Rational design and identification of immuno-oncology drug combinations

A target map of clinical combination therapies in oncology: an analysis of clinicaltrials.gov

Combination therapies have taken center stage for cancer treatment, however, there is a lack of a comprehensive portrait to quantitatively map the current clinical combination progress. This study aims to capture clinical combination therapies of the validated FDA-approved new oncology drugs by a macro data analysis and to summarize combination mechanisms and strategies in the context of the existing literature. A total of 72 new molecular entities or new therapeutic biological products for cancer treatment approved by the FDA from 2017 to 2021 were identified, and the data on their related 3334 trials were retrieved from the database of ClinicalTrials.gov. Moreover, these sampled clinical trials were refined by activity status and combination relevance and labeled with the relevant clinical arms and drug combinations, as well as drug targets and target pairs. Combination therapies are increasingly prevalent in clinical trials of new oncology drugs. From retrospective work, existing clinical combination therapies in oncology are driven by different patterns (i.e., rational design and industry trends). The former can be represented by mechanism-based or structure-based combinations, such as targeting different domains of HER2 protein or in-series co-targeting in RAF plus MEK inhibitors. The latter is an empirically driven strategy, including redundant combinations in hot targets, such as PD-1/PD-L1, PI3K, CDK4/6, and PARP. Because of an explosion in the number of clinical trials and the resultant shortage of available patients, it is essential to rationally design drug combinations.

Discoidin Domain Receptor-Driven Gene Signatures as Markers of Patient Response to Anti-PD-L1 Immune Checkpoint Therapy

BACKGROUND

Anti-programmed cell death 1 (anti-PD-1) and PD ligand 1 (PD-L1) immune checkpoint therapies (ICTs) provided durable responses only in a subset of cancer patients. Thus, biomarkers are needed to predict nonresponders and offer them alternative treatments. We recently implicated discoidin domain receptor tyrosine kinase 2 (DDR2) as a contributor to anti-PD-1 resistance in animal models; therefore, we sought to investigate whether this gene family may provide ICT response prediction.

METHODS

We assessed mRNA expression of DDR2 and its family member DDR1. Transcriptome analysis of bladder cancer (BCa) models in which DDR1 and 2 were perturbed was used to derive DDR1- and DDR2-driven signature scores. DDR mRNA expression and gene signature scores were evaluated using BCa-The Cancer Genome Atlas (n = 259) and IMvigor210 (n = 298) datasets, and their relationship to BCa subtypes, pathway enrichment, and immune deconvolution analyses was performed. The potential of DDR-driven signatures to predict ICT response was evaluated and independently validated through a statistical framework in bladder and lung cancer cohorts. All statistical tests were 2-sided.

RESULTS

DDR1 and DDR2 showed mutually exclusive gene expression patterns in human tumors. DDR2high BCa exhibited activation of immune pathways and a high immune score, indicative of a T-cell-inflamed phenotype, whereas DDR1high BCa exhibited a non-T-cell-inflamed phenotype. In IMvigor210 cohort, tumors with high DDR1 (hazard ratio [HR] = 1.53, 95% confidence interval [CI] = 1.16 to 2.06; P = .003) or DDR2 (HR = 1.42, 95% CI = 1.01 to 1.92; P = .04) scores had poor overall survival. Of note, DDR2high tumors from IMvigor210 and CheckMate 275 (n = 73) cohorts exhibited poorer overall survival (HR = 1.56, 95% CI = 1.20 to 2.06; P < .001) and progression-free survival (HR = 1.77 95%, CI = 1.05 to 3.00; P = .047), respectively. This result was validated in independent cancer datasets.

CONCLUSIONS

These findings implicate DDR1 and DDR2 driven signature scores in predicting ICT response.

Enhancing the Therapeutic Efficacy of Gefitinib in Human Non-Small-Cell Lung Cancer through Drug Combination

The interaction between tumor cells and the tumor microenvironment (TME) significantly influences tumorigenesis, so TME-targeted therapy has attracted widespread attention. We have previously demonstrated that the combination of dipyridamole, bestatin, and dexamethasone (DBD mix, DBDx) is effective against heterogeneous human pancreatic cancer and hepatocellular carcinoma in mouse xenograft models. To further expand the therapeutic potential of this drug combination, herein, we investigated the antitumor efficacy and the underlying mechanism of DBDx and the combination of DBDx and gefitinib in different mouse xenograft models of human non-small-cell lung cancer (NSCLC). Three human cancer cell lines H460, PG, and A431 were used to determine the apoptosis and growth inhibition induced by DBDx, gefitinib, and their combinations. Changes in epidermal growth factor receptor (EGFR) signaling pathway-related proteins were analyzed following treatment using western blotting. In vitro, DBDx strongly inhibited the proliferation of tumor cells, whereas the combined treatment exhibited a significant synergistic effect. Compared with DBDx, the combination treatment further induced apoptosis and downregulated the expression of molecules associated with EGFR signaling pathway. In vivo, compared with DBDx alone, the combination treatment distinctly inhibited tumor growth in mouse xenograft models of human NSCLC. Overall, our results indicate that the combination of DBDx and gefitinib in the treatment of human NSCLC is very promising, which warrants further translational studies.

