Neuberg, Donna

Abstract Patients with multiple myeloma have an elevated risk of severe complications from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; COVID-19) infection. To demonstrate proof of concept for a pandemic preparedness and response network, the American Society of Hematology Research Collaborative Multiple Myeloma Program developed a prospective, decentralized study within a site-based provider network. In the COVID-19 outcomes in myeloma and the impact of vaccines (COSMIC) study, patients provided consent, permitted electronic health record data collection, completed patient-reported outcomes at baseline, day 30, and day 180 through an electronic study portal, and were compensated for participation. Clinicians at participating sites facilitated study recruitment and shared best practices monthly to accelerate study execution. The COSMIC study rapidly consented 201 patients from 8 participating sites over 4 months (22 January 2024 through 17 May 2024). Consented participants were 30 to 93 years old, with a median age of 67; 46% were female, 69% were non-Hispanic White, 17% were non-Hispanic Black, and 8% were Hispanic/Latino. Patient-reported data completion was 97% at baseline, 81% at 1 month, and 79% at 6 months. At baseline, 55% of patients reported a history of COVID-19 infection and had received a median of 4 SARS-CoV-2 vaccines. For all patients, there were an additional 18 COVID-19 infections and 57 vaccines received during the 6-month observation period. There were no significant differences in health-related quality of life over time or by history of COVID-19 infection. COSMIC demonstrates the feasibility of an innovative evidence generation strategy to drive rapid accrual of patients to address an evolving area of need. This trial was registered at www.clinicaltrials.gov as #NCT05831787.

Abstract BACKGROUND Whilst the broad clonal architecture of naturally progressing chronic lymphocytic leukemia (CLL) has been described, a comprehensive picture of how chemotherapy and targeted agents reshape that landscape is lacking. Here we integrate clone-specific growth kinetics with mutational profiles, transcriptomic subtypes, and CpG-methylation-based epigenetic classes to capture the multidimensional evolutionary responses of CLL under native conditions and during treatment. METHODS We previously reported a systematic whole-exome sequencing (WES) analysis of 417 leukemia–germline pairs from 169 treatment-naïve patients ≥ 65 y (Karisani et al., Blood 2024). The treatment arm comprised 83 patients who received ibrutinib and 39 who received chlorambucil in the RESONATE-2 trial (NCT01722487). Forty-seven age-matched “watch-and-wait” patients from the CLL Research Consortium served as untreated controls. Peripheral-blood samples were collected at baseline and again ~300 d and ~600 d after therapy initiation (or diagnosis in the watch-and-wait cohort), enabling longitudinal clonal tracking. WES data were processed with PhylogicNDT to reconstruct clonal architectures, define phylogenies, and identify significant clonal shifts. MutSig tools identified mutations enriched in expanding or contracting clones. Pretreatment RNA-sequencing profiles were grouped into transcriptional subtypes by consensus clustering based on the Louvain algorithm. Differentially methylated regions plus epitypes were called from reduced representation bisulfite sequencing data. RESULTS Across the 169 patients with WES, we resolved 579 subclonal clusters. Significant clonal shifts (defined as a distribution shift of cancer cell fraction in a subclone of >95% between two timepoints) were uncommon during watch-and-wait (36%) but markedly higher after therapy — chlorambucil 67% (q = 0.01), and ibrutinib 77% (q < 10-4). In ibrutinib-treated CLL, regressing subclones were enriched for KRAS (q<10-3) and SF3B1 mutations (q<10-3), whereas expanding subclones preferentially harboured mutations in BIRC3 (q<10-4), NOTCH1 (q <10-3), TP53 (q=0.02), and POT1 (q=0.098). In chlorambucil-treated patients, no specific mutations were found to be enriched in expanding or regressing subclones. Using the recently defined CLL expression clusters (ECs) in patients treated with chemoimmunotherapy as defined by Knisbacher et al. (Nat Genet 2022), we were unable to identify any relationship between ECs and outcomes. We therefore sought to identify de novo ECs with prognostic relevance in the context of BTK-inhibition (BTKi-ECs). We identified a transcriptionally defined patient subgroup (C4, n=20) associated with inferior progression-free survival (median PFS = 63 months, log-rank q=0.05), independent of known prognostic clinical and molecular features, including epitypes, in multivariate analysis (Cox HR=3.7, 95% CI: 1.6-8.6, q=0.02). Gene-set enrichment in this cluster highlighted TNF-ɑ signalling via NF-κB (REL, TNFAIP3, NFKB2) and G2M checkpoint pathways (SMC4, EZH2). We also identified a distinct transcriptionally defined patient subgroup (C3, n=18) that was enriched for patients harboring at least one contracting subclone and no expanding subclones (q=0.005). This cluster showed a relative depletion of mutations within the chromatin modification pathway (6% vs 50%, q<0.01). Gene-set enrichment in this cluster highlighted a downregulation of genes in the TNF-ɑ signalling via NF-κB pathway (SOCS3, ICOSLG, NFKB2). CONCLUSION BTK inhibition and chemotherapy drive distinct evolutionary trajectories in CLL. Multi-omics profiling reveals mutation-defined subclones with divergent sensitivity to targeted therapy and delineates two informative expression states: a high-risk NF-κB/inflammatory cluster (C4; poor PFS) and a cluster enriched with contracting subclones with depleted TNF-ɑ signalling via NF-κB (C3). Integrating clonal growth kinetics with genomic, transcriptomic, and methylation data can potentially identify patients likely to progress on BTK inhibitors and identify actionable pathways for adaptive or combination therapy.

