Kinetics of hematologic response in AL amyloidosis: Insights from clinical and cytogenetic subgroup analysis in the daratumumab era.

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INTRODUCTION

The primary goal of treatment in immunoglobulin light chain (AL) amyloidosis is to rapidly eliminate the amyloidogenic plasma cell clone, thereby halting the production of toxic misfolded free light chains (FLCs) that aggregate into insoluble fibrils. Effective management of AL hinges on achieving a rapid and profound reduction in FLCs to prevent further organ injury, complemented by supportive care to minimize treatment‐related toxicities. 1

While fibril‐directed therapies remain investigational without proven benefit in randomized clinical trials yet, 2 , 3 , 4 rapid control of the plasma cell clone remains the only strategy to improve outcomes. The ANDROMEDA trial demonstrated that adding daratumumab to bortezomib‐cyclophosphamide‐dexamethasone (Dara–VCd) increased the rates of very good partial response or better (≥VGPR) from ~50% to ~80%, and improved overall survival (OS) versus VCd alone. 5 , 6 Furthermore, profound hematologic responses (involved FLC [iFLC] ≤ 2 mg/dL or difference between iFLC and uninvolved FLC [dFLC] < 1 mg/dL) correlated with superior outcomes in both pre‐Dara studies and in the ANDROMEDA trial. 7 , 8 , 9 Notably, the median time to hematologic complete response (heme‐CR) was substantially shorter with Dara–VCd versus VCd (60 vs. 85 days, respectively). However, kinetics of FLC reduction and hematologic response with frontline Dara‐based therapy remain poorly characterized. Understanding these dynamics is crucial, as response speed/depth profoundly influences organ recovery and survival. 10 , 11 , 12 , 13 Moreover, emerging therapies enable timely treatment modifications for suboptimal early responses, potentially improving long‐term outcomes. 1

In this study, we examined FLC reduction kinetics and hematologic response during the first 6 months of treatment in patients with newly diagnosed AL amyloidosis who received frontline Dara‐based combination regimens. Specifically, we aimed to evaluate the influence of clone‐specific features, such as light chain isotype, disease burden, and cytogenetic abnormalities detected by fluorescence in situ hybridization (FISH), on response kinetics. We also aimed to identify the predictors of early hematologic response in the context of Dara‐based frontline therapy and the impact of early response on hematologic event‐free survival (heme‐EFS) and OS.

METHODS

Study design and data collection

This multicenter retrospective cohort study was conducted across eight institutions across the United States and Europe (Supporting Information S3: Table 1 ). Each site's institutional review board approved the study, and all methods adhered to the principles outlined in the Declaration of Helsinki. Patients were eligible if they had a newly diagnosed systemic AL amyloidosis confirmed by biopsy, with typing confirmed by mass spectrometry, immunohistochemistry, or immunofluorescence, as per institutional practice, diagnosed before January 2023, with data cutoff being February 2025. Individuals were excluded if Dara was introduced to the VCd or Vd backbone after the first cycle or >4 weeks following the start of treatment.

Disease burden was assessed by baseline dFLC and the percentage of bone marrow plasma cells (BMPCs). Baseline FISH cytogenetics were collected at each institution according to local criteria for distinguishing between positive and negative results. The probes of primary interest included t(11;14), gain(1q), hyperdiploidy, del(13q) or monosomy 13, del(17p), and high‐risk multiple myeloma translocations (t[4;14], t[14;16], or t[14;20]). Due to the small number of patients with either del(17p) or high‐risk translocations, they were combined into one (high‐risk myeloma abnormalities) for analysis. Disease staging followed the European modification of the Mayo 2004 staging system, the Mayo 2012 staging system, and renal staging as outlined by Palladini et al. 14 , 15 , 16 Data on levels of iFLC and uninvolved FLCs were collected at 1, 3, and 6 months, along with hematologic response assessments based on validated criteria at these time points. Patients were monitored in accordance with each institution's standard follow‐up protocols.

Definitions of response evaluation

Hematologic response was evaluated in all individuals with dFLC ≥ 2 mg/dL at baseline. Standard validated criteria governed the assessment of patients with a baseline dFLC greater than 5 mg/dL. 17 , 18 For those with a baseline dFLC ranging from 2 to 5 mg/dL, a low‐dFLC partial response (low‐dFLC‐PR) was defined by a post‐treatment dFLC below 1 mg/dL, without meeting the criteria for a heme‐CR. 19 , 20 , 21 Patients who achieved VGPR, low‐dFLC‐PR, or heme‐CR were collectively classified as “VGPR or better (≥VGPR).” For heme‐EFS, the following outcomes were considered as events: hematologic relapse or progression as per International Society of Amyloidosis criteria, 22 the start of a subsequent line of therapy, or death, whichever occurred first.

