Предсказательная и прогнозирующая значимость результатов лечения пациентов в Даратумумаб-Трейд Множественной миеломы.

Introduction

Multiple myeloma (MM) is a hematological malignancy characterized by the proliferation of plasma cells in the bone marrow [ 1 ]. Despite advancements in treatment, MM remains incurable, with a 5‐year survival rate of around 60% [ 2 ], underscoring the need for more effective treatment strategies that improve survival and quality of life.

Recent years have witnessed a transformation in MM treatment with the introduction of novel agents such as daratumumab, bortezomib, and lenalidomide. These therapies have shown significant efficacy in clinical trials and have been incorporated into combination regimens [ 3 , 4 ]. Notably, daratumumab, a humanized monoclonal antibody targeting the CD38 antigen on myeloma cells, has shown significant efficacy in both newly diagnosed and relapsed/refractory MM patients [ 3 ].

Although these therapeutic advancements are crucial, optimizing treatment strategies requires a more holistic approach that accounts for patient‐reported perspectives. A critical aspect of this approach is the integration of patient‐reported outcomes (PROs) [ 2 , 5 ] in patient care. PROs are patient self‐reported measures capturing diverse dimensions of a patient's well‐being, including social, emotional, physical, cognitive, and disease‐specific factors [ 2 , 6 ]. PROs are extensively employed in hematology trials to evaluate the quality of life in patients receiving cancer treatment, offering valuable insights into the patient experience that complement conventional clinical metrics [ 2 , 6 ].

Recent studies have demonstrated the prognostic utility of PROs for predicting treatment outcomes across various cancers, including advanced HER2‐positive breast cancer, urothelial cancer, and metastatic lung cancer [ 7 , 8 , 9 ]. PROs may therefore offer vital patient‐centered data to inform treatment selection and outcome evaluations. Among the various PRO domains, physical function has garnered particular attention because of its association with overall mortality and treatment outcomes in cancer populations, including advanced solid tumors [ 10 , 11 , 12 ]. Physical function reflects patients' capacity to perform daily activities and endure cancer treatments, which is particularly relevant for MM, a disease that predominantly affects older adults [ 13 , 14 , 15 , 16 ].

Moreover, emerging evidence suggests patient‐reported physical function is an independent prognostic factor for survival and grade ≥ 3 adverse events across multiple cancer types [ 7 , 8 , 9 , 17 , 18 , 19 ]. Studies comparing physical function to Eastern Cooperative Oncology Group Performance Status (ECOG‐PS), a clinician‐assessed performance metric, have shown that physical function offers additional prognostic information [ 7 , 11 ]. Although the ECOG‐PS is commonly utilized to assess patient eligibility for clinical trials and as a treatment effect discriminator [ 20 ], physical function, with its patient‐centered focus, may also serve as a valuable treatment effect modifier to inform personalized therapeutic decisions [ 18 ], yet it is not routinely investigated in such a fashion.

Thus, this study aims to assess the prognostic significance of PROs for survival and adverse outcomes in patients with MM undergoing contemporary multi‐drug immunomodulatory combinations and to compare patient‐reported physical function with ECOG‐PS in terms of their prognostic and predictive value for treatment effect modification with daratumumab‐based regimens.

Methods

Patient Population

Individual patient data from three open‐label, randomized trials were pooled for this analysis: MAIA ( NCT02252172 , data cut‐off: February 19, 2021) [ 21 ], POLLUX ( NCT02076009 , data cut‐off: March 7, 2016) [ 22 ], and CASTOR ( NCT02136134 , data cut‐off: January 11, 2016) [ 23 ]. All trials enrolled patients who were 18 years of age or older. The POLLUX and CASTOR trials evaluated the effectiveness of daratumumab in patients with relapsed or refractory MM who had undergone at least one previous therapy. The MAIA trial, on the other hand, focused on newly diagnosed MM patients who were deemed ineligible for high‐dose chemotherapy or autologous stem cell transplantation because of their age (≥ 65 years) or the presence of comorbidities that could lead to severe side effects.

In the MAIA and POLLUX trials, treatment cycles of 28 days continued until disease progression, an unacceptable level of toxic events, withdrawal of consent, or death. Patients in the daratumumab arms received intravenous daratumumab at a dose of 16 mg per kilogram of body weight once weekly during Cycles 1 and 2, every 2 weeks during Cycles 3 through 6, and then every 4 weeks thereafter. In both arms, patients received oral lenalidomide at a dose of 25 mg on Days 1 through 21 of each cycle if the creatinine clearance was more than 60 mL per minute. For patients with a creatinine clearance between 30 and 50 mL per minute, a reduced dose of lenalidomide of 10 mg was recommended. Oral dexamethasone at a dose of 40 mg was administered weekly. In the CASTOR trial, all patients received up to 8 cycles (21 days per cycle) of bortezomib and dexamethasone. In the daratumumab group, daratumumab was administered intravenously at a dose of 16 mg per kilogram once per week (Days 1, 8, and 15) during Cycles 1–3, once every 3 weeks (on Day 1) during Cycles 4–8, and once every 4 weeks thereafter until the patient withdrew consent, the disease progressed, or unacceptable toxic effects developed. Bortezomib was administered subcutaneously at a dose of 1.3 mg per square meter on Days 1, 4, 8, and 11 of Cycles 1–8, and dexamethasone was administered orally or intravenously at a dose of 20 mg on Days 1, 2, 4, 5, 8, 9, 11, and 12, for a total dose of 160 mg per cycle.

