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Introduction: MDS are primarily diseases of the elderly, with median age at diagnosis of 72 years. Focused data on clinical and molecular characteristics, as well as outcomes, in younger pts under age 60 years are limited. We conducted this multicenter retrospective study to describe pt characteristics, cytogenetic and molecular profiles, treatment modalities, and outcomes in younger pts with MDS, including adolescent and young adults (AYA). Methods: The study included pts diagnosed with MDS between 2002-2023 at 7 academic centers. We compared baseline characteristics, treatment, and outcomes of AYA (18-39 years) and younger adult (YA) pts (40-59 years). Responses were assessed using the modified IWG 2006 response criteria which included complete remission (CR), marrow CR (mCR), and hematologic improvement (HI). Wilcoxon rank sum test and Fisher's exact (and Chi-square) test were used to compare pt and disease characteristics for continuous and categorical variables, respectively. Overall survival (OS) was estimated using the Kaplan-Meier method and compared using log-rank test. Results: A total of 173 pts were identified (AYA, N=50; YA, N=123). Median age at diagnosis was 50 years (range, 18-59), with 51% females. Baseline characteristics are shown in Table 1. Therapy-related MDS (t-MDS) was observed in 40 pts (23%) with the remaining classified as de novo MDS (N=133; 77%). Inherited bone marrow failure syndromes were documented in 15 pts (9%), with Shwachman-Diamond Syndrome (N=4) and Fanconi anemia (N=4) being the most frequent. Using the WHO 2016 classification, the most common subtypes of MDS in both the AYA and YA cohorts were MDS with multilineage dysplasia (38% vs 19%), MDS with excess blasts-2 (MDS-EB) (24% vs 29%), and MDS-EB-1 (16% vs 20%). Similarly, using the WHO 2022 classification, the most common subtypes were MDS with low blasts (50% vs 21%), MDS with increased blasts-2 (MDS-IB-2) (24% vs 24%), and MDS-IB-1 (16% vs 18%). Stratifying pts using the IPSS-R cytogenetic (CG) risk group, the most common CG risk category identified was good CG (34% vs 52%, p=0.012). Poor/very poor CG were present in 34% and 37% of the AYA and YA groups, respectively. STAG2 and NRAS mutations occurred more frequently in the AYA pts compared to the YA group, while the YA cohort was enriched for the following mutations: TP53, DNMT3A, SF3B1, SRSF2, and TET2 (Figure 1). In the AYA population, 18 pts (38%) had very low/low, 10 (21%) intermediate, and 20 pts (42%) had high/very high risk MDS using IPSS-R. In the YA population, 32 pts (26%) had very low/low, 32 (26%) intermediate, and 57 pts (47%) had high/very high risk MDS using IPSS-R. Twenty-seven percent of pts with very low to intermediate-risk MDS using IPSS-R were upstaged to moderate high to very high-risk MDS using IPSS-M. The majority of pts (AYA: 75%; YA: 63%, p=0.5) were transfusion independent at baseline. Single-agent hypomethylating agents were the most frequently used frontline therapy, used in 17 AYA pts (34%) and 58 YA pts (48%), with more AYA pts receiving upfront allogeneic stem cell transplantation (Allo-SCT) compared to YA pts (28% vs 5%). After frontline therapy (Table 1), an additional 14 AYA (28%) and 52 YA (42%) pts received Allo-SCT. In pts with IPSS-R intermediate to very high-risk, response rates (HI+CR+mCR+mCR with HI) to frontline therapies were 46% and 54.5% in the AYA and YA groups, respectively. After median follow-up of 24 months [range, 1-218], median OS was numerically longer in the AYA versus YA group (155 vs 53 months, p=0.2). AML transformation occurred in 24% and 20% of the pts in the AYA and YA groups, respectively. Median OS was significantly longer in de novo compared to t-MDS pts (56 vs 27 months, p=0.03). Median OS among Allo-SCT compared to non-SCT pts were not reached vs 155 months ( p=0.9) in the AYA group and 53 vs 54 months ( p=0.4) in the YA group. IPSS-R (very low+low vs intermediate vs high+very high) was able to distinguish differences in OS in the YA (133 vs 53 vs 17 months, p=0.0002) and AYA (155 vs not reached vs 18 months, p=0.006) pts, with the exception of the intermediate-risk group in the AYA cohort. Conclusions: In this study, d e novoMDS was seen in the majority of younger pts with MDS (both AYA and YA). Mutations in STAG2 and NRAS were more common in the AYA pts, while YA pts had MDS more enriched for TP53, DNMT3A, TET2 and splicing mutations. This study is still ongoing with a plan to compare the abovementioned groups to older pts (≥60 years) with MDS.