Abstract Introduction: Graft Failure (GF) is a rare but devastating outcome of HCT. Primary GF (PGF), defined as a failure of neutrophil recovery by Day+28, is straightforward to diagnose. Diagnosing secondary GF (SGF) is more challenging, with its broad time-range, multiple confounding diagnoses, and lack of predictive biomarkers. In BMT CTN1703/1801, we analyzed patients receiving RIC HCT for heme malignancies with either Tac/MTX (n=159) or PT-Cy (n=165) GVHD prophylaxis. SGF was defined as donor chimerism <5% after initial donor engraftment. With Tac/MTX, there were 3 PGF and 1 SGF diagnoses. With PT-Cy, there were 4 PGF and 6 SGF. Median SGF diagnosis was Day +64 (range: Day+28-215). There were too few Tac/MTX patients to analyze SGF, but sufficient events with PT-Cy. To identify PT-Cy SGF predictors, we leveraged lymphocyte, T, B, and NK cell reconstitution analysis. In SGF we found an early, profound deficit in the reconstitution of all major lymphocyte populations, including total lymphocytes, T, B, and NK cells, as early as Day+28. This enabled the modeling of a SGF risk classifier based on the Absolute Lymphocyte Count (ALC). Methods: Clinical ALC measurements were performed on all SGF patients (n=6) and on non-GF controls with ALCs available (146 of 155 non-GF patients). Flow cytometry was performed on all PT-Cy SGF patients (n = 6) and from a subset of non-GF controls (n = 18: controls were chosen as patients without relapse or severe GVHD, to reduce confounders introduced by immunologic interventions, and for whom all samples, including from the graft infusion, were available). T, B, and NK counts, as well as T cell subsets, were compared (using Welch's T test) on Days 7, 14, 21, 28, 42, 63, 98, 180, 270, 365, 730, with SGF patients censored on the day of GF diagnosis. To interrogate the optimal ALC cutoff, the cumulative incidence of SGF was computed at Days +28 and +42, with death without GF as a competing risk. Results: We have previously demonstrated that, compared to Tac/MTX, PT-Cy patients exhibited an early decrease in reconstitution of all T cell populations (with normal NK and B cell reconstitution). Here we focused specifically on PT-Cy patients with or without SGF. Graft CD34 counts/kg were not different between SGF patients and non-GF patients (mean CD34/kg = 1.38 x10e6 (SGF) vs 0.67 x10e6 (non-GF, p = 0.61) However, even amidst the overarching early suppression in T cell reconstitution with PT-Cy, SGF patients could be easily distinguished from the larger PT-Cy cohort, based on more profound deficits in ALC, T, B and NK cells reconstitution, measured using 2 strategies: (1) SGF patients demonstrated significant early (Day +28) quantitative defects in the reconstitution of the ALC (mean +/- SEM 262+/-32 cells/µL (non-GF) vs 60 +/- 25 (SGF, p<0.0001), CD4 T cells (62 +/- 12 cells/µL vs 19+/-15, p=0.048), CD8 T cells (15 +/-3 cells/µL vs 3.5 +/-1 p=0.0009), all CD8 T cell subpopulations, as well as NK cells (116+/-31 cells/µL vs 2.9+/-1.4 cells/µL, p= 0.002) and B cells (3.5 +/-1.4 cells/µL vs 0.16+/-0.07 p =0.03). (2) In SGF, there was a significant deficit in the rate of rise of all major lymphocyte populations between Days 28-42-60 vs non-GF, including CD4 T cells (p<0.0001), CD8 T cells (p = 0.0002), B cells (p<0.0001), and NK cells (p =0.048). These discoveries suggested that a classifier could be identified to risk-stratify patients for SGF. We explored an ALC cutpoint, amenable to standard clinical lab analysis. A statistically significant threshold was identified at both Days+28 and +42, with Day+42 being most predictive: A threshold of 120 cells/µL was identified as optimal, with landmark analysis documenting a SGF rate of 34.7% below the cutpoint, and SGF of 1% above it (HR = 45.9, 95% CI 5.7 - 366). Conclusions: Despite the small number of events, PT-Cy patients with SGF demonstrated a distinctive reconstitution trajectory that encompassed an early, substantial, and sustained deficit in all lymphocyte counts, as well as a lack of their longitudinal expansion. This enabled the discovery of a candidate ALC biomarker cutpoint at Day+42 that could distinguish patients who were more likely to develop SGF. If confirmed, these data could generate a predictive biomarker for SGF, which would enable the design of trials evaluating early interventions (e.g. CD34+ boosts, DLI, modification of immunosuppression) to improve outcomes for these patients.

