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

Executive summary of the American Radium Society appropriate use criteria for brain metastases in epidermal growth factor receptor mutated-mutated and ALK-fusion non-small cell lung cancer

The American Radium Society (ARS) Central Nervous System (CNS) committee reviewed literature on epidermal growth factor receptor mutated (EGFRm) and ALK-fusion (ALK+) tyrosine kinase inhibitors (TKIs) for the treatment of brain metastases (BrMs) from non-small cell lung cancers (NSCLC) to generate appropriate use guidelines addressing use of TKIs in conjunction with or in lieu of radiotherapy (RT). The panel developed three key questions to guide systematic review: can radiotherapy be deferred in patients receiving EGFR or ALK TKIs at (1) diagnosis or (2) recurrence? Should TKI be administered concurrently with RT (3)? Two literature searches were performed (May 2019 and December 2023). The panel developed 8 model cases and voted on treatment options using a 9-point scale, with 1-3, 4-6 and 7-9 corresponding to usually not appropriate, may be appropriate, and usually appropriate (respectively), per the UCLA/RAND Appropriateness Method. Consensus was achieved in only 4 treatment scenarios, all consistent with existing ARS-AUC guidelines for multiple BrM. The panel did not reach consensus that RT can be appropriately deferred in patients with BrM receiving CNS penetrant ALK or EGFR TKIs, though median scores indicated deferral may be appropriate under most circumstances. Whole brain RT with concurrent TKI generated broad disagreement except in cases with 2-4 BrM, where it was considered usually not appropriate. We identified no definitive studies dictating optimal sequencing of TKIs and RT for EGFRm and ALK+ BrM. Until such studies are completed, the committee hopes these cases guide decision- making in this complex clinical space.

Emerging therapeutics and evolving assessment criteria for intracranial metastases in patients with oncogene-driven non-small-cell lung cancer

The improved survival outcomes of patients with non-small-cell lung cancer (NSCLC), largely owing to the improved control of systemic disease provided by immune-checkpoint inhibitors and novel targeted therapies, have highlighted the challenges posed by central nervous system (CNS) metastases as a devastating yet common complication, with up to 50% of patients developing such lesions during the course of the disease. Early-generation tyrosine-kinase inhibitors (TKIs) often provide robust systemic disease control in patients with oncogene-driven NSCLCs, although these agents are usually unable to accumulate to therapeutically relevant concentrations in the CNS owing to an inability to cross the blood-brain barrier. However, the past few years have seen a paradigm shift with the emergence of several novel or later-generation TKIs with improved CNS penetrance. Such agents have promising levels of activity against brain metastases, as demonstrated by data from preclinical and clinical studies. In this Review, we describe current preclinical and clinical evidence of the intracranial activity of TKIs targeting various oncogenic drivers in patients with NSCLC, with a focus on newer agents with enhanced CNS penetration, leptomeningeal disease and the need for intrathecal treatment options. We also discuss evolving assessment criteria and regulatory considerations for future clinical investigations.

Effect of EGFR-TKIs combined with craniocerebral radiotherapy on the prognosis of EGFR-mutant lung adenocarcinoma patients with brain metastasis: A propensity-score matched analysis

Background and Purpose

Epidermal growth factor receptor (EGFR)-mutant lung cancers are associated with a high risk of developing brain metastases (BM). Craniocerebral radiotherapy is a cornerstone for the treatment of BM, and EGFR-TKIs act on craniocerebral metastases". However, whether EGFR-TKIs combined with craniocerebral radiotherapy can further increase the efficacy and improve the prognosis of patients is unclear. This study aimed to evaluate the difference in efficacy between targeted-therapy alone and targeted-therapy combined with radiotherapy in EGFR-mutant lung adenocarcinoma patients with BM.

Materials and Methods

A total of 291 patients with advanced non-small cell lung cancer (NSCLC) and EGFR mutations were enrolled in this retrospective cohort study. Propensity score matching (PSM) was conducted using a nearest-neighbor algorithm (1:1) to adjust for demographic and clinical covariates. Patients were divided into two groups: EGFR-TKIs alone and EGFR-TKIs combined with craniocerebral radiotherapy. Intracranial progression-free survival (iPFS) and overall survival (OS) were calculated. Kaplan-Meier analysis was used to compare iPFS and OS between the two groups. Brain radiotherapy included WBRT, local radiotherapy, and WBRT+Boost.

