The tumor-immune system plays a critical role in colorectal cancer progression. Recent preclinical and clinical studies showed that combination therapy with anti-PD-L1 and cancer vaccines improved treatment response. In this study, we developed a multiscale mathematical model of interactions among tumors, immune cells, and cytokines to investigate tumor evolutionary dynamics under different therapeutic strategies. Additionally, we established a computational framework based on approximate Bayesian computation to generate virtual tumor samples and capture inter-individual heterogeneity in treatment response. The results demonstrated that a multiple low-dose regimen significantly reduced advanced tumor burden compared to baseline treatment in anti-PD-L1 therapy. In contrast, the maximum dose therapy yielded superior tumor growth control in cancer vaccine therapy. Furthermore, cytotoxic T cells were identified as a consistent predictive biomarker both before and after treatment initiation. Notably, the cytotoxic T cells-to-regulatory T cells ratio specifically served as a robust pre-treatment predictive biomarker, offering potential clinical utility for patient stratification and ther
Breast Cancer is a major public health problem and the most common diagnosed malignancy in woman. There have been significant developments in clinical approaches and theoretical experimental to understand the interactions of cancer cells dynamics with the immune system, also developments on analytical and computational models to help provide insights into clinical observations for a better understanding of cancer cells, but more are needed, especially at the genetic and molecular levels mathematically. Treatments such as immunotherapy, chemotherapy, hormone therapy, radiotherapy, and gene therapy are the main strategies in the fight against breast cancer. The present study aims at investigating the effects of estrogen derived from recent models, but this time combined with immunotherapy as a way to treat or inhibit the cancer growth by a mathematical model of breast cancer in situ, governed by a simplified model of nonlinear-coupled ordinary differential equations, that combines important interactions between natural cells, tumor cells, immune cells, ketogenic diet in the presence of an anticancer drug. Another contribution was to introduce the inhibition effect epsilon for new res
Treatment-related lymphopenia is a frequent and clinically significant consequence of cancer therapy that can compromise immune-mediated tumor control and worsen patient outcomes. Despite its importance, no mechanistic framework exists to accurately predict the severity of lymphopenia from patient-specific data. Here, we present a biokinetic model that quantitatively describes lymphocyte depletion and recovery during and after radiotherapy, integrating radiation dose-volume distributions, blood circulation dynamics, and distinct kinetics of fast- and slow-recovering lymphocyte populations. The model was calibrated and validated using 56 independent clinical datasets encompassing various tumor sites and treatment modalities. It reproduces observed lymphocyte counts and enables prediction of individual severity of lymphopenia from baseline or early-treatment counts. Applying this framework, we demonstrate that particle therapy reduces lymphocyte depletion by ~30% compared with photon therapy, providing a quantitative explanation for its observed immune-sparing benefit. By linking radiation physics, immune kinetics, and clinical outcomes, our model establishes a mechanistically ground
In recent years, cancer genome sequencing and other high-throughput studies of cancer genomes have generated many notable discoveries. In this review, Novel genomic alteration mechanisms, such as chromothripsis (chromosomal crisis) and kataegis (mutation storms), and their implications for cancer are discussed. Genomic alterations spur cancer genome evolution. Thus, the relationship between cancer clonal evolution and cancer stems cells is commented. The key question in cancer biology concerns how these genomic alterations support cancer development and metastasis in the context of biological functioning. Thus far, efforts such as pathway analysis have improved the understanding of the functional contributions of genetic mutations and DNA copy number variations to cancer development, progression and metastasis. However, the known pathways correspond to a small fraction, plausibly 5-10%, of somatic mutations and genes with an altered copy number. To develop a comprehensive understanding of the function of these genomic alterations in cancer, an integrative network framework is proposed and discussed. Finally, the challenges and the directions of studying cancer omic data using an in
Background : depression and anxiety are common in patients with cancer, classical antidepressant has no proven efficacy on this type of distress compared to placebo. A Psilocybin (serotoninergic hallucinogen) based therapy appear to give promising results among recent studies. Aims : to examine if a Psilocybin based therapy could be considered for patients with cancer related depression and anxiety and to assume it's safety. To sum up Heffter institute work, as the main institute working on this topic. Method : following PRISMA (Preferred Reporting Items for Systematic reviews and Meta Analyses) guidelines, a systematic review was conducted, for quantitative and qualitative studies about psilocybin for treating cancer related depression and anxiety. Pubmed and the Heffter institute databases have been reached for this purpose, separating studies in types : qualitative or quantitative. We studied the effects on cancer related depression and anxiety separately and investigated the psychological and neurobiological mechanisms. Results : the four studies included a total of 105 randomized patients, meta analysis on depression and anxiety with pooled Peto odds ratio showed a significant
