Paiva, Bruno

Solitary plasmacytomas (SPs) are rare localized tumors of clonal plasma cells, either in the bone (solitary bone plasmacytoma) or in soft tissue/extraosseous (extramedullary) with either no or with minimal bone marrow (BM) infiltration (<10% clonal plasma cells by immunohistochemistry) and no evidence of systemic involvement or myeloma defining events. Approximately 50% of SPs will progress to symptomatic myeloma within 5 years after initial definitive local radiotherapy. Increased availability of improved diagnostic and monitoring tools has increased the sensitivity of detection of additional lesions and marrow involvement and has implications for the follow-up strategy after treatment. Thus, the definitions and requirements for the diagnosis and follow-up of SPs are evolving. The diagnosis of SP requires the careful exclusion of multiple myeloma (MM) that would require systemic therapy, by using all the available methods to detect systemic disease (advanced imaging, sensitive BM assessment methods, blood and urine tests). Local radiotherapy remains the mainstay of therapy, and despite the availability of innovative drugs for MM, the clinical benefit of systemic therapy currently remains poorly defined. The International Myeloma Working Group provides here updated recommendations for the diagnosis, evaluation, treatment, and response assessment of patients with SPs, incorporating recent data and advances in diagnostic tools.

Abstract Improved understanding of how chimeric antigen receptor (CAR) and endogenous T‐cell phenotypes associate with progression‐free survival (PFS) is needed to individualize and improve immunotherapy outcomes in multiple myeloma (MM). We characterized and investigated the prognostic value of immune dynamics defined with multidimensional and computational flow cytometry in 258 bone marrow aspirates from 107 MM patients treated with idecabtagene vicleucel in the KarMMa trial. At screening, there was an association between longer PFS and increased percentages of PD1 + cytotoxic T cells and ICOS − TIGIT + Tregs. At Month 1 after infusion, the presence of >1% CAR‐T cells, a CD4/CD8 CAR‐T‐cell ratio >0.09, and greater abundance of non‐activated non‐cytolytic CAR‐T cells identified patients with longer PFS. Notably, 47 of the 115 endogenous T‐cell clusters were prognostic at the latest assessment performed after CAR‐T infusion. Lower percentages of ICOS + TIGIT − Tregs showed the strongest association with longer PFS. Altogether, this large dataset of dynamic immune profiling throughout treatment with anti‐B‐cell maturation antigen (anti‐BCMA) CAR‐T cells uncovered that the T‐cell composition prior to apheresis as well as CAR‐T and endogenous T‐cell phenotypes after infusion associates with PFS. These findings have potential for translation into routine laboratory practice for improved understanding of response and resistance to CAR‐T cells in relapsed/refractory MM.

Abstract The detection of genetic abnormalities is required during diagnostic workup and for potential individualization of therapy selection in multiple myeloma (MM) and its precursor conditions. At present, this relies on invasive bone marrow (BM) biopsies, severely limiting early detection, frequent longitudinal monitoring, and the precise selection of therapy. The current standard for detecting genetic alterations in MM is fluorescence in situ hybridization (FISH), which cannot detect point mutations and other clinically relevant alterations. Consequently, the IMS-IMWG guidelines were recently updated to require next-generation sequencing for the classification of high-risk MM. To address these needs, we recently launched GenoPredicta, a CLIA-approved LDT that enables routine monitoring, informing diagnosis, and treatment selection by comprehensively characterizing MM genomes with whole genome sequencing (WGS) from as few as 50 circulating tumor cells (CTCs) isolated from peripheral blood (PB) or tumor cells from BM. Briefly, tumor cells are isolated from samples using fluorescence-activated cell sorting and subjected to WGS, from which copy number alterations, structural variants, and short variants (SNVs/indels) are identified using a fully automated pipeline generating physician-ready clinical reports from raw sequencing data in ∼6h. Analytical validation of GenoPredicta showed complete concordance with FISH results. Identifying alterations in therapeutic targets (e.g., BCMA, GPRC5D) for guiding MM immunotherapies is becoming increasingly important. Here, we describe GenoPredicta results from relapsed/refractory MM patients, highlighting resistance-conferring alterations that can only be detected by WGS, such as deletions in the kilo- to megabase scales that are observed in conjunction with SNVs and indels, leading to biallelic loss/inactivation of the gene, including at subclonal levels. In addition to biallelic loss of BCMA and GPRC5D in response to CAR T or T cell engager therapies, we observed similar resistance mechanisms for targets of immunomodulatory drugs, e.g., CRBN. The observation of these resistance mechanisms was consistent with patients’ clinical histories. In summary, we demonstrate that low input WGS-based characterization of MM from BM or CTCs is a viable replacement for FISH for clinical diagnosis and monitoring, with CTC-based measurements enabling comprehensive profiling of the MM genome. Crucially, this includes genetic alterations that confer resistance to therapy, allowing for both early detection of such alterations and more precise selection and guidance of therapy. The dramatically improved variant calling ability from WGS, especially in low-input CTC applications, extends to other malignancies and will gain wider adoption as sequencing costs continue to decrease. Citation Format: Bruno Paiva, Peter Voorhees, Patricia T. Greipp, Danielle Sookiasian, Julian Hess, Marisa DeMeo, Vicki Pounder, Sarah Calkins, Reid Meyer, Linda B. Baughn, Christine-Ivy Liacos, Meletios-Athanasios Dimopoulos, Alexandra Papadimou, Taouxi Konstantina, Efstathios Kastritis, Jesus Berdeja, Daniel Auclair, Valentina Nardi, Thomas Mullen, Francois Aguet, Shaji Kunnathu Kumar. Whole genome sequencing of multiple myeloma genomes with a novel clinical assay enables identification of genetic alterations underlying immunotherapy resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1067.

