Rocci, Alberto

Abstract Background: Real world data (RWD) is one of the key components to our understanding of the treatment trajectory of patients with multiple myeloma (MM) in clinical practice. However, treatment responses recorded in patients' medical records are not often standardized. We have developed an algorithm to derive real-world response (dR) for MM patients based on the International Myeloma Working Group (IMWG) criteria to overcome issues with missing values (Xu et al. Pharmacoepidemiol Drug Saf 2021). In this study, we evaluate its association with overall survival (OS) in a RWD cohort. Methods: This study included patients diagnosed with MM between Jan 1, 2011 and Jan 31, 2021 from the US-based Flatiron Health electronic health record (EHR)-derived de-identified database. We derived overall response (partial response [PR] or better, e.g. > 50% reduction of serum M-protein from baseline) to 1L treatment in newly diagnosed MM patients based on the IMWG response criteria, taking into account missing data. Associations between dR and OS were evaluated at individual- and treatment-level. At individual-level, associations were assessed using landmark analyses at approximating 3/4/5 cycles of treatment and at 6 months. Patients who died before the landmark were excluded from the analysis. At treatment-level, we stratified the cohort by year of treatment initiation (every two years from 2011-2021). Within each stratum, treatment groups were compared to estimate the hazard ratios (HRs) of OS and odds ratios (ORs) of dR, both adjusted for potential confounding factors (age, ECOG status, cytogenetic risk groups [high vs standard], time between diagnosis and first-line [1L] start date). The association between HRs and ORs was assessed by coefficient of determination (R 2) from a stratum-size weighted linear regression model, where values close to 1 imply a strong correlation and 0 indicates no association. Only significant HRs and ORs (P < 0.05) in the multivariable analysis were used in the analysis. Results: Of 6806 patients in the Flatiron Health MM database, 70% (n=4830) had serum myeloma (M) protein measured by protein electrophoresis within 30 days before the start of 1L treatment, 27% (n=1838) had urine M protein and 57.8% (n=3935) had serum free light chains (FLCs). Patients with at least one serum M protein or FLC measurement were eligible for study entry (n=5609). During 1L treatment, 4727 patients (46% female, mean ± standard deviation age 67.8 ± 10.4 years) had valid laboratory test results for dR assessment (for each patient we required a baseline measurement plus at least one additional measurement after treatment start of the same lab type), of which 71.7% (n=3387) had PR or better. The majority of the patients (n=2188, 46.3%) received a proteasome inhibitor (PI)+steroid+immunomodulatory drug (IMiD) treatment regimen, followed by PI+chemo+steroid (n=639, 13.5%), PI+steroid (n=621, 13.1%), and steroid+IMiD (n=607, 12.8%). Other treatment groups had fewer than 100 patients and were not considered in the treatment-level analysis (Table 1). At the individual-level analysis, dR was significantly associated with OS at all landmarks (HRs 0.80 to 0.81, P < 0.001, Table 2). At treatment-level, the association between dR and OS was R 2=0.67 (P < 0.001, Figure 1). Subgroup analyses were conducted for each pair of treatment groups; however, only PI+IMiD+steroid vs PI+steroid had sufficient sample sizes, which showed R 2 of 0.82 (P = 0.02). Conclusions: In the absence of recorded response data in a patient's EHR, laboratory assessments can successfully be applied using our algorithm to determine, based on part of the IMWG response criteria, the response status for patients with MM. In the RWD cohort, patients who had PR or better to 1L treatment demonstrated longer survival than non-responders. A moderate association was observed at the treatment-level between dR at 1L and OS. However, these associations might be specific to certain treatment groups and the underlying mode of action of the drugs. Further analysis will be needed to validate these results. Figure 1 Figure 1. Disclosures Xu: F. Hoffmann-La Roche AG: Current Employment. Tyanova: F. Hoffmann-La Roche: Current Employment. Williamson: Amgen: Current equity holder in publicly-traded company; Genentech: Current Employment, Current equity holder in publicly-traded company. Rocci: Novartis: Other: Wife is and employee of Novartis and holds Novartis stocks; Sanofi