM = 81) from 99 different patients (Online Supplementary Table S1). First, we ranked

aminopeptidase gene expression levels based on mRNA abundance in all samples and ob-

served that most aminopeptidases were expressed (Figure 1A, Table 2, Online Supplemen-

tary Table S5). The most abundantly expressed aminopeptidase genes were LAP3, ERAP2,

methionyl aminopeptidase 2 (METAP2), tripeptidyl peptidase 2 (TTP2), DPP7, ERAP1,

LTA4H, and leucyl and cystinyl aminopeptidase (LNPEP) with a median log2(RPKM) range

of 5.00 (LAP3) to 2.93 (LNPEP). The least abundantly expressed aminopeptidase genes

were thyrotropin-releasing hormone-degrading enzyme (TRHDE), F coagulation factor

XI (F11), X-prolyl aminopeptidase 2 (XPNPEP2), cathepsin V (CTSV), laeverin (LVRN),

and archaelysin family metallopeptidase 1 (AMZ1) with a median log2(RPKM) range of

−

6.57 (TRDHE) to

−

3.60 (AMZ1). We further veriﬁed that aminopeptidase gene expression

proﬁles are comparable between the Multiple Myeloma Research Foundation (MMRF)

CoMMpass dataset (n = 892) and our internal (FIMM) dataset with a correlation coefﬁcient

of 0.93 (p = 2.2

×

10⁻¹⁶) (Online Supplementary Figure S3). In the FIMM dataset, aminopep-

tidases clustered hierarchically into four subgroups, with group I displaying the highest

level of gene expression and group IV the lowest (Figure 1B). We observed correlation

between expression levels of some aminopeptidase genes in group I, for example, TPP2

and DPP7, and certain cytogenetic markers and with patient age (Online Supplementary

Figure S4). TPP2 was expressed at a lower level in patients with del 13q and in patients

≥

65 years of age. DPP7 was expressed at a lower level in patients with either t(4;14) or

t(11;14), but was more highly expressed in patients ≥65 years of age.

Liquid chromatography–tandem mass spectrometry-based proteomics data were

collected from 23 MM patients (n = 14 NDMM; n = 9 RRMM) (Online Supplementary

Table S1). The proteomic results showed a positive correlation with mRNA expression

in CD138+ cells enriched from MM patient BM-MNCs. Out of the 39 aminopeptidases,

peptides positively identifying 17 of these proteolytic enzymes were detected by mass spec-

trometry (Online Supplementary Figure S5A, Table S6). Of these 17 aminopeptidases, six,

namely, LAP3, bleomycin hydrolase (BLMH), DPP3, DPP7, RNPEP, and ERAP2, showed

the highest correlation between protein and gene expression levels (Online Supplementary

Figure S5B).

3.2. Association between Aminopeptidase Gene Expression and MM Disease Status

Next, we investigated whether the expression of aminopeptidase genes could be linked

to disease status. We compared aminopeptidase gene expression proﬁles between the 41

NDMM and 81 RRMM patient samples in the FIMM dataset. In total, ﬁve aminopeptidases,

ERAP2, XPNPEP1, DPP3, RNPEP, and CTSV, were differentially expressed in the RRMM

and NDMM samples (p < 0.05; adjusted p < 0.1), indicating potential roles in disease

progression (Figure 2 A). Of the ﬁve differentially expressed aminopeptidase genes, only

ERAP2 was decreased in RRMM vs. NDMM samples (Figure 2B). In the MMRF CoMMpass

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dataset using paired samples (NDMM, n = 39; RRMM, n = 45), similar differences were

observed, although only CTSV had an adjusted p-value < 0.1 (Online Supplementary

Figure S6). We observed correlation between the expression levels of some of the ﬁve

differentially expressed aminopeptidase genes in RRMM vs. NDMM, for example, DPP3,

RNPEP, and XPNPEP1, and certain cytogenetic markers and with International Staging

System (ISS) stage (Online Supplementary Figure S7A,B). DPP3 was expressed at a lower

level in patients with t(11;14), and also in ISS stage 2 compared with ISS stage 3. RNPEP

was more highly expressed in patients with either del 13q or t(4;14) or 1q gain, and in ISS

stage 3 compared with both ISS stages 1 and 2. XPNPEP1 was more highly expressed in

patients with 1q gain, and in ISS stage 3 compared with ISS stage 2.

Figure 1. Expression of aminopeptidase gene family in MM patient CD138+ cells. (

expression of 39 aminopeptidase and 17 housekeeping genes from 122 MM samples (NDMM (n = 41), RRMM (n = 81)) taken

from 99 different patients. Genes are ranked based on median expression values. ( ) Heatmap showing the hierarchical

A) Boxplot showing the log2(RPKM)

B

clustering of aminopeptidase gene expression, with group I (blue) containing genes with the highest and group IV (magenta)

containing genes with the lowest level of expression. Groups II (red) and III (green) contain genes with an intermediate level

of expression. Sample cytogenetics (del 13q, del 17p, t(4;14), t(11;14), 1q gain) are indicated above the heatmap. Disease

stage indicates whether the sample was from NDMM (shown as cyan; samples on the left) or RRMM (pink; samples on

the right). RPKM: reads per kilobase of transcript per million mapped reads; MM: multiple myeloma; NDMM: newly

diagnosed multiple myeloma; RRMM: relapsed/refractory multiple myeloma.