Abstract Introduction Detecting genetic abnormalities is mandatory during diagnostic workup and for potential individualization of therapy selection in multiple myeloma (MM) and its precursor conditions. At present, this requires invasive bone marrow (BM) biopsies, severely limiting early detection, patient monitoring, and the precise selection of therapy. The current standard for detecting genetic alterations in MM is fluorescence in situ hybridization (FISH), which cannot detect point mutations and other clinically relevant alterations. Consequently, the IMS-IMWG guidelines were recently updated to require next-generation sequencing for the classification of high-risk MM. Moreover, the importance of identifying alterations in therapeutic targets (e.g., BCMA, GPRC5D) for guiding immunotherapies is becoming increasingly recognized. To address this need, we recently launched GenoPredicta, a CLIA-approved Laboratory Developed Test that enables routine monitoring, informing diagnosis, and treatment selection by comprehensively and robustly characterizing MM genomes with whole genome sequencing (WGS) from a minimum of ~50 circulating tumor cells (CTCs) isolated from peripheral blood (PB) or tumor cells from BM. Here, we describe the characterization of clinical and research samples with GenoPredicta, highlighting resistance-conferring genetic alterations that can only be detected by sequencing and that are consistent with patients' clinical histories, while further demonstrating the complete concordance of GenoPredicta with prior FISH results. Methods Tumor cells were isolated from BM and/or PB samples using fluorescence-activated cell sorting (FACS) (after CD138+ enrichment for PB) and subjected to whole genome sequencing (WGS), from which we identified copy number alterations, structural variants, and single nucleotide variants and indels. For a subset of samples, the BM was also characterized by FISH, using a standard clinical panel for characterizing MM; for other samples prior FISH results were available from patients' clinical records. Results The analytical validation of GenoPredicta, conducted on a retrospective cohort of &gt;60 viably frozen samples from &gt;30 patients, showed complete concordance between BM and PB for detected genomic abnormalities, with all clinically relevant events (11 translocations and 125 CNVs) observed in BM recapitulated in PB (100% recall). All events identified by FISH in 14 samples (8 translocations and 30 CNVs) were also identified in the BM (100% recall), with 50 translocations and 30 CNVs not detected by FISH also absent in the WGS data (100% precision). The abnormalities detected in these samples also included multiple high-risk events not assayed by FISH, including biallelic TP53 loss (Del 17p coupled with a deleterious TP53 mutation, observed in 4 patients) and MYC::IGL translocations (2 patients). Moreover, for all fresh peripheral blood clinical samples with FISH results available from patients' histories, GenoPredicta recalled 100% of reported events. Among these clinical samples, we notably observed a case with both a biallelic GPRC5D deletion and a TNFRSF17 (BCMA) deletion combined with a truncating mutation on the remaining allele. This was consistent with the patient's clinical history showing progression after therapy with talquetamab (anti-GPRC5D bispecific), and previous nonresponse to teclistamab (anti-BCMA bispecific) after prior therapy with Cilta-cel (BCMA-directed CAR T). GenoPredicta also detected other mutations likely to confer resistance to immunotherapies, including loss-of-function mutations in IKZF3 and deletions of FCRL5 in frozen peripheral blood research samples. Conclusion We demonstrate that WGS-based characterization of MM from BM or CTCs is a viable replacement for FISH for clinical diagnosis and monitoring, with blood-based measurements enabling comprehensive profiling of the myeloma cancer genome. Importantly, this includes genetic alterations that confer resistance to therapy, allowing for both early detection of such alterations and more precise selection of therapy.