Setting the Dose of Checkpoint Inhibitors: The Role of Clinical Pharmacology

Cancer immunotherapy is based on checkpoint inhibitors (CPIs) that significantly improve the clinical outcome of several malignant diseases. These inhibitors are monoclonal antibodies (mAbs) directed at cytotoxic T lymphocyte-associated protein 4 (CTLA-4), programmed cell death 1 (PD-1), or programmed death-ligand 1 (PD-L1), sharing most of the clinical pharmacokinetic characteristics of mAb targeted therapies, all of which differ from those of cytotoxics and small molecules. Establishing the labeled dose of mAbs, and particularly of the CPIs, represents a true challenge. This review therefore examines the main criteria used for dose selection, along with their limits. The relationships between CPI pharmacokinetic parameters and treatment outcome (efficacy and/or toxicity) differ somewhat among the various drugs, but general features can be identified. Nevertheless, the interpretation of these relationships remains quite controversial. A first interpretation asserts that inter-individual pharmacokinetic variability in clearance has an impact on outcome and should be taken into consideration for dosing individualization. The second considers that higher clearance values observed in some patients result from characteristics associated with poor predictive factors of efficacy. Finally, the schedule, and particularly its frequency of administration, merits rethinking.

The landscape of novel and complementary targets for immunotherapy: an analysis of gene expression in the tumor microenvironment

Background: Immunotherapies targeting immune checkpoint proteins CTLA-4, PD-1, and PD-L1 have saved lives, but these therapies have only been effective in some patients. Patients positive for expression of immune checkpoint proteins in the tumor microenvironment show better response to immune checkpoint inhibitors. Consequently, knowledge of which genes are consistently expressed in lymphocytes within the tumor microenvironment can convey potentially effective and complementary new immunotherapy targets.

Results: We identified 54 genes that have higher co-expression with the pan T-cell marker CD3E than CTLA4 or PDCD1. In a dataset of 26 patients who received anti-PD-1 therapy, we observed that co-expression between CD3E and PDCD1 was higher among responders than non-responders, supporting our correlation-based approach.

Conclusions: The genes highlighted in these analyses, which include CD6, TIGIT, CD96, and SLAMF6, warrant further investigation of their therapeutic potential.

Methods: We analyzed and ranked genes that were co-expressed with the pan T-cell marker CD3E in 9,601 human tumors, spanning 31 cancer types. To further identify targets that may be complementary to existing PD-1 therapy, we examined and ranked genes with high CD3E co-expression and relatively low PDCD1 co-expression.

Turning cold tumors into hot tumors: harnessing the potential of tumor immunity using nanoparticles

INTRODUCTION

Immune checkpoint inhibitors have considerably changed the landscape of oncology. However apart from world-acclaimed success stories limited to melanoma and lung cancer, many solid tumors failed to respond to immune checkpoint inhibitors due to limited immunogenicity, unfavorable tumor micro-environments (TME), lack of infiltrating T lymphocytes or increases in Tregs. Areas covered: Combinatorial strategies are foreseen as the future of immunotherapy and using cytotoxics or modulating agents is expected to boost the efficacy of immune checkpoint inhibitors. In this respect, nanoparticles displaying unique pharmacokinetic features such as tumor targeting properties, optimal payload delivery and long-lasting interferences with TME, are promising candidates for such combinations. This review covers the basis, expectancies, limits and pitfalls of future combination between nanoparticles and immune check point inhibitors. Expert opinion: Nanoparticles allow optimal delivery of variety of payloads in tumors while sparing healthy tissue, thus triggering immunogenic cell death. Depleting tumor stroma could further help immune cells and monoclonal antibodies to better circulate in the TME, plus immune-modulating properties of the charged cytotoxics. Finally, nanoparticles themselves present immunogenicity and antigenicity likely to boost immune response at the tumor level.

Systems Pharmacology: Defining the Interactions of Drug Combinations

The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.

Immune Profiling of Cancer Patients Treated with Immunotherapy: Advances and Challenges

The recent advances in immunotherapy and the availability of novel drugs to target the tumor microenvironment have dramatically changed the paradigm of cancer treatment. Nevertheless, a significant proportion of cancer patients are unresponsive or develop resistance to these treatments. With the aim to increase the clinical efficacy of immunotherapy, combinations of agents and standard therapies with complementary actions have been developed mostly on an empirical base, since their mechanisms of actions are not yet fully dissected. The characterization of immune responsiveness and its monitoring along with the treatment of cancer patients with immunotherapy can provide insights into the mechanisms of action of these therapeutic regimens and contribute to the optimization of patients’ stratification and of combination strategies and to the prediction of treatment-related toxicities. Thus far, none of the immunomonitoring strategies has been validated for routine clinical practice. Moreover, it is becoming clear that the genomic and molecular make-up of tumors and of the infiltrating immune system represent important determinants of the clinical responses to immunotherapy. This review provides an overview of different approaches for the immune profiling of cancer patients and discusses their advantages and limitations. Recent advances in genomic-based assays and in the identification of host genomic relationships with immune responses represent promising approaches to identify molecular determinants and biomarkers to improve the clinical efficacy of cancer immunotherapy.