Optimization of dosing for EGFR-mutant non-small cell lung cancer with evolutionary cancer modeling

DataXflow: Synergizing data-driven modeling with best parameter fit and optimal control - An efficient data analysis for cancer research

Building data-driven models is an effective strategy for information extraction from empirical data. Adapting model parameters specifically to data with a best fitting approach encodes the relevant information into a mathematical model. Subsequently, an optimal control framework extracts the most efficient targets to steer the model into desired changes via external stimuli. The DataXflow software framework integrates three software pipelines, D2D for model fitting, a framework solving optimal control problems including external stimuli and JimenaE providing graphical user interfaces to employ the other frameworks lowering the barriers for the need of programming skills, and simultaneously automating reoccurring modeling tasks. Such tasks include equation generation from a graph and script generation allowing also to approach systems with many agents, like complex gene regulatory networks. A desired state of the model is defined, and therapeutic interventions are modeled as external stimuli. The optimal control framework purposefully exploits the model-encoded information by providing those external stimuli that effect the desired changes most efficiently. The implementation of DataXflow is available under https://github.com/MarvelousHopefull/DataXflow. We showcase its application by detecting specific drug targets for a therapy of lung cancer from measurement data to lower proliferation and increase apoptosis. By an iterative modeling process refining the topology of the model, the regulatory network of the tumor is generated from the data. An application of the optimal control framework in our example reveals the inhibition of AURKA and the activation of CDH1 as the most efficient drug target combination. DataXflow paves the way to an agile interplay between data generation and its analysis potentially accelerating cancer research by an efficient drug target identification, even in complex networks.

Characterization of the in vitro metabolic profile of nazartinib in HLMs using UPLC-MS/MS method: In silico metabolic lability and DEREK structural alerts screening using StarDrop software

The orally given, irreversible, third-generation inhibitor of the epidermal growth factor receptor (EGFR), known as Nazartinib (EGF816), is now undergoing investigation in Phase II clinical trials conducted by Novartis for Non-Small Cell Lung Cancer. The primary aim of the current research was to establish a rapid, specific, environmentally friendly, and highly versatile UPLC-MS/MS methodology for the determination of nazartinib (NZT) levels in human liver microsomes (HLMs). Subsequently, same approach was used to examine the metabolic stability of NZT. The UPLC-MS/MS method employed in HLMs was validated as stated in the bioanalytical method validation criteria outlined by the US- FDA. The evaluation of the metabolic stability of NZT and the identification of potentially structural alarms were performed using the StarDrop software package that includes the P450 and DEREK software. The calibration curve for NZT showed a linearity in the range from 1 to 3000 ng/mL. The inter-day accuracy and precision exhibited a range of values between -4.33 % and 4.43 %, whereas the intra-day accuracy and precision shown a range of values between -2.78 % and 7.10 %. The sensitivity of the developed approach was verified through the determination of a LLOQ of 0.39 ng/mL. The intrinsic clearance and in vitro half-life of NZT were assessed to be 46.48 mL/min/kg and 17.44 min, respectively. In our preceding inquiry, we have effectively discerned the bioactivation center, denoted by the carbon atom between the unsaturated conjugated system and aliphatic linear tertiary amine. In the context of computational software, making minor adjustments or substituting the dimethylamino-butenoyl moiety throughout the drug design process may increase the metabolic stability and safety properties of new synthesized derivatives. The efficiency of utilizing different in silico software approaches to conserve resources and reduce effort was proved by the outcomes attained from in vitro incubation experiments and the use of NZT in silico software.

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

Osimertinib plus local treatment for brain metastases versus osimertinib alone in patients with EGFR-Mutant Non-Small Cell Lung Cancer

OBJECTIVES

Osimertinib is a standard treatment for patients with EGFR-mutant non-small cell lung cancer (NSCLC) and is highly effective for brain metastases (BMs). However, it is unclear whether local treatment (LT) for BMs prior to osimertinib administration improves survival in EGFR-mutant NSCLC. We aimed to reveal the survival benefit of upfront local treatment (LT) for BMs in patients treated with osimertinib.

MATERIALS AND METHODS

This multicenter retrospective study included consecutive patients with EGFR mutation (19del or L858R)-positive NSCLC who had BMs before osimertinib initiation between August 2018 and October 2021. We compared overall survival (OS) and central nervous system progression-free survival (CNS-PFS) between patients who received upfront LT for BMs (the upfront LT group), and patients who received osimertinib only (the osimertinib-alone group). Inverse-probability treatment weighting (IPTW) analysis was performed to adjust for potential confounding factors.

RESULTS

Of the 121 patients analyzed, 57 and 64 patients had 19del and L858R, respectively. Forty-five and 76 patients were included in the upfront LT group and the osimertinib-alone groups, respectively. IPTW-adjusted Kaplan-Meier curves showed that the OS of the upfront LT group was significantly longer than that of the osimertinib-alone group (median, 95 % confidence intervals [95 %CI]: Not reached [NR], NR-NR vs. 31.2, 21.7-33.2; p = 0.021). The hazard ratio (HR) for OS and CNS-PFS was 0.37 (95 %CI, 0.16-0.87) and 0.36 (95 %CI, 0.15-0.87), respectively.

