Research ArticleExperimental Studies

Satraplatin Demonstrates High Cytotoxic Activity Against Genetically Defined Lymphoid Malignancies

THILO ZANDER, JIA XUE, GABRIEL MARKSON, FELIX DAHM and CHRISTOPH RENNER
Anticancer Research April 2022, 42 (4) 1821-1832; DOI: https://doi.org/10.21873/anticanres.15658

Abstract

Background/Aim: Satraplatin is an oral platinum analog with proven clinical efficacy and a more favorable toxicity profile, although with increased hematotoxicity, when compared to cisplatin. Hence, we carried out a systematic biomarker analysis to identify hematological malignancies with high susceptibility to satraplatin. Materials and Methods: Half-maximal inhibitory concentrations (IC50) for satraplatin and cisplatin were determined for 66 different cancer cell lines by CTG Luminescent Cell Viability Assay. In a second step, whole transcriptome RNA sequencing and whole-exome DNA sequencing technology followed by unbiased analysis of gene expression, gene mutation and copy number levels were performed and correlated with drug efficacy. Results: Satraplatin was significantly more active against hematological malignancies compared to solid organ cancer. In addition, satraplatin showed a significantly more potent antiproliferative activity compared to cisplatin in most lymphoma cell lines achieving sub micromolar IC50 values. Single BCL2 apoptosis regulator (BCL2) gene mutation and 9p21 copy-number deletions including S-methyl-5’-thioadenosine phosphorylase (MTAP) deficiency were identified as key characteristics for high sensitivity to satraplatin. Conclusion: Satraplatin demonstrated a high cytotoxic activity in genetically well-defined hematological malignancies which is distinct from that of cisplatin. MTAP deficiency was identified as biomarker of enhanced satraplatin efficacy in hematological cancer-derived cell lines. These data in combination with the lipophilicity of satraplatin provide the rationale for targeting specific lymphatic entities such as primary central nervous system lymphoma and cutaneous T-cell lymphoma to improve clinical outcome.

Key Words:

Cisplatin, carboplatin and oxaliplatin are platinum-based drugs that are used throughout the world for cancer treatment (1). All three are intravenously administered and primarily hydrophilic, with similar or even overlapping side-effects including nephrotoxicity, ototoxicity, neurotoxicity, cardiotoxicity, hematological toxicity, hepatotoxicity, and gastrointestinal toxicity. Platinum toxicity is usually the main reason for dose adaptations or even treatment suspension. This is particularly true for cisplatin, with the two most common nephrotoxic side-effects being acute kidney injury and hypomagnesemia, which is reported to affect up to 90% of cisplatin-treated patients (2). Neurotoxicity and ototoxicity (60-90% of all patients) are the second and third most common platinum toxicities experienced with oxaliplatin, primarily causing neurotoxicity, and cisplatin causing ototoxicity.

In order to develop better tolerated platinum compounds with less toxicity, platinum(IV) complexes featuring an octahedral geometry with two additional ligand sites were developed. They follow a classical prodrug concept since it is essential for their anticancer activity that they are reduced to the corresponding platinum(II) analogs in the tumor cell, leading to apoptosis (3). Satraplatin (bis-acetato-amminedichloro-cyclohexylamine-platinum) is a prime example of a novel platinum(IV) compound and was developed as the first oral fourth-generation platinum compound with activity in platinum-sensitive and even some platinum-resistant preclinical models (4). Over the past two decades, satraplatin was studied extensively in different in-vitro and in-vivo solid tumor models (5) and demonstrated consistently high activity (6).

Clinical development was performed in a variety of cancer entities, including prostate cancer, with encouraging results, although prostate cancer was traditionally considered a platinum-resistant disease. Phase I/II monotherapy trials of satraplatin in patients with hormone-refractory prostate cancer demonstrated an objective response rate of up to 31% (7). On the basis of these positive data, a large phase III registration trial (SPARC) was initiated evaluating satraplatin as second-line therapy in combination with prednisone versus prednisone and placebo for hormone-refractory prostate cancer in patients whose disease progressed after one line of chemotherapy. The trial met its primary endpoint and showed a 33% reduction in risk of progression or death (progression-free survival; hazard ratio=0.67; p<0.001), a 36% reduction in the risk of pain progression, prostate-specific antigen responses (decline of at least 50%) in 25% versus 12% (p=0.001) and an objective response rate by Response Evaluation Criteria for Solid Tumors of 8% versus 0.7% (p<0.002). Unfortunately, no statistically significant benefit in median overall survival (14.3 vs. 14.3 months; hazard ratio=0.98; p=0.80) was noted and the clinical development stopped (8). As a consequence, satraplatin is not approved for cancer treatment.

In agreement with data from previous clinical trials, the SPARC trial confirmed that satraplatin-related toxicity was generally mild, including moderate nausea, fatigue, anorexia, diarrhea, and altered taste. In particular, there was no nephrotoxicity, hair loss nor added neurotoxicity noted, which renders this drug unique within this class of drugs. In an early phase II study (CA142-008), satraplatin and cisplatin were directly compared as monotherapy in frontline treatment of patients with non-small cell lung cancer (9). In terms of tolerability, more pronounced hematotoxicity was documented for satraplatin. For example, grade 3 or more leukopenia was found in 29% (satraplatin) versus 11% (cisplatin) of patients, indicative of targeted activity of satraplatin against hematopoietic cells. Following the discovery of this activity profile, we hypothesized that satraplatin has particular activity against hematological malignancies. To gain a better understanding of the signaling pathways relevant to satraplatin activity, we performed a biomarker analysis on 66 tumor cell lines, with a special focus on lymphoma entities.

Materials and Methods

Cell lines and reagents. The cell lines listed in Table I were studied. All cell lines were cultured in appropriate media supplemented with 10-15% fetal bovine serum (ExCell Bio, Shanghai, PR China) at a temperature of 37°C, with 5% CO2 and 95% humidity. Cell viability was measured using the CTG Luminescent Cell Viability Assay (Promega, Madison, WI, USA) by standard procedures as described below. Satraplatin was purchased from MedChemExpress (Shanghai, PR China) and cisplatin from Qilu Pharm (Jinan, Shandong, PR China). Stock solutions of both drugs at 1 mM were established for storage at –20°C as previously described (5).