Background: Monoclonal B-cell lymphocytosis (MBL) is a precursor condition to chronic lymphocytic leukemia (CLL) characterized by the presence of circulating monoclonal B-cell populations in the peripheral blood of otherwise healthy individuals. MBL increases risk of several adverse clinical outcomes, including development of hematological malignancies. Mosaic chromosomal alterations (mCAs) are a form of clonal hematopoiesis which affect large segments of DNA and include gains, losses, and copy number neutral loss of heterozygosity events. mCAs can be detected from SNP-array genotyping. Prior work has suggested mCAs as a potent risk factor for hematological malignancies. However, the nature of the relationship between mCAs and MBL is unknown. We sought to determine 1) the association between mCAs and MBL and 2) their combined effect on risk of hematological malignancies, in a large, prospectively collected, and well-annotated cohort of individuals with known MBL, mCA, and clinical outcomes. Methods: We examined the prevalence of mCA in a cohort of individuals from the Mayo Clinic Biobank who were screened for MBL using a sensitive eight-color flow cytometry assay. We classified MBL by the size of the clone relative to total B-cells, with high count (HC) defined as those individuals who had a percent clonal B-cell count ≥85% out of total B-cell count and low-count (LC) otherwise. Autosomal mCAs were detected from whole blood DNA collected at the time of MBL screening using a SNP-array-based assay. The MoChA pipeline was used to identify mCA events. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CIs). To examine incident hematological cancers, we identified and validated all hematological cancers through medical record abstraction using the International Statistical Classification of Diseases codes. Cox regression was used to calculate hazard ratios (HR) and 95% CIs, stratified by MBL status or autosomal mCA status. All models were adjusted for age and sex. Results: We evaluated the prevalence of autosomal mCA in 3,764 individuals (792 individuals with MBL of whom 64 had HC-MBL and 2,972 without MBL). The prevalence of autosomal mCA was 7.7% ( n=288) with a median mCA cell fraction of 0.09 (range 0.01-1). Among those with and without MBL overall, the prevalence of mCA was 15.5% ( n=123) and 5.6% ( n=165), respectively (Figure 1). Compared to those without an autosomal mCA, individuals with an autosomal mCA were 2.4-fold more likely to have MBL (95% CI 1.9-3.2; P =2.0x10 -11; Figure 1). CLL-associated mCA (i.e., those commonly altered in CLL including trisomy 12 and deletion of 6q, 11q, 13q, and 17p) were present in 42 individuals, of whom 38 (90.5%) had MBL overall and 30 (71.4%) individuals had HC-MBL. Compared to those without a CLL-associated mCA, individuals with a CLL-associated mCA were 539.39-fold more likely to have HC-MBL (95% CI 190.3-1975.9; P <2.0x10 -16) and 6.7-fold more likely to have LC-MBL (95% CI 2.0-26.5; P =0.0031; Figure 1). Next, we evaluated the relationship between MBL and mCA with risk of developing a hematologic malignancy in 3,009 individuals with follow-up data (median 10 years). Forty-one individuals developed an incident hematological malignancy (68.3% lymphoid). When we evaluated the effect of autosomal mCA stratified by MBL status we observed elevated but non-significant risk of hematological malignancies (Figure 2). When we evaluated the effect of MBL stratified by autosomal mCA status we observed elevated and significant increased risk of developing a hematological malignancy (Figure 2). Conclusions: We found that the presence of an autosomal mCA is highly associated with having MBL. In particular, >90% of CLL-associated mCA carriers had MBL with ~7- and ~540-fold increased association for LC- and HC-MBL, respectively. These results suggest that almost all individuals in large biobanks with CLL-associated mCAs have undiagnosed MBL. However, not all individuals with MBL have an mCA. When evaluating risk of hematological malignancies, we found autosomal mCAs had >2-fold elevated but non-significant effects when stratified by MBL status while MBL remained a significant risk factor regardless of autosomal mCA status. Collectively these results support that MBL remains an important risk factor and suggests that mCAs hold potential to identify a subgroup of individuals at greater risk of developing a hematological malignancy.

