Combinatorial immunotherapy strategies: most gods throw dice, but fate plays chess

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Treatment outcomes were used to validate a novel mathematical kinetics-pharmacodynamics model predictive of perioperative disease progression. Longitudinal measurements of presurgical primary tumor size and postsurgical metastatic burden were compiled using 128 mice receiving variable neoadjuvant treatment doses and schedules (released publicly at https://zenodo.org/records/10607753). A non-linear mixed-effects modeling approach quantified inter-animal variabilities in metastatic dynamics and survival, and machine-learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (CD31 and Ki67). Our computational simulations show that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery and treatment cessation. Machine learning algorithms that included support vector machines, random forests, and artificial neural networks, confirmed a lack of definitive biomarkers, which shows the value of preclinical modeling studies to identify potential failures that should be avoided clinically.

Overcoming immuno-resistance by rescheduling anti-VEGF/cytotoxics/anti-PD-1 combination in lung cancer model

Background: Many tumors are refractory to immune checkpoint inhibitors, but their combination with cytotoxics is expected to improve sensitivity. Understanding how and when cytotoxics best re-stimulate tumor immunity could help overcome resistance to immune checkpoint inhibitors. Methods: In vivo studies were performed in C57BL/6 mice grafted with immune-refractory LL/2 lung cancer model. A longitudinal immunomonitoring study on tumor, spleen, and blood after multiple treatments including Cisplatin, Pemetrexed, and anti-VEGF, either alone or in combination, was performed, spanning a period of up to 21 days, to determine the optimal time window during which immune checkpoint inhibitors should be added. Finally, an efficacy study was conducted comparing the antiproliferative performance of various schedules of anti-VEGF, Pemetrexed-Cisplatin doublet, plus anti-PD-1 (i.e., immunomonitoring-guided scheduling, concurrent dosing or a random sequence), as well as single agent anti-PD1. Results: Immunomonitoring showed marked differences between treatments, organs, and time points. However, harnessing tumor immunity (i.e., promoting CD8 T cells or increasing the T CD8/Treg ratio) started on D7 and peaked on D14 with the anti-VEGF followed by cytotoxics combination. Therefore, a 14-day delay between anti-VEGF/cytotoxic and anti-PD1 administration was considered the best sequence to test. Efficacy studies then confirmed that this sequence achieved higher antiproliferative efficacy compared to other treatment modalities (i.e., -71% in tumor volume compared to control). Conclusions: Anti-VEGF and cytotoxic agents show time-dependent immunomodulatory effects, suggesting that sequencing is a critical feature when combining these agents with immune checkpoint inhibitors. An efficacy study confirmed that sequencing treatments further enhance antiproliferative effects in lung cancer models compared to concurrent dosing and partly reverse the resistance to cytotoxics and anti-PD1.

METRO-PD1: Phase 1 study of nivolumab in combination with metronomic chemotherapy in children and adolescents with relapsing/refractory solid tumors

BACKGROUND

This multicenter Phase I study (NCT03585465) evaluated nivolumab in combination with 3 metronomic chemotherapy (MC) regimens in children with refractory/relapsing solid tumors.

OBJECTIVES

To evaluate the feasibility and safety of the three regimens METHODS: Patients aged < 18 years were enrolled. Nivolumab was combined with cyclophosphamide and vinblastine (arm A), capecitabine (arm B), or cyclophosphamide, vinblastine and capecitabine (arm C). Arm A and B were allocated sequentially. Arm C opened only if A and B were deemed safe. Dose-limiting toxicities (DLTs) were evaluated over the first two cycles. Patients were evaluable if they received > 2 cycles and > 70% of the planned dose.

POPULATION

Sixteen patients were enrolled, 3 in arm A, 6 in arm B, and 7 in arm C. Median age was 11.5 years (range, 5-19). Patients previously received a median of 3.5 (range, 1-4) lines of systemic treatment, 14 patients had surgery and 11 had radiotherapy.