Results

The median age at diagnosis was 54 years (range: 28-81 years). Most patients were female (55.9%) and non-smokers (75.5%). Fifty-one pairs of patients were matched using PSM. The median iPFS for EGFR-TKIs alone (n=37) and EGFR-TKIs+craniocerebral radiotherapy (n=24) was 8.9 and 14.7 months, respectively. The median OS for EGFR-TKIs alone (n=52) and EGFR-TKIs+craniocerebral radiotherapy (n=52) was 32.1 and 45.3 months, respectively.

Conclusion

In EGFR-mutant lung adenocarcinoma patients with BM, targeted therapy combined with craniocerebral radiotherapy is an optimal treatment.

The role of targeted therapy and immune therapy in the management of non-small cell lung cancer brain metastases

Brain metastases are a significant source of morbidity and mortality in patients with non-small cell lung cancer. Historically, surgery and radiation therapy have been essential to maintaining disease control within the central nervous system due to poorly penetrant conventional chemotherapy. With the advent of targeted therapy against actionable driver mutations, there is potential to control limited and asymptomatic intracranial disease and delay local therapy until progression. In this review paper, intracranial response rates and clinical outcomes to biological and immune therapies are summarized from the literature and appraised to assist clinical decision making and identify areas for further research. Future clinical trials ought to prioritize patient-centered quality of life and neurocognitive measures as major outcomes and specifically stratify patients based on mutational marker status, disease burden, and symptom acuity.

Cross-Resistance Among Sequential Cancer Therapeutics: An Emerging Issue

Over the past two decades, cancer treatment has benefited from having a significant increase in the number of targeted drugs approved by the United States Food and Drug Administration. With the introduction of targeted therapy, a great shift towards a new era has taken place that is characterized by reduced cytotoxicity and improved clinical outcomes compared to traditional chemotherapeutic drugs. At present, targeted therapies and other systemic anti-cancer therapies available (immunotherapy, cytotoxic, endocrine therapies and others) are used alone or in combination in different settings (neoadjuvant, adjuvant, and metastatic). As a result, it is not uncommon for patients affected by an advanced malignancy to receive subsequent anti-cancer therapies. In this challenging complexity of cancer treatment, the clinical pathways of real-life patients are often not as direct as predicted by standard guidelines and clinical trials, and cross-resistance among sequential anti-cancer therapies represents an emerging issue. In this review, we summarize the main cross-resistance events described in the diverse tumor types and provide insight into the molecular mechanisms involved in this process. We also discuss the current challenges and provide perspectives for the research and development of strategies to overcome cross-resistance and proceed towards a personalized approach.

Emerging Systemic Treatment Perspectives on Brain Metastases: Moving Toward a Better Outlook for Patients

The diagnosis of brain metastases has historically been a dreaded, end-stage complication of systemic disease. Additionally, with the increasing effectiveness of systemic therapies that prolong life expectancy and improved imaging tools, the incidence of intracranial progression is becoming more common. Within this context, there has been increasing attention directed at understanding the molecular underpinnings of intracranial progression. Exploring the unique features of brain metastases compared with their extracranial counterparts to identify aberrant signaling pathways, which can be targeted pharmacologically, may help lead to new treatments for this patient population. Additionally, critical discoveries outside the sphere of the central nervous system are increasingly being applied to brain metastases with the emergence of immune checkpoint inhibition, becoming a prevalent treatment option for patients with brain metastases across multiple histologies. As novel treatment strategies are considered, they require thoughtful incorporation of agents that can cross the blood-brain barrier and can synergize with pre-existing agents through rational combinations. Lastly, as clinicians and scientists continue to understand key molecular features of these tumors, they will continue to influence the treatment algorithms that are developing for the management of these patients. Due to the complexity of treatment decisions for patients with brain metastases, an emerging tool is the utilization of multidisciplinary brain metastasis tumor boards to ensure optimal treatment decisions are made and that patients are provided access to applicable clinical trials. Looking to the future, the collective effort to understand the various tumor-intrinsic and tumor-extrinsic factors that promote central nervous system seeding and propagation will have the potential to change the clinical trajectory for these patients.