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Table 2. List of evaluated aminopeptidase genes (n = 39), sorted by median log2(RPKM) expression from RNAseq data

from 122 multiple myeloma patient samples in the FIMM dataset.

Median log2(RPKM)

Expression (n = 122)

Peptidase/Protein

Family

Gene Symbol ^a

Genomic Location

Gene Name ^a

LAP3

4p15.32

5q15

5.00

4.40

Leucine aminopeptidase 3

Endoplasmic reticulum

aminopeptidase 2

M

ERAP2

METAP2

TPP2

DPP7

12q22

13q33.1

9q34.3

4.22

4.13

4.04

Methionyl aminopeptidase 2

Tripeptidyl peptidase 2

Dipeptidyl peptidase 7

Endoplasmic reticulum

aminopeptidase 1

Leukotriene A4 hydrolase

Leucyl and cystinyl

M

S

ERAP1

LTA4H

LNPEP

5q15

12q23.1

5q15

4.03

3.44

2.93

M

aminopeptidase

XPNPEP1

METAP1

DPP3

10q25.1

4q23

11q13.2

15q22.31

2.66

2.46

2.28

2.21

X-prolyl aminopeptidase 1

Methionyl aminopeptidase 1

Dipeptidyl peptidase 3

Dipeptidyl peptidase 8

Aminopeptidase puromycin

sensitive

M

S

DPP8

NPEPPS

17q21.32

2.12

M

BLMH

JMJD7

RNPEP

PGPEP1

TPP1

PEPD

DNPEP

DPP9

17q11.2

15q15.1

1q32.1

19p13.11

11p15.4

19q13.11

2q35

1.83

1.78

1.76

1.72

1.59

1.41

1.16

Bleomycin hydrolase

Jumonji domain containing 7

Arginyl aminopeptidase

Pyroglutamyl-peptidase I

Tripeptidyl peptidase 1

Peptidase D

Aspartyl aminopeptidase

Dipeptidyl peptidase 9

Methionyl aminopeptidase

type 1D, mitochondrial

Aminopeptidase like 1

Cathepsin H

X-prolyl aminopeptidase 3

Arginyl aminopeptidase like 1

Aminopeptidase O (putative)

Lysine demethylase 8

Alanyl aminopeptidase,

membrane

C

*

M

C

S

M

S

19p13.3

METAP1D

2q31.1

0.38

M

NPEPL1

CTSH

XPNPEP3

RNPEPL1

AOPEP

KDM8

20q13.32

15q25.1

22q13.2

2q37.3

9q22.32

16p12.1

0.37

0.36

−0.04

−0.1

−0.38

−1.3

M

C

M

*

ANPEP

15q26.1

−2.13

M

ENPEP

MMP14

DPP4

4q25

14q11.2

2q24.2

−2.15

−2.83

−2.84

−3.39

Glutamyl Aminopeptidase

Matrix Metallopeptidase 14

Dipeptidyl Peptidase 4

N-Acetylated Alpha-Linked

Acidic Dipeptidase Like 1

Archaelysin Family

M

S

NAALADL1

11q13.1

M

AMZ1

7p22.3

−3.6

M

Metallopeptidase 1

LVRN

CTSV

XPNPEP2

F11

5q23.1

9q22.33

Xq26.1

4q35.2

−3.64

−4.39

−6.15

−6.22

Laeverin

Cathepsin V

X-Prolyl Aminopeptidase 2

Coagulation Factor XI

Thyrotropin Releasing

Hormone Degrading Enzyme

M

C

M

S

TRHDE

12q21.1

−6.57

M

a

Source of gene symbols and names: the HUGO Gene Nomenclature Committee (HGNC); RPKM: reads per kilobase of transcript per

million mapped reads; C: cysteine peptidase; M: metallopeptidase; S: serine peptidase; *: Jumonji oxygenase family.

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Figure 2. Aminopeptidase genes are differentially expressed in NDMM versus RRMM. (A) Five aminopeptidase genes

(ERAP2, XPNPEP1, DPP3, RNPEP, CTSV) were differentially expressed in RRMM (n = 82) and NDMM (n = 41) samples

(p < 0.05; adjusted p < 0.1) in the FIMM dataset. Differential gene expression between NDMM and RRMM was deter-

mined using the DESeq2 tool. (B) Among these ﬁve genes, only the expression level of ERAP2 was decreased in RRMM

versus NDMM samples. NDMM: newly diagnosed multiple myeloma; RRMM: relapsed/refractory multiple myeloma;

RPKM: reads per kilobase of transcript per million mapped reads; ap: adjusted p-value.

