Citations

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 Multiple studies including ours have shown that the interaction of chronic lymphocytic leukemia (CLL) B cells with the bone marrow (BM) microenvironment promotes cell survival and protects these cells from the cytotoxic effects of therapy. A detailed understanding of this interaction will help better understand CLL leukemic cell survival and drug resistance mechanisms. We performed RNA-seq in paired CLL B cells isolated from untreated blood and BM (n=6) and in untreated CLL B cells (n=4) cultured alone or co-cultured with bone marrow stromal cells (BMSCs) derived from either normal individuals (n=2) or CLL patients (n=2). We found upregulation of 232 and 917 genes in CLL B cells from BM and in co-cultured CLL B cells compared to the CLL B cells from the same patient’s blood and CLL B cells cultured alone respectively (p<0.05, fold change >1.5). However we found only 13 genes overlapped between BM CLL B cells and co-cultured CLL B cells. To explore the functional importance of these 13 genes, we analyzed the expression of these genes in CLL blood B cells from an additional 169 untreated patients by RNA-seq and found a positive relationship between 5 of these 13 genes (MKI67, PNP, C16orf54, MOB3A, CDK2AP2) with CLL patient’s clinical outcome including overall survival (OS), progression free survival (PFS), and time to first treatment (TTFT) [univariate Cox regressions analysis (UCRA) p<0.05]. Out of 5 genes, purine nucleotide phosphorylase (PNP) which is an enzyme in the purine salvage pathway, showed the significant association for patient’s OS, PFS, and TTFT using UCRA [hazard ratio (HR) {95% CI}: 2.8 {1.8-4.3}, 2.7 {1.9-3.8}, 2.9 {2.0-4.2}, respectively, p<.0001], suggesting purine metabolism plays an important role in BM induced CLL B cell survival. We also observed an increase in PNP protein levels in CLL B cells co-cultured with BMSCs or from BM using Western blot analysis. Untargeted metabolomic profiling (LC-MS+GC-MS) showed an increased level of purine salvage pathway metabolites adenosine, inosine, and hypoxanthine in co-cultured CLL B cells. Consistent with this altered nucleotide metabolism data, we also found increased pyrimidine pathway enzyme thymidylate synthase (TYMS) at RNA and protein levels in co-cultured CLL B cells and in BM CLL B cells. We further analyzed the RNA-seq data generated from the 169 CLL patients and it also showed a positive association of TYMS gene with their OS, PFS and TTFT using UCRA [HR (95% CI): 1.35 (1.0-1.8), 1.3 (1.0-1.6), 1.63 (1.3-2.1) respectively, p<0.05]. In conclusion, these results indicate that active purine/pyrimidine metabolism in CLL B cells may be augmented by the BM environment to support CLL B cell survival. Future studies to understand the exact mechanism of PNP and TYMS mediated CLL B cell survival for both untreated and treated CLL B cells will uncover unique approaches for therapy. Citation Format: Sutapa Sinha, Zhiquan Wang, Weiguo Han, Kari G. Rabe, Susan L. Slager, Chantal E. McCabe, Daniel R. O'Brien, Sameer A. Parikh, Esteban Braggio, Neil E. Kay. Role of purine and pyrimidine metabolism in CLL B cell survival and drug resistance mediated via interaction with bone marrow microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1252.

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.

Background and Aims: This study aimed to investigate safety and efficacy of silmitasertib, an oral small molecule casein kinase 2 inhibitor, plus gemcitabine and cisplatin (G+C) versus G+C in locally advanced/metastatic cholangiocarcinoma. Approach and Results: This work is a Phase 1b/2 study (S4‐13‐001). In Phase 2, patients received silmitasertib 1000 mg twice daily for 10 days with G+C on Days 1 and 8 of a 21‐day cycle. Primary efficacy endpoint was progression‐free survival (PFS) in the modified intent‐to‐treat population (defined as patients who completed at least one cycle of silmitasertib without dose interruption/reduction) from both phases (silmitasertib/G+C n = 55, G+C n = 29). The response was assessed by Response Evaluation Criteria in Solid Tumors v1.1. The median PFS was 11.2 months (95% confidence interval [CI], 7.6, 14.7) versus 5.8 months (95% CI, 3.1, not evaluable [NE]) ( p = 0.0496); 10‐month PFS was 56.1% (95% CI, 38.8%, 70.2%) versus 22.2% (95% CI, 1.8%, 56.7%); and median overall survival was 17.4 months (95% CI, 13.4, 25.7) versus 14.9 months (95% CI, 9.9, NE) with silmitasertib/G+C versus G+C. Overall response rate was 34.0% versus 30.8%; the disease control rate was 86.0% versus 88.5% with silmitasertib/G+C versus G+C. Almost all silmitasertib/G+C (99%) and G+C (93%) patients reported at least one treatment emergent adverse event (TEAE). The most common TEAEs (all grades) with silmitasertib/G+C versus G+C were diarrhea (70% versus 13%), nausea (59% vs. 30%), fatigue (47% vs. 47%), vomiting (39% vs. 7%), and anemia (39% vs. 30%). Twelve patients (10%) discontinued treatment because of TEAEs during the study. Conclusions: Silmitasertib/G+C demonstrated promising preliminary evidence of efficacy for the first‐line treatment of patients with locally advanced/metastatic cholangiocarcinoma.

