![Sample our Bioscience journals, sign in here to start your access, Latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5925/TOC-Subject-Sample-2021-Bioscience.jpg"})
ABSTRACT
Introduction
Treatment resistance poses a significant obstacle in oncology, especially in biliary tract cancer (BTC) and pancreatic cancer (PC). Current therapeutic options include chemotherapy, targeted therapy, and immunotherapy. Resistance to these treatments may arise due to diverse molecular mechanisms, such as genetic and epigenetic modifications, altered drug metabolism and efflux, and changes in the tumor microenvironment. Identifying and overcoming these mechanisms is a major focus of research: strategies being explored include combination therapies, modulation of the tumor microenvironment, and personalized approaches.
Areas covered
We provide a current overview and discussion of the most relevant mechanisms of resistance to chemotherapy, target therapy, and immunotherapy in both BTC and PC. Furthermore, we compare the different strategies that are being implemented to overcome these obstacles.
Expert opinion
So far there is no unified theory on drug resistance and progress is limited. To overcome this issue, individualized patient approaches, possibly through liquid biopsies or single-cell transcriptome studies, are suggested, along with the potential use of artificial intelligence, to guide effective treatment strategies. Furthermore, we provide insights into what we consider the most promising areas of research, and we speculate on the future of managing treatment resistance to improve patient outcomes.
Article highlights
Therapeutic options for BTC and PC include chemotherapy, targeted therapy, and immunotherapy, however the prognosis of these patients remains poor.
Resistance to these treatments is common and it may arise from diverse molecular mechanisms, such as genetic alterations, epigenetic modifications, altered drug metabolism and efflux, and changes in the tumor microenvironment. Identifying and overcoming these mechanisms is a major focus of research.
Addressing each patient individually, utilizing tools such as liquid biopsy or single-cell transcriptome studies can help identify primary mechanisms of resistance, allowing for targeted treatments.
The article explores the emerging possibility of using artificial intelligence to integrate data on different treatment resistance mechanisms, aiming for a unified understanding and guiding the development of more effective strategies.
Examples of success in personalized medicine, such as the development of new generation FGFR inhibitors in BTC and maintenance therapy with PARP inhibitor olaparib in BRCA1/2 mutated PC patients, are acknowledged.
Abbreviations
| 5-AZA | = | 5-Azacytidine |
| ABC | = | ATP-binding Cassette |
| ACT | = | Adoptive Cell Transfer |
| AI | = | Artificial Intelligence |
| AKT | = | Serine/Threonine Kinase |
| ALK | = | Anaplastic Lymphoma Kinase |
| ATM | = | Ataxia Telangiectasia Mutated |
| ATP | = | Adenosine Triphosphate |
| Bcl-2 | = | B-Cell Lymphoma 2 |
| BRAF | = | B-Raf Proto-oncogene, Serine/Threonine Kinase |
| BTC | = | Biliary Tract Cancer |
| CAFs | = | Cancer-associated Fibroblasts |
| CCA | = | Cholangiocarcinoma |
| ctDNA | = | Circulating Tumor DNA |
| DCR | = | Disease Control Rate |
| DNA | = | Deoxyribonucleic Acid |
| DSB | = | Double Strand Breaks |
| ECC | = | Extrahepatic Cholangiocarcinoma |
| ECM | = | Extracellular Matrix |
| EGFR | = | Epidermal Growth Factor Receptor |
| EMT | = | Epithelial-mesenchymal Transition |
| ERK | = | Extracellular Regulated Kinase |
| FA | = | Fanconi Anemia |
| FDA | = | Food and Drug Administration |
| FGFR | = | Fibroblast Growth Factor Receptor |
| GEM | = | Gemcitabine |
| HA | = | Hyaluronic Acid |
| hENT | = | Human Equilibrative Nucleoside Transporter |
| HDR | = | Homology‐directed Repair |
| ICC | = | Intrahepatic Cholangiocarcinoma |
| IDH1 | = | Isocitrate Dehydrogenase 1 |
| KRAS | = | Kirsten Rat Sarcoma Virus |
| MAPK | = | Mitogen-activated Protein Kinase |
| MCH | = | Antigen Presenting Molecules |
| MSI | = | Microsatellite Instability |
| NF-kβ | = | Nuclear Factor Kappa-light-chain-enhancer of Activated B Cells |
| NSCLC | = | Non-small Cell Lung Cancer |
| NTRK | = | Neurotrophic Tyrosine Receptor Kinase |
| ORR | = | Overall Response Rate |
| OS | = | Overall Survival |
| PARP | = | Poly (ADP-ribose) Polymerase |
| PARPi | = | Poly (ADP-ribose) Polymerase Inhibitor |
| PC | = | Pancreatic Cancer |
| PD-L1 | = | Programmed Death-ligand 1 |
| PDX | = | Patient-Derived Xenograft |
| PFS | = | Progression Free Survival |
| PI3K | = | Phosphoinositide-3-kinase |
| PLOD2 | = | 2-Oxoglutarate 5-Dioxygenase 2 |
| RNA | = | Ribonucleic Acid |
| ROS | = | Reactive Oxygen Species |
| Shh | = | Sonic hedgehog |
| TAM | = | Tumor Associated Macrophage |
| TGF-β | = | Transforming Growth Factor Beta |
| TKI | = | Tyrosine Kinase Inhibitor |
| TME | = | Tumor Microenvironment |
| Treg | = | Regulatory T-cells |
| VEGF | = | Vascular Endothelial Growth Factor |
| ZEB1 | = | Zinc Finger E-box Binding Homeobox 1 |
Declaration of interest
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
Reviewer disclosures
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Author contributions
B Toledo and C Deiana wrote the manuscript. E Giovannetti revised and corrected thoroughly the manuscript. All authors revised the manuscript critically and agreed to the published version of the manuscript.
Acknowledgments
The images were made using Biorender.com.