Background With the advent of tyrosine kinase inhibitor (TKI)-based therapy the outcome of Philadelphia chromosome-positive (Ph +) acute lymphoblastic leukemia (ALL) has significantly improved. Moreover, patients (pts) who achieved undetectable (u) BCR::ABL1 at 3 months (mo) from the time of initiating therapy, tend to have improved outcomes (Short et al. Blood 2016; 128 [4];504-507). We conducted a retrospective multicenter study to analyze the predictors of achieving uBCR::ABL1 in adult (> 18 years) Ph + ALL pts. Methods A total of 458 adult Ph + ALL pts from 10 US academic institutions, who were diagnosed between May 2003 and April 2023, were evaluated to assess the rates of uBCR::ABL1 at 3 mo after initiating therapy and factors that predict the achievement of molecular response. Continuous variables were summarized as median (range), while categorical variables were reported as frequency (percentage). Unadjusted comparisons of patient/ treatment related characteristics and molecular response were made using a non-parametric test (continuous variables) or Fisher's exact test (categorical variables). Survival curves were estimated using the Kaplan-Meier method and compared between molecular responses via the log-rank test. Results Baseline characteristics The median age of the pts was 53 years (range [R] 19-85), median white blood cell count (WBC) was 17.6 (10 9/L) (R, 1.2-571), 249 (55%) pts had additional cytogenetic (CG) abnormalities, 98 (22%) pts had p210 fusion protein, and 35 (8%) pts had CNS disease at diagnosis. A higher proportion of pts received TKI in combination with intensive chemotherapy (IC) (69%), than TKI + steroids (15%), TKI + low intensity chemotherapy (LC; combination of vincristine, steroids +/- rituximab) (9%), TKI + blinatumomab (3%) and IC without TKI (4%) during induction. During induction, the highest proportion of pts received 2 nd generation TKI combinations (67%; majority dasatinib 98%), compared to imatinib (19%) or ponatinib (10%): 4% of pts did not receive TKI with induction. The complete remission (CR) rate was 94%; measurable residual disease (MRD) negative by flow cytometry 51% (181/352 evaluable pts) and 58.5% (209/354 evaluable pts) of pts had uBCR::ABL1 by PCR at 3 mo from induction. A total of 212 (46%) pts received allogeneic stem cell transplantation (allo-HCT) in CR1, among them 61% of pts received TKI maintenance post allo-HCT. Predictors of uBCR::ABL1 at 3 months Predictors for uBCR::ABL1 at 3 mo are summarized in Table 1. The proportion of pts with WBC > 100 (45% vs 60%, p= 0.04), p210 fusion protein (35% vs 85%, p= <0.001), additional CG abnormality with monosomy 7 (40% vs 61%, p= 0.03), additional somatic myeloid co-mutation (39% vs 61%, p= 0.09; 94 pts evaluable), received induction with steroid plus TKI (41% vs 63%, p= 0.002) or received imatinib based induction (47% vs 61%,p= 0.07) had inferior uBCR::ABL1 rates at 3 mo. Conversely pts who received ponatinib based induction had significantly better uBCR::ABL1 rates (79% vs 57%, p= 0.009). Survival The median relapse free survival (RFS) was 72.3 mo (95% CI; 50.1-94.4), RFS was numerically higher among pts who achieved uBCR::ABL1 at 3 mo but not statistically significant (84.2 mo vs 62.9 mo, p= 0.20). The median overall survival (OS) was 129.2 mo (95% CI; 81.2-177.2), OS was not significantly different among pts who achieved uBCR::ABL1 at 3 mo (129.2) versus no uBCR::ABL1 (not reached; 50% alive at 94 mo), p= 0.47. In sub-set analysis, among non-transplanted pts, RFS was not statistically significant between those who achieved uBCR::ABL1 vs those who did not (38.9 vs 22.0 mo, respectively p= 0.24). However, the median OS was significantly higher in pts who achieved uBCR::ABL1 at 3 mo compared to pts who did not achieve uBCR::ABL1 at 3 mo: 149.3 vs 77.5 mo, p= 0.04 ( Figure 1). Conclusion In this large multicenter study, we observed significantly inferior uBCR::ABL1 rates among pts with WBC > 100, p210 fusion protein, monosomy 7 CG abnormality, pts receiving induction with steroid plus TKI or imatinib based induction. Conversely, uBCR::ABL1 rates were significantly better in pts receiving ponatinib based induction therapy. Overall, we did not see significant impact of uBCR::ABL1 at 3 mo, on RFS or OS. It is likely subsequent therapies and allo-HCT abrogates the negative impact of not achieving uBCR::ABL1 at 3 mo. However, among non-transplanted pts achievement of uBCR::ABL1 at 3 mo showed a significantly improved OS.

Scoring systems designed to improve the accuracy of prognostication in chronic lymphocytic leukemia (CLL) usually rely on discretized/dichotomic values of the clinical and biological variables. Here we analyzed the immunophenotypic and (immuno)genetic profiles of 2,243 CLL patients with Rai stage 0-I-II, by applying unsupervised machine learning methods, elaborating prognostic factors as continuous variables, to identify novel relationships and interactions likely missed in conventional hierarchical models. The study included an internal training cohort (n=863) and two external validation cohorts provided from Cardiff University/Brighton Medical School (n=455) and the Mayo Clinic (Rochester, MN; n=925). Primary endpoint was Time to First Treatment (TTFT). Laboratory-based markers evaluated in the training cohort were: surface antigen expression by flow cytometry (as % of positive cells) of CD20, FMC7, CD49d, CD49c, CD38, CD23, CD43, CD22, ZAP-70; chromosomal aberrations (% of nuclei with abnormal signal) del13q, tris12, del11q and del17p,; mutational status of TP53 by NGS as % VAF; IGHV mutational status as % mutation. 