An intelligent hypoxia-relieving chitosan-based nanoplatform for enhanced targeted chemo-sonodynamic combination therapy on lung cancer

The clinical efficacy of epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs)-based targeted molecular therapies (TMT) is inevitably hampered by the development of acquired drug resistance in non-small cell lung cancer (NSCLC) treatment. Sonodymanic therapy (SDT) is a promising new cancer treatment approach, but its effects are restricted by tumor hypoxia. Herein, a nanoplatform fabricated by erlotinib-modified chitosan loading sonosensitizer hematoporphyrin (HP) and oxygen-storing agent perfluorooctyl bromide (PFOB), namely CEPH, was developed to deliver HP to erlotinib-sensitive cells. CEPH with ultrasound could alleviate hypoxia inside the three-dimensional multicellular tumor spheroids, suppress NSCLC cell growth under normoxic or hypoxic condition, and enhance TMT/SDT synergistic effects through elevated production of reactive oxygen species, decrease of mitochondrial membrane potential, and down-regulation of the expression of the proteins EGFR, p-EGFR, and HIF-1α. Hence, CEPH could be a potential nanoplatform to improve the efficacy of oxygen-dependent SDT and overcome hypoxia-induced TMT resistance for enhanced synergistic TMT/SDT.

Identification of optimal dosing schedules of dacomitinib and osimertinib for a phase I/II trial in advanced EGFR-mutant non-small cell lung cancer

Despite the clinical success of the third-generation EGFR inhibitor osimertinib as a first-line treatment of EGFR-mutant non-small cell lung cancer (NSCLC), resistance arises due to the acquisition of EGFR second-site mutations and other mechanisms, which necessitates alternative therapies. Dacomitinib, a pan-HER inhibitor, is approved for first-line treatment and results in different acquired EGFR mutations than osimertinib that mediate on-target resistance. A combination of osimertinib and dacomitinib could therefore induce more durable responses by preventing the emergence of resistance. Here we present an integrated computational modeling and experimental approach to identify an optimal dosing schedule for osimertinib and dacomitinib combination therapy. We developed a predictive model that encompasses tumor heterogeneity and inter-subject pharmacokinetic variability to predict tumor evolution under different dosing schedules, parameterized using in vitro dose-response data. This model was validated using cell line data and used to identify an optimal combination dosing schedule. Our schedule was subsequently confirmed tolerable in an ongoing dose-escalation phase I clinical trial (NCT03810807), with some dose modifications, demonstrating that our rational modeling approach can be used to identify appropriate dosing for combination therapy in the clinical setting.

Osimertinib and dacomitinib are approved as first-line treatment of EGFR-mutant NSCLC but resistance can arise. Here, the authors use a computational model to identify an optimal dosing schedule for osimertinib and dacomitinib combination therapy that was confirmed tolerable and effective in an ongoing phase I clinical trial.

A Quantitative Paradigm for Decision-Making in Precision Oncology

The complexity and variability of cancer progression necessitate a quantitative paradigm for therapeutic decision-making that is dynamic, personalized, and capable of identifying optimal treatment strategies for individual patients under substantial uncertainty. Here, we discuss the core components and challenges of such an approach and highlight the need for comprehensive longitudinal clinical and molecular data integration in its development. We describe the complementary and varied roles of mathematical modeling and machine learning in constructing dynamic optimal cancer treatment strategies and highlight the potential of reinforcement learning approaches in this endeavor.

Clone Wars: Quantitatively Understanding Cancer Drug Resistance

A key aim of early clinical development for new cancer treatments is to detect the potential for efficacy early and to identify a safe therapeutic dose to take forward to phase II. Because of this need, researchers have sought to build mathematical models linking initial radiologic tumor response, often assessed after 6 to 8 weeks of treatment, with overall survival. However, there has been mixed success of this approach in the literature. We argue that evolutionary selection pressure should be considered to interpret these early efficacy signals and so optimize cancer therapy.

Modeling Resistance and Recurrence Patterns of Combined Targeted-Chemoradiotherapy Predicts Benefit of Shorter Induction Period

Optimal integration of molecularly targeted therapies, such as tyrosine kinase inhibitors (TKI), with concurrent chemotherapy and radiation (CRT) to improve outcomes in genotype-defined cancers remains a current challenge in clinical settings. Important questions regarding optimal scheduling and length of induction period for neoadjuvant use of targeted agents remain unsolved and vary among clinical trial protocols. Here, we develop and validate a biomathematical framework encompassing drug resistance and radiobiology to simulate patterns of local versus distant recurrences in a non-small cell lung cancer (NSCLC) population with mutated EGFR receiving TKIs and CRT. Our model predicted that targeted induction before CRT, an approach currently being tested in clinical trials, may render adjuvant targeted therapy less effective due to proliferation of drug-resistant cancer cells when using very long induction periods. Furthermore, simulations not only demonstrated the competing effects of drug-resistant cell expansion versus overall tumor regression as a function of induction length, but also directly estimated the probability of observing an improvement in progression-free survival at a given cohort size. We thus demonstrate that such stochastic biological simulations have the potential to quantitatively inform the design of multimodality clinical trials in genotype-defined cancers. SIGNIFICANCE: A biomathematical framework based on fundamental principles of evolution and radiobiology for in silico clinical trial design allows clinicians to optimize administration of TKIs before chemoradiotherapy in oncogene-driven NSCLC.

