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Introduction: Although endomyocardial biopsy is the gold standard, echocardiography, and Cardiac Magnetic Resonance (CMR) play a pivotal role in the diagnosis of cardiac amyloidosis. In this systematic review, we aim to estimate the diagnostic test accuracy (DTA) of CMR and echocardiography for cardiac amyloidosis. Methods: As part of the American Society of Hematology guidelines on the diagnosis of Amyloidosis, we searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials from inception till January 2024 for relevant studies. Two reviewers independently performed title and abstract screening and full-text article screening on LASER Al and extracted relevant data using a piloted Excel sheet. We performed statistical pooling using Stata 18.0 software. We evaluated risk of bias using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of the evidence. We report diagnostic test accuracy as sensitivity (95% confidence interval) and specificity (95% confidence interval). Results: After screening 29,237, we included 9 studies assessing DTA of late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) using an endomyocardial biopsy (EMB) as a reference test in patients with amyloidosis (light chain and ATTR). The majority of studies didn't report on the type of amyloidosis where only 2 out of 9 were in patients with light chain amyloidosis. The pooled estimates for sensitivity and specificity of LGE are 0.95 (0.88. 0.98), and 0.81 (0.60, 0.92), respectively. We did a sensitivity analysis and included only LGE suggestive for amyloidosis which didn't change sensitivity but led to an increase in specificity for amyloidosis to 0.87 (0.76, 0.94), respectively. The pooled estimates were based on moderate certainty in the evidence. For 2-dimensional echocardiography, we addressed DTA for different echocardiographic findings and we did not limit the reference to EMB. A panel of experts judged whether the reference was acceptable or not. Acceptable reference was EMB alone or a combination of extracardiac biopsy plus cardiac imaging (CMR or echocardiography) with or without positive cardiac biomarkers (troponin or BNP or NT-proBNP). Six studies (n=2063) addressed interventricular septum thickness (IVS), sensitivity was 0.77 (0.69, 0.84) and specificity was 0.71 (0.60, 0.81). 4 out of 6 studies used 12 mm as a cutoff while the other 2 studies used 13 mm as a cutoff. Only 2 studies (n=1667) assessed the DTA of posterior wall thickness (PWT), with sensitivity ranging from 0.76 to 1.00, and specificity ranging from 0.84 to 0.89. Six studies (n=438) addressed diastolic dysfunction (grade 2,3), with a sensitivity of 0.71 (0.40, 0.90), and specificity of 0.75 (0.64, 0.84). 7 studies (n=472) reported DTA of only diastolic dysfunction grade 3. When compared to diastolic dysfunction (grade 2,3, sensitivity decreases to 0.42 (0 28. 0.58) and specificity increases to 0.89 (0.83. 0.94). For E/A ratio, only 2 studies (n=127) were included. Sensitivity ranged from 0.45 to 0.65, and specificity ranged from 0.85 to 0.98. Four studies (n=1596) yielded a sensitivity of 0.86 (0.65, 0.95) and specificity of 0.76 (0.55, 0.89) of left ventricular global longitudinal strain (GLS). All except one used -17% as a cutoff for GLS. For apical sparing on GLS, pooled estimates from 8 studies (n=2253) led to a sensitivity of 0.72 (0.64, 0.78) and specificity of 0.78 (0.64, 0.88). There was a variation of cutoff across studies. The certainty of evidence for echocardiographic findings was low to moderate. Conclusion: CMR is a more accurate test with better performance compared to echocardiography.

Introduction: The primary analysis of the phase 3 ECHO trial (NCT02972840) demonstrated that acalabrutinib plus bendamustine-rituximab (ABR) significantly improved progression-free survival (PFS) vs placebo plus bendamustine-rituximab (PBR) in older patients (pts) with previously untreated mantle cell lymphoma (MCL) (Wang et al. EHA 2024, Abstract #LB3439). Here we present additional efficacy and safety results from the ECHO trial. Methods: Pts aged ≥65 years (y) with previously untreated MCL and ECOG PS ≤2 were randomly assigned 1:1 to receive ABR or PBR. Acalabrutinib (100 mg twice daily) or placebo was administered until progressive disease (PD) or unacceptable toxicity. Induction with BR was administered for 6 cycles followed by rituximab maintenance (A±R maintenance, cycles 7-30) in pts achieving a partial or complete response (PR or CR). Pts progressing on PBR could cross over to receive acalabrutinib monotherapy. PFS (primary endpoint) was assessed by independent review committee. Minimal residual disease (MRD) negativity rate (10-5) was assessed in peripheral blood (every 24 weeks [wks], at CR, and at PD) and bone marrow (at CR) by NGS-based ClonoSEQ assay (Adaptive Biotechnologies). Results: Among the 598 pts randomized to ABR or PBR (299 in each arm), high-risk baseline characteristics included high-risk simplified MIPI score in 24.1% and 24.4%, blastoid histology in 8.7% and 6.7%, pleomorphic histology in 5.0% and 6.0%, centrally tested Ki-67 ≥30% in 46.5% and 49.2%, and known TP53 mutation in 7.4% and 9.7%, respectively. PFS hazard ratios (HRs) for subgroups were 0.78 (95% confidence interval [CI] 0.51-1.19) in pts with high-risk simplified MIPI score, 