RESULTS

Median number of cycles was 2 (1-24), median treatment duration was 56 days (18-714). In arm C, median number of cycles was 4 with median treatment duration of 95 days. No DLT was observed. Grade 3 adverse events (AE) and serious AE were observed in 8 patients (50%) and 1 patient (6%), respectively, over the first 2 cycles. No grade 4 AE occurred. The 6-month PFS and OS were 12% and 44%, respectively, in the whole population. Prolonged stable disease was observed in a high-grade glioma and an atypical teratoid rhabdoid tumor.

CONCLUSION

Arm C appears safe. A randomized phase II trial evaluating the addition of nivolumab to the triple MC is ongoing.

Harnessing tumor immunity with cytotoxics: T cells monitoring in mice bearing lung tumors treated with anti-VEGF and pemetrexed-cisplatin doublet

BACKGROUND

Successful immunotherapy is restricted to some cancers only, and combinatorial strategies with other drugs could help to improve their efficacy. Here, we monitor T cells in NSCLC model after treatment with cytotoxics (CT) and anti-VEGF drugs, to understand when immune checkpoint inhibitors should be best associated next.

METHODS

In vivo study was performed on BALB/c mice grafted with KLN205 cells. Eight treatments were tested including control, cisplatin and pemetrexed as low (LD CT) and full (MTD CT) dose as single agents, flat dose anti-VEGF and the association anti-VEGF + CT. Full immunomonitoring was performed by flow cytometry on tumor, spleen and blood over 3 weeks.

RESULTS

Immunomodulatory effect was dependent upon both treatments and time. In tumors, combination groups shown numerical lower Treg cells on Day 21. In spleen, anti-VEGF and LD CT group shown higher CD8/Treg ratio on Day 7; on Day 14, higher T CD4 were observed in both combination groups. Finally, in blood, Tregs were lower and CD8/Treg ratio higher, on Day 14 in both combination groups. On Day 21, CD4 and CD8 T cells were higher in the anti-VEGF + MTD CT group.

CONCLUSIONS

Anti-VEGF associated to CT triggers notable increase in CD8/Tregs ratio. Regarding the scheduling, a two-week delay after using anti-VEGF and CT could be the best sequence to optimize antitumor efficacy.

A graph model of combination therapies

The simultaneous use of multiple medications causes drug-drug interactions (DDI) that impact therapeutic efficacy. Here, we argue that graph theory, in conjunction with game theory and ecosystem theory, can address this issue. We treat the coexistence of multiple drugs as a system in which DDI is modeled by game theory. We develop an ordinary differential equation model to characterize how the concentration of a drug changes as a result of its independent capacity and the dependent influence of other drugs through the metabolic response of the host. We coalesce all drugs into personalized and context-specific networks, which can reveal key DDI determinants of therapeutical efficacy. Our model can quantify drug synergy and antagonism and test the translational success of combination therapies to the clinic.

Antitumor Peptide-Based Vaccine in the Limelight

The success of the immune checkpoint blockade has provided a proof of concept that immune cells are capable of attacking tumors in the clinic. However, clinical benefit is only observed in less than 20% of the patients due to the non-specific activation of immune cells by the immune checkpoint blockade. Developing tumor-specific immune responses is a challenging task that can be achieved by targeting tumor antigens to generate tumor-specific T-cell responses. The recent advancements in peptide-based immunotherapy have encouraged clinicians and patients who are struggling with cancer that is otherwise non-treatable with current therapeutics. By selecting appropriate epitopes from tumor antigens with suitable adjuvants, peptides can elicit robust antitumor responses in both mice and humans. Although recent experimental data and clinical trials suggest the potency of tumor reduction by peptide-based vaccines, earlier clinical trials based on the inadequate hypothesis have misled that peptide vaccines are not efficient in eliminating tumor cells. In this review, we highlighted the recent evidence that supports the rationale of peptide-based antitumor vaccines. We also discussed the strategies to select the optimal epitope for vaccines and the mechanism of how adjuvants increase the efficacy of this promising approach to treat cancer.