CONCLUSIONS

The OS and CNS-PFS of patients who received upfront LT for BMs followed by osimertinib were significantly longer than those of patients who received osimertinib alone. Upfront LT for BMs may be beneficial in patients with EGFR-mutant NSCLC treated with osimertinib.

Rapid Response to Lenvatinib and Disease Flare After Discontinuation in a Patient With Thymic Carcinoma Harboring KIT Exon 11 Mutation: A Case Report

Lenvatinib, a multitarget tyrosine kinase inhibitor for c-Kit and other kinases, has exhibited promising efficacy in treating advanced or metastatic thymic carcinoma (TC). Here, we present the case of a patient with metastatic TC harboring a KIT exon 11 deletion and amplification. The patient exhibited a remarkable response to lenvatinib but experienced rapid disease progression after discontinuation of lenvatinib, referred to as a "disease flare." This case report indicates that KIT mutations and amplification can predict lenvatinib response in patients with TC. However, in such cases, there might be a risk of disease flares after lenvatinib discontinuation.

The study of primary and acquired resistance to first-line osimertinib to improve the outcome of EGFR-mutated advanced Non-small cell lung cancer patients: the challenge is open for new therapeutic strategies

The development of targeted therapy in epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) patients has radically changed their clinical perspectives. Current first-line standard treatment for advanced disease is commonly considered third-generation tyrosine kinase inhibitors (TKI), osimertinib. The study of primary and acquired resistance to front-line osimertinib is one of the main burning issues to further improve patients' outcome. Great heterogeneity has been depicted in terms of duration of clinical benefit and pattern of progression and this might be related to molecular factors including subtypes of EGFR mutations and concomitant genetic alterations. Acquired resistance can be categorized into two main classes: EGFR-dependent and EGFR-independent mechanisms and specific pattern of progression to first-line osimertinib have been demonstrated. The purpose of the manuscript is to provide a comprehensive overview of literature about molecular resistance mechanisms to first-line osimertinib, from a clinical perspective and therefore in relationship to emerging therapeutic approaches.

Diverse mutant selection windows shape spatial heterogeneity in evolving populations

Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes in both cancer and infectious diseases. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N * 2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more fully reflect the selection of drug resistant genotypes. Furthermore, using synthetic data and empirical dose-response data in cancer, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. By simulating a tumor treated with cyclic drug therapy, we find that mutant selection windows introduced by drug diffusion promote the proliferation of drug resistant cells. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine.

Interplay of mutations, alternate mechanisms, and treatment breaks in leukaemia: Understanding and implications studied with stochastic models

Bcr-Abl1 kinase domain mutations are the most prevalent cause of treatment resistance in chronic myeloid leukaemia (CML). Alternate resistance pathways nevertheless exist, and cell line experiments show certain patterns in the gain, and loss, of some of these alternate adaptations. These adaptations have clinical consequences when the tumour develops mechanisms that are beneficial to its growth under treatment, but slow down its growth when not treated. The results of temporarily halting treatment in CML have not been widely discussed in the clinic and there is no robust theoretical model that could suggest when such a pause in therapy can be tolerated. We constructed a dynamic model of how mechanisms such as Bcr-Abl1 overexpression and drug transporter upregulation evolve to produce resistance in cell lines, and investigate its behaviour subject to different treatment schedules, in particular when the treatment is paused ('drug holiday'). Our study results suggest that the presence of additional resistance mechanisms creates an environment which favours mutations that are either preexisting or occur late during treatment. Importantly, the results suggest the existence of tumour drug addiction, where cancer cells become dependent on the drug for (optimal) survival, which could be exploited through a treatment holiday. All simulation code is available at https://github.com/Sandalmoth/dual-adaptation.

Alectinib continuation beyond progression in ALK-positive non-small cell lung cancer with alectinib-refractory

Background

Alectinib, a next-generation anaplastic lymphoma kinase tyrosine kinase inhibitor (ALK-TKI), has demonstrated noteworthy efficacy in the treatment of non-small cell lung cancer (NSCLC). Unfortunately, 53.3% of untreated patients receiving first-line treatment with alectinib developed resistance to alectinib. However, despite the widespread use of alectinib, studies on the efficacy and safety of continuing alectinib with other necessary therapies after progression of alectinib and possible population of benefit are still limited.

Methods

This retrospective cohort study included fifteen patients with ALK-positive NSCLC from nine institutions in China who experienced disease progression after first- or second-line treatment and continued to receive alectinib treatment between 2019 and 2022. This study aimed to evaluate the median progression-free survival (mPFS), objective response rate (ORR), median overall survival (mOS), and adverse events (AEs) of continuing alectinib combined with other therapies after the emergence of drug resistance.

