Because of our imperfect knowledge of complicated cancer biology and the rarity of preclinical models that accurately replicate tumor complexity, new treatments in clinical oncology studies have a high failure rate, a significant contributor to the high failure rate in clinical oncology trials. Widespread use of patient-derived xenograft (PDX) models is presently being developed, enabling us to objectively assess models’ capacity to imitate and investigate critical clinical scenarios. These possibilities include tumor heterogeneity and clonal growth, contributions to the tumor microenvironment, finding novel drugs and biomarkers, and drug-resistance mechanisms.
The Different Types of Cancer
According to biomarker studies for predictive and prognostic malignancies in creating personalized cancer treatment, clinical judgment and experience are more important than published clinical data. Below is a list of various types of cancer.
Gallbladder Cancer
Biliary tumors are rare but highly aggressive cancers with poor results. Their low occurrence hindered adequate therapy trials. Novel study platforms are urgently required. Successful biliary cancer PDX models are possible and may be used to guide future high-risk patients treatment.
Head and Neck Cancer
Imprinting PDXs from head and neck cancer samples at different stages of the illness for head and neck oncology clinical trials is feasible. They retain the genetic features of their human donor. In addition, chemotherapy and radiation may also treat them, allowing therapeutically helpful research.
Endometrial Cancer
Endometrial cancer mixed Mullerian molecular classification was recently conducted. It gives a method that consistently improves EC categorization along with histology findings and optimizes patient treatment. PDX models were previously utilized in EC, mainly as a tailored tool for evaluating the effectiveness of new treatments and finding treatment-response biomarkers.
Acute Myeloid Leukemia
Xenograft (PDX)-derived models are typically transitory and non-transferable. Therefore, they are not causing symptoms or death. Because blood cancer PDX models are permanent, they may be utilized in clinical trials to investigate disease recurrence after a treatment challenge and the effectiveness of new drugs in treating drug-resistant malignancies.
Brain Cancer
Patient survival in pediatric oncology has improved in many areas in recent decades, but the prognosis remained poor for most children with malignant brain tumors. Current pediatric brain cancer PDXs are generated by xenografting fresh tissue, freshly acquired cell suspensions, or short-cropped neurospheres into immunosuppressed rats or mice.
Cholangiocarcinoma
Cholangiocarcinoma is a low-prognosis biliary cancer. This deadly illness requires effective tailored treatments. Biliary tumors are rare but highly aggressive with a poor prognosis. Their scarcity challenges successful gallbladder cancer clinical trials.
Prostate Cancer
Prostate cancer is a complex, varied disease that presents substantial challenges to drug development and scientific research. Preclinical models such as patient-derived xenografts (PDX) must thus be used to assess medicines mainly intended to treat prostate cancer.
Testicular Cancer
Testicular cancer is one of the most common cancers in young men aged 20–40, growing worldwide. PDX models are generally regarded as the most fantastic way of predicting medication efficacy before clinical trials. In addition, these models may be used for mechanistic study and preclinical testing of testicular cancer therapies.
Conclusion
From understanding disease biology to developing novel treatment approaches, preclinical models are essential in cancer research. Although significant limitations have been found in PDX models’ capacity to predict clinical outcomes, they remain the model of choice for preclinical research at this time. Continuous multi-institutional initiatives are thus ongoing to create and disseminate these tools to maximize the translation potential of considerable, well-annotated PDX resources. This study offers an in-depth assessment of PDX models’ current state while discussing potential possibilities and problems for future PDX development.