Implementing biological markers as a tool to guide clinical care of patients with pancreatic cancer

Small molecular inhibitors: Therapeutic strategies for pancreatic cancer

Pancreatic cancer (PC), a disease with high heterogeneity and a dense stromal microenvironment, presents significant challenges and a bleak prognosis. Recent breakthroughs have illuminated the crucial interplay among RAS, epidermal growth factor receptor (EGFR), and hedgehog pathways in PC progression. Small molecular inhibitors have emerged as a potential solution with their advantages of oral administration and the ability to target intracellular and extracellular sites effectively. However, despite the US FDA approving over 100 small-molecule targeted antitumor drugs, challenges such as low response rates and drug resistance persist. This review delves into the possibility of using small molecules to treat persistent or spreading PC, highlighting the challenges and the urgent need for a diverse selection of inhibitors to develop more effective treatment strategies.

Tumour organoids and assembloids: Patient-derived cancer avatars for immunotherapy

BACKGROUND

Organoid technology is an emerging and rapidly growing field that shows promise in studying organ development and screening therapeutic regimens. Although organoids have been proposed for a decade, concerns exist, including batch-to-batch variations, lack of the native microenvironment and clinical applicability.

MAIN BODY

The concept of organoids has derived patient-derived tumour organoids (PDTOs) for personalized drug screening and new drug discovery, mitigating the risks of medication misuse. The greater the similarity between the PDTOs and the primary tumours, the more influential the model will be. Recently, 'tumour assembloids' inspired by cell-coculture technology have attracted attention to complement the current PDTO technology. High-quality PDTOs must reassemble critical components, including multiple cell types, tumour matrix, paracrine factors, angiogenesis and microorganisms. This review begins with a brief overview of the history of organoids and PDTOs, followed by the current approaches for generating PDTOs and tumour assembloids. Personalized drug screening has been practised; however, it remains unclear whether PDTOs can predict immunotherapies, including immune drugs (e.g. immune checkpoint inhibitors) and immune cells (e.g. tumour-infiltrating lymphocyte, T cell receptor-engineered T cell and chimeric antigen receptor-T cell). PDTOs, as cancer avatars of the patients, can be expanded and stored to form a biobank.

CONCLUSION

Fundamental research and clinical trials are ongoing, and the intention is to use these models to replace animals. Pre-clinical immunotherapy screening using PDTOs will be beneficial to cancer patients.

KEY POINTS

The current PDTO models have not yet constructed key cellular and non-cellular components. PDTOs should be expandable and editable. PDTOs are promising preclinical models for immunotherapy unless mature PDTOs can be established. PDTO biobanks with consensual standards are urgently needed.

Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis

Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.

MicroRNAs and long non-coding RNAs in pancreatic cancer: From epigenetics to potential clinical applications

MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two relevant classes of non-coding RNAs (ncRNAs) that play a pivotal role in a number of molecular processes through different epigenetic regulatory mechanisms of gene expression. As a matter of fact, the altered expression of these types of RNAs leads to the development and progression of a varied range of multifactorial human diseases. Several recent reports elucidated that miRNA and lncRNAs have been implicated in pancreatic cancer (PC). For instance, dysregulation of such ncRNAs has been found to be associated with chemoresistance, apoptosis, autophagy, cell differentiation, tumor suppression, tumor growth, cancer cell proliferation, migration, and invasion in PC. Moreover, several aberrantly expressed miRNAs and lncRNAs have the potential to be used as biomarkers for accurate PC diagnosis. Additionally, miRNAs and lncRNAs are considered as promising clinical targets for PC. Therefore, in this review, we discuss recent experimental evidence regarding the clinical implications of miRNAs and lncRNAs in the pathophysiology of PC, their future potential, as well as the challenges that have arisen in this field of study in order to drive forward the design of ncRNA-based diagnostics and therapeutics for PC.

Artificial intelligence in pancreatic cancer

Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.

Advances in bioengineering pancreatic tumor-stroma physiomimetic Biomodels

Pancreatic cancer exhibits a unique bioarchitecture and desmoplastic cancer-stoma interplay that governs disease progression, multi-resistance, and metastasis. Emulating the biological features and microenvironment heterogeneity of pancreatic cancer stroma in vitro is remarkably complex, yet highly desirable for advancing the discovery of innovative therapeutics. Diverse bioengineering approaches exploiting patient-derived organoids, cancer-on-a-chip platforms, and 3D bioprinted living constructs have been rapidly emerging in an endeavor to seamlessly recapitulate major tumor-stroma biodynamic interactions in a preclinical setting. Gathering on this, herein we showcase and discuss the most recent advances in bio-assembling pancreatic tumor-stroma models that mimic key disease hallmarks and its desmoplastic biosignature. A reverse engineering perspective of pancreatic tumor-stroma key elementary units is also provided and complemented by a detailed description of biodesign guidelines that are to be considered for improving 3D models physiomimetic features. This overview provides valuable examples and starting guidelines for researchers envisioning to engineer and characterize stroma-rich biomimetic tumor models. All in all, leveraging advanced bioengineering tools for capturing stromal heterogeneity and dynamics, opens new avenues toward generating more predictive and patient-personalized organotypic 3D in vitro platforms for screening transformative therapeutics targeting the tumor-stroma interplay.

Artificial intelligence: Emerging player in the diagnosis and treatment of digestive disease

Given the breakthroughs in key technologies, such as image recognition, deep learning and neural networks, artificial intelligence (AI) continues to be increasingly developed, leading to closer and deeper integration with an increasingly data-, knowledge- and brain labor-intensive medical industry. As society continues to advance and individuals become more aware of their health needs, the problems associated with the aging of the population are receiving increasing attention, and there is an urgent demand for improving medical technology, prolonging human life and enhancing health. Digestive system diseases are the most common clinical diseases and are characterized by complex clinical manifestations and a general lack of obvious symptoms in the early stage. Such diseases are very difficult to diagnose and treat. In recent years, the incidence of diseases of the digestive system has increased. As AI applications in the field of health care continue to be developed, AI has begun playing an important role in the diagnosis and treatment of diseases of the digestive system. In this paper, the application of AI in assisted diagnosis and the application and prospects of AI in malignant and benign digestive system diseases are reviewed.

Identification of treatment-induced vulnerabilities in pancreatic cancer patients using functional model systems

Despite the advance and success of precision oncology in gastrointestinal cancers, the frequency of molecular-informed therapy decisions in pancreatic ductal adenocarcinoma (PDAC) is currently neglectable. We present a longitudinal precision oncology platform based on functional model systems, including patient-derived organoids, to identify chemotherapy-induced vulnerabilities. We demonstrate that treatment-induced tumor cell plasticity in vivo distinctly changes responsiveness to targeted therapies, without the presence of a selectable genetic marker, indicating that tumor cell plasticity can be functionalized. By adding a mechanistic layer to precision oncology, adaptive processes of tumors under therapy can be exploited, particularly in highly plastic tumors, such as pancreatic cancer.

The clinical significance of circulating tumor cells and T lymphocyte subtypes in pancreatic cancer patients

Circulating tumor cells (CTCs) are sensitive and reliable biomarkers for tracing relapsed and metastatic cancer. Here, we explore the clinical significance of CTCs and T lymphocyte subtypes in patients with pancreatic cancer. A total of 106 patients with the pancreatic cancer were enrolled in this study. The enrichment and identification of CTCs were achieved before treatment by a PatrolCTC detection technique. Flow cytometry (FACS) was used to characterize CD4, CD8, natural killer (NK) cells, and Tregulatory (Treg) lymphocyte subtypes. Interleukin-2 (IL-2), Interleukin-4 (IL-4), Interleukin-17A (IL-17A), Interleukin-10 (IL-10), and Interferon γ (IFN-γ) were measured by meso-scale discovery (MSD) assay. Among these patients, 44 (41.5%) patients with pancreatic ductal adenocarcinoma (PDAC) were female and 62 (58.5%) cases were male. Case numbers with II-IV tumor-node-metastasis (TNM) stages were 32 (30.2%), 50 (47.2%), and 24 (22.6%), respectively. The positive rate of CTCs before surgery was 37.5% (12/32), 88.0% (44/50) and 100% (24/24) in stage II, III, and IV patients, respectively. Total CTCs, mixed CTCs, and mesenchymal CTCs (MCTCs) were strongly relevant to shorter progression-free survival (PFS) of the patients. In addition, total CTCs (≥6) and positive MCTCs were also significantly correlated with recurrence and metastasis. The patients with high CTCs also had low levels of CD4, CD4/CD8 ratio, NK cells, IL-2, and IFNγ. In contrast, Treg cells had significant elevation in PDAC patients. These results indicated that CTCs number in PDAC patients was an independent indicator for worse PFS. High CTCs also had strong correlation with weak cellular immunity functions.