0.68 (95% CI 0.29-1.58) in pts with blastoid histology, 0.64 (95% CI 0.25-1.66) in pts with pleomorphic histology, 0.69 (95% CI 0.49-0.98) in pts with Ki-67 ≥30%, and 0.88 (95% CI 0.42-1.78) in pts with known TP53 mutation. In the ABR arm, median PFS was 22.2 months (mo) among pts who discontinued acalabrutinib during induction for reasons other than PD or death, 29.4 mo in pts who discontinued acalabrutinib during A±R maintenance (cycles 7-30) for reasons other than PD or death, and not reached in pts who received acalabrutinib for 31 cycles or more. Among evaluable patients who were MRD negative at the end of induction (24 wks), the rate of conversion to MRD positive during maintenance was lower in the ABR arm (5.85%; n=11/188) than in the PBR arm (15%; n=26/171). Conversely, among evaluable pts who were MRD positive at the end of induction (24 wks), 37.5% (n=3/8) in the ABR arm and 20% (n=3/15) in the PBR arm converted to MRD negative during maintenance. Overall rates of AEs were similar in each arm during induction, but were higher in ABR during A±R maintenance (cycles 7-30) and more so in the monotherapy phase (cycle 31+). Grade ≥3 TEAEs (ABR vs PBR) were 70.0% vs 68.7% during induction, 64.9% vs 62.9% during A±R maintenance (cycles 7-30), and 57.0% vs 45.3% during monotherapy (cycle 31+). Similarly, rates of TEAEs leading to discontinuation of acalabrutinib/placebo were 8.1% vs 8.8% during induction, 23.9% vs 19.0% during A±R maintenance (cycles 7-30), and 25.9% vs 13.7% during monotherapy (cycle 31+). Exposure-adjusted incidence rates per 100 person-years reduced the difference between ABR and PBR for clinically relevant TEAE rates (32.6 vs 29.3 for grade ≥3 serious TEAEs; 4.2 vs 4.0 for grade 5 TEAEs; 15.5 vs 12.4 for TEAEs leading to acalabrutinib/placebo discontinuation), possibly due to longer median exposure to acalabrutinib (28.6 vs 24.6 mo). Conclusion: Acalabrutinib in combination with BR provides a significant PFS benefit in pts with previously untreated MCL, including those with high-risk features. Furthermore, the addition of acalabrutinib to BR provided a greater PFS effect among pts in the ABR arm with the longest exposure to acalabrutinib. The PFS improvement may be partially driven by the contribution of acalabrutinib to sustained MRD-negative status and deepened responses after the end of induction. ABR provides this clinical benefit without excess toxicity as shown by attenuated differences between arms in exposure-adjusted incidence rates, suggesting that higher AE rates in the ABR arm are likely due in part to the longer duration of acalabrutinib treatment vs placebo. These data emphasize the benefits of acalabrutinib in frontline MCL treatment in high-risk pts and the additional benefit of continuous therapy.

Introduction: NPM1 mutations, seen in up to 30% of adults with acute myeloid leukemia (AML), often co-occur with FLT3 internal tandem duplication (FLT3-ITD). Measurable residual disease (MRD) testing before allogeneic hematopoietic cell transplant (alloHCT) for persistence of mutated NPM1 and/or FLT3-ITD can identify patients with AML in complete remission (CR) at highest risk of relapse and death. Quantitative RT-PCR assays are currently recommended for NPM1 MRD testing, while FLT3-ITD MRD testing is DNA-based using next generation sequencing (NGS). DNA-based NGS assays for mutated NPM1 MRD testing may have several advantages but require validation. We performed NPM1 MRD detection in blood collected from patients with AML during first CR (CR1) prior to alloHCT on a subset of patients from the Pre-MEASURE study (PMID: 36881031) using a highly-sensitive, commercially available DNA-based NPM1 NGS MRD assay. Methods: Of the 531 patients aged 18 or older from the Pre-MEASURE study who received an alloHCT for NPM1mutated AML during CR1 at a CIBMTR reporting site in the USA between the years of 2013-2019, a subset of 190 were randomly selected to be included in this analysis. A commercially available research testing kit (IVS, Invivoscribe, San Diego, CA), which can detect DNA-based NPM1 MRD, was used to establish a workflow following clinical testing standards and validated to detect NPM1 insertion variants down to a variant allele fraction (VAF) of at least 0.005%. Results were compared to those previously reported using an anchored multiplex PCR-based (AMP) targeted NGS assay (detection limit 0.01%) and FLT3-ITD MRD results, where applicable. Overall survival (OS) and cumulative incidence of relapse were estimated with the day of transplant as time 0 using Kaplan-Meier estimation (log-rank tests), Fine and Gray model, and Cox proportional hazards models. Results: NPM1 NGS MRD testing was successful for 186 (98%) patients, of which 48 (26%) relapsed (median: 4.0 months; range: 0.7-52.5) and 64 (34%) died (median: 9.7 months; range: 0.4-58.9) post-alloHCT. The IVS assay detected NPM1 insertion variants in 71 patients with a median VAF of 0.0026% (range:0.0002-2.1%). Using a VAF threshold of ≥0.01% for MRD positivity, the IVS test showed that 24 patients tested positive for NPM1 MRD pre-alloHCT, which was associated with significantly increased rates of relapse (52% vs 20% at 3yrs; HR=4.3; P<0.001) and decreased OS (34% vs 71% at 3yrs; HR=3.6; P<0.001) compared to testing negative, in close alignment with those previously reported using