420/863 cases received treatment (median 46 months). Univariate Cox regression identified FISH del11q, del17p and tris12, TP53 and IGHV mutations, CD38 and CD49d expression, as features associated with TTFT (p-value <0.001). An unsupervised k-means algorithm partitioned cases into 6 clusters (C1-C6), as selected by the elbow method, and centroid analysis evaluated which feature most contributed to each cluster (Panel A): C1 (n=275) was heavily IGHV-mutated (mutation range 4.5-22.0%) with low representation of all other features; C2 (n=208) was mostly IGHV-unmutated/intermediate (up to 4.5%) in the absence of other features; C3 (n=169) were CD49d-expressing cases (>50% expression), with low representation of other features, except CD38; C4 (n=127) were cases with trisomy 12 (>28%), also expressing CD49d and CD38; C5 (n=48) were cases with highly clonal del11q (44-98%) and IGHV-unmutated, mutually exclusive with TP53 disruption; C6 (n=34) contained TP53-disrupted cases with high mutation burden (VAF 36-97%) and/or del17p (range 40-96%), with low importance of all the other features. Of note, alternative use of either TP53 mutation or del17p as defining features of cluster 6 did not result in changes of the classification accuracy. Clinically, clusters 3-4-5-6 showed median TTFT of 26, 22, 9, 5 months respectively whereas median TTFT was not reached for clusters 1-2. Hierarchical agglomerative clustering aggregated the 6 clusters in 3 major risk tiers: high (C5-6), intermediate (C2-3-4) and low (C1), with median TTFT of 7, 45 and not reached, respectively (Panel B). Multivariate Cox analysis (MVA) with Rai staging demonstrated that the 3-tier risk score contributed significantly and independently to TTFT estimate (p<0.0001; Harrel's c-index=0.71). To classify novel patients, we designed a hierarchical algorithm, based on each clusters' estimated risk and according to calculated cut-off, i.e. C6, del17p/ TP53>36%; C5, del11q>43%; C4, tris12>28%; C3, CD49d>50%; C2, IGHV≤4.5%; C1, the remaining patients with IGHV>4.5%. This approach was highly robust by an internal training-validation bootstrap (cluster classification accuracy = 0.96±0.02). External samples from two independent cohorts were classified into the six clusters using the cut-offs defined by the hierarchical classifier, and then aggregated into the 3-risk tiers. The classifier performed robustly in both cohorts, again adding independent prognostic information to Rai stage in MVA, with p<0.0001 and c-index of 0.76 for Cardiff/Brighton cohort and p<0.0001 and c-index of 0.74 for Mayo cohort. In conclusion, our machine-learning-driven, laboratory-based classification identifies clusters of patients who at higher risk of requiring early treatment, independent of clinical staging, and may help to better identify patients who may benefit from early treatment or more frequent disease surveillance. For some features, TP53 or IGHV mutational status, our unsupervised approach selected cut-offs not recapitulating the canonical ones, envisioning different biological activities in the TTFT setting. For example, TP53 mutations were clinically relevant only if present in the vast majority of the clone , suggesting the involvement of specific activities of the TP53 mutant in this context.

INTRODUCTION We aim to identify factors that impact outcomes of venetoclax for patients with CLL treated in routine practice at a tertiary center. We report on the use of venetoclax in the most frequently encountered disease scenarios: first-line, relapsed/Bruton tyrosine kinase inhibitor (BTKi)-naïve, and relapsed/BTKi-exposed. METHODS We identified patients who received venetoclax therapy for CLL (between 4/2012-4/2023) from the Mayo Clinic CLL Database. Undetectable measurable residual disease (uMRD) was defined as <1 CLL cell per 10,000 leukocytes using 8-color flow cytometry on peripheral blood (PB) or bone marrow (BM). Overall survival (OS) was defined as the time from venetoclax start until date of death or last known to be alive. Treatment-free survival (TFS) after venetoclax was defined as the time from venetoclax start until the earliest of date of next treatment, or death. Kaplan-Meier was used to display OS and TFS. Multivariable Cox proportional hazards regression models were used to estimate associations of factors with time-to-event outcomes. RESULTS A total of 155 patients received venetoclax: firstline therapy (in combination with obinutuzumab, n=55) and relapsed CLL (n=100; 17 had relapsed/BTKi-naïve CLL, and 83 had previously received BTKi [55 with progression after BTKi], relapsed/BTKi-exposed). The median follow-up for the cohorts of first-line therapy, relapsed/BTKi-naïve, and relapsed/BTKi-exposed was 12.9 months, 37.0 months, and 27.6 months, respectively. Baseline characteristics at the time of venetoclax initiation for all patients are shown in Table 1. The median TFS for the overall cohort was 39.0 months. The median OS was 54.6 months. Among patients treated with venetoclax as first-line therapy (n=55), the 2-year TFS ( Figure 1) and 2-year OS rates were both 91%. MRD testing was performed in 28 patients and was uMRD in 23 (82%) patients (only PB assessed, n=7; only BM assessed, n=2; PB and BM assessed, n=14). Detectable MRD was identified in 3 patients, and 2 patients had discordant results (PB uMRD and BM detectable disease). Among patients treated with venetoclax in the relapsed/BTKi-naïve setting (n=17), the 2-year TFS rate was 73% ( Figure 1) and the 2-year OS rate was 100%. MRD testing was performed in 7 patients and was uMRD in all 7 (100%) (only PB assessed, n=3; only BM assessed, n=2; PB and BM assessed, n=2). The median time to first uMRD result was 14.1 months. Among relapsed/BTKi-exposed venetoclax-treated patients (n=83), the median TFS was 26.9 months ( Figure 1), and the median OS was 39.4 months.In this subgroup, the median TFS for patients with (n=55) and without (n=28) prior disease progression on prior BTKi were 22.3 and 42.3 months, respectively. Median TFS with venetoclax monotherapy (n=30) was 24.0 months, venetoclax in combination with rituximab (n=37) was 26.9 months, and venetoclax in combination with obinutuzumab (n=16) was 39.0 months. BTKi-exposed patients that were chemotherapy-naïve (n=27) and chemotherapy-exposed (n=56) had median TFS of 29.1 and 24.0 months, respectively. MRD testing was performed in 28 patients and was uMRD in 16 (57%) patients (only PB assessed, n=9; only BM assessed, n=2; PB and BM assessed, n=5). Detectable MRD was identified in 11 patients, and 1 had discordant results (PB uMRD and BM detectable disease). The median time to first uMRD result was 11.5 months. TP53 disruption, unmutated IGHV genes, older age, complex karyotype (CK; defined as more than 3 chromosomal aberrations on CpG stimulated karyotype), and disease progression on prior BTKi were associated with shorter TFS in the overall cohort on univariate analysis. TP53 disruption, older age, CK, and disease progression on prior BTKi were associated with shorter OS in the overall cohort on univariate analysis. Multivariable analysis was performed by including only those patients where all variables significant in univariable analysis were available (OS model n=65, TFS model n=53). In these models, only CK was significantly associated with shorter TFS (HR 8.5; 95%CI 2.5-29.1; P<0.001) and shorter OS (HR 4.1; 95%CI 1.2-14; P=0.03). CONCLUSIONS Patients with BTKi-exposed CLL, particularly those with prior disease progression on BTKi, had worse outcomes. Our study identified CK as one of the most important baseline predictors of adverse TFS and OS in the overall cohort of patients, supporting karyotype assessment for prognostication prior to venetoclax treatment.

Introduction: Recent studies have shown that clonal B cells derived from patients with chronic lymphocytic leukemia (CLL) have altered histone modification landscapes compared to B cells from healthy controls. We have also shown that the BCR signaling pathway impacts the chromatin landscapes of CLL B cells, implicating the interaction between oncogenic signaling and epigenetic machinery in CLL (Wang et al., Blood Cancer J. 2022; Wang et al., Cancer Res (2023) 83 (7_Supplement): 4725). However, the majority of the chromatin studies in CLL concentrate on the active enhancers associated with oncogene expression; there is a very limited understanding of lost enhancers in CLL: 1) What is the function of lost enhancers; 2) How are these lost enhancers maintained in CLL; 3) Can these lost enhancers be detected at the precursor state of CLL, monoclonal B-cell lymphocytosis (MBL); 4) if this aberrant chromatin signature affected by CLL treatment. Methods: We systematically mapped the genome-wide histone modification profile (histone marks: H3K4me1, H3K4me3, H3K9me3, H3K27ac, H3K27me3, and H3K36me3) in 22 previously untreated CLL samples, 6 high count MBL samples, and peripheral blood B cells from 6 healthy donors by Cleavage Under Targets and Tagmentation (CUT&Tag). We also analyzed B cells from 8 patients before and after one year of continuous Bruton tyrosine kinase inhibitor (BTKi) ibrutinib treatment. The genomic regions with a decrease in both H3K4me1 and H3K27ac enrichment (analyzed by Deseq2, fold change > 1, p < 0.01) were named lost enhancers. We used CRISPR-mediated epigenetic editing to study the role of gained H3K9me3 on EBF1 gene expression. Results: Compared to normal B cells, the lost enhancers (genomic regions defined by decreased enrichment of H3K4me1 and H3K27ac) in CLL B cells showed increased heterochromatin marks (H3K9me3 and H3K27me3) enrichment. Importantly, these enhancers are associated with genes downregulated in CLL and involved in the immune response, indicating the source of epigenetic impact on impaired immunity in CLL. We further found that this aberrant chromatin structure also exits in clonal B cells from the precursor stage of MBL, implicating the importance of epigenetic silencing in CLL evolution. In particular, we found the lost enhancer signature across the gene locus EBF1, an important transcription factor that defines B cell lineage and regulates normal B cell activation. Using CRISPR-mediated epigenetic editing, we demonstrated that the gain of H3K9me3 is sufficient to silence the EBF1 enhancer and its gene expression. Finally, we found that while BTKi ibrutinib treatment partially inhibits the CLL-gained enhancers in some patients, it failed to restore the lost enhancers in CLL B cells. Conclusion: In summary, we have elucidated an epigenetic silencing mechanism that may contribute to the loss of physiological function of normal B cells, which places the clonal B cells at a selection advantage during CLL evolution and increases the risk of serious infections in CLL and MBL ( Fig 1).