Results

Among fifteen patients eligible for this study, all patients started continuing treatment with alectinib after oligoprogression or central nervous system (CNS) progression. The mPFS for the whole cohort receiving continuing alectinib with other necessary therapies was 8 months [95% confidence interval (CI): 4 to not applicable (NA)], with an ORR of 46.7%. The mOS was not reached. During continuing alectinib treatment, only one patient experienced grade 2 elevation of aspartate aminotransferase (AST) and serum glutamic-oxaloacetic transaminase (SGOT).

Conclusions

The continuation of alectinib treatment combined with other necessary therapies demonstrates favorable response and safety in patients with ALK-positive NSCLC who experienced oligoprogression or CNS progression following alectinib in first- or second-line therapy. Instead of immediately switching to another ALK-TKI, continuing alectinib combined with other necessary therapies may offer greater survival benefits to the patients.

Correlation Analysis between T790M Status and Clinical Characteristics of Patients with EGFR-sensitive Mutation Advanced NSCLC who Progressed after the First Generation and First-line EGFR-TKIs Administration: A Real-world Exploratory Study

AIM

The present study is to investigate the association between T790M status and clinical characteristics of patients with EGFR-sensitive advanced non-small cell lung cancer (NSCLC) who progressed the initial epidermal growth factor receptor tyrosine kinase inhibitors (EGFRTKIs) administration.

METHODS

A total of 167 patients with EGFR-sensitive mutations advanced NSCLC who had successful genetic tests and progressed the initial EGFR-TKI treatment were included in this study retrospectively. The clinical and demographic characteristics of these patients were collected, which were manifested as pathological type, metastasis location, initial biopsy method, initial genetic test specimens, and baseline gene mutations status. Correlation analysis between T790M status and these characteristics was performed and prognostic analysis regarding the different subgroups was carried out accordingly.

RESULTS

The prevalence of secondary T790M after resistance to initial EGFR-TKIs among the 167 patients was 52.7%. Correlation analysis indicated that the median progression-free Survival (PFS) to initial EGFR-TKIs >12 months were more likely to develop secondary T790M in univariate analysis. However, the conclusion failed to show statistically significant in multivariate analysis. Additionally, patients with intracranial progression of initial EGFR-TKIs therapy were associated with secondary EGFR-T790M. However, it should be noted that those whose best overall response was partial response (PR) during the EGFR-TKI therapy were relevant to secondary T790M. Furthermore, The median PFS of the initial EGFR-TKIs administration was longer among patients with T790M positive mutation and patients with PR reaction than those without T790M mutation and patients with stable disease (SD), respectively (median PFS: 13.6 vs. 10.9 months, P=0.023) and (median PFS: 14.0 vs. 10.1 months, P=0.001).

CONCLUSION

This retrospective study highlighted the real-world evidence that the best efficacy and intracranial progression with initial EGFR-TKIs therapy among patients with advanced NSCLC might be the promising indicators to predict the occurrence of EGFR-T790M. Patients with PR reaction and T790M positive mutation conferred longer PFS of the initial EGFR-TKIs administration. Also, the conclusion should be confirmed in more patients with advanced NSCLC subsequently.

Adaptive therapy: a tumor therapy strategy based on Darwinian evolution theory

Cancer progression is a dynamic process of continuous evolution, in which genetic diversity and heterogeneity are generated by clonal and subclonal amplification based on random mutations. Traditional cancer treatment strategies have a great challenge, which often leads to treatment failure due to drug resistance. Integrating evolutionary dynamics into treatment regimens may be an effective way to overcome the problem of drug resistance. In particular, a potential treatment is adaptive therapy, which strategy advocates containment strategies that adjust the treatment cycles according to tumor evolution to control the growth of treatment-resistant cells. In this review, we first summarize the shortcomings of traditional tumor treatment methods in evolution and then introduce the theoretical basis and research status of adaptive therapy. By analyzing the limitations of adaptive therapy and exploring possible solutions, we can broaden people's understanding of adaptive therapy and provide new insights and strategies for tumor treatment.

CT Radiomics Predict EGFR-T790M Resistance Mutation in Advanced Non-Small Cell Lung Cancer Patients After Progression on First-line EGFR-TKI

RATIONALE AND OBJECTIVES

We aim to explore the value of chest CT radiomics in predicting the epidermal growth factor receptor (EGFR)-T790M resistance mutation of advanced non-small cell lung cancer (NSCLC) patients after the failure of first-line EGFR-tyrosine kinase inhibitor (EGFR-TKI).

MATERIALS AND METHODS

A total of 211 and 135 advanced NSCLC patients with tumor tissue-based (Cohort-1) or circulating tumor DNA (ctDNA)-based (Cohort-2) EGFR-T790M testing were included, respectively. Cohort-1 was used for modeling and Cohort-2 was for models' validation. Radiomic features were extracted from tumor lesions on chest nonenhanced CT (NECT) and/or contrast-enhanced CT (CECT). We used eight feature selectors and eight classifier algorithms to establish radiomic models. Models were evaluated by area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).