the AMP assay. After removing the VAF threshold, an additional 47 patients were classified as NPM1 MRD positive, which remained associated with increased rates of relapse (40% vs 15% at 3yrs; HR=3.4; P<0.001) and decreased OS (50% vs 75% at 3yrs; HR=2.9; P<0.001). In multivariable analysis, residual NPM1 MRD burden prior to alloHCT was associated with risk of relapse and death in a dose-dependent manner. Patients were further subdivided based on their reported FLT3-ITD mutational status at baseline. Increased risk of relapse was seen for NPM1 MRD positive patients regardless of baseline FLT3-ITD. 123 (66%) patients were co-mutated for FLT3-ITD at baseline and previously tested for FLT3-ITD MRD using the IVS assay (PMID: 38696205). Defining MRD based on the presence of NPM1 vs NPM1 and/or FLT3-ITD revealed equivalent levels of relapse (45% vs 44%) and OS (45% vs 46%) at 3 years, with NPM1 MRD evaluation identifying 5 additional relapses. These results were confirmed in the full Pre-MEASURE cohort (n=317) using the AMP assay. NPM1 MRD positive patients receiving nonmyeloablative conditioning or reduced-intensity conditioning (RIC) without melphalan (mel) had increased risk of relapse or death compared to patients receiving myeloablative conditioning or RIC with mel, regardless of FLT3-ITD co-mutational status (3yrs: relapse 87% vs 55%, P=0.006; OS 15% vs 42%, P=0.013). Conclusions: In patients with NPM1 mutated AML from the Pre-MEASURE study, we show that detection of residual NPM1 variants in pre-transplant blood during CR1 using a highly sensitive DNA-based assay is associated in a dose-dependent manner with a significantly increased risk of relapse and death after allo-HCT, which can be mitigated in part by conditioning regimen. In patients co-mutated for both FLT3-ITD and NPM1 at diagnosis, NPM1 should be prioritized as a target for NGS-MRD if only one test is available.

Background: Despite recent major advances, multiple myeloma (MM) remains incurable using therapies that are currently available to patients. There are no available/approved targeted therapies for MM that allow for individualized therapeutic approaches to the disease. Fifteen to 20% of newly diagnosed MM patients have a translocation of chromosomes 4 and 14 [t(4;14)]. This translocation puts transcription of the gene encoding Multiple Myeloma SET domain-containing protein (MMSET or NSD2), a histone 3 lysine 36 methyltransferase, under the control of the IgH super enhancer. The super enhancer drives the overexpression of MMSET resulting in an abnormal histone code that promotes myelomagenesis. Presence of t(4;14) is associated with poor clinical prognosis (Caro Am Soc Clin Oncol Educ Book 2021). KTX-1001 is a first-in-class, selective and potent small molecule inhibitor of MMSET, and is the first investigational drug that targets the t(4;14) MM patient population. Methods: This Phase 1, first-in-human open-label study (NCT05651932) is designed to assess the safety, tolerability, and initial clinical activity of KTX-1001 in relapsed/refractory MM (RRMM) and has two parts: dose escalation (Part A) and dose expansion (Part B). In Part B, only t(4;14)+ MM patients will be evaluated in single agent and combination expansion cohorts. Patients with RRMM who have received ≥3 prior lines of therapy, including a proteasome inhibitor (PI), immunomodulatory drug (IMiD), and anti-CD38 antibody are eligible for the dose escalation part. The primary objective of the study is to determine a recommended phase 2 dose (RP2D) and schedule of KTX-1001. Secondary and exploratory objectives include pharmacokinetic (PK) and pharmacodynamic (PD) evaluation of KTX-1001, and assessment of preliminary efficacy. KTX-1001 is administered orally until disease progression, unacceptable toxicity, or patient withdrawal from the study. A Safety Review Committee (SRC) reviews all data prior to each dose escalation decision and on a regular basis. Results: As of June 14, 2024, 17 patients (pts) have been treated with KTX-1001 in Part A across 6 dose levels (SRC reviewed). Dose Level 7 has been reached with no dose limiting toxicities (DLTs). Ten of these patients are t(4;14) positive. Patients have received a median of 5 prior lines of therapy (range: 3-17), including 7 pts with prior BCMA CAR-T, 6 pts with prior BCMA bispecific and/or ADC, 5 pts with prior non-BCMA bispecific, and 12 pts with prior transplant. Patient demographics include a median age of 68 years (range: 50-83), 10 male/ 7 female, 13 white, 2 black or African American, 2 as other identified race, and 3 Hispanic or Latino pts. Six pts remain on treatment. KTX-1001 shows excellent tolerability to date, with treatment emergent AEs (TEAEs) (CTCAEv5.0) predominantly of grade 1 and 2 severity. Most frequently observed TEAEs suspected to be related to KTX-1001 include fatigue (35%), diarrhea (24%), and constipation (24%), for which no grade 3 or 4 events have been observed. Frequency and/or severity of TEAEs have not increased with escalating doses nor treatment duration, and no patients have discontinued due to an AE. Two heavily pre-treated patients [one known to be t(4;14)+] have demonstrated long-lasting stable disease and clinical benefit at 10 and 7 months respectively. Both patients have been intra-patient dose escalated. For PK and PD data collected to date, drug