All trials adhered to the International Conference on Harmonisation Good Clinical Practice guidelines and the Declaration of Helsinki. Participants gave their written informed consent. The secondary analysis of anonymized data reported in this study was deemed low‐risk research and received approval from the University of Sharjah Ethics Committee (approval reference number: REC‐23‐11‐07‐01‐F). Data were accessed in accordance with the Johnson & Johnson policy and were made available through Vivli Inc. ( www.vivli.org ).

Predictors and Outcomes

The primary predictors investigated in this study were the patients' pretreatment PRO measures concerning their symptoms, functioning, financial difficulties, and overall global health status. Pretreatment PROs were collected using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ‐C30) version 3.0. This questionnaire facilitates the assessment of symptom‐related scales (pain, fatigue, insomnia, diarrhea, constipation, appetite loss, dyspnea, nausea and vomiting), functional scales (emotional, physical, role, social, and cognitive functioning), financial difficulties, and global health status [ 24 ]. For functional scales, higher scores denote a more positive perception of functioning, whereas for symptom‐related scales, higher scores indicate a more negative perception of symptoms [ 24 ].

The primary assessed outcome was the overall survival (OS) with progression‐free survival (PFS) and grade ≥ 3 adverse events evaluated as secondary outcomes. Additionally, an exploratory analysis was conducted to stratify grade ≥ 3 adverse events into hematologic (including blood and lymphatic system disorders such as anemia, neutropenia, thrombocytopenia, lymphopenia, and leukopenia) and nonhematologic events. Across all trials, OS was consistently defined as the duration from randomization to death from any cause, whereas PFS was defined as the duration from randomization to either disease progression or death from any cause. Adverse events were documented in accordance with the National Cancer Institute's Common Terminology Criteria for Adverse Events (NCI CTCAE) version 4.0 [ 25 ].

Statistical Analysis

The analysis was conducted in two phases. In the first phase, a broad evaluation was conducted to assess the prognostic association of all PRO domains with survival and adverse outcomes in patients with MM. The second phase of the analysis focused on a more detailed comparison of the prognostic and predictive roles of patient‐reported physical function versus ECOG‐PS.

Cox proportional hazard modeling was utilized to examine the prognostic association between pretreatment PROs and OS, PFS, and grade ≥ 3 adverse events. Each analysis was stratified by the study and treatment arm to mitigate trial‐to‐trial variability. The associations were expressed as hazard ratios (HRs) along with their 95% confidence intervals (95% CIs). The threshold for statistical significance was established at p < 0.05. Analyses were performed on complete cases. The c ‐statistic was used to evaluate discrimination performance. The Akaike information criterion and visual inspections were employed to check for potential nonlinear effects of continuous variables. Univariable and multivariable analyses, adjusted for age, sex, weight, MM international staging system (ISS) stage, ECOG‐PS score, and comorbidity count, were conducted. In the second phase, the predictive and prognostic performance of patient‐reported physical function compared with ECOG‐PS was evaluated for key outcomes, including OS, PFS, and grade ≥ 3 adverse events. Discrepancies between ECOG‐PS and patient‐reported physical function were further examined using descriptive statistics to assess alignment or divergence in patient status as reported by clinicians versus patients. Additionally, interaction terms were incorporated to assess ECOG‐PS and physical function as treatment effect modifiers in daratumumab versus non‐daratumumab treatment arms.

The Kaplan–Meier method was used to graphically depict the association between PROs and survival/toxicity outcomes. For plotting, PROs were categorized as “low” and “high” based on the 50th percentile of each subscale in the study population. We also explored alternative stratifications (25th and 75th percentiles), but these thresholds did not provide sufficient discrimination between groups. Forest plots were utilized to visually represent the variability between PROs and survival/toxicity outcomes across different studies and treatment arms. All analyses were performed using R version 4.0.

Results

Patient Population

In the pooled cohort of 1804 patients, 654 (36%) and 251 (14%) patients were randomized to the DRd (daratumumab, lenalidomide, and dexamethasone) or DVd (daratumumab, bortezomib, and dexamethasone) arms, respectively, whereas 652 (36%) and 247 (14%) patients were randomized to the Rd (lenalidomide and dexamethasone) or Vd (bortezomib and dexamethasone) arms, respectively, as summarized in Table S1 . A total of 1469 patients (81%) experienced grade ≥ 3 adverse events. Regarding pretreatment PRO values, 268 (15%) patients had missing data across all values (Table S2 ). All pretreatment PRO values significantly differed in distribution between clinical trials apart from diarrhea and cognitive function. Patient‐reported physical function measures were available for 1535 (85%) patients, with 842 (47%) patients reporting “high” physical function and 693 (38%) reporting “low” physical function (Table S3 ). Clinician‐assessed ECOG‐PS was recorded for 1803 (99%) patients, with 761 (42%) patients classified with an ECOG‐PS score of 0, and 884 (58%) patients with an ECOG‐PS score of ≥ 1 (Tables S3 and S4 ). The median OS follow‐up time was 56.2 months for MAIA, 7.43 months for CASTOR, and 13.5 months for the POLLUX trial.

