Dose-Dependent Mutation Rates Determine Optimum Erlotinib Dosing Strategies for EGFR Mutant Non-Small Cell Lung Cancer Patients

Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer

The promise of precision medicine has been limited by the pervasive resistance to many targeted therapies for cancer. Inferring the timing (i.e., pre-existing or acquired) and mechanism (i.e., drug-induced) of such resistance is crucial for designing effective new therapeutics. This paper studies cetuximab resistance in head and neck squamous cell carcinoma (HNSCC) using tumor volume data obtained from patient-derived tumor xenografts. We ask if resistance mechanisms can be determined from this data alone, and if not, what data would be needed to deduce the underlying mode(s) of resistance. To answer these questions, we propose a family of mathematical models, with each member of the family assuming a different timing and mechanism of resistance. We present a method for fitting these models to individual volumetric data, and utilize model selection and parameter sensitivity analyses to ask: which member(s) of the family of models best describes HNSCC response to cetuximab, and what does that tell us about the timing and mechanisms driving resistance? We find that along with time-course volumetric data to a single dose of cetuximab, the initial resistance fraction and, in some instances, dose escalation volumetric data are required to distinguish among the family of models and thereby infer the mechanisms of resistance. These findings can inform future experimental design so that we can best leverage the synergy of wet laboratory experimentation and mathematical modeling in the study of novel targeted cancer therapeutics.

Drug-induced resistance evolution necessitates less aggressive treatment

Increasing body of experimental evidence suggests that anticancer and antimicrobial therapies may themselves promote the acquisition of drug resistance by increasing mutability. The successful control of evolving populations requires that such biological costs of control are identified, quantified and included to the evolutionarily informed treatment protocol. Here we identify, characterise and exploit a trade-off between decreasing the target population size and generating a surplus of treatment-induced rescue mutations. We show that the probability of cure is maximized at an intermediate dosage, below the drug concentration yielding maximal population decay, suggesting that treatment outcomes may in some cases be substantially improved by less aggressive treatment strategies. We also provide a general analytical relationship that implicitly links growth rate, pharmacodynamics and dose-dependent mutation rate to an optimal control law. Our results highlight the important, but often neglected, role of fundamental eco-evolutionary costs of control. These costs can often lead to situations, where decreasing the cumulative drug dosage may be preferable even when the objective of the treatment is elimination, and not containment. Taken together, our results thus add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and motivate further experimental and clinical investigation of the mutagenicity and other hidden collateral costs of therapies.

Pharmacokinetic Profiles Determine Optimal Combination Treatment Schedules in Computational Models of Drug Resistance

Identification of optimal schedules for combination drug administration relies on accurately estimating the correct pharmacokinetics, pharmacodynamics, and drug interaction effects. Misspecification of pharmacokinetics can lead to wrongly predicted timing or order of treatments, leading to schedules recommended on the basis of incorrect assumptions about absorption and elimination of a drug and its effect on tumor growth. Here, we developed a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data. The software can be used to compare prespecified schedules on the basis of the number of resistant cells where drug interactions and pharmacokinetic curves can be estimated from user-provided data or models. We applied our approach to publicly available in vitro data of treatment with different tyrosine kinase inhibitors of BT-20 triple-negative breast cancer cells and of treatment with erlotinib of PC-9 non-small cell lung cancer cells. Our approach is publicly available in the form of an R package called ACESO (https://github.com/Michorlab/aceso) and can be used to investigate optimum dosing for any combination treatment. SIGNIFICANCE: These findings introduce a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data.

Mathematical Approach to Differentiate Spontaneous and Induced Evolution to Drug Resistance During Cancer Treatment

Purpose

Drug resistance is a major impediment to the success of cancer treatment. Resistance is typically thought to arise from random genetic mutations, after which mutated cells expand via Darwinian selection. However, recent experimental evidence suggests that progression to drug resistance need not occur randomly, but instead may be induced by the treatment itself via either genetic changes or epigenetic alterations. This relatively novel notion of resistance complicates the already challenging task of designing effective treatment protocols.

Materials and Methods

To better understand resistance, we have developed a mathematical modeling framework that incorporates both spontaneous and drug-induced resistance.

Results

Our model demonstrates that the ability of a drug to induce resistance can result in qualitatively different responses to the same drug dose and delivery schedule. We have also proven that the induction parameter in our model is theoretically identifiable and propose an in vitro protocol that could be used to determine a treatment’s propensity to induce resistance.