The cardiac organ response was assessed per consensus criteria. 17 Recently established graded cardiac response criteria were also implemented. 23 Renal organ response was determined using validated criteria, including graded renal response. 16 , 24 Laboratory measurements for troponins and B‐type natriuretic peptide (BNP)/N‐terminal pro B‐type natriuretic peptide (NT‐proBNP), along with the various troponin assays, were standardized according to the conversion table described by Muchtar et al. 25

Endpoints and statistical analysis

The primary outcomes examined were dFLC and iFLC measurements, as well as hematologic response at 1, 3, and 6 months, categorized according to clone‐specific features including light chain isotype (kappa vs. lambda), disease burden (baseline dFLC and percentage BMPC), and cytogenetics by FISH (t[11;14], gain[1q], hyperdiploidy, del[13q], high‐risk myeloma abnormalities). For assessment of light chain kinetics, data were censored at the initiation of a subsequent line of therapy or autologous stem cell transplantation (ASCT) consolidation to isolate the impact of Dara–VCd/Dara–Vd. A log scale was implemented when plotting dFLC and iFLC values for better visual interpretation. Secondary outcomes comprised organ response rates, heme‐EFS, and OS among patients who experienced early (≤1 month) versus late (>1 but ≤3 months) hematologic response (≥VGPR). Organ responses were captured at 3, 6, and 12 months, as well as at the best response. For missing hematologic responses, the following imputation methods were implemented: (1) a patient with documented ≥VGPR at 1 and 6 months was assumed to also have ≥VGPR at 3 months. (2) Patients with documented <VGPR at 1 and 6 months were assumed to also have <VGPR at 3 months. (3) Patients with documented <VGPR at 3 months were assumed to have <VGPR at 1 month. (4) Patients with documented <VGPR at 6 months were assumed to have <VGPR at preceding time points. (5) Patients with ≥VGPR at 1 month who had not initiated a second‐line treatment by 4 months were assumed to have ≥VGPR at 3 months. (6) Patients with ≥VGPR at 3 months who had not initiated a second‐line treatment by 7 months were assumed to have ≥VGPR at 6 months. Heme‐EFS and OS probabilities were estimated using the Kaplan–Meier method, with group comparisons conducted using the log‐rank test. The median follow‐up time was derived using the reverse Kaplan–Meier approach. 26 The Kruskal–Wallis test was applied to compare continuous variables, while Pearson's χ ² test was used for categorical variables. Statistical significance was defined as a P value less than 0.05. For landmark analysis of heme‐EFS, patients who had deceased or progressed to second‐line therapy prior to that time point were excluded. For landmark analysis of OS, patients who had deceased prior to that time point were excluded. All analyses were performed in R version 4.0.2 (R Core Team, 2020).

RESULTS

Patient characteristics

In this multicenter cohort, 315 consecutive patients with newly diagnosed AL amyloidosis treated with upfront Dara‐based therapy were included (Table 1 ). The median age at diagnosis was 66.8 years (range 23–91), 63.8% of patients were male, and the majority were White (87.3%). Most patients had good performance status, with an Eastern Cooperative Oncology Group (ECOG) score of 0 (34.5%) or 1 (44.7%), and New York Heart Association (NYHA) functional Class I (33.3%) or II (36.0%).

Most patients had lambda light chain involvement (79.0%), with a median baseline iFLC of 25.21 mg/dL (range 1.08–1093.91) and a median dFLC of 23.04 mg/dL (range 0.2–1093.36). Over half of patients (58.5%) had a baseline dFLC above 18 mg/dL at diagnosis. The median percentage of BMPC was 12% (range 0–90), with most having BMPCs of over 10% (69.5%). Cytogenetically, t(11;14) was present in 53.4%, del(13q)/monosomy 13 in 29.1%, gain(1q) in 22.3%, hyperdiploidy in 20.9%, and high‐risk myeloma abnormalities in 9.7%.

The median NT‐proBNP was 2473 pg/mL (range 13–70,000), with 20.9% ≥8500 pg/mL, and the median high‐sensitivity troponin‐T was 52.5 ng/L (range 2–492). Most patients had cardiac involvement (73.3%) and renal involvement (61.9%), and 32.1% had at least three organs involved at diagnosis. Half of the patients presented with advanced disease, specifically modified Mayo 2004 Stage IIIA (29.8%) or IIIB (19.9%).