Prognostic Associations of PROs With Treatment Outcomes

Table 1 presents the univariate analysis of continuous PRO associations with OS, PFS, and grade ≥ 3 adverse events, expressed per 10‐unit increase in PRO scores. In the pooled cohort, several PROs demonstrated significant associations with OS, including physical function, global health status, fatigue, pain, social function, cognitive function, role function, appetite loss, emotional function, constipation, nausea and vomiting, insomnia, and dyspnea ( p < 0.05). Similar associations were observed with PFS and grade ≥ 3 adverse events, except for dyspnea lacking statistical significance for PFS, whereas insomnia and emotional function lacked statistical significance for grade ≥ 3 adverse events.

Among the assessed PROs, physical function (OS c = 0.6, PFS c = 0.56, grade ≥ 3 c = 0.55), global health status (OS c = 0.6, PFS c = 0.56, grade ≥ 3 c = 0.54), and fatigue (OS c = 0.6, PFS c = 0.57, grade ≥ 3 c = 0.55) were the top three most prognostic PROs for OS, PFS, and grade ≥ 3 adverse events (Table 1 ). Figure 1 presents the Kaplan–Meier estimates of OS, PFS, and grade ≥ 3 adverse events by physical function, stratified by study. Kaplan–Meier estimates for OS, PFS, and grade ≥ 3 adverse events by global health status and fatigue are shown in Figures S1 and S2 , respectively. Table S6 presents the adjusted analysis of continuous PRO associations with OS, PFS, and grade ≥ 3 adverse events, expressed per 10‐unit increase in PRO scores.

Comparison of Patient‐Reported Physical Function Versus ECOG‐PS

In the univariable analysis, both patient‐reported physical function and ECOG‐PS were significantly associated with OS and PFS (Table 2 ). The predictive performance ( c ‐statistic) of both patient‐reported physical function (low vs. high) and ECOG‐PS (1 or 2 vs. 0) was 0.58 ( p < 0.001) and 0.54 ( p < 0.001) for OS and PFS, respectively. In the multivariable analysis, both patient‐reported physical function and ECOG‐PS remained statistically significant prognostic factors for OS ( p < 0.001) and PFS ( p < 0.05), indicating that these measures provide independent prognostic information (Table 2 ). Similarly, both patient‐reported physical function and ECOG‐PS were significantly associated with grade ≥ 3 adverse events in both the univariable analysis ( p < 0.001) and multivariable analysis ( p < 0.05), demonstrating their independent prognostic value (Table 2 ).

Upon stratifying grade ≥ 3 adverse events into hematologic and nonhematologic categories (Table S7 ), both physical function and ECOG‐PS were significantly associated with nonhematologic grade ≥ 3 adverse events in the univariable analysis ( p < 0.001) and remained independent prognostic factors in the adjusted analysis ( p < 0.05). However, no statistically significant associations were observed between physical function or ECOG‐PS and hematologic grade ≥ 3 adverse events.

Interestingly, from the 761 (42%) patients with a clinician‐assessed ECOG‐PS score of 0, 140 (18%) patients reported low physical function, with 55 (7%) reporting trouble doing strenuous activities, 44 (14%) reporting trouble taking a long walk, 11 (11%) reporting trouble taking a short walk or staying in a bed or chair during the day, and 3 (< 1%) reporting needing help eating, dressing, or washing (Table S3 ).

To further clarify the differences between the high and low physical functioning groups, Table S5 presents a comprehensive breakdown of physical function impairments across both groups. As presented in the table, 688 (99%) of the patients in the low functioning group reported at least one severe impairment (i.e., score of 3 = “quite a bit” or score of 4 = “very much”), compared with only 192 (23%) patients in the high‐functioning group.

Physical Function Versus ECOG for Daratumumab Efficacy

In the randomized arms of the pooled trials (daratumumab vs. therapies without daratumumab), patient‐reported physical function was predictive of OS treatment benefit from daratumumab ( p interaction = 0.020; Figure 2 ). Specifically, in patients reporting low physical function, a statistically significant improvement in OS was observed for the daratumumab‐treatment arms (vs. therapies without daratumumab) (adjusted HR [aHR] [95% CI] = 0.53 [0.40–0.70], p < 0.001). Conversely, patients reporting high‐physical function exhibited less OS benefit from daratumumab (vs. therapies without daratumumab) (aHR [95% CI] = 0.86 [0.62–1.19], p = 0.364). Kaplan–Meier estimates of OS efficacy of daratumumab (vs. therapies without daratumumab), subgrouped by low‐ and high‐physical function, are presented in Figure 3 . In contrast, ECOG‐PS was not predictive of OS treatment efficacy ( p interaction = 0.598; Figure S3 ). Figure S4 presents the Kaplan–Meier estimates of the OS efficacy of daratumumab (vs. therapies without daratumumab), subgrouped by ECOG‐PS 1 or 2 versus 0.