exposure and steady-state concentration of KTX-1001 (at C1D15, when steady state concentration has been reached) appear to be dose-proportional. PD data indicate a dose-dependent decrease in the H3K36me2 mark on treatment versus baseline across dose levels 1 through 6, suggesting that the RP2D may be reached by year end. Updated results will be presented at the congress. Conclusions: Dose escalation of KTX-1001 is ongoing with an excellent tolerability profile to date and demonstration of on-target pharmacodynamics that will inform the determination of the RP2D(s). These PD data support the targeted mechanism of action of KTX-1001 to selectively inhibit MMSET and break the t(4;14) histone code. Given KTX-1001's promising safety profile, single-agent and combination expansion cohorts with standard-of-care MM agents/classes will begin in 2025 to further optimize doses and provide proof of concept in Part B of the Phase 1 study.

Introduction: Progression-free survival (PFS), a common endpoint used in multiple myeloma (MM) trials, is defined as the time to the earliest occurrence of disease progression or death. Disease progression in MM is determined by the International Myeloma Working Group (IMWG) Uniform Response Criteria based on imaging and biomarkers from blood, urine, and bone marrow biopsy testing. In clinical trials, IMWG-recommended assessments are protocol-based. However, in routine care, they are often performed and recorded differently based on a multitude of factors. Algorithms to ascertain progression events using routinely collected biomarker data are important to classify disease progression and align with trial-defined endpoints. The aim of this study was to construct and evaluate an optimized real-world Progression (rwP) algorithm using real-world data (RWD) and demonstrate how biomarker data are captured and used to derive endpoints in routine practice. Method: A cohort of 270 transplant ineligible newly diagnosed MM (NDMM) patients starting on lenalidomide and dexamethasone (Rd) and meeting select MAIA (NCT02252172) trial eligibility criteria were included from the nationwide Flatiron Health electronic health record-derived deidentified database from 2015 to 2023. A rwP algorithm was developed using key IMWG-recommended biomarkers, including serum protein electrophoresis (SPEP), urine protein electrophoresis (UPEP), and serum free light chains (FLCs). The initial baseline value for each biomarker was identified, if available, during either the pre-treatment period (45 days prior through 7 days after first line treatment (1L) start) or the on-treatment period (8 days after 1L start through 1L end). Available biomarkers were followed for disease assessment. If any subsequent lab value during 1L treatment was less than the initial baseline value, the subsequent “on-treatment baseline” value was adopted as the new lowest value; otherwise, the initial value was used as the “on-treatment baseline” value for assessing progression events. Three hierarchies, which describe the order of utilizing key IMWG-recommended biomarkers to define rwP events, were explored, including: i) strict hierarchy (SH) [SPEP > UPEP > FLCs], ii) partial hierarchy (PH) [SPEP = UPEP > FLCs], and iii) no hierarchy (NH) [SPEP = UPEP = FLCs]. The PH option closely resembles the IMWG criteria, with FLCs only used for rwP assessments when no measurable SPEP/UPEP are identified. The NH approach considers all available biomarkers equally and resembles how they are utilized in real-world clinical practice. The feasibility of requiring confirmatory events following the first suggestion of progression was also explored. Assays following the progression event up to the end of 1L were considered as confirmatory progression events if they met IMWG criteria. Results: We applied the rwP algorithm with each hierarchy to all eligible NDMM patients (N = 270). The SH, PH, and NH approaches respectively identified 106 (39.3%), 106 (39.3%), and 123 (45.6%) patients with progression events. Results from the SH and PH approach are consistent likely due to the rarity of UPEP usage in clinical practice. The NH approach captured more progression events because FLCs are used in the presence of measurable SPEP or UPEP, resulting in 73 progression events captured by FLCs in comparison to 32 from other approaches, potentially increasing false-positive capture due to the volatility of FLC results. Among the 106 PH-identified patients who progressed, 84 (79.2%) had IMWG-recommended tests following their progression event, and 74 (69.8%) had their progression confirmed before the end of 1L, with 35 (33.0%) receiving their confirmatory reading within 2 months after progression. These results suggest that requiring confirmatory testing as part of the rwP is feasible, though it may introduce false negatives for patients receiving clinical progression confirmation instead of getting confirmatory labs in routine clinical practice. Conclusion: We present a comprehensive assessment of important design choices for an rwP algorithm for NDMM, including a hierarchy of key assays and the inclusion of confirmatory testing. Further validation of the algorithm with health care provider assessments and the inclusion of bone marrow biopsy and imaging data is important for evaluating the performance of the algorithm in RWD.