Background TP53-mutated (m) acute myeloid leukemia (AML) is an adverse risk disease, which continues to have dismal outcome in the contemporary era of novel therapies. Venetoclax (VEN) in combination with hypomethylating agents (HMA; azacitidine or decitabine) have shown to improve complete remission (CR) rates compared to HMA alone, however no significant differences in overall survival (OS) have been observed. Methods We conducted a retrospective study through the COMMAND consortium (a collaboration of acute leukemia experts from 10 US academic institutions) to analyze the impact on survival outcomes of HMA plus (+) VEN compared to HMA-based therapies in adult (≥18 years) pts with TP53m AML. The AML Database of 381 pts with TP53m AML diagnosed between 2012-2022 was queried and 154 (40%) (HMA [n=50] and HMA +VEN [n=104]) pts were identified eligible for this analysis. Continuous variables were summarized as median (range) while categorical variables were reported as frequency (percentage). The Kaplan-Meier method was used to estimate OS, defined as time from diagnosis to death or last follow-up. Logistic regression models were used to determine the univariate and multivariate of OS. Multivariable models included all significant univariate predictors. All tests were two-sided with a p value <0.05 considered statistically significant. Results Baseline characteristics and responses The median age (years [yrs]) was 74 (range [R], 38-87) and 71 (R,29-88) yrs in the HMAand HMA + VEN groups (gps) (p= 0.23), respectively ( Table 1). Thirty-four (68%) and 57 (55%) patients (pts) were ≥ 70 yrs in the HMA and HMA + VEN gps, respectively (p= 0.16). Twenty-six percent vs 31% ( p=0.57), 86% vs 90% ( p=0.58) and 68% vs 73% ( p=0.56) of pts had secondary AML, had complex cytogenetics (CG), and had multi-hit TP53m in the HMA and HMA + VEN gps, respectively. The proportion of pts with most frequently occurring (> 5%) myeloid co-mutations ( RUNX1, ASXL1, TET2, DNMT3A, RAS, PTPN11 and JAK2) were not significantly (NS) different between the HMA and HMA + VEN gps, as shown in Table 1. The early treatment related mortality in first 30 days of induction was higher in the HMA+VEN gp (25%) when compared with the HMA gp (6%), p=0.004. The complete remission rates (CR/CRi) were higher in the HMA + VEN gp compared to the HMA gp (35% vs 18%, p=0.05). Similarly, the proportion of pts receiving allogeneic stem cell transplant (allo-HCT) after induction was higher in the HMA + VEN gp when compared with the HMA gp (13% vs 4%; p=0.14). Duration of response and overall survival The median follow up was 9.8 months (mo) (R, 1-56) and 5.80 mo (R, 1-30.4) in the HMA and HMA + VEN gps, respectively ( p=0.12). The median duration of response (DOR) was higher in the HMA +VEN gp (15.6 mo) when compared with the HMA gp (7.93 mo), but NS ( p=0.26). The median OS was NS different when comparing the HMA and HMA + VEN gps (9.23 vs 7.3 mo, p=0.80 [ Figure 1]). Multivariable analysis for duration of response and overall survival We did multivariable analysis (MVA) for DOR and OS using baseline variables ( Table 1) that showed significance/trend towards significance in univariate analysis ( p<0.1). Allo-HCT (HR; 0.11, 95% CI: 0.02-0.52, p=0.005) retained significance for better DOR, whereas age ≥ 70 yrs (HR; 1.7, 95% CI: 0.63-4.65, p=0.28) and complex CG (HR; 3.05, 95% CI: 0.79-11.73, p=0.10) did not retain significance in MVA. In MVA for OS, achievement of CR/CRi (HR; 0.35, 95% CI; 0.20-0.59, p=<0.001) and allo-HCT after induction (HR; 0.06, 95% CI; 0.09-0.46, p=0.007) retained favorable significance for OS, whereas complex CG (HR; 2.36, 95% CI: 1.11-4.99, p=0.02) retained significance for shorter OS. PTPN11m (HR; 1.57, 95%CI: 0.75-3.28, p=0.23) and TET2m (HR; 1.02, 95% CI: 0.56-1.87, p= 0.92) did not retain significance in MVA. Conclusion In this multi-center real-world study, while HMA+VEN led to improvement in CR rates and a higher proportion of pts were bridged to allo-HCT, it did not associate with an improvement in OS when compared with HMA monotherapy. More effective treatment strategies are warranted to improve outcome of pts with TP53m AML.