Abstract Gallbladder cancer (GBC) is one of the deadliest cancers, with a 5-year-survival-rate of less than 5 percent for late-stage disease. The response rate to chemotherapy among GBC patients is generally poor. Recent research has attempted to identify diagnostic, prognostic, and predictive biomarkers, however, currently, no biomarkers can accurately diagnose GBC and predict patients’ prognosis. Integrative analysis of molecular and clinical characterization has not been fully established, and minimal improvement has been made to the survival of these patients, in part due to the heterogeneity of GBC. Machine learning techniques have been proven to empower analysis of big data in oncology, allowing for improvement in the generation of biomarkers to predict patient outcomes. Using machine learning, we can utilize high-throughput RNA sequencing with clinicopathologic data to develop a predictive tool for GBC prognosis. Current predictive models for GBC outcomes often utilize clinical data only, with the highest C-statistic reported being 0.71. C-statistic values over 0.7 generally indicate good models, however 0.8 is the threshold for strong predictive models. We aim to build a superior algorithm to predict overall survival in GBC patients with advanced disease, using machine learning approaches to prioritize biomarkers for GBC prognosis. We have identified over 80 fresh frozen GBC tissue samples from Mayo Clinic Rochester, Dongsan Medical Center in Daegu, Korea, University of the Witwatersrand, in Johannesburg, South Africa, Lithuanian University of Health Science in Vilnius, Lithuania, and University of Calgary in Calgary, Canada, from patients enrolled between 2012 and 2021. We will perform next-generation RNA sequencing on these tissue samples. The patients’ clinical, pathologic and survival data will be abstracted from the medical record uniformly across sites. Feature engineering and dimensionality reduction will be performed. Then random forests, support vector machines, and gradient boosting machines will be applied to train the data. Variable importance will prioritize multi-omic markers. Standard 5-fold cross validation will be used to assess performance of each ML algorithm. If overall survival can be better predicted with the addition patients’ transcriptional sequencing data compared to using clinical profiles alone, we can gain a greater understanding of key biomarkers driving the tumor phenotype. Citation Format: Linsey Jackson, Loretta Allotey, Valles Kenneth, Gavin Oliver, Asha Nair, Daniel O'Brien, Rondell Graham, Mitesh Borad, Arjun Athreya, Lewis Roberts. Prognostic biomarkers for gallbladder cancer: A machine learning approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1944.

Abstract Chronic lymphocytic leukemia (CLL) is characterized by multiple copy number abnormalities (CNVs) with prognostic value. Identifying these structural variations is central to defining CLL pathogenesis, risk stratification, and therapeutic approaches. Fluorescence in situ hybridization (FISH) is the clinical gold standard in detecting prognostic CNVs in CLL. However, next-generation sequencing (NGS) techniques have become more readily available for clinical genomic applications and can also be used to identify CNVs. Here we present bioinformatic methods to accurately identify CNVs in CLL using NGS data. We used the CNV-calling algorithm PatternCNV to detect clinically relevant CNVs: deletion 17p13 [del(17p)], deletion 11q23 [del(11q)], deletion 13q14 [del(13q)], and trisomy 12. PatternCNV was run on 2274 samples (1500 somatic and 774 germline samples) from six different sequencing batches, screened using a targeted sequencing panel that covers all exons of 59 recurrently CLL mutated genes and additional amplicons covering the minimal affected regions of relevant CNVs. To correct for potential batch effects, PatternCNV was initially run to quantify exon coverage behavior without the chromosomes containing recurrent CNV events, 11, 12, 13, and 17. Principal component analyses and correlation matrices were analyzed, grouping the samples into four distinct clusters that contain similar exon coverage patterns. Samples in each of the four clusters were then independently re-run through PatternCNV using all chromosomes. Visual analysis of CNV plots revealed a bias in normalization. To correct this, the log2ratios were corrected to center the log ratio on the median coverage. Sample noisiness was calculated from the difference in the median absolute deviation (DiffMAD) and samples with a DiffMAD score greater than 0.3 were excluded. All CNV analyses were blinded to clinical FISH results. The effectiveness of our CNV calling was evaluated in 522 CLL patients who had FISH conducted within three months of the sample date. We excluded samples with low tumor metrics identified by FISH (less than 20% of cells with either del(17p), del(11q), trisomy 12 or del(13q)). When we compared our CNV analyses with the FISH data, we found high concordance 99.6% for del(17p), 97.5% for del(11p), 99.1% for trisomy 12, and 93.7% for del(13q). N=46 total discordant pairs were identified, with the highest discordance for del(13q), N=28, followed by del(11q), N=12. These novel bioinformatic methods allow for accurate detection of CNVs across NGS sequencing batches. The high concordance in detecting CNVs between targeted NGS and the gold standard of FISH, suggest NGS is an accurate tool for calling CNVs in CLL. Further, NGS can infer clinically relevant CNVs in genomic locations not targeted by FISH. Citation Format: Chantal E. McCabe, Erik Jessen, Daniel R. O'Brien, Julia E. Wiedmeier-Nutor, Susan L. Slager, Esteban Braggio. Identifying copy number variations in chronic lymphocytic leukemia using targeted next generation sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3352.

