Citations

Abstract Introduction: A subset of patients with chronic lymphocytic leukemia (CLL) experiences disease progression from CLL to Richter transformation (RT), characterized by the occurrence of a high-grade lymphoma. The outcomes of patients with RT are typically dismal and identifying better therapeutic strategies remains critically important for these patients. To discern the biologic underpinnings driving this transformation, we studied immune changes and genetic evolution in samples from CLL patients who developed RT. Methods: We identified 18 patients with CLL who developed RT; and performed single cell (sc) RNA-sequencing(seq) on sorted B and T cells from peripheral blood mononuclear cells (PBMCs) at the time of RT (n=18) and at the earlier time of CLL diagnosis (n=11). The median time between the CLL sample and the one closest to RT was 4.3 years (range, 0.2-7.3); and patients received a median of 1 CLL directed treatment (range, 0 – 7). A total of 749,906 cells and 36,601 genes were captured during scRNAseq and 720,580 cells and 21,892 genes were retained for downstream analysis, respectively. Analysis was performed using the immunopipe (https://github.com/pwwang/immunopipe) that wraps around Seurat (v5.1.0) and identified enriched pathways using Enrichr service. The sc-T cell receptor (TCR)-repertoire(R)-seq data was analyzed and visualized using scplotter (). The whole exome sequencing (WES) analysis with tumor B-cells from same patients of CLL & RT stages were done using the Agilent sureselect Human all exon v5 + UTR capture kit. Reads from paired end sequencing were processed with the sarek pipeline. Results: Analysis of 720,580 cells from 11 CLL and 18 RT samples showed 16 distinct cell types- 3 B and 13 T cell subtypes. We observed an increasing trend of CD8 TEM [mean 0.548 vs. 0.384, p = 0.12 (Wilcoxon test)] but a decreasing trend of CD4 TCM (mean 0.236 vs. 0.337, p = 0.08) and B-memory (mean 0.0085 vs. 0.0195, p = 0.51) subsets in RT compared to their CLL stage. When comparing transcriptional changes, we found that among the top 18 highly variable genes, the expression of FP236383.3 (lncRNA) and TCL1A was significantly higher (p<0.0001) in the B cell subsets of patients with RT vs. CLL stage. Additionally, in T cell subsets—primarily dnT, γδT, and CD8/CD4 naïve cells—the expression of FP236383.3, NKG7, GNLY, and CCL5 was significantly higher (p<0.0001), while the expression of LDHB, GZMK, and IL32 was significantly lower (p<0.0001) in RT vs. CLL stage samples. Comparison of specific gene set enrichment analysis showed significantly increased expression of GZMB in gdT (p<0.001) and decreased expression of GZMA, GZMK, TIGIT and TCF7 in dnT and CD8 Naïve cells (p<0.001) for RT vs. CLL stage. The top 10 differentially expressed pathways in RT included oxidative phosphorylation, allograft rejection, IFN-α/-γ responses, TNF-α signaling via NF-κβ, complement, Myc targets V1, mTORC1, hypoxia, and p53 signaling across various B and T cell subsets compared to the CLL stage. Initial analysis of TCR-R data showed that patients with RT stage have lower number of unique clones (p<0.5) and reduced clonal diversity (p<0.5), and a higher number of large and hyperexpanded clones compared to their CLL stage. Additionally, WES data indicated that the most frequently mutated gene is TP53 in tumor B cells of both CLL and RT stages. However, a higher percentage of RT-stage samples carry mutations in MGA, ATM, and IRF2BP2 genes compared to their CLL-stage. WES data also showed a trend toward differing frequencies of copy number amplification at 19q12 (p=0.10) and 4q13.1 (p=0.52) between RT and CLL stages. Conclusions: The main findings of our study are as follows:The transcriptional landscape showed significant changes in multiple genes and pathway enrichment related to cell growth, differentiation and immune response in RT vs. CLL stage.A decreased fraction of CD4 TCM cells and reduced TCR clonal diversity were observed in samples at the RT stage and may indicate a poor prognosis for the patients at this stage. WES data of tumor B cells also indicated possible genetic alterations in the RT vs. CLL stage. Data from in-depth analyses on understanding of the dynamics of T cell response and additional experiments on these patients are expected to provide valuable insights into tumor genetic heterogeneity, transcriptional and immune regulation in blood-based immune subsets.