Regarding PFS, patient‐reported physical function was significantly associated with a change in the magnitude of daratumumab treatment benefit ( p interaction = 0.025; Figure 4 ). Specifically, the low physical function cohort demonstrated a greater treatment benefit (aHR [95% CI] = 0.34 [0.22–0.53], p < 0.001) compared with the high‐physical function group (aHR [95% CI] = 0.53 [0.42–0.67], p = 0.034), relative to daratumumab (vs. therapies without daratumumab). This suggests that patients with low physical function may derive more substantial gains from daratumumab treatment. Conversely, ECOG‐PS was not predictive of PFS efficacy for the daratumumab treatment arms ( p interaction = 0.166; Figure S5 ). Kaplan–Meier estimates of the PFS efficacy of daratumumab (vs. therapies without daratumumab), subgrouped by low‐ and high‐function, are presented in Figure 3 . Figure S6 presents the Kaplan–Meier estimates of the PFS efficacy of daratumumab (vs. therapies without daratumumab), sub‐grouped by ECOG‐PS 1 or 2 versus 0.

Discussion

In this analysis of data pooled from rigorously conducted MM clinical trials, it was demonstrated for the first time that pretreatment PROs serve as independent prognostic markers for survival outcomes and grade ≥ 3 adverse effects in patients diagnosed with MM initiating contemporary immunomodulatory regimens, including daratumumab. Among the PROs evaluated, physical function provided independent prognostic value beyond the clinician‐defined ECOG‐PS. Moreover, physical function emerged as a predictive factor for treatment outcomes, with patients reporting low physical function demonstrating greater OS and PFS benefits with daratumumab (vs. therapies without daratumumab) compared with those reporting high‐physical function.

A growing body of evidence highlights the importance of integrating PRO assessments in oncology to enhance symptom management, improve patients' quality of life, facilitate patient‐clinician communication, increase patient satisfaction, and optimize survival outcomes [ 6 , 18 , 26 , 27 ]. In concordance with our findings, various studies across different cancers [ 27 , 28 ] such as breast cancer [ 9 , 18 ], urothelial carcinoma [ 8 ], and lung cancer [ 7 , 11 , 12 ] have identified PROs as independent prognostic factors for OS, PFS, or grade ≥ 3 adverse events. Additionally, a meta‐analysis including 138 studies and 158 127 solid and hematological cancer patients revealed that 87% of studies reported at least one PRO to be significantly prognostic for OS [ 27 ]. Despite this growing recognition, the use of PROs in MM, which has a high symptom burden [ 29 , 30 ], remains in the exploratory phase [ 31 ]. In line with our results, a study developed a prognostic survival model for transplant‐ineligible MM patients, incorporating PROs such as self‐reported performance status and symptom burden, which revealed a significant association between high performance or symptom severity and 1‐year all‐cause mortality [ 29 ]. Our present study extends this body of knowledge by investigating the predictive performance of PROs in relation to survival outcomes in MM initiating contemporary daratumumab‐based regimens, evaluating their independence from other prognostic markers, and potentially facilitating the development of patient‐friendly prognostic tools to enhance shared decision‐making.

The ECOG‐PS is a well‐established tool for treatment decision‐making and patient stratification in clinical trials [ 32 ] and is recognized as a strong independent prognostic factor of treatment outcomes across various oncology populations [ 33 ]. Nevertheless, a critical examination of ECOG‐PS scales reveals several inherent limitations, primarily due to the subjective nature of the clinician‐based assessment, which introduces potential biases. For instance, multiple studies [ 11 , 12 , 18 ] have identified discrepancies between patient‐reported physical function and clinician‐assessed ECOG‐PS across various malignancies, with each assessment method providing independent prognostic information. Moreover, a study reported that up to 50% of patient‐reported functional limitations are missed by clinicians, consequently leading to implications with treatment eligibility as well as prognosis prediction [ 32 , 34 ]. This discordance in scoring was also demonstrated in this study's findings, where patient‐reported physical function and ECOG‐PS served as independent prognostic markers for survival outcomes. These findings highlight patient‐reported physical function as an effective and inexpensive prognostic factor to incorporate into the development of clinical prediction models. Furthermore, the independent information provided by patient‐reported physical function may aid in improving risk stratification in research and clinical trial eligibility as the misclassification of clinician‐based performance status is a well‐documented limitation in the literature [ 18 , 32 , 35 , 36 ]. For example, studies have reported instances where patients may be incorrectly deemed fit to undergo treatment despite underlying impairments, whereas others with sufficient physiological reserve might be unjustly excluded from potentially beneficial treatments because of underestimated performance status scores [ 32 , 35 , 37 ].

