The evolution to hepta-refractory myeloma involves sequential loss of CD38, BCMA and GPRC5D
Bibliographic record
Abstract
Abstract Introduction: No other hematologic malignancy has seen more drug approvals than multiple myeloma (MM). Yet, MM remains largely incurable, even in the era of T-cell engaging therapies. Patients who have exhausted all treatment options can be considered “hepta-refractory” (i.e., resistant to 2 immunomodulatory drug (IMiD) classes, 2 proteasome inhibitor classes, CD38 antibodies, and both BCMA- and GPRC5D-targeted therapies). These patients are increasingly encountered in clinical practice. Often in good general condition, they actively seek further treatment. Understanding the mechanisms of relapse and resistance is key to guiding future therapy. Here, we investigated the clinical outcomes and underlying genomic landscape of hepta-refractory MM using whole genome sequencing (WGS). Methods: We included 33 patients meeting the criteria for hepta-refractory MM. WGS was performed in 17 patients, including 3 with sequential samples and 8 with extramedullary biopsies. WGS (depth 107x) was performed with purified CD138+ cells on NovaSeq instruments. Variants were called with Strelka2, Manta, GATK4 and HadoopCNV. Immunohistochemical (IHC) staining for BCMA was performed in 12 patients using an anti-BCMA antibody (E6D7B, Cell Signaling; 1:100) . Results: Median progression-free survival (PFS) in subsequent therapy lines and overall survival (OS) in hepta-refractory MM were 3.0 and 11.9 months, respectively. Despite this poor outcome, eight patients experienced a PFS of more than 6 months following various salvage therapies, which suggests underlying biological differences. WGS revealed frequent inactivation of tumor suppressor genes, including biallelic events affecting TP53 (n=6/17, 35%) and CDKN2C (n=6), consistent with a strong enrichment of high-risk disease. According to the latest IMWG consensus, 13/17 (77%) patients met criteria for high-risk MM. Next, we examined drug resistance mechanisms. Alterations in the CRBN pathway (comprising CRBN, CUL4A/B, DDB1, IKZF1/3, and COP9 signalosome) were detected in 12/17 (71%) patients, highlighting a major mechanism of IMiD resistance. CD38, the target of daratumumab or isatuximab, was affected by 4p deletions in 5/17 (29%) patients, including 2 patients with focal biallelic loss. The BCMA-encoding gene TNFRSF17 was altered in 9/17 (53%) patients: monoallelic loss in 2, and biallelic events in 7 patients. GPRC5D loss was also frequent: monoallelic in 2, and biallelic in 6/17 (35%). Notably, 5 patients (29%) showed biallelic loss of both target genes, TNFRSF17 and GPRC5D, severely limiting further immunotherapy options. In all patients with biallelic TNFRSF17 loss, BCMA protein expression was absent by IHC. Conversely, BCMA expression in TNFRSF17 wildtype cases varied from negative to strong, suggesting epigenetic modulation beyond WGS-detectable events. To gain insight into the evolution of ultra-refractory disease, we reconstructed phylogenetic trees for 3 patients with longitudinal samples during T-cell–engaging therapies. All showed branching evolution, consistent with initial deep responses. Of two patients treated with talquetamab, one acquired biallelic GPRC5D events at relapse, having already harbored a TNFRSF17 biallelic event after BCMA-directed therapies. The second developed monoallelic loss of GPRC5D. The third patient, treated sequentially with talquetamab, teclistamab and CAR-T, exhibited pronounced parallel evolution with the emergence of four distinct biallelic GPRC5D events and two independent TNFRSF17 deletions, underscoring the adaptive capacity of late-stage MM. We also explored the utility of WGS in guiding clinical decisions. In 6 patients retreated with BCMA-targeted therapies, 4 had no TNFRSF17 alterations and all responded. Vice versa, two patients with biallelic TNFRSF17 deletions were refractory to salvage BCMA-directed CAR-T therapy. Conclusion: Hepta-refractory MM is marked by extensive genetic alterations associated with high-risk disease and multidrug resistance. WGS proved helpful in selecting further therapies for this ultra-refractory population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".