AbstractContextSARS-CoV-2 infects the gastrointestinal tract and may be associated with symptoms that resemble diabetic gastroparesis. Why patients with diabetes who contract COVID-19 are more likely to have severe disease is unknown.ObjectiveWe aimed to compare the duodenal mucosal expression of SARS-CoV-2 and inflammation-related genes in diabetes gastroenteropathy (DGE), functional dyspepsia (FD), and healthy controls.MethodsGastrointestinal transit, and duodenal mucosal mRNA expression of selected genes were compared in 21 controls, 39 DGE patients, and 37 FD patients from a tertiary referral center. Pathway analyses were performed.ResultsPatients had normal, delayed (5 FD [13%] and 13 DGE patients [33%]; P = 0.03 vs controls), or rapid (5 FD [12%] and 5 DGE [12%]) gastric emptying (GE). Compared with control participants, 100 SARS-CoV-2-related genes were increased in DGE (FDR < 0.05) vs 13 genes in FD; 71 of these 100 genes were differentially expressed in DGE vs FD but only 3 between DGE patients with normal vs delayed GE. Upregulated genes in DGE include the SARS-CoV2 viral entry genes CTSL (|Fold change [FC]|=1.16; FDR < 0.05) and CTSB (|FC|=1.24; FDR < 0.05) and selected genes involved in viral replication (eg, EIF2 pathways) and inflammation (CCR2, CXCL2, and LCN2, but not other inflammation-related pathways eg, IL-2 and IL-6 signaling).ConclusionSeveral SARS-CoV-2-related genes were differentially expressed between DGE vs healthy controls and vs FD but not between DGE patients with normal vs delayed GE, suggesting that the differential expression is related to diabetes per se. The upregulation of CTSL and CTSB and replication genes may predispose to SARS-CoV2 infection of the gastrointestinal tract in diabetes.

463 Background: Molecularly targeted therapy is gaining traction in cholangiocarcinoma with the first FDA approval of a targeted agent in 2020, the FGFR inhibitor pemigatinib. Yet response rates and efficacy are modest, and most cholangiocarcinoma patients still lack targeted therapy options. The hippo pathway effector, YAP, is an oncogene that is activated in the majority of cholangiocarcinoma, yet attempts to develop YAP inhibitors have not yet been clinically successful. Recently, we described a novel mechanism of YAP regulation, via tyrosine phosphorylation by the Src-family kinase LCK. Therefore we hypothesized that LCK inhibition by may be therapeutic in cholangiocarcinoma through suppression of YAP activity. Methods: NTRC 0652-0 is a novel tyrosine kinase inhibitor with specificity for LCK. In vitro pharmacodynamics were defined. In cholangiocarcinoma cells, specificity for LCK inhibition was determined by mass spectrometry-based unbiased analysis of the tyrosine phosphoproteome following treatment with NTRC 0652-0 or genetic deletion of LCK. YAP phosphorylation and cotranscriptional activity, cell viability, and apoptosis were assessed. A panel of eight cholangiocarcinoma patient derived organoids (PDO) were characterized and tested for sensitivity to NTRC 0652-0. Two patient-derived xenograft (PDX) models bearing FGFR2-rearrangements were utilized for in vivo assessment of pharmacokinetics, toxicity, and efficacy. Results: NTRC 0652-0 demonstrated selectivity for LCK inhibition in vitro and in cholangiocarcinoma cells. NTRC 0652-0 treatment led to YAP inhibition and apoptotic cell death in cholangiocarcinoma cell lines, associated with inhibition of MCL1 expression. PDOs demonstrated variable sensitivity to NTRC 0652-0, correlated with basal YAP tyrosine phosphorylation and drug-induced suppression of YAP co-transcriptional activity. FGFR2-altered cholangiocarcinoma was also identified as a subset with enrichment of YAP target genes. Cells from an FGFR2-altered PDX were sensitive to NTRC 0652-0 despite being primarily resistant to pemigatinib. In two PDX models of FGFR2-altered cholangiocarcinoma, daily oral treatment with NTRC 0652-0 inhibited YAP tyrosine phosphorylation, and resulted in stable plasma and tumor drug levels, acceptable toxicity, and significantly decreased tumor growth. Conclusions: A novel LCK inhibitor, NTRC 0652-0, reduced YAP signaling and demonstrated preclinical efficacy in multiple patient-derived models of cholangiocarcinoma. LCK is a novel therapeutic target in cholangiocarcinoma, and YAP activation or FGFR2-alteration are potential biomarkers for response.