Management of brain metastases according to molecular subtypes

The incidence of brain metastases has markedly increased in the past 20 years owing to progress in the treatment of malignant solid tumours, earlier diagnosis by MRI and an ageing population. Although local therapies remain the mainstay of treatment for many patients with brain metastases, a growing number of systemic options are now available and/or are under active investigation. HER2-targeted therapies (lapatinib, neratinib, tucatinib and trastuzumab emtansine), alone or in combination, yield a number of intracranial responses in patients with HER2-positive breast cancer brain metastases. New inhibitors are being investigated in brain metastases from ER-positive or triple-negative breast cancer. Several generations of EGFR and ALK inhibitors have shown activity on brain metastases from EGFR and ALK mutant non-small-cell lung cancer. Immune-checkpoint inhibitors (ICIs) hold promise in patients with non-small-cell lung cancer without druggable mutations and in patients with triple-negative breast cancer. The survival of patients with brain metastases from melanoma has substantially improved after the advent of BRAF inhibitors and ICIs (ipilimumab, nivolumab and pembrolizumab). The combination of targeted agents or ICIs with stereotactic radiosurgery could further improve the response rates and survival but the risk of radiation necrosis should be monitored. Advanced neuroimaging and liquid biopsy will hopefully improve response evaluation.

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.

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology

The amount of "big" data generated in clinical oncology, whether from molecular, imaging, pharmacological, or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive algorithms and simulations are required, with applications for diagnosis, prognosis, drug development, or prediction of the response to therapy. Such mathematical and computational constructs can be subdivided into two broad classes: biologically agnostic, statistical models using artificial intelligence techniques, and physiologically based, mechanistic models. In this review, recent advances in the applications of such methods in clinical oncology are outlined. These include machine learning applied to big data (omics, imaging, or electronic health records), pharmacometrics and quantitative systems pharmacology, as well as tumor kinetics and metastasis modeling. Focus is set on studies with high potential of clinical translation, and particular attention is given to cancer immunotherapy. Perspectives are given in terms of combinations of the two approaches: "mechanistic learning."

Management of CNS metastases in patients with EGFR mutation-positive NSCLC

Central nervous system (CNS) metastases are a frequent and severe complication associated with epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC). The first- and second-generation EGFR tyrosine kinase inhibitors (TKIs) have shown considerable efficacy in EGFR-mutated NSCLC. However, their limited potential to cross the blood-brain barrier (BBB) renders them less effective in the management of CNS metastases in NSCLC. Osimertinib, a third-generation irreversible EGFR-TKI with good potential to cross the BBB, has shown significant clinical activity and acceptable safety profile in patients with EGFR-positive NSCLC brain and leptomeningeal metastases. The progression-free survival (PFS) of up to 15.2 months in CNS metastases patients in the FLAURA trial and the CNS objective response rates (ORRs) of 54% and 43% in the AURA/AURA2 and BLOOM trials, respectively, have established the role of osimertinib in patients with NSCLC with CNS metastases. The AURA3 trial also reported a PFS of 8.5 months and overall ORR of 71%. These data have supported osimertinib to be recognized as a "preferred" first-line treatment for EGFR-positive metastatic NSCLC by the National Comprehensive Cancer Network (NCCN). With limited treatment options available, upfront administration of osimertinib in patients with NSCLC irrespective of EGFR T790M and CNS metastases may improve the overall response rate and potentially reduce the adverse effects of radiotherapy. Our review focuses on the management of EGFR-mutated NSCLC CNS metastases in the context of recent NCCN guidelines.