RESULTS

CT morphological manifestations of peripheral location and pleural indentation sign were associated with EGFR-T790M. For NECT, CECT, and NECT+CECT radiomic features, the feature selector and classifier algorithms of LASSO and Stepwise logistic regression, Boruta and SVM, and LASSO and SVM were chosen to develop the optimal model, respectively (AUC: 0.844, 0.811, and 0.897). All models performed well in calibration curves and DCA. Independent validation of models in Cohort-2 revealed that both NECT and CECT models individually had limited power for predicting EGFR-T790M mutation detected by ctDNA (AUC: 0.649, 0.675), while the NECT+CECT radiomic model had a satisfactory AUC (0.760).

CONCLUSION

This study proved the feasibility of using CT radiomic features to predict the EGFR-T790M resistance mutation, which could be helpful in guiding personalized therapeutic strategies.

Cascade strategy for glucose oxidase-based synergistic cancer therapy using nanomaterials

Nanomaterial-based cancer therapy faces significant limitations due to the complex nature of the tumor microenvironment (TME). Starvation therapy is an emerging therapeutic approach that targets tumor cell metabolism using glucose oxidase (GOx). Importantly, it can provide a material or environmental foundation for other diverse therapeutic methods by manipulating the properties of the TME, such as acidity, hydrogen peroxide (H2O2) levels, and hypoxia degree. In recent years, this cascade strategy has been extensively applied in nanoplatforms for ongoing synergetic therapy and still holds undeniable potential. However, only a few review articles comprehensively elucidate the rational designs of nanoplatforms for synergetic therapeutic regimens revolving around the conception of the cascade strategy. Therefore, this review focuses on innovative cascade strategies for GOx-based synergetic therapy from representative paradigms to state-of-the-art reports to provide an instructive, comprehensive, and insightful reference for readers. Thereafter, we discuss the remaining challenges and offer a critical perspective on the further advancement of GOx-facilitated cancer treatment toward clinical translation.

Inhibition of a lower potency target drives the anticancer activity of a clinical p38 inhibitor

The small-molecule drug ralimetinib was developed as an inhibitor of the p38α mitogen-activated protein kinase, and it has advanced to phase 2 clinical trials in oncology. Here, we demonstrate that ralimetinib resembles EGFR-targeting drugs in pharmacogenomic profiling experiments and that ralimetinib inhibits EGFR kinase activity in vitro and in cellulo. While ralimetinib sensitivity is unaffected by deletion of the genes encoding p38α and p38β, its effects are blocked by expression of the EGFR-T790M gatekeeper mutation. Finally, we solved the cocrystal structure of ralimetinib bound to EGFR, providing further evidence that this drug functions as an ATP-competitive EGFR inhibitor. We conclude that, though ralimetinib is >30-fold less potent against EGFR compared to p38α, its ability to inhibit EGFR drives its primary anticancer effects. Our results call into question the value of p38α as an anticancer target, and we describe a multi-modal approach that can be used to uncover a drug's mechanism-of-action.

Treatment of evolving cancers will require dynamic decision support

Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.

Reuniting philosophy and science to advance cancer research

Cancers rely on multiple, heterogeneous processes at different scales, pertaining to many biomedical fields. Therefore, understanding cancer is necessarily an interdisciplinary task that requires placing specialised experimental and clinical research into a broader conceptual, theoretical, and methodological framework. Without such a framework, oncology will collect piecemeal results, with scant dialogue between the different scientific communities studying cancer. We argue that one important way forward in service of a more successful dialogue is through greater integration of applied sciences (experimental and clinical) with conceptual and theoretical approaches, informed by philosophical methods. By way of illustration, we explore six central themes: (i) the role of mutations in cancer; (ii) the clonal evolution of cancer cells; (iii) the relationship between cancer and multicellularity; (iv) the tumour microenvironment; (v) the immune system; and (vi) stem cells. In each case, we examine open questions in the scientific literature through a philosophical methodology and show the benefit of such a synergy for the scientific and medical understanding of cancer.

Final Report on Real-World Effectiveness of Sequential Afatinib and Osimertinib in EGFR-Positive Advanced Non-Small Cell Lung Cancer: Updated Analysis of the RESET Study

PURPOSE

This study aimed to report the final analysis of time-on-treatment (TOT) and overall survival (OS) in patients with advanced-stage epidermal growth factor receptor (EGFR)+ non-small cell lung cancer (NSCLC) who received sequential afatinib and osimertinib and to compare the outcomes with other second-line regimens (comparator group).

MATERIALS AND METHODS

In this updated report, the existing medical records were reviewed and rechecked. TOT and OS were updated and analyzed according to clinical features using the Kaplan-Meier method and log-rank test. TOT and OS were compared with those of the comparator group, in which most patients received pemetrexed-based treatments. A multivariable Cox proportional hazard model was used to evaluate features that could affect survival outcomes.

RESULTS

The median observation time was 31.0 months. The follow-up period was extended to 20 months. A total of 401 patients who received first-line afatinib were analyzed (166 with T790M+ and second-line osimertinib, and 235 with unproven T790M and other second-line agents). Median TOTs on afatinib and osimertinib were 15.0 months (95% confidence interval [CI], 14.0 to 16.1) and 11.9 months (95% CI, 8.9 to 14.6), respectively. The median OS in the osimertinib group was 54.3 months (95% CI, 46.7 to 61.9), much longer than that in the comparator group. In patients who received osimertinib, the OS was longest with Del19+ (median, 59.1; 95% CI, 48.7 to 69.5).