BACKGROUND Our knowledge of how the genomic landscape of chronic lymphocytic leukemia (CLL) is impacted by the selective pressure imposed by chemotherapy and targeted agents remains incomplete. We hypothesize that the extent of clonal evolution, baseline transcriptional state, and the overall and clone-specific growth kinetics of CLL will vary under native conditions or following exposure to chemotherapy and targeted therapy. METHODS We report the systematic genomic analysis of leukemia-normal whole-exome sequencing (WES) of 420 samples collected from 170 patients who were >65 years of age with treatment-naïve CLL. The treatment-cohort consisted of 83 patients who received ibrutinib (420mg daily) and 40 patients who received chlorambucil (0.5 mg/kg up to maximum 0.8 mg/kg, on days 1 and 15 of a 28-day cycle, up to 12 cycles) as part of the RESONATE-2 trial (NCT01722487). The ‘watch and wait’ (WW) cohort comprised 47 age-matched patients whose samples originated from the CLL Research Consortium and were untreated for their CLL during sampling. Sampling occurred at baseline and at day 300, and/or day 600, following initiation of therapy or following a diagnosis of CLL for those in the WW cohort. The WW cohort had more cases with a mutated IGHV (72% vs 40%), del17p (14% vs 0%), and del13q (72% vs 45%) when compared to the treatment cohort. Baseline CLL RNA-seq data were available for 150 patients. We used PhylogicNDT to reconstruct the clonal architecture from WES. By determining the fraction of cancer cells with each mutation (cancer cell fraction [CCF]) and their multiplicities (number of DNA molecules carrying a mutation per cancer cell), subclones were identified through clustering of CCFs of mutational events across samples from each patient. This allowed us to estimate the phylogenetic relationships and significant clonal shifts (defined as a distribution shift of CCF in a subclone of >95% between two timepoints, corresponding to a median ΔCCF of 18%) for each patient's CLL and to relate these shifts to somatic and clinical features. RESULTS In total, we identified 791 subclonal clusters across the three patient cohorts (ibrutinib = 406, chlorambucil = 189, WW = 196). We observed higher rates of significant clonal shifts between any two time points in patients exposed to chlorambucil (68%, q=0.008) and ibrutinib (77%, q<0.0001) when compared to patients on WW (36%). There was no significant difference in the number of patients exhibiting a clonal shift between those receiving ibrutinib and chlorambucil (q=0.3). More patients treated with ibrutinib (54%, q=0.0002) and chlorambucil (50%, q=0.004) harbored a clone that reduced in size than those on WW (17%). More patients treated with ibrutinib (71%, q<0.0002) and chlorambucil (55%, q=0.12) possessed at least one clone that increased in size during therapy than patients receiving WW (34%). In ibrutinib-treated patients, SF3B1 mutations (q=0.13, 14% vs 5%) and XPO1 mutations (q=0.13, 5% vs 0.3%) were enriched in subclones that significantly reduced over the complete time period. We also observed more BIRC3 mutations in subclones that had a significant shift in CCF when compared to subclones that remained stable over time (q=0.2, 5% vs 2%). Consistent with the overall study, with 8 years of follow-up in this patient subset, long-term progression-free survival (PFS) was significantly higher in the patients treated with ibrutinib when compared to chlorambucil (P<0.0001). This PFS benefit was also seen regardless of subclonal growth patterns. Using baseline RNA-seq data, we identified 7 of the 8 recently defined CLL expression clusters (EC), that were identified in the context of chemoimmunotherapy for CLL (Knisbacher et al. Nat Gen 2022), and could recapitulate the relationship to outcomes in individuals treated with chlorambucil. However, despite the larger cohort, no significant relationship between EC clusters and PFS were observed in ibrutinib-treated patients, suggesting that a distinct predictive baseline transcriptional signature may be present in ibrutinib-treated patients. CONCLUSION Our analyses highlight the need to interrogate the molecular features of CLL in relation to the impact of therapeutic selective pressure imposed by BTK inhibition, which appear distinct from exposure to chemotherapy. Ongoing analyses aim to identify relationships between patterns of growth and subclonal growth rates molecular and clinical features.