Towards Multidrug Adaptive Therapy

A new ecologically inspired paradigm in cancer treatment known as "adaptive therapy" capitalizes on competitive interactions between drug-sensitive and drug-resistant subclones. The goal of adaptive therapy is to maintain a controllable stable tumor burden by allowing a significant population of treatment-sensitive cells to survive. These, in turn, suppress proliferation of the less-fit resistant populations. However, there remain several open challenges in designing adaptive therapies, particularly in extending these therapeutic concepts to multiple treatments. We present a cancer treatment case study (metastatic castrate-resistant prostate cancer) as a point of departure to illustrate three novel concepts to aid the design of multidrug adaptive therapies. First, frequency-dependent "cycles" of tumor evolution can trap tumor evolution in a periodic, controllable loop. Second, the availability and selection of treatments may limit the evolutionary "absorbing region" reachable by the tumor. Third, the velocity of evolution significantly influences the optimal timing of drug sequences. These three conceptual advances provide a path forward for multidrug adaptive therapy. SIGNIFICANCE: Driving tumor evolution into periodic, repeatable treatment cycles provides a path forward for multidrug adaptive therapy.

Tyrosine Kinase Inhibitors for the Treatment of EGFR Mutation-Positive Non-Small-Cell Lung Cancer: A Clash of the Generations

The availability of 3 generations of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) with different pharmacologic characteristics and clinical profiles has provided oncologists with a potentially confusing choice for the treatment of EGFR mutation-positive non-small-cell lung cancer. Although recent head-to-head clinical trials have demonstrated improved efficacy with second-generation (ie, afatinib, dacomitinib) and third-generation (ie, osimertinib) TKIs compared with the first-generation TKIs (eg, erlotinib, gefitinib), acquired resistance has been inevitable, regardless of which agent has been chosen as first-line therapy. Thus, the potential availability of subsequent treatment options is an important consideration. Recent data have demonstrated that osimertinib confers an overall survival benefit compared with first-generation EGFR TKIs, and dacomitinib has shown an overall survival benefit compared with gefitinib in an exploratory analysis. However, the relative benefits of different sequential EGFR-TKI regimens, especially those involving second- and third-generation agents, have remained uncertain and require prospective evaluation. Few such data currently exist to inform treatment choices. In the present review, we examined the pharmacologic characteristics and current clinical data for EGFR TKIs, including emerging information on the molecular mechanisms of resistance across the different generations of TKIs. Given the uncertainties regarding the optimal treatment choice, we have focused on the factors that might help determine the treatment decisions, such as efficacy and safety in patient subgroups. We also discussed the emerging real-world data, which have provided some insights into the benefits of sequential regimens in everyday clinical practice.

Third-generation epidermal growth factor receptor tyrosine kinase inhibitors for the treatment of non-small cell lung cancer

Mutations in the epidermal growth factor receptor (EGFR) gene are the most common targetable genomic drivers of non-small cell lung cancer (NSCLC), occurring in approximately 50% and 10-15% of adenocarcinomas of the lung in Asian and Western populations, respectively. The most common EGFR-activating mutations, the exon 19 deletion and the L858R point mutation occurring in the receptor tyrosine kinase domain, are susceptible to inhibition. The first EGFR tyrosine kinase inhibitors (TKIs) to be evaluated were the reversible first-generation EGFR TKIs, gefitinib and erlotinib, followed by the irreversible second-generation EGFR TKIs, afatinib and dacomitinib. The study of acquired resistance mechanisms to first- and second-generation EGFR TKIs in patients with activating EGFR-mutated NSCLC identified the gatekeeper T790M point mutation, present in over 50% of cases, as the most common mechanism of acquired resistance. The need to overcome this resistance mechanism led to the development of third-generation EGFR TKIs, of which osimertinib is the only one to date with regulatory approval. In this review, we present the clinical context leading to the development of third-generation EGFR TKIs, the mode of action of these inhibitors and the clinical data supporting their use. We review third-generation TKI agents that are approved, in development, and those that failed in clinical trials. Finally, we will touch upon ongoing studies and future directions, such as combination treatment strategies, currently being explored to improve the efficacy of treatment with third-generation EGFR TKIs.

Optimization of Cancer Treatment in the Frequency Domain

Thorough exploration of alternative dosing frequencies is often not performed in conventional pharmacometrics approaches. Quantitative systems pharmacology (QSP) can provide novel insights into optimal dosing regimen and drug behaviors which could add a new dimension to the design of novel treatments. However, methods for such an approach are currently lacking. Recently, we illustrated the utility of frequency-domain response analysis (FdRA), an analytical method used in control engineering, using several generic pharmacokinetic-pharmacodynamic case studies. While FdRA is not applicable to models harboring ever increasing variables such as those describing tumor growth, studying such models in the frequency domain provides valuable insight into optimal dosing frequencies. Through the analysis of three distinct tumor growth models (cell cycle-specific, metronomic, and acquired resistance), we demonstrate the application of a simulation-based analysis in the frequency domain to optimize cancer treatments. We study the response of tumor growth to dosing frequencies while simultaneously examining treatment safety, and found for all three models that above a certain dosing frequency, tumor size is insensitive to an increase in dosing frequency, e.g., for the cell cycle-specific model, one dose per 3 days, and an hourly dose yield the same reduction of tumor size to 3% of the initial size after 1 year of treatment. Additionally, we explore the effect of drug elimination rate changes on the tumor growth response. In summary, we show that the frequency-domain view of three models of tumor growth dynamics can help in optimizing drug dosing regimen to improve treatment success.

