Bruins, Laura A.

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

Abstract Introduction: Although therapies targeting Bruton Tyrosine Kinase (BTK), either as monotherapy or in combination have revolutionized the treatment in chronic lymphocytic leukemia (CLL), the disease remains incurable. Continuous treatment with these agents frequently leads to the development of resistance and disease progression, mostly associated with mutations in BTK or PLCG2. However, a significant fraction of BTKi-resistant patients does not harbor mutations in these genes, revealing that multiple distinct mechanisms of resistance can independently develop in these patient cohorts. Identifying novel molecular pathways associated with drug resistance in the relapsed/refractory (RR) setting of CLL requires more exploration. Materials and Methods: Using the Mayo Clinic CLL Database and tissue bank, we identified 30 RR patients, who had had disease progressionon a BTKi. The median lines of prior therapies were four. The median age at start of treatment was 57.9 (range, 36-78), 77% were male. Rai stage at diagnosis was 0 in 10% of patients, stage I in 37%, stage II in 20%, stage III in 6%, and stage IV in 27%. IGHV unmutated was observed in 87% of cases. Five patients subsequently evolved to Richter's Transformation (RT). A total of 64 blood samples were collected at different timepoints: baseline (before BTKi therapy), during treatment and at disease progression from BTKi (Ibrutinib/Acalabrutinib/Pirtobrutinib/Nemtabrutinib). The CD5+/CD19+ tumor population was enriched by flow sorting. We performed whole genome sequencing (WGS) in tumor/germline pairs with an average sequencing depth of 60X. Copy number variants (CNVpytor), structural variants – SV (Manta and DELLY2), single nucleotide variants (SNV) and indels (MuTect2, Strelka) were calculated. Mutational signature analysis was conducted independently on each disease timepoint with sigProfiler. De novo signatures were extracted with non-negative matrix factorization, then decomposed and mapped to known signatures from the COSMIC Human Cancer Signatures database. Results: Prior to start of BTKi treatment, the most common abnormality was del13q (65%), followed by abnormalities in TP53 (mutation and/or del17p) in 59% of patients, del8p, del11q (23% each), and mutations in SF3B1 (35%), NFKIE, NOTCH1 and MAPK signaling pathway (BRAF, KRAS, NRAS, PTPN11) in 23% of cases, each. Complex karyotype, defined as >3 copy number abnormalities was found in 59% of cases. Disease progression was observed 4.4 years (median; range 1.9-10.8 yrs) after starting BTKi therapy. At disease progression, there was an overall increase in genomic complexity. Overall, 17 cases (56%) showed mutations in BTK (p.C481S, p.C481Y, p.L528W, p.A428D) and/or PLCG2 (p.P844H). Four cases showed mutations in both genes or two or more independent mutations in BTK. Del17p and/or TP53 mutations were found in 20 (67%) cases, 9 of them with biallelic abnormalities. A third of patients (N=10) were found to have MAPK mutations, and 9 (30%) cases had mutations in the epigenetic regulators CREBBP, SETD2, SETD1A andKMT2D. Loss of heterozygosity, including 11q and 17p was found in 13% of cases. Chromothripsis was found in 23% of cases. In patients without BTK or PLCG2 mutations (N=13), TP53 abnormalities were present in 92% of cases and mutations in the MAPK pathway in 46% of these patients. Finally, we identified several SVs involving driver genes at disease progression, including inversions in RB1 (N=1), NOTCH1 (N=1) and translocations in MYC (N=1) and IGH (N=2). Analyzing the mutational signatures, baseline timepoints were dominated heavily by aging signatures (SBS5, SBS1). However, in cases that progressed after therapy, we found enrichment of mutational signatures associated with defective DNA mismatch repair (SBS44, 6%), chemotherapy treatment (SBS25, 14%), homologous and nucleotide excision repair deficiency (SBS8, 48%), and indirect effects of AID-induced somatic mutagenesis (SBS85, 7%). Additionally, the Melphalan signature (SBS99, 10%) was found in patients treated alkylating agents. Conclusions: Disease progression of CLL on BTKi confirmed the existence of multiple driver genes beyond BTK, PLCG2 andTP53, including the high prevalence of mutations in MAPK pathway and epigenetic regulators, and increased genomic complexity. Ongoing efforts are focused on integrating these findings with clinical demographics, epigenetic modifications, and preclinical drug sensitivity profiles.