Abstract Background Chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) is the most common leukemia in US adults and incidence increases with age. Although the treatment (tx) landscape has evolved, it is unclear whether tx selection differs for older patients (pts), given concerns around comorbidities, toxicity, and efficacy. There is a need to better understand pt characteristics, tx patterns, and biomarker use among pts aged ≥65 yrs with CLL/SLL in the current therapeutic era. Methods A retrospective longitudinal study was conducted using the de-identified 100% Medicare Fee-for-Service (FFS) Research Identifiable Files. Included pts were diagnosed with CLL/SLL at age ≥65 and initiated first-line (1L) tx 1/1/2020-9/30/2024 (with ≥12 mo in database prior to 1L tx start). Index date was defined as date of 1L tx start, and pts were followed to death, end of Medicare enrollment, or study end, whichever occurred first. 1L tx was defined as any CLL pharmacologic tx within the first 90 days of index date. Tx utilization was summarized as the number and proportion of pts who received each tx at each line. Descriptive statistics were used to summarize demographic and social characteristics and baseline comorbidities, in all pts and by 1L tx. Proportion and timing of documented biomarker orders any time prior to index were summarized by test type (CLL fluorescence in situ hybridization [FISH], TP53 DNA sequencing and immunoglobulin heavy chain variable region [IGHV] test) and by 1L tx. Results Of the 21,008 pts included, median age was 76.4 yrs at 1L tx, with 43% of pts aged 65-74 yrs, 43% aged 75-84 and 14% aged ≥85. The majority of pts were male (58%) and White (91%). About 8% and 9% of pts had dual eligibility status and low-income subsidy, respectively. Median Charlson Comorbidity Index (CCI) was 4 (range: 1-21), with 40% of pts having CCI ≥5. Covalent Bruton tyrosine kinase inhibitor (cBTKi) monotherapies (mono) were most common at 1L tx (41%), including 10% zanubrutinib (zanu), 15% ibrutinib (ibr), and 16% acalabrutinib (acala). B-cell lymphoma 2 (BCL2)+anti-CD20 monoclonal antibody (mAb) was less common (11%), and chemoimmunotherapy (CIT) was used by 13% of pts, mostly BR (8%) and FCR (3%). Other less common tx included cBTKi+anti-CD20mAb (6%) or cBTKi+BCL2 therapies (1%). Among remaining pts, most received anti-CD20mAb (22%). The use of cBTKi mono increased from 41.7% in 2020 (ibr 30.6%; acala 11.1%) to 47.6% in 2024, driven by increased use of zanu (27.8%; acala 15.7%; ibr 4.2%). BCL2+anti-CD20mAb increased from 9.2% in 2020 to 12.6% in 2024. Pts treated with cBTKi mono were older than those with BCL2+anti-CD20mAb (median age 77.2 vs 74.5), and more likely to have dual eligibility status (10% vs 4%) or low-income subsidy (11% vs 5%). More pts who received zanu mono had CCI ≥5 (39%) versus acala (36%), ibr (35%), or BCL2+anti-CD20mAb (32%). Pts with BCL2+anti-CD20mAb were less likely to have diabetes (23% vs 26%), congestive heart failure (CHF; 16% vs 19%), chronic pulmonary disease (23% vs 27%), and renal diseases (23% vs 27%). Pts with ibr were less likely to have myocardial infarction (29% vs 34%), coronary artery disease (22% vs 25%), CHF (16% vs 19%), and atrial fibrillation (12% vs 18%). Overall, 69% of pts had FISH testing prior to 1L tx, but test orders for TP53 (12%) or IGHV (28%) status were infrequent. Pts with ibr had the lowest rates of TP53 tests (6%), whereas pts with CIT had the lowest rates of IGHV tests (16%). The timing of tests showed a binomial distribution where most pts received FISH, TP53, or IGHV tests either ≤60 days prior to 1L tx (46%, 50%, 33%), or >1 yr before 1L tx (30%, 22%, 43%). Among pts who received ≥2 lines of therapy for CLL (n=4428; 21%), cBTKi mono was the most common in 2L (41%), including 14% zanu, 17% acala, and 10% ibr. CIT and BCL2+anti-CD20mAb were used in 12% and 9% of pts, respectively. Only 634 (3%) pts had 3L+ tx, of whom 33% received cBTKi mono, including 16% zanu, 12% acala, and 5% ibr (5%). Conclusion This large real-world study found that cBTKi mono was the most common 1L tx choice among pts aged ≥65 with CLL/SLL, with increased use of next-generation cBTKi. Pts with more baseline comorbidities were less likely to receive ibr or BCL2-based tx. FISH testing was not performed in nearly one-third of pts, and most pts did not have TP53 or IGVH sequencing prior to tx. Future studies are needed to continue to evaluate evolving tx paradigms in older pts with CLL/SLL.

