Targeted Therapies for Pancreatic Cancer

Amanam, Idoroenyi; Chung, Vincent

doi:10.3390/cancers10020036

Cited by 75 publications

(54 citation statements)

References 67 publications

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“…Haas et al reported no significant difference in overall survival for KRAS wild-type vs. mutant patients, and KRAS mutation status is predictive rather than prognostic in advanced PC (34). Efforts focused on targeting PC have been disappointing (7,36), so the standard of care for PC continues to be chemotherapy. However, chemoresistance is a major challenge in the treatment of PC (14,15,17,18).…”

Section: Discussionmentioning

confidence: 99%

Pharmacometabolomics Identifies 3-Hydroxyadipic Acid, d-Galactose, Lysophosphatidylcholine (P-16:0), and Tetradecenoyl-l-Carnitine as Potential Predictive Indicators of Gemcitabine Efficacy in Pancreatic Cancer Patients

Zhang

et al. 2020

Front. Oncol.

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Gemcitabine (GEM)-based chemotherapy is the standard regimen for the treatment of pancreatic cancer (PC). However, chemoresistance is a major challenge in PC treatment. Reliable biomarkers are urgently needed to predict the response to GEM-based therapies. GEM-sensitive (GEM-S) and GEM-resistant (GEM-R) pancreatic carcinoma xenograft models were established, and GEM monotherapy and GEM plus nanoparticle albumin-bound paclitaxel (nab-PTX) doublet therapy were administered to GEM-S/R tumor-bearing mice. Metabolomic mass spectrometry (MS) analysis of serum, liver, and tumor samples was performed using an ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometer. The results showed that both GEM monotherapy and combination therapy significantly inhibited the tumor growth in GEMS subgroup. However, in the GEM-R subgroup, tumor growth was not significantly inhibited by GEM monotherapy, but was significantly suppressed by GEM combination therapy. Metabolic profiling analysis by hierarchical cluster analysis and partial least squares discriminant analysis showed that the differences in metabolites were most significant in serum of three types of samples in the GEM-S/R subgroups, regardless of the administration of GEM monotherapy or combination therapy. The differential metabolite analysis of serum samples revealed 38 and 26 differential metabolites between the GEM-R and GEMS subgroups treated with GEM monotherapy or combination therapy, and four common discriminating metabolites were investigated: 3-hydroxyadipic acid, D-galactose, lysophosphatidylcholine (LysoPC) (P-16:0), and tetradecenoyl-L-carnitine. The relative amounts of the four metabolites changed significantly and consistently after GEM monotherapy or combination therapy. The levels of these four metabolites Wu et al.

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Section: Discussionmentioning

confidence: 99%

Pharmacometabolomics Identifies 3-Hydroxyadipic Acid, d-Galactose, Lysophosphatidylcholine (P-16:0), and Tetradecenoyl-l-Carnitine as Potential Predictive Indicators of Gemcitabine Efficacy in Pancreatic Cancer Patients

Zhang

et al. 2020

Front. Oncol.

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“…Amanam and Chung systematically investigated all currently available targeted therapies and drug targets for pancreatic cancer [47]. Many studies have reported HER2 overexpression in up to 45% of patients with PDAC [48].…”

Section: Targets Accordance Comparison Between Clinical Drug Treatmenmentioning

confidence: 99%

SCNrank: spectral clustering for network-based ranking to reveal potential drug targets and its application in pancreatic ductal adenocarcinoma

