Validation of four candidate pancreatic cancer serological biomarkers that improve the performance of CA19.9

Abstract Background The identification of new serum biomarkers with high sensitivity and specificity is an important priority in pancreatic cancer research. Through an extensive proteomics analysis of pancreatic cancer cell lines and pancreatic juice, we previously generated a list of candidate pancreatic cancer biomarkers. The present study details further validation of four of our previously identified candidates: regenerating islet-derived 1 beta (REG1B), syncollin (SYCN), anterior gradient homolog 2 protein (AGR2), and lysyl oxidase-like 2 (LOXL2). Methods The candidate biomarkers were validated using enzyme-linked immunosorbent assays in two sample sets of serum/plasma comprising a total of 432 samples (Sample Set A: pancreatic ductal adenocarcinoma (PDAC, n = 100), healthy (n = 92); Sample Set B: PDAC (n = 82), benign (n = 41), disease-free (n = 47), other cancers (n = 70)). Biomarker performance in distinguishing PDAC from each control group was assessed individually in the two sample sets. Subsequently, multiparametric modeling was applied to assess the ability of all possible two and three marker panels in distinguishing PDAC from disease-free controls. The models were generated using sample set B, and then validated in Sample Set A. Results Individually, all markers were significantly elevated in PDAC compared to healthy controls in at least one sample set (p ≤ 0.01). SYCN, REG1B and AGR2 were also significantly elevated in PDAC compared to benign controls (p ≤ 0.01), and AGR2 was significantly elevated in PDAC compared to other cancers (p \u3c 0.01). CA19.9 was also assessed. Individually, CA19.9 showed the greatest area under the curve (AUC) in receiver operating characteristic (ROC) analysis when compared to the tested candidates; however when analyzed in combination, three panels (CA19.9 + REG1B (AUC of 0.88), CA19.9 + SYCN + REG1B (AUC of 0.87) and CA19.9 + AGR2 + REG1B (AUC of 0.87)) showed an AUC that was significantly greater (p \u3c 0.05) than that of CA19.9 alone (AUC of 0.82). In a comparison of early-stage (Stage I-II) PDAC to disease free controls, the combination of SYCN + REG1B + CA19.9 showed the greatest AUC in both sample sets, (AUC of 0.87 and 0.92 in Sets A and B, respectively). Conclusions Additional serum biomarkers, particularly SYCN and REG1B, when combined with CA19.9, show promise as improved diagnostic indicators of pancreatic cancer, which therefore warrants further validation

Translational and oncologic significance of tertiary lymphoid structures in pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PDAC) is an aggressive tumor with poor survival and limited treatment options. PDAC resistance to immunotherapeutic strategies is multifactorial, but partially owed to an immunosuppressive tumor immune microenvironment (TiME). However, the PDAC TiME is heterogeneous and harbors favorable tumor-infiltrating lymphocyte (TIL) populations. Tertiary lymphoid structures (TLS) are organized aggregates of immune cells that develop within non-lymphoid tissue under chronic inflammation in multiple contexts, including cancers. Our current understanding of their role within the PDAC TiME remains limited; TLS are complex structures with multiple anatomic features such as location, density, and maturity that may impact clinical outcomes such as survival and therapy response in PDAC. Similarly, our understanding of methods to manipulate TLS is an actively developing field of research. TLS may function as anti-tumoral immune niches that can be leveraged as a therapeutic strategy to potentiate both existing chemotherapeutic regimens and potentiate future immune-based therapeutic strategies to improve patient outcomes. This review seeks to cover anatomy, relevant features, immune effects, translational significance, and future directions of understanding TLS within the context of PDAC

Bioinformatic identification of proteins with tissue-specific expression for biomarker discovery

<p>Abstract</p> <p>Background</p> <p>There is an important need for the identification of novel serological biomarkers for the early detection of cancer. Current biomarkers suffer from a lack of tissue specificity, rendering them vulnerable to non-disease-specific increases. The present study details a strategy to rapidly identify tissue-specific proteins using bioinformatics.</p> <p>Methods</p> <p>Previous studies have focused on either gene or protein expression databases for the identification of candidates. We developed a strategy that mines six publicly available gene and protein databases for tissue-specific proteins, selects proteins likely to enter the circulation, and integrates proteomic datasets enriched for the cancer secretome to prioritize candidates for further verification and validation studies.</p> <p>Results</p> <p>Using colon, lung, pancreatic and prostate cancer as case examples, we identified 48 candidate tissue-specific biomarkers, of which 14 have been previously studied as biomarkers of cancer or benign disease. Twenty-six candidate biomarkers for these four cancer types are proposed.</p> <p>Conclusions</p> <p>We present a novel strategy using bioinformatics to identify tissue-specific proteins that are potential cancer serum biomarkers. Investigation of the 26 candidates in disease states of the organs is warranted.</p

Integrative Preoteomic Analysis of Cell Line Conditioned Media and Pancreatic Juice for the Identification of Candidate Pancreatic Cancer Biomarkers

Novel serological biomarkers to aid in the detection and clinical management of pancreatic cancer patients are urgently needed. In the present study, we performed in-depth proteomic analysis of conditioned media from six pancreatic cancer cell lines (MIA-PaCa2, PANC1, BxPc3, CAPAN1, CFPAC1 and SU.86.86), the normal pancreatic ductal epithelial cell line HPDE, and pancreatic juice samples from cancer patients for identification of novel biomarker candidates. Using 2D-LC-MS/MS, a total of 3479 non-redundant proteins were identified with ≥2 peptides. Subsequent label-free protein quantification and integrative analysis of the biological fluids resulted in the generation of candidate biomarkers, of which five proteins were shown to be significantly elevated in plasma from pancreatic cancer patients in a preliminary assessment. Further verification of two of the proteins in ~200 serum samples demonstrated the ability of these proteins to significantly improve the area under the receiver operating characteristic curve of CA19.9 from 0.84 to 0.91.MAS

Validation of four candidate pancreatic cancer serological biomarkers that improve the performance of CA19.9

Abstract Background The identification of new serum biomarkers with high sensitivity and specificity is an important priority in pancreatic cancer research. Through an extensive proteomics analysis of pancreatic cancer cell lines and pancreatic juice, we previously generated a list of candidate pancreatic cancer biomarkers. The present study details further validation of four of our previously identified candidates: regenerating islet-derived 1 beta (REG1B), syncollin (SYCN), anterior gradient homolog 2 protein (AGR2), and lysyl oxidase-like 2 (LOXL2). Methods The candidate biomarkers were validated using enzyme-linked immunosorbent assays in two sample sets of serum/plasma comprising a total of 432 samples (Sample Set A: pancreatic ductal adenocarcinoma (PDAC, n = 100), healthy (n = 92); Sample Set B: PDAC (n = 82), benign (n = 41), disease-free (n = 47), other cancers (n = 70)). Biomarker performance in distinguishing PDAC from each control group was assessed individually in the two sample sets. Subsequently, multiparametric modeling was applied to assess the ability of all possible two and three marker panels in distinguishing PDAC from disease-free controls. The models were generated using sample set B, and then validated in Sample Set A. Results Individually, all markers were significantly elevated in PDAC compared to healthy controls in at least one sample set (p ≤ 0.01). SYCN, REG1B and AGR2 were also significantly elevated in PDAC compared to benign controls (p ≤ 0.01), and AGR2 was significantly elevated in PDAC compared to other cancers (p < 0.01). CA19.9 was also assessed. Individually, CA19.9 showed the greatest area under the curve (AUC) in receiver operating characteristic (ROC) analysis when compared to the tested candidates; however when analyzed in combination, three panels (CA19.9 + REG1B (AUC of 0.88), CA19.9 + SYCN + REG1B (AUC of 0.87) and CA19.9 + AGR2 + REG1B (AUC of 0.87)) showed an AUC that was significantly greater (p < 0.05) than that of CA19.9 alone (AUC of 0.82). In a comparison of early-stage (Stage I-II) PDAC to disease free controls, the combination of SYCN + REG1B + CA19.9 showed the greatest AUC in both sample sets, (AUC of 0.87 and 0.92 in Sets A and B, respectively). Conclusions Additional serum biomarkers, particularly SYCN and REG1B, when combined with CA19.9, show promise as improved diagnostic indicators of pancreatic cancer, which therefore warrants further validation

Infigratinib in patients with advanced cholangiocarcinoma with gene fusions/translocations: the PROOF 301 trial.

Cholangiocarcinoma is an aggressive malignancy with poor overall survival. Approximately 15% of intrahepatic cholangiocarcinomas contain alterations. Infigratinib is an oral FGFR 1-3 kinase inhibitor. Favorable results from a Phase II trial of infigratinib in advanced/metastatic -altered cholangiocarcinomas has led to its further investigation in the front-line setting. In this article we describe the design, objectives and rationale for PROOF 301, a Phase III multicenter, open label, randomized trial of infigratinib in comparison to standard of care gemcitabine and cisplatin in advanced/metastatic cholangiocarcinoma with translocations. The results of this study have the potential to define a new role for a chemotherapy-free, targeted therapy option in the front-line setting for these patients. Clinical Trial Registration: NCT03773302 (ClincalTrials.gov)

Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) has been left behind in the evolution of personalized medicine. Predictive markers of response to therapy are lacking in PDAC despite various histological and transcriptional classification schemes. We report an artificial intelligence (AI) approach to histologic feature examination that extracts a signature predictive of disease-specific survival (DSS) in patients with PDAC receiving adjuvant gemcitabine. We demonstrate that this AI-generated histologic signature is associated with outcomes following adjuvant gemcitabine, while three previously developed transcriptomic classification systems are not (n&nbsp;= 47). We externally validate this signature in an independent cohort of patients treated with adjuvant gemcitabine (n&nbsp;= 46). Finally, we demonstrate that the signature does not stratify survival outcomes in a third cohort of untreated patients (n&nbsp;= 161), suggesting that the signature is specifically predictive of treatment-related outcomes but is not generally prognostic. This imaging analysis pipeline has promise in the development of actionable markers in other clinical settings where few biomarkers currently exist