Genotoxic potential of a novel PDE-4B inhibitor Apremilast by chromosomal aberration and micronucleus assay in mice

Objective

Researchers have confirmed that chronic administration of drugs at high doses causes genotoxicity which serve as first step in development of cancers. Apremilast, a phosphodiesterase-4 inhibitor is Food and Drug Administration (FDA) approved drug for Psoriatic Arthritis. The present study designed to conduct genotoxicity testing using the genotoxic study which give simple, sensitive, economical and fast tools for the assessment of damage of genetic material.

Methods

To conduct genotoxicity study of Apremilast, 60 Swiss albino male mice divided into 6 groups (n = 10). Group1 served as a normal control group without any treatment, Group 2 treated as a disease control and administered with cyclophosphamide 40 mg/kg, IP. Group 3, 4, 5 and 6 treated as test groups and received 10, 20, 40 and 80 mg/kg/day Apremilast respectively. The total duration of study was 13 weeks. At termination day animals were sacrificed and chromosomal aberration assay (BMCAA) and micronucleus assay (BMMNA) were performed to know the genotoxicity potential of Apremilast.

Results

The results indicates significant rise in chromosomal aberrations (CA) frequency in bone marrow cells and decrease in the MI of the disease control animals as well as Apremilast treated groups. Further significant (p < 0.001; p < 0.0001) increase in score of micronucleated polychromatic erythrocytes (MNPCEs) and percentage of micronucleated PCEs per 1000 PCEs and decrease in the ratio of polychromatic/normochromatic erythrocytes (PCE/NCE) was observed in micronucleus assay. Genotoxic effect increases with the increase of Apremilast dose. Conclusion: Finding of present indicates that Apremilast shows genotoxic potential on high administration although further detailed toxicity studies required for confirmations.

A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors

Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer evolution has improved the understanding of anticancer treatment resistance. A better characterization of cancer evolution and subsequent use of this knowledge for personalized treatment would increase the chance to overcome cancer treatment resistance. Model‐based approaches may help achieve this goal. In this review, we comprehensively summarized mathematical models of tumor dynamics for solid tumors and of drug resistance evolution. Models displayed by ordinary differential equations, algebraic equations, and partial differential equations for characterizing tumor burden dynamics are introduced and discussed. As for tumor resistance evolution, stochastic and deterministic models are introduced and discussed. The results may facilitate a novel model‐based analysis on anticancer treatment response and the occurrence of resistance, which incorporates both tumor dynamics and resistance evolution. The opportunities of a model‐based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment.

Hyaluronic acid-functionalized gelatin hydrogels reveal extracellular matrix signals temper the efficacy of erlotinib against patient-derived glioblastoma specimens

Therapeutic options to treat primary glioblastoma (GBM) tumors are scarce. GBM tumors with epidermal growth factor receptor (EGFR) mutations, in particular a constitutively active EGFRvIII mutant, have extremely poor clinical outcomes. GBM tumors with concurrent EGFR amplification and active phosphatase and tensin homolog (PTEN) are sensitive to the tyrosine kinase inhibitor erlotinib, but the effect is not durable. A persistent challenge to improved treatment is the poorly understood role of cellular, metabolic, and biophysical signals from the GBM tumor microenvironment on therapeutic efficacy and acquired resistance. The intractable nature of studying GBM cell in vivo motivates tissue engineering approaches to replicate aspects of the complex GBM tumor microenvironment. Here, we profile the effect of erlotinib on two patient-derived GBM specimens: EGFR + GBM12 and EGFRvIII GBM6. We use a three-dimensional gelatin hydrogel to present brain-mimetic hyaluronic acid (HA) and evaluate the coordinated influence of extracellular matrix signals and EGFR mutation status on GBM cell migration, survival and proliferation, as well as signaling pathway activation in response to cyclic erlotinib exposure. Comparable to results observed in vivo for xenograft tumors, erlotinib exposure is not cytotoxic for GBM6 EGFRvIII specimens. We also identify a role of extracellular HA (via CD44) in altering the effect of erlotinib in GBM EGFR + cells by modifying STAT3 phosphorylation status. Taken together, we report an in vitro tissue engineered platform to monitor signaling associated with poor response to targeted inhibitors in GBM.

Identification of Withaferin A as a Potential Candidate for Anti-Cancer Therapy in Non-Small Cell Lung Cancer

