Genomic Determinants of Resistance to Anti-BCMA Chimeric Antigen Receptor T-Cell (CART) Therapies in Patients with Relapsed/Refractory Multiple Myeloma
Bibliographic record
Abstract
The introduction of chimeric antigen receptor T cells (CART) and bispecific T-cell engagers (TCE) has revolutionized the treatment landscape in patients with relapsed/ refractory multiple myeloma (RRMM). However, despite impressive responses reported to date, the mechanisms responsible for resistance or treatment failure remain inadequately determined. To investigate the genomic mechanisms involved in primary refractoriness and resistance to anti-BCMA immunotherapies we interrogated 122 whole genomes (WGS; 80X median coverage) and 10 whole exomes (WES) generated from a total of 96 patients treated with either CART (n=74) or T-cell engagers (TCE, n=22). 74 and 13 patients had samples collected before treatment with CART (idecel n=58; ciltacel n=16) and TCE, respectively. Patients treated with CART had a median progression-free survival (PFS) of 394 days, with 19 (25%) patients progressing within the first 100 days (i.e. refractory). The presence of pre-treatment extramedullary disease (EMD, 12%) and prior anti-BCMA exposure (20%) was associated with inferior progression free survival (PFS) (both p<0.0001). The MyCARe score high-risk patients (n=3, 4%) in this cohort had poor outcomes ; however, it failed to discriminate between low (n=21, 37.5%) and intermediate risk (n=32, 57%) (p=0.10). Loss of TNFRSF17 was observed in 5/96 (5%) patients, 4 of whom were treated with CART. Of these, 3 had previously been exposed to anti-BCMA therapies, such as belantamab mafodotin (n=2), and these genomic events were present before CART treatment, causing complete refractoriness to CART. Interestingly, all patients with biallelic loss of BCMA were also noted to have CYLD or TRAF3 biallelic loss, key regulator of NFkB signaling. We hypothesize that as BCMA is a driver of NFkB activation in MM cells and that only in the presence of genomic alterations involving NFkB, can this absence of BCMA be tolerated by the tumor cell, promoting resistance to CART. Next, we investigated what other alternations in pre-CART samples associate with inferior PFS and treatment refractory disease. Among known high-risk features 1q gain was significantly associated with inferior PFS. In investigating a large catalogue of driver genes, we identified multiple genomic drivers involved in resistance and primary refractoriness to anti-BCMA CAR-T. These drivers can be categorized into five major groups: one associated with favorable PFS and four associated with unfavorable PFS. The favorable group included patients with RPL10 mutations (84% patients in remission at 1 year). The second group included loss of genes involved in genomic instability and complexity such as RPL5, TP53, CDKN2C and presence of hyper-APOBEC. The third group included genes involved in the NFkB signaling (CYLD, TRAF3, NFKB2, MAP3K14). The fourth group included loss of function events involving transcription factors and regulators (e.g. SP140, KMT2C, DIS3). The last group had genomic events known to be involved in plasma cell differentiation (e.g. IKFZ3, CD38, XBP1, TNFRSF17). Overall, patients with genomic events from any two of the unfavorable groups (n=32) had significantly worse outcomes compared with the other patients (median PFS 75 vs 763 days, p<0.0001), accounting for 84% of all refractory patients. By employing a Cox proportional-hazards model, we demonstrated that these genomic features independently and more accurately predict refractoriness to anti-BCMA CAR-T therapy [p<0.0001; Hazard ratio (HR): 5.5497] compared to traditional risk scores like EMD (p=0.59, HR: 0.5945) and MyCARe (p=0.03, HR: 0.1694). Comparing WGS data from samples collected at the time of progression after CART (n=12) and post-TCE (n=9) patients, only one BCMA mutation (P33S) was observed after CART, and its impact on CART binding was not confirmed in functional studies. This is different from TCE where these mutations and antigen escape account for >50% of relapse (5/9; Lee et al. Nat Med 2023). Furthermore, it supports the hypothesis that the high prevalence of BCMA mutations seen post-TCE is a consequence of continuous selective pressure by TCE-based therapies. Overall, these data suggest that comprehensive genomic profiling can accurately predict clinical outcomes in MM patients treated with anti-BCMA CART outperforming current clinical predictors of risk and potentially serving as tool to select different treatment strategies.
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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 | 0.001 |
| 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) | 0.001 | 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".