Evolutionary therapy (ET) aims to steer tumor evolution by adjusting treatment timing and dosing to control rather than eradicate tumor burden. Clinical use requires reliable monitoring of tumor dynamics to inform mathematical models that guide therapy. In cancers such as metastatic castrate-resistant prostate cancer and relapsed platinum-sensitive ovarian cancer, ET models are informed by serial serum biomarkers. For cancers lacking reliable biomarkers, such as metastatic non-small cell lung cancer (NSCLC), radiographic imaging remains the primary method for treatment response assessment, typically using RECIST 1.1 criteria. RECIST, which tracks a few lesions with one-dimensional (1D) measurements and defines progression relative to the nadir, the smallest tumor burden recorded after treatment, was not designed to support ET. It may miss early regrowth, underrepresent tumor burden, and obscure disease trends. Using a virtual NSCLC patient model, we demonstrate that lesion selection and measurement dimensionality strongly affect progression detection. Two-dimensional metrics provide modest improvement, but only 3D volumetric measurements accurately capture both tumor burden and its
Cytotoxic chemotherapy is a common treatment for advanced prostate cancer. These tumors are also known to rely on angiogenesis, i.e., the growth of local microvasculature via chemical signaling produced by the tumor. Thus, several clinical studies have been investigating antiangiogenic therapy for advanced prostate cancer, either as monotherapy or combined with standard cytotoxic protocols. However, the complex genetic alterations promoting prostate cancer growth complicate the selection of the best chemotherapeutic approach for each patient's tumor. Here, we present a mathematical model of prostate cancer growth and chemotherapy that may enable physicians to test and design personalized chemotherapeutic protocols in silico. We use the phase-field method to describe tumor growth, which we assume to be driven by a generic nutrient following reaction-diffusion dynamics. Tumor proliferation and apoptosis (i.e., programmed cell death) can be parameterized with experimentally-determined values. Cytotoxic chemotherapy is included as a term downregulating tumor net proliferation, while antiangiogenic therapy is modeled as a reduction in intratumoral nutrient supply. Another equation coupl
We present a general computational theory of cancer and its developmental dynamics. The theory is based on a theory of the architecture and function of developmental control networks which guide the formation of multicellular organisms. Cancer networks are special cases of developmental control networks. Cancer results from transformations of normal developmental networks. Our theory generates a natural classification of all possible cancers based on their network architecture. Each cancer network has a unique topology and semantics and developmental dynamics that result in distinct clinical tumor phenotypes. We apply this new theory with a series of proof of concept cases for all the basic cancer types. These cases have been computationally modeled, their behavior simulated and mathematically described using a multicellular systems biology approach. There are fascinating correspondences between the dynamic developmental phenotype of computationally modeled {\em in silico} cancers and natural {\em in vivo} cancers. The theory lays the foundation for a new research paradigm for understanding and investigating cancer. The theory of cancer networks implies that new diagnostic methods
Nanorobots are a promising development in targeted drug delivery and the treatment of neurological disorders, with potential for crossing the blood-brain barrier (BBB). These small devices leverage advancements in nanotechnology and bioengineering for precise navigation and targeted payload delivery, particularly for conditions like brain tumors, Alzheimer's disease, and Parkinson's disease. Recent progress in artificial intelligence (AI) and machine learning (ML) has improved the navigation and effectiveness of nanorobots, allowing them to detect and interact with cancer cells through biomarker analysis. This study presents a new reinforcement learning (RL) framework for optimizing nanorobot navigation in complex biological environments, focusing on cancer cell detection by analyzing the concentration gradients of surrounding biomarkers. We utilize a computer simulation model to explore the behavior of nanorobots in a three-dimensional space with cancer cells and biological barriers. The proposed method uses Q-learning to refine movement strategies based on real-time biomarker concentration data, enabling nanorobots to autonomously navigate to cancerous tissues for targeted drug d
