Endometrial cancer (EC) is rising both in incidence and mortality, is involving younger women, and is leading in the US for gynecologic cancer incidence. The application of molecular characterization and targeting treatment to selected molecular types of EC is exemplified by the marked benefit of mismatch repair deficient (dMMR) EC to immune checkpoint inhibitor (ICI) treatment. However, the response to immunotherapy has been less significant in other EC molecular types. We reported previously on the public health relevance of molecular analysis of endometrial cancer types to direct treatment considerations and discussed the limitation in biomarkers predictive of response to immunotherapy or available to examine for treatment selection, outside of mismatch repair deficiency. The current follow-on commentary addresses how new thinking can lead to optimization of immunotherapy applications for endometrial cancer molecular types, how to consider timing and sequencing of immunotherapy with other interventions, and directions for novel immunotherapy combinations. This report outlines key background studies and preclinical observations, directions to overcome inherent resistance, how to leverage ICI to augment clinical response to standard treatments, and considerations for how and when to re-expose patients to ICI treatment(s). The discussions led to potential clinical trial concepts now under development.

Immunotherapy has become a pivotal therapy for various cancers, including esophageal cancer, showing promising potential in improving survival rates and enabling personalized care. However, significant challenges occur in identifying predictive biomarkers, refining combination therapies, and managing immunotherapy-related adverse effects. This bibliometric study analyzed publications related to the use of immunotherapy in esophageal cancer management over a 10-y period (January 1, 2015, to October 14, 2024), retrieved from the Web of Science Core Collection. Keyword co-occurrence and co-citation analyses were performed to identify key contributors, central themes, and influential publications. A total of 545 publications on esophageal cancer immunotherapy were included in the analysis. A sharp increase in publication volume was observed beginning in 2019, with a peak between 2021 and 2023. China emerged as the leading contributor, accounting for 67.7% of the total output, while Zhengzhou University produced the highest number of publications among all institutions. Prominent individual contributors included Ken Kato and Shen Lin. Research hotspots centered on PD-1/PD-L1 inhibitors, combination therapies, and tumor microenvironment modulation. Notably, a clear temporal evolution in research focus was observed, with early studies emphasizing specific immune checkpoint targets and agents (e.g., PD-1, Pembrolizumab, and CTLA-4), followed by a shift toward mechanistic investigations involving the tumor microenvironment, treatment resistance, and prognosis. This study provides a comprehensive view of immunotherapy in the management of esophageal cancer, offering direction for future research and valuable insights for clinical innovation.

Immune checkpoint inhibitor (ICI)-based strategies have become a consensus in the preoperative treatment of mismatch repair-deficient (dMMR)/microsatellite instability-high (MSI-H) gastric cancer (GC). However, the necessity and optimal strategy of combining ICIs with chemotherapy remain uncertain. This retrospective study aimed to evaluate the efficacy of different preoperative chemo-immunotherapy combinations in patients with dMMR/MSI-H GC. According to their therapeutic regimens, patients were divided into three cohorts: ICI-alone cohort; immunotherapy with induction chemotherapy (IC) cohort (ICI + IC): 1-2 cycles of IC followed by ICI; concurrent chemo-immunotherapy cohort (ICI + chemo): ICI combined with chemotherapy throughout the entire preoperative treatment. The pathological complete response (pCR) rate and major pathological response (MPR) rate were analyzed. Peripheral blood parameters before and after preoperative treatment were analyzed. A total of 45 patients with locally advanced or oligometastatic dMMR/MSI-H GC were included. Baseline characteristics were well balanced among the three cohorts. The pCR rates were 18.2% (95% CI, 2.3-51.8%) in the ICI-alone cohort, 85.7% (95% CI, 42.1-99.6%) in the ICI + IC cohort, and 37.0% (95% CI, 19.4-57.6%) in the ICI + chemo cohort. Notably, the ICI + IC cohort showed a significantly higher pCR rate than the other two cohorts (p=0.015). The MPR rates were 54.5%, 85.7%, and 48.1% in the three cohorts, respectively, with no statistical significance. After preoperative treatment, monocyte-to-lymphocyte ratio exhibited an upward trend in the ICI + IC (p=0.100) and ICI + chemo (p=0.058) cohorts, indicating enhanced antigen presentation activity and immune activation. A preoperative strategy of IC followed by ICIs significantly increased pCR rate compared to ICI monotherapy or concurrent chemo-immunotherapy, suggesting a more effective strategy for patients with resectable dMMR/MSI-H GC. Given its retrospective design, small sample size, and lack of safety data, this study warrants validation in prospective clinical trials.