Abstract Introduction Understanding the tumor immune microenvironment (TIME) in multiple myeloma (MM) is critical for identifying prognostic biomarkers and therapeutic targets. We hypothesized that specific immune cell populations within the bone marrow TIME are associated with clinical outcomes and could enhance patient stratification. To this end, we conducted the largest CyTOF-based immune profiling study to date in MM, profiling >300 million bone marrow immune cells from patients enrolled in the CoMMpass study. Methods We analyzed >1,000 bone marrow samples from ~700 newly diagnosed MM patients enrolled in the MMRF CoMMpass study (NCT01454297), using a harmonized protocol and 41-antibody CyTOF panel at five academic centers. The majority were baseline samples (n=671), with additional samples spanning up to 3.5 years post-diagnosis (n>300). Each site contributed ~200 samples on average. The cohort included 40% females, with a median age of 63 years and BMI of 27.8. ISS stages were evenly distributed (I: 36%, II: 36%, III: 29%), and 84% had ECOG 0-1. Autologous stem cell transplant was performed in 58% of patients, and 22% received maintenance therapy. Results We profiled 311 million single cells across the cohort, identifying major immune compartments: Neutrophils (36.0%), CD4 T cells (19.7%), NK cells (9.9%), monocytes (7.6%), B cells (5.7%), and CD8 T cells (5.6%). Less abundant populations included dendritic cells, γδ T cells, plasmablasts, and pDCs. In our preliminary analysis with multivariate cox regression modeling, we identified clinical factors including stem cell transplant, ISS stage, and ECOG status, as independent predictors of overall survival (OS). After adjusting for age and sex, higher proportions of naïve B cells, pDCs, and γδ T cells were independently associated with improved OS, while CD4+ T cell abundance was positively associated with prolonged progression-free survival (PFS). In contrast, elevated neutrophil proportions were significantly linked to inferior OS and demonstrated a trend toward shorter PFS. Longitudinal immune profiling in more than 200 patients identified a gradual accumulation of naïve B cells and a pronounced post-treatment rise in CD45+ plasmablasts, highlighting persistent B cell activity as a candidate mechanism for sustained therapeutic efficacy. Conclusion This study presents the largest immune proteomic atlas in MM to date, linking TIME composition to clinical outcomes. A favorable immune landscape was marked by preserved humoral immunity (naïve B cells, plasmablast expansion), enrichment of pDCs and γδ T cells, and lower neutrophil abundance, collectively associated with improved survival outcomes. These findings lay the groundwork for immune-guided prognostication and highlight new opportunities for therapeutic targeting in MM. Ongoing analyses aim to further resolve baseline and longitudinal immune dynamics in relation to treatment response.

