Evaluating IPMN and pancreatic carcinoma utilizing quantitative histopathology

Single-cell omics: a new perspective for early detection of pancreatic cancer?

Pancreatic cancer is one of the most lethal cancers, mostly due to late diagnosis and limited treatment options. Early detection of pancreatic cancer in high-risk populations bears the potential to greatly improve outcomes, but current screening approaches remain of limited value despite recent technological advances. This review explores the possible advantages of liquid biopsies for this application, particularly focusing on circulating tumour cells (CTCs) and their subsequent single-cell omics analysis. Originating from both primary and metastatic tumour sites, CTCs provide important information for diagnosis, prognosis and tailoring of treatment strategies. Notably, CTCs have even been detected in the blood of subjects with pancreatic precursor lesions, suggesting their suitability as a non-invasive tool for the early detection of malignant transformation in the pancreas. As intact cells, CTCs offer comprehensive genomic, transcriptomic, epigenetic and proteomic information that can be explored using rapidly developing techniques for analysing individual cells at the molecular level. Studying CTCs during serial sampling and at single-cell resolution will help to dissect tumour heterogeneity for individual patients and among different patients, providing new insights into cancer evolution during disease progression and in response to treatment. Using CTCs for non-invasive tracking of cancer features, including stemness, metastatic potential and expression of immune targets, provides important and readily accessible molecular insights. Finally, the emerging technology of ex vivo culturing of CTCs could create new opportunities to study the functionality of individual cancers at any stage and develop personalised and more effective treatment approaches for this lethal disease.

Evaluating IPMN and pancreatic carcinoma utilizing quantitative histopathology

Intraductal papillary mucinous neoplasms (IPMN) are pancreatic lesions with uncertain biologic behavior. This study sought objective, accurate prediction tools, through the use of quantitative histopathological signatures of nuclear images, for classifying lesions as chronic pancreatitis (CP), IPMN, or pancreatic carcinoma (PC). Forty-four pancreatic resection patients were retrospectively identified for this study (12 CP; 16 IPMN; 16 PC). Regularized multinomial regression quantitatively classified each specimen as CP, IPMN, or PC in an automated, blinded fashion. Classification certainty was determined by subtracting the smallest classification probability from the largest probability (of the three groups). The certainty function varied from 1.0 (perfectly classified) to 0.0 (random). From each lesion, 180 ± 22 nuclei were imaged. Overall classification accuracy was 89.6% with six unique nuclear features. No CP cases were misclassified, 1/16 IPMN cases were misclassified, and 4/16 PC cases were misclassified. Certainty function was 0.75 ± 0.16 for correctly classified lesions and 0.47 ± 0.10 for incorrectly classified lesions (P = 0.0005). Uncertainty was identified in four of the five misclassified lesions. Quantitative histopathology provides a robust, novel method to distinguish among CP, IPMN, and PC with a quantitative measure of uncertainty. This may be useful when there is uncertainty in diagnosis.