Patients were treated with either Dara–VCd (92.1%) or Dara–Vd at the investigator's discretion. Fourteen percent underwent subsequent ASCT. The median time from treatment initiation to ASCT was 6.9 months (range 2.6–22.7). At a median follow‐up of 35.3 months (range 4.11–77.32), 27.6% required a subsequent line of therapy. A total of 298 patients (94.6%) were evaluable for hematologic response (Table 2 ). Most achieved heme‐CR (51.5%), low‐dFLC PR (4.4%), or VGPR (24.2%) at the best hematologic response, with PR in 14.0% and no response in 5.8% of patients.

Kinetics of evolution of dFLC and iFLC

We examined dFLC and iFLC levels at the 1‐, 3‐, and 6‐month time points in the overall cohort and then stratified these analyses by (1) light chain isotype, (2) disease burden (based on percentage BMPC and baseline dFLC), and (3) FISH cytogenetic groups (Figure 1 ). The median baseline iFLC in the entire cohort was 25.21 mg/dL (range 1.08–1093.91), which decreased to 4.35 (range 0.19–978), 1.92 (range 0.13–85.88), and 1.21 (range 0.14–62.8) at 1, 3, and 6 months, respectively. The median baseline dFLC was 23.04 mg/dL (range 0.2–1093.36), which decreased to 3.95 (range 0–191.9), 0.9 (range 0–85.48), and 0.46 (range 0–42.91) at the same respective time points.

Light chain isotypes

At baseline, patients with the kappa FLC isotype had numerically higher iFLC and dFLC levels compared to those with lambda, although this was not statistically significant (median iFLC 30.4 vs. 23.1 mg/dL, respectively, P = 0.14; and median dFLC 27.4 vs. 21.3 mg/dL, respectively, P = 0.09; Figure 1A,B ). Notably, patients with the kappa FLC isotype demonstrated a slower reduction in FLCs, resulting in significantly higher iFLC and dFLC levels by 1 month (P < 0.001 and P = 0.0015, respectively). This difference persisted at the 3‐month (P < 0.001 for both) and 6‐month (P < 0.001 for both) time points. By 3 months, 11.3% of patients with either kappa or lambda involvement had deceased or transitioned to second‐line treatment, with rates increasing to 33.9% and 28.3% for kappa versus lambda, respectively, by 6 months.

Disease burden

First, we investigated response kinetics by percentage BMPC at baseline, stratifying patients into three groups (<10%, 10%–20%, and ≥20%). There was a significant stepwise increase in baseline iFLC and dFLC across subgroups with increasing percentages of BMPC (Figure 1A,B ; P < 0.001 for both). The median dFLC in the three subgroups was 14.1 (range 0.46–693.2), 26.4 (range 1.34–948.9), and 40.3 (range 0.2–1088) mg/dL, respectively. These differences persisted at 1 month (P < 0.001 for both iFLC and dFLC) and 3 months (P < 0.001 and P = 0.0029 for iFLC and dFLC, respectively). However, there was no significant difference in iFLC and dFLC across subgroups stratified by percentage BMPC at 6 months. By 3 months, 8.3%, 13.5%, and 12.2% of patients with <10%, 10%–20%, and ≥20% BMPC, respectively, had either deceased or transitioned to second‐line treatment, with rates increasing to 21.1%, 29.4%, and 37.7%, respectively, by 6 months.

Next, we investigated response kinetics by baseline dFLC (≥18 mg/dL vs. <18 mg/dL), which revealed a pattern similar to that of percentage BMPC. Although patients in both subgroups showed a gradual decline over time, the gap in iFLC and dFLC across the two subgroups remained significant at 1 month (P < 0.001 for both iFLC and dFLC) and 3 months (P < 0.001 for both); however, there was no significant difference at the 6‐month mark. By 3 months, 7.7% and 13.7% of patients with <18 and ≥18 mg/dL, respectively, had deceased or transitioned to second‐line treatment, with rates increasing to 20.0% and 34.2%, respectively, by 6 months.

FISH cytogenetic groups

First, we investigated response kinetics in patients with t(11;14), which was the largest cytogenetic subgroup. Compared to patients without t(11;14), those with t(11;14) had significantly lower baseline iFLC (22.8 mg/dL vs. 34.0 mg/dL, P = 0.0046) and dFLC levels (20.4 mg/dL vs. 32.4 mg/dL, P = 0.00042). However, there was no significant difference in iFLC and dFLC at 1, 3, and 6 months among patients with versus without t(11;14). By 3 months, 12.1% and 8.7% of patients with and without t(11;14), respectively, had deceased or transitioned to second‐line treatment, with rates increasing to 26.1% and 32.8%, respectively, by 6 months. In patients with gain(1q), there was a trend toward higher iFLC and dFLC at baseline compared to those without gain(1q) (iFLC: 41 vs. 23 mg/dL, respectively; P = 0.063; dFLC: 36.8 vs. 21.8 mg/dL, respectively; P = 0.062). Furthermore, the dFLC level in patients with gain(1q) was numerically higher at 3 months (2.8 vs. 0.7 mg/dL, respectively; P = 0.096) and statistically significant at 6 months (0.9 vs. 0.2 mg/dL, respectively; P = 0.032).