Abstract In this study, we investigated tumor intrinsic mechanisms of resistance to cevostamab, a bispecific T-cell engager targeting Fc receptor-like protein 5 (FCRL5) and CD3 that has shown promising clinical activity and durable responses in advanced MM. Longitudinal bone marrow aspirates from nine MM patients treated with cevostamab were analyzed. All patients had previously been treated with both immunomodulatory drugs (IMiDs) and proteasome inhibitors (PI) and eight patients had received daratumumab prior to cevostamab, which was, on average, the 6th line of treatment (mean treatment duration 220±273 days). Five of nine patients responded with ≥PR. Whole genome and transcriptome sequencing was performed on sorted CD138+ MM cells at baseline, and whole exome and transcriptome sequencing was performed on five and three samples, respectively, following cevostamab discontinuation. Baseline FCRL5 expression levels did not correlate with clinical response. However, in one non-responder patient, FCRL5 expression increased four-fold after 28 days of treatment. Given the location of the FCRL5 gene on chromosome 1q—commonly amplified in advanced MM—we evaluated the presence of Gain(1q) in our study cohort. All patients whose best response was ≤PR (n=6) harbored a Gain(1q), whereas it was absent in those who achieved VGPR and sCR (n=3). Crucially, one of the patients that achieved sCR was initially treated with 60 mg of cevostamab for one year and then escalated to a higher dose cohort (90 mg) in the second year. At progression, a clonal FCRL5E115A missense mutation (allelic frequency = 56%) and a subclonal FCRL5G455Efs*19(allelic frequency = 5%) frameshift deletion mutation were detected, both absent at baseline. The missense mutation was located on the extracellular domain of FCRL5 targeted by cevostamab. To validate the functional impact of FCRL5E115Aand FCRL5G455Efs*19 on the sensitivity to cevostamab in vitro, we generated a FCRL5 knockout (KO) model utilizing CRISPR-Cas9 technology in OPM2 MM cells. In vitro cevostamab cytotoxicity assay with PBMCs and pan T cells from healthy donors at various effector to target (E:T) ratios confirmed a complete loss of cevostamab sensitivity in our FCRL5KO models. cDNA encoding patient-derived FCRL5 alterations (FCRL5E115Aand FCRL5G455Efs*19) as well as FCRL5wtwere cloned into mammalian expression vector and reintroduced into FCRL5KO cells. Cevostamab sensitivity was reinstated by FCRL5wt expression in KO cells. In contrast, FCRL5E115A conferred complete resistance to cevostamab (p<0,001) and FCRL5G455Efs*19 depicted a significant decline in cevostamab sensitivity relative to FCRL5wt expressing models. Additional experiment utilizing direct stochastic optical reconstruction microscopy (dSTORM) are ongoing to monitor the membrane localization of FCRL5 mutants. By integrating genomic, transcriptomic and functional data with clinical outcomes, this work is the first to report on acquired FCRL5 mutations causing resistance to cevostamab. We hypothesize that the prolonged treatment duration may have contributed to the acquisition of the target mutations and note a subsequent durable sCR response to BCMA-targeted immunotherapy in the same patient, suggesting immune-related factors such as T cell exhaustion, were not drivers of cevostamab resistance. Due to the limited sample size, these findings require validation in larger cohorts. Nonetheless, our findings begin to elucidate tumor intrinsic mechanisms associated with cevostamab resistance.

Abstract Introduction: Multiple Myeloma (MM) is a complex hematologic malignancy, where patients often display one or more potentially high risk cytogenetic alterations associated with poor outcomes. Traditional high risk cytogenetics events in myeloma, such as NSD2 t(4;14) translocations, gain of 1q21, or deletion of 17p13, have often been studied in isolation, but emerging evidence suggests the combination of these high risk alterations has a greater negative prognostic impact than any on their own. This has been recently reflected in risk stratification guidelines from IMS and IMWG, which consider certain events, such as t(4;14), as high risk only in combination with gain of 1q21 or deletion of 1p32. Still, the mechanisms through which multi-hit high risk events disrupt the microenvironment and promote poor outcomes remains unclear. We hypothesize that myelomas with multiple high risk alterations remodel the immune environment to promote tumor progression and immune escape. Methods: To study the role of multi-hit high risk cytogenetics on the immune microenvironment, we analyzed CD138-bone marrow aspirate single-cell RNA sequencing (scRNAseq) data from 337 newly diagnosed MM patients from the MMRF CoMMpass study, generated as part of the Immune Atlas initiative. Cytogenetic alterations for these patients were derived from whole-genome and whole-exome sequencing of CD138+ bone marrow aspirates from the MMRF CoMMpass cohort. Patients were classified as multi-hit high-risk MM if they exhibited either (i) two high-risk cytogenetic events, including NSD2 t(4;14), MAF t(14;16), del(17p13), or (ii) one high-risk event with gain of 1q21. Results: Based on cytogenetic information, 33 patients in the Immune Atlas cohort were classified as 'multi-hit’ high-risk, while 34 patients had only a single high-risk alteration. These multi-hit high risk patents displayed significantly worse progression free survival compared to standard risk patients, adjusting for age, therapy, and ASCT (HR=2.34, p<7.2e-5), while single-hit patients showed a non-significant trend towards worse outcomes (HR=1.51, p=0.071). The most frequent multi-hit risk event in the cohort was NSD2 t(4;14) in combination with 1q21, observed in 23 patients. Compositional