Biomarker Discovery In Pancreatic Cancer Research Articles

Abstract 1074: Plasma EV profiling facilitates low abundance biomarker discovery in pancreatic cancer

Abstract The lack of reliable biomarkers for early detection of pancreatic ductal adenocarcinoma (PDAC) results in poor prognosis due to advanced disease at diagnosis. Biopsies for pancreatic cancer are known to be particularly difficult to perform because of the location of the pancreas and low volume of primary tumors that metastasize aggressively. Currently, there is no blood test or other screening modality in use, for stratification of individuals with higher-than-average risk (5-8-fold) of developing PDAC in their lifetime. Given this existing void in the US and worldwide, an FDA-compliant, minimally invasive, quantitative biomarker test for the early detection of PDAC would be critical for screening subpopulations with, a) inherent genetic or familial predisposition; history of chronic pancreatitis; c) precursor lesions of pancreas; and d) older patients (>50 years) with new onset diabetes or sudden weight loss. Extracellular vesicles (EVs) are shed by normal and cancer cells and carry a rich molecular cargo that can be leveraged for biomarker discovery. Whereas molecular profiling of bio-fluids poses intrinsic limitations for detection of low abundant, disease-specific biomarkers due to matrix effects; EVs, on the other hand, offer promise as a robust and minimally invasive biomarker resource. Mass spectrometry data were developed in our laboratory, based on multi-omics (proteomics, lipidomics and metabolomics) analyses of plasma EVs derived from patients diagnosed with early-stage PC (N=60), pancreatitis (N=39), precursor lesions of pancreas (N=45) as well as normal controls (N=50). These clinically annotated samples were made available by the Georgetown University Medical Center (GUMC) repository. Feature selection using a training-validation design allowed the development of a multiplexed biomarker panel comprising of 12-analytes (5-lipids, 2-metabolites and 5-proteins) that can robustly stratify early-stage PC from normal controls with high accuracy (AUC>95%). Availability of a FDA compliant, multi-analyte-based classification algorithm that can stratify patients with precursor lesions of the pancreas that are at potential risk of progression to PC with > 90% specificity and sensitivity is a critical clinical need. Refinement of panel to improve positive predictive value as well as identification of markers predictive of progression of precursor lesions to malignant transformation is ongoing. Citation Format: Shivani Bansal, Shu Wang, Yaoxiang Li, Sunil Bansal, Jill Smith, John B. Tyburski, Keith Unger, Amrita Cheema. Plasma EV profiling facilitates low abundance biomarker discovery in pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1074.

Quantitative proteomics for therapeutic biomarker discovery in pancreatic cancer

Quantitative proteomics for therapeutic biomarker discovery in pancreatic cancer

Quantitative proteomics for therapeutic biomarker discovery in pancreatic cancer

Quantitative proteomics for therapeutic biomarker discovery in pancreatic cancer

A Precision Clinical-Omics Approach in Predicting Therapeutic Response to Pancreatic Cancer

Background: Pancreatic ductal adenocarcinoma is one of the most aggressive solid tumours. Despite numerous advances in surgical techniques, survival has remained largely unchanged over the last decade. Pancreatic cancer is thus considered a ‘systemic disease’ where chemotherapy plays a pivotal role in both the pre- and post-surgery setting. Tools that can predict the most effective chemotherapeutic regimens for patients are urgently needed to improve survival by optimising biological effects while minimising side effects, thereby not comprising their fitness for surgery. The aim of the study was to characterise the proteome of patient derived pancreatic cancer organoids to identify biomarkers predictive of chemotherapeutic sensitivity. Method: Using quantitative mass spectrometry we performed deep proteomic profiling of patient derived pancreatic cancer organoids with matched primary tumour tissue, and performed high-throughput combinatorial drug screening on organoid lines. Results: We demonstrated that common pancreatic cancer characteristics were conserved in organoids derived from different patients, and through comparative analysis between organoids revealed individual diversity that correlated with patient specific features. By correlating personalized organoid proteomes with their matched drug sensitivity profiles derived using high-throughput combinatorial drug screening, we identified protein biomarkers that correlated with therapeutic response to both standard of care and other novel therapeutics. Candidate biomarkers were verified by immunohistochemistry of organoids, with further validation to be performed in an interventional trial using organoids to predict second-line chemotherapy in patients with metastatic disease. Conclusions: We present the integration of deep proteomic profiling with a patient derived organoid platform for therapeutic biomarker discovery in pancreatic cancer.