View this table:
Table I.

Name and origin of cell lines analyzed listed by disease entity.

Determination of the half-maximal inhibitory concentration (IC50). Cells were harvested during the logarithmic growth period and cell numbers counted using Count-star (Inno-Alliance Biotech, Cupertino, CA, USA). Cell density was adjusted to 4.44×104 cells/ml with the respective culture medium. Then, 90 μl of cell suspensions were added in triplicate to each well of a 96-well plate with a final cell density of 4×103 cells/well. After 24 h, 10 μl serial solutions of either cisplatin or satraplatin were transferred to each well in triplicate to achieve final drug concentrations per well of 100 μM, 31.6 μM, 10 μM, 3.16 μM, 1.0 μM, 316 nM, 100 nM, 31.6 nM and 10 nM, respectively. The test plate was incubated for 72 h in a humidified incubator at 37°C with 5% CO2, and then evaluated using CTG cell viability assay (Promega). For that purpose, the plate was equilibrated at room temperature for approximately 30 min. Then, 50 μl CTG reagent was added to each well. The content was mixed for 5 min on an orbital shaker to induce cell lysis and the plate incubated at room temperature for 20 min to stabilize the luminescent signal. Luminescence was recorded using EnVision Multi Label Reader (Perkin Elmer, Richmond, CA, USA).

Data analysis. The data were analyzed and visualized using GraphPad Prism 5.0 (GraphPad Software, San Diego, CA, USA) and Think-Cell 11 (Think-Cell Software, Berlin, Germany). In order to calculate absolute IC50, a dose–response curve was fitted using a non-linear regression model with a sigmoidal dose response. The formula for calculating the cell survival rate was: Cell survival (%) at Cx=(Cx – M)/(N – M)×100%, Cx being the luminescence signal of cells cultured with any concentration of the test compound, M being the luminescence signal of the blank control (with no cells) and N being the vehicle control with culture medium containing 0.25% (v/v) dimethyl sulfoxide. The absolute IC50 was calculated according to the dose–response curve generated by GraphPad Prism 5.0. Raw cell viability data were then used and standard four-parameter monotonic log-logistic dose–response curves fitted using R package dr4pl (10). To investigate drug efficacy in the 66 cell lines tested, we computed several metrics from the dose–response curves and used the values for the area under the curve (AUC) as a representation of drug efficacy. Efficacy among cell lines from different tissue origin was compared by analysis of variance, with p-values >0.05 representing no significant difference in sensitivity to the drug across test indications.

Whole-transcriptome sequencing (RNAseq). Total RNA extraction for baseline cell line samples was proceeded according to Qiagen Cat#74106 protocol (Qiagen, Hilden, Germany). The integrity of the total RNA was determined by a 2100 Bioanalyser (Agilent, Santa Clara, CA, USA) and quantified using NanoDrop (Thermo Fisher Scientific, Waltham, MA, USA). Only high-quality RNA samples (OD260/280=1.8-2.2, OD260/230≥2.0, RNA integrity number ≥7, total RNA >500 ng) was used to construct the sequencing library. PolyA mRNA was purified from total RNA using oligo-dT-attached magnetic beads and then fragmented by fragmentation buffer. Taking these short fragments as templates, first-stranded cDNA was synthesized using reverse transcriptase and random primers, followed by second-stranded cDNA synthesis by standard measures. The synthesized cDNA was subjected to end-repair, phosphorylation and ‘A’ base addition according to a library construction protocol (TruSeq RNA Sample Preparation v2 Guide; https://support.illumina.com/content/dam/illumina-support/documents/documentation/chemistry_documentation/samplepreps_truseq/truseqrna/truseq-rna-sample-prep-v2-guide-15026495-f.pdf). Then, sequencing adapters were added to both sites of the cDNA fragments. After polymerase chain reaction amplification for cDNA fragments, the targets of 250-350 bp were cleaned. After library construction, Qubit 2.0 fluorometer dsDNA HS Assay (Thermo Fisher Scientific) was used to quantify the concentration of the resulting sequencing libraries, while the size distribution was analyzed using an Agilent BioAnalyzer 2100. After library validation, Illumina cBOT cluster generation system with HiSeq PE Cluster Kits (Illumina, San Diego, CA, USA) was used to generate clusters. Paired-end sequencing was performed using Illumina NovaSeq 6000 platform following Illumina-provided protocols for 2×150 paired-end sequencing.

Gene-expression analysis. The quality of RNAseq fastq raw reads was checked by FastQC software (Babraham Bioinformatics, https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The adapter and low-quality sequences were trimmed by Trimmomatic software (11). The reads were mapped to reference genes (ENSEMBL GRCh37.66) by Bowtie (12) software, and gene expression was calculated by MMSEQ (13) software. The final expression values are log2-transformed fragments per kilobase of exon model per million mapped fragment values. For each cohort of tested cell lines, we started with gene expression matrix on 49,442 annotated genes for the studied cell lines with expression data available. Expression of 49,442 genes was correlated with AUC values across 66 cell lines to identify positively correlated genes and negatively correlated genes to drug sensitivity. Spearman correlation coefficient <–0.55 and p<0.05 were used as cutoffs for genes correlated with enhanced sensitivity whereas Spearman correlation coefficient >0.55 and p<0.05 were used as cutoffs for genes correlated with resistance.

Whole-exome sequencing (WES). Genomic DNA extraction from baseline cell line samples for WES was performed on KingFisher Flex (Thermo Fisher Scientific) with MagMAX™ DNA Multi-Sample Ultra 2.0 Kit (ABI, Waltham, MA, USA). Genomic DNA was quantified by Nanodrop™ 2000 spectrophotometer (Thermo Fisher Scientific). Only high-quality DNA (concentration >100 ng/μl, OD260/280≥1.8-2.2, OD260/230≥2) was used for library construction. Library construction was performed following the protocol of SureSelect XT HS2 DNA Reagent Kit (Agilent). In brief, fragmentation was first performed by ME220 Focused-ultrasonicator (Covaris, Woburn, MA, USA) after DNA quality control, followed by end-repair, dA-tailing and ligation. The ligation product was then purified using AMPure XP beads (Beckman Coulter, Indianapolis, IN, USA), the purified adaptor-ligated library was amplified, followed by another step of purification using AMPure XP beads (Beckman Coulter). Finally, the purified WES libraries were analyzed by 2×150 paired-end sequencing [NovaSeq 6000 S2 Reagent Kit (Illumina)] on NovaSeq 6000 (Illumina) according to the manufacturer’s protocol.