CONCLUSION

This is one of the largest real-world studies reporting the encouraging activity of sequential afatinib and osimertinib in Asian patients with EGFR+ NSCLC who acquired the T790M mutation, particularly Del19+.

Loss of Key EMT-Regulating miRNAs Highlight the Role of ZEB1 in EGFR Tyrosine Kinase Inhibitor-Resistant NSCLC

Despite recent advances in the treatment of non-small cell lung cancer (NSCLC), acquired drug resistance to targeted therapy remains a major obstacle. Epithelial-mesenchymal transition (EMT) has been identified as a key resistance mechanism in NSCLC. Here, we investigated the mechanistic role of key EMT-regulating small non-coding microRNAs (miRNAs) in sublines of the NSCLC cell line HCC4006 adapted to afatinib, erlotinib, gefitinib, or osimertinib. The most differentially expressed miRNAs derived from extracellular vesicles were associated with EMT, and their predicted target ZEB1 was significantly overexpressed in all resistant cell lines. Transfection of a miR-205-5p mimic partially reversed EMT by inhibiting ZEB1, restoring CDH1 expression, and inhibiting migration in erlotinib-resistant cells. Gene expression of EMT-markers, transcription factors, and miRNAs were correlated during stepwise osimertinib adaptation of HCC4006 cells. Temporally relieving cells of osimertinib reversed transition trends, suggesting that the implementation of treatment pauses could provide prolonged benefits for patients. Our results provide new insights into the contribution of miRNAs to drug-resistant NSCLC harboring EGFR-activating mutations and highlight their role as potential biomarkers and therapeutic targets.

Diverse mutant selection windows shape spatial heterogeneity in evolving populations

Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N*2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more accurately reflect the selection fo drug resistant genotypes. Furthermore, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine.

The acquisition order of leukemic drug resistance mutations is directed by the selective fitness associated with each resistance mechanism

In Chronic Myeloid Leukemia, the transition from drug sensitive to drug resistant disease is poorly understood. Here, we used exploratory sequencing of gene transcripts to determine the mechanisms of drug resistance in a dasatinib resistant cell line model. Importantly, cell samples were collected sequentially during drug exposure and dose escalation, revealing several resistance mechanisms which fluctuated over time. BCR::ABL1 overexpression, BCR::ABL1 kinase domain mutation, and overexpression of the small molecule transporter ABCG2, were identified as dasatinib resistance mechanisms. The acquisition of mutations followed an order corresponding with the increase in selective fitness associated with each resistance mechanism. Additionally, it was demonstrated that ABCG2 overexpression confers partial ponatinib resistance. The results of this study have broad applicability and help direct effective therapeutic drug usage and dosing regimens and may be useful for clinicians to select the most efficacious therapy at the most beneficial time.

Stratification of non-small cell lung adenocarcinoma patients with EGFR actionable mutations based on drug-resistant stem cell genes

EGFR-TKIs were used in NSCLC patients with actionable EGFR mutations and prolong prognosis. However, most patients treated with EGFR-TKIs developed resistance within around one year. This suggests that residual EGFR-TKIs resistant cells may eventually lead to relapse. Predicting resistance risk in patients will facilitate individualized management. Herein, we built an EGFR-TKIs resistance prediction (R-index) model and validate in cell line, mice, and cohort. We found significantly higher R-index value in resistant cell lines, mice models and relapsed patients. Patients with an elevated R-index had significantly shorter relapse time. We also found that the glycolysis pathway and the KRAS upregulation pathway were related to EGFR-TKIs resistance. MDSC is a significant immunosuppression factor in the resistant microenvironment. Our model provides an executable method for assessing patient resistance status based on transcriptional reprogramming and may contribute to the clinical translation of patient individual management and the study of unclear resistance mechanisms.

The Resistance to EGFR-TKIs in Non-Small Cell Lung Cancer: From Molecular Mechanisms to Clinical Application of New Therapeutic Strategies

Almost 17% of Western patients affected by non-small cell lung cancer (NSCLC) have an activating epidermal growth factor receptor (EGFR) gene mutation. Del19 and L858R are the most-common ones; they are positive predictive factors for EGFR tyrosine kinase inhibitors (TKIs). Currently, osimertinib, a third-generation TKI, is the standard first-line therapy for advanced NSCLC patients with common EGFR mutations. This drug is also administered as a second-line treatment for those patients with the T790M EGFR mutation and previously treated with first- (erlotinib, gefitinib) or second- (afatinib) generation TKIs. However, despite the high clinical efficacy, the prognosis remains severe due to intrinsic or acquired resistance to EGRF-TKIs. Various mechanisms of resistance have been reported including the activation of other signalling pathways, the development of secondary mutations, the alteration of the downstream pathways, and phenotypic transformation. However, further data are needed to achieve the goal of overcoming resistance to EGFR-TKIs, hence the necessity of discovering novel genetic targets and developing new-generation drugs. This review aimed to deepen the knowledge of intrinsic and acquired molecular mechanisms of resistance to EGFR-TKIs and the development of new therapeutic strategies to overcome TKIs' resistance.

