Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling.
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ABSTRACT:
SUBMITTER: Butner JD
PROVIDER: S-EPMC8629426 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeAlthough oncolytic viral vectors show promise for the treatment of various cancers, ineffective initial distribution and propagation throughout the tumor mass often limit the therapeutic response. A mathematical model is developed to describe the spread of herpes simplex virus from the initial injection site.Experimental designThe tumor is modeled as a sphere of radius R. The model incorporates reversible binding, interstitial diffusion, viral degradation, and internalization and physiologic parameters. Three species are considered as follows: free interstitial virus, virus bound to cell surfaces, and internalized virus.ResultsThis analysis reveals that both rapid binding and internalization as well as hindered diffusion contain the virus to the initial injection volume, with negligible spread to the surrounding tissue. Unfortunately, increasing the dose to saturate receptors and promote diffusion throughout the tumor is not a viable option: the concentration necessary would likely compromise safety. However, targeted modifications to the virus that decrease the binding affinity have the potential to increase the number of infected cells by 1.5-fold or more. An increase in the effective diffusion coefficient can result in similar gains.ConclusionsThis analysis suggests criteria by which the potential response of a tumor to oncolytic herpes simplex virus therapy can be assessed. Furthermore, it reveals the potential of modifications to the vector delivery method, physicochemical properties of the virus, and tumor extracellular matrix composition to enhance efficacy.
| S-EPMC2872130 | biostudies-literature
Project description:The distribution of nanomedicines inside solid tumors is often restricted to perivascular areas, leaving most distal tumor cells out of reach. This partly explains modest patient benefit of many nanomedicines compared to their free-form counterparts. The objective for this study is to develop a mathematical model to quantitatively analyze this phenomenon and the influencing factors to such perivascular distribution and seek for effective strategies to alleviate this. A spatial tumor distribution model was firstly constructed to mimic the geometrical structure of tumor vessels and the surrounding tumor cells. This tumor model was further integrated with a systemic pharmacokinetics model for nanoparticles. A variety of factors on the tumor spatial distributions of nanomedicines were considered in the model. With the model, we quantified the effect of these influencing factors on tumor delivery efficacy (ID %), the magnitude of heterogeneous distribution (H index), and the effect of enhanced permeability and retention (EPR). In particularly, we compared the spatial distributions of the nanoparticles and the free payloads insides tumors. The model predicted high degrees of distributional heterogeneity for both nanoparticles and free payloads. The degree of heterogeneity and the influencing factors for free payloads were markedly different from those for nanoparticles. We found that nanoparticle diffusion coefficient was the most effective factor in reducing the nanoparticle H index but exerted moderate influence on the free payloads H index. The most effective factor in reducing the H index of free payload was payload diffusion coefficient. The factors that improved free payload distribution were closely associated with higher drug efficacy. In contrast, the factors that improved nanoparticle spatial distributions did not always confer improved anti-tumor efficacy of the delivered drug. These findings highlight the importance of assessing the heterogeneous free payload distribution in tumors for the development of effective nanomedicines.
| S-EPMC6717548 | biostudies-literature
Project description:Neuroendocrine tumors (NETs) occur in various regions of the body and present with complex clinical and biochemical phenotypes. The molecular underpinnings that give rise to such varied manifestations have not been completely deciphered. The management of neuroendocrine tumors (NETs) involves surgery, locoregional therapy, and/or systemic therapy. Several forms of systemic therapy, including platinum-based chemotherapy, temozolomide/capecitabine, tyrosine kinase inhibitors, mTOR inhibitors, and peptide receptor radionuclide therapy have been extensively studied and implemented in the treatment of NETs. However, the potential of immune checkpoint inhibitor (ICI) therapy as an option in the management of NETs has only recently garnered attention. Till date, it is not clear whether ICI therapy holds any distinctive advantage in terms of efficacy or safety when compared to other available systemic therapies for NETs. Identifying the characteristics of NETs that would make them (better) respond to ICIs has been challenging. This review provides a summary of the current evidence on the value of ICI therapy in the management of ICIs and discusses the potential areas for future research.
| S-EPMC9731788 | biostudies-literature
Project description:The emergence of immune 'checkpoint inhibitors' such as cytotoxic T-lymphocyte antigen 4 (CTLA-4) and programmed death receptor 1 (PD-1) has revolutionized treatment of solid tumors including melanoma, lung cancer, among many others. The goal of checkpoint inhibitor combination therapy is to improve clinical response and minimize toxicities. Rational design of checkpoint combinations considers immune-mediated mechanisms of antitumor activity: immunogenic cell death, antigen release and presentation, activation of T-cell responses, lymphocytic infiltration into tumors and depletion of immunosuppression. Potential synergistic combinations include checkpoint blockade with conventional (radiation, chemotherapy and targeted therapies) and newer immunotherapies (cancer vaccines, oncolytic viruses, among others). Reliable biomarkers are necessary to define patients who will achieve best clinical benefit with minimal toxicity in combination therapy.