et al. 2020

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Background: Pancreatic ductal adenocarcinoma (PDAC) is the most common pancreatic malignancy. Due to its wide heterogeneity, PDAC acts aggressively and responds poorly to most chemotherapies, causing an urgent need for the development of new therapeutic strategies. Cell lines have been used as the foundation for drug development and disease modeling. CRISPR-Cas9 plays a key role in every step-in drug discovery: from target identification and validation to preclinical cancer cell testing. Using cell-line models and CRISPR-Cas9 technology together make drug target prediction feasible. However, there is still a large gap between predicted results and actionable targets in real tumors. Biological network models provide great modus to mimic genetic interactions in real biological systems, which can benefit gene perturbation studies and potential target identification for treating PDAC. Nevertheless, building a network model that takes cell-line data and CRISPR-Cas9 data as input to accurately predict potential targets that will respond well on real tissue remains unsolved. Methods: We developed a novel algorithm 'Spectral Clustering for Network-based target Ranking' (SCNrank) that systematically integrates three types of data: expression profiles from tumor tissue, normal tissue and cell-line PDAC; protein-protein interaction network (PPI); and CRISPR-Cas9 data to prioritize potential drug targets for PDAC. The whole algorithm can be classified into three steps: 1. using STRING PPI network skeleton, SCNrank constructs tissuespecific networks with PDAC tumor and normal pancreas tissues from expression profiles; 2. With the same network skeleton, SCNrank constructs cell-line-specific networks using the cell-line PDAC expression profiles and CRISPR-Cas 9 data from pancreatic cancer cell-lines; 3. SCNrank applies a novel spectral clustering approach to reduce data dimension and generate gene clusters that carry common features from both networks. Finally, SCNrank applies a scoring scheme called 'Target Influence score' (TI), which estimates a given target's influence towards the cluster it belongs to, for scoring and ranking each drug target.

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“…Targeting specific mutations in pancreatic cancer has been a matter of research for over several decades now. The difficulties and obstacles that have been encountered during multiple therapeutic approaches to the RAS/RAF/MEK/ERK pathway are exemplarily on the so far fruitless way to an effective, personalized treatment [51]. Although more than 90% of PDACs harbour a KRAS mutation, inhibition of RAS activation or its' downstream signalling with farnesyltransferase inhibitors (tipifarnib), MEK 1/2 inhibitors (selumetinib, trametinib) alone or in combination with EGFR inhibitors (erlotinib) either did not show a significant benefit in survival or were not clinically applicant due to overlapping toxicities of small molecule inhibitors and necessary dose reductions.…”

Section: Molecularly Targeted Therapiesmentioning

confidence: 99%

Molecular biology in pancreatic ductal adenocarcinoma: implications for future diagnostics and therapy

et al. 2019

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Background Novel technology has enabled researchers to better characterize pancreatic cancers at the molecular level. We wanted to explore some of the emerging discoveries, such as molecular subclassification, use of liquid biopsy and use of organoids in cancer assessment. Methods A literature review with a search specific to the topic, with recent reviews in major journals and a focus on the last 5 years (until December 2018), was done. Results Pancreatic ductal adenocarcinoma (PDAC) may now be classified into clinical subgroups based on the predominant genomic profiles, but consensus on one classification system is lacking. Several subtypes have been suggested, including categories such as basal-like, stroma-activated, desmoplastic, pure classical and immune classical types. Further refinement may translate into clinically meaningful groups for therapeutic or prognostic purposes. Liquid

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Targeted Therapies for Pancreatic Cancer

Cited by 75 publications

References 67 publications

Pharmacometabolomics Identifies 3-Hydroxyadipic Acid, d-Galactose, Lysophosphatidylcholine (P-16:0), and Tetradecenoyl-l-Carnitine as Potential Predictive Indicators of Gemcitabine Efficacy in Pancreatic Cancer Patients

Pharmacometabolomics Identifies 3-Hydroxyadipic Acid, d-Galactose, Lysophosphatidylcholine (P-16:0), and Tetradecenoyl-l-Carnitine as Potential Predictive Indicators of Gemcitabine Efficacy in Pancreatic Cancer Patients

SCNrank: spectral clustering for network-based ranking to reveal potential drug targets and its application in pancreatic ductal adenocarcinoma

Molecular biology in pancreatic ductal adenocarcinoma: implications for future diagnostics and therapy

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