Evolution of hematologic response

We evaluated the evolution of hematologic response depth during the first 6 months. In the overall study cohort, the proportion of patients achieving ≥VGPR with first‐line treatment increased from 49.8% at 1 month to 66.0% at 3 months and was 62.6% at 6 months. Upon including patients who progressed to second‐line therapy, additional 0.9% of patients achieved ≥VGPR at 1 month, 2.1% at 3 months, and 7.7% at 6 months, resulting in a cumulative ≥VGPR rate of 70.3% at the 6‐month time point (Figure 2A ).

Light chain isotypes

When comparing patients by light chain isotype, those with kappa light chain had significantly lower ≥VGPR rates at 1 month (28.3% vs. 52.0%, P = 0.004), 3 months (48.4% vs. 70.6%, P = 0.002), and 6 months (48.4% vs. 66.7%, P = 0.003) compared to those with lambda light chain, mirroring the trend seen with iFLC/dFLC.

Disease burden

Compared to patients with BMPC ≥ 10%, patients with BMPC < 10% had significantly higher ≥VGPR rates at 1 month (67.5% vs. 35.3%, P < 0.001), 3 months (76.5% vs. 59.9%, P = 0.05), and 6 months (68.9% vs. 59.1%, P = 0.05).

Patients with baseline dFLC ≥ 18 mg/dL similarly experienced significantly lower ≥VGPR rates at 1 month (33.5% vs. 69.1%, P < 0.001), 3 months (55.4% vs. 82.1%, P < 0.001), and 6 months (54.0% vs. 76.2%, P < 0.001) compared to those who had <18 mg/dL at baseline.

FISH cytogenetic groups

None of the cytogenetic subgroups showed a statistically significant impact on ≥VGPR rates at the 1‐, 3‐, and 6‐month time points. At best hematologic response, patients with gain(1q) had a numerically lower ≥VGPR rate (70% vs. 82%, respectively; P = 0.1) as well as heme‐CR rate (37% vs. 52%, respectively; P = 0.1) compared to those without gain(1q).

Factors associated with early ≥VGPR and its impact on organ response

Among the 298 patients evaluable for a hematologic response, 232 (73.7%) were alive at 3 months and had available hematologic response data at the 1‐ and 3‐month time points. Of these, 113 achieved an early ≥VGPR, 47 achieved a late ≥VGPR, and 72 did not achieve VGPR by 3 months. The baseline characteristics of these 3 groups are shown in Supporting Information S4: Table 2 .

We investigated the co‐occurrence and mutual exclusivity among various clone characteristics (Supporting Information S5: Table 3 ) and then their associations with achieving early ≥VGPR (Supporting Information S4: Table 2 and Supporting Information S6: Table 4 ). As expected, baseline dFLC ≥ 18 mg/dL and BMPC ≥ 10% were strongly correlated (adjusted P value < 0.0001). The presence of t(11;14) showed significant mutual exclusivity with other cytogenetic abnormalities, specifically gain(1q) (adjusted P < 0.0001), hyperdiploidy (adjusted P = 0.009), del(13q) (adjusted P = 0.016), and high‐risk cytogenetics (adjusted P = 0.029), while gain(1q) was significantly correlated with del(13q) (adjusted P = 0.005) and with hyperdiploidy (adjusted P = 0.016).

Factors significantly associated with achieving early ≥VGPR in univariate analysis included lambda light chain involvement (85.8% in early ≥VGPR, 76.6% in late ≥VGPR, and 63.4% in no VGPR, P = 0.001), baseline dFLC < 18 mg/dL (54% vs. 25.5% vs. 17.1%, respectively, P < 0.001), and lower baseline BMPC percentage (median 10% vs. 15% vs. 15%; P = 0.01). In a multivariate logistic regression analysis incorporating these three variables (Supporting Information S6: Table 4 ), baseline dFLC < 18 mg/dL (odds ratio [OR] 2.56; P = 0.019) and BMPC < 10% (OR 2.86; P = 0.02) were independently associated with achieving early ≥VGPR, while lambda light chain was no longer significant (OR 2.02; P = 0.14).