Aventis: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Other: Received travel sponsorship ; AbbVie: Other: Received travel sponsorship ; F. Hoffmann-La Roche Ltd.: Current Employment; NHS: Ended employment in the past 24 months; Sanofi: Consultancy; Takeda: Consultancy, Honoraria, Other: Received travel sponsorship , Speakers Bureau; Roche: Current equity holder in publicly-traded company; Owner of 100% stocks of Harlock Healthcare Consulting Ltd (UK privately-held company currently not active).: Current holder of individual stocks in a privately-held company; Celgene: Honoraria, Other: Received travel sponsorship ; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen-Cilag Ltd: Honoraria, Speakers Bureau. Yousefi: Roche: Current Employment, Current equity holder in publicly-traded company. Kumar: Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; Roche-Genentech: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Carsgen: Research Funding; Beigene: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Bluebird Bio: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Background: Real-world data (RWD) derived from electronic health records are becoming increasingly important for deriving insights from clinical practice to complement findings from clinical trials. Response to treatment in multiple myeloma (MM) is assessed using the International Myeloma Working Group (IMWG) response criteria based on MM-specific laboratory measures (i.e. monoclonal [M] protein in serum and urine, free light chain [FLC] levels), as well as radiological images and bone marrow (BM) investigations when appropriate. As healthcare providers do not routinely collect all the information required by the IMWG response criteria, RWD data are often incomplete. Here, we present a derived real-world response (dR) algorithm based on IMWG criteria that accounts for lab measures routinely collected in the clinical care of MM patients and evaluate its ability to accurately assess response to treatment using clinical trial patient-level data. Methods: Treatment response is assessed using a rule-based algorithm integrating longitudinal laboratory measures routinely captured in RWD (Xu et al. Pharmacoepidemiol Drug Saf 2021) as e.g. by the Flatiron MM database. The rules are based on 'relaxed' IMWG criteria, which entail exclusion of BM biopsies data and of imaging results, and reduction in either serum or urine M protein levels (rather than both) to assign partial response. This algorithm was applied to patient-level clinical trial data from the Bellini trial (Kumar et al. Lancet Oncol 2020) with response assignment made by an independent review committee (IRC) in the trial used as the 'ground truth'. Agreement between the IRC's and algorithm's assignments of response was estimated using Cohen's Kappa statistic. Differences in overall response rate (ORR) between treatment and placebo arms were calculated using stratified Cochran-Mantel-Haenszel tests based on strata at randomization with number of prior lines of therapy and previous proteasome inhibitor treatment status variables used for stratification. Results: The Bellini trial is a Phase 3 clinical trial with 2:1 design enrolling 194 and 97 patients in its treatment and placebo arms, respectively (291 patients in total). Regular assessments of M protein and FLC levels were performed (median 13 or 14 measurements), providing detailed trajectory of patient response (Table 1). Comparison between the IRC's and algorithm's assignments of responders classified as Partial Response or better (PR+) resulted in almost perfect agreement with Cohen's Kappa 0.82 (274/291 assignments in agreement). It is worth noting that the Cohen's Kappa between the IRC's and Investigator's assessments is 0.85, indicating that the algorithm's error is within some expected uncertainty. Due to the exclusion of BM information, agreement decreased to 0.56 when depths of response were considered separately as opposed to grouping PR+ patients together with most cases being overestimated as complete response (CR) or stringent CR. In assessing treatment effect, differences in ORR between the intervention and placebo arms in the trial based on IRC's assessment could be accurately recapitulated (68% [66/97] vs 71% [69/97] responders in the placebo arm and 82% [159/194] vs 87% [169/194] in the intervention arm for IRC's and algorithm's assessment, respectively). Based on these results, algorithm response assignment led to consistent conclusions about treatment efficacy in the Bellini