Intratumoral Heterogeneity in Lung Cancer

The diagnosis and treatment of lung cancer (LC) is always a challenge. The difficulty in the decision of therapeutic schedule and diagnosis is directly related to intratumoral heterogeneity (ITH) in the progression of LC. It has been proven that most tumors emerge and evolve under the pressure of their living microenvironment, which involves genetic, immunological, metabolic, and therapeutic components. While most research on ITH revealed multiple mechanisms and characteristic, a systemic exposition of ITH in LC is still hard to find. In this review, we describe how ITH in LC develops from the perspective of space and time. We discuss elaborate details and affection of every aspect of ITH in LC and the relationship between them. Based on ITH in LC, we describe a more accurate multidisciplinary therapeutic strategy on LC and provide the newest opinion on the potential approach of LC therapy.

Data integration between clinical research and patient care: A framework for context-depending data sharing and in silico predictions

The transfer of new insights from basic or clinical research into clinical routine is usually a lengthy and time-consuming process. Conversely, there are still many barriers to directly provide and use routine data in the context of basic and clinical research. In particular, no coherent software solution is available that allows a convenient and immediate bidirectional transfer of data between concrete treatment contexts and research settings. Here, we present a generic framework that integrates health data (e.g., clinical, molecular) and computational analytics (e.g., model predictions, statistical evaluations, visualizations) into a clinical software solution which simultaneously supports both patient-specific healthcare decisions and research efforts, while also adhering to the requirements for data protection and data quality. Specifically, our work is based on a recently established generic data management concept, for which we designed and implemented a web-based software framework that integrates data analysis, visualization as well as computer simulation and model prediction with audit trail functionality and a regulation-compliant pseudonymization service. Within the front-end application, we established two tailored views: a clinical (i.e., treatment context) perspective focusing on patient-specific data visualization, analysis and outcome prediction and a research perspective focusing on the exploration of pseudonymized data. We illustrate the application of our generic framework by two use-cases from the field of haematology/oncology. Our implementation demonstrates the feasibility of an integrated generation and backward propagation of data analysis results and model predictions at an individual patient level into clinical decision-making processes while enabling seamless integration into a clinical information system or an electronic health record.

Adaptive therapy for ovarian cancer: An integrated approach to PARP inhibitor scheduling

Toxicity and emerging drug resistance are important challenges in PARP inhibitor (PARPi) treatment of ovarian cancer. Recent research has shown that evolutionary-inspired treatment algorithms which adapt treatment to the tumor's treatment response (adaptive therapy) can help to mitigate both. Here, we present a first step in developing an adaptive therapy protocol for PARPi treatment by combining mathematical modelling and wet-lab experiments to characterize the cell population dynamics under different PARPi schedules. Using data from in vitro Incucyte Zoom time-lapse microscopy experiments and a step-wise model selection process we derive a calibrated and validated ordinary differential equation model, which we then use to test different plausible adaptive treatment schedules. Our model can accurately predict the in vitro treatment dynamics, even to new schedules, and suggests that treatment modifications need to be carefully timed, or one risks losing control over tumour growth, even in the absence of any resistance. This is because our model predicts that multiple rounds of cell division are required for cells to acquire sufficient DNA damage to induce apoptosis. As a result, adaptive therapy algorithms that modulate treatment but never completely withdraw it are predicted to perform better in this setting than strategies based on treatment interruptions. Pilot experiments in vivo confirm this conclusion. Overall, this study contributes to a better understanding of the impact of scheduling on treatment outcome for PARPis and showcases some of the challenges involved in developing adaptive therapies for new treatment settings.

A Wnt/β-catenin signaling inhibitor, IMU1003, suppresses the emergence of osimertinib-resistant colonies from gefitinib-resistant non-small cell lung cancer cells

Drug resistance has become a challenge in effective longterm molecular targeted therapy. Longterm non-small cell lung cancer (NSCLC) treatments with the first-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) shorten the effective duration of the third-generation EGFR-TKI, osimertinib, via genetic or epigenetic mechanisms in addition to the gatekeeper mutation T790M. This study reproduced this persistence in vitro using gefitinib-resistant NSCLC PC-9 cells (GR cells) and revealed that pharmacological nuclear localization inhibition of β-catenin suppressed the osimertinib resistance. Osimertinib effectively reduced GR cell survival but left significantly more resistant colonies than parental PC-9 cells. The nuclear fraction of β-catenin was enriched in GR cells during acquisition of osimertinib resistance. A chemical nuclear localization inhibitor of β-catenin, IMU1003, dramatically decreased the emergence of osimertinib-resistant colonies. Forced nuclear localization of β-catenin reduced IMU1003 efficacy. Thus, suppression of the nuclear β-catenin function may overcome the transgenerational EGFR-TKI-resistance.