Despite ongoing efforts in cancer research, a fully effective treatment for glioblastoma multiforme (GBM) is still unknown. Since adoptive cell transfer immunotherapy is one of the potential cure candidates, efforts have been made to assess its effectiveness using mathematical modeling. In this paper, we consider a model of GBM immunotherapy proposed by Abernathy and Burke (2016), which also takes into account the dynamics of cancer stem cells, i.e., the type of cancer cells that are hypothesized to be largely responsible for cancer recurrence. We modify the initial ODE system by applying simplifying assumptions and analyze the existence and stability of steady states of the obtained simplified model depending on the treatment levels.

Immunotherapy constitutes a paradigm shift in cancer treatment. Its FDA approval for several indications has yielded improved prognosis for cases where traditional therapy has shown limited efficiencey. However, many patients still fail to benefit from this treatment modality, and the exact mechanisms responsible for tumor response are unknown. Noninvasive treatment monitoring is crucial for longitudinal tumor characterization and the early detection of non-responders. While various medical imaging techniques can provide a morphological picture of the lesion and its surrounding tissue, a molecular-oriented imaging approach holds the key to unraveling biological effects that occur much earlier in the immunotherapy timeline. Magnetic resonance imaging (MRI) is a highly versatile imaging modality, where the image contrast can be tailored to emphasize a particular biophysical property of interest using advanced engineering of the imaging pipeline. In this review, recent advances in molecular-MRI based cancer immunotherapy monitoring are described. Next, the presentation of the underlying physics, computational, and biological features are complemented by a critical analysis of the results obtained in preclinical and clinical studies. Finally, emerging artificial intelligence (AI)-based strategies to further distill, quantify, and interpret the image-based molecular MRI information are discussed in terms of perspectives for the future.

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 results and conclusions, A qualitative study was performed and biological interpretations were included to understand the conditions of stability in a realistic way.

<p>This chapter presents an overview of cancer prognosis and current treatments such as surgery, radiotherapy, and chemotherapy. It begins by looking at cancer prevention. Predicting the expected outcome for patients diagnosed with cancer is a critical step in their management; however, prognostication has remained somewhat subjective, leading to suboptimal clinical outcomes. The chapter then considers immunotherapy, which aims to treat cancer by generating or enhancing an immune response against the tumour. Immunotherapy differs from other methods of cancer treatment in that it does not target the tumour cell directly but instead targets the immune system. Principally, three strategies are utilized: immune checkpoint blockade, adoptive T cell transfer, and cancer vaccines. The chapter also describes how clinical trials of new candidate drugs are currently undertaken.</p>

This study proposes an effective positive control design strategy for cancer treatment by resorting to the combination of immunotherapy and chemotherapy. The treatment objective is to transfer the initial number of tumor cells and immune-competent cells from the malignant region into the region of benign growth where the immune system can inhibit tumor growth. In order to achieve this goal, a new modeling strategy is used that is based on Takagi-Sugen. A Takagi-Sugeno fuzzy model is derived based on the Stepanova nonlinear model that enables a systematic design of the controller. Then, a positive Parallel Distributed Compensation controller is proposed based on a linear copositive Lyapunov Function so that the tumor volume and administration of the chemotherapeutic and immunotherapeutic drugs is reduced, while the density of the immune-competent cells is reached to an acceptable level. Thanks to the proposed strategy, the entire control design is formulated as a Linear Programming problem, which can be solved very efficiently. Finally, the simulation results show the effectiveness of the proposed control approach for the cancer treatment. Keywords: Co-positive linear Lyapunov function, Cancer, Chemotherapy, Immunotherapy, Positive system, Takagi-Sugeno fuzzy system.

<p>CAR-T is a kind of cell immunotherapy, and it is currently the most emerging technology to treat tumors with subversive potential. It is also one of the most effective methods for the treatment of malignant tumors. </p>

The goal of cancer immunotherapy is to boost a patient's immune response to a tumor. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumor microenvironment, immune-modulating effects of conventional treatments, and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modeling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumor classification, optimal treatment scheduling, and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modelers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumor-immune biology. We conclude the review with recommendations for modelers both with respect to methodology and biological direction that might help keep modelers at the forefront of cancer immunotherapy development.