Gene-mutation analysis. The quality of WES fastq raw data was checked by FastQC software. The adapter and low-quality sequences were trimmed by Trimmomatic software (11). The reads were aligned to the hg19 genome by BWA software (14), and the variants were called by GATK software (15) and annotated by VEP software (16). The driver status of mutations was predicted following the method of the CancerGenomeInterpreter database (17). Wilcoxon signed-rank test and Benjamini and Hochberg adjusted p-values were calculated between mutation status and efficacy endpoint (AUC values). The calculation for the difference in AUC between cell lines with mutated and those with wild-type gene was as follows: AUC difference=mean AUC for cell lines with somatic point mutation – mean AUC for cell lines with no somatic point mutation. AUC difference >0 indicates that a gene mutation is enriched in resistant cell lines (with high AUC) whereas an AUC difference <0 indicates that a gene mutation is enriched in sensitive cell lines (with low AUC).

Analysis of copy-number variation. Aligned sequences from WES data were used to estimate gene copy numbers by CopyWriteR R package (18). Genes with copy-number values >3 were defined as a copy number-amplified while copy-number values <1 were defined as a copy number-deleted. Wilcoxon signed-rank test and Benjamini and Hochberg-adjusted p-values were calculated between amplification/deletion status and efficacy endpoints (AUC values) as described above.

Results

Satraplatin has high cytotoxicity against lymphoid malignancies. The cytotoxic profile of satraplatin was tested in a standard cell-based assay of 66 cancer cell lines (Table I) as described above. To investigate and compare drug efficacy, AUC values were used. By analysis of variance, we found significant differences in AUC values across all cancer types (Figure 1) for satraplatin. Satraplatin was significantly more active against hematological malignancies compared to solid organ cancers (p<0.0001). Therefore, the cohort was split into groups with solid organ cancer (n=22) and hematological malignancies (n=44) and only the latter was used for further analysis.

Figure 1.
Figure 1.

Cytotoxic activity of satraplatin against tumor cell lines of different origin as determined by the area under the dose–response curve (AUC). The median AUC value across all tested cell lines was 2.35. Tumor entities are indicated on the x-axis. Sensitivity of cells of hematopoietic and lymphoid malignancies was significantly higher compared with those from cancer of solid organs (analysis of variance, p=2.3×10–8).

Satraplatin was significantly more active against hematological malignancies than standard cisplatin (p=0.009) (Figure 2), with IC50 values at low to sub-micromolar levels. When grouped by histology, satraplatin revealed high activity in diffuse large B-cell lymphoma (DLBCL), T-cell lymphoma, mantle-cell lymphoma (MCL), multiple myeloma and Burkitt lymphoma cell lines. In addition, statistical significances were calculated for some cell lines of special interest, and it was shown that especially for the cell line OCI-LY3, satraplatin was significantly more active than cisplatin, with a p-value of 0.0006. Similar results were obtained for other DLBCL cell lines such as SU-DHHL-5 (p=0.003) and SU-DHL-6 (p=0.007).

Figure 2.
Figure 2.

Comparison of cytotoxicity to using satraplatin and cisplatin. The difference in half-maximal inhibitory concentration (IC50) values (cisplatin minus satraplatin; μM) was plotted for each cell line. The origin of each cell line is given in Table I. Enhanced killing of hematopoietic and lymphoid cancer cell lines was noted for satraplatin.

Identification of genes overexpressed leading to enhanced satraplatin sensitivity. Using gene-expression analysis, seven genes were identified that positively correlated with enhanced sensitivity to satraplatin with a Spearman correlation coefficient <-0.55 and p<0.05 as cutoff (Figure 3). The gene conferring the highest level of sensitivity was TUB like protein 3 (TULP3) followed by poly(A)-specific ribonuclease subunit PAN3 (PAN3), PHD finger protein 2 (PHF2), kelch domain containing 1 (KLHDC1) and WRN RecQ like helicase (WRN), respectively. Other overexpressed genes known to be associated with platinum sensitivity, such as BRCA2 DNA repair-associated (BRCA2), were identified for both platinum compounds, supporting the validity of the analysis. In total, 31 overexpressed genes leading to satraplatin resistance were identified, with S-methyl-5’-thioadenosine phosphorylase (MTAP) achieving the highest Spearman correlation (Figure 3).

Figure 3.
Figure 3.

Volcano plot of analysis of correlation between the expression of specific genes and the area under the satraplatin dose–response curve (AUC) for hematopoietic and lymphoid cancer cell lines. Genes conferring enhanced satraplatin sensitivity are depicted on the left [e.g. BRCA2 DNA repair-associated (BRCA2), kelch domain-containing 1 (KLHDC1), poly(A)-specific ribonuclease subunit PAN3 (PAN3), PHD finger protein 2 (PHF2), TUB like protein 3 (TULP3), and WRN RecQ-like helicase (WRN)], those conferring resistance on the right [e.g. ATP synthase peripheral stalk subunit D (ATP5H), DEP domain-containing MTOR-interacting protein (DEPTOR), dual specificity phosphatase 23 (DUSP23), S-methyl-5’-thioadenosine phosphorylase (MTAP), neurochondrin (NCDN), solute carrier family 17 member 5 (SLC17A5), SLC9A3 regulator 1 (SLC9A3R1), tight junction protein 1 (TJP1)]. Genes achieving statistical significance are marked as black squares (sensitive) and black triangles (resistant).