Patients with early ≥VGPR were significantly more likely to achieve heme‐CR at any point than those with late ≥VGPR or no VGPR by 3 months (72.7% vs. 52.3% vs. 22.2%, respectively, P < 0.001), and were less likely to require a subsequent line of therapy (16.8% vs. 21.3% vs. 62.5%, respectively, P < 0.001). Comparing organ responses, patients with early heme ≥VGPR showed higher rates of cardiac ≥VGPR compared to those with late heme ≥VGPR or no heme VGPR by 3 months (71% vs. 50% vs. 47.4%, P = 0.03). Renal responses were generally similar between early and late hematologic responders, but significantly higher than those who did not achieve heme ≥VGPR (74.5% vs. 71.4% vs. 37.5%, P = 0.01).

Survival outcomes

At a median follow‐up of 35.3 months (range 4.11–77.32), the 1‐ and 3‐year heme‐EFS rates were 69% and 57%, respectively, and the 1‐ and 3‐year OS were 86% and 78%, respectively (Table 3 , Supporting Information S1: Figure 1 ). OS stratified by Mayo 2004 and Mayo 2012 staging systems, as well as baseline dFLC (<2 mg/dL, 2–5 mg/dL, >5 mg/dL), is shown in Supporting Information S2: Figure 2 . Baseline factors significantly associated with heme‐EFS on univariate analysis were ECOG performance status, NYHA functional class, dFLC ≥ 18 mg/dL, NT‐proBNP ≥ 8500 pg/mL, HS‐Troponin‐T ≥ 50 ng/L, BMPC ≥ 10%, and gain(1q). Factors significantly affecting OS on univariate analysis included ECOG performance status, NYHA functional class, estimated glomerular filtration rate (eGFR) < 50 mL/min, NT‐proBNP ≥ 8500 pg/mL, HS‐Troponin‐T ≥ 50 ng/L, and involvement of three or more organs.

On multivariate Cox analysis for heme‐EFS (Table 4 ), including significant variables on univariate analysis (excluding NYHA and HS‐Troponin‐T, as they are highly correlated with NT‐proBNP), the hazard ratios (HRs) for dFLC ≥ 18 mg/dL, NT‐proBNP ≥ 8500 pg/mL, BMPC ≥ 10%, and gain(1q) were 2.08 (95% CI 1.29–3.36, P = 0.0026), 2.39 (95% CI 1.56–3.67, P < 0.0001), 1.54 (95% CI 0.919–2.59, P = 0.1), and 1.56 (95% CI 1.01–2.41, P = 0.043), respectively. Similarly, on multivariate analysis for OS, the HRs for NT‐proBNP ≥ 8500 pg/mL, eGFR < 50 mL/min, and having three or more organs involved were 7.44 (95% CI 3.49–15.9, P < 0.0001), 1.05 (95% CI 0.54–2.07, P = 0.88), and 1.78 (95% CI 0.589–5.39, P = 0.31), respectively.

Subsequently, we performed landmark analysis at 1 and 3 months to isolate the impact of profound light chain reduction (iFLC < 2 mg/dL and dFLC < 1 mg/dL) on heme‐EFS and OS among patients achieving ≥VGPR. In a 1‐month landmark analysis, patients achieving ≥VGPR with iFLC ≥ 2 mg/dL showed similar heme‐EFS to those with ≥VGPR and iFLC < 2 mg/dL (HR 1.53, P = 0.21). Similarly, patients with ≥VGPR had comparable outcomes regardless of dFLC being <1 or ≥1 mg/dL at 1 month (HR 1.99, P = 0.12). No significant differences in OS were observed between these groups at the 1‐month landmark (Figure 3A,C ).

However, performing a similar analysis at the 3‐month landmark revealed significant differences. Patients with ≥VGPR and iFLC ≥ 2 mg/dL had worse heme‐EFS compared to those with ≥VGPR and iFLC < 2 mg/dL (HR 2.68, P < 0.001). Likewise, patients with ≥VGPR and dFLC ≥ 1 mg/dL had significantly worse heme‐EFS compared to those with ≥VGPR and dFLC < 1 mg/dL (HR 2.78, P = 0.0015). These differences were mirrored by OS, showing worse survival for patients with ≥VGPR and iFLC ≥ 2 mg/dL (HR 2.34, P = 0.03) and for those with ≥VGPR and dFLC ≥ 1 mg/dL (HR 2.79, P = 0.031), compared to their counterparts with lower light chain levels (Figure 3B,D ).

When comparing patients who achieved an early ≥VGPR versus those who attained it later, the early responders demonstrated significantly superior heme‐EFS. Three‐year heme‐EFS rates were 78% (95% CI 70%–86%) for early responders and 58% (95% CI 45%–74%) for late responders (P < 0.001, Figure 3E ). These differences also translated into improved OS, with 3‐year survival rates of 90% (95% CI 84%–96%) and 75% (95% CI 63%–89%), respectively (P = 0.02, Figure 3F ).