analysis of the immune populations in the scRNAseq data revealed that relative to both standard and single-hit high risk patients, multi-hit high risk patients displayed significant reductions in IFN-I stimulated CD4+ and CD8+ T cell populations, marked by decreased expression for antiviral response markers such as ISG15, XAF1, and IFI44L. Enrichment of inflammatory CD16+ non-classical monocytes was also observed. Further analysis of the most frequent multi-hit risk event, NSD2 with 1q21, revealed distinct enrichment of late-activated cytotoxic HLA+ CD8+T cells and cytotoxic CD4+.T cells. Neither alteration was observed in patients with single-hit NSD2 or 1q21. Further analysis on the interactions between high risk events revealed distinct transcriptomic profiles dependent on the high risk events involved, particularly in multi-hit events involving mutations or deletions of TP53 or gain of 1q21. Reduction in IFN-I signaling across various immune populations was primarily restricted to multi-hit risk events involving gain of 1q21. Conversely, patients with biallelic loss of TP53 displayed significant enrichment of these same IFN-I populations. Given this contrast in IFN-I signaling spanning multiple immune subtypes, we hypothesized that the malignant myeloma cells might similarly display altered IFN-I activity. Utilizing CD138+ myeloma bulk RNA sequencing data from CoMMpass, we found that IFN-I response scores strongly correlated between paired scRNA immune and bulk RNAseq tumor datasets (R2: 0.75, p=1.63e-32). Across 660 patients with bulk RNAseq and cytogenetic data, we confirmed significant reductions in IFN-I response scores in tumor cells in multi-hit high risk involving 1q21, and significant enrichment in those with complete loss of TP53 relative to standard-risk MM. Conclusions: Overall, we do observe that MM patients with multiple high risk cytogenetic alterations display distinct immunologic traits relative to standard risk or single-hit high risk patients in addition to significantly worse outcomes. Furthermore, we find variability in the immune alterations observed based on the cytogenetic events involved, further illustrating the heterogeneity in how MM shapes the immune microenvironment.

Abstract Background: Recent clinical trials of immune checkpoint inhibitors (ICI) in multiple myeloma (MM) demonstrated the need to prioritize patients most likely to benefit from this type of therapy (Richard, et al., Nat. Cancer, 2024). Immune checkpoints in MM function through receptor-ligand (RL) interactions between CD8+ T cells and malignant plasma cells. However, there is a gap in our understanding of the immune checkpoint landscape in newly diagnosed MM (NDMM). To explore this, we mapped known immune checkpoint RL pairs in subjects enrolled in the CoMMpass Study (NCT01454297), a prospective, longitudinal observational trial. Methods Bone marrow aspirates from study participants were collected and separated into CD138 positive and CD138 negative fractions for comprehensive molecular characterization of the tumor (using bulk RNA sequencing and whole genome sequencing) and tumor immune microenvironment (using 3' single cell RNA sequencing (scRNA-seq)). 191 patients had paired bulk tumor and single cell immune expression profiles. To evaluate the co-expression of receptor-ligand pairs, we developed two approaches that combine gene expression values across single cell and bulk RNA-seq. The first approach uses joint percentiles to identify patients with high expression (top 75%) of both receptor and ligand (referred to as high-high). The second approach uses a rank-based interaction score to create a single value for each patient within a distribution defined for each receptor-ligand pair; patients with extreme values have high expression of both receptor and ligand. The two methods identified similar sets of patients with high co-expression of receptor and ligand. We then compared progression free survival (PFS) across receptor-ligand co-expression groups (high-high vs. others) and tested for other clinical associations. From a pre-defined list of 17 receptor-ligand pairs (Liu, et al., Exp. Hematol. Oncol., 2023), we focused on the following receptors (from CD8+ T cells) and ligands (from tumor plasma cells): PD-1 with PD-L1; LAG-3 with MHC-II; TIGIT with CD115; and TIM-3 with phosphatidylserine. Results Overall, 91 patients (91/191, 47.6%) showed high-high co-expression for at least 1 out of 17 receptor-ligand pairs, and 100 patients were high-high for no receptor-ligand pairs. Among patients with at least one high-high pair, there was a median of 2.0 high-high pairs (range: 1-11). Hierarchical clustering of high-high status and rank-based interaction score correlations showed that for a given receptor, patients often showed high-high co-expression across multiple related ligands. For TIM-3 (receptor, CD8+ T cells) and phosphatidylserine (ligand, tumor plasma cells), we identified 16 patients (16/191, 8.4%) with high-high co-expression and significantly worse PFS (median survival 485 vs. 1503 days, log-rank p-value 0.043). For PD-1-PD-L1, we identified 7 patients with high-high co-expression; for LAG-3-MHC-II, 12 patients (6.3%); and for TIGIT-CD115, 13 patients (6.8%). Conclusion ICI therapy remains an emerging area of study in MM. Here, we developed methods to explore immune checkpoint receptor-ligand co-expression from paired immune microenvironment and tumor cell populations. Our approach demonstrates the value of data integration across gene expression modalities, especially when standard sample collection methodologies segregate tumor and immune populations. Co-expression analysis may be one factor in prioritizing patients for immune checkpoint inhibitor therapy.