3.3. Prognostic Signiﬁcance of Aminopeptidase Gene Expression

High expression of four aminopeptidase genes associated with signiﬁcantly poorer

prognosis in MM patients from both FIMM (Figure 3A–E and Online Supplementary

Figure S8A–F) and CoMMpass datasets (Online Supplementary Figure S9A–M). The me-

dian overall survival associated with patient samples showing high versus low gene

expression was 55 vs. 122 months for XPNPEP1 (hazard ratio (HR), 3.263; 95% conﬁdence

limit (CL), 1.749–6.087; p = 0.00012) (Figure 3B), 73 vs. 100 months for RNPEP (HR, 2.271;

95% CL, 1.248–4.129; p = 0.0062) (Figure 3C), 73 vs. 120 months for DPP3 (HR, 1.956; 95%

CL, 1.108–3.453; p = 0.02) (Figure 3 D), and 68 vs. 122 months for BLMH (HR, 1.815; 95% CL,

1.035–3.184; p = 0.037) (Figure 3E, Online Supplementary Table S7).

3.4. Aminopeptidase Somatic Mutation and CNV Characteristics in Myeloma

Exome sequence analysis of 169 samples (n = 56 NDMM and n = 113 RRMM) from

132 different MM patients in the FIMM dataset (Online Supplementary Table S1) indicated

that small somatic variants within the protein coding regions of aminopeptidase genes are

relatively rare. In this dataset, NPEPPS was mutated in 2.37% of the samples (4/169), LVRN

in 2.37% of the samples (4/169), and BLMH in 1.78% of the samples (3/169), while the

frequency of mutations in other aminopeptidases was below 1.2% (Online Supplementary

Figure S10, Table S8). Mutation frequencies were similarly infrequent in the CoMMpass

dataset samples (n = 1164), with aminopeptidase mutations identiﬁed in less than 1.2% of

the samples (Online Supplementary Figure S11).

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Figure 3. Prognostic signiﬁcance of aminopeptidase gene expression in MM. (A) In the FIMM dataset consisting of 122 MM

samples, NDMM (n = 41) and RRMM (n = 81), from 99 different patients, 11 aminopeptidase genes were predicted to

be associated with poor prognosis (p < 0.05). From these 11 genes, 4 were further validated in the CoMMpass dataset.

Survival curves are shown for the 4 aminopeptidase genes based on the FIMM dataset, dividing the patients with low gene

expression (n = 61) and high gene expression (n = 61): (B) XPNPEP1, (C) RNPEP, (D) DPP3, and (E) BLMH. NDMM: newly

diagnosed multiple myeloma; RRMM: relapsed/refractory multiple myeloma; HR: hazard ratio; CI: conﬁdence interval.

CNV analysis showed that gains in the copy number of the aminopeptidase genes

DPP9 (31.95%), PGPEP1 (27.22%), RNPEP (20.12%), PEPD (20.12%), and DPP7 (17.75%)

were frequent, whereas deletions of TPP2 (40.24%), XPNPEP2 (13.61%), and XPNPEP3

(8.87%) were observed in more than 8% of the samples (Online Supplementary Figure S12A,

Figure S13, Table S9). Results in the FIMM dataset were validated using the CoMMpass

dataset: the correlation coefﬁcient for aminopeptidase gain percentage was 0.73, and for

aminopeptidase deletion, the percentage was 0.7 (Online Supplementary Figure S12B). As

expected, RRMM samples exhibited a higher number of CNVs associated with aminopepti-

dase genes than NDMM samples (RRMM: n = 467 overall, with gains in 317 and deletions

in 150 vs. NDMM: n = 159, with gains in 95 and deletions in 64).

3.5. MM Patient Bone Marrow CD138+CD38+ Plasma Cells Are Sensitive to Melﬂufen

Aminopeptidases not only have a role in disease progression, but these enzymes are

required to direct the activity of peptide–drug conjugates, such as melﬂufen. To determine

whether melﬂufen activity can be detected and differs in BM samples from MM patients,

we evaluated the efﬁcacy of melﬂufen and four additional drugs (melphalan, selinexor,

bortezomib, and 4-hydroperoxycyclophosphamide) towards CD138+CD38+ plasma cells

in 15 BM-MNC samples (NDMM = 6; RRMM = 9) obtained from 14 different MM pa-

tients (Online Supplementary Figure S14, Tables S1). CD138+CD38+ plasma cell-speciﬁc

responses to the drugs were measured by multiplexed high-throughput FC. The fraction of

CD138+CD38+ cells from all live cells in the tested samples varied between 0.4% and 27.9%

after 72 h incubation in the ex vivo culturing conditions without drug treatment (Online

Supplementary Table S10). Of the 5 drugs tested, melﬂufen showed the highest activity

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against CD138+CD38+ cells (median EC50 = 0.9 nM) (Online Supplementary Table S10).

Of the 15 samples tested, 5 were highly sensitive to melﬂufen (median EC50 < 0.1 nM),

6 were intermediately sensitive (median EC50 0.1–5 nM), and the 4 least-sensitive sam-

ples had a median EC50 >5 nM (Figure 4 A). In general, CD138+CD38+ cells were more

sensitive to melﬂufen than melphalan (median EC50 = 1473 nM) (Online Supplementary

Table S11). There was a positive correlation between melﬂufen and melphalan response in

CD138+CD38+ cells (r = 0.66; p = 0.009) (Figure 4B). We did not observe correlation between

myeloma patient sample CD138+CD38+ cell melﬂufen sensitivity and aminopeptidase

gene expression in a small set of 10 myeloma samples (Online Supplementary Figures S15

and S16, Table S12).