Working with Convex Responses: Antifragility from Finance to Oncology

We extend techniques and learnings about the stochastic properties of nonlinear responses from finance to medicine, particularly oncology, where it can inform dosing and intervention. We define antifragility. We propose uses of risk analysis for medical problems, through the properties of nonlinear responses (convex or concave). We (1) link the convexity/concavity of the dose-response function to the statistical properties of the results; (2) define "antifragility" as a mathematical property for local beneficial convex responses and the generalization of "fragility" as its opposite, locally concave in the tails of the statistical distribution; (3) propose mathematically tractable relations between dosage, severity of conditions, and iatrogenics. In short, we propose a framework to integrate the necessary consequences of nonlinearities in evidence-based oncology and more general clinical risk management.

Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse

BACKGROUND

Adaptive CD19-targeted chimeric antigen receptor (CAR) T-cell transfer has become a promising treatment for leukemia. Although patient responses vary across different clinical trials, reliable methods to dissect and predict patient responses to novel therapies are currently lacking. Recently, the depiction of patient responses has been achieved using in silico computational models, with prediction application being limited.

METHODS

We established a computational model of CAR T-cell therapy to recapitulate key cellular mechanisms and dynamics during treatment with responses of continuous remission (CR), non-response (NR), and CD19-positive (CD19+) and CD19-negative (CD19-) relapse. Real-time CAR T-cell and tumor burden data of 209 patients were collected from clinical studies and standardized with unified units in bone marrow. Parameter estimation was conducted using the stochastic approximation expectation maximization algorithm for nonlinear mixed-effect modeling.

RESULTS

We revealed critical determinants related to patient responses at remission, resistance, and relapse. For CR, NR, and CD19+ relapse, the overall functionality of CAR T-cell led to various outcomes, whereas loss of the CD19+ antigen and the bystander killing effect of CAR T-cells may partly explain the progression of CD19- relapse. Furthermore, we predicted patient responses by combining the peak and accumulated values of CAR T-cells or by inputting early-stage CAR T-cell dynamics. A clinical trial simulation using virtual patient cohorts generated based on real clinical patient datasets was conducted to further validate the prediction.

CONCLUSIONS

Our model dissected the mechanism behind distinct responses of leukemia to CAR T-cell therapy. This patient-based computational immuno-oncology model can predict late responses and may be informative in clinical treatment and management.

Non-Small Cell Lung Cancer Targeted Therapy: Drugs and Mechanisms of Drug Resistance

The advent of precision medicine has brought light to the treatment of non-small cell lung cancer (NSCLC), expanding the options for patients with advanced NSCLC by targeting therapy through genetic and epigenetic cues. Tumor driver genes in NSCLC patients have been uncovered one by one, including epidermal growth factor receptor (EGFR), mesenchymal lymphoma kinase (ALK), and receptor tyrosine kinase ROS proto-oncogene 1 (ROS1) mutants. Antibodies and inhibitors that target the critical gene-mediated signaling pathways that regulate tumor growth and development are anticipated to increase patient survival and quality of life. Targeted drugs continue to emerge, with as many as two dozen approved by the FDA, and chemotherapy and targeted therapy have significantly improved patient prognosis. However, resistance due to cancer drivers' genetic alterations has given rise to significant challenges in treating patients with metastatic NSCLC. Here, we summarized the main targeted therapeutic sites of NSCLC drugs and discussed their resistance mechanisms, aiming to provide new ideas for follow-up research and clues for the improvement of targeted drugs.

Adaptive therapy to circumvent drug resistance to tyrosine kinase inhibitors in cancer: is it clinically relevant?

INTRODUCTION

Cancer is highly adaptable and is constantly evolving against current targeted therapies such as tyrosine kinase inhibitors. Despite advances in recent decades, the emergence of drug resistance to tyrosine kinase inhibitors constantly hampers therapeutic efficacy of cancer treatment. Continuous therapy versus intermittent clinical regimen has been a debate in drug administration of cancer patients. An ecologically-inspired shift in cancer treatment known as 'adaptive therapy' intends to improve the drug administration of drugs to cancer patients that can delay emergence of drug resistance.

AREAS COVERED

We discuss improved understanding of the concept of drug resistance, the basis of continuous therapy, intermittent clinical regimens, and adaptive therapy will be reviewed. In addition, we discuss how adaptive therapy provides guidance for future cancer treatment.

EXPERT OPINION

The current understanding of drug resistance in cancer leads to poor prognosis and limited treatment options in patients. Fighting drug resistance mutants is constantly followed by new forms of resistance. In most reported cases, continuous therapy leads to drug resistance and an intermittent clinical regimen vaguely delays it. However, adaptive therapy, conceptually, exploits multiple parameters that can suppress the growth of drug resistance and provides safe treatment for cancer patients in the future.