Abstract Introduction: Monoclonal gammopathy of undetermined significance (MGUS) affects over 2 million individuals aged ≥70 in the US. Earlier detection of smoldering multiple myeloma (SMM) and multiple myeloma (MM) in individuals with suspected MGUS via bone marrow biopsy allows for earlier active monitoring and potential intervention, including with daratumumab for high-risk SMM. Risk prediction for the presence of SMM or MM with the iStopMM and Mayo Clinic models leads to decreased individual-patient and population-level resource utilization burden of bone marrow testing with the tradeoff of some missed non-MGUS diagnoses. We sought to quantify the value of earlier diagnosis, while additionally accounting for the recent results of the AQUILA trial, by assessing the lifetime cost-effectiveness of bone marrow testing strategies for patients with suspected MGUS. Methods: In this independent analysis free of industry influence, we used a decision tree with nested Markov cohort models to evaluate diagnosis and disease progression from MGUS and SMM to MM across 3 diagnostic strategies employing: (1) iStopMM risk prediction model versus (2) Mayo Clinic risk stratification and prediction model versus (3) a test-all strategy. Both risk prediction model performance characteristics were derived from the largest population-level prospective cohort study. Individuals in the MGUS state were monitored annually for disease progression with blood tests. The SMM state included two groups: those correctly diagnosed through biopsy and those misclassified as MGUS due to not undergoing biopsy under the prediction model triage rules (missed SMM diagnosis). As recommended by guidelines, diagnosed patients with non–low-risk SMM were monitored with blood tests every three months; those with low-risk SMM were tested every six months. All diagnosed SMM patients underwent imaging for bone lesions at diagnosis and annually for up to five years. Missed SMM patients were monitored at the same frequency as those with MGUS until disease progression. Early versus delayed myeloma diagnosis complications were informed by real-world multi-institutional quasi-experimental data of consecutively diagnosed patients with MM. MM treatment and outcomes across up to four lines of therapy were modeled exactly as previously published in a validated model. In additional scenario analyses, we assessed the impact of daratumumab monotherapy in the care of people with high-risk SMM across all 3 testing strategies by incorporating the progression free survival and overall survival benefit as reported in the phase 3 AQUILA study. The primary outcome was the incremental net monetary benefit (iNMB), reported across all accepted US willingness-to-pay (WTP) thresholds from a US health system perspective. We concluded with extensive deterministic sensitivity analyses and additionally quantified uncertainty across all model parameters simultaneously with 10,000 Monte Carlo simulations. Results: The iStopMM model was the cost-effective strategy yielding the highest NMB of all strategies at every WTP threshold, with per-patient cost and effectiveness of $188,300 and 10.21 QALYs, respectively. This strategy was dominant—costing less and producing more QALYs—than both the Mayo Clinic and test-all strategies. At the population level, use of the iStopMM model over the Mayo Clinic model and the test-all strategy yields iNMBs of $0.67 [95% CI: 0.2 – 1.14] billion and $3.13 [95%CI: 2.80-3.47] billion, respectively, in favor of the iStopMM strategy. The iStopMM strategy was the cost-effective strategy in 100% of 10,000 second order Monte Carlo iterations. In deterministic sensitivity analyses, the top three parameters the iNMB was most sensitive to were prediction model test characteristics (i.e., sensitivity and specificity) as well as the baseline prevalence of SMM. However, no parameter variation could change the conclusion that iStopMM is the cost-effective strategy. In scenario analyses incorporating benefit of daratumumab monotherapy in the care of individuals with high-risk SMM, the iStopMM model remained the cost-effective strategy. Conclusion: Using the iStopMM risk prediction model for marrow sampling in patients with suspected MGUS leads to cost savings per patient evaluated, both with and without incorporating daratumumab benefit in the care of individuals with high-risk SMM, as compared to both the Mayo Clinic model and testing all with bone marrow biopsy.