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

Background: Immune dysfunction is a hallmark of chronic lymphocytic leukemia (CLL), and several studies suggest that hypogammaglobulinemia at the time of CLL diagnosis may predict shorter time to first therapy (TTFT; Parikh et al, 2015). However, it is unclear if hypogammaglobulinemia can predict TTFT independent of the CLL-International Prognostic Index (CLL-IPI) and the tumor mutational load (TML). We sought to determine if serum immunoglobulin (Ig) levels at diagnosis can independently predict outcomes in patients with CLL and its precursor, high-count monoclonal B-cell lymphocytosis (MBL). Methods: We used the Mayo Clinic CLL Database to identify newly diagnosed CLL/MBL consented within 2 years of diagnosis. Next generation sequencing (NGS) was performed using a 59 gene panel (SureSelect) to detect mutated CLL genes. Serum Ig levels were quantitated by radial immunodiffusion (Immunoplates). Serum Ig levels below the lower limit of normal were defined as hypogammaglobulinemia. We used Chi-squared or Fisher's exact tests to compare discrete variables and the Kruskal Wallis test for continuous variables. TTFT was calculated from date of diagnosis to date of first treatment or date last known to be untreated. We used Cox regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for TTFT and overall survival (OS) associations. Results: We identified 895 newly diagnosed patients (588 CLL and 307 MBL) who had undergone NGS for recurrent somatic mutations and had serum Ig levels available at the time of diagnosis. The median absolute B-cell count was 6.7 x109/L; 613 (69%) were male, 375 (42%) had unmutated IGHV genes, and 81 (9%) had TP53 disruption. Based on CLL-IPI, 412 (46%) patients had low risk, 285 (32%) had intermediate risk, and 198 (22%) had high/very high risk. The most commonly mutated genes were NOTCH1 (14%), SF3B1 (11%), TP53 (10%), NFKBIE (8%), ATM (6%), and BIRC3 (6%). Based on the number of genes with high impact or hotspot mutations, we classified 40%, 29%, and 31% patients with TML scores of 0, 1, or 2+, respectively. At CLL/MBL diagnosis, the median serum IgG, IgA, and IgM was 891 mg/dL (111-2620), 121 mg/dL (1-1150), and 44 mg/dL (5-1320), respectively; 284 (32%) patients had low serum IgG, 128 (17%) had low serum IgA, and 322 (42%) had low serum IgM. Low serum IgA was associated with mutations in NOTCH1, SF3B1, ATM, BIRC3 and NFKBIE, low IgM was associated with SF3B1 and BIRC3 mutations (all comparisons, P < .05), and low IgG was associated with BIRC3 mutation (P = .05). The median follow-up for the cohort was 8.4 years. A total of 359 patients progressed requiring therapy (299 CLL and 60 MBL), and 265 patients died (177 CLL and 88 MBL). The TTFT was significantly shorter for patients with low serum IgG compared to normal serum IgG at diagnosis (median 4.4 vs 10.8 years) and similar findings were observed in individuals with low serum IgA (1.7 vs 10.8 years) and those with low serum IgM (4.6 vs 11.1 years). On univariable analysis, the following were associated with a shorter TTFT: low serum IgG (HR: 1.8, 95% CI 1.4-2.2), low serum IgA (HR: 3.5, 95% CI 2.7-4.4), low serum IgM (HR: 1.9, 95% CI 1.5-2.3), TML score of 1 (HR: 1.7, 95% CI 1.3-2.2), TML of 2+ (HR: 4.4, 95% CI 3.4-5.7), CLL-IPI intermediate risk (HR: 3.7, 95% CI 2.8-4.8), and CLL-IPI high/very high risk (HR: 7.6, 95% CI 5.8-10.1) (all comparisons, P < .0001). In multivariable analyses after adjusting for CLL-IPI and TML, low serum levels of IgG (HR: 1.8, 95% CI: 1.5-2.3), IgA (HR: 2.4, 95% CI: 1.8-3.0), and IgM (HR: 1.8, 95% CI: 1.5-2.3) were independently associated with a shorter TTFT (all comparisons, P < .0001). No significant differences in OS were detected between patients with low and normal serum IgG, IgM, and IgA on univariate analysis. Similar results for TTFT and OS were observed after adding clonal B-cell count to the Cox model. Conclusions: Our study shows that low serum Ig levels at the time of initial diagnosis is predictive of TTFT independently of the CLL-IPI and TML, which can further risk stratify individuals with newly diagnosed CLL/MBL. Additionally, low serum IgA, IgM, and IgG levels at diagnosis are significantly associated with specific somatic mutations in individuals with newly diagnosed CLL/MBL.