Localized delivery of immunotherapy via implantable scaffolds for breast cancer treatment

Breast cancer remains a leading global cause of morbidity and mortality. While the field of immunotherapy is a promising avenue of investigation and has revolutionized the standard of care for melanoma and lung cancer, modest response rates and a high incidence of immune-related adverse events often necessitate the administration of a sub-therapeutic dose or treatment cessation. Injectable and implantable drug delivery devices present a novel strategy to achieve sustained delivery of potent concentrations of drug directly to the tumor site and minimize systemic toxicity. This review will address the current limitations with conventional immunotherapy for breast cancer treatment, and the recent developments and future prospects in localized delivery strategies. We describe implantable scaffolds and injectable biomaterials for the localized delivery of immunotherapy, which can improve the safety and efficacy of immunotherapies. We discuss the limitations of these delivery systems, such as the influence of shape and material type on drug release and tumor uptake. The challenges of clinical translation, such as the availability of appropriate preclinical animal models and accurate reporting are also discussed. Considerations of these issues will pave the way for effective new therapies that will improve treatment response, patient survival and quality of life for breast cancer patients.

Tumor-immune ecosystem dynamics define an individual Radiation Immune Score to predict pan-cancer radiocurability

Radiotherapy efficacy is the result of radiation-mediated cytotoxicity coupled with stimulation of antitumor immune responses. We develop an in silico 3-dimensional agent-based model of diverse tumor-immune ecosystems (TIES) represented as anti- or pro-tumor immune phenotypes. We validate the model in 10,469 patients across 31 tumor types by demonstrating that clinically detected tumors have pro-tumor TIES. We then quantify the likelihood radiation induces antitumor TIES shifts toward immune-mediated tumor elimination by developing the individual Radiation Immune Score (iRIS). We show iRIS distribution across 31 tumor types is consistent with the clinical effectiveness of radiotherapy, and in combination with a molecular radiosensitivity index (RSI) combines to predict pan-cancer radiocurability. We show that iRIS correlates with local control and survival in a separate cohort of 59 lung cancer patients treated with radiation. In combination, iRIS and RSI predict radiation-induced TIES shifts in individual patients and identify candidates for radiation de-escalation and treatment escalation. This is the first clinically and biologically validated computational model to simulate and predict pan-cancer response and outcomes via the perturbation of the TIES by radiotherapy.

Cancer Immunotherapy Dosing: A Pharmacokinetic/Pharmacodynamic Perspective

Immune check-point inhibitors are drugs that are markedly different from other anticancer drugs because of their indirect mechanisms of antitumoral action and their apparently random effect in terms of efficacy and toxicity. This marked pharmacodynamics variability in patients calls for reconsidering to what extent approved dosing used in clinical practice are optimal or whether they should require efforts for customization in outlier patients. To better understand whether or not dosing could be an actionable item in oncology, in this review, preclinical and clinical development of immune checkpoint inhibitors are described, particularly from the angle of dose finding studies. Other issues in connection with dosing issues are developed, such as the flat dosing alternative, the putative role therapeutic drug monitoring could play, the rise of combinatorial strategies, and pharmaco-economic aspects.

Personalized Cancer Vaccines: Clinical Landscape, Challenges, and Opportunities

Tremendous innovation is underway among a rapidly expanding repertoire of promising personalized immune-based treatments. Therapeutic cancer vaccines (TCVs) are attractive systemic immunotherapies that activate and expand antigen-specific CD8⁺ and CD4⁺ T cells to enhance anti-tumor immunity. Our review highlights key issues impacting TCVs in clinical practice and reports on progress in development. We review the mechanism of action, immune-monitoring, dosing strategies, combinations, obstacles, and regulation of cancer vaccines. Most trials of personalized TCVs are ongoing and represent diverse platforms with predominantly early investigations of mRNA, DNA, or peptide-based targeting strategies against neoantigens in solid tumors, with many in combination immunotherapies. Multiple delivery systems, routes of administration, and dosing strategies are used. Intravenous or intramuscular administration is common, including delivery by lipid nanoparticles. Absorption and biodistribution impact antigen uptake, expression, and presentation, affecting the strength, speed, and duration of immune response. The emerging trials illustrate the complexity of developing this class of innovative immunotherapies. Methodical testing of the multiple potential factors influencing immune responses, as well as refined quantitative methodologies to facilitate optimal dosing strategies, could help resolve uncertainty of therapeutic approaches. To increase the likelihood of success in bringing these medicines to patients, several unique development challenges must be overcome.