Figure 4. MM patient bone marrow CD138+CD38+ plasma cells are sensitive to melﬂufen. (A) Drug sensitivity was

measured after 72 h incubation with melﬂufen or with DMSO alone (control). Cell viability was assessed by multicolor

high-throughput ﬂow cytometry using annexin V and 7AAD viability markers. The viability (%) of CD138+CD38+ cells

was calculated, and dose-response curves drawn. The MM patient samples can be divided into three groups based on the

sensitivity of their CD138+CD38+ cells to melﬂufen: highly sensitive (red: EC50 < 0.1 nM), intermediately sensitive (gray:

EC50 0.1–5 nM), not sensitive (black: EC50 > 5 nM). (

B) Correlation of CD138+CD38+ cell sensitivity to melﬂufen (y-axis)

with melphalan sensitivity (x-axis). Drug sensitivity was measured using EC50 values. (

C

) Comparison of CD138+CD38+

cell sensitivity (EC50) between NDMM (dots) and RRMM (triangles) in patient samples treated with melﬂufen (black;

p = 0.0004), melphalan (blue; p = 0.025), selinexor (red; p = 0.44), bortezomib (brown; p = 0.34), and 4-HC (purple; p = 1).

DMSO: dimethyl sulfoxide; 4-HC: 4-hydroperoxycyclophosphamide; NDMM: newly diagnosed multiple myeloma; RRMM:

relapsed/refractory multiple myeloma.

Of the other tested drugs, selinexor, a XPO1/CRM1 inhibitor, also showed high toxicity

against CD138+CD38+ cells (median EC50 = 35.4 nM) (Online Supplementary Figure S14,

Table S11). MM sample sensitivity to selinexor did not correlate with melﬂufen sensitivity

or any of the other tested drugs (Online Supplementary Figure S17A–C). Bortezomib, a pro-

teasome subunit S26 inhibitor, showed a very narrow window of response in CD138+CD38+

cells in the ex vivo assay with a median EC50 = 5.7 nM (Online Supplementary Figure S14

,

Table S11). There were ﬁve samples that could be considered sensitive to bortezomib

(median EC50 = 4.2 nM) and three samples insensitive (median EC50 = 12.3 nM). None of

the samples showed sensitivity to 4-hydroperoxycyclophosphamide, the active metabolite

of cyclophosphamide, with an EC50

Figure S14, Table S11).

≥

1900 nM in the ex vivo assay (Online Supplementary

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3.6. RRMM Samples Are More Sensitive to Melﬂufen than NDMM Samples

Interestingly, our FC-based drug sensitivity testing results suggested that CD138+CD38+

cells from RRMM patients were more sensitive ex vivo to melﬂufen compared with

CD138+CD38+ cells in NDMM samples (RRMM median EC50 = 0.04 nM; NDMM median

EC50 = 7.7 nM; p = 0.0004) (Figure 4C). A similar trend was observed with melphalan,

although the median EC50 values were clearly higher (RRMM median EC50 = 556 nM;

NDMM median EC50 = 3193 nM; p = 0.025) when compared with melﬂufen (Figure 4C).

CD138+CD38+ cells from patients with poor prognosis indicators, such as cytogenetic

markers del 17p and t(4;14), included both melﬂufen-sensitive (n = 5) and insensitive

(n = 5) samples (Online Supplementary Figure S18). No clear biomarkers for sensitivity to

melﬂufen were identiﬁed based on cytogenetic markers alone. Both melﬂufen-sensitive

and insensitive samples expressed aminopeptidase genes (Figure 1B).

3.7. Melﬂufen Activity Is Dependent on Aminopeptidase Activity

To determine whether melﬂufen activity is dependent on aminopeptidase activity,

MM cell lines RPMI-8226 and MM.1S were ﬁrst treated with the metalloaminopeptidase

inhibitors bestatin and tosedostat, then treated with melﬂufen or melphalan, and the viabil-

ity of cells measured after 48 h. The viability of RPMI-8226 and MM.1S cells decreased in a

dose-dependent manner upon 48 h treatment with tosedostat alone (Figure 5A). Treating

RPMI-8226 cells with 10

creased the viability of the cells from 60.41% to 78.15% with 3

from 21.77% to 54.55% with 5 M melﬂufen (p 0.0001), compared with untreated controls

(Figure 5B). Likewise, treating MM.1S cells with 10 M tosedostat prior to treatment with

melﬂufen signiﬁcantly increased the viability of the cells from 39.82% to 73.78% for 0.5

melﬂufen (p = 0.0175) and from 19.81% to 51.60% for 1 M melﬂufen (p = 0.0046) compared

µM tosedostat prior to treatment with melﬂufen signiﬁcantly in-

µM melﬂufen (p = 0.0086) and

µ

≤

µ

µM

µ

with untreated control cells (Figure 5C). In addition, treating RPMI-8226 and MM.1S cells

with bestatin reduced their sensitivity to melﬂufen, albeit not signiﬁcantly.

3.8. Aminopeptidases LAP3, LTA4H, RNPEP, DPP7, and ANPEP Hydrolyze Melﬂufen

Melﬂufen can be hydrolyzed to melphalan and para-ﬂuoro-L-phenylalanine ethyl es-

ter (4-F-Phe-OEt) under suitable conditions (Figure 6A, Online Supplementary Figure S19

,

Online Supplementary Table S4). We tested seven aminopeptidases, DPP3, DPP7, XPN-

PEP1, LAP3, LTA4H, RNPEP, and ANPEP, to determine whether they induce melﬂufen

amide hydrolysis in vitro. The release of 4-F-Phe-OEt from melﬂufen was measured after

incubating melﬂufen with the aminopeptidases for 2 h. Release of 4-F-Phe-OEt was mea-

sured using liquid chromatography–high-resolution mass spectrometry, which showed

that the peak area of melﬂufen decreases while the peak area for 4-F-Phe-OEt increases

when a speciﬁc aminopeptidase is added. A clear increase of 4-F-Phe-OEt was observed

after melﬂufen incubation with aminopeptidases LAP3 (Figure 6B), LTA4H (Figure 6C),

RNPEP (Figure 6 D), and ANPEP (Online Supplementary Figure S19), suggesting that

these aminopeptidases can hydrolyze melﬂufen. However, no such increase was observed

with DPP3 and XPNPEP1. Interestingly, aminopeptidase DPP7 induced hydrolysis of

the terminal ester and not the amide of the melﬂufen molecule. The DPP family of en-

zymes hydrolyzes the peptide bond between two amino acids from the N-terminal. Due

to the structure of melﬂufen, it is likely that DPP7 hydrolyzes the ester rather than the

amide bond.

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Figure 5. Aminopeptidase inhibitors reduce sensitivity to melﬂufen in MM cell lines. (

MM.1S were treated with different concentrations of the aminopeptidase inhibitor tosedostat for 48 h, and cell viability

was measured using PrestoBlue cell viability reagent. ( ) RPMI-8226 cells or ( ) MM.1S cells were pretreated with DMSO

(control) or the aminopeptidase inhibitors bestatin (10 M) or tosedostat (10 M) for 1 h, then either left without additional

A) MM cell lines RPMI-8226 and

B

C

µ

treatment or treated with three different concentrations of melﬂufen or melphalan for 15 min, after which the medium was

replaced. After 48 h, cell viability was measured using PrestoBlue cell viability reagent. Statistical signiﬁcance is indicated

as *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ****p ≤ 0.0001. DMSO: dimethyl sulfoxide

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Figure 6. Aminopeptidases LAP3, LTA4H, and RNPEP can hydrolyze melﬂufen to melphalan and 4-F-Phe-OEt. (

pathway of the hydrolysis of melﬂufen peptide bond by the aminopeptidases LAP3, LTA4H, and RNPEP. (

buffer and substrates were mixed and incubated without and with ( ) LAP3, ( ) LTA4H, and (

A) Reaction

B

–D

) Incubation

◦

B

C

D

) RNPEP for 2 h at 37 C

(with duplicate incubations). The incubation was stopped by adding ice-cold acetonitrile. Using LC-HRMS, 4-F-Phe-OEt

was then quantiﬁed, with concentrations presented as the peak area in the ﬁgure. 4-F-Phe-OEt: para-ﬂuoro-L-phenylalanine

ethyl ester; LC-HRMS: liquid chromatography–high-resolution mass spectrometry.

4. Discussion

Aminopeptidases are ubiquitous enzymes with important roles in cell development,

growth, and maintenance. They are widely expressed in tissues, including hematopoietic

cells of the bone marrow and peripheral blood, and in lymphoid organs [27

tidases are thought to have basic roles in regulating cell homeostasis, but more speciﬁc

functions have recently been identiﬁed, particularly in malignancies [ 29]. The utilization

,28]. Aminopep-

1

,

of aminopeptidases for the processing of novel therapeutic drugs, such as antibody–drug

conjugates and peptide–drug conjugates, is an active research area in drug development,

especially for plasma cell diseases. The chemical linker in melﬂufen is made of a dipeptide,

which is a substrate for aminopeptidases, resulting in hydrolysis and rapid release of the

cytotoxic alkylator payload and intermediate metabolites [30]. Therefore, the expression

and activity of aminopeptidases in MM cells is of particular interest.

Analysis of gene expression proﬁles of CD138+ cells from MM patient samples from

two datasets showed that the majority of aminopeptidases are expressed at relatively

similar levels. LAP3, one of the most highly expressed aminopeptidases in MM, is an

enzyme that primarily catalyzes the hydrolysis of leucine residues from the amino-termini

of proteins or peptides [31]. LAP3 is also implicated in antigen and major histocompatibility

complex class I peptide processing, and is associated with the proinﬂammatory effects of

IFN-

invasion [33

cell and hepatocellular carcinoma [35

hematological cells is currently not known.

γ

[

32]. In cancer, LAP3 has also been linked to tumor cell proliferation, migration, and

34]. Altered LAP3 expression has also been observed in esophageal squamous

36]. The role of LAP3 in plasma cells and other

,

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Only ﬁve aminopeptidases, namely, ERAP2, XPNPEP1, DPP3, RNPEP, and CTSV,

were differentially expressed between NDMM and RRMM. In the endoplasmic reticulum,

ERAP enzymes trim peptides for presentation on major histocompatibility complex class I

molecules [37]. A small number of studies demonstrate the direct involvement of ERAP

enzymes in tumor growth and the generation of tumor epitopes for the presentation of

cytotoxic T cells [38]. For example, the absence of ERAP2 in choriocarcinoma has been

linked to the reduction of the activation of T lymphocytes by the tumor cells [39]. One could

speculate that the reduction of ERAP2 expression in myeloma cells could be beneﬁcial

by reducing their immunogenicity and leading to their increased survival by immune

evasion. The selection for lower ERAP2-expressing cells would naturally occur during

disease progression. Another hypothetical link between MM and ERAP2 could be the

excessive plasma protein production in MM cells that induces endoplasmic reticulum

stress. The proteasomal degradation of the paraprotein is required to avoid endoplasmic

reticulum stress-induced cell death. If this machinery is inhibited or altered, it might lead

to drug resistance. In addition, CTSV is a potential prognostic biomarker for progression

with high CTSV expression shown to be associated with poor outcome in breast ductal

carcinomas in situ [40].

We also found that elevated expression of the aminopeptidase genes XPNPEP1, RN-

PEP, DPP3, and BLMH could predict poor survival of MM patients. The role of these and

other highly expressed aminopeptidases in MM remains to be explored. Proline-speciﬁc

DPPs are emerging drug targets, and DPP4 inhibitors (gliptins) are already approved

globally for the treatment of diabetes. Inhibition of DPP7 leads to apoptosis of the majority

of resting cells in chronic lymphocytic leukemia [41]. Another study found that inhibition

of DPP7 leads to upregulation of RB1 and MYC and decrease of BCL2, leading to potential

apoptosis induction [42]. However, TP53 inactivation prevents DPP7 downregulation [42].

Biallelic inactivation of TP53 is a hallmark of MM and could therefore explain DPP7 overex-

pression in MM [43

and correlates with poor prognosis, and it is also more highly expressed in endometrial

carcinomas vs. normal tissue [45 46]. Off-target DPP8/9 inhibition in MM cells leads to

,44]. DPP3 is overexpressed in estrogen receptor-positive breast cancer

,

apoptotic cell death signaling in the presence of DPP4 inhibitors (gliptins), whereas in AML

the direct inhibition of DPP8/9 induces the pro-caspase-1-dependent inﬂammatory form

of programmed cell death, pyroptosis [14,15].

There are only a very limited number of studies demonstrating the prognostic value

of aminopeptidases, namely, BLMH, RNPEP, and XPNPEP1, in human tumors. BLMH

may have multiple roles in different physiological and pathological conditions, such as

resistance to bleomycin therapy in several cancer types, cellular detoxiﬁcation, and training

of peptides for antigen presentation [47,48]. The XPNPEP1 (alias aminopeptidase P) protein

is speciﬁcally found in the blood vessels of lungs and also in lung tumors [49]. RNPEP

(alias aminopeptidase B) mRNA levels have been demonstrated to be altered during

colorectal adenoma–carcinoma evolution, and RNPEP plasma levels are also independent

prognostic factors for colorectal cancer patients [50]. Both XPNPEP1 and RNPEP enzyme

activities were found to be signiﬁcantly elevated in thyroid neoplasms when compared

with nonmalignant adjacent tissues [51].

Peptide–drug conjugates such as melﬂufen require aminopeptidases to unload their

payload into cells. We found that melﬂufen could target CD138+CD38+ plasma cells in MM

patient samples and was particularly active in RRMM samples. The higher expression of

speciﬁc aminopeptidases in RRMM compared with NDMM could explain the differential

activity of melﬂufen in samples from these two patient groups. However, from our limited

set of samples, we did not observe signiﬁcant correlation between melﬂufen sensitivity

and expression of an individual aminopeptidase gene. Nevertheless, hydrolysis analysis of

seven aminopeptidases demonstrated that LAP3, LTA4H, RNPEP, DPP7, and ANPEP are

able to hydrolyze melﬂufen. In addition, we demonstrated that the efﬁcacy of melﬂufen,

but not melphalan, could be reduced with the addition of the metalloaminopeptidase

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inhibitors tosedostat or bestatin in MM cells lines. This indicates a difference in the mode

of action between melphalan and melﬂufen.

Melﬂufen is being investigated in several clinical trials. The ﬁrst study reported shows

that melﬂufen combined with dexamethasone leads to clinical improvement with an overall

response rate of 31%, median progression-free survival of 5.7 months, and median overall

survival of 20.7 months [52]. It also demonstrated that melﬂufen is equally effective in

melphalan-naïve and melphalan-refractory patients [52]. The second study, focusing on

heavily pretreated RRMM, showed that melﬂufen combined with dexamethasone leads

to clinical improvement in the triple-class RRMM population with an overall response

rate of 26%, median progression-free survival of 3.9 months, and median overall survival

of 11.2 months [53]. In this study, we demonstrated that melﬂufen is highly effective

at targeting plasma cells, particularly in samples from RRMM patients, including those

with poor prognostic markers, such as del 17p or t(4;14). We also showed that melﬂufen

can be hydrolyzed to its active form by aminopeptidases that are highly expressed in

MM. Although additional investigations are needed to understand the speciﬁc role of

aminopeptidases in MM pathogenesis, our results highlight that this group of enzymes

can be exploited to beneﬁt MM patients who may not respond to other available therapies.