Single gene mutations leading to enhanced sensitivity to satraplatin. On the single gene level, 19 genes were identified which either mediated enhanced (12 genes) or reduced (seven genes) satraplatin cytotoxic when mutated (Figure 4). B-cell lymphoma 2 (BCL2) apoptosis regulator was the single most significantly mutated gene linked to enhanced satraplatin cytotoxicity. Of importance, BCL2 gene mutation was not identified as a marker for cisplatin activity and seems to be a unique hallmark for sensitivity to satraplatin. Additional gene mutations conferring satraplatin sensitivity were e.g., amine oxidase copper containing 1 (AOC1), BOC cell adhesion associated, oncogene regulated (BOC), DEAD-box helicase 3 X-linked (DDX3X), ethanolamine kinase 2 (ETNK2) and ubiquitin specific peptidase 22 (USP22). Again, BRCA2 gene mutation was identified as a marker of sensitivity to both satraplatin and cisplatin.

Figure 4.
Figure 4.

Volcano plot of the area under the satraplatin dose–response curve (AUC) by gene signature at the mutation level. More and less sensitive groups of hematopoietic and lymphoid cancer cell lines were defined by considering the difference in AUC between those with gene deletion and those with wild-type gene. Gene deletions conferring enhanced satraplatin sensitivity are depicted on the left [adaptor-related protein complex 4 subunit epsilon 1 (AP4E1), amine oxidase copper-containing 1 (AOC1), B-cell lymphoma 2 (BCL2), BOC cell adhesion-associated oncogene-regulated (BOC), BRCA2 DNA repair-associated (BRCA2), crystallin beta-gamma domain-containing 3 (CRYBG3), DEAD-box helicase 3 X-linked (DDX3X), ethanolamine kinase 2 (ETNK2), FSHD region gene 1 family member B (FRG1B), IQ motif-containing N (KIAA1683), schlafen family member 5 (SLFN5), ubiquitin-specific peptidase 22 (USP22)], and those conferring resistance on the right [EPH receptor A8 (EPHA8), leucine-rich repeat-containing 61 (LRRC6), mitotic spindle-positioning (MISP), phospholipase D1 (PLD1), pregnancy-specific beta-1-glycoprotein 9 (PSG9), SNF2-related CREBBP activator protein (SRCAP) and taste 1 receptor member 2 (TAS1R2)]. Genes achieving statistical significance are marked as black squares (sensitive) and black triangles (resistant).

Analysis of DNA copy-number deletions identifies recurrent chromosomal abnormalities of the 9p21 locus. Finally, copy-number analysis was performed as described and 10 genes [chromosome 8 open reading frame 76 (C8orf76), collagen type XIV alpha 1 chain (COL14A1), derlin 1 (DERL1), family with sequence similarity 83 member A (FAM83A), hyaluronan synthase 2 (HAS2), mitochondrial ribosomal protein L13 (MRPL13), zinc fingers and homeoboxes 2 (ZHX2), NBPF member 9 (NBPF9), phosphodiesterase 4D-interacting protein (PDE4DIP), and peptidylprolyl isomerase A-like 4G (PPIAL4G)] were identified by DNA copy-number amplification level as reducing satraplatin sensitivity (Figure 5). However, when DNA copy-number deletions were analyzed, four genes cyclin-dependent kinase inhibitor 2A (CDKN2A), CDKN2B, MTAP and DMRT-like family A1 (DMRTA1) known to be located at the 9p21 locus were identified as acting as prominent biomarkers for enhanced satraplatin sensitivity (Figure 5). Since we had identified MTAP overexpression as marker of satraplatin resistance, we performed an in-depth analysis linking MTAP deficiency to satraplatin activity (Figure 6). Plotting the IC50 values for satraplatin against MTAP status as defined by Marjon et al. (19), a significantly higher efficacy of satraplatin in MTAP-deficient cell lines was demonstrated (p<0.0001).

Figure 5.
Figure 5.

Volcano plot of the area under the satraplatin dose–response curve (AUC) by gene signature at the copy-number level. Again, more (left) and less (right) sensitive groups of hematopoietic and lymphoid cancer cell lines were defined by considering the difference in AUC between those with gene deletion and those with wild-type gene. Four genes highlighted as black squares were identified by copy-number deletion as biomarkers for enhanced satraplatin sensitivity [cyclin-dependent kinase inhibitor 2A (CDKN2A), cyclin-dependent kinase inhibitor 2B (CDKN2B), S-methyl-5’-thioadenosine phosphorylase (MTAP) and DMRT-like family A1 (DMRTA1)].

Figure 6.
Figure 6.

Satraplatin cytotoxicity as half-maximal inhibitory concentration (IC50) for the hematopoietic and lymphoid cancer cell lines examined and by S-methyl-5’-thioadenosine phosphorylase (MTAP) status. Cell lines are listed according to their sensitivity to satraplatin, with the most sensitive one listed at the top. MTAP status: Grey: wild-type; black: deficient; white: status undefined/unknown.

Discussion

We completed a systematic analysis of satraplatin activity in common hematological malignancies and identified DLBCL and its respective cell lines to be very sensitive to satraplatin, with IC50 values at low micromolar or even sub-molar doses. Satraplatin is not only significantly superior in killing DLBCL cell lines such as OCI-LY7 when compared to cisplatin, but in addition, and more importantly, satraplatin very efficiently killed the cell line OCI-LY3. The OCI-LY3 cell line is of special interest since it has a stable mutational pattern (20), including mutations of MYD88 innate immune signal transduction adaptor (MYD88), Pim-1 proto-oncogene, serine/threonine kinase (PIM1), CD79b molecule (CD79B), caspase recruitment domain family member 11 (CARD11) and PR/SET domain 1 (PRDM1). These mutations resemble the genetic landscape of the so-called cluster 5 DLBCL as recently described by Chapuy and colleagues (21). Cluster 5 DLBCL gene alterations include frequent BCL2 gains, concordant MYD88L265P/CD79B mutations, and additional mutations of ETS variant transcription factor 6 (ETV6), PIM1, glyoxylate and hydroxypyruvate reductase (GRHPR), TBL1X receptor 1 (TBL1XR1) and PRDM1. These mutations are typical for patients with DLBCL with extranodal lymphoma involvement such as primary central nervous system lymphoma (PCNSL) and testicular lymphoma. As described in their pivotal publication, lymphoma tissue from eight out of nine patients with PCNSL fulfilled the C5 cluster criteria (Fisher’s exact test, p<0.001) and this laid the basis for the acceptance of the C5 genetic signature resembling PCNSL. Recently, the linkage between the C5 DLBCL cluster and PCNSL was confirmed by mutational profiling of DLBCL samples from an additional 48 patients with PCNSL at the MD Anderson Cancer Center (22). Therefore, and based on the mutational pattern, OCI-LY3 is suggested to be the most accepted PCNSL-like human DLBCL cell line, and highly suitable for predicting and comparing the efficacy of investigational compounds such as satraplatin for PCNSL treatment.

A similar DLBCL classification system based on genetic features was developed by Wright and colleagues (23). They published a probabilistic classification of patient samples of DLBCL and used the data of Chapuy et al. as a validation cohort. Again, this classification revealed genetic similarities between the different DLBCL subtypes. It is noteworthy that the MCD cohort, i.e., those with co-occurrence of MYD88L265P and CD79B mutations, as characterized by them shows major similarities with the C5 cohort as defined by Chapuy and colleagues regarding the mutational pattern. MCD DLBCL is known to spread secondarily to extranodal sites in 30% of all cases, with the central nervous system being the most common site. As for the C5 DLBCL cluster, MCD DLBCL is best represented by the OCI-LY3 cell line (23). In view of the data presented herein, satraplatin should be particularly useful in the treatment of DLBCL with primary extranodal lymphoma presentation, for example with lymphoma of the central nervous system, vitreo-retina, testis, or breast. The lipophilic characteristics of satraplatin enables the drug to access the cerebrospinal fluid at sufficient dose levels (24). In addition, its oral availability, with a favorable side-effect profile such as lack of neuro- and ototoxicity, provide the rationale for further investigation of satraplatin in extranodal lymphomas with a special focus on PCNSL as outlined.

In a second step, we carried out a more comprehensive molecular biomarker analysis to better understand the possible linkage between genetic characteristics of different lymphoma entities and sensitivity to satraplatin. With the multitude of possible mechanisms of actions of satraplatin, a predictive biomarker is critical for better selecting appropriate patients who will benefit most. To our surprise, we were clearly able to demonstrate meaningful differences in biomarker-defined activity between satraplatin and cisplatin as only few genetic alterations such as BRCA2 mutations or reduced BRCA2 expression were common to both platinum compounds but most of the other genetic alterations identified clearly differentiated the two. When focusing on single-gene mutation analysis, satraplatin was found to be particular active against cell lines that harbor BCL2, USP22 or ETNK2 mutations. Interestingly, these mutations were not related to sensitivity to cisplatin. The linkage of high satraplatin activity to BCL2 mutations in particular may be a potential explanation for the more severe hematotoxicity of satraplatin observed in previous clinical trials. Moreover, platinum resistance and refractoriness have been linked to the enhanced expression of anti-apoptotic genes, such as BCL2 in solid organ tumor-derived cell lines (25). In that study, satraplatin in contrast to oxaliplatin was significantly more effective and able to reduce BCL2 expression up to 13-fold, independently of P53 mutational status. The BCL2-inhibiting function of satraplatin again establishes a link to hematological diseases in which BCL2 overexpression is a key driver, and BCL2 inhibition by venetoclax has shown tremendous clinical efficacy, e.g., for MCL, acute myeloid leukemia and chronic lymphocytic leukemia treatment (26).

Finally, satraplatin was more active against a wide variety of lymphoma cell lines harboring gene copy-number deletions in the 9p21 locus including MTAP, CDKN2A, CDKN2B, and DMRTA1. CDKN2A/CDKN2B deletions are frequent characteristics of different lymphoma entities such as T-cell lymphoma/T-cell acute leukemia/cutaneous T-cell lymphoma (CTCL), DLBCL including PCNSL, MCL, and chronic lymphocytic leukemia. MTAP gene is adjacent to the CDKN2A tumor-suppressor gene and is frequently co-deleted with CDKN2A, the most commonly deleted gene in human cancer. Deletion of MTAP occurs in approximately 15% of all cancer cases and has been reported to be linked to more aggressive tumors associated with shorter survival when compared to wild-type MTAP (27). MTAP deletion increases cellular concentrations of its substrate MTA and, by competing with S-adenosyl-L-methionine, MTA binds to and partially inhibits protein arginine methyltransferase 5 (PRMT5). Due to its essential role in the spliceosome machinery, normal PRMT5 function is particularly important for the hematopoietic compartment to maintain homologous recombination and DNA repair (28). As demonstrated in PRMT5 knockout mice, complete inhibition leads to severe, lethal pancytopenia (29). It was recently shown that pharmacological inhibition of PRMT5 in MCL cell lines restarted a pro-apoptotic program and created a synergism with the BCL2 inhibitor venetoclax (30). As described above, satraplatin has high activity in MTAP-deleted hematopoietic cell lines, most likely because it causes severe DNA-damaging lesions in the context of a homologous recombination-deficiency (functional PRMT inhibition). In addition, a key point might be the simultaneously observed unique BCL2 inhibition which specifically elicits superior satraplatin cytotoxicity compared to cisplatin.

Satraplatin is ideally suited to exploiting vulnerability in p16/CDKN2A- and MTAP-deficient hematologicaI malignancies and in tumors of solid organs. We postulate that satraplatin is the preferred platinum agent for the treatment of tumors harboring the indicated gene mutations, those with reduced gene expression and, in particular, in 9p21 deleted/methylated tumors. Within the lymphoma entities, satraplatin is of particular interest for the treatment of PCNSL and CTCL. For PCNSL, del9p21 with/without BCL2 mutations are present in nearly 80% of cases (31). For CTCL, deletion of CDKN2A/CDKN2B has been described as a hallmark in most subtypes such as mycosis fungoides and Sezary syndrome. This deletion is a continuum that occurs more frequently with disease progression and is associated with more aggressive clinical behavior (32). There is an increasing loss of CDKN2A/B copy numbers and, thus, MTAP deletion in localized forms of mycosis fungoides when progressing towards systemic Sezary syndrome. Again, the high efficacy of satraplatin, with low-micromolar IC50, in the representative CTCL cell line HUT78 supports this notion. One can also assume that in this disease – as in PCNSL – the lipophilic characteristic of satraplatin is well suited and high treatment efficacy is to be anticipated. Following these data, clinical trials in both entities are in preparation.

Footnotes

  • Authors’ Contributions

    Jia Xue: Study design, performing experiments, data analysis and interpretation, article writing and final approval. Thilo Zander: Study design, data analysis and interpretation, article writing and final approval. Gabriel Markson: Study design, data analysis and interpretation, article writing and final approval. Felix Dahm: Study design, data analysis and interpretation, article writing and final approval. Christoph Renner: Study design, data analysis and interpretation, article writing and final approval.

  • Conflicts of Interest

    TZ, GM, FD and CR are directors of Dayton Therapeutics AG and are co-inventors in new patent applications related to this article. JX is employed by Crown Bioscience Inc., Suzhou, PRC.

  • Received January 8, 2022.
  • Revision received February 14, 2022.
  • Accepted February 16, 2022.

References

  1. ↵
    1. Chu E and
    2. DeVita VT Jr.
    : Physicians’ Cancer Chemotherapy Drug Manual 2019. Jones & Bartlett Pub Inc., 2019.
  2. ↵
    1. Pabla N and
    2. Dong Z
    : Cisplatin nephrotoxicity: mechanisms and renoprotective strategies. Kidney Int 73(9): 994-1007, 2008. PMID: 18272962. DOI: 10.1038/sj.ki.5002786
    OpenUrlCrossRefPubMed
  3. ↵
    1. Doshi G,
    2. Sonpavde G and
    3. Sternberg CN
    : Clinical and pharmacokinetic evaluation of satraplatin. Expert Opin Drug Metab Toxicol 8(1): 103-111, 2012. PMID: 22098065. DOI: 10.1517/17425255.2012.636352
    OpenUrlCrossRefPubMed
  4. ↵
    1. Bhargava A and
    2. Vaishampayan UN
    : Satraplatin: leading the new generation of oral platinum agents. Expert Opin Investig Drugs 18(11): 1787-1797, 2009. PMID: 19888874. DOI: 10.1517/13543780903362437
    OpenUrlCrossRefPubMed
  5. ↵
    1. Wosikowski K,
    2. Lamphere L,
    3. Unteregger G,
    4. Jung V,
    5. Kaplan F,
    6. Xu JP,
    7. Rattel B and
    8. Caligiuri M
    : Preclinical antitumor activity of the oral platinum analog satraplatin. Cancer Chemother Pharmacol 60(4): 589-600, 2007. PMID: 17541592. DOI: 10.1007/s00280-007-0502-z
    OpenUrlCrossRefPubMed
  6. ↵
    1. Sova P,
    2. Mistr A,
    3. Kroutil A,
    4. Zak F,
    5. Pouckova P and
    6. Zadinova M
    : Comparative anti-tumor efficacy of two orally administered platinum(IV) drugs in nude mice bearing human tumor xenografts. Anticancer Drugs 17(2): 201-206, 2006. PMID: 16428939. DOI: 10.1097/00001813-200602000-00012
    OpenUrlCrossRefPubMed
  7. ↵
    1. Latif T,
    2. Wood L,
    3. Connell C,
    4. Smith DC,
    5. Vaughn D,
    6. Lebwohl D and
    7. Peereboom D
    : Phase II study of oral bis (aceto) ammine dichloro (cyclohexamine) platinum (IV) (JM-216, BMS-182751) given daily x 5 in hormone refractory prostate cancer (HRPC). Invest New Drugs 23(1): 79-84, 2005. PMID: 15528984. DOI: 10.1023/B:DRUG.0000047109.76766.84
    OpenUrlCrossRefPubMed
  8. ↵
    1. Sternberg CN,
    2. Petrylak DP,
    3. Sartor O,
    4. Witjes JA,
    5. Demkow T,
    6. Ferrero JM,
    7. Eymard JC,
    8. Falcon S,
    9. Calabrò F,
    10. James N,
    11. Bodrogi I,
    12. Harper P,
    13. Wirth M,
    14. Berry W,
    15. Petrone ME,
    16. McKearn TJ,
    17. Noursalehi M,
    18. George M and
    19. Rozencweig M
    : Multinational, double-blind, phase III study of prednisone and either satraplatin or placebo in patients with castrate-refractory prostate cancer progressing after prior chemotherapy: the SPARC trial. J Clin Oncol 27(32): 5431-5438, 2009. PMID: 19805692. DOI: 10.1200/JCO.2008.20.1228
    OpenUrlAbstract/FREE Full Text
  9. ↵
    1. Groen H and
    2. Weil C
    : A randomized, multicenter phase II study of BMS-182751 (JM216) or cisplatin in patients with stage IIIB or IV non-small cell lung cancer. Investigator’s Brochure, Satraplatin, pp. 234-241, 2012.
  10. ↵
    1. Ritz C,
    2. Baty F,
    3. Streibig JC and
    4. Gerhard D
    : Dose-response analysis using R. PLoS One 10(12): e0146021, 2015. PMID: 26717316. DOI: 10.1371/journal.pone.0146021
    OpenUrlCrossRefPubMed
  11. ↵
    1. Bolger AM,
    2. Lohse M and
    3. Usadel B
    : Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30(15): 2114-2120, 2014. PMID: 24695404. DOI: 10.1093/bioinformatics/btu170
    OpenUrlCrossRefPubMed
  12. ↵
    1. Langmead B,
    2. Trapnell C,
    3. Pop M and
    4. Salzberg SL
    : Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3): R25, 2009. PMID: 19261174. DOI: 10.1186/gb-2009-10-3-r25
    OpenUrlCrossRefPubMed
  13. ↵
    1. Turro E,
    2. Su SY,
    3. Gonçalves Â,
    4. Coin LJ,
    5. Richardson S and
    6. Lewin A
    : Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads. Genome Biol 12(2): R13, 2011. PMID: 21310039. DOI: 10.1186/gb-2011-12-2-r13