Abstract The CoMMpass study (NCT01454297) is a prospective, longitudinal observational trial involving 1,141 newly diagnosed, treatment-naïve multiple myeloma (NDMM) patients. Bone marrow aspirates from the study participants were collected, separated into CD138 negative and CD138 positive fractions for comprehensive molecular characterization of tumor (using bulk RNA sequencing and whole genome sequencing) and tumor immune microenvironment using 3' single-cell RNA sequencing (scRNA-seq). To support exploration of this rich multi-omic dataset, we developed CoMMpass Explorer (CE), an intuitive interactive platform that enables real-time analysis of clinical and genomic data from these study participants. CE provides four main views: Overall Summary for clinical feature distribution and Kaplan-Meier curves; Mutational Profile for visualizing and comparing somatic mutations; Tumor Profile for differential expression and gene set enrichment; and Immune Microenvironment for comparing cell type abundance and cell cycle dynamics between cohorts using scRNA-seq data. A central feature of CE is cohort building, where users can filter and stratify patients by clinical, genomic, and survival data elements to create custom cohorts for analysis. We validated CE by reproducing findings from two prior CoMMpass-based studies. In the first study by Simhal et al, we stratified patients using transcript-level expression by WEE1 into tertiles and confirmed that high WEE1 expression is associated with poorer progression-free survival across multiple genomic subgroups. In the second study by Manojlovic et al, we replicated mutation frequencies between African American and Caucasian patients by creating race-based cohorts. We further demonstrate CE's utility by identifying and comparing two NDMM cohorts of patients at baseline who have not received autologous stem cell transplant: cohort 1 includes patients that received up-front triplet therapy (N=342) and cohort 2 includes those who did not receive a triplet up-front (N=235). While survival differences between these two cohorts were not statistically significant (p>0.05), CE revealed elevated epithelial-mesenchymal transition (EMT) pathway activity in cohort 1. Immune profiling showed lower abundance of CD8+ T cells (p < 0.05) and NK cells (p = 0.01), but a higher abundance of B cells (p < 0.05) in cohort 1. Genomic data showed fewer EIF2AK3 mutations and higher transcript-level expression of EIF2AK3 in cohort 1. Notably, patients in cohort 1 with lower transcript-level expression of EIF2AK3 had better survival outcomescompared to patients in cohort 2. CE democratizes access to the CoMMpass multi-omic resource, allowing researchers to interrogate clinical and molecular heterogeneity with ease. By enabling hypothesis generation and replication of published results, CE serves as a valuable tool for identifying meaningful patterns in gene expression, mutation profiles, and immune contexture. It also highlights potential prognostic biomarkers, such as EIF2AK3 expression, that may inform treatment decisions in NDMM, especially in the context of triplet-based regimens.

With the rapid development of artificial intelligence (AI), various deep learning (DL) methods have been introduced into radiation oncology. Among them, the generation of synthetic Computed Tomography (sCT) images has attracted increasing attention, as it supports different clinical scenarios, from image-guided adaptive radiotherapy (IGART) to the simulation-free workflow. This review provides a comprehensive overview of recent studies on DL-based sCT synthesis in radiotherapy from multiple imaging modalities, including Cone-Beam CT (CBCT), Magnetic Resonance Imaging (MRI), and diagnostic CT, and discusses their clinical applications in CBCT-based online adaptive radiotherapy, MRI-guided radiotherapy, and simulation-free workflows. We also examine the architectures of representative DL models such as convolutional neural networks (CNNs) and generative adversarial networks (GANs) and summarize emerging training strategies. Finally, we discuss current challenges of clinical translation of DL algorithms into clinical practice and suggest potential directions for future research. Overall, this paper highlights the potential of AI-driven sCT generation to advance treatment planning by reducing imaging burden, improving dose accuracy, and accelerating workflow efficiency, thus ultimately improving the treatment outcome of patient care.