Introduction Survival rates for people with mantle cell lymphoma (MCL) have improved in recent years. Among the potential factors accounting for this improvement is the approval of multiple new therapies over the past two decades, including bortezomib, bendamustine, ibrutinib, lenalidomide, acalabrutinib, and zanubrutinib. We hypothesized that the availability of multiple agents with non-overlapping mechanisms of action would result in significant improvements in overall survival following first-line therapy. Methods Patients diagnosed with MCL between 2002 and 2017 were identified using the SEER-Medicare linked database. Selected patients were >65 years of age at the time of diagnosis with MCL. Lines of therapy were determined using billing codes, with inpatient/outpatient claims used for traditional agents and Medicare Part D dataset used for oral agents. Next lines of therapy were defined based on claims for new anticancer agents separated from the end of the prior line by some certain number of days. Sensitivity analyses were performed varying the number of days between therapies. Overall survival (OS) was defined as initiation of initial therapy to death, while OS2 was defined as time from initiation of second-line therapy to death. Time to next therapy 1 (TTNT1) was defined as time from start of first-line therapy to death or start of second-line therapy, whichever came first. Cox models and Kaplan-Meier (KM) survival curves were generated for time-to-event analyses. Analyses were stratified by categories of years of first- and second-line therapies corresponding to eras of key drug approvals (e.g., 2002-2005, 2006-2009, 2010-2013, >2013). Results In total, 4,832 patients were included, among whom 3,840 patients (79.5%) had claims for first-line regimens and 1,648 (34.1% of total and 42.9% of patients with first-line regimens) had second-line regimens. Of the 4,832 patients, the majority were male (67.4%), White (94.6%), and non-Hispanic (94.7%), with good performance status (74.8%) and a Charlson Comorbidity Index <2 (64.8%). The median age at diagnosis was 75 (IQR 70-81). Patients with first- and/or second-line regimens did not differ significantly from the entire population of 4,832 patients with MCL in terms of baseline characteristics. In the first-line setting, use of bendamustine-rituximab (BR) increased from 2.3% of patients treated in 2006-2009 to 49.5% in 2014-2017. Similarly, use of single-agent BTK-inhibitors (BTKi) increased from nonexistent in patients treated in 2010-2013 to 13.8% in 2014-2017. In the second-line setting, use of single-agent BTKi similarly increased from 2.6% of those treated in 2010-2013 to 44.9% in 2014-2017. Median OS for the 3,840 patients with fist-line regimens was 4.4 years (95% CI, 4.1-4.5); median OS for those receiving first-line therapy in 2002-2005 was 3.5 years (3.1-3.9), compared to 3.7 (3.4-4.0), 4.5 (4.2-5.0), and not reached (4.8-NR) among those treated in 2006-2009, 2010-2013, and 2014-2017, respectively. Patients treated in 2014-2017 had significantly improved OS compared with those in 2002-2005 (log rank p<0.0001). Median TTNT1 overall was 2.5 years (2.4-2.6); median TTNT1 was 3.2 years (2.9-3.5) vs 2.3 years (2.1-2.6) for patients who received first-line therapy in 2014-2017 compared to 2002-2005 (log rank p<0.0001) ( Figure A). In addition, there was a significant difference across KM curves for OS2 stratified by categories of years of second-line therapy, with median OS2 of 2.5 (2.0-3.2) in patients treated in 2014-2017 compared to median OS2 of 2.1 (1.7-2.5) in 2010-2013 ( Figure B). Discussion In this analysis, we observed improvements survival of MCL patients over time, along with similar improvements in outcomes following first-line therapy, which may correlate with increased BR and BTKi use as first-line agents. Unexpectedly, improvements in survival following first-line therapy were considerably more modest. Potential explanations include lack of data from recent years during which we expect continued increased utilization of oral therapies vs unique impact of repeated therapies in the elderly. Overall, these data support the need for ongoing development of first- and subsequent-line therapies in MCL.

Background: Treatment of chronic lymphocytic leukemia (CLL) has been transformed with targeted therapies including inhibitors of Bruton's tyrosine kinase (BTKi). Currently three covalent BTK inhibitors (cBTKi) are approved for CLL, but most patients eventually relapse, commonly through acquisition of the C481S BTK mutation (Woyach et al. 2014). Pirtobrutinib and nemtabrutinib are non-covalent BTK inhibitors (ncBTKi) developed to target and inhibit C481S mutant BTK. However novel secondary site mutations in BTK, namely T474I and L528W, have been found to confer resistance to both cBTKi and ncBTKi (Wang et al. 2022). Regardless of these mutations, BCR signaling remains intact, suggesting that inhibition of BTK maintains its therapeutic importance. LP-168 is a novel ultra-selective 4 th generation BTKi with an active warhead capable of covalent interaction with WT BTK or non-covalent binding when a BTK C481 mutation is present. Methods: To determine the target selectivity of LP-168 we screened the compound against 468 kinases in the scanMAX Kinase Assay. For validation of covalent binding, dialysis against WT BTK was performed. Primary CLL B cells were isolated by negative selection and treated with LP-168 for all experiments. BCR signaling alterations were assessed via immunoblot to observe changes in target protein phosphorylation. Changes in CLL migration towards CXCL12 or CXCL13 were interrogated by transwell migration. Cytotoxicity towards primary CLL cells was measured via Annexin V/PI flow cytometry with and without HS-5 stromal cell support. Experiments using TMD8 cells were performed following CRISPR modification to insert WT, C481S, or T474I BTK. In vivo survival studies were performed using both the Eμ-TCL1 and Eμ-MTCP1 mouse engraftment models. Results: LP-168 demonstrated selectivity towards BTK with roughly 700-fold selectivity for BTK vs its next off-target. In an enzymatic assay, LP-168 displayed nanomolar potency towards both WT BTK (IC 50=0.11 nM) and C481S BTK (IC 50=1.0 nM). Following 2-hour drugging, LP-168 inhibited BCR signaling in primary CLL B cells at increasing