AbstractDespite advances in acute myeloid leukemia (AML) treatment, significant unmet medical needs remain. Surrogate end points for overall survival can accelerate the approval of novel therapies. Measurable residual disease (MRD) is a promising surrogate end point candidate, providing a sensitive and quantitative assessment of disease burden. Numerous studies have demonstrated that negative MRD status—across diverse methodologies, assessment timepoints and thresholds, patient subgroups, and clinical settings—independently predicts improved survival. Although MRD can inform therapeutic decisions at the patient level, its formal integration as a primary surrogate end point in regulatory frameworks for clinical trials requires rigorous validation. MRD Partnership and Alliance in AML Clinical Treatment (MPAACT) is a research consortium among industry and academic leaders. MPAACT actively engages with regulatory agencies, health technology assessment bodies, technology vendors, and patient groups to establish a pathway for validating MRD as a surrogate end point in AML clinical trials. For the current article, the authors reviewed the status of MRD assessment, its use in recent clinical trials, current MRD assessment methodologies, standardization, regulatory guidance, statistical approaches, and patient access considerations necessary for MRD to become a surrogate clinical trial end point. The extensive collaboration between MPAACT, global industry, and academic partners—including data sharing and resource integration—underscores the collective commitment to advancing AML therapies. Establishing MRD as a surrogate end point could accelerate the development and approval of innovative treatments, ultimately improving patient outcomes.

Despite significant improvements in survival of patients with multiple myeloma (MM), outcomes remain heterogeneous, and a significant proportion of patients experience suboptimal outcomes. Importantly, traditional prognostic factors based on data from patients treated with older therapies no longer capture prognosis accurately in the contemporary era of novel triplet or quadruplet therapies. Therefore, risk stratification requires refinement in the context of available and investigational treatment options in routine practice and clinical trials, respectively. The current identification of high-risk MM (HRMM) in routine practice is based on the Revised International Staging System, which stratifies patients using a combination of widely available serum biomarkers and chromosomal abnormalities assessed via fluorescence in situ hybridization. In recent years, a substantial body of evidence concerning additional clinical, biological, and molecular/genomic prognostic factors has accumulated, along with new MM risk stratification tools and consensus reports. The International Myeloma Society, along with the International Myeloma Working Group, convened an Expert Panel with the primary aim of revisiting the definition of HRMM and formulating a practical and data-driven consensus definition, based on new evidence from molecular/genomic assays, updated clinical data, and contemporary risk stratification concepts. The Panel proposes the following Consensus Genomic Staging (CGS) of HRMM which relies upon the presence of at least one of these abnormalities: (1) del(17p), with a cutoff of >20% clonal fraction, and/or TP53 mutation; (2) an IgH translocation including t(4;14), t(14;16), or t(14;20) along with 1q+ and/or del(1p32); (3) monoallelic del(1p32) along with 1q+ or biallelic del(1p32); or (4) β2 microglobulin ≥5.5 mg/L with normal creatinine (<1.2 mg/dL).

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

PURPOSE Newly approved drugs and combinations treating multiple myeloma (MM) have resulted in substantial improvements in patients' survival. To deliver rapid access to newer therapies, an earlier end point to expedite clinical trials is needed. Our objective was to evaluate the minimal residual disease–negative complete response (MRD-CR) as an intermediate end point for progression-free survival (PFS) and overall survival (OS) in newly diagnosed (ND) transplant-eligible (NDTE) patients, ND transplant-ineligible (NDTinE) patients, and patients with relapsed/refractory (RR) MM. PATIENTS AND METHODS Individual patient data from 20 randomized multicenter trials were collected. Eleven studies (4,773 patients) with sufficient data were analyzed to evaluate whether 9- or 12-month MRD-CR classified at a 10 –5 threshold could be reasonably likely to predict the clinical benefit of new agents regarding PFS and OS. Global odds ratio (OR) was estimated using the bivariate Plackett Copula model. Supportive evaluation included correlations of the treatment effects on MRD-CR end points and PFS/OS, evaluated by both linear regression ( R 2 weighted least squared ) and Copula ( R 2 Copula ) models. RESULTS The analysis demonstrated that both 9- and 12-month MRD-CR strongly correlated with PFS at patient level in NDTE patients, NDTinE patients, and patients with RRMM. Global ORs ranged from 3.06 to 16.24, all with 95% CIs excluding 1.0. Encouraging trial-level correlations ( R 2 , 0.61-0.70) were observed by pooling three populations and were stronger ( R 2 , 0.67-0.78) in the ND population. Similar results were observed for OS. CONCLUSION Our findings provided the support for use of MRD-CR classified at a 10 –5 threshold at either 9 or 12 months after starting of the treatment, as an intermediate end point to support accelerated approvals, in future trials in NDTE patients, NDTinE patients, and patients with RRMM.