DISCUSSION

Our study provides an in‐depth evaluation of hematologic response kinetics in newly diagnosed AL patients treated with frontline Dara‐based regimens. We observed four key findings: First, kappa FLC isotype showed significantly slower reductions in FLC levels compared to lambda. Second, disease burden (BMPC ≥ 10% and baseline dFLC ≥18 mg/dL) significantly predicted early hematologic ≥VGPR likelihood, even with daratumumab‐based therapy. Third, while t(11;14) patients presented with lower baseline iFLC/dFLC levels, no significant kinetic differences emerged later. Patients with gain(1q) showed numerically higher baseline iFLC/dFLC levels and remained an independent predictor of worse heme‐EFS at long‐term follow‐up, as shown in prior studies with Dara‐based regimens. 27 , 28 , 29 Finally, achieving early ≥VGPR within 1 month or a profound light chain reduction (iFLC < 2 and/or dFLC < 1 mg/dL) within 3 months was associated with superior EFS and OS, underscoring the importance of a rapid hematologic response in AL amyloidosis.

Kappa and Lambda FLC isotypes represent distinct entities with differing proteotoxicity, immunoglobulin variable gene usage, organ tropism, and survival. 30 , 31 , 32 , 33 Lambda light chains—particularly IGLV1‐44—show cardiac tropism, IGLV6‐57 skews renal (glomerular) involvement, while kappa AL is relatively enriched for hepatic disease. 31 , 32 , 34 Patients with kappa FLC present with higher serum creatinine levels, elevated amyloid scores, and more frequent concurrent cast nephropathy or light chain deposition disease. 35 Our data set confirmed these patterns, with kappa FLC associated with higher creatinine, elevated alkaline phosphatase, and significantly more liver involvement (Supporting Information S7: Table 5 ). Consistent with prior studies showing higher baseline kappa FLC levels, 32 we observed that the iFLC/dFLC gap between isotypes significantly increased over time at 1, 3, and 6 months, despite low absolute iFLC levels at later time points.

Baseline disease burden remains an important prognostic factor in AL amyloidosis. Higher BMPC percentages or elevated dFLC indicate increased toxicity and organ damage, and is a key component of the Mayo 2012 staging system. 15 Although the advent of Dara initially raised hopes that disease burden might become less influential, 5 , 6 our findings and those of others indicate that both baseline dFLC and BMPC percentages continue to predict the need for second‐line therapy. 36 , 37 Additionally, higher disease burden is an independent predictor of delayed hematologic response. Patients with BMPC ≥ 10% were 1.5 times more likely to require a second‐line therapy by 6 months compared to those with BMPC < 10%. However, with a 3‐year median follow‐up, dFLC > 18 mg/dL or BMPCs > 10% did not significantly predict OS in our cohort, indicating successful salvage by subsequent lines of therapy. Future clinical trials should test T‐cell redirecting immunotherapies such as bispecific antibodies or chimeric antigen receptor T‐cell therapies in patients presenting with high clone burden to potentially further reduce the time to achieve deep hematologic response.

The timing and depth of VGPR are pivotal in AL amyloidosis, as earlier and deeper hematologic responses translate into better organ outcomes. 10 , 11 , 12 , 38 , 39 Studies show that half of the patients who achieve a normal FLC ratio show an organ response by 6 months. 11 Beyond response speed, light chain suppression depth increasingly predicts outcomes. While traditional response criteria emphasize heme‐CR as optimal, 17 emerging evidence indicates that patients achieving a normal FLC ratio or a stringent dFLC response (dFLC < 1 mg/dL) may experience survival rates comparable to heme‐CR. 10 Manwani et al demonstrated in 915 newly diagnosed AL patients that stringent dFLC responses significantly improved OS and time‐to‐next‐treatment compared to dFLC ≥ 1 mg/dL at best response. 40 A complementary study by Muchtar et al. expanded the definition of heme‐CR by highlighting the independent prognostic value of iFLC ≤ 2 mg/dL and dFLC ≤ 1 mg/dL. 7 Milani et al. showed that achieving either iFLC < 2 mg/dL or traditional heme‐CR conferred comparable OS, though heme‐CR uniquely predicted prolonged time‐to‐next‐therapy or death. 8 Additionally, Sarosiek et al found that heme‐CR remained an optimal treatment goal associated with superior survival compared to isolated stringent dFLC response, but iFLC < 2 mg/dL improved survival even among CR patients. 41 Taken together, these studies advocate a revised approach to defining deep hematologic response in AL amyloidosis, integrating stringent quantitative FLC parameters and BM minimal residual disease/serum FLC mass spectrometry, when available. 42 In our cohort, early VGPR achievers showed higher organ response rates at the 3‐year follow‐up. At the 3‐month landmark, deep VGPR (iFLC < 2 mg/dL or dFLC < 1 mg/dL) translated into superior heme‐EFS and OS compared to VGPR with higher light chains. These data further support the use of highly effective clone‐directed therapies in the first‐line setting in AL.