5. Conclusions

In this study we have analyzed the expression of aminopeptidase genes in MM patients

and how aminopeptidase gene expression correlates with patient overall survival. We

also tested the sensitivity of MM plasma cells to peptide–drug conjugate melﬂufen, which

is known to be activated by aminopeptidases, and how inhibition of aminopeptidases

affect melﬂufen activity in MM cell lines. Based on ranked abundance, LAP3, ERAP2,

METAP2, TTP2, and DPP7 were highly expressed in MM. ERAP2, XPNPEP1, DPP3, RNPEP,

and CTSV were differentially expressed between RRMM and NDMM samples (p < 0.05).

Survival analysis revealed that high expression of XPNPEP1, RNPEP, DPP3, and BLMH

(p < 0.05) was associated with shorter overall survival. Sensitivity to melﬂufen was detected

ex vivo in the majority of MM patient samples, and high sensitivity was observed, especially

in RRMM samples. Hydrolysis analysis demonstrated that melﬂufen is a substrate for

aminopeptidases LAP3, LTA4H, RNPEP, and ANPEP, which are expressed in MM. The

sensitivity of MM cell lines to melﬂufen was reduced by aminopeptidase inhibitors. These

results indicate critical roles of aminopeptidases in disease progression and the activity of

melﬂufen in MM.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/

10.3390/cancers13071527/s1: Figure S1: Drug plate layout and drug concentrations used in the

ﬂow cytometry-based drug sensitivity testing, Figure S2: Flow cytometry gating strategy used in

drug sensitivity testing, Figure S3: Aminopeptidase gene family genes showed similar expression

in both the CoMMpass and FIMM datasets, Figure S4: Correlation of aminopeptidase genes LAP3,

ERAP2, METAP2, TPP2, DPP7, ERAP1, LTA4H, and LNPEP (Group I) expression with myeloma

patient cytogenetics and age in the FIMM dataset, Figure S5: Aminopeptidase gene expression posi-

tively correlates with aminopeptidase protein expression, especially for LAP3, BLMH, DPP3, DPP7,

RNPEP, and ERAP2 aminopeptidases, Figure S6: CoMMpass dataset conﬁrms that aminopeptidase

genes are differentially expressed in NDMM vs. RRMM, Figure S7: Correlation of aminopeptidase

genes differentially expressed between RRMM and NDMM with myeloma patient cytogenetics and

ISS stage in the FIMM dataset, Figure S8: Prognostic signiﬁcance of three aminopeptidase genes

XPNPEP1, RNPEP, and DPP3 expression in NDMM and RRMM samples separately in the FIMM

dataset, Figure S9: Prognostic signiﬁcance of aminopeptidase gene expression in the CoMMpass

dataset, Figure S10: Somatic mutation predictions for aminopeptidase genes in the FIMM dataset

samples (n = 169), Figure S11: Somatic mutation predictions for aminopeptidase genes in CoMMpass

dataset (n = 1164), Figure S12: Aminopeptidase gene CNVs in FIMM dataset MM samples (n = 169),

Figure S13: Aminopeptidase gene CNVs in CoMMpass dataset (n = 1044), Figure S14: Ex vivo

sensitivity of MM CD138+CD38+ plasma cells to melﬂufen, melphalan, bortezomib, selinexor, and

4-HC, Figure S15: Ex vivo sensitivity of MM CD138+CD38+ plasma cells to melﬂufen in 10 MM

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patients with gene expression data also available from MM CD138+ plasma cells., Figure S16: Corre-

lation of the 39 aminopeptidase genes’ expression with melﬂufen sensitivity in bone marrow plasma

cells in 10 myeloma patient samples, Figure S17: Correlation of drug sensitivity (EC50 values) to

selinexor vs. melﬂufen, melphalan, and bortezomib in CD138+CD38+ plasma cells from MM patient

samples, Figure S18: Cytogenetics and melﬂufen sensitivity in CD138+CD38+ plasma cells from MM

patient samples, Figure S19: Aminopeptidase ANPEP can hydrolyze melﬂufen to melphalan and

4-F-Phe-OEt, Table S1: FIMM dataset patient sample characteristics and sample analysis informa-

tion, Table S2: List of the 39 annotated aminopeptidase genes in the human genome utilizing the

Ensembl and NCBI databases and further conﬁrming the molecular function (gene ontology) of the

identiﬁed genes, Table S3: List of compounds used in the experiments, Table S4: Aminopeptidases

used in the hydrolysis assay and their incubation buffers, Table S5: Log2(RPKM) expression val-

ues for 39 aminopeptidase genes and 17 housekeeping genes in MM patient samples in the FIMM

dataset (n = 122), Table S6: LC–MS/MS-based proteomics label-free quantitation intensity values

for 17 aminopeptidase proteins in CD138+ cells isolated from MM patient samples in the FIMM

dataset (n = 23), Table S7: Statistical overview of genes identiﬁed as prognostic markers (p

≤

0.05)

in the FIMM dataset (n = 122), Table S8: Aminopeptidase gene somatic mutation frequencies from

MM patient samples in the FIMM dataset (n = 169), Table S9: Aminopeptidase gene copy number

variation scores from MM patient samples in the FIMM dataset (n = 169), Table S10: Summary of

live cells and CD138+CD38+ plasma cells present in the BM-MNC samples after 72 h incubation in

DMSO (control), Table S11: Melﬂufen, melphalan, selinexor, bortezomib, and 4-HC EC50 values from

15 MM patient samples’ CD138+CD38+ plasma cells with sample disease stage indicated, Table S12:

Mean log2(RPKM) values for the 39 aminopeptidase genes in melﬂufen high-sensitivity (n = 5) and

low-sensitivity (n = 5) myeloma samples.