Background: Anti-BCMA CAR T cells are a new standard of care for the treatment of RRMM patients. Hence, it is paramount to identify determinants of response and resistance for individualized approaches. There is emerging data about the importance of depth of response in prolonged progression-free survival (PFS) and of genetic antigen escape in treatment resistance. By contrast, there is limited understanding of how patient' outcomes are shaped by immune fitness prior aphaeresis, and virtually no data about CAR T and endogenous profiles post infusion. Aim: Characterize immune dynamics and investigate its prognostic value in RRMM patients treated with CAR T cells. Methods: Immune profiling was performed using multidimensional and computational flow cytometry in 285 bone marrow aspirates from 120 RRMM patients enrolled in the KarMMa phase 2 trial. In total, 170 immune clusters were identified and systematically quantified in a pileup analysis of samples collected before and at months 1, 3, 6 and 12 after CAR T infusion. Immune scores were developed using cox regression with 10-fold cross-validation. Results: At baseline (n = 63), there were significant associations between the bone marrow immune composition and the number of prior lines of treatment, prior autologous transplant and staging. Namely, there were higher proportions of ICOS, PD1, TIGIT and TIM3 positive cells within the CD4+ and CD8+ T cell compartments from heavily pretreated patients and those with R-ISS 3. A baseline immune score defined by the percentages of GrzmB+HLADR+ cytotoxic NK cells, TIGIT+ Tregs, CD4+CD127+ICOS+ and CD4+CD25+CD127+TIGIT+, CD8+TIGIT+, CD8+GrzmB+HLADR+ and CD8+CD27+CD95+CD45RAloCD197lo T cells was associated with PFS. Patients with low vs high risk immune score at baseline had median PFS of 25 vs 6 months, respectively (p &lt; .001). Interestingly, there were no significant associations between the baseline risk score and treatment or staging, suggesting potential complementarity of immune profiling with other prognostic factors. At month 1 after infusion (n = 55), the presence of &gt;1% vs &lt;1% CAR T cells was associated with longer PFS (medians of 20 vs 8 months, p = .002). In addition to the magnitude of CAR T persistence, it was interesting to observe increased percentages of CD8+ CAR T cells in patients with &lt;5 prior lines of therapy, of CD4+HLADR+ and CD8+TEMRA CAR T cells in patients with R-ISS 1/2, as well as of PD1+TIM3+ CD4+ and CD8+ CAR T cells in patients with R-ISS 3. Moreover, the phenotype of CAR T cells was associated with PFS. Patients with low vs high risk immune score (ie, based on the percentages of CD4+CD57+, CD8+CD27-EM, CD8+PD1+TIM3+ and CD4+CD8+ CAR T cells) had median PFS not reached vs 11 months, respectively (p &lt; .0001). Similarly to the above, there were no significant associations between the CAR T immune risk score and treatment or staging, suggesting potential complementarity of CAR T quantification and phenotyping post-infusion with other patient' characteristics. Next, we investigated endogenous T cell dynamics in 51 RRMM patients with paired samples prior and after CAR T infusion. There were significant differences in the frequencies of 30 immune clusters, including CD27+ naïve and EM CD4+ and CD8+ T cells, ICOS+ and TIGIT+ Tregs, CD4+ and CD8+ T cells, as well as of CD25+CD127+ CD4+ and CD8+ T cells. Furthermore, an immune risk score defined at the latest available time point (n = 95) was associated with PFS (p &lt; .0001). Patients with lower levels of GrzmB+ cytotoxic NK cells and of immunoregulatory HLADR- NK cells, as well as of CD4+CD27+CD95+CM, CD4+HLADR+, CD8+PD1+ and CD8+CD27-CD95+EM T cells showed a high risk score and a median PFS of 6 vs 20 months in patients with low risk immune score. Conclusions: This is one of the largest dynamic studies of immune profiling in a clinical trial investigating anti-BCMA CAR T cells in RRMM. Our results uncovered that patient' immune status is shaped by prior treatment and staging, and that the distribution of different NK, Treg, CD4+ and CD8+ T cells prior aphaeresis is associated with PFS. Furthermore, the phenotype of CAR T cells at month 1 after infusion may be as prognostic as its percentage. We also showed how treatment with CAR T cells modulates the bone marrow immune composition and how it may contribute to immune escape vs surveillance. Altogether, this study supports immune profiling for improved understanding of response and resistance to CAR T cells in RRMM.

Summary: While the current approach to precursor hematologic conditions is to “watch and wait,” this may change with the development of therapies that are safe and extend survival or delay the onset of symptomatic disease. The goal of future therapies in precursor hematologic conditions is to improve survival and prevent or delay the development of symptomatic disease while maximizing safety. Clinical trial considerations in this field include identifying an appropriate at-risk population, safety assessments, dose selection, primary and secondary trial endpoints including surrogate endpoints, control arms, and quality-of-life metrics, all of which may enable more precise benefit–risk assessment.