Like a Rolling Stone: Sting-Cgas Pathway and Cell-Free DNA as Biomarkers for Combinatorial Immunotherapy

Combining immune checkpoint inhibitors with other treatments likely to harness tumor immunity is a rising strategy in oncology. The exact modalities of such a combinatorial regimen are yet to be defined, and most attempts have relied so far on concomitant dosing, rather than sequential or phased administration. Because immunomodulating features are likely to be time-, dose-, and-schedule dependent, the need for biomarkers providing real-time information is critical to better define the optimal time-window to combine immune checkpoint inhibitors with other drugs. In this review, we present the various putative markers that have been investigated as predictive tools with immune checkpoint inhibitors and could be used to help further combining treatments. Whereas none of the current biomarkers, such as the PDL1 expression of a tumor mutational burden, is suitable to identify the best way to combine treatments, monitoring circulating tumor DNA is a promising strategy, in particular to check whether the STING-cGAS pathway has been activated by cytotoxics. As such, circulating tumor DNA could help defining the best time-window to administrate immune checkpoint inhibitors after that cytotoxics have been given.

Towards Rational Cancer Therapeutics: Optimizing Dosing, Delivery, Scheduling, and Combinations

The current trend to personalize anticancer therapies mostly relies on selecting the best drug or combination of drugs to achieve optimal efficacy in patients. In addition to the comprehensive genetic and molecular knowledge of each tumor before choosing the drugs to be given, there is probably much room left for improvement by further personalizing the very modes by which the drugs are given, once they have been carefully selected. In particular, shifting from standard dosing to tailored dosing should help in maintaining drug exposure levels in the right therapeutic window, thus ensuring that the efficacy/toxicity balance is optimal. This paper covers the current knowledge regarding pharmacokinetic/pharmacodynamic relationships of anticancer agents, from decades-old cytotoxics to the latest immune checkpoint inhibitors, the most frequent sources for long-neglected interpatient variability impacting on drug exposure levels, and what could be done to achieve real personalized medicine in oncology such as implementing therapeutic drug monitoring with adaptive dosing strategies or using model-driven modalities for personalized dosing and scheduling.

Deciphering the response and resistance to immune-checkpoint inhibitors in lung cancer with artificial intelligence-based analysis: when PIONeeR meets QUANTIC

This project aims to generate dense longitudinal data in lung cancer patients undergoing anti-PD1/PDL1 therapy. Mathematical modelling with mechanistic learning algorithms will help decipher the mechanisms underlying the response or resistance to immunotherapy. A better understanding of these mechanisms should help identifying actionable items to increase the efficacy of immune-checkpoint inhibitors.

Metronomic Maintenance for High-Risk Pediatric Malignancies: One Size Will Not Fit All

Maintenance therapy sometimes relies on the use of metronomic chemotherapy (MC); that is, the continuous administration of low-dose chemotherapy. Maintenance therapy has been successfully used for decades in pediatric patients with acute lymphoblastic leukemia (ALL) and recent results have demonstrated improved outcomes in patients with pediatric high-risk rhabdomyosarcoma (RMS) on maintenance therapy. Here, we review the use of metronomic maintenance therapy in pediatric cancer and discuss its mechanisms of action on the tumor microenvironment and cancer cells. We also discuss its potential use as a chemotherapy alone or in combination with targeted therapies, immunotherapies, or agents for drug repurposing.