Development of Pancreatic Cancer: Targets for Early Detection and Treatment

Background: Pancreatic ductal adenocarcinoma (PDAC) is the 4th leading cause of cancer death worldwide and compared to other malignancies its share in cancer mortality is expected to rise further. This is due to a lack of sensitive diagnostic tools that would permit earlier detection in a potentially curable stage and the very slow progress in finding effective drug treatments for pancreatic cancer. Key Messages: Aside from genetic predispositions and environmental agents, chronic pancreatitis is by far the greatest risk factor for PDAC. It also shares several etiological factors with pancreatic cancer and represents its most challenging differential diagnosis. Biomarkers that can distinguish between chronic pancreatitis and PDAC may therefore be suitable for the latter's early detection. Moreover, targeting the natural history of chronic pancreatitis would be one approach to prevent PDAC. Targeting tumor-cell signaling directly by interfering with receptor tyrosine kinases has shown some efficacy, although the results in clinical trials were less encouraging than for other cancers. Other compounds developed have targeted the formation of extracellular matrix around the tumor, the proteolytic activity in the tumor environment, histone deacetylases, hedgehog signaling and heat shock proteins, but none has yet found its way into routine patient care. Attempts to individualize treatment according to the tumor's somatic mutation profile are novel but so far impractical. Conclusions: Progress in the treatment of pancreatic cancer has been exceedingly slow and mostly dependent on improved pharmaceutical preparations or combinations of established chemotherapeutic agents. The promise of major breakthroughs implied in targeting tumor signal transduction events has so far not materialized

Metabolic biomarker signature to differentiate pancreatic ductal adenocarcinoma from chronic pancreatitis

Objective Current non-invasive diagnostic tests can distinguish between pancreatic cancer (pancreatic ductal adenocarcinoma (PDAC)) and chronic pancreatitis (CP) in only about two thirds of patients. We have searched for blood-derived metabolite biomarkers for this diagnostic purpose. Design For a case-control study in three tertiary referral centres, 914 subjects were prospectively recruited with PDAC (n=271), CP (n=282), liver cirrhosis (n=100) or healthy as well as non-pancreatic disease controls (n=261) in three consecutive studies. Metabolomic profiles of plasma and serum samples were generated from 477 metabolites identified by gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry. Results A biomarker signature (nine metabolites and additionally CA19-9) was identified for the differential diagnosis between PDAC and CP. The biomarker signature distinguished PDAC from CP in the training set with an area under the curve (AUC) of 0.96 (95% CI 0.93-0.98). The biomarker signature cut-off of 0.384 at 85% fixed specificity showed a sensitivity of 94.9% (95% CI 87.0%-97.0%). In the test set, an AUC of 0.94 (95% CI 0.91-0.97) and, using the same cut-off, a sensitivity of 89.9% (95% CI 81.0%-95.5%) and a specificity of 91.3% (95% CI 82.8%-96.4%) were achieved, successfully validating the biomarker signature. Conclusions In patients with CP with an increased risk for pancreatic cancer (cumulative incidence 1.95%), the performance of this biomarker signature results in a negative predictive value of 99.9% (95% CI 99.7%-99.9%) (training set) and 99.8% (95% CI 99.6%-99.9%) (test set). In one third of our patients, the clinical use of this biomarker signature would have improved diagnosis and treatment stratification in comparison to CA19-9

Cathepsin D Expression and Gemcitabine Resistance in Pancreatic Cancer.

