Citations

Abstract Background: Cholangiocarcinoma is an aggressive malignancy with limited systemic therapeutics. We have recently identified LCK, a non-receptor tyrosine kinase, as a potential therapeutic target in select subtypes of cholangiocarcinoma. Proteomic profiling in other cancer types has proven beneficial in predicting response to systemic therapies. Herein, we present proteomic profiling of cholangiocarcinoma can predict sensitivity to a novel LCK inhibitor in patient derived xenograft models. Methods: Proteins from 28 unique cholangiocarcinoma patient derived xenograft models (PDX) were isolated, labeled with tandem mass tags (TMT) and then analyzed by liquid chromatography and mass spectroscopy (LC/MS). Phosphopeptides were separately evaluated both by Immobilized Metal Affinity Chromatography and pY-enrichment techniques. Following normalization, the global proteome, serine/threonine phosphoproteome, and tyrosine phosphoproteome were analyzed and a proteomic signature was created for each tumor. These signatures were compared to PDX models with known response profiles to NTRC0652-0, an LCK inhibitor (1 resistant and 1 sensitive). The top 200 differentially abundant proteins (log2FC 2.6) were then utilized to develop a sensitive and a resistant signature. Correlation indices were then calculated across the 28 PDX model library. An additional predicted resistant and predicted sensitive model was expanded. Tumor bearing mice were randomized based on tumor volume to either treatment with NTRC (30mg/kg PO daily), n=5, or vehicle, n=4, (10% Kolliphor, 10% DMSO, 80% Water, 2 mol equivalents HCl). Tumors were measured weekly with calipers and then weighed at the end of the study. Results: In the top 200 differentially abundant proteins that represented the proteomic signature there was over representation of proteins with decreased abundance. There were only 12 proteins with higher abundance in the sensitive model as compared to the resistant. Utilizing the signature developed from the global proteomics data, nine tumors were predicted to be sensitive while 17 were predicted to be resistant to NTRC. PDX115 is a an intrahepatic CCA PDX known to contain an IDH1 mutation and was predicted to be resistant by modeling. The model was resistant to single agent NTRC with no demonstrable effect on tumor growth in vivo. PDX175 is a distal CCA PDX and was predicted to be sensitive by modeling. In vivo growth was inhibited by NTRC as compared to vehicle treated mice following two weeks of treatment (983% vs 435%, p=0.03). Additionally, final tumor weight was significantly lower in NTRC treated mice (0.651 g vs 0.317 g, p=0.04). Conclusion: Cholangiocarcinoma proteomic signatures can be used to predict therapeutic response to a novel LCK inhibitor in preclinical models. Whether or not this could be applied to other treatment modalities in cholangiocarcinoma remains to be determined. Citation Format: Ryan Watkins, Roberto Alva-Ruiz, Caitlin Conboy, Dong-Gi Mun, Erik Jessen, Diep Vu, Jos de Man, Rogier Buijsman, Akhilesh Pandey, Mitesh Borad, Gregory Gores, Rory Smoot. Proteomic profiling of cholangiocarcinoma predicts response to a novel small molecule inhibitor in preclinical models [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 3094.

Abstract Background: As a key receptor tyrosine kinase (RTK) regulating tumorigenesis, AXL is overexpressed in multiple different types of cancers. AXL can be activated by homodimerization after binding with its ligand, growth arrest specific 6 protein (Gas6) or by heterodimerization with other RTKs. AXL overexpression and/or activation have been reported to promote cancer cell proliferation, migration/invasion and to induce resistance to targeted therapy. However, the AXL downstream signaling network has not yet been fully elucidated. Methods: We employed a CRISPR-CAS9-based knockout technology to create AXL inducible knockout cells in a human triple negative breast cancer (TNBC) cell line, HCC1395. In order to explore the downstream signaling events driven by AXL, we induced AXL knockout for 72 hours and used a 6-plex TMT-labeling-based quantitative proteomic approach to characterize the protein phosphorylation alterations induced by AXL knockout. IMAC and anti-phosphotyrosine antibody (pY1000)-based phosphopeptide enrichment methods were used to comprehensively quantify the phosphoproteome changes induced by AXL knockout. Results: In this study, we identified 38,525 phospho serine/threonine sites and 1,427 phosphotyrosine sites. In response to AXL knockout, 1,581 phospho serine/threonine sites and 40 phosphotyrosine sites showed significant differences between AXL-KO and AXL-WT groups with >1.5-fold change and p<0.05 as the cutoff. Among them, 982 phosphoserine/threonine sites and 30 phosphotyrosine sites were decreased, while 599 phospho serine/threonine sites and 10 phosphotyrosine sites were increased in AXL-KO cells. Signaling pathway enrichment analysis revealed that suppressing AXL expression could inhibit multiple important cancer related signaling pathways, including cell cycle, focal adhesion, MAPK signaling pathway, p53 signaling pathway and mTOR signaling pathway. Conclusion: Quantitative analysis of the phosphoproteome driven by AXL demonstrated that AXL plays a pivotal role in regulating canonical oncogenic signaling but also signaling pathways involved in RNA processing and DNA repair. Our data also suggest that targeting AXL could suppress these oncogenic signaling pathways and have therapeutic potential to improve clinical outcomes for patients with AXL overexpression. Citation Format: Li Wang, Nicole Pearson, Yuning Xiong, Santosh Renuse, Ran Cheng, Jodi M. Carter, Akhilesh Pandey, Xinyan Wu. Quantitative phosphoproteomic analysis of AXL signaling network in breast cancer [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 3913.

AbstractPIK3CA is one of the most frequently mutated genes in human cancers, with the two most prevalent activating mutations being E545K and H1047R. Although the altered intracellular signaling pathways in these cells have been described, the effect of these mutations on their extracellular vesicles (EVs) has not yet been reported. To study altered cellular physiology and intercellular communication through proteomic analysis of EVs, MCF10A cells and their isogenic mutant versions (PIK3CA E545K and H1047R) were cultured and their EVs enriched by differential ultracentrifugation. Proteins were extracted, digested with trypsin and the peptides labeled with tandem mass tag (TMT) reagents and analyzed by liquid chromatography tandem mass spectrometry (LC‐MS/MS). Four thousand six hundred and fifty‐five peptides were identified from 579 proteins of which 522 proteins have been previously described in EVs. Relative quantitation revealed altered levels of EV proteins including several cell adhesion molecules. Mesothelin, E‐cadherin, and epithelial cell adhesion molecule were elevated in both mutant cell‐derived EVs. Markers of tumor invasion and progression like galectin‐3 and transforming growth factor beta induced protein were increased in both mutants. Overall, activating mutations in PIK3CA result in altered EV composition with characteristic changes associated with these hotspot mutations.

Uncontrolled growth of breast cells due to altered gene expression is a key feature of breast cancer. Alterations in the expression of ion channels lead to variations in cellular activities, thus contributing to attributes of cancer hallmarks. Changes in the expression levels of ion channels were observed as a consequence of EMT. Additionally, ion channels were reported in the activation of EMT and maintenance of a mesenchymal phenotype. Here, to identify altered ion channels in breast cancer patients, differential gene expression and weighted gene co-expression network analyses were performed using transcriptomic data. Protein–protein interactions network analysis was carried out to determine the ion channels interacting with hub EMT-related genes in breast cancer. Thirty-two ion channels were found interacting with twenty-six hub EMT-related genes. The identified ion channels were further correlated with EMT scores, indicating mesenchymal phenotype. Further, the pathway map was generated to represent a snapshot of deregulated cellular processes by altered ion channels and EMT-related genes. Kaplan–Meier five-year survival analysis and Cox regressions indicated the expression of CACNA1B, ANO6, TRPV3, VDAC1 and VDAC2 to be potentially associated with poor survival. Deregulated ion channels correlate with EMT-related genes and have a crucial role in breast cancer-associated tumorigenesis. Most likely, they are potential candidates for the determination of prognosis in patients with breast cancer.

Abstract Background: Cyclin-dependent 4/6 kinase inhibitors (CDK4/6i) and endocrine therapy (ET) have improved progression-free survival (PFS) and overall survival in hormone-receptor-positive (HR+) metastatic breast cancer (MBC), but endocrine resistance is a major challenge. PROMISE [NCT0281902; n=63] is a multicenter study that enrolled women with HR+ HER2- MBC commencing palbociclib (Pb) with letrozole (1st line [1L]) or fulvestrant (2nd line [2L]), and was designed to perform a comprehensive “omic” assessment of prospectively collected biospecimens (pre-treatment (M1), at 2 months (M2), and at disease progression). The goal is to identify novel genomic variants and pathways associated with resistance to CDK4/6i and ET and PFS outcomes. Here we report the association between the proteomic, metabolomics, and lipidomics data generated from pre-Pb and 2-month serum samples and PFS. Methods: Untargeted mass spectrometry data was generated from Metabolon, assaying 1308 metabolites and 831 lipids. Additionally, 1436 proteins were assayed on the Olink platform. Cox proportional hazard models were used to evaluate the univariate hazard ratio (HR) for all features with respect to PFS. The analyses were performed on samples from 45 patients (N=33 1Lwith 9 progression events and 12 2L with 8 progression events), obtained from M1 and M2 timepoints on Pb + ET. Enrichment analysis p-values are calculated using Fisher’s exact test. Results: Proteomics: In the M1 timepoint, 93 and 43 proteins were associated with PFS in the 1L and 2L settings, respectively; inflammation genes were enriched among the 1L setting (p= 0.034); 33 proteins presented HRs ranging between 0.026 and 0.56. The FABP9 protein (HR of 1.98, 95% CI 1.02-3.83) was associated with worse PFS. Conversely, inflammation genes were not observed to be enriched in 2L. In the M2 timepoint, we observed 60 and 21 proteins significantly associated with PFS, but no biological function was enriched in 1L and 2L. Metabolites: In the M1 timepoint, metabolism of the sulfur-containing amino acids (methionine, cysteine, SAM and taurine) were enriched in the 1L setting (p= 0.035, HR range 0.15-0.33); and the branched-chain amino acids (leucine, isoleucine, and valine) were significantly associated with PFS in the 2L setting (p= 0.028, HR range 0.013-0.33). At the M2 timepoint, the amino acids were no longer enriched, but fatty acid metabolism was significantly enriched for both 1L and 2L (p= 0.048 and 0.067, respectively). Pathways involving lipids, amino acids, and xenobiotics were enriched in metabolites related to PFS (p <0.05) for both treatment lines at M1 and M2. Lipidomics: In the M1 timepoint, 10 and 19 lipids were associated with PFS for 1L and 2L, respectively. The most notable lipid associated with worse PFS in the 1L was an 18 carbon phosphatidylinositol, PI(18:1/18:2), (HR 7.34 (CI 1.27-42.50); 8 triglycerides were associated with improved PFS (HR range 0.39 and 0.55). In 2L, the 19 lipids associated with PFS included 12 phosphatidylcholines (enrichment p = 5.6X10-8). In the M2 timepoint, 15 and 8 lipids were significantly associated with PFS for 1L and 2L. An enrichment of phosphatidylinositols was observed in 1L (p= 1.2X10-5); none were observed in the 2L.Future Directions: Networks are being constructed using the proximity scores of the proteins, lipids, and metabolites associated with PFS in M1 and M2 for 1L and 2L. Network similarities and analyses will be conducted.Conclusion: Distinct multi-omic changes identified in serum samples obtained from PROMISE participants M1 and M2 on Pb correlate with disease progression in both 1L and 2L settings. Additionally, validation studies will determine the significance of these findings. Citation Format: Krishna R. Kalari, Kevin J. Thompson, Jason Sinnwell, Xiaojia Tang, Vera J. Suman, Jun He, Seul Kee Byeon, Akhilesh Pandey, Abe Eyman Casey, Peter T. Vedell, Ann M. Moyer, Alvaro Moreno-Aspitia, Donald W. Northfelt, Minetta C. Liu, Tufia C. Haddad, Saranya Chumsri, Prema Peethambaram, Kathryn J. Ruddy, Karthik V. Giridhar, Roberto A. Leon-Ferre, Richard M. Weinshilboum, Liewei Wang, Ciara C. O’ Sullivan, Matthew P. Goetz. Multiomics data reveal novel biomarkers for CDK4/6 resistance [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-01-03.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is accountable for the protracted COVID-19 pandemic. Its high transmission rate and pathogenicity led to health emergencies and economic crisis. Recent studies pertaining to the understanding of the molecular pathogenesis of SARS-CoV-2 infection exhibited the indispensable role of ion channels in viral infection inside the host. Moreover, machine learning (ML)-based algorithms are providing a higher accuracy for host-SARS-CoV-2 protein–protein interactions (PPIs). In this study, PPIs of SARS-CoV-2 proteins with human ion channels (HICs) were trained on the PPI-MetaGO algorithm. PPI networks (PPINs) and a signaling pathway map of HICs with SARS-CoV-2 proteins were generated. Additionally, various U.S. food and drug administration (FDA)-approved drugs interacting with the potential HICs were identified. The PPIs were predicted with 82.71% accuracy, 84.09% precision, 84.09% sensitivity, 0.89 AUC-ROC, 65.17% Matthews correlation coefficient score (MCC) and 84.09% F1 score. Several host pathways were found to be altered, including calcium signaling and taste transduction pathway. Potential HICs could serve as an initial set to the experimentalists for further validation. The study also reinforces the drug repurposing approach for the development of host directed antiviral drugs that may provide a better therapeutic management strategy for infection caused by SARS-CoV-2.

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.

Gastric cancer is a leading cause of death from cancer globally. Gastric cancer is classified into intestinal, diffuse and indeterminate subtypes based on histology according to the Laurén classification. The intestinal and diffuse subtypes, although different in histology, demographics and outcomes, are still treated in the same fashion. This study was designed to discover proteomic signatures of diffuse and intestinal subtypes. Mass spectrometry-based proteomics using tandem mass tags (TMT)-based multiplexed analysis was used to identify proteins in tumor tissues from patients with diffuse or intestinal gastric cancer with adjacent normal tissue control. A total of 7448 or 4846 proteins were identified from intestinal or diffuse subtype, respectively. This quantitative mass spectrometric analysis defined a proteomic signature of differential expression across the two subtypes, which included gremlin1 (GREM1), bcl-2-associated athanogene 2 (BAG2), olfactomedin 4 (OLFM4), thyroid hormone receptor interacting protein 6 (TRIP6) and melanoma-associated antigen 9 (MAGE-A9) proteins. Although GREM1, BAG2, OLFM4, TRIP6 and MAGE-A9 have all been previously implicated in tumor progression and metastasis, they have not been linked to intestinal or diffuse subtypes of gastric cancer. Using immunohistochemical labelling of a tissue microarray comprising of 124 cases of gastric cancer, we validated the proteomic signature obtained by mass spectrometry in the discovery cohort. Our findings should help investigate the pathogenesis of these gastric cancer subtypes and potentially lead to strategies for early diagnosis and treatment.