Although various categories of PROs assist in the detection and management of treatment‐related toxicities and symptoms [ 38 , 39 ], they are not commonly conceptualized as potential biomarkers for informing clinical decision‐making and treatment benefit. However, this study's findings revealed a significant increase in OS and PFS benefit from daratumumab in patients reporting low pretreatment physical function, suggesting that patient‐reported physical function, may present a pragmatic strategy for identifying patient subgroups more likely to derive superior benefits from daratumumab‐based regimens. This observed benefit may stem from several interconnected factors. Patients with low physical function may represent a subgroup with advanced or aggressive disease, marked by high baseline disease burden, which positions them for greater potential benefits from daratumumab‐based regimens. Starting from a poorer baseline, these patients may have more “room for improvement,” leading to more pronounced gains in physical function, which potentially translates into better overall outcomes. Moreover, daratumumab targets CD38, a molecule frequently upregulated in active or proliferative myeloma clones [ 40 ], suggesting enhanced efficacy in high‐burden disease states [ 41 , 42 ]. Furthermore, daratumumab‐based regimens have demonstrated efficacy in treating patients with MM‐related burdens that may impair physical functioning, [ 43 ] such as bone disease [ 44 ] or renal impairment [ 45 ], as well as improving overall quality of life, including physical functioning, particularly in frail patients [ 43 , 46 ]. Although daratumumab provided significant survival benefits in low functioning patients, the lack of benefit observed in high‐functioning patients may be attributed to differences in access to effective salvage therapies. High‐functioning patients are more likely to receive subsequent lines of therapy, potentially mitigating the impact of initial treatment on their OS and PFS survival outcomes. In contrast, patients with low physical functioning may have limited eligibility for later treatments, making the initial therapeutic benefit from daratumumab particularly critical for their survival. Additionally, differences in disease aggressiveness or tolerability of subsequent therapies may also play a role in shaping these survival outcomes.

Similar to our findings, a study on advanced breast cancer identified physical function as a predictor of survival outcomes from abemaciclib (vs. comparator therapy) [ 18 ]. These results highlight the potential of pretreatment physical function as a predictive marker for assessing therapeutic benefits directly from the patient's perspective. Such insights could aid in tailoring treatment regimens that not only enhance patient health and quality of life but also offer additional advantages, including potential economic benefits through reduced hospital readmissions and emergency room visits [ 47 , 48 ].

Given the improved survival rates achieved with contemporary MM treatment regimens, ensuring a high quality of life for patients becomes paramount. Identifying predictive factors for grade ≥ 3 adverse effects is essential to implement preventative measures and mitigate the symptom burden associated with these life‐extending therapies. Our findings indicate that both physical function and ECOG‐PS provide independent prognostic information for grade ≥ 3 adverse events. Moreover, although these measures were strongly associated with non‐hematologic grade ≥ 3 adverse events, they did not significantly correlate with hematological adverse events. This suggests that a lower functional status may be more indicative of susceptibility to systemic complications such as infections, respiratory disorders, gastrointestinal toxicities, and metabolic disturbances, rather than hematologic abnormalities. In agreement with our findings, a study assessing breast cancer patients reported PROs, including physical well‐being, as prognostic factors for grade ≥ 3 AEs [ 9 ]. Moreover, studies have demonstrated the benefit of incorporating PROs into symptom monitoring for cancer patients, highlighting a reduced incidence of serious AEs, decreased emergency room visits, less symptom burden, and a higher quality of life [ 36 , 49 ].

This study has several limitations, including the stringent inclusion criteria of clinical trials, which restrict the generalizability of findings to real‐world populations. For example, the POLLUX and CASTOR trials evaluated daratumumab‐based regimens in specific patient populations, such as those with relapsed or refractory MM, whereas the MAIA trial focused on newly diagnosed, transplant‐ineligible MM patients. Moreover, all three trials were restricted to intravenous daratumumab administration, limiting the ability to assess whether the observed associations extend to subcutaneous administration, which is now the standard of care because of its improved tolerability and convenience [ 50 ]. Additionally, over 80% of the cohort was of white race, underscoring the underrepresentation of other ethnic groups at higher risk for MM [ 51 ]. There was also a small proportion of missing PRO data, likely due to some patients not completing the baseline questionnaire. Moreover, the EORTC QLQ‐C30 utilized in clinical trials is a generic questionnaire and does not assess condition‐specific QoL in MM patients. Despite these limitations, a notable strength of this study is the pooling of large data ( n = 1840) from three rigorous, high‐quality trials (MAIA, POLLUX, CASTOR), which enhance study power and help mitigate issues related to generalizability and missing data.

In conclusion, pretreatment PROs were identified as independent prognostic factors of survival outcomes and grade ≥ 3 adverse events in patients with MM initiating contemporary immunomodulatory regimens. Patient‐reported physical function emerged as an independent predictor of survival outcomes, providing prognostic information that extends beyond the clinician‐assessed ECOG‐PS. Notably, patients with low physical function showed greater benefits from daratumumab treatment arms for both OS and PFS. These findings underscore the significant potential for PROs, particularly patient‐reported physical function, as an inexpensive and feasible predictive marker to inform on potential treatment benefits from daratumumab‐based regimens. Further research and prospective studies are warranted to confirm the identified associations and explore whether they extend to other contemporary MM treatment regimens.

Author Contributions

Conception and design: A.Y.A. and A.M.H. Development of methodology: A.Y.A. and A.M.H. Acquisition of data: A.Y.A. Analysis and interpretation of data (e.g., statistical analysis, biostatistics, and computational analysis): S.A.A., E.B., A.M.H., Z.A., H.O.A.‐S., M.H.S., Z.K., S.M.A., R.M., Y.B., R.A.M., G.K., M.J.S., K.H.A., and A.Y.A. Writing, review, and/or revision of the manuscript: S.A.A., E.B., A.M.H., Z.A., H.O.A.‐S., M.H.S., Z.K., S.M.A., R.M., Y.B., R.A.M., G.K., M.J.S., K.H.A., and A.Y.A. Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): S.A.A. and A.Y.A. Study supervision: A.M.H. and A.Y.A. All authors have read and agreed to the published version of the manuscript.

Ethics Statement

This secondary analysis of anonymized clinical trial data was confirmed to be negligible‐risk research and has been approved by the University of Sharjah Research and Ethics Committee (approval reference number: REC‐23‐11‐07‐01‐F).

Consent

All authors have read and agreed to submit the manuscript.

Conflicts of Interest

H.O.A.‐S. is on the advisory board of ROCHE, MSD, BMS, ASTRAZENECA, and Novartis. All other authors declare no conflicts of interest.

Supporting information

Data S1.

Acknowledgments

This study, carried out under YODA Project # 2022‐5048, used data obtained from the Yale University Open Data Access Project, which has an agreement with Janssen Research & Development, L.L.C. The interpretation and reporting of research using this data are solely the responsibility of the authors and do not necessarily represent the official views of the Yale University Open Data Access Project or Janssen Research & Development, L.L.C. Open access publishing facilitated by Flinders University, as part of the Wiley ‐ Flinders University agreement via the Council of Australian University Librarians.

Funding: This work was supported by University of Sharjah Targeted Research (grant no. 2301110392). A.M.H. is a National Health and Medical Research Council (NHMRC) Fellow (APP2008119), who is supported by research funding from Tour De Cure, Australia (RSP‐117‐FY2023).

Data Availability Statement

Data were accessed according to YODA policy and process for clinical study data sharing and is available for request at https://yoda.yale.edu/ .

References

1. Kyle R. A. and Rajkumar S. V., “Multiple Myeloma,” New England Journal of Medicine 351, no. 18 (2004): 1860–1873.

2. Salek S., Ionova T., Oliva E. N., et al., “The Reporting, Use, and Validity of Patient‐Reported Outcomes in Multiple Myeloma in Clinical Trials: A Systematic Literature Review,” Cancers 14, no. 23 (2022): 6007.

3. De Luca F., Allegra A., Di Chio C., Previti S., Zappalà M., and Ettari R., “Monoclonal Antibodies: The Greatest Resource to Treat Multiple Myeloma,” International Journal of Molecular Sciences 24, no. 4 (2023): 3136.

4. Mian A., Naqvi S. A. A., Ayaz A., et al., “Incidence of Second Primary Malignancies in Patients With Multiple Myeloma Receiving Anti‐CD38 Monoclonal Antibodies: A Systematic Review and Meta‐Analysis,” Leukemia Research 131 (2023): 107324.

5. Gotay C. C., Kawamoto C. T., Bottomley A., and Efficace F., “The Prognostic Significance of Patient‐Reported Outcomes in Cancer Clinical Trials,” Journal of Clinical Oncology 26, no. 8 (2008): 1355–1363.

6. Lai‐Kwon J., Thorner E., Rutherford C., Crossnohere N., and Brundage M., “Integrating Patient‐Reported Outcomes Into the Care of People With Advanced Cancer—A Practical Guide,” American Society of Clinical Oncology Educational Book 44, no. 3 (2024): e438512.

7. Badaoui S., Shahnam A., McKinnon R. A., Abuhelwa A. Y., Sorich M. J., and Hopkins A. M., “The Predictive Utility of Patient‐Reported Outcomes and Performance Status for Survival in Metastatic Lung Cancer Patients Treated With Chemoimmunotherapy,” Translational Lung Cancer Research 11, no. 3 (2022): 432.

8. Tan E., Abuhelwa A. Y., Badaoui S., et al., “Association Between Patient‐Reported Outcomes and Survival in Patients With Advanced Urothelial Carcinoma Treated With Atezolizumab,” Bladder Cancer 8, no. 1 (2022): 81–88.

9. Modi N., Danell N., Perry R., et al., “Patient‐Reported Outcomes Predict Survival and Adverse Events Following Anticancer Treatment Initiation in Advanced HER2‐Positive Breast Cancer,” ESMO Open 7, no. 3 (2022): 100475, 10.1016/j.esmoop.2022.100475.

10. Kerrigan K., Patel S. B., Haaland B., et al., “Prognostic Significance of Patient‐Reported Outcomes in Cancer,” JCO Oncology Practice 16, no. 4 (2020): e313–e323, 10.1200/JOP.19.00329.

11. Hopkins A. M., Wagner J., Kichenadasse G., Modi N., Rowland A., and Sorich M. J., “Patient‐Reported Outcomes as a Prognostic Marker of Survival in Patients With Advanced Nonsmall Cell Lung Cancer Treated With Immunotherapy,” International Journal of Cancer 147, no. 11 (2020): 3085–3089.

12. Pompili C., Omar S., Ilyas M. H., et al., “Patient‐Reported Physical Function Is Associated With Survival After Lung Resection for Non‐Small Cell Lung Cancer,” Annals of Thoracic Surgery 116, no. 3 (2023): 563–569.

13. Stokke K., Halvorsen T. O., Grønberg B. H., et al., “Associations Between Measured and Patient‐Reported Physical Function and Survival in Advanced NSCLC,” Healthcare 10 (2022): 922.

14. Ezzatvar Y., Ramírez‐Vélez R., Sáez de Asteasu M. L., et al., “Physical Function and All‐Cause Mortality in Older Adults Diagnosed With Cancer: A Systematic Review and Meta‐Analysis,” Journals of Gerontology: Series A 76, no. 8 (2021): 1447–1453.