Management of Brain Metastases in Epidermal Growth Factor Receptor Mutant Non-Small-Cell Lung Cancer

Lung cancer remains a leading cause of mortality with 1.69 million deaths worldwide. Activating mutations in epidermal growth factor receptor (EGFR), predominantly exon 19 deletions and exon 21 L858R mutations, are known oncogenic drivers identified in 20-40% of non-small-cell lung cancers (NSCLC). 70% of EGFR-mutant NSCLC patients develop brain metastases (BM), compared to 38% in EGFR wild-type patients. First-generation tyrosine kinase inhibitors (TKIs), such as erlotinib and gefitinib have proven to be superior to chemotherapy in the front-line treatment of EGFR-mutant NSCLC, as has afatinib, a second-generation TKI. The most common acquired resistance mechanism is the development of a gatekeeper mutation in exon 20 T790M. Osimertinib has emerged as a third-generation EGFR TKI with proven activity in the front-line setting as well as in patients with a T790M acquired resistance mutation with remarkable CNS activity. As long-term survival outcomes in EGFR-mutant NSCLC continue to improve, the burden of BM becomes a greater challenge. Here, we review the literature related to the management of BM in EGFR-mutant NSCLC including the role of the three generations of EGFR TKIs, immunotherapy, and brain radiation.

Medical Treatment Options for Patients with Epidermal Growth Factor Receptor Mutation-Positive Non-Small Cell Lung Cancer Suffering from Brain Metastases and/or Leptomeningeal Disease

Brain metastases and/or leptomeningeal disease (LMD) with associated central nervous system (CNS) metastases are known complications of advanced epidermal growth factor receptor (EGFR) mutation-positive non-small cell lung cancer (NSCLC). It is important, therefore, to assess the activity of EGFR tyrosine kinase inhibitors (TKIs) versus such CNS complications. This review explores the literature reporting the intracranial activity of EGFR TKIs, and finds that there is evidence for varying efficacy of the approved agents, erlotinib, gefitinib, afatinib, and osimertinib in patients with CNS metastases. Other EGFR TKIs in development, such as AZD3759, may have a future role as therapeutic options in this setting. Emerging evidence indicates that the second- and third-generation EGFR TKIs, afatinib and osimertinib, effectively penetrate the blood-brain barrier, and therefore represent viable treatment options for CNS lesions, and can reduce the risk of CNS progression. These agents should therefore be considered as first-line treatment options in patients with EGFR mutation-positive NSCLC who have brain metastases and/or LMD. While there are currently no prospective data comparing the intracranial efficacy of second- and third-generation EGFR TKIs in this setting, CNS activity and protection offered by different EGFR TKIs should be an additional consideration when making decisions about the optimal sequence of treatment with EGFR TKIs in order to maximize survival benefit in individual patients.

Sequencing Therapy for Genetically Defined Subgroups of Non-Small Cell Lung Cancer

The practice of precision medicine for patients with metastatic non-small cell lung cancer (NSCLC), particularly those patients with adenocarcinoma histology (the predominant subtype of NSCLC), has become the accepted standard of care worldwide. Implementation of prospective tumor molecular profiling and rational therapeutic decision-making based on the presence of recurrently detected oncogenic "driver" alterations in the tumor genome has revolutionized the way that lung cancer is diagnosed and treated in the clinic. Over the past two decades, there has been a deluge of therapeutically actionable driver alterations and accompanying small molecule inhibitors to target these drivers. Herein, we synthesize a large and rapidly growing body of literature regarding therapeutic inhibition of driver mutations. We focus on established targets, including EGFR, anaplastic lymphoma kinase (ALK), ROS1, BRAF, RET, MET, HER2, and neurotrophic tyrosine kinase receptor (NTRK), with a particular emphasis on the sequencing of small molecule inhibitors in these genetically defined cohorts of patients with lung cancer.

