Santos, David M. Cordas dos

Abstract Accurate prediction of risk of progression from smoldering (SMM) to active multiple myeloma (MM) is paramount to individualized early therapeutic strategies with minimum risk of overtreatment. Current risk stratification models do not account for evolving biomarker trajectories. We assembled the largest cohort to date of 2,270 SMM patients from six international centers with longitudinal clinical and biological data to train and validate the PANGEA 2.0 risk models. Four evolving biomarkers were significantly associated with shorter time-to-progression: M-protein increase ≥0.2g/dL, involved:uninvolved serum free light chain ratio increase ≥20, creatinine increase >25%, and hemoglobin decrease ≥1.5g/dL. PANGEA 2.0 outperforms established models including the 20/2/20 and IMWG models by more accurately predicting progression (C-statistics=0.69–0.84), even without biomarker history (C-statistics=0.69–0.83) or recent bone marrow biopsy. PANGEA 2.0 is an easy-to-use, open-access tool (https://ghobrial.shinyapps.io/pangea_2_calculator) to improve and individualize SMM risk stratification. Validation tools are available to compare PANGEA 2.0 to established models (https://ghobrial.shinyapps.io/pangea_validation).

Introduction Genomic alterations are common in multiple myeloma precursor stages, monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). However, commonly used stratification systems do not include somatic alterations that play a role in the disease. To address this clinical need, we generated a comprehensive genomic map of MM and precursors by harmonizing and analyzing mostly WGS data from 1,030 patients. We used these data to define a simple MM-like score, that is able to discriminate which premalignant patients are at risk to progress. Methods We collected, enriched, and performed WGS of tumor plasma cells and matched normals from 138 participants with MGUS or SMM, including those in low, intermediate, and high-risk groups. We combined our precursor dataset with genomic profiles from MM (n=20) and external sources of 872 individuals with MGUS (n=18), SMM (n=62), and MM (n=792) for a cohort of 1,030 individuals with MM or a precursor condition. In 16 patients, we could perform WGS serially from the bone marrow or circulating tumor cells (n=39) and reassess the presence of mutations. Results To develop a simple score that reflects the similarity of precursor genomes to full-blown multiple myeloma disease, we searched among the 107 common driver events (occurring in >1% of patients) and initiating translocations for ones that are differently altered in precursor conditions versus MM. We found 26 differentially altered events: only MAF and MAFB translocations were enriched in MGUS/SMM whereas 24 events were enriched in MM, including known drivers MYC, KRAS, FAM46C, del(1p) and gain(1q). We then defined a simple “MM-like” score for each tumor as the number of MM-enriched drivers minus the number of MGUS-enriched drivers. As expected, the MM-like score in our cohort increased throughout MM development (MM-like score median of 1 in MGUS, 2 in SMM, and 3 in MM). Even among SMM patients, we found a gradual increase of the MM-like score from low- (median=1), to intermediate (median=2), to high-risk (median=3) groups that match the overall medians in MGUS, SMM and MM (p=2e-5). We tested whether the MM-like score can be used to estimate risk of progression. Among untreated SMM patients characterized with WGS, an MM-like score greater than 2 was associated with a higher likelihood of progression (HR=5.3, P=0.03). Using an independent dataset of 87 patients with SMM, we were able to validate that a MM-like score greater than 2 was indeed associated with a higher risk of progression (HR=3.3, P=5x10-5). 11 patients who did not progress and for whom we performed longitudinal WGS, has stable MM-like scores over time, while three patients clinically progressed and their MM-like score increased at the time of progression (due to newly detected mutations in KRAS and NRAS). For the two remaining patients, their MM-like score increased, and we observed growth of the subclone that acquired the additional MM-like driver. In the first patient with MGUS/low-risk SMM, a novel hyperdiploid clone was detected in addition to a TP53 bi-allelic hit and t(11;14) translocation and was shown to increase in clonal fraction with tumor burden (light chains +104% within three years). In the second non-progressor with an increasing MM-like score, a novel del(1p) was detected, and other subclones in the phylogenetic tree either shrank or were stable over three years. Finally, because the MM-like score can place tumors on a gradual axis of evolution towards a more genomically-mature tumor, we tested whether the MM-like score was also predictive of survival in patients treated with a newly diagnosed MM. To do so, we calculated the MM-like score in patients from the publicly available CoMMpass database. We found that an increased score significantly correlated with shorter progression-free survival (median PFS1: 3 years in high score, versus 5 years in low/intermediate score; p=1e-3) and overall survival (median OS: 4.5 years in high score, versus not reached in low/intermediate score; p=1e-5). Conclusion Overall, we demonstrate that it is possible to stratify precursor stages of MM by discovering drivers, detecting those that are associated with the stage of the disease, and defining a genomically-based score that can be used to assess risk of progression and is easy to implement in the clinic.