BackgroundCathepsin-D (CatD), owing to its dual role as a proteolytic enzyme and as a ligand, has been implicated in cancer progression. The role of CatD in pancreatic ductal adenocarcinoma is unknown.MethodsCatD expression quantified by immunohistochemistry of tumor-tissue microarrays of 403 resected pancreatic cancer patients from the ESPAC-Tplus trial, a translational study within the ESPAC (European Study Group for Pancreatic Cancer) trials, was dichotomously distributed to low and high H scores (cut off 22.35) for survival and multivariable analysis. The validation cohort (n = 69) was recruited based on the hazard ratio of CatD from ESPAC-Tplus. 5-fluorouracil-, and gemcitabine-resistant pancreatic cancer cell lines were employed for mechanistic experiments. All statistical tests were two-sided.ResultsMedian overall survival was 23.75 months and median overall survival for patients with high CatD expression was 21.09 (95% confidence interval [CI] = 17.31 to 24.80) months vs 27.20 (95% CI = 23.75 to 31.90) months for low CatD expression (χ2 LR, 1DF = 4.00; P = .04). Multivariable analysis revealed CatD expression as a predictive marker in gemcitabine-treated (z stat = 2.33; P = .02) but not in 5-fluorouracil-treated (z stat = 0.21; P = .82) patients. An independent validation cohort confirmed CatD as a negative predictive marker for survival (χ2 LR, 1DF = 6.80; P = .009) and as an independent predictive marker in gemcitabine-treated patients with a hazard ratio of 3.38 (95% CI = 1.36 to 8.38, P = .008). Overexpression of CatD was associated with a concomitant suppression of the acid sphingomyelinase, and silencing of CatD resulted in upregulation of acid sphingomyelinase with rescue of gemcitabine resistance.ConclusionsAdjuvant gemcitabine is less effective in pancreatic ductal adenocarcinoma with high CatD expression, and thus CatD could serve as a marker for biomarker-driven therapy

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.Comment: Technical report of BraSy

Immune Cell and Stromal Signature Associated with Progression-free Survival of Patients with Resected Pancreatic Ductal Adenocarcinoma

Background & Aims: Changes to the microenvironment of pancreatic ductal adenocarcinomas (PDACs) have been associated with poor outcomes of patients. We studied the associations between composition of the pancreatic stroma (fibrogenic, inert, dormant, or fibrolytic stroma) and infiltration by inflammatory cells and times of progression-free survival (PFS) of patients with PDACs after resection. Methods: We obtained 1824 tissue microarray specimens from 385 patients included in the European Study Group for Pancreatic Cancer trial 1 and 3 and performed immunohistochemistry to detect alpha smooth muscle actin, type 1 collagen, CD3, CD4, CD8, CD68, CD206, and neutrophils. Tumors that expressed high and low levels of these markers were compared with patient outcomes using Kaplan-Meier curves and multivariable recursive partitioning for discrete-time survival tree analysis. Prognostic index was delineated by a multivariable Cox proportional hazards model of immune cell and stromal markers and PFS. Findings were validated using 279 tissue microarray specimens from 93 patients in a separate cohort. Results: Levels of CD3, CD4, CD8, CD68, and CD206 were independently associated with tumor recurrence. Recursive partitioning for discrete-time survival tree analysis identified a high level of CD3 as the strongest independent predictor for longer PFS. Tumors with levels of CD3 and high levels of CD206 associated with a median PFS time of 16.6 months and a median prognostic index of –0.32 (95% confidence interval [CI] –0.35 to –0.31), whereas tumors with low level of CD3 cell and low level of CD8 and high level of CD68 associated with a median PFS time of 7.9 months and a prognostic index of 0.32 (95% CI 0.050–0.32); we called these patterns histologic signatures. Stroma composition, when unassociated with inflammatory cell markers, did not associate significantly with PFS. In the validation cohort, the histologic signature resulted in an error matrix accuracy of predicted response of 0.75 (95% CI 0.64–0.83; accuracy P < .001). Conclusions: In an analysis of PDAC tissue microarray specimens, we identified and validated a histologic signature, based on leukocyte and stromal factors, that associates with PFS times of patients with resected PDACs. Immune cells might affect the composition of the pancreatic stroma to affect progression of PDAC. These findings provide new insights into the immune response to PDAC

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing