Getz, Gad

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 The development of targeted therapy for patients with multiple myeloma (MM) is hampered by the low frequency of actionable genetic abnormalities. Gain or amplification of chromosome 1q (1q+) is the most frequent arm-level copy number gain in patients with MM and is associated with higher risk of progression and death despite recent therapeutic advances. Thus, developing targeted therapy for patients with MM with 1q+ stands to benefit a large portion of patients in need of more effective management. Here, we used large-scale dependency screens and drug screens to systematically characterize the therapeutic vulnerabilities of MM with 1q+ and displayed increased sensitivity to myeloid cell leukemia-1 (MCL1) and phosphatidyl inositol 3-kinase (PI3K) inhibitors. Using single-cell RNA sequencing, we compared subclones with and without 1q+ within the same patient tumors and demonstrated that 1q+ is associated with higher levels of MCL1 and the PI3K pathway. Furthermore, by isolating isogenic clones with different copy number profiles for part of the chromosome 1q arm, we observed increased sensitivity to MCL1 and PI3K inhibitors with arm-level gain. Lastly, we demonstrated synergy between MCL1 and PI3K inhibitors and dissected their mechanism of action in MM with 1q+, uncovering a cytostatic effect. In conclusion, this study highlights that MM with 1q+ may present enhanced sensitivity to MCL1 and PI3K inhibitors, enabling their use at lower doses without sacrificing efficacy, and may thus accelerate the development of targeted therapy for patients with MM and 1q+.

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.

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

Introduction Genomic alterations are common in multiple myeloma precursor stages, monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). However, commonly used stratification systems do not include somatic alterations that play a role in the disease. To address this clinical need, we generated a comprehensive genomic map of MM and precursors by harmonizing and analyzing mostly WGS data from 1,030 patients. We used these data to define a simple MM-like score, that is able to discriminate which premalignant patients are at risk to progress. Methods We collected, enriched, and performed WGS of tumor plasma cells and matched normals from 138 participants with MGUS or SMM, including those in low, intermediate, and high-risk groups. We combined our precursor dataset with genomic profiles from MM (n=20) and external sources of 872 individuals with MGUS (n=18), SMM (n=62), and MM (n=792) for a cohort of 1,030 individuals with MM or a precursor condition. In 16 patients, we could perform WGS serially from the bone marrow or circulating tumor cells (n=39) and reassess the presence of mutations. Results To develop a simple score that reflects the similarity of precursor genomes to full-blown multiple myeloma disease, we searched among the 107 common driver events (occurring in >1% of patients) and initiating translocations for ones that are differently altered in precursor conditions versus MM. We found 26 differentially altered events: only MAF and MAFB translocations were enriched in MGUS/SMM whereas 24 events were enriched in MM, including known drivers MYC, KRAS, FAM46C, del(1p) and gain(1q). We then defined a simple “MM-like” score for each tumor as the number of MM-enriched drivers minus the number of MGUS-enriched drivers. As expected, the MM-like score in our cohort increased throughout MM development (MM-like score median of 1 in MGUS, 2 in SMM, and 3 in MM). Even among SMM patients, we found a gradual increase of the MM-like score from low- (median=1), to intermediate (median=2), to high-risk (median=3) groups that match the overall medians in MGUS, SMM and MM (p=2e-5). We tested whether the MM-like score can be used to estimate risk of progression. Among untreated SMM patients characterized with WGS, an MM-like score greater than 2 was associated with a higher likelihood of progression (HR=5.3, P=0.03). Using an independent dataset of 87 patients with SMM, we were able to validate that a MM-like score greater than 2 was indeed associated with a higher risk of progression (HR=3.3, P=5x10-5). 11 patients who did not progress and for whom we performed longitudinal WGS, has stable MM-like scores over time, while three patients clinically progressed and their MM-like score increased at the time of progression (due to newly detected mutations in KRAS and NRAS). For the two remaining patients, their MM-like score increased, and we observed growth of the subclone that acquired the additional MM-like driver. In the first patient with MGUS/low-risk SMM, a novel hyperdiploid clone was detected in addition to a TP53 bi-allelic hit and t(11;14) translocation and was shown to increase in clonal fraction with tumor burden (light chains +104% within three years). In the second non-progressor with an increasing MM-like score, a novel del(1p) was detected, and other subclones in the phylogenetic tree either shrank or were stable over three years. Finally, because the MM-like score can place tumors on a gradual axis of evolution towards a more genomically-mature tumor, we tested whether the MM-like score was also predictive of survival in patients treated with a newly diagnosed MM. To do so, we calculated the MM-like score in patients from the publicly available CoMMpass database. We found that an increased score significantly correlated with shorter progression-free survival (median PFS1: 3 years in high score, versus 5 years in low/intermediate score; p=1e-3) and overall survival (median OS: 4.5 years in high score, versus not reached in low/intermediate score; p=1e-5). Conclusion Overall, we demonstrate that it is possible to stratify precursor stages of MM by discovering drivers, detecting those that are associated with the stage of the disease, and defining a genomically-based score that can be used to assess risk of progression and is easy to implement in the clinic.

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

Abstract Multiple myeloma (MM) develops from well-defined precursor stages; however, invasive bone marrow (BM) biopsy limits screening and monitoring strategies for patients. We enumerated circulating tumor cells (CTC) from 261 patients (84 monoclonal gammopathy of undetermined significance, 155 smoldering multiple myeloma, and 22 MM), with neoplastic cells detected in 84%. We developed a novel approach, MinimuMM-seq, which enables the detection of translocations and copy-number abnormalities through whole-genome sequencing of highly pure CTCs. Application to CTCs in a cohort of 51 patients, 24 with paired BM, was able to detect 100% of clinically reported BM biopsy events and could replace molecular cytogenetics for diagnostic yield and risk classification. Longitudinal sampling of CTCs in 8 patients revealed major clones could be tracked in the blood, with clonal evolution and shifting dynamics of subclones over time. Our findings provide proof of concept that CTC detection and genomic profiling could be used clinically for monitoring and managing disease in MM. Significance: In this study, we established an approach enabling the enumeration and sequencing of CTCs to replace standard molecular cytogenetics. CTCs harbored the same pathognomonic MM abnormalities as BM plasma cells. Longitudinal sampling of serial CTCs was able to track clonal dynamics over time and detect the emergence of high-risk genetic subclones. This article is highlighted in the In This Issue feature, p. 247