Background: Contemporary questions about treatment and outcomes of patients (pts) living with multiple myeloma (MM) demand efficient and timely evidence generation methods, where real-world/ everyday practice data can play an important role. Relevant potential applications include understanding and addressing disparities in treatment access, mitigating toxicities of novel therapies while maintaining quality of life, appropriately sequencing treatments within long-term management strategies, and disseminating and appropriately using surrogate endpoints in practice, such as minimal residual disease. However, existing real-world data (RWD) repositories are limited by an inability to implement interventions in response to issues identified through retrospective data analysis, which is pivotal for the use of newer therapies. The ASH Research Collaborative (ASH RC) MM Data Hub Program was developed to address these gaps. Methods: We developed a multicenter network of US-based tertiary academic medical centers, engaging site clinicians, patients, biostatisticians, informaticians, and data scientists. Initial activities included constructing a shared real-world MM data model comprised of consensus core data elements. Pt data from each site, dating back to at least 2019, is currently being entered through electronic case report forms, batch uploaded utilizing existing institutional data repositories, and soon direct electronic health record import in standard data acquisition formats. Data are aggregated in the Data Hub and shared in a confidential Site Portal with each site. The Portal allows sites to track their own pts, analyze outcomes, and identify ways to improve the quality of care provided to individuals living with MM. We also developed an innovative evidence generation learning community of site clinicians who meet monthly and collaborate on the “COVID-19 OutcomeS in Myeloma and the Impact of VaCcines (COSMIC)” study, a prospective, decentralized observational study that rapidly accrued pts and collects relevant practice information. The group also shares best practices in study conduct to accelerate study enrollment and execution. Results: The MM Data Hub Program now contains twelve centers: Memorial Sloan Kettering Cancer Center, Mayo Clinic, Icahn School of Medicine at Mount Sinai, University of California San Francisco, University of California Davis, Medical College of Wisconsin, Barbara Ann Karmanos Cancer Institute, University of North Carolina Medical Center at Chapel Hill, Emory University Hospital, Washington University School of Medicine in St. Louis, Dana-Farber Cancer Institute, and Atrium Health. From this Network, six centers have contributed data from 8,824 pts (2,825 with sufficiently complete data are included in this analysis). The median age at time of MM diagnosis is 63, and of the 627 deceased pts, the median age at death is 70.6 years, with median time to death from first MM diagnosis of 6 years. The proportions of pts by decade of diagnoses are 34.9% from 2020-2024; 57.6% from 2010-2019; 7.3% from 2000-2009; and 0.9% prior to 2000. 83.1% of pts had a diagnosis of plasma cell myeloma (non-light chain), and 14.4% of pts had a diagnosis of light chain myeloma. 42.3% of pts are female, and the self-reported patient races are: 85.0% white; 9.2% black or African; and 4.3% all other races (race was not documented in 1.5% of pts). 2.8% of pts are of Hispanic ethnicity. Participant data files are currently undergoing completion and cleaning procedures; the analysis will include updated results at the meeting, and a Network-wide analytic data set will be available for initial retrospective data analyses in early 2025. The COSMIC study opened for enrollment on January 22, 2024, and completed enrollment of 200 pts on May 17, 2024. Conclusions: The ASH RC Data Hub MM Program launched in 2023 and is comprised of 12 sites that collectively have contributed participant files for 8,824 pts thus far. Utilizing the structure of the Network, we successfully implemented and established the feasibility of collecting data directly from pts electronically to monitor SARS-CoV-2 vaccines and COVID-19 related outcomes. The Program serves as a successful proof-of-concept model, providing an enduring RWD resource and a prospective evidence generation platform for the myeloma community.