| S-EPMC5619130 | biostudies-literature
Project description:BackgroundImmune checkpoint inhibitors (ICIs) have been increasingly used in cancer treatment, and a subset of patients undergo pseudoprogression. Recognizing the incidence of pseudoprogression is critical for clinical practice.PurposeTo evaluate by systematic review and meta-analysis the incidence of pseudoprogression in cancer treatment with ICIs, and compare the incidence according to response criteria, tumor types, and immunotherapeutic agents.Materials and MethodsMedline and Embase were searched to identify relevant studies published before December 31, 2018. Clinical trials, post hoc analysis of clinical trials, and prospective studies on ICI treatment in patients with malignant solid tumors were included. Pooled incidence of pseudoprogression for all included studies, per definition of pseudoprogression, cancer type, and drug type, was obtained by random-effects models with inverse variance weighting model.ResultsSeventeen studies with 3402 patients were analyzed. The pooled incidence of pseudoprogression was 6.0% (95% confidence interval: 5.0%, 7.0%). The definition of pseudoprogression were divided into four categories: progressive disease followed by partial response (PR) or complete response (CR) but not stable disease (SD) with Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 (six studies); progressive disease followed by SD or PR or CR with RECIST 1.1 (five studies); progressive disease followed by SD or PR or CR with RECIST 1.0 (three studies); and progressive disease followed by SD or PR or CR with immune-related response criteria (irRC) (three studies). Incidence of pseudoprogression varied from 4.5% to 8.0% per definition, ranged from 5.0% to 7.0% per cancer type, and was 5.6% with the monotherapy of programmed cell death-1 inhibitor.ConclusionThe overall incidence of pseudoprogression was 6.0% and was less than 10% in subgroup analyses according to the definitions of pseudoprogression, cancer type, and immune checkpoint inhibitor type. Varying definitions across trials and studies indicates the need for uniform criteria of pseudoprogression for solid tumors.© RSNA, 2020Online supplemental material is available for this article.See also the article by Dodd and MacDermott in this issue.
| S-EPMC7526949 | biostudies-literature
Project description:We aimed to identify key principles of targeted therapy of protein kinases and their application to the management of solid tumors.Concurrent advances in tumor genomic analysis and molecular inhibitor development have dramatically impacted the diagnosis and treatment of solid tumors, and common themes regarding the use of kinase inhibitors are developing.The list of kinase inhibitors that have been approved by the US Food and Drug Administration was reviewed and articles related to the agents were searched in the PubMed database up until December 2015. We included pivotal, randomized controlled phase 2 and 3 trials, and also pertinent preclinical studies.Small molecule inhibitors targeted against driver kinases, overactive in selected subsets of solid tumors, elicit improved response rates and survival compared with standard chemotherapy. Disease control has been proven in the metastatic and, to a limited extent, the adjuvant setting. However, tumor eradication is rare, and duration of treatment response is limited by the development of drug resistance.Kinase inhibitors induce response in diverse types of solid tumors. Although the agents are often effective in defined molecular subsets, cure is rare and resistance is common. This broad review provides rationale for further investigation of multimodality therapy combining kinase inhibitors with additional systemic and local therapies, including surgery.
| S-EPMC5199628 | biostudies-literature
Project description:Genomic instability is a hallmark of cancer related to DNA damage response (DDR) deficiencies, offering vulnerabilities for targeted treatment. Poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) interfere with the efficient repair of DNA damage, particularly in tumors with existing defects in DNA repair, and induce synthetic lethality. PARPi are active across a range of tumor types harboring BRCA mutations and also BRCA-negative cancers, such as ovarian, breast or prostate cancers with homologous recombination deficiencies (HRD). Depending on immune contexture, immune checkpoint inhibitors (ICIs), such as anti-PD1/PD-L1 and anti-CTLA-4, elicit potent antitumor effects and have been approved in various cancers types. Although major breakthroughs have been performed with either PARPi or ICIs alone in multiple cancers, primary or acquired resistance often leads to tumor escape. PARPi-mediated unrepaired DNA damages modulate the tumor immune microenvironment by a range of molecular and cellular mechanisms, such as increasing genomic instability, immune pathway activation, and PD-L1 expression on cancer cells, which might promote responsiveness to ICIs. In this context, PARPi and ICIs represent a rational combination. In this review, we summarize the basic and translational biology supporting the combined strategy. We also detail preclinical results and early data of ongoing clinical trials indicating the synergistic effect of PARPi and ICIs. Moreover, we discuss the limitations and the future direction of the combination.