Our retrospective design, sample size constraints, and relatively short follow‐up period represent important limitations. Additionally, we did not exclude patients who met the SLiM‐CRAB criteria for myeloma at baseline. Nevertheless, just ~2% of patients in our data set had BMPCs > 60% and ~13% had sFLC ratio > 100, with the overall distribution of cytogenetic abnormalities consistent with other AL cohorts (e.g., t(11;14) in ~50%).

CONCLUSION

Our multicenter study provides the first detailed evaluation of hematologic response kinetics with frontline Dara‐based therapy. Response trajectories were shaped by light chain isotype, baseline disease burden, and FISH abnormalities such as t(11;14) and gain(1q). Findings underscore the importance of early, profound light chain suppression for optimizing outcomes, in line with the broader literature on AL amyloidosis. 10 , 11 , 12 Evolving treatments including BCL2 inhibition, CAR‐T cells, and bispecific antibodies may deliver even swifter and more complete responses, 43 , 44 improving long‐term outcomes further. Larger retrospective studies with extended follow‐up and prospective trials are needed to clarify whether early therapeutic modification could benefit delayed responders.

AUTHOR CONTRIBUTIONS

Abdul‐Hamid Bazarbachi : Conceptualization; writing—review and editing; writing—original draft; methodology; formal analysis; project administration; data curation; resources. Saurabh Zanwar : Writing—review and editing; project administration; resources; data curation. Ute Hegenbart : Writing—review and editing; project administration; resources; data curation. Despina Trajanova : Writing—review and editing; project administration; resources; data curation. Divaya Bhutani : Writing—review and editing; project administration; resources; data curation. Morie A. Gertz : Writing—review and editing; project administration; resources; data curation. Angela Dispenzieri : Writing—review and editing; project administration; resources; data curation. Shaji Kumar : Writing—review and editing; project administration; resources; data curation. Anita D'Souza : Writing—review and editing; project administration; resources; data curation. Anannya Patwari : Writing—review and editing; project administration; resources; data curation. Andrew Cowan : Writing—review and editing; project administration; resources; data curation. GuiZhen Chen : Writing—review and editing; project administration; resources; data curation. Paolo Milani : Writing—review and editing; project administration; resources; data curation. Giovanni Palladini : Writing—review and editing; project administration; resources; data curation. Vaishali Sanchorawala : Writing—review and editing; project administration; resources; data curation. Geethika Bodanapu : Writing—review and editing; project administration; resources; data curation. Mohamad Mohty : Writing—review and editing; project administration; resources; data curation. Suzanne Lentzsch : Writing—review and editing; project administration; resources; data curation. Stefan O. Schönland : Writing—review and editing; project administration; resources; data curation. Eli Muchtar : Writing—review and editing; project administration; resources. Rajshekhar Chakraborty : Writing—review and editing; writing—original draft; conceptualization; methodology; project administration; resources; data curation; formal analysis.