Rapid sample preparation workflow based on enzymatic nanoreactors for potential serum biomarker discovery in pancreatic cancer

Mass spectrometry (MS)-based proteomics have been extensively applied in clinical practice to discover potential protein and peptide biomarkers. However, the traditional sample pretreatment workflow remains labor-intensive and time-consuming, which limits the application of MS-based proteomic biomarker discovery studies in a high throughput manner. In the current work, we improved the previously reported procedure of the simple and rapid sample preparation methods (RSP) by introducing macroporous ordered siliceous foams (MOSF), namely RSP-MOSF. With the aid of MOSF, we further reduced the digestion time to 10 min, facilitating the whole sample handling process within 30 min. Combining with 30 min direct data independent acquisition (DIA) of LC-MS/MS, we accomplished a serum sample analysis in 1 h. Comparing with the RSP method, the performance of protein and peptide identification, quantitation, as well as the reproducibility of RSP-MOSF is comparable or even outperformed the RSP method. We further applied this workflow to analyze serum samples for potential candidate biomarker discovery of pancreatic cancer. Overall, 576 serum proteins were detected with 41 proteins significantly changed, which could serve as potential biomarkers for pancreatic cancer. Additionally, we evaluated the performance of RSP-MOSF method in a 96-well plate format which demonstrated an excellent reproducibility of the analysis. These results indicated that RSP-MOSF method had the potential to be applied on an automatic platform for further scaled analysis.

Abstract 2860: Impact of hemolysis on multi-omic pancreatic cancer biomarker discovery: De-risking precision medicine biomarker development

Abstract Biomarker analysis is critically dependent on the quality of biofluid or tissue samples obtained from human research studies. Although proteomic, lipidomic, and metabolomic analyses can be dramatically impacted by the time of sample collection, fasting status, and participant demographics, the hemolysis status of plasma, serum or buffy coat samples is a poorly understood confounder of sample quality. Hemoglobin levels can range between 0 - 10 g/L in samples (referred to as 1-4 in plasma/serum and 0-4 level in buffy coat) as a marker of hemolysis severity and sample contamination by reticulocyte-derived analytes. In oncology clinical trials, patients can be more susceptible to hemolysis due to chemotherapy treatment, which can impact sample assessment and study results. Herein, we analyzed 941 plasma and 950 serum samples using comprehensive proteomics, structural lipidomics, signaling lipidomics, and metabolomics in a pancreatic cancer biomarker discovery program referred to as Project Survival as well as 951 buffy coat samples using only proteomic analysis. Project survival is a 7-year longitudinal pancreatic cancer biomarker discovery trial analyzing 400+ pancreatic cancer and at-risk patients using multi-omic and multiple biofluid assessment. To date this study yielded samples in plasma with 92.3% - #1, 6.5% - #2, 1.2% - #3, and 0% - #4 hemolysis, serum with 94.8% - #1, 3.8% - #2, 1.4% - #3, and 0% - #4 hemolysis and buffy coat 42.7% - #0, 25.6% - #1, 20.8% -#2, 10.4% - #3, and 0.5% - #4 hemolysis. Multi-omic and regression analysis of sample data for hemolysis status revealed a distinct pattern of OMIC variables correlated with the degree of hemolysis. Proteomics analysis was the greatest impacted in terms of the protein identification and quantitation. Additionally, pathway analysis revealed expected pathways associated with hemolysis and coagulation, but also unknown pathways and corresponding proteins that were differentially correlated with hemolysis state. Additionally, metabolomics and lipidomics analysis also revealed distinct differentials associated with hemolysis state. Herein, our analysis is the first to analyze thousands of samples using multi-omics revealing critically informative molecular differentials across OMIC technologies demonstrating that caution should be given to avoid these identified biomarkers for translational development. Citation Format: Michael A. Kiebish, Punit Shah, Valerie Bussberg, Vladimir Tolstikov, Rick Searfoss, Kennedy Ofori-Mensa, Eric M. Grund, Abena Darkwah, Emily Y. Chen, Bennett Greenwood, Ellaine Adu Ntoso, Leonardo Rodrigues, Mia Liu, Elder Granger, Chas Bountra, Rangaprasad Sarangarajan, A J. Moser, Niven R. Narain. Impact of hemolysis on multi-omic pancreatic cancer biomarker discovery: De-risking precision medicine biomarker development [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2860.