Abstract High-count monoclonal B-cell lymphocytosis (HCMBL) is a precursor condition to chronic lymphocytic leukemia (CLL). We have shown that among individuals with HCMBL, the CLL-International Prognostic Index (CLL-IPI) is prognostic for time-to-first therapy (TTFT). Little is known about the prognostic impact of somatically mutated genes among individuals with HCMBL. We sequenced DNA from 371 individuals with HCMBL using a targeted sequencing panel of 59 recurrently mutated genes in CLL to identify high-impact mutations. We compared the sequencing results with that of our treatment-naïve CLL cohort (N = 855) and used Cox regression to estimate hazard ratios and 95% confidence intervals (CIs) for associations with TTFT. The frequencies of any mutated genes were lower in HCMBL (52%) than CLL (70%). At 10 years, 37% of individuals with HCMBL with any mutated gene had progressed requiring treatment compared with 10% among individuals with HCMBL with no mutations; this led to 5.4-fold shorter TTFT (95% CI, 2.6-11.0) among HCMBL with any mutated gene vs none, independent of CLL-IPI. When considering individuals with low risk of progression according to CLL-IPI, those with HCMBL with any mutations had 4.3-fold shorter TTFT (95% CI, 1.6-11.8) vs those with none. Finally, when considering both CLL-IPI and any mutated gene status, we observed individuals with HCMBL who were high risk for both prognostic factors had worse prognosis than patients with low-risk CLL (ie, 5-year progression rate of 32% vs 21%, respectively). Among HCMBL, the frequency of somatically mutated genes at diagnosis is lower than that of CLL. Accounting for both the number of mutated genes and CLL-IPI can identify individuals with HCMBL with more aggressive clinical course.

Abstract TP53 aberrations, including mutations and deletion of 17p13, are important adverse prognostic markers in chronic lymphocytic leukemia (CLL) but are less studied in high count monoclonal B-cell lymphocytosis (HCMBL), an asymptomatic pre-malignant stage of CLL. Here we estimated the prevalence and impact of TP53 aberrations in 1,230 newly diagnosed treatment-naïve individuals (849 CLL, 381 HCMBL). We defined TP53 state as: wild-type (no TP53 mutations and normal 17p), single-hit (del(17p) or one TP53 mutation), or multi-hit (TP53 mutation and del(17p), TP53 mutation and loss of heterozygosity, or multiple TP53 mutations). Cox regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for time to first treatment and overall survival by TP53 state. We found 64 (7.5%) CLL patients and 17 (4.5%) HCMBL individuals had TP53 mutations with variant allele fraction >10%. Del(17p) was present in 58 (6.8%) of CLL and 11 (2.9%) of HCMBL cases. Most individuals had wild-type (N=1,128, 91.7%) TP53 state, followed by multi-hit (N=55, 4.5%) and then single-hit (N=47, 3.8%) TP53 state. The risk of shorter time to therapy and death increased with the number of TP53 abnormalities. Compared to wild-type patients, multi-hit patients had 3-fold and single-hit patients had 1.5-fold increased risk of requiring therapy. Multi-hit patients also had 2.9-fold increased risk of death compared to wild-type. These results remained stable after accounting for other known poor prognostic factors. Both TP53 mutations and del(17p) may provide important prognostic information for HCMBL and CLL that would be missed if only one were measured.

AbstractIdentifying biomarkers associated with disease progression and drug resistance are important for personalized care. We investigated the expression of 121 curated genes, related to immunomodulatory drugs (IMiDs) and proteasome inhibitors (PIs) responsiveness. We analyzed 28 human multiple myeloma (MM) cell lines with known drug sensitivities and 130 primary MM patient samples collected at different disease stages, including newly diagnosed (ND), on therapy (OT), and relapsed and refractory (RR, collected within 12 months before the patients’ death) timepoints. Our findings led to the identification of a subset of genes linked to clinical drug resistance, poor survival, and disease progression following combination treatment containing IMIDs and/or PIs. Finally, we built a seven‐gene model (MM‐IMiD and PI sensitivity‐7 genes [IP‐7]) using digital gene expression profiling data that significantly separates ND patients from IMiD‐ and PI‐refractory RR patients. Using this model, we retrospectively analyzed RNA sequcencing (RNAseq) data from the Mulltiple Myeloma Research Foundation (MMRF) CoMMpass (n = 578) and Mayo Clinic MM patient registry (n = 487) to divide patients into probabilities of responder and nonresponder, which subsequently correlated with overall survival, disease stage, and number of prior treatments. Our findings suggest that this model may be useful in predicting acquired resistance to treatments containing IMiDs and/or PIs.