Type 2 Diabetes Mellitus Intersects With Pancreatic Cancer Diagnosis and Development

The relationship between type 2 diabetes mellitus (T2DM) and pancreatic cancer (PC) is complex. Diabetes is a known risk factor for PC, and new-onset diabetes (NOD) could be an early manifestation of PC that may be facilitate the early diagnosis of PC. Metformin offers a clear benefit of inhibiting PC, whereas insulin therapy may increase the risk of PC development. No evidence has shown that novel hypoglycemic drugs help or prevent PC. In this review, the effects of T2DM on PC development are summarized, and novel strategies for the prevention and treatment of T2DM and PC are discussed.

18F-FDG PET/CT imaging biomarkers for early and late evaluation of response to first-line chemotherapy in patients with pancreatic ductal adenocarcinoma

Purpose: The purpose of this study was to evaluate ¹⁸F-FDG-PET/CT as an early and late interim imaging biomarker in patients with pancreatic ductal adenocarcinoma (PDAC) who undergo first-line systemic therapy. Methods: This was a prospective, single-center, single-arm, open-label study (IRB12-000770). Patient receiving first line chemotherapy were planned to undergo a baseline ¹⁸F-FDG-PET/CT (PET1), early interim ¹⁸F-FDG PET/CT (PET2) and late interim ¹⁸F-FDG-PET/CT (PET3). ROC selected and established (mPERCIST / RECIST1.1) cut-offs for metabolic and radiographic tumor response assessment were applied. Patients were followed to collect data on further treatments and overall survival (OS). Results: The study population consisted of 28 patients who underwent PET1. Twenty-three of these (82%) underwent PET2 and 21 (75%) PET3, respectively. Twenty-three deaths occurred during a median follow up period of 14 months (maximum follow up, 58.3 months). The median OS was 36.2 months (95%CI, 28-NYR) in early metabolic responders (6/23 (26%), P = 0.016) and 25.4 months (95%CI, 19.6-NYR) in early radiographic responders (7/23 (30%), P = 0.16). The median overall survival was 27.4 months (95%CI, 21.4-NYR) in late metabolic responders (10/21 (48%), P = 0.058) and 58.2 months (95%CI, 21.4-NYR) in late radiographic responders (7/21 (33%), P = 0.008). Conclusion: ¹⁸F-FDG PET may serve as early interim imaging biomarker (~ at 4 weeks) for evaluation of response to first-line chemotherapy in patients with PDAC. Radiographic changes might be sufficient for response evaluation after the completion of first line chemotherapy.

Patient-Derived Organoids as a Model for Cancer Drug Discovery

In the search for the ideal model of tumours, the use of three-dimensional in vitro models is advancing rapidly. These are intended to mimic the in vivo properties of the tumours which affect cancer development, progression and drug sensitivity, and take into account cell–cell interactions, adhesion and invasiveness. Importantly, it is hoped that successful recapitulation of the structure and function of the tissue will predict patient response, permitting the development of personalized therapy in a timely manner applicable to the clinic. Furthermore, the use of co-culture systems will allow the role of the tumour microenvironment and tissue–tissue interactions to be taken into account and should lead to more accurate predictions of tumour development and responses to drugs. In this review, the relative merits and limitations of patient-derived organoids will be discussed compared to other in vitro and ex vivo cancer models. We will focus on their use as models for drug testing and personalized therapy and how these may be improved. Developments in technology will also be considered, including the use of microfluidics, 3D bioprinting, cryopreservation and circulating tumour cell-derived organoids. These have the potential to enhance the consistency, accessibility and availability of these models.