In oncology, treating cancer with a beam of photons is a well established therapeutic technique, developed over 100 years, and today over 50% of cancer patients will undergo traditional X-ray radiotherapy. However, ionizing radiation therapy is not the only option, as the high-energy photons delivering their cell-killing radiation energy into cancerous tumor can lead to significant damage to healthy tissues surrounding the tumor, located throughout the beam's path. Therefore, in nowadays, advances in ionizing radiation therapy are competitive to non-ionizing ones, as for example the laser light based therapy, resulting in a synergism that has revolutionized medicine. The use of non-invasive or minimally invasive (e.g. through flexible endoscopes) therapeutic procedures in the management of patients represents a very interesting treatment option. Moreover, as the major breakthrough in cancer management is the individualized patient treatment, new biophotonic techniques, e.g. photo-activated drug carriers, help the improvement of treatment efficacy and/or normal tissue toxicity. Additionally, recent studies support that laser technology progresses could revolutionize cancer proton th
Intermittent Androgen Suppression (IAS) is a treatment strategy for delaying or even preventing time to relapse of advanced prostate cancer. IAS consists of alternating cycles of therapy (in the form of androgen suppression) and off-treatment periods. The level of prostate specific antigen (PSA) in a patient's serum is frequently monitored to determine when the patient will be taken off therapy and when therapy will resume. In spite of extensive recent clinical experience with IAS, the design of an ideal protocol for any given patient remains one of the main challenges associated with effectively implementing this therapy. We use a threshold-based policy for optimal IAS therapy design that is parameterized by lower and upper PSA threshold values and is associated with a cost metric that combines clinically relevant measures of therapy success. We apply Infinitesimal Perturbation Analysis (IPA) to a Stochastic Hybrid Automaton (SHA) model of prostate cancer evolution under IAS and derive unbiased estimators of the cost metric gradient with respect to various model and therapy parameters. These estimators are subsequently used for system analysis. By evaluating sensitivity estimates
Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence, variability across populations and across time, and, in the case of cancers that use radiotherapy, by further symptom dependency on the tumor location and prescribed treatment. We describe THALIS, an environment for visual analysis and knowledge discovery from cancer therapy symptom data, developed in close collaboration with oncology experts. Our approach leverages unsupervised machine learning methodology over cohorts of patients, and, in conjunction with custom visual encodings and interactions, provides context for new patients based on patients with similar diagnostic features and symptom evolution. We evaluate this approach on data collected from a cohort of head and neck cancer patients. Feedback from our clinician collaborators indicates that THALIS supports knowledge discovery beyond the limits of machines or humans alone, and that it serves as a valuable t
While the use of combination therapy is increasing in prevalence for cancer treatment, it is often difficult to predict the exact interactions between different treatment forms, and their synergistic/antagonistic effects on patient health and therapy outcome. In this research, a system of ordinary differential equations is constructed to model nonlinear dynamics between tumor cells, immune cells, and three forms of therapy: chemotherapy, immunotherapy, and radiotherapy. This model is then used to generate optimized combination therapy plans using optimal control theory. In-silico experiments are conducted to simulate the response of the patient model to various treatment plans. This is the first mathematical model in current literature to introduce radiotherapy as an option alongside immuno- and chemotherapy, permitting more flexible and effective treatment plans that reflect modern therapeutic approaches.
We use a Stochastic Hybrid Automaton (SHA) model of prostate cancer evolution under intermittent androgen suppression (IAS) to study a threshold-based policy for therapy design. IAS is currently one of the most widely used treatments for advanced prostate cancer. Patients undergoing IAS are submitted to cycles of treatment (in the form of androgen deprivation) and off-treatment periods in an alternating manner. One of the main challenges in IAS is to optimally design a therapy scheme, i.e., to determine when to discontinue and recommence androgen suppression. The level of prostate specific antigen (PSA) in a patient's serum is frequently monitored to determine when the patient will be taken off therapy and when therapy will resume. The threshold-based policy we propose is parameterized by lower and upper PSA threshold values and is associated with a cost metric that combines clinically relevant measures of therapy success. Using Infinitesimal Perturbation Analysis (IPA), we derive unbiased gradient estimators of this cost metric with respect to the controllable PSA threshold values based on actual data and show how these estimators can be used to adaptively adjust controllable para
Mechanical force is tightly connected to human health status, and the occurrence of disease can generally be ascribed to certain loss of force balance. However, the role of mechanical approaches in tumor therapy is largely neglected, while the currently available cancer prevention and treatment methods are generally either expensive or just cause too much side effect. In this article, we present a systematic interpretation on a promising strategy which was termed here as Mechanical Therapy for the first time based on the fact that mechanical force closely accompanies the whole life (growth and death) of a cell, and plays a crucial role in biological functions. In order to mold the mechanical force as a practical tool for cancer therapy, we expound the effects of the mechanical force related to the tumors from molecule to tissue level and evaluate its feasibility for treatment purpose. It can be conceived that given enough investigations, the mechanical therapy may generate big potential to open new windows for cancer prevention, relieving or even destroying if well administrated. As an easy going and convenient method, a systematic and thorough study of mechanical therapy may clari
The growth of many solid tumors has been found to be driven by chemo- and radiotherapy-resistant cancer stem cells (CSCs). A suitable therapeutic avenue in these cases may involve the use of a differentiating agent (DA) to force the differentiation of the CSCs and of conventional therapies to eliminate the remaining differentiated cancer cells (DCCs). To describe the effects of a DA that reprograms CSCs into DCCs, we adapt a differential equation model developed to investigate tumorspheres, which are assumed to consist of jointly evolving CSC and DCC populations. We analyze the mathematical properties of the model, finding the equilibria and their stability. We also present numerical solutions and phase diagrams to describe the system evolution and the therapy effects, denoting the DA strength by a parameter \(a_{dif}\).To obtain realistic predictions, we choose the other model parameters to be those determined previously from fits to various experimental datasets. These datasets characterize the progression of the tumor under various culture conditions. Typically, for small values of \(a_{dif}\) the tumor evolves towards a final state that contains a CSC fraction, but a strong the
The $^{10}$B isotope has been almost exclusively used in the neutron-capture radiation therapy (NCT) of cancer for decades. We have identified two other nuclides suitable for the radiotherapy, which have ca.10 times larger cross section of absorption for neutrons and emit heavy charged particles. This would provide several key advantages for potential NCT, such as the possibility to use either a lower nuclide concentration in the target tissues, or a lower neutron irradiation flux. By detecting the characteristic $γ$ radiation from the spontaneous decay of the radionuclides, one can image and control their accumulation. These advantages could be critical for the revival of the NCT as a safer, more efficient and more widely used cancer therapy.
Purpose. Patients with advanced cancer may undergo multiple lines of treatment, switching therapies as their disease progresses. Motivated by a study of metastatic prostate cancer, we develop a microsimulation framework to study therapy sequence. Methods. We propose a discrete-time state transition model to study two lines of anti-cancer therapy. Based on digitized published progression-free survival (PFS) and overall survival (OS) curves, we infer event types (progression or death), and estimate transition probabilities using cumulative incidence functions with competing risks. Our model incorporates within-patient dependence over time, such that response to first-line therapy informs subsequent event probabilities. Parameters governing the degree of within-patient dependence can be used to calibrate the model-based results to those of a target trial. We demonstrate these methods in a study of two therapy sequences for metastatic prostate cancer, where Docetaxel (DCT) and Abiraterone Acetate (AA) are both appropriate for use in either first or second line treatment. We assess costs, Quality-Adjusted Life Years (QALYs) and Incremental Cost Effectiveness Ratio (ICER) for two treatme
In biomedical research and the practice of cancer therapy, gold nanoparticles have been used to visualize malignant tumors, as the heated bodies for hyperthermia of cancer cells, as drug carriers to deliver drugs to a cancer cell, but, to the best of our knowledge, they have not yet been used consciously as the sources of terahertz (THz) radiation delivered to a cancer cell that contributes to the inhibition of cell activity. It is predicted here that gold nanoparticles less than 8 nm in size are sources of spontaneous THz radiation, and the possibility of their application in oncology is due to the known effects of THz radiation on the cells of living organisms. There are indications that nanoparticles with a size comparable to the width of the major groove of the DNA molecule will be the most effective. Another effect that has not yet been taken into account in biomedical studies using gold nanoparticles is that of local electric fields due to the contact potential difference above edges and vertices of gold nanoclusters. The prerequisites and possibilities for searching for the manifestations of these two effects when gold nanoparticles are introduced into living cells of organi
Biosyntheses of proteins, nucleotides and fatty acids, are essential for the malignant proliferation and survival of cancer cells. Cumulating research findings show that amino acid restrictions are potential strategies for cancer interventions. Meanwhile, dietary strategies are popular among cancer patients. However, there is still lacking solid rationale to clarify what is the best strategy, why and how it is. Here, integrated analyses and comprehensive summaries for the abundances, signalling and functions of amino acids in proteomes, metabolism, immunity and food compositions, suggest that, intermittent fasting or intermittent dietary lysine restriction with normal maize as an intermittent staple food for days or weeks, might have the value and potential for cancer prevention or therapy. Moreover, dietary supplements were also discussed for cancer cachexia including dietary immunomodulatory.