A Comparative Analysis of ARX and ANFIS Models for Tumor Growth Prediction Under Single and Multi-agent Chemotherapy.

BACKGROUND/AIM: Despite the advances in oncology and cancer treatment over the past decades, cancer remains one of the deadliest diseases. This study focuses on further understanding the complex nature of cancer by using mathematical tumor modeling to understand, capture as best as possible, and describe its complex dynamics under chemotherapy treatment. MATERIALS AND METHODS: Focusing on autoregressive with exogenous inputs, i.e., ARX, and adaptive neuro-fuzzy inference system, i.e., ANFIS, models, this work investigates tumor growth dynamics under both single and combination anticancer agent chemotherapy treatments using chemotherapy treatment data on xenografted mice. RESULTS: Four ARX and ANFIS models for tumor growth inhibition were developed, estimated, and evaluated, demonstrating a strong correlation with tumor weight data, with ANFIS models showing superior performance in handling the multi-agent tumor growth complexities. These findings suggest potential clinical applications of the ANFIS models through further testing. Both types of models were also tested for their prediction capabilities across different chemotherapy schedules, with accurate forecasting of tumor growth up to five days in advance. The use of adaptive prediction and sliding (moving) data window techniques allowed for continuous model updating, ensuring more robust predictive capabilities. However, long-term forecasting remains a challenge, with accuracy declining over longer prediction horizons. CONCLUSION: While ANFIS models showed greater reliability in predictions, the simplicity and rapid deployment of ARX models offer advantages in situations requiring immediate approximations. Future research with larger, more diverse datasets and by exploring varying model complexities is recommended to improve the models' reliability and applicability in clinical decision-making, thereby aiding the development of personalized chemotherapy regimens.

Advanced Non-linear Mathematical Model for the Prediction of the Activity of a Putative Anticancer Agent in Human-to-mouse Cancer Xenografts.

BACKGROUND/AIM: Mathematical models have long been considered as important tools in cancer biology and therapy. Herein, we present an advanced non-linear mathematical model that can predict accurately the effect of an anticancer agent on the growth of a solid tumor. MATERIALS AND METHODS: Advanced non-linear mathematical optimization techniques and human-to-mouse experimental data were used to develop a tumor growth inhibition (TGI) estimation model. RESULTS: Using this mathematical model, we could accurately predict the tumor mass in a human-to-mouse pancreatic ductal adenocarcinoma (PDAC) xenograft under gemcitabine treatment up to five time periods (points) ahead of the last treatment. CONCLUSION: The ability of the identified TGI dynamic model to perform satisfactory short-term predictions of the tumor growth for up to five time periods ahead was investigated, evaluated and validated for the first time. Such a prediction model could not only assist the pre-clinical testing of putative anticancer agents, but also the early modification of a chemotherapy schedule towards increased efficacy.

Proteomic studies of pediatric medulloblastoma tumors with 17p deletion.

CNS tumors are the leading cause of cancer-related death in children. Medulloblastoma is the commonest pediatric CNS malignancy, wherein, despite multimodal therapy with surgery, radiation, and chemotherapy, 5 year survival rates merely approach 60%. Until present, gene expression and cytogenetic studies have produced contradicting findings regarding the molecular background of the specific disease. Through integration of genomics, bioinformatics, and proteomics, the current study aims to shed light at the proteomic-related molecular events responsible for MBL pathophysiology, as well as to provide molecular/protein/pathway answers concerning tumor-onset. Experiments were performed on tissues collected at surgery. With 17p loss being the commonest chromosomal aberrance observed in our sample set, array-CGH were employed to first distinguish for 17p-positive cases. 2-DE coupled to mass spectrometry identification exposed the MBL-specific protein profile. Protein profiles of malignant tissues were compared against profiles of normal cerebellar tissues, and quantitative protein differences were determined. Bioinformatics, functional and database analyses, characterization, and subnetwork profiling generated information on MBL protein interactions. Key molecules of the PI3K/mTOR signaling network were identified via the techniques applied herein. Among the findings IGF2, PI3K, Rictor, MAPKAP1, S6K1, 4EBP1, and ELF4A, as part of the IGF network (implicating PI3K/mTOR), were founded to be deregulated.

Review: Chios mastic gum: a plant-produced resin exhibiting numerous diverse pharmaceutical and biomedical properties.

Chios mastic gum (CMG) is a resin produced by the plant Pistacia lentiscus var. chia. CMG is used to extract the mastic gum essential oil (MGO). CMG and MGO consist of nearly 70 constituents and have demonstrated numerous and diverse biomedical and pharmacological properties including (a) eradication of bacteria and fungi that may cause peptic ulcers, tooth plaque formation and malodor of the mouth and saliva; (b) amelioration or dramatic reduction of symptoms of autoimmune diseases by inhibiting production of pro-inflammatory substances by activated macrophages, production of cytokines by peripheral blood mononuclear cells in patients with active Crohn's disease, and suppression of production of inflammatory cytokines and chemokines in an asthma model in mice; (c) protection of the cardiovascular system by effectively lowering the levels of total serum cholesterol, low-density lipoprotein and triglycerides in rats, and protection of low-density lipoprotein from oxidation in humans; (d) induction of apoptosis in human cancer cells in vitro and extensive inhibition of growth of human tumors xenografted in immunodeficient mice; and (e) improvement of symptoms in patients with functional dyspepsia. Collectively taken, these numerous and diverse medical and pharmaceutical properties of CMG and MGO warrant further research in an effort to enhance specific properties and identify specific constituent(s) that might be associated with each property.

Loss of CCDC6 affects cell cycle through impaired intra-S-phase checkpoint control.

In most cancers harboring Ccdc6 gene rearrangements, like papillary thyroid tumors or myeloproliferative disorders, the product of the normal allele is supposed to be functionally impaired or absent. To address the consequence of the loss of CCDC6 expression, we applied lentiviral shRNA in several cell lines. Loss of CCDC6 resulted in increased cell death with clear shortening of the S phase transition of the cell cycle. Upon exposure to etoposide, the cells lacking CCDC6 did not achieve S-phase accumulation. In the absence of CCDC6 and in the presence of genotoxic stress, like etoposide treatment or UV irradiation, increased accumulation of DNA damage was observed, as indicated by a significant increase of pH2Ax Ser139. 14-3-3σ, a major cell cycle regulator, was down-regulated in CCDC6 lacking cells, regardless of genotoxic stress. Interestingly, in the absence of CCDC6, the well-known genotoxic stress-induced cytoplasmic sequestration of the S-phase checkpoint CDC25C phosphatase did not occur. These observations suggest that CCDC6 plays a key role in cell cycle control, maintenance of genomic stability and cell survival and provide a rational of how disruption of CCDC6 normal function contributes to malignancy.

Proteomics studies of childhood pilocytic astrocytoma.

Childhood pilocytic astrocytoma is the most frequent brain tumor affecting children. Proteomics analysis is currently considered a powerful tool for global evaluation of protein expression and has been widely applied in the field of cancer research. In the present study, a series of proteomics, genomics, and bioinformatics approaches were employed to identify, classify and characterize the proteome content of low-grade brain tumors as it appears in early childhood. Through bioinformatics database construction, protein profiles generated from pathological tissue samples were compared against profiles of normal brain tissues. Additionally, experiments of comparative genomic hybridization arrays were employed to monitor for genetic aberrations and sustain the interpretation and evaluation of the proteomic data. The current study confirms the dominance of MAPK pathway for the childhood pilocytic astrocytoma occurrence and novel findings regarding the ERK-2 expression are reported.