| S-EPMC7352466 | biostudies-literature
Project description:ObjectivesImmune checkpoint inhibitors (ICIs) are being increasingly used across cancer types. Emergency room (ER) and inpatient (IP) care, common in patients with cancer, remain poorly defined in this specific population, and risk factors for such care are unknown.MethodsWe retrospectively reviewed charts for patients with solid tumors who received >1 ICI dose at 1 of 2 sites from January 1, 2011 to April 28, 2017. Demographics, medical history, cancer diagnosis/therapy/toxicity details, and outcomes were recorded. Descriptive data detailing ER/IP care at the 2 associated hospitals during ICI therapy (from first dose to 3 mo after last dose) were collected. The Fisher exact test and multivariate regression analysis was used to study differences between patients with versus without ER/IP care during ICI treatment.ResultsAmong 345 patients studied, 50% had at least 1 ER visit during ICI treatment and 43% had at least 1 IP admission. Six percent of ER/IP visits eventually required intensive care. A total of 12% of ER/IP visits were associated with suspected or confirmed immune-related adverse events. Predictors of ER care were African-American race (odds ratio [OR]: 3.83, P=0.001), Hispanic ethnicity (OR: 3.12, P=0.007), and coronary artery disease (OR: 2.43, P=0.006). Predictors of IP care were African-American race (OR: 2.38, P=0.024), Hispanic ethnicity (OR: 2.29, P=0.045), chronic kidney disease (OR: 3.89, P=0.006), angiotensin converting enzyme inhibitor/angiotensin receptor blocker medication use (OR: 0.44, P=0.009), and liver metastasis (OR: 2.32, P=0.003).ConclusionsUnderstanding demographic and clinical risk factors for ER/IP care among patients on ICIs can help highlight disparities, prospectively identify high-risk patients, and inform preventive programs aimed at reducing such care.
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Project description:The B-raf gene is mutated in up to 66% of human malignant melanomas, and its protein product, BRAF kinase, is a key part of RAS-RAF-MEK-ERK (MAPK) pathway of cancer cell proliferation. BRAF-targeted therapy induces significant responses in the majority of patients, and the combination BRAF/MEK inhibitor enhances clinical efficacy, but the response to BRAF inhibitor and to BRAF/MEK inhibitor is short lived. On the other hand, treatment of melanoma with an immune checkpoint inhibitor, such as anti-PD-1, has lower response rate but the response is much more durable, lasting for years. For this reason, it was suggested that combination of BRAF/MEK and PD-1 inhibitors will significantly improve overall survival time.This paper develops a mathematical model to address the question of the correlation between BRAF/MEK inhibitor and PD-1 inhibitor in melanoma therapy. The model includes dendritic and cancer cells, CD 4+ and CD 8+ T cells, MDSC cells, interleukins IL-12, IL-2, IL-6, IL-10 and TGF- β, PD-1 and PD-L1, and the two drugs: BRAF/MEK inhibitor (with concentration γ B ) and PD-1 inhibitor (with concentration γ A ). The model is represented by a system of partial differential equations, and is used to develop an efficacy map for the combined concentrations (γ B ,γ A ). It is shown that the two drugs are positively correlated if γ B and γ A are at low doses, that is, the growth of the tumor volume decreases if either γ B or γ A is increased. On the other hand, the two drugs are antagonistic at some high doses, that is, there are zones of (γ B ,γ A ) where an increase in one of the two drugs will increase the tumor volume growth, rather than decrease it.It will be important to identify, by animal experiments or by early clinical trials, the zones of (γ B ,γ A ) where antagonism occurs, in order to avoid these zones in more advanced clinical trials.
| S-EPMC5517842 | biostudies-other
Project description:We present a mechanistic mathematical model of immune checkpoint inhibitor therapy to address the oncological need for early, broadly applicable readouts (biomarkers) of patient response to immunotherapy. The model is built upon the complex biological and physical interactions between the immune system and cancer, and is informed using only standard-of-care CT. We have retrospectively applied the model to 245 patients from multiple clinical trials treated with anti-CTLA-4 or anti-PD-1/PD-L1 antibodies. We found that model parameters distinctly identified patients with common (n = 18) and rare (n = 10) malignancy types who benefited and did not benefit from these monotherapies with accuracy as high as 88% at first restaging (median 53 days). Further, the parameters successfully differentiated pseudo-progression from true progression, providing previously unidentified insights into the unique biophysical characteristics of pseudo-progression. Our mathematical model offers a clinically relevant tool for personalized oncology and for engineering immunotherapy regimens.
| S-EPMC7190324 | biostudies-literature