concentrations, demonstrated by reduced phosphorylation of BTK (92%, p<0.0001, n=8) and its immediate downstream target PLCγ2 (41%, p=0.0013, n=8). Migration capacity of primary CLL cells towards CXCL12 and CXCL14 was also found to decrease after 2-hours of treatment with LP-168 (52%, p<0.001, n=10 and 51%, p<0.001, n=10, respectively). After 48 hours of exposure, LP-168 was able to induce cytotoxicity of primary CLL cells alone and in co-culture with HS-5 stromal cells in a dose-dependent manner, with similar or improved efficacy to other BTKi. LP-168 decreased CLL cell production of CCL3 and CCL4 chemokines (CCL3: 93%, p<0.001, n=8; CCL4: 93%, p<0.001, n=8). LP-168 also demonstrated modest cytotoxicity towards TMD8 cells harboring either C481S BTK (24%, p=0.0008 , n=3) or T474I BTK (13%, p=0.0690, n=3), as well as decreased production of CCL3 (C481S: 95%, p<0.0001, n=3; T474I: 99%, p<0.0001, n=3) and CCL4 (C481S: 97%, p<0.0001, n=3; T474I: 98%, p<0.0001, n=3) (Figure 1A). Further, we observed inhibition of downstream BCR signaling in primary CLL B cells harboring C481S via immunoblotting and cytokine production. Finally, to evaluate the in vivo efficacy of LP-168 we tested this compound in both the Eµ-TCL1 and Eµ-MTCP1 models. We treated mice daily via oral gavage and found that 50 mg/kg of LP-168 significantly improved survival in the Eµ-TCL1 model when compared to vehicle (median 51 vs 44 days; p=0.0018) or ibrutinib at 50 mg/kg (median 51 vs 45 days; p=0.0098) and the Eµ-MTCP1 model when compared with vehicle (median 122 vs 62 days; p<0.0001) or ibrutinib at 50 mg/kg (median 122 vs 96 days; p=0.0162) (Figure 1B). Conclusions: Collectively, our data demonstrate that LP-168 is a potent and selective inhibitor of BTK with activity against CLL, even in the presence of mutations that mediate resistance to cBTKi and ncBTKi. These data support the continued preclinical and clinical investigation of LP-168, which is currently being studied in the phase 1 setting of CLL (NCT04775745) and NHL (NCT04993690).

Introduction: In many cases, treatment decisions for multiple myeloma patients must be made in the absence of high quality randomized controlled clinical trials. As a result, many clinicians lean on prognostic models to drive treatment selection. The most widely used of these models incorporate a limited number of patient related factors that reflect tumor burden, cytogenetic features, or gene expression profiling. These prediction scores are derived from regression models that incorporate these variables at the time of diagnosis. They do not account for variables that change dynamically over time, do not explore the relationship between treatment selection and progression, and imperfectly predict survival outcomes. We designed a prognostic model based on an ensemble machine learning platform to predict progression in NDMM using Extreme Gradient Boosting Machine (XGBoost) combined with accelerated failure time modeling for survival analysis. Methods: We utilized a large retrospective data set containing treatment and response information for 1127 patients with newly diagnosed multiple myeloma (NDMM) treated at the Cleveland Clinic Foundation between 2000 and 2023. Following data preprocessing based on data completeness, 953 patient records were included in our training, testing, and cross validation datasets. For the purposes of our analysis, the initial dataset was randomly split into training (70%), testing (15%) and validation (15%) subsets at the patient level. We also defined the lower and upper bounds for each subset, which is critical due to the right-censored nature of survival data. Hyperparameters for XGBoost were optimized using Bayesian search, minimizing the negative log-likelihood. A random forest method was applied for the imputation of missing data (MissForest). We then trained the XGBoost model using GPU acceleration for enhanced computational efficiency. Finally, a log-rank test on predicted survival times was used to test the model's performance on patients with and without known progression. Results: Our preprocessed data set had 953 patients with a mean age at disease onset of 65 years and a slight male predominance (55%). Approximately 27% of patients harbored high cytogenetic risk disease and 3.5% of the cohort presented with extramedullary disease a diagnosis. At a median follow up of 35 months, 47% of patients had experienced disease progression or death. Induction therapy included an immunomodulatory drug in 40% of patients and proteasome inhibitors in 22%. Frontline autologous stem cell transplantation followed induction in 28% of patients. Median progression free survival in the overall data set was 44 months. These features were consistent across the randomly assigned training, testing, and validation cohorts. Following data preprocessing, 34 independent clinical and genomic variables including selection of first line treatment were assessed as model inputs each available record. Under optimized parameters, the model was trained with a maximum tree depth = 7 and a learning rate ~ 0.22 for the desired output of progression free survival (PFS). We then queried PFS for both the training and validation sets and divided patients into those known to have progressed and those without a progression event. Survival analysis was undertaken using predicted PFS values for patients known to have progressed and those who remained progression free. For the validation data set, our model successfully discerned between progressed and non-progressed cases (log-rank test statistic = 22.07, p < 0.005) (Figure 1). Discussion: We present a machine learning approach based on regularized gradient boosting that accurately discerns between patients who experience progression and those who remain progression free at a median ~35 months of follow-up in a large retrospective data set in patients presenting with NDMM. Further elaboration of our model will allow for the incorporation of large amounts data to predict survival outcomes on the basis of dynamic variables such as depth of response and treatment selection. With the ability to parse outcomes among multiple myeloma patients at high resolution, future clinicians and clinical trialists may be able to overcome limitations in trial design and patient-level therapy selection for both newly diagnosed and relapsed patients.