LC-MS/MS and SWATH based serum metabolomics enables biomarker discovery in pancreatic cancer

BackgroundPancreatic cancer (PC) is the fourth leading cause of cancer death because of its subtle clinical symptoms in the early stage. To discover particular serum metabolites as potential biomarkers to differentiate pancreatic carcinoma from benign disease (BD) is on urgent demand. MethodTo comprehensively analyze serum metabolites obtained from 14 patients with PC, 10 patients with BD and 10 healthy individuals (normal control, NC), we separated the metabolites using both reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC). The data were acquired on a high-resolution quadrupole time-of-flight mass spectrometer operated in negative (ESI–) and positive (ESI+) ionization modes, respectively. Differential metabolites were selected by univariate (Student’s t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Sequential window acquisition of all theoretical spectra (SWATH) analysis was further utilized to validate the metabolites found in discovery stage. The receiver operator characteristics (ROC) curve analysis was performed to evaluate predictive clinical usefulness of 8 metabolites. ResultsA total of 8 metabolites including taurocholic acid, glycochenodexycholic acid, glycocholic acid, L-glutamine, glutamic acid, L-phenylalanine, L-tryptophan, and L-arginine were identified and relatively quantified as differential metabolites for discriminating PC, BD and NC. The 8 metabolites and their combination discriminated PC from BD and NC with well-performed area under the curve (AUC) values, sensitivity and specificity. ConclusionBile acids (especially taurocholic acid) performed to be potential biomarkers in PC diagnosis. Other amino acids (such as L-glutamine, glutamic acid, L-phenylalanine, L-tryptophan, and L-arginine) in serum samples from PC patients might provide a sensitive, blood-borne diagnostic signature for the presence of PC or its precursor lesions.

Isomer-specific profiling of N-glycans derived from human serum for potential biomarker discovery in pancreatic cancer

Glycosylation is one of the most important post-translational modifications of protein. Recently, global profiling of human serum glycomics has become a noninvasive method for cancer-related biomarker discovery and many studies have focused on compositional glycan profiling. In contrast, structure-specific glycan profiling may provide more potential biomarkers with higher specificity than compositional profiling. In this work, N-glycans released from human serum were neutralized with methylamine and reduced by ammonia-borane complex prior to profiling using nanoLC-ESI-MS with porous graphitized carbon (PGC) and relative abundances of over 280 isomers were compared between pancreatic cancer (PC) cases (n = 32) and healthy controls (n = 32). Statistical analysis identified 25 specific-isomeric biomarkers with significant differences (p-value < 0.05). ROC and PCA analysis were performed to assess the potential biomarkers which were identified as being significantly altered in cancer. The AUCs of the significantly changed specific-isomers were ranging from 0.712 to 0.949. In addition, with the combination of all potential biomarkers, a higher AUC of 0.976 with sensitivity (93.5%) and specificity (90.6%) was obtained. Overall, the proposed strategy coupled to relative quantitative analysis of isomeric glycans make it possible to discover new biomarkers for the diagnosis of PC. SignificancePancreatic cancer (PC) has a poor prognosis with a five-year survival rate <5%. Therefore, a strategy for accurate diagnosis of PC is indeed required. In this paper, a dual-derivatized strategy for structure-specific glycan profiling has been used and according to our best knowledge, this is the first application of this strategy for PC biomarker discovery, in which the separation, identification and relative quantification of isomeric glycans can be simultaneously obtained. In addition, by in-depth analysis of isomeric glycans, the full description of the stereo- and region- diversity of glycans can also be achieved, which might provide more potential information for PC biomarker discovery.

PCMdb: Pancreatic Cancer Methylation Database

Pancreatic cancer is the fifth most aggressive malignancy and urgently requires new biomarkers to facilitate early detection. For providing impetus to the biomarker discovery, we have developed Pancreatic Cancer Methylation Database (PCMDB, http://crdd.osdd.net/raghava/pcmdb/), a comprehensive resource dedicated to methylation of genes in pancreatic cancer. Data was collected and compiled manually from published literature. PCMdb has 65907 entries for methylation status of 4342 unique genes. In PCMdb, data was compiled for both cancer cell lines (53565 entries for 88 cell lines) and cancer tissues (12342 entries for 3078 tissue samples). Among these entries, 47.22% entries reported a high level of methylation for the corresponding genes while 10.87% entries reported low level of methylation. PCMdb covers five major subtypes of pancreatic cancer; however, most of the entries were compiled for adenocarcinomas (88.38%) and mucinous neoplasms (5.76%). A user-friendly interface has been developed for data browsing, searching and analysis. We anticipate that PCMdb will be helpful for pancreatic cancer biomarker discovery.