Low response rate and recurrence are common issues in lung cancer; thus, identifying a potential compound for these patients is essential. Utilizing an in silico screening method, we identified withaferin A (WA), a cell-permeable steroidal lactone initially extracted from Withania somnifera, as a potential anti-lung cancer and anti-lung cancer stem-like cell (CSC) agent. First, we demonstrated that WA exhibited potent cytotoxicity in several lung cancer cells, as evidenced by low IC₅₀ values. WA concurrently induced autophagy and apoptosis and the activation of reactive oxygen species (ROS), which plays an upstream role in mediating WA-elicited effects. The increase in p62 indicated that WA may modulate the autophagy flux followed by apoptosis. In vivo research also demonstrated the anti-tumor effect of WA treatment. We subsequently demonstrated that WA could inhibit the growth of lung CSCs, decrease side population cells, and inhibit lung cancer spheroid-forming capacity, at least through downregulation of mTOR/STAT3 signaling. Furthermore, the combination of WA and chemotherapeutic drugs, including cisplatin and pemetrexed, exerted synergistic effects on the inhibition of epidermal growth factor receptor (EGFR) wild-type lung cancer cell viability. In addition, WA can further enhance the cytotoxic effect of cisplatin in lung CSCs. Therefore, WA alone or in combination with standard chemotherapy is a potential treatment option for EGFR wild-type lung cancer and may decrease the occurrence of cisplatin resistance by inhibiting lung CSCs.

BCR-ABL V280G Mutation, Potential Role in Imatinib Resistance: First Case Report

INTRODUCTION

The identification of BCR-ABL expression as the defining leukemogenic event in chronic myeloid leukemia (CML) and the introduction of BCR-ABL tyrosine kinase inhibitors in 2001 have revolutionized disease management, leading to a reduction in mortality rates and accordingly an increase in the estimated prevalence of CML.

CASE REPORT

Based on medical records and clinical follow-up, the authors present the case of a Philadelphia chromosome-positive CML patient who developed resistance to imatinib. Quantitative reverse transcription-polymerase chain reaction testing revealed a V280G BCR-ABL mutation.

DISCUSSION AND CONCLUSIONS

This is the first report describing a new BCR-ABL kinase domain mutation-V280G-that might be associated with resistance to imatinib. Approximately 15% to 30% of patients treated with imatinib discontinue treatment due to resistance or intolerance. More than 90 BCR-ABL mutations were detected so far, conferring variable degrees of drug resistance, with consequent clinical, therapeutic, and prognostic impact.

Phase 1 study of twice weekly pulse dose and daily low-dose erlotinib as initial treatment for patients with EGFR-mutant lung cancers

Background

Patients with EGFR-mutant lung cancers treated with EGFR tyrosine kinase inhibitors (TKIs) develop clinical resistance, most commonly with acquisition of EGFR T790M. Evolutionary modeling suggests that a schedule of twice weekly pulse and daily low-dose erlotinib may delay emergence of EGFR T790M. Pulse dose erlotinib has superior central nervous system (CNS) penetration and may result in superior CNS disease control.

Methods

We evaluated toxicity, pharmacokinetics, and efficacy of twice weekly pulse and daily low-dose erlotinib. We assessed six escalating pulse doses of erlotinib.

Results

We enrolled 34 patients; 11 patients (32%) had brain metastases at study entry. We observed 3 dose-limiting toxicities in dose escalation: transaminitis, mucositis, and rash. The MTD was erlotinib 1200 mg days 1-2 and 50 mg days 3-7 weekly. The most frequent toxicities (any grade) were rash, diarrhea, nausea, fatigue, and mucositis. 1 complete and 24 partial responses were observed (74%, 95% CI 60-84%). Median progression-free survival was 9.9 months (95% CI 5.8-15.4 months). No patient had progression of an untreated CNS metastasis or developed a new CNS lesion while on study (0%, 95% CI 0-13%). Of the 18 patients with biopsies at progression, EGFR T790M was identified in 78% (95% CI 54-91%).

Conclusion

This is the first clinical implementation of an anti-cancer TKI regimen combining pulse and daily low-dose administration. This evolutionary modeling-based dosing schedule was well-tolerated but did not improve progression-free survival or prevent emergence of EGFR T790M, likely due to insufficient peak serum concentrations of erlotinib. This dosing schedule prevented progression of untreated or any new central nervous system metastases in all patients.

Epidermal growth factor receptor and notch signaling in non-small-cell lung cancer

Lung cancer is the most common reason of cancer deaths and about 85% of these are non-small-cell lung cancer. Currently, lung cancer therapy is mainly based on the tumor node metastasis (TNM) disease staging and tumor histological classification. Despite therapeutic innovations, the prognosis for lung cancer patients has not significantly changed in the last years. Therefore, a proper understanding of cell signaling pathways involved in cancer pathogenesis seems to be essential for improvement in cancer therapy field. The knowledge of crosstalk between epidermal growth factor receptor (EGFR) and Notch pathway can lead to enhanced screening for the expression of these genes allowing patients to optimize treatment options and predict potential treatment resistance. This review focuses on recent advances related to the mechanisms of EGFR and Notch signaling in non-small-cell lung cancer and the effectiveness of current Notch- and EGFR-targeted therapies.