Introduction The major primary genetic events in multiple myeloma (MM) include translocations of immunoglobulin heavy chain (IgH) and a hyperdiploidy (HRD) karyotype (Kuehl, 2002). The main drivers of pathogenesis in HRD MM are unknown (Fonseca, 2003), therefore further research is needed in this subgroup. Translational research in HRD MM has relied on analysis of genomic and transcriptomic alterations, however changes at the genome and transcriptome level do not always correlate directionally with protein abundance. Because proteins are considered the main and final effectors of most cellular processes, proteogenomics can improve our understanding of the drivers of HRD MM. We employed it here to identify potential biomarkers in HRD MM and determine whether any given trisomy may lead to a selective advantage. Methods: Protein and mRNA were extracted from CD138+ cells from 47 HRD MM and 14 t(11;14) MM samples. RNA-seq data was processed using MAPR-Seq pipeline using default parameters and gene-wise raw counts were extracted for each sample. Proteomics data was processed using Specranaut and protein-wise intensities were extracted for each sample. A generalized linear model configured with negative binomial distribution was used to analyze gene-wise count data in edgeR software. Genes with an adjusted p-value of <=0.05 were considered significantly differentially expressed. Protein intensity data were normalized using trimmed mean of M-values and log2 transformed. A generalized linear model configured with Gaussian distribution was used to compare protein intensities between the groups. Proteins with an adjusted p-value <= 0.05 were considered significantly differentially expressed. Two separate rank lists for protein and mRNA data were generated for pathway analysis. Results When comparing our small cohort of HRD MM patient samples to those with t(11;14) MM, out of approximately 16,600 total transcripts and 9,700 proteins evaluated we identified approximately 2,100 significantly variant transcripts and 500 significantly variant proteins in HRD MM. GSEA pathway analysis of the transcriptome and proteome revealed significantly upregulated translational and ribosomal processing pathways in HRD MM. Transcriptional MYC target pathways were also significantly upregulated. RNAseq revealed significantly higher expression of MYC (Rank 24.5, p=5.3E-6) in HRD MM. It is known that patients with t(11;14) MM have increased expression of CCND1 (Kuehl and Bergsagel, 2012). As expected, we found that the HRD MM subgroup had significantly lower expression of CCND1 compared to the t(11;14) MM subgroup (Rank -79.9, p=2.8E-21) and this was also true at the protein level (Rank -21.7, p=2.2E-5). The most significantly abundant protein in the HRD MM subgroup compared to t(11;14) MM was BAHD1, which is a protein that targets H3K27me3 and recruits transcriptional co-repressors (Fan, 2020). BAHD1 has been shown to repress erythroid genes and may recruit HDAC5, which plays a role in osteoblast differentiation (Rank 72.3, p=2.9E-20; Xu, 2021). The second most abundant protein in HRD MM was CKS2, which binds to the catalytic subunit of cyclin-dependent kinases (CDKs) and therefore positively regulates cell proliferation, invasion, and migration and has been shown to be implicated in tumor progression in several solid tumors (Rank 72.3, p=2.9E-20; You, 2015). TRIM proteins promote tumorigenesis and cancer development. TRIM14 mediates cancer development through mediating the JAK/STAT, PI3K/AKT, NFkB, and p53 pathways and was found to be significantly higher in HRD MM (Rank 70.6, p=9.0E-20; Huang, 2022). We also found increased protein abundance of LAMP5 in HRD MM (Rank 20.1, p=6.0E-5). LAMP5 may play an important role in MM progression and has been shown to be highly expressed in patients with mixed-lineage leukemia and is associated with a poor prognosis (Wang, 2023; Gracia-Maldonado, 2022). We identified many proteins (not all discussed here) that have not been well described in HRD MM and thus may be biomarkers/targets of disease pathogenesis of this subgroup. Conclusion Proteins are the final effectors of most cellular processes. Proteogenomics can improve our understanding of the drivers of HRD MM. Using a proteogenomic approach we identified proteins that may lead to selective advantage and/or pathogenesis of trisomies in HRD MM.