15. Facon T., Leleu X., and Manier S., “How I Treat Multiple Myeloma in Geriatric Patients,” Blood 143, no. 3 (2024): 224–232.

16. Rosko A., Giralt S., Mateos M.‐V., and Dispenzieri A., “Myeloma in Elderly Patients: When Less Is More and More Is More,” American Society of Clinical Oncology Educational Book 37 (2017): 575–585.

17. Verweij N. M., Schiphorst A. H., Pronk A., van den Bos F., and Hamaker M. E., “Physical Performance Measures for Predicting Outcome in Cancer Patients: A Systematic Review,” Acta Oncologica 55, no. 12 (2016): 1386–1391.

18. Badaoui S., Kichenadasse G., Rowland A., Sorich M. J., and Hopkins A. M., “Patient‐Reported Outcomes Predict Progression‐Free Survival of Patients With Advanced Breast Cancer Treated With Abemaciclib,” Oncologist 26, no. 7 (2021): 562–568.

19. Modi N., Abuhelwa A. Y., Rowland A., et al., “Association Between Patient‐Reported Outcomes and Therapeutic Outcomes in Patients With Breast Cancer: A Pooled Individual‐Participant Data Analysis,” Journal of Clinical Oncology 41, no. 16_suppl (2023): 530, 10.1200/JCO.2023.41.16_suppl.530.

20. Azam F., Latif M. F., Farooq A., et al., “Performance Status Assessment by Using ECOG (Eastern Cooperative Oncology Group) Score for Cancer Patients by Oncology Healthcare Professionals,” Case Reports in Oncology 12, no. 3 (2020): 728–736.

21. Facon T., Kumar S. K., Plesner T., et al., “Daratumumab, Lenalidomide, and Dexamethasone Versus Lenalidomide and Dexamethasone Alone in Newly Diagnosed Multiple Myeloma (MAIA): Overall Survival Results From a Randomised, Open‐Label, Phase 3 Trial,” Lancet Oncology 22, no. 11 (2021): 1582–1596.

22. Dimopoulos M. A., Oriol A., Nahi H., et al., “Daratumumab, Lenalidomide, and Dexamethasone for Multiple Myeloma,” New England Journal of Medicine 375, no. 14 (2016): 1319–1331.

23. Palumbo A., Chanan‐Khan A., Weisel K., et al., “Daratumumab, Bortezomib, and Dexamethasone for Multiple Myeloma,” New England Journal of Medicine 375, no. 8 (2016): 754–766.

24. Aaronson N. K., Ahmedzai S., Bergman B., et al., “The European Organization for Research and Treatment of Cancer QLQ‐C30: A Quality‐Of‐Life Instrument for Use in International Clinical Trials in Oncology,” Journal of the National Cancer Institute 85, no. 5 (1993): 365–376.

25. Department of Health and Human Services NIoH NCI , Common Terminology Criteria for Adverse Events (CTCAE), Version 4.0 (Department of Health and Human Services NIoH NCI, 2009).

26. Kotronoulas G., Kearney N., Maguire R., et al., “What Is the Value of the Routine Use of Patient‐Reported Outcome Measures Toward Improvement of Patient Outcomes, Processes of Care, and Health Service Outcomes in Cancer Care? A Systematic Review of Controlled Trials,” Journal of Clinical Oncology 32, no. 14 (2014): 1480–1501.

27. Efficace F., Collins G. S., Cottone F., et al., “Patient‐Reported Outcomes as Independent Prognostic Factors for Survival in Oncology: Systematic Review and Meta‐Analysis,” Value in Health 24, no. 2 (2021): 250–267.

28. Patel S. B., Haaland B., Ose D., et al., “Risk Stratification Using Patient‐Reported Outcomes (PROs) in Patients (Pts) With Advanced Cancer,” American Society of Clinical Oncology 36 (2018): 10101.

29. Mian H., Seow H., Balitsky A. K., et al., “A Prognostic Survival Model Incorporating Patient‐Reported Outcomes for Transplant‐Ineligible Patients With Multiple Myeloma,” Oncologist 29, no. 6 (2024): 519–526.

30. Ebraheem M. S., Seow H., Balitsky A. K., et al., “Trajectory of Symptoms in Patients Undergoing Autologous Stem Cell Transplant for Multiple Myeloma: A Population‐Based Cohort Study of Patient‐Reported Outcomes,” Clinical Lymphoma, Myeloma and Leukemia 21, no. 9 (2021): e714–e721.

31. Wang T., Lu Q., and Tang L., “Assessment Tools for Patient‐Reported Outcomes in Multiple Myeloma,” Supportive Care in Cancer 31, no. 7 (2023): 431.

32. Scott J. M., Stene G., Edvardsen E., and Jones L. W., “Performance Status in Cancer: Not Broken, but Time for an Upgrade?,” Journal of Clinical Oncology 38, no. 25 (2020): 2824–2829.

33. Mollica V., Rizzo A., Marchetti A., et al., “The Impact of ECOG Performance Status on Efficacy of Immunotherapy and Immune‐Based Combinations in Cancer Patients: The MOUSEION‐06 Study,” Clinical and Experimental Medicine 23, no. 8 (2023): 5039–5049.