trial. Implementation of criteria to characterize very good partial response or better (VGPR+) also led to a conclusion consistent with the Bellini trial IRC's assessment, while implementation of criteria of CR+ resulted in estimates of treatment efficacy that were higher, but directionally consonant (Table 2). Conclusions: We present a fully automated rule-based algorithm for response assessment in MM relying on longitudinal lab measurements during treatment. The algorithm uses 'relaxed' IMWG criteria to account for routine clinical practice settings and demonstrates very high agreement with the assessment of trained clinicians, while reliably reproducing the efficacy analysis in the Bellini trial when ORR and VGPR+ are considered. We envision that, if the concordance observed here is confirmed in other independent cohorts, this algorithm can be used in the assessment of response in RWD and can facilitate response assessment when suboptimal amounts of data are available. Figure 1 Figure 1. Disclosures Tyanova: F. Hoffmann-La Roche: Current Employment. Xu: F. Hoffmann-La Roche AG: Current Employment. Williamson: Amgen: Current equity holder in publicly-traded company; Genentech: Current Employment, Current equity holder in publicly-traded company. Rocci: Novartis: Other: Wife is and employee of Novartis and holds Novartis stocks; Sanofi Aventis: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Other: Received travel sponsorship ; Janssen-Cilag Ltd: Honoraria, Speakers Bureau; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Honoraria, Other: Received travel sponsorship ; Owner of 100% stocks of Harlock Healthcare Consulting Ltd (UK privately-held company currently not active).: Current holder of individual stocks in a privately-held company; Roche: Current equity holder in publicly-traded company; Takeda: Consultancy, Honoraria, Other: Received travel sponsorship , Speakers Bureau; Sanofi: Consultancy; NHS: Ended employment in the past 24 months; F. Hoffmann-La Roche Ltd.: Current Employment; AbbVie: Other: Received travel sponsorship . Hong: Imago BioSciences: Current Employment; Genentech, Inc.: Ended employment in the past 24 months; Stock options in Imago BioSciences: Current equity holder in publicly-traded company. Jin: Genentech Inc: Current Employment; Roche: Current equity holder in publicly-traded company. Karve: AbbVie: Current Employment, Current equity holder in publicly-traded company. Roose: Flatiron Health, Inc.: Current Employment; Roche: Current equity holder in publicly-traded company. Sawas: Seattle Genetics: Honoraria; Affimed: Research Funding; Roche: Current equity holder in publicly-traded company; Flat Iron Health: Current Employment; Acrotech: Honoraria; Daiichi-Sankyo: Speakers Bureau; Seattle Genetics: Speakers Bureau; Gilead: Speakers Bureau. Kumar: Antengene: Consultancy, Honoraria; Novartis: Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Tenebio: Research Funding; Beigene: Consultancy; Oncopeptides: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Bluebird Bio: Consultancy; Roche-Genentech: Consultancy, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

High levels of circulating miR-16 in the serum of multiple myeloma (MM) patients are independently associated with longer survival. Although the tumor suppressor function of intracellular miR-16 in MM plasma cells (PCs) has been elucidated, its extracellular role in maintaining a nonsupportive cancer microenvironment has not been fully explored. Here, we show that miR-16 is abundantly released by MM cells through extracellular vesicles (EVs) and that differences in its intracellular expression as associated with chromosome 13 deletion (Del13) are correlated to extracellular miR-16 levels. We also demonstrate that EVs isolated from MM patients and from the conditioned media of MM-PCs carrying Del13 more strongly differentiate circulating monocytes to M2-tumor supportive macrophages (TAMs), compared with MM-PCs without this chromosomal aberration. Mechanistically, our data show that miR-16 directly targets the IKKα/β complex of the NF-κB canonical pathway, which is critical not only in supporting MM cell growth, but also in polarizing macrophages toward an M2 phenotype. By using a miR–15a-16-1–KO mouse model, we found that loss of the miR-16 cluster supports polarization to M2 macrophages. Finally, we demonstrate the therapeutic benefit of miR-16 overexpression in potentiating the anti-MM activity by a proteasome inhibitor in the presence of MM-resident bone marrow TAM.