Introduction: Recent efforts have shed light on the clinical and laboratory factors associated with toxicity and response to BCMA-directed CAR-T therapy in patients with relapsed/refractory multiple myeloma (RRMM). However, a major limitation of current scoring systems lies in their assessment at time of lymphodepletion. Because they are not determined before apheresis, they are ill-equipped to guide patient selection, anticipate level-of-care, or plan pre-emptive toxicity management strategies. We therefore aimed to characterize early predictors of CAR-T toxicity and response. Study Design: In this multicenter observational study, we examined demographic, clinical, and laboratory data across three distinct timepoints: indication (e.g., treatment decision), apheresis, and pre-lymphodepletion (LD). CRS and ICANS were graded according to ASTCT criteria. Hematotoxicity (ICAHT) was graded using EHA/EBMT criteria. Early infections (day 0-100) were defined based on microbiologic data or as a clinical infection syndrome, and graded per Hill et al, Blood 2018. Kaplan-Meier estimates of progression-free (PFS) and overall survival (OS) were compared by log-rank test. Cox proportional hazard models were applied to explore associations between pre-apheresis risk factors and key toxicity and survival outcomes. The CAR-HEMATOTOX (HT) score - encompassing markers of inflammation (e.g., CRP, ferritin) and marrow reserve (e.g., hemoglobin, ANC, platelet count) - was determined at each timepoint (Rejeski et al, Blood 2021). Results: We included 530 RRMM patients treated with standard-of-care cilta-cel (n=184) or ide-cel (n=346) across nine centers. Median age was 65 (range 31-88), median ECOG was 1 (IQR 0-1), and patients received a median of 5 prior lines of therapy (IQR 4-7). The median time from indication to apheresis was 16 days (IQR 7-52), while the median time from apheresis to infusion was 56 days (IQR 46-69). The proportion of high-risk patients by HT score did not change between the pre-apheresis and LD timepoints (19% vs. 20%, p=0.82). Most patients stayed either low-risk (n=388, 73.2%) or high-risk (n=68, 12.8%). However, 35 patients (6.6%) shifted from high-to-low risk, while 39 patients (7.4%) progressed from low-to-high risk - reflecting their response to bridging therapies. We noted considerable differences in morbidity and mortality based on the pre-apheresis HT score. Compared to their low-risk counterparts (score 0-2, n=427), high-risk patients (score ≥3, n=103) were hospitalized longer (12 vs. 15 days, p=0.009) and required more frequent ICU admissions (6% vs. 12%, p=0.05). They exhibited more severe early cytopenias (G3+: 8% vs. 25%, p<0.001) and late cytopenias (G3+: 7% vs. 21%, p<0.001), resulting in increased use of TPO agonists and stem cell boosts. Grade 3 or higher ICANS was also more common in HThigh patients (3% vs. 11%, p=0.006), as were severe infections (14% vs. 27%, p=0.007). We found increased non-relapsed mortality in HThigh patients (1-yr NRM 4.6% vs. 16.1%, p<0.001), but no significant difference in the occurrence of secondary malignancies (p>0.9). With a median follow-up of 12.4 months, HThigh patients showed inferior PFS (1-yr PFS 29% vs. 63%, p<0.0001) and OS (1-yr OS 49% vs. 83%, p<0.0001). Transitioning from a low-to-high score was linked to poor survival (1-yr PFS 26%), while the shift from high-to-low risk was associated with favorable outcomes (1-yr PFS 51%). In a multivariable Cox regression model of pre-apheresis features, a high HT score (aHR 2.52, p<0.0001), prior anti-BCMA therapy (aHR 1.55, p=0.04), history of extramedullary disease (aHR 1.42, p=0.02), and penta-refractoriness (aHR 1.45, p=0.02) were independently associated with PFS. Notably, the presence of more than 3 of these high-risk features defined an ultra-high-risk cohort of 28 patients (5.3%) with particularly dismal survival (1-year PFS 15%). Conclusions: Leveraging granular pre-apheresis data from a large international cohort, we identify early markers of toxicity and response in CAR-T-treated RRMM patients. The HT score - reflecting baseline inflammation and hematopoietic reserve - identified high-risk patients likely to benefit from pre-emptive toxicity mitigation strategies such as prophylactic stem cell collection. Most importantly, we characterize a small yet ultra-high-risk group of RRMM patients in need of more effective bridging and therapeutic options.