Introduction Multiple Myeloma (MM) precursors Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM) have variable risk of progression to active MM, and identifying which patients may progress remains a clinical challenge. Deep proteome profiling of peripheral blood (PB) plasma may advance non-invasive precursor disease staging, monitoring and characterization. Here, we performed plasma proteomic profiling on patients across the MM disease continuum as well as progressive and stable disease to provide insights into MM disease biology and identify protein-based high-risk disease features that may improve prognostication. Methods We performed high-throughput profiling of >5400 proteins using the Olink® Explore HT library and Proximity Extension Assay (PEA) technology. We profiled 529 PB plasma samples from 485 individuals, including MGUS (n=100), SMM (n=203), MM (n=100), and healthy donors (HDs) (n=82). Sequential samples from SMM-MM progressors (n=32) and stable SMM-SMM non-progressors (n=32) were also profiled, where precursor samples ranged 1.04-6.91 years (median 2.33 years) prior to MM progression. T-tests, ANOVAs, and linear mixed effect (LME) models were used to identify significant proteins across disease stages, progression status, and time. Results were adjusted for multiple testing using Benjamini-Hochberg procedure. A subset of 86 individuals (13 HDs, 14 MGUS, 41 SMM, 18 MM) also had single-cell RNA sequencing (scRNA-seq) performed on tumor and immune cells from paired PB/BM collected at the same timepoint to enable cellular mapping of signals detected in plasma. Results We successfully captured significantly dysregulated proteins including proteins highly expressed on the surface of plasma cells, such as BCMA, SLAMF7 and CD38, highlighting the utility of PEA technology to monitor soluble levels of clinically relevant targets. Given SMM patients are clinically heterogeneous and can resemble MGUS or MM, we aimed to improve discrimination of disease states using biological features obtained from the plasma proteome. We developed a classifier using an Elastic Net model for each stage, where plasma proteins such as SLAMF7, BCMA, TACI, FCRL5 and others had the highest importance scores for the model. We demonstrated 97% SMM/MM samples could be identified from healthy samples (AUC=0.82), indicating our classifier could reliably screen disease-related cases. Moreover, 84% of SMM cases were correctly classified as clinically defined SMM, while misclassified samples were labelled as MM cases. Interestingly, the misclassified patients had consistently rising M-spike levels during clinical follow-up but without a shift in clinical stage or risk status, suggesting the plasma proteome may provide earlier indication of evolving disease. Since proteomic information may also hold value for improved risk stratification, we evaluated protein levels in precursor stage timepoint samples from SMM-MM progressors and stable SMM non-progressors. We identified a prognostic five-protein signature that was significantly elevated in SMM-MM progressors, of which BCMA and TACI were top proteins, as well as proteins vital for calcium homeostasis and integrin-mediated cell adhesion. BCMA and TACI levels also had a strong positive correlation with BM plasma cell infiltration, which suggests these proteins may be useful surrogates of BM tumor burden during routine blood-based assessment of precursor patients. Lastly, integrative analysis of scRNA-seq of tumor and immune cells was used to elucidate cell origin level information of the prognostic signature proteins. Four proteins were highly expressed in malignant versus non-malignant plasma cells, suggesting our prognostic signature partially provides a readout of plasma cell biology. Meanwhile, one protein was constitutively expressed on most leukocytes, suggesting that the interplay of other cell types in the immune microenvironment involved in progression may also be reflected in the plasma. Conclusion We performed the first comprehensive analysis of the plasma proteome of MM and its precursor conditions. Overall, we developed a classifier that utilizes plasma proteins alone to accurately classify disease stages and identified a prognostic protein signature associated with progressive disease.

Introduction: Telomere biology disorders (TBD) are multisystemic accelerated aging syndromes due to abnormal telomere length (TL) shortening and increased cancer risk. Pediatric-onset TBD is characterized by severely shortened TL and specific associations with germline pathogenic variants mainly in DKC1, TINF2, PARN genes and characteristic clinical features [bone marrow failure (BMF), abnormal nail/skin/oral mucosa]. Adult-onset TBD is genetically distinct (predominantly, TERT, TERC, and RTEL1 mutations) with varied organ involvement [interstitial lung disease (ILD), BMF, cirrhosis, or bone disease] and heterogenous outcomes. In our previous study of predominantly adult patients with short telomeres, multiple organ involvement (Mangaonkar A et al. Blood Cancer J 2021) predicted poor overall survival (OS). We sought to validate these findings in a larger, well annotated adult cohort of TBD patients. Methods: Patients with suspected TBD underwent comprehensive clinical and molecular evaluation through our TBD-dedicated clinic after regulatory approval. TL was assessed through clinical flow cytometry fluorescence in-situ hybridization (FlowFISH).Clinical and laboratory features, including pulmonary imaging, lung volumes/function testing, hepatic imaging (MR elastography/fibroscan) and bone density assessments were retrospectively abstracted. Germline and somatic genetic testing was performed on peripheral blood through targeted panels for known TBD and myeloid genes respectively. Cox proportional hazard model was used to identify significant variables that also retained independent prognostic impact. Results: Overall, 122 patients met criteria for TBD as defined by lymphocyte TL (LTL) <=10th percentile and/or a presence of a germline-TBD variant; 34 patients LTL <1st percentile, 84 1st and the 10th percentile (27 with TBD-germline variants) and 4 with only TBD-germline variants. Median age at diagnosis was 56 (18-77) years, with 72 (59%) males. Clinical features included presence of ILD (n=81, 66%), BMF (n=39, 32%), cirrhosis/hepatic fibrosis (n=19, 38%), bone disease osteopenia/osteoporosis/fractures, n=53, 