Author Contributions: Conceptualization, J.J.M., R.K., N.N.N., F.L. and C.A.H.; data curation, J.J.M.

and J.L.; formal analysis, J.J.M., R.K., G.A.T., P.S., M.M.M., A.S. (Alexander Schepsky), D.B., P.D. and

A.S. (Ana Slipicevic); funding acquisition, C.A.H.; methodology, J.J.M., R.K., G.A.T., M.-E.H., P.S.,

M.M.M., A.S. (Alexander Schepsky) and P.D.; project administration, C.A.H.; resources, J.L., P.A.

and R.S.; supervision, T.G., P.O., A.S. (Ana Slipicevic), N.N.N., F.L. and C.A.H.; visualization, J.J.M.,

R.K., G.A.T., P.S., and A.S. (Ana Slipicevic); writing—original draft, J.J.M., R.K., N.N.N. and C.A.H.;

writing—review and editing, J.J.M., R.K., G.A.T., M.-E.H., P.S., M.M.M., A.S. (Alexander Schepsky),

T.G., J.L., D.B., P.D., P.O., A.S. (Ana Slipicevic), P.A., R.S., N.N.N., F.L. and C.A.H. All authors have

read and agreed to the published version of the manuscript.

Funding: This work has been supported by funding from Oncopeptides AB, Academy of Finland

(grant 1320185), Cancer Society of Finland, and Sigrid Jusélius Foundation.

Institutional Review Board Statement: The study was conducted according to the guidelines of the

Declaration of Helsinki, and approved by an ethical committee of the Helsinki University Hospital

(permit numbers 239/13/03/00/2010 and 303/13/03/01/2011, date of approval, 5.11.2012, and

approval date for project extension, 13.2.2018)

Informed Consent Statement: Written informed consent has been obtained from the patients to

collect patient samples and to publish the data used in this paper.

Data Availability Statement: The data presented in this study are available on request from the

corresponding author. The data are not publicly available due to privacy and ethical limitations.

Acknowledgments: These data were generated in part by the Multiple Myeloma Research Foun-

dation Personalized Medicine Initiatives (https://research.themmrf.org and www.themmrf.org,

accessed on 5 February 2018). This work has been supported by funding from Oncopeptides AB,

Academy of Finland (grant 1320185), Cancer Society of Finland, and Sigrid Jusélius Foundation. The

authors acknowledge use of the Q-Exactive quantitative mass spectrometer, funded under the Re-

search Infrastructure Call 2012 by Science Foundation Ireland (SFI-12/RI/2346/3). The authors would

like to thank Alun Parsons, Minna Suvela, the FIMM Sequencing Unit, the FIMM High Throughput

Biomedicine Unit, and the FIMM Bioinformatics Unit for their excellent technical support. A&M

Labor GmbH (Bergheim, Germany) is acknowledged for performing the aminopeptidase hydrolysis

assay experiments. The authors are very grateful to the patients who generously donated samples

for our research. The authors would also like to thank the Finnish Hematology Research Biobank

(FHRB; Helsinki, Finland) for providing the viably frozen samples for the ﬂow cytometry-based drug

sensitivity testing experiments.

Cancers 2021, 13, 1527

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Conﬂicts of Interest: All authors met the criteria set forth by the International Committee of Medical

Journal Editors (ICMJE) and hence adequately contributed to manuscript development. A.S. and

F.L. are employees of Oncopeptides AB. N.N.N. is a consultant for Oncopeptides AB. C.A.H. has

received research funding from Oncopeptides AB, Kronos Bio, Novartis, Celgene, Orion Pharma,

and the IMI2 consortium project HARMONY. T.G., G.A.T., and A.S. have a research contract with

Oncopeptides AB. R.S. has received research funding from Amgen, BMS, Celgene, and Takeda and

honoraria from Amgen, Celgene, Janssen-Cilag, Takeda, and Sanoﬁ. J.L. and P.A. have received

personal fees from Amgen, Bristol Myers Squibb, Celgene, Janssen, Sanoﬁ, and Takeda. J.J.M., R.K.,

M.E.H., P.S., M.M.M., D.B., P.D., and P.O. declare no conﬂicts of interest.

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Article Doi

Aminopeptidase expression in multiple myeloma associates with disease progression and sensitivity to melflufen

DOI: 10.3390/cancers13071527

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Article abstract of DOI:10.3390/cancers13071527

Full text of DOI:10.3390/cancers13071527

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