Abstract Venetoclax (VTX) is a selective small-molecule inhibitor of BCL-2 that exhibits antitumoral activity against MM cells presenting lymphoid features and those with translocation t(11;14). Despite its impressive clinical activity, VTX therapy for a prolonged duration can lead to drug resistance. Therefore, it is important to understand the underlying mechanisms of resistance in order to develop strategies to prevent or overcome resistance. In the present study, we established four VTX resistant human myeloma cell lines (HMCLs) from four sensitive HMCLs, including three with t(11;14), in culture with a stepwise increase in treatment dose with VTX. To identify the molecular basis of acquired VTX resistance, whole exon sequencing (WES), mRNA-sequencing (mRNAseq), and protein expression assays were performed in the four isogenic VTX-sensitive/resistant HMCLs and three MM patients with samples collected before VTX administration and after clinical resistance to the drug. Compared with sensitive cell lines and patient samples collected before VTX administration, mRNAseq analysis identified downregulation of BIM and upregulation of BCLXL in both resistant cell lines and MM cells from relapse patients. Other transcriptional changes detected included upregulation of AURKA, BIRC3, BIRC5, and IL32. Enrichment analysis of differentially expressed genes suggested involvement of PI3K and MAPK signaling, likely associated with cytokines, growth factors (EGF, FGF and IGF family members), and receptor tyrosine kinase (EGF and FGF). Western blot analysis was performed to compare BCL2 family expression in resistant cell lines versus sensitive cell lines and it showed upregulation of BCL2 survival members (such as MCL-1 and BCLXL), and downregulation of pro-apoptotic BH3 members (such as BIM and PUMA). BIM expression was completely lost in one resistant cell line, and introduction of exogenous BIM into this cell line enhanced VTX sensitivity. Interestingly, BCL2 was upregulated in some resistant cell lines generated after a long-term treatment with VTX, suggesting BCL2 expression level may not be suitable as a marker of VTX sensitivity for acquired resistance. Unlike in CLL, BCL2 mutations were not identified through WES in any resistant cell lines or primary patient sample harvested after relapse. While 8 genes were mutated in two resistant samples , no clear mutational pattern emerged . Based on the above, we further tested some specific inhibitors in in vitro or ex vivo cell models to help understanding resistant mechanism and identify strategies to overcome VTX resistance. We found that inhibition of MCL-1, with the compound S68345, substantially enhanced VTX sensitivity in three resistant HMCLs and in primary cells from one relapsed MM patient. A BCLXL inhibitor (A155463) only significantly enhanced VTX sensitivity in one resistant cell line after co-treatment with VTX. Co-treatment of the other three resistant cell lines with VTX, S68345 and A155463 resulted in the most synergistic anti-myeloma activity, suggesting those cell lines are co-dependent on MCL-1, BCLXL, and BCL2 for survival, although they are more dependent on MCL-1. We also found that inhibition of PI3K signaling, IGF1, RTK (EGF and FGF) and AURKA significantly increased VTX sensitivity, partially through downregulation of MCL-1, and BCLXL, and upregulation of BIM. Conventional anti-MM drugs such as dexamethasone, bortezomib and lenalidomide, were shown to have little activity on augmenting VTX sensitivity in most resistant cell lines. In summary, we find that acquired resistance to VTX in MM is largely associated with BCL2 family regulation, including upregulation of survival members such as MCL-1, BCLXL, BCL2, and downregulation of pro-apoptotic members, especially BIM. Our study also indicates that upstream signaling involved in BCL2 family regulation during acquired resistance is likely related to cytokine, growth factor, and/or RTK-induced cell signaling such as PI3K. Co-inhibition of MCL-1, or BCLXL, as well as the upstream PI3K, RTK (FGF and EGF), IGF-1 mediated signaling were effective in overcoming VTX resistance. Disclosures Fonseca: Mayo Clinic in Arizona: Current Employment; Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Takeda: Consultancy; Bayer: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Merck: Consultancy; Juno: Consultancy; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; AbbVie: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees.