RNA-Sequencing Of Paired Pre-Treatment and Relapse Samples Reveals Differentially Expressed Genes Associated With Lenalidomide Resistance In Multiple Myeloma
Bibliographic record
Abstract
Abstract Background Immunomodulatory (IMiDs) drugs’ cytotoxicity in MM cells is mediated through their binding to cereblon (CRBN) an adaptor protein of the Cul4a-DDB1-ROC1 ubiquitin E3 ligase complex. Loss of CRBN is clearly associated with resistance to IMiDs (Zhu et al. Blood. 2011) however this does not appear to be the sole mechanism through which MM cells may acquire resistance to this class of drugs. In addition, the acquisition of CRBN mutations within the Thalidomide binding domain (exons 10-11) have not been well studied. Similarly and while splice variants of CRBN were recently reported (Ghandi et al. Blood. 2012), no correlations has been made between the presence of these isoforms and IMiDs resistance in primary samples. Methods and Results In this study, we first validated CRBN as biomarker of clinical response to lenalidomide. At the mRNA level, using qRT-PCR (n=26, amplicons with 2 sets of primers overlapping exons 8-9 and exons 10-11) low CRBN in CD138 sorted bone plasma cells was significantly associated with shorter PFS (p=0.008) and lack of response to lenalidomide. We next compared CRBN mRNA (qRT-PCR) expression in paired samples (n=17 patients - 34 pairs) collected immediately pre-treatment and at the time of disease progression post-lenalidomide. In 10/17 (58.8%), a significant reduction (2-ΔΔCT < 0.8) in the CRBN amplicon levels was observed between the paired pre- and post-treatment samples. These data suggest that in nearly half of the patients, CRBN-independent mechanisms may be mediating resistance to IMiDs. In order to elucidate these CRBN-independent mechanisms of resistance to lenalidomide and identify potential targets that may overcome it, RNA-seq analysis was performed in 12 paired patients samples obtained sequentially from the same patient prior to lenalidomide treatment initiation and after development of resistance. Transcriptome sequence data was generated by RNA-seq performed for each sample on Ion Torrent Proton sequencer with a minimum of 70 million reads per sample. Poly(A) is captured with poly(T) magnetic beads, fragmented and copied to cDNA libraries with reverse transcriptase and primers. Filtered Fastq files are processed with TopHat-Fusion alignment against hg19 as reference genome. Cufflinks and Cuffdiff algorithms were used to detect differentially expressed transcripts and spliced isoforms. A total of 830 genes were identified as differentially expressed (p value and FDR <0.05) between the pre- and post-lenalidomide paired samples. Selected differentially expressed genes were also measured by RT-PCR analysis to confirm the validity of the RNA-seq analysis. In order to gain insight into the cellular and molecular functions of this identified gene set, we compared it to the expression of 395 gene sets curated in the Molecular Signatures Database (MSigDB), using the Gene set enrichment analysis (GSEA) algorithm. Among the most enriched gene sets in this analysis were KRAS.KIDNEY_UP.V1_UP; KRAS.LUNG_UP.V1_UP; KRAS.600.LUNG.BREAST_UP.V1_UP; RAF_UP.V1_DN; MEK_UP.V1_DN; and STK33_DN suggesting a role for the RAS-RAF-MAPK pathway in the acquired resistance to IMiDs. Other gene sets of interest included AKT_UP_MTOR_DN.V1_DN, E2F3_UP.V1_UP; EIF4E_UP and RPS14_DN.V1_DN; consistent with a role for the translational machinery activity (mTOR-4EBP1-eIF4E pathway) in IMiDs resistance. Of interest variant splice isoforms of CRBN were visualized using the Integrative Genomics Viewer (IGV) tool including isoforms lacking exon 10, which contains a portion of the IMiD-binding domain. However these CRBN splice variants are unlikely to be implicated in resistance to IMiDs since they were not enriched in the relapsed paired samples. Conclusions Study of the transcriptome of paired pre- and post-IMIDs in myeloma primary cells confirms that acquired resistance to this class of drugs is associated with the direct loss of CRBN as well as through the modulation of other CRBN-independent pathways. Disclosures: No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".