70%), MDS/AML (n=11, 9%), non-hematological cancer (n=38, 31%), and head and neck cancer (n=2). Germline pathogenic variants were detected in 22 (18%), most frequently in TERT (n=10), RTEL1 (n=7) and TERC (n=2), while variants of uncertain significance (VUS) were seen in 35 (29%) patients, especially in TERT (n=20), TERC/RTEL1(n=6), NAF1 (n=4). Somatic variants in hematopoietic cells were detected in 37 (30%) patients with frequent alterations seen in DNA damage response and repair (PPM1D/TP53, n=11), and splicing component (U2AF1/SF3B1/ZRSR2, n=9) genes. At last follow-up [median of 3 (0-18) years], there were 26 (21%) deaths (most commonly, progressive ILD-18, cancer of lung/breast/pancreas-3, transplant-related complications-2). After censoring patients at the time of any transplantation (lung transplant-26, liver transplant-5, bone marrow transplant-8), the Kaplan-Meier estimate of median OS was not reached. Univariate survival analysis found adverse OS predicted by older age at diagnosis (P=0.006), presence of ILD (P=0.0002), hepatic fibrosis/cirrhosis (P=0.02), and somatic DNA repair variants (P=0.03). Multivariate model analysis found that only presence of ILD retained independent prognostic impact (HR=2, P=0.02). Among radiographic ILD subtypes, UIP was the most common (51%), followed by NSIP (10%), and others (6%), with median FEV1 of 2L (range: 0.5-4.6), FVC 2.4L (0.7-6.2), and TLC 4L (1.9-7.4). TBD-ILD patients were older (P<0.0001) with a male preponderance (P=0.03), 52 (64%) with multi-organ involvement, and had a less severe hematological phenotype [lower incidence of BMF (P<0.0001) and fetal hemoglobin elevation (P=0.01)], more frequent bone disease (P=0.0009), relatively longer telomeres in lymphocytes (P=0.04), and no particular association with any germline or somatic genetic alterations. Conclusions: Presence of ILD is an independent and significant predictor of adverse survival in adult TBD. Within adult TBD, patients with ILD represent a unique clinical subtype without any obvious genetic or pathologic correlates. Focusing on definitive therapeutic measures in this subgroup of adult TBD has the best chance to alter natural history and improve outcomes.

Background: Anaplastic large cell lymphomas (ALCLs) represent a heterogeneous group of T-cell lymphomas that currently are classified by the presence or absence of ALK tyrosine kinase (TK) fusion genes (ALK+ or ALK−) and clinical presentation (systemic, cutaneous, or breast implant-associated). Two overarching molecular types of ALCL recently were discovered, defined by the presence (Type I) or absence (Type II) of a gene expression signature highly enriched for JAK-STAT3 activation. Fusions involving non-ALK TK genes occur in some ALK− cases, but the fusion landscape of ALCL remains incompletely characterized. Methods: Expert consensus pathology review was conducted in the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). RNAseq was performed and fusions were identified using FusionCatcher. Here, we focused on recurrent in-frame coding fusions. Previously unreported fusions were validated by RT-PCR. Type I/II was assigned using a previously validated gene expression-based model. Genes with adjusted (adj) P<0.05 were considered differentially expressed. Overall survival (OS) was assessed for systemic ALCL, when available. Results: We evaluated 379 ALCLs (229M/150F; mean age, 56 y). Of 199 candidate fusions (excluding reciprocal events), 28 were recurrent and passed quality metrics. At least 1 of these 28 fusions was present in 150 cases (40%). ALK fusions were present in 106 ALK+ ALCLs (all Type I; P<0.0001). Of these, NPM1::ALK was seen in 68/106 (64%). Alternate partners (X::ALK) included ATIC (N=24) and CTLC, COL1A2, MSN, MYH9, RNF213, SATB1, TFH, TPM3, and TRAF1 (1-3 cases each). X::ALK was associated with older age (mean, 52 y) than NPM1::ALK (32 y; P<0.0001) and showed relative overexpression of 49 genes, including multiple activators of small GTPases such as CGNL1 (FC, 5.6; Padj=2.4×10-6), SRGAP1, ALS2, DOCK1, and NCKAP1. ALK expression was similar in cases with NPM1::ALK and X::ALK and there was no significant difference in OS. Non-ALK TK fusions were seen in 17/273 ALK− cases (6%), including TYK2 (N=9); JAK2 (N=6); and ROS1 (N=2). All were Type I ALCLs (P<0.0001). ALK− cases with non-ALK TK fusions overexpressed 38 genes compared to ALK− cases without TK fusions, including cytokines involved in the IL17 signaling pathway such as CXCL6 (FC, 12.5; Padj=4.2×10-3), CXCL1, and CSF3. We then assessed the relationship between ALK− cases with non-ALK TK fusions and ALK+ cases by separately comparing each subset to ALK− cases without TK fusions. FC values for ALK− cases with non-ALK TK fusions were significantly correlated with FC values for ALK+ ALCLs over the set of expressed genes (R=0.54; P<0.0001). At a median follow-up of 32 months, 0/4 systemic ALCL patients with non-ALK TK fusions had died, while 30% of those with ALK− ALCL without TK fusions had died, but this was not statistically significant (P=0.26). The other 13 ALK− cases with non-ALK TK fusions were either localized ALCLs or did not have outcome data available. Of 20 TP63 fusions, 17 (85%) were TBL1XR1::TP63 and 3 were X::TP63. As previously reported, TP63 fusions were associated with poor OS (median OS, 10 months; not reached for other ALK− ALCLs; P=0.007). Four ALCLs had a novel NCL::UBTF fusion involving genes encoding the nucleolar proteins nucleolin and nucleolar (upstream binding) transcription factor-1. Four cases each had PARG::BMS1 and TNK1::GPS2 (the latter seen only in Type I ALCLs). Conclusions: This large consortium-based analysis of coding fusions further elucidates the molecular distinction between Type I and Type II ALCLs. Both ALK and non-ALK TK fusions were seen exclusively in Type I ALCLs, which we have shown previously are enriched for JAK-STAT3 pathway genes and correlate strongly with pSTAT3Y705 positivity by immunohistochemistry. These fusions were not identified in Type II ALCLs, which previous data suggest are associated with epigenetic alterations. ALK+ ALCLs with NPM1::ALK and X::ALK showed differences in gene expression, suggesting biological differences. Furthermore, ALCLs with non-ALK TK fusions shared gene expression features with ALK+ ALCL. Clinical fusion detection could enhance molecular classification of Type I and Type II ALCLs and may guide precision therapy.