Combination Therapy and the Evolution of Resistance: The Theoretical Merits of Synergism and Antagonism in Cancer

The search for effective combination therapies for cancer has focused heavily on synergistic combinations because they exhibit enhanced therapeutic efficacy at lower doses. Although synergism is intuitively attractive, therapeutic success often depends on whether drug resistance develops. The impact of synergistic combinations (vs. antagonistic or additive combinations) on the process of drug-resistance evolution has not been investigated. In this study, we use a simplified computational model of cancer cell numbers in a population of drug-sensitive, singly-resistant, and fully-resistant cells to simulate the dynamics of resistance evolution in the presence of two-drug combinations. When we compared combination therapies administered at the same combination of effective doses, simulations showed synergistic combinations most effective at delaying onset of resistance. Paradoxically, when the therapies were compared using dose combinations with equal initial efficacy, antagonistic combinations were most successful at suppressing expansion of resistant subclones. These findings suggest that, although synergistic combinations could suppress resistance through early decimation of cell numbers (making them "proefficacy" strategies), they are inherently fragile toward the development of single resistance. In contrast, antagonistic combinations suppressed the clonal expansion of singly-resistant cells, making them "antiresistance" strategies. The distinction between synergism and antagonism was intrinsically connected to the distinction between offensive and defensive strategies, where offensive strategies inflicted early casualties and defensive strategies established protection against anticipated future threats. Our findings question the exclusive focus on synergistic combinations and motivate further consideration of nonsynergistic combinations for cancer therapy.Significance: Computational simulations show that if different combination therapies have similar initial efficacy in cancers, then nonsynergistic drug combinations are more likely than synergistic drug combinations to provide a long-term defense against the evolution of therapeutic resistance. Cancer Res; 78(9); 2419-31. ©2018 AACR.

Brain Metastases in Oncogene-Addicted Non-Small Cell Lung Cancer Patients: Incidence and Treatment

Brain metastases (BM) are common in non-small cell lung cancer patients including in molecularly selected populations, such as EGFR-mutant and ALK-rearranged tumors. They are associated with a reduced quality of life, and are commonly the first site of progression for patients receiving tyrosine kinase inhibitors (TKIs). In this review, we summarize incidence of BM and intracranial efficacy with TKI agents according to oncogene driver mutations, focusing on important clinical issues, notably optimal first-line treatment in oncogene-addicted lung tumors with upfront BM (local therapies followed by TKI vs. TKI monotherapy). We also discuss the potential role of newly emerging late-generation TKIs as new standard treatment in oncogene-addicted lung cancer tumors compared with sequential strategies.

Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients

Human primary glioblastomas (GBM) often harbor mutations within the epidermal growth factor receptor (EGFR). Treatment of EGFR-mutant GBM cell lines with the EGFR/HER2 tyrosine kinase inhibitor lapatinib can effectively induce cell death in these models. However, EGFR inhibitors have shown little efficacy in the clinic, partly because of inappropriate dosing. Here, we developed a computational approach to model the in vitro cellular dynamics of the EGFR-mutant cell line SF268 in response to different lapatinib concentrations and dosing schedules. We then used this approach to identify an effective treatment strategy within the clinical toxicity limits of lapatinib, and developed a partial differential equation modeling approach to study the in vivo GBM treatment response by taking into account the heterogeneous and diffusive nature of the disease. Despite the inability of lapatinib to induce tumor regressions with a continuous daily schedule, our modeling approach consistently predicts that continuous dosing remains the best clinically feasible strategy for slowing down tumor growth and lowering overall tumor burden, compared to pulsatile schedules currently known to be tolerated, even when considering drug resistance, reduced lapatinib tumor concentrations due to the blood brain barrier, and the phenotypic switch from proliferative to migratory cell phenotypes that occurs in hypoxic microenvironments. Our mathematical modeling and statistical analysis platform provides a rational method for comparing treatment schedules in search for optimal dosing strategies for glioblastoma and other cancer types.