New trends on bio-inspired computation

Biological systems are self-organizing, tolerant of manufacturing defects and they adapt, rather than being programmed, to their environments. The problems they solve involve the interaction of an organism/system with the real world. Bio-inspired computation is the use of computers to model nature, and simultaneously the study of natural biological systems to improve the usage of computers. It is a major subset of natural computation. Recently, bio-inspired computation, such as evolutionary computation, swarm intelligence bacterial foraging, cultural algorithms, neural networks, fuzzy systems, rough sets, molecular computing and other bionics, is becoming increasingly important in face of the complexity of today’s demanding applications. This special issue on bio-inspired computation is dedicated to the latest work in the theory and applications in this exciting area. Our aim is to provide a useful reference for understanding new trends in bio-inspired computation. After a detailed review process, eight papers were selected to reflect the thematic vision. The contents of these studies are briefly described as follows. The group search optimizer (GSO) was inspired by animal social searching behaviour. It has been shown that the global search performance of the GSO is competitive to other biologically inspired optimization algorithms. In the paper, ‘Analysis of premalignant pancreatic cancermass spectrometry data for biomarker selection using a group search optimizer’, S. He et al. apply a GSO as a feature selection method to mass spectrometry (MS) data analysis for premalignant pancreatic cancer biomarker discovery. After applying a smooth nonlinear energy operator (SNEO) to detect peaks, then a GSO with linear discriminant analysis (LDA) is used to select a parsimonious set of peak windows ((biomarkers) that can distinguish cancer. After selecting a set of biomarkers, a support vector machine (SVM) is then applied to build a classifier to diagnosis premalignant cancer cases. The GSO algorithm was compared with a genetic algorithm (GA), evolution strategies (ES), evolutionary programming (EP) and a particle swarm optimizer (PSO). The results showed that the GSO-based feature selection algorithm is capable of selecting a parsimonious set of biomarkers to achieve better classification performance than other algorithms. In the paper, ‘Viral system algorithm: foundations and comparison between selective and massive infections’, Pablo Cortes et al. present a guided and deep introduction to viral systems (VS), a novel bio-inspired methodology based on the natural biological process taking part when the organism has to give a response to an external infection. VS has proven to be very efficient when dealing with problems of high complexity. The paper discusses on the foundations of viral systems, presents the main pseudocodes that need to be implemented and illustrates the methodology application. A comparison between VS and other metaheuristics, as well between different VS approaches, is presented. Finally, trends and new research opportunities are presented for this bio-inspired methodology. Population-based heuristic optimization methods like differential evolution (DE) depend largely on the generation of the initial population. The initial population not only affects the search for several iterations but often also has an influence on the final solution. The conventional method for generating an initial population is the use of computergenerated pseudorandom numbers, which may not be very effective. In the paper entitled with ‘A simplex differential evolution algorithm: development and applications’, Musrrat Ali et al. have investigated the potential of generating the initial population by integrating the non-linear simplex method (NSM) of Nelder and Mead with pseudorandom numbers in a DE algorithm. The resulting algorithm named the non-linear simplex differential evolution (NSDE) is tested on a set of 20 benchmark problems with box constraints and two real-life problems. Numerical results show that the proposed scheme for generating the random numbers significantly improves the performance of DE in terms of fitness function value, convergence rate and average CPU time. In the paper, ‘How to design a powerful family of particle swarm optimizers in inverse modelling’, Juan Luis Fernandez Martinez et al. show how to design a powerful set of particle swarm optimizers for application in inverse modelling.

Metabolomic profiling for biomarker discovery in pancreatic cancer

Pancreatic cancer (PC) is the fourth leading cause of cancer death in the United States, with 4% survival, 5 years after diagnosis. Patients with pancreatic cancer are usually diagnosed at late stages, when the disease is incurable. Sensitive and more specific markers are critical for supporting new prevention, diagnostic or therapeutic strategies. Here, we report mass spectrometry-based metabolomic profiling of human pancreas matched tumor and normal tissue. Multivariate data analysis using two independent methods shows significant alterations in the profiles of the tumor metabolome as compared to the normal tissue. These findings offer an information-rich matrix for discovering novel candidate biomarkers with diagnostic or prognostic potential.