34. Fromme E. K., Eilers K. M., Mori M., Hsieh Y.‐C., and Beer T. M., “How Accurate Is Clinician Reporting of Chemotherapy Adverse Effects? A Comparison With Patient‐Reported Symptoms From the Quality‐Of‐Life Questionnaire C30,” Journal of Clinical Oncology 22, no. 17 (2004): 3485–3490.

35. Roman M. A., Koelwyn G. J., Eves N. D., et al., “Comparison of Performance Status With Peak Oxygen Consumption in Operable Patients With Non‐Small‐Cell Lung Cancer,” Respirology 19, no. 1 (2014): 105–108.

36. Basch E., Deal A. M., Dueck A. C., et al., “Overall Survival Results of a Trial Assessing Patient‐Reported Outcomes for Symptom Monitoring During Routine Cancer Treatment,” Journal of the American Medical Association 318, no. 2 (2017): 197–198.

37. Cheng S., Qureshi M., Pullenayegum E., Haynes A., and Chan K. K., “Do Patients With Reduced or Excellent Performance Status Derive the Same Clinical Benefit From Novel Systemic Cancer Therapies? A Systematic Review and Meta‐Analysis,” ESMO Open 2, no. 4 (2017): e000225.

38. Di Maio M., Gallo C., Leighl N. B., et al., “Symptomatic Toxicities Experienced During Anticancer Treatment: Agreement Between Patient and Physician Reporting in Three Randomized Trials,” Journal of Clinical Oncology 33, no. 8 (2015): 910–915.

39. Di Maio M., Basch E., Bryce J., and Perrone F., “Patient‐Reported Outcomes in the Evaluation of Toxicity of Anticancer Treatments,” Nature Reviews. Clinical Oncology 13, no. 5 (2016): 319–325.

40. Van de Donk N. W., Richardson P. G., and Malavasi F., “CD38 Antibodies in Multiple Myeloma: Back to the Future,” Blood 131, no. 1 (2018): 13–29.

41. Nijhof I. S., Casneuf T., Van Velzen J., et al., “CD38 Expression and Complement Inhibitors Affect Response and Resistance to Daratumumab Therapy in Myeloma,” Blood, the Journal of the American Society of Hematology 128, no. 7 (2016): 959–970.

42. Kitadate A., Kobayashi H., Abe Y., et al., “Pre‐Treatment CD38‐Positive Regulatory T Cells Affect the Durable Response to Daratumumab in Relapsed/Refractory Multiple Myeloma Patients,” Haematologica 105, no. 1 (2020): e37–e40.

43. Horigome Y., Suzuki K., and Suzuki T., “Daratumumab Treatment for ‘Truly Frail’ Elderly Myeloma Patients,” Life 14, no. 3 (2024): 389.

44. Terpos E., Ntanasis‐Stathopoulos I., Kastritis E., et al., “Daratumumab Improves Bone Turnover in Relapsed/Refractory Multiple Myeloma; Phase 2 Study ‘REBUILD’,” Cancers 14, no. 11 (2022): 2768.

45. Cejalvo M. J., Legarda M., Abella E., et al., “Single‐Agent Daratumumab in Patients With Relapsed and Refractory Multiple Myeloma Requiring Dialysis: Results of a Spanish Retrospective, Multicentre Study,” British Journal of Haematology 190, no. 5 (2020): e289–e292.

46. Perrot A., Facon T., Plesner T., et al., “Health‐Related Quality of Life for Frail Transplant‐Ineligible Patients With Newly Diagnosed Multiple Myeloma Treated With Daratumumab, Lenalidomide and Dexamethasone: Subgroup Analysis of MAIA Trial,” Blood 140, no. Supplement 1 (2022): 1142–1145.

47. Lizan L., Perez‐Carbonell L., and Comellas M., “Additional Value of Patient‐Reported Symptom Monitoring in Cancer Care: A Systematic Review of the Literature,” Cancers 13, no. 18 (2021): 4615.

48. Caminiti C., Maglietta G., Diodati F., et al., “The Effects of Patient‐Reported Outcome Screening on the Survival of People With Cancer: A Systematic Review and Meta‐Analysis,” Cancers 14, no. 21 (2022): 5470.

49. Zhang L., Zhang X., Shen L., Zhu D., Ma S., and Cong L., “Efficiency of Electronic Health Record Assessment of Patient‐Reported Outcomes After Cancer Immunotherapy: A Randomized Clinical Trial,” JAMA Network Open 5, no. 3 (2022): e224427‐e.

50. Usmani S. Z., Nahi H., Legiec W., et al., “Final Analysis of the Phase III Non‐Inferiority COLUMBA Study of Subcutaneous Versus Intravenous Daratumumab in Patients With Relapsed or Refractory Multiple Myeloma,” Haematologica 107, no. 10 (2022): 2408–2417.

51. Habr D. and Corsaro M., “Reimagining Diversity in Multiple Myeloma Clinical Trials,” Hematological Oncology 40, no. 4 (2022): 689–694.

Associated Data

Supplementary Materials

Data S1.

Data Availability Statement

Data were accessed according to YODA policy and process for clinical study data sharing and is available for request at https://yoda.yale.edu/ .