    OpenUrlCrossRefPubMed
  14. ↵
    1. Li H
    : Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv Prepr arXiv13033997, 2013.
  15. ↵
    1. McKenna A,
    2. Hanna M,
    3. Banks E,
    4. Sivachenko A,
    5. Cibulskis K,
    6. Kernytsky A,
    7. Garimella K,
    8. Altshuler D,
    9. Gabriel S,
    10. Daly M and
    11. DePristo MA
    : The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20(9): 1297-1303, 2010. PMID: 20644199. DOI: 10.1101/gr.107524.110
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. McLaren W,
    2. Gil L,
    3. Hunt SE,
    4. Riat HS,
    5. Ritchie GR,
    6. Thormann A,
    7. Flicek P and
    8. Cunningham F
    : The Ensembl Variant Effect Predictor. Genome Biol 17(1): 122, 2016. PMID: 27268795. DOI: 10.1186/s13059-016-0974-4
    OpenUrlCrossRefPubMed
  17. ↵
    1. Tamborero D,
    2. Rubio-Perez C,
    3. Deu-Pons J,
    4. Schroeder MP,
    5. Vivancos A,
    6. Rovira A,
    7. Tusquets I,
    8. Albanell J,
    9. Rodon J,
    10. Tabernero J,
    11. de Torres C,
    12. Dienstmann R,
    13. Gonzalez-Perez A and
    14. Lopez-Bigas N
    : Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med 10(1): 25, 2018. PMID: 29592813. DOI: 10.1186/s13073-018-0531-8
    OpenUrlCrossRefPubMed
  18. ↵
    1. Kuilman T,
    2. Velds A,
    3. Kemper K,
    4. Ranzani M,
    5. Bombardelli L,
    6. Hoogstraat M,
    7. Nevedomskaya E,
    8. Xu G,
    9. de Ruiter J,
    10. Lolkema MP,
    11. Ylstra B,
    12. Jonkers J,
    13. Rottenberg S,
    14. Wessels LF,
    15. Adams DJ,
    16. Peeper DS and
    17. Krijgsman O
    : CopywriteR: DNA copy number detection from off-target sequence data. Genome Biol 16: 49, 2015. PMID: 25887352. DOI: 10.1186/s13059-015-0617-1
    OpenUrlCrossRefPubMed
  19. ↵
    1. Marjon K,
    2. Cameron MJ,
    3. Quang P,
    4. Clasquin MF,
    5. Mandley E,
    6. Kunii K,
    7. McVay M,
    8. Choe S,
    9. Kernytsky A,
    10. Gross S,
    11. Konteatis Z,
    12. Murtie J,
    13. Blake ML,
    14. Travins J,
    15. Dorsch M,
    16. Biller SA and
    17. Marks KM
    : MTAP deletions in cancer create vulnerability to targeting of the MAT2A/PRMT5/RIOK1 axis. Cell Rep 15(3): 574-587, 2016. PMID: 27068473. DOI: 10.1016/j.celrep.2016.03.043
    OpenUrlCrossRefPubMed
  20. ↵
    1. Juskevicius D,
    2. Müller A,
    3. Hashwah H,
    4. Lundberg P,
    5. Tzankov A and
    6. Menter T
    : Characterization of the mutational profile of 11 diffuse large B-cell lymphoma cell lines. Leuk Lymphoma 59(7): 1710-1716, 2018. PMID: 29043881. DOI: 10.1080/10428194.2017.1387903
    OpenUrlCrossRefPubMed
  21. ↵
    1. Chapuy B,
    2. Stewart C,
    3. Dunford AJ,
    4. Kim J,
    5. Kamburov A,
    6. Redd RA,
    7. Lawrence MS,
    8. Roemer MGM,
    9. Li AJ,
    10. Ziepert M,
    11. Staiger AM,
    12. Wala JA,
    13. Ducar MD,
    14. Leshchiner I,
    15. Rheinbay E,
    16. Taylor-Weiner A,
    17. Coughlin CA,
    18. Hess JM,
    19. Pedamallu CS,
    20. Livitz D,
    21. Rosebrock D,
    22. Rosenberg M,
    23. Tracy AA,
    24. Horn H,
    25. van Hummelen P,
    26. Feldman AL,
    27. Link BK,
    28. Novak AJ,
    29. Cerhan JR,
    30. Habermann TM,
    31. Siebert R,
    32. Rosenwald A,
    33. Thorner AR,
    34. Meyerson ML,
    35. Golub TR,
    36. Beroukhim R,
    37. Wulf GG,
    38. Ott G,
    39. Rodig SJ,
    40. Monti S,
    41. Neuberg DS,
    42. Loeffler M,
    43. Pfreundschuh M,
    44. Trümper L,
    45. Getz G and
    46. Shipp MA
    : Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat Med 24(5): 679-690, 2018. PMID: 29713087. DOI: 10.1038/s41591-018-0016-8
    OpenUrlCrossRefPubMed
  22. ↵
    1. Ou A,
    2. Sumrall A,
    3. Phuphanich S,
    4. Spetzler D,
    5. Gatalica Z,
    6. Xiu J,
    7. Michelhaugh S,
    8. Brenner A,
    9. Pandey M,
    10. Kesari S,
    11. Korn WM,
    12. Mittal S,
    13. Westin J and
    14. Heimberger AB
    : Primary CNS lymphoma commonly expresses immune response biomarkers. Neurooncol Adv 2(1): vdaa018, 2020. PMID: 32201861. DOI: 10.1093/noajnl/vdaa018
    OpenUrlCrossRefPubMed
  23. ↵
    1. Wright GW,
    2. Huang DW,
    3. Phelan JD,
    4. Coulibaly ZA,
    5. Roulland S,
    6. Young RM,
    7. Wang JQ,
    8. Schmitz R,
    9. Morin RD,
    10. Tang J,
    11. Jiang A,
    12. Bagaev A,
    13. Plotnikova O,
    14. Kotlov N,
    15. Johnson CA,
    16. Wilson WH,
    17. Scott DW and
    18. Staudt LM
    : A probabilistic classification tool for genetic subtypes of diffuse large B cell lymphoma with therapeutic implications. Cancer Cell 37(4): 551-568.e14, 2020. PMID: 32289277. DOI: 10.1016/j.ccell.2020.03.015
    OpenUrlCrossRefPubMed
  24. ↵
    1. Marcus L,
    2. Murphy R,
    3. Fox E,