PKPD modeling of acquired resistance to anti-cancer drug treatment

Non-small cell lung cancer (NSCLC) patients greatly benefit from the treatment with tyrosine kinase inhibitors (TKIs) targeting the epidermal growth factor receptor (EGFR). However, emergence of acquired resistance inevitable occurs after long-term treatment in most patients and limits clinical improvement. In the present study, resistance to drug treatment in patient-derived NSCLC xenograft mice was assessed and modeling and simulation was applied to understand the dynamics of drug resistance as a basis to explore more beneficial drug regimen. Two semi-mechanistic models were fitted to tumor growth inhibition profiles during and after treatment with erlotinib or gefitinib. The base model proposes that as a result of drug treatment, tumor cells stop proliferating and undergo several stages of damage before they eventually die. The acquired resistance model adds a resistance term to the base model which assumes that resistant cells are emerging from the pool of damaged tumor cells. As a result, tumor cells sensitive to drug treatment will either die or be converted to a drug resistant cell population which is proliferating at a slower growth rate as compared to the sensitive cells. The observed tumor growth profiles were better described by the resistance model and emergence of resistance was concluded. In simulation studies, the selection of resistant cells was explored as well as the time-variant fraction of resistant over sensitive cells. The proposed model provides insight into the dynamic processes of emerging resistance. It predicts tumor regrowth during treatment driven by the selection of resistant cells and explains why faster tumor regrowth may occur after discontinuation of TKI treatment. Finally, it is shown how the semi-mechanistic model can be used to explore different scenarios and to identify optimal treatment schedules in clinical trials.

Twice weekly pulse and daily continuous-dose erlotinib as initial treatment for patients with epidermal growth factor receptor-mutant lung cancers and brain metastases

BACKGROUND

In a phase 1 study of pulse/continuous-dose erlotinib, no patient had disease progression in the central nervous system (CNS). This expansion cohort of the phase 1 study tested the same regimen in a cohort of individuals with epidermal growth factor receptor (EGFR)-mutant lung cancers with untreated brain metastases.

METHODS

Patients had not received EGFR tyrosine kinase inhibitors or radiation for brain metastases. All received 1200 mg of erlotinib on days 1 and 2 and 50 mg on days 3 to 7 weekly. The primary endpoints were the overall and CNS response rates (according to version 1.1 of the Response Evaluation Criteria in Solid Tumors).

RESULTS

Between May 2015 and August 2016, 19 patients were enrolled. Forty-two percent of the patients had target brain lesions, and the median size of the target brain lesions was 13 mm. Overall, 14 patients (74%; 95% confidence interval [CI], 51%-89%) had partial responses. The response rate in brain metastases was 75%. The overall median progression-free survival was 10 months (95% CI, 7 months to not reached). Only 3 patients (16%) had CNS progression. To date, 4 patients required CNS radiation at some time during their course. The adverse events (any grade) seen in 10% or more of the patients were rash, diarrhea, nausea, an increase in alanine aminotransferase, and fatigue.

CONCLUSIONS

Pulse/continuous-dose erlotinib produced a 74% overall response rate and a 75% response rate in brain metastases in patients with EGFR-mutant lung cancers and untreated brain metastases. CNS control persisted even after progression elsewhere. Although this regimen did not improve progression-free survival or delay the emergence of EGFR T790M, it prevented progression in the brain and could be useful in situations in which CNS control is critical. Cancer 2018;124:105-9. © 2017 American Cancer Society.

A Neuro-oncologist's Perspective on Management of Brain Metastases in Patients with EGFR Mutant Non-small Cell Lung Cancer

Management of non-small cell lung cancer (NSCLC) with brain metastasis (BrM) has been revolutionized by identification of molecular subsets that have targetable oncogenes. Historically, survival for NSCLC with symptomatic BrM was weeks to months. Now, many patients are surviving years with limited data to guide treatment decisions. Tumors with activating mutations in epidermal growth factor receptor (EGFRact+) have a higher incidence of BrM, but a longer overall survival. The high response rate of both systemic and BrM EGFRact+ NSCLC to tyrosine kinase inhibitors (TKIs) has led to the rapid incorporation of new therapies but is outpacing evidence-based decisions for BrM in NSCLC. While whole brain radiation therapy (WBRT) was the foundation of management of BrM, extended survival raises concerns for the subacute and late effects radiotherapy. We favor the use of TKIs and delaying the use of WBRT when able. At inevitable disease progression, we consider alternative dosing schedules to increase CNS penetration (such as pulse dosing of erlotinib) or advance to next generation TKI if available. We utilize local control options of surgery or stereotactic radiosurgery (SRS) for symptomatic accessible lesions based on size and edema. At progression despite available TKIs, we use pemetrexed-based platinum doublet chemotherapy or immunotherapy if the tumor has high expression of PDL-1. We reserve the use of WBRT for patients with more than 10 BrM and progression despite TKI and conventional chemotherapy, if performance status is appropriate.