Background: The COVID-19 pandemic had an outsized impact on individuals with immune compromise, such as multiple myeloma, leading to increased rates of severe COVID, hospitalization, and death in these groups. The pandemic also demonstrated the need for rapid and efficient evidence generation to address rapidly changing medical needs and accelerated the use of innovative trial designs to accomplish this goal. Methods: We designed and established a prospective, decentralized study within a site-based provider network, COVID-19 OutcomeS in Myeloma and the Impact of VaCcines (COSMIC), for patients with multiple myeloma at ASH Research Collaborative sites to monitor outcomes related to COVID-19 infection and vaccination. The direct-to-patient approach was accomplished using a study application accessed with a QR code provided to patients during usual clinical care. All study activities occurred within the patient portal at Baseline, Day 30, and Day 180, minimizing burden on site personnel and staff. Study activities included eligibility confirmation, consenting, electronic health record (EHR) data permissions, and patient-reported outcome (PRO) surveys. PRO data (EORTC QLQ-C30, EORTC QLQ MY-20) were made available to clinicians through the portal to visualize results from their patients. Patients shared if they received a COVID-19 vaccination or booster and if they had a COVID-19 infection. Patients with a history of COVID-19 infection included method of confirmation and if hospitalization was required. Patients were provided compensation for participation. Automated text and email messages were sent at three separate intervals: 24 hours, Day 8, and Day 15 for participants who initiated but did not complete study activities. A personal email reminder per participant was sent for those whose second study visit window was approaching. ASH Research Collaborative clinicians met regularly to share best practices related to enrollment and study conduct, thereby creating an innovative evidence generation learning community. We studied the feasibility of data collection in a decentralized format by focusing on the number of participants who interacted with the system, those who consented, and those who completed baseline PRO assessments. Results: The COSMIC study rapidly accrued and consented 200 patients from eight participating ASH Research Collaborative sites over four months (1/22/2024 through 5/17/2024): 69% self-reported as non-Hispanic White (NHW). Of enrolled participants, 57.5% opted to permission sharing of their EHR data, which included disease and treatment-related clinical data, laboratory results, pathology reports, and imaging reports. A total of 196 patients (98%) provided demographic and COVID-related disease and vaccine data at baseline; nearly all of these patients (n=195) provided baseline PRO data, and 150 patients provided further information at Day 30 at the time of this analysis. Those who did not provide Day 30 data were not significantly different from those who did in terms of age (median: 68 [no Day 30 data] vs. 67 [Day 30 data] years; p=0.44), sex (41% [no Day 30 data] vs. 47% [Day 30 data] Female; p=0.47), or race (65% [no Day 30 data] vs. 71% [Day 30 data] NHW; p=0.60). A higher proportion of Hispanic patients were retained at Day 30 (13/15=87%) vs. 78% of non-Hispanic patients or those who declined reporting ethnicity (2/7=29%; p=0.007). COVID-19 vaccination data were submitted by 196 patients, 186 of whom had ever received a COVID-19 vaccination. Data on number, type, brand, and dates for these vaccinations were provided. Conclusions: The COSMIC study conducted by the ASH Research Collaborative was able to rapidly accrue a diverse cohort of patients using a decentralized workflow within a site-based provider network, with high consent and compliance rates. This platform, and the innovative evidence generation learning community of ASH Research Collaborative investigators and patients, sets a precedent to address a variety of real-world questions related to disease management, infections, vaccinations, and PROs in this population of patients with multiple myeloma.