Analysis of premalignant pancreatic cancer mass spectrometry data for biomarker selection using a group search optimizer

This paper presents a novel group search optimizer (GSO)-based biomarker discovery method for pancreatic cancer diagnosis using mass spectrometry (MS) data. The GSO was inspired by animal social searching behaviour. It has been shown that the global search performance of the GSO is competitive to other biologically inspired optimization algorithms. In this study, we applied a GSO as a feature selection method to MS data analysis for premalignant pancreatic cancer biomarker discovery. We first applied a smooth non-linear energy operator to detect peaks. Then a GSO with linear discriminant analysis was used to select a parsimonious set of peak windows (biomarkers) that can distinguish cancer. After selecting a set of biomarkers, a support vector machine was then applied to build a classifier to diagnosis premalignant cancer cases. We compared the GSO algorithm with a genetic algorithm, evolution strategies, evolutionary programming and a particle swarm optimizer. The results showed that the GSO-based feature selection algorithm is capable of selecting a parsimonious set of biomarkers to achieve better classification performance than other algorithms. The source code of the proposed GSO-based feature selection algorithm is available at www.cs.bham.ac.uk/~szh .

Stable Isotope Dilution Multidimensional Liquid Chromatography-Tandem Mass Spectrometry for Pancreatic Cancer Serum Biomarker Discovery

A novel approach to pancreatic cancer biomarker discovery has been developed, which employs a stable isotope labeled proteome (SILAP) standard coupled with extensive multidimensional separation coupled with tandem mass spectrometry (MS/MS). Secreted proteins from CAPAN-2 human pancreatic cancer derived cells were collected after conducting stable isotope labeling by amino acids in cell culture (SILAC). The resulting SILAP standard contained <0.5% of individual unlabeled proteins. Pooled sera from patients with early stage pancreatic cancer or controls were prepared, and an equal amount of the SILAP standard was added to each sample. Proteins were separated by isoelectric focusing (IEF) prior to two-dimensional liquid chromatography (2D-LC)–MS/MS analysis. A total of 1065 proteins were identified of which 121 proteins were present at 1.5-fold or greater concentrations in the sera of patients with pancreatic cancer. ELISA validation of these findings was successfully performed for two proteins, ICAM-1 and BCAM. Results of these studies have provided proof of principle that a SILAP standard derived from the CAPAN-2 secreted proteome can be used in combination with extensive multidimensional LC-MS/MS for the identification and relative quantitation of potential biomarkers of pancreatic cancer. This technique allows for the detection of low-abundance proteins, and focuses only on biologically relevant proteins derived from pancreatic cancer cells.

In vivo platform for translational drug development and biomarker discovery in pancreatic cancer

4000 While there are many new agents entering clinical development, there is very little data to prioritize which agents should be explored in pancreas cancer. Furthermore, often there is no information on biomarkers that may predict the activity of these drugs in pancreas cancer. In this project, we have generated a cohort of 30 pancreatic cancer xenografts by implanting in nude mice tumor materials from surgical specimens. Molecular studies show that a) these tumors maintain the genetic features of the originator cancer such as KRAS, p53 and DPC4 gene status; b) tumors represent the heterogeneity of pancreatic cancer well (there is not selection of any genotype in the xenograft ) and c) features do not changed over time. We have used this platform to explore the activity of a battery with a total of 10 new anticancer agents including inhibitors of MAPK, EGFR, mTOR, src kinase, Ras oncogene, mitosis regulators, angiogenesis, heat shock protein and hedgehog pathway inhibitors using a methodology similar to a two stage phase II clinical trial. All agents are tested against 10 xenografts. Those inactive are not explored anymore. Active agents are tested against the full set of tumors. Thus far we have shown that inhibitors of mTOR and MAPK have substantial activity in this model while Ras interacting agents and EGFR targeted drugs have no single agent activity. For active agents, we have characterized the tumors for potential strategies and biormarkers that may predict activity using both a target-focus approach as well as general profiling approach. Signaling inhibitors are more active in tumors with evidence of activation of the targeted pathway. In addition, ex vivo assays indicate that the ability to inhibit the targeted pathway is associated with agent activity. This information may help to prioritize agents for clinical development in pancreatic cancer. [Table: see text]

Proteomic Profiling of Pancreatic Cancer for Biomarker Discovery

Pancreatic cancer is a uniformly lethal disease that is difficult to diagnose at early stage and even more difficult to cure. In recent years, there has been a substantial interest in applying proteomics technologies to identify protein biomarkers for early detection of cancer. Quantitative proteomic profiling of body fluids, tissues, or other biological samples to identify differentially expressed proteins represents a very promising approach for improving the outcome of this disease. Proteins associated with pancreatic cancer identified through proteomic profiling technologies could be useful as biomarkers for the early diagnosis, therapeutic targets, and disease response markers. In this article, we discuss recent progress and challenges for applying quantitative proteomics technologies for biomarker discovery in pancreatic cancer.