    4. McCully C,
    5. Cruz R,
    6. Warren KE,
    7. Meyer T,
    8. McNiff E,
    9. Balis FM and
    10. Widemann BC
    : The plasma and cerebrospinal fluid pharmacokinetics of the platinum analog satraplatin after intravenous administration in non-human primates. Cancer Chemother Pharmacol 69(1): 247-252, 2012. PMID: 21706317. DOI: 10.1007/s00280-011-1659-z
    OpenUrlCrossRefPubMed
  25. ↵
    1. Kalimutho M,
    2. Minutolo A,
    3. Grelli S,
    4. Formosa A,
    5. Sancesario G,
    6. Valentini A,
    7. Federici G and
    8. Bernardini S
    : Satraplatin (JM-216) mediates G2/M cell cycle arrest and potentiates apoptosis via multiple death pathways in colorectal cancer cells thus overcoming platinum chemo-resistance. Cancer Chemother Pharmacol 67(6): 1299-1312, 2011. PMID: 20734047. DOI: 10.1007/s00280-010-1428-4
    OpenUrlCrossRefPubMed
  26. ↵
    1. Al-Sawaf O,
    2. Zhang C,
    3. Tandon M,
    4. Sinha A,
    5. Fink AM,
    6. Robrecht S,
    7. Samoylova O,
    8. Liberati AM,
    9. Pinilla-Ibarz J,
    10. Opat S,
    11. Sivcheva L,
    12. Le Dû K,
    13. Fogliatto LM,
    14. Niemann CU,
    15. Weinkove R,
    16. Robinson S,
    17. Kipps TJ,
    18. Tausch E,
    19. Schary W,
    20. Ritgen M,
    21. Wendtner CM,
    22. Kreuzer KA,
    23. Eichhorst B,
    24. Stilgenbauer S,
    25. Hallek M and
    26. Fischer K
    : Venetoclax plus obinutuzumab versus chlorambucil plus obinutuzumab for previously untreated chronic lymphocytic leukaemia (CLL14): follow-up results from a multicentre, open-label, randomised, phase 3 trial. Lancet Oncol 21(9): 1188-1200, 2020. PMID: 32888452. DOI: 10.1016/S1470-2045(20)30443-5
    OpenUrlCrossRefPubMed
  27. ↵
    1. Bertino JR,
    2. Waud WR,
    3. Parker WB and
    4. Lubin M
    : Targeting tumors that lack methylthioadenosine phosphorylase (MTAP) activity: current strategies. Cancer Biol Ther 11(7): 627-632, 2011. PMID: 21301207. DOI: 10.4161/cbt.11.7.14948
    OpenUrlCrossRefPubMed
  28. ↵
    1. Hamard PJ,
    2. Santiago GE,
    3. Liu F,
    4. Karl DL,
    5. Martinez C,
    6. Man N,
    7. Mookhtiar AK,
    8. Duffort S,
    9. Greenblatt S,
    10. Verdun RE and
    11. Nimer SD
    : PRMT5 regulates DNA repair by controlling the alternative splicing of histone-modifying enzymes. Cell Rep 24(10): 2643-2657, 2018. PMID: 30184499. DOI: 10.1016/j.celrep.2018.08.002
    OpenUrlCrossRefPubMed
  29. ↵
    1. Liu F,
    2. Cheng G,
    3. Hamard PJ,
    4. Greenblatt S,
    5. Wang L,
    6. Man N,
    7. Perna F,
    8. Xu H,
    9. Tadi M,
    10. Luciani L and
    11. Nimer SD
    : Arginine methyltransferase PRMT5 is essential for sustaining normal adult hematopoiesis. J Clin Invest 125(9): 3532-3544, 2015. PMID: 26258414. DOI: 10.1172/JCI81749
    OpenUrlCrossRefPubMed
  30. ↵
    1. Brown F,
    2. Hwang I,
    3. Sloan S,
    4. Hinterschied C,
    5. Helmig-Mason J,
    6. Long M,
    7. Youssef Y,
    8. Chan W,
    9. Prouty A,
    10. Chung J,
    11. Zhang Y,
    12. Chen-Kiang S,
    13. Diliberto M,
    14. Elemento O,
    15. Sehgal L,
    16. Alinari L,
    17. Scherle P,
    18. Vaddi K,
    19. Lapalombella R,
    20. Paik J and
    21. Baiocchi R
    : PRMT5 inhibition restarts a pro-apoptotic program and creates vulnerability to combination treatment with BCL-2 inhibitor venetoclax in mantle cell lymphoma. Hematological Oncology 39(S2), 2022. DOI: 10.1002/hon.143_2880
    OpenUrlCrossRef
  31. ↵
    1. Schwindt H,
    2. Vater I,
    3. Kreuz M,
    4. Montesinos-Rongen M,
    5. Brunn A,
    6. Richter J,
    7. Gesk S,
    8. Ammerpohl O,
    9. Wiestler OD,
    10. Hasenclever D,
    11. Deckert M and
    12. Siebert R
    : Chromosomal imbalances and partial uniparental disomies in primary central nervous system lymphoma. Leukemia 23(10): 1875-1884, 2009. PMID: 19494841. DOI: 10.1038/leu.2009.120
    OpenUrlCrossRefPubMed
  32. ↵
    1. Laharanne E,
    2. Chevret E,
    3. Idrissi Y,
    4. Gentil C,
    5. Longy M,
    6. Ferrer J,
    7. Dubus P,
    8. Jouary T,
    9. Vergier B,
    10. Beylot-Barry M and
    11. Merlio JP
    : CDKN2A-CDKN2B deletion defines an aggressive subset of cutaneous T-cell lymphoma. Mod Pathol 23(4): 547-558, 2010. PMID: 20118908. DOI: 10.1038/modpathol.2009.196
    OpenUrlCrossRefPubMed
Back to top

In this issue

Print
Download PDF
Article Alerts
Email Article
Citation Tools
Reprints and Permissions
Satraplatin Demonstrates High Cytotoxic Activity Against Genetically Defined Lymphoid Malignancies
THILO ZANDER, JIA XUE, GABRIEL MARKSON, FELIX DAHM, CHRISTOPH RENNER
Anticancer Research Apr 2022, 42 (4) 1821-1832; DOI: 10.21873/anticanres.15658
Reddit logo Twitter logo Facebook logo Mendeley logo

Jump to section

Keywords