CONFLICT OF INTEREST STATEMENT

Abdul‐Hamid Bazarbachi: Research funding: ASCO Conquer Cancer Foundation (Young Investigator Award). Saurabh Zanwar: No conflicts of interest. Ute Hegenbart: No conflicts of interest. Despina Trajanova: No conflicts of interest. Divaya Bhutani: No conflicts of interest. Morie A. Gertz: Honoraria: Alnylam, Aptitude Health, Ashfield, Celgene, Ionis/Akcea, Janssen, Johnson & Johnson, Juno, Physicians' Education Resource, Prothena, Research To Practice, Sanofi, and Sorrento. Consulting or advisory role: Alnylam, Aptitude Health, Ashfield, Celgene, Ionis/Akcea, Janssen, Johnson & Johnson, Juno, Physicians' Education Resource, Prothena, Research To Practice, Sanofi, and Sorrento. Other relationships: AbbVie (Data Safety Monitoring Board), i3Health (educational material development). Angela Dispenzieri: Research funding (institutional): AbbVie, Caelum, Janssen, Novartis, Prothena, Sanofi, Takeda, and TeneoBio. Consulting or advisory role: Prothena and Janssen. Honoraria: BMS, Janssen, and Pfizer. Shaji Kumar: Research funding (institutional): Celgene, Adaptive Biotechnologies, Sanofi, AbbVie, Takeda, Janssen, Kite, Merck, MedImmune/AstraZeneca, Novartis, and Roche. Consulting or advisory role: AbbVie, Adaptive Biotechnologies, Celgene, Janssen, Kite, MedImmune/AstraZeneca, and Takeda. Other relationships: Oncopeptides (Independent Review Committee). Anita D'Souza: Research funding (institutional): AbbVie, Caelum, Janssen, Novartis, Prothena, Sanofi, Takeda, and TeneoBio. Consulting or advisory role: Prothena and Janssen. Honoraria: BMS and Pfizer. Anannya Patwari: No conflicts of interest. Andrew Cowan: Research funding (institutional): Adaptive Biotechnologies, Harpoon, Nektar, BMS, Janssen, Sanofi, and AbbVie. Consulting or advisory role: BMS and Adaptive Biotechnologies. GuiZhen Chen: No conflicts of interest. Paolo Milani: Honoraria: Pfizer and Janssen‐Cilag. Consulting or advisory role: Siemens and Janssen‐Cilag. Giovanni Palladini: Honoraria: Pfizer, Sebia, Siemens, The Binding Site, Janssen, Protego, and Zentalis. Consulting or advisory role: Janssen, Protego, and Zentalis. Vaishali Sanchorawala: Research funding (institutional): Caelum, Celgene, Janssen, Karyopharm, Millennium‐Takeda, Oncopeptides, Prothena, Sanofi, and Sorrento. Consulting or advisory role: Attralus, Pfizer, Janssen, AbbVie, Proclara, Protego, PharmaTrace, Regeneron, and Telix. Geethika Bodanapu: No conflicts of interest. Mohamad Mohty: Employment: Assistance Publique – Hôpitaux de Paris, Université Sorbonne Paris Cité. Consulting or advisory role: Adaptive Biotechnologies, Amgen, Astellas Pharma, Bristol‐Myers Squibb, GlaxoSmithKline, Janssen Cilag EMEA, Jazz Pharmaceuticals, MEDAC PHARMA, INC., Novartis, OncoPep, Inc., Pfizer, Sanofi, Stemline Therapeutics Inc., Takeda Oncology, and Therakos. Research funding (institutional): Janssen Cilag EMEA, Jazz Pharmaceuticals, and Sanofi. Other relationships: EBMT, Intergroupe Francophone du Myélome, International Academy for Clinical Hematology, and International Myeloma Society (board member; volunteer, no compensation); Springer (journal editor honoraria). Suzanne Lentzsch: Research funding (institutional): Celgene, Sanofi, and Zentalis. Consulting or advisory role: Adaptive Biotechnologies, Alexion, BMS, Caelum, GSK, Janssen, Karyopharm, Oncopeptides, AbbVie, Pfizer, and Takeda. Honoraria: Clinical Care Options and Regeneron. Patents, royalties, other intellectual property: CAEL‐101 (Royalties paid to Columbia University). Stefan O. Schönland: Research funding (institutional): Janssen, Prothena, and Sanofi. Consulting or advisory role: Janssen, Prothena, and Telix. Honoraria: Pfizer and Takeda. Travel, accommodations, expenses: The Binding Site, Celgene, and Jazz. Eli Muchtar: Consulting or advisory role: Protego. Rajshekhar Chakraborty: Consulting or advisory role: Janssen, Sanofi, and Adaptive Biotechnologies. Research funding: Janssen, BMS, Juno/Celgene, Sanofi, Regeneron, IGM BIosciences, Nektar, Harpoon, Caelum, Opna Bio, and Karyopharm. Advisory/consulting: Sebia, Janssen, BMS, Sanofi, HopeAI, Adaptive Biotechnologies, AbbVie, and Kite/Arcellx.

ETHICS STATEMENT

This study was conducted in accordance with the ethical principles set forth in the Declaration of Helsinki and was approved by our institutional review board (IRB). As this is a retrospective analysis of previously collected data, the requirement for informed consent was waived by the IRB. All patient data were anonymized to maintain confidentiality and privacy. The study protocol ensured that no identifiable information was accessible to the researchers during data analysis. We adhered to all applicable data protection and privacy regulations throughout the study.

FUNDING

No external funding supported this study.

Supporting information

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ACKNOWLEDGMENTS

The authors acknowledge patients and caregivers.

DATA AVAILABILITY STATEMENT

The data sets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Due to the nature of this research and the need to protect patient privacy, data access is restricted and will be granted following review and approval by our IRB. Additionally, any requests for data sharing must comply with applicable data protection and privacy regulations.

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Associated Data

Supplementary Materials

Supporting Information.

Supporting Information.

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Supporting Information.

Supporting Information.

Supporting Information.

Data Availability Statement

The data sets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Due to the nature of this research and the need to protect patient privacy, data access is restricted and will be granted following review and approval by our IRB. Additionally, any requests for data sharing must comply with applicable data protection and privacy regulations.