Background: Data from the ASH Research Collaborative COVID-19 Registry for Hematology demonstrated that patients with multiple myeloma and other immune compromise had elevated risk for severe complications from COVID-19 infection. To address current evidence gaps and to demonstrate proof of concept for a pandemic preparedness and response network, the ASH Research Collaborative Multiple Myeloma Program developed a prospective, decentralized study within a site-based provider network to investigate COVID-19 infections, vaccinations, and quality of life in patients with multiple myeloma. Methods: We designed and established the COVID-19 OutcomeS in Myeloma and the Impact of VacCines (COSMIC) to study COVID-19 in patients with multiple myeloma at ASH Research Collaborative network sites. A study application was accessed with a QR code and shared with patients during usual clinical care. Patients provided consent, optionally gave permission for electronic health record data collection, and completed patient-reported outcomes (EORTC QLQ-C30 and MY20) at baseline, Day 30, and Day 180 through the study portal. Clinicians at participating sites facilitated study enrollment and shared best practices monthly to accelerate study execution. Study objectives related to feasibility of data collection in a decentralized format are reported in a separate abstract. Here, we report interim results related to SARS-CoV-2 vaccination history, COVID-19 infection, and how these relate to patient reported outcomes and clinical outcomes in enrolled participants. Results: The COSMIC study rapidly accrued and consented 200 patients from eight participating ASH Research Collaborative sites over four months (1/22/2024 through 5/17/2024). Consented participants' ages ranged from 29 to 93 years, with median of 67 years; 46% were female, 69% were non-Hispanic White (NHW), 17% were non-Hispanic Black (NHB), and 8% were Hispanic/Latino. Of the 196 patients who reported COVID-19 data, 111 (57%) reported at baseline that they had a history of COVID-19 infection, with 12 having required hospitalization for this (12/111=11%). Overall, 95% (186/196) reported a history of COVID-19 vaccination, where the median number of vaccinations was 4 (range: 1 to 6); 44 patients (23%) expressed willingness to discuss getting a yearly booster vaccine with their provider. At 1-month follow-up, there were 3 additional patients who reported a COVID-19 infection and 18 patients who received an additional COVID-19 vaccination. The baseline EORTC QLQ-C30 median Global Health Status/QOL score was 66.7 (mean=64.4, range: 8.3 - 100, n=196). This overall global QOL score was not significantly different between those who did vs. did not have a history of COVID-19 infection (mean [SD]: 63.7 [19.3] vs. 65.8 [17.6]; p=0.57). In analyses adjusted for age, biological sex, and race/ethnicity group, history of COVID-19 infection was not associated with HRQOL (odds ratio=0.93, 95% CI: 0.78 - 1.10; p=0.38). Data collection is ongoing and final study results will be reported at a later date. Conclusion: COSMIC is a prospective, decentralized study within a site-based provider network conducted by the ASH Research Collaborative Multiple Myeloma Program that demonstrates the feasibility of using an innovative evidence generation strategy to drive rapid accrual of patients to address a rapidly evolving area of need. The COSMIC study also provides proof of concept that a real-world evidence generation network can play an important role in national pandemic preparedness and response. The COSMIC experience reinforces the ability to accrue diverse patient populations representing the demographic of myeloma patients in the US.

Abstract Clonal hematopoiesis of indeterminate potential (CHIP), an emerging biomarker for personalized risk-directed interventions, is increased in cancer survivors. However, little is known about patient preferences for CHIP testing. We surveyed participants in a prospective cohort study of young women with breast cancer (BC). The emailed survey included an introduction to CHIP and a vignette eliciting participants’ preferences for CHIP testing, considering sequentially: population-based 10-year risk of BC recurrence, hematologic malignancy, and heart disease; increased CHIP-associated risks; current CHIP management; dedicated CHIP clinic; and hypothetical CHIP treatment. Preference changes were evaluated using the McNemar test. The survey response rate was 82.2% (528/642). Median age at time of survey was 46 years and median time from diagnosis was 108 months. Only 5.9% had prior knowledge of CHIP. After vignette presentation, most survivors (87.1%) recommended CHIP testing for the vignette patient. Presented next with CHIP-independent, population-based risks, 11.1% shifted their preference from testing to not testing. After receiving information about CHIP-associated risks, an additional 10.1% shifted their preference to testing. Preference for testing increased if vignette patient was offered a CHIP clinic or hypothetical CHIP treatment, with 7.2% and 14.1% switching preferences toward testing, respectively. Finally, 75.8% of participants desired CHIP testing for themselves. Among participants, 28.2% reported that learning about CHIP caused at least moderate anxiety. Most young survivors favored CHIP testing, with preferences influenced by risk presentation and potential management strategies. Our findings highlight the importance of risk communication and psychosocial support when considering biomarkers for future risk in cancer survivors. This trial has been registered at www.clinicaltrials.gov as #NCT01468246.