Introduction: Understanding patient preferences and decision-making processes is crucial in managing relapsed multiple myeloma (RRMM). Shared decision-making has become a key element of patient-centered care, allowing patients to collaborate with healthcare providers in choosing treatments that align with their health and quality of life goals. This study examines how the perceived severity of treatment side effects influences patient decision-making autonomy and communication patterns, providing insights into decision-making practices in hematology. Methods: A survey was conducted with 784 RRMM patients as of January 1, 2024, using HealthTree Cure Hub (PMID: 35271305). Among the participants, 333 experienced a relapse and responded to the question about how side effect severity affected their treatment choices. Of these, 54% (n=181) considered side effects to be very or extremely influential (VE), whereas 46% (n=152) regarded them as somewhat, slightly, or not influential at all (SSN). Participants evaluated these factors on 5-point Likert scales: 1 (very poor) to 5 (very good); 0 (not at all influential) to 4 (extremely influential). The survey assessed patient involvement in final treatment decisions, discussions with fellow patients, family or caregivers, and the time allocated and used for decision-making. Results: Patients in the VE group reported a better understanding of the likely side effects of treatment options compared to the SSN group (VE: 3.9±1.0; SSN: 3.6±1.0, p<0.05), with a higher proportion reporting a very good understanding (VE: 31%, n=45; SSN: 18%, n=29, p=0.036). A weak relationship confirmed VE patients engaged in additional side effect education before making a change in treatment decisions (r=0.164, p=0.004). VE patients also reported a better understanding of the balance between effectiveness and potential side effects of treatments than SSN patients (VE: 3.8±1.0; SSN: 3.5±1.1, p<0.05), with more VE patients reporting a very good understanding (29%, n=41 vs. 15%, n=24, p=0.006). A weak relationship confirmed that VE patients more closely evaluated the trade-offs of treatment options when choosing a new treatment regimen (r=0.197, p<0.001). Likely to achieve a better understanding, more VE patients talked with fellow patients (VE: 19%, n=24; SSN: 7%, n=11, p = 0.003) and rated these conversations as more influential than SSN (VE: 1.5±1.5; SSN: 1.1±1.4, p<0.05). Additionally, VE patients were more likely to discuss treatment options with family or caregivers before making a final decision (VE: 49%, n=61; SSN: 29%, n=43, p<0.05). Moreover, VE patients reported greater involvement in their doctor-patient communication approach (VE: 3.2±0.8; SSN: 2.8±0.8, p<0.05), with scores ranging from 1 (“...follow the doctor's suggestions”) to 4 (“...we discuss my preferences... and I make the final decision”). Furthermore, VE patients reported more self-determination in the treatment decision-making process than SSN patients, with more VE patients making the final treatment decision themselves rather than having their doctor decide (“I made the final decision” - VE: 66%, n=93; SSN: 52%, n=81; “My doctor made the final decision” - VE: 34%, n=47; SSN: 46%, n=72, p=0.013). VE patients reported being provided 1.5 more weeks of time to make a treatment decision than the SSN (4.8±8.9 v. 3.3±6.0 wks, p<0.05). Subsequently, VE patients reported using 79% more time than SSN patients to arrive at a final decision (VE: 2.5±6.0; SSN: 1.4±1.9 wks, p<0.05), likely attributed to time spent gaining a better understanding, having more conversations with patients and family, more shared decision making and self-determination in their treatment decision. Conclusion: The study highlights that RRMM patients who consider side effects to be very or extremely influential in their treatment decisions report being more proactive in understanding and discussing their treatment options. These patients engage more with healthcare providers, peers, and family members and are granted more time to make informed decisions. This emphasis on understanding and communication underscores the importance of personalized patient education and shared decision-making in the treatment of RRMM, especially for patients who are highly influenced by potential side effects. Together, these findings underscore the ongoing need for patient-centered care and education to optimize treatment choices.