Abstract Richter syndrome (RS), an aggressive lymphoma that develops in patients with chronic lymphocytic leukemia (CLL), is a striking example of histologic transformation. While recent therapeutic advances have transformed the treatment landscape of CLL and lymphoma, RS remains associated with dismal overall survival. Despite an advanced genomic and molecular characterization of CLL over the past decade, the current understanding of the genetic factors driving evolution of CLL to RS is limited. To decipher the genetics underlying this transformation, we have performed an integrative analysis of exome, genome and transcriptome data generated from matched RS and CLL samples from a discovery cohort of 53 patients with newly diagnosed RS of DLBCL histology. Through computational deconvolution of CLL and RS clones, we constructed phylogenetic relationships and traced evolution of CLL to RS, confirming both clonal related (87%) and unrelated cases (13%). In addition to identifying recognized RS-risk genetic lesions, we discovered novel RS-specific alterations, including 5 putative somatic driver genes (IRF2BP2, SRSF1, B2M, DNMT3A and EZH2), frequent copy number alterations beyond del(9p21)(CDKN2A/B), (including amp(7q21.2) (CDK6), amp(9p24) (PDL1/L2), and amp(1q23)(MCL1)), and recurrent whole genome duplication and chromothripsis. Integration of exome and genome sequencing data led to the identification of distinct molecular subtypes of RS with prognostic importance. To confirm these molecular subtypes, a validation cohort of 47 RS cases has been assembled with paired exome and transcriptome data. To further investigate the stepwise clonal evolution of CLL to RS, we performed single-cell RNA-sequencing on biopsy samples obtained at diagnosis from 5 individuals with clonally related transformation. Using a novel tool, CNVSingle, we inferred allele specific single-cell copy number alterations that enabled identification of the single-cell clusters representing distinct CLL and RS genetic subclones as well as intermediate, or transitional, evolutionary states. RS cells displayed gene expression enriched in pathways of MYC targets and cell cycle, in line with similar analysis on bulk transcriptomes. Finally, by ultra-low pass (ULP)-WGS sequencing of plasma samples from RS patients, we demonstrate detection of RS tumor DNA in plasma months prior to initial clinical diagnosis (n=3 of 6) or post-allogeneic stem cell transplant relapse (n=2 of 2). cfDNA is thus a promising tool for early detection of emerging RS and RS relapse as well as for non-invasive detection surrounding diagnosis. Altogether, our study defines RS-specific alterations and provides a molecular definition of RS, identifies distinct genetic subtypes of RS with prognostic significance, traces the evolutionary path to RS and suggests future strategies for improved detection. Citation Format: Erin M Parry, Ignaty Leshchiner, Romain Guieze, Connor Johnson, Eugen Tausch, Sameer A Parikh, Camilla K Lemvigh, Conor Messer, Filippo Utro, Chaya Levovitz, Kahn Rhrissorrakrai, Matthew S Davids, Julien Broseus, Shuqiang Li, Ziao Lin, Binyamin A Knisbacher, Christof Schneider, Laura Z Rassenti, Thomas J Kipps, Nitin Jain, William Wierda, Florence Cymbalista, Neil E Kay, Kenneth J Livak, Brian P Danysh, Chip Stewart, Donna Neuberg, Jennifer R Brown, Laxmi Paridi, Stephan Stilgenbauer, Gaddy Getz, Catherine Wu. Evolutionary history of transformation from chronic lymphocytic leukemia to Richter syndrome [abstract]. In: Proceedings of the Third AACR International Meeting: Advances in Malignant Lymphoma: Maximizing the Basic-Translational Interface for Clinical Application; 2022 Jun 23-26; Boston, MA. Philadelphia (PA): AACR; Blood Cancer Discov 2022;3(5_Suppl):Abstract nr A13.