Assessing chemotherapy dosing strategies in a spatial cell culture model

Predicting patient responses to chemotherapy regimens is a major challenge in cancer treatment. Experimental model systems coupled with quantitative mathematical models to calculate optimal dose and frequency of drugs can enable improved chemotherapy regimens. Here we developed a simple approach to track two-dimensional cell colonies composed of chemo-sensitive and resistant cell populations via fluorescence microscopy and coupled this to computational model predictions. Specifically, we first developed multiple 4T1 breast cancer cell lines resistant to varying concentrations of doxorubicin, and demonstrated how heterogeneous populations expand in a two-dimensional colony. We subjected cell populations to varied dose and frequency of chemotherapy and measured colony growth. We then built a mathematical model to describe the dynamics of both chemosensitive and chemoresistant populations, where we determined which number of doses can produce the smallest tumor size based on parameters in the system. Finally, using an in vitro model we demonstrated multiple doses can decrease overall colony growth as compared to a single dose at the same total dose. In the future, this system can be adapted to optimize dosing strategies in the setting of heterogeneous cell types or patient derived cells with varied chemoresistance.

Antifragile Control Systems: The Case of an Anti-Symmetric Network Model of the Tumor-Immune-Drug Interactions

A therapy’s outcome is determined by a tumor’s response to treatment which, in turn, depends on multiple factors such as the severity of the disease and the strength of the patient’s immune response. Gold standard cancer therapies are in most cases fragile when sought to break the ties to either tumor kill ratio or patient toxicity. Lately, research has shown that cancer therapy can be at its most robust when handling adaptive drug resistance and immune escape patterns developed by evolving tumors. This is due to the stochastic and volatile nature of the interactions, at the tumor environment level, tissue vasculature, and immune landscape, induced by drugs. Herein, we explore the path toward antifragile therapy control, that generates treatment schemes that are not fragile but go beyond robustness. More precisely, we describe the first instantiation of a control-theoretic method to make therapy schemes cope with the systemic variability in the tumor-immune-drug interactions and gain more tumor kills with less patient toxicity. Considering the anti-symmetric interactions within a model of the tumor-immune-drug network, we introduce the antifragile control framework that demonstrates promising results in simulation. We evaluate our control strategy against state-of-the-art therapy schemes in various experiments and discuss the insights we gained on the potential that antifragile control could have in treatment design in clinical settings.

High Tumor Mutation Burden Is Associated with Poor Clinical Outcome in EGFR-Mutated Lung Adenocarcinomas Treated with Targeted Therapy

This study aimed to determine the association between TMB and treatment outcomes in patients with epidermal growth factor receptor (EGFR)-mutated lung cancer that were treated with tyrosine kinase inhibitors (TKIs). The TMB was assessed using a 409-gene targeted next-generation sequencing panel. We compared the response rate (RR), progression-free survival (PFS), overall survival (OS), and frequency of secondary T790M mutations among the different TMB groups. The median TMB of the study population (n = 88) was 3.36/megabases. We divided 52 (59%) and 36 (41%) patients into the low and high TMB groups, respectively. A high TMB level was significantly associated with liver metastasis and more advanced stage (all p < 0.05). RR was significantly lower in the high TMB group than that of the low TMB group (50.0% vs. 80.7%, all p = 0.0384). In multivariate analysis, high TMB was independently associated with a shorter PFS (hazard ratio [HR] = 1.80, p = 0.0427) and shorter OS (HR = 2.05, p = 0.0397) than that of the low TMB group. Further, high TMB was independently associated with decreased T790M mutation development. These results suggest that high TMB may be a predictive biomarker for adverse treatment outcomes and represent a patients’ subgroup warranting tailored therapeutic approaches.

Osimertinib for lung cancer cells harboring low-frequency EGFR T790M mutation

Osimertinib, a third-generation EGFR tyrosine kinase inhibitor, shows significant benefit among patients with EGFR T790M mutation at disease progression. We analyzed the whole exome sequence of 48 samples obtained from 16 lung cancer patients with a longitudinal follow-up: treatment-naïve-baseline primary tumors positive for EGFR activating-mutations, paired re-biopsies upon disease progression but negative for EGFR T790M mutation based on qPCR, and their matched normal blood samples. Our Next generation sequencing (NGS) analysis identified an additional set of 25% re-biopsy samples to harbor EGFR T790M mutation occurring at a low-allele frequency of 5% or less, undetectable by conventional qPCR-based assays. Notably, the clinical utility of osimertinib among patients harboring low-allele frequency of EGFR T790M in tissue biopsy upon disease progression remains less explored. We established erlotinib-resistant PC-9R cells and twenty single-cell sub-clones from erlotinib-sensitive lung cancer PC-9 cells using in vitro drug-escalation protocol. NGS and allele-specific PCR confirmed the low-allele frequency of EGFR T790M present at 5% with a 100-fold higher resistance to erlotinib in the PC-9R cells and its sub-clones. Additionally, luciferase tagged PC-9, and PC-9R cells were orthotopically injected through the intercostal muscle into NOD-SCID mice. The orthotopic lung tumors formed were observed by non-invasive bioluminescence imaging. Consistent with in vitro data, osimertinib, but not erlotinib, caused tumor regression in mice injected with PC-9R cells, while both osimertinib and erlotinib inhibited tumors in mice injected with PC-9 cells. Taken together, our findings could extend the benefit of osimertinib treatment to patients with low EGFR T790M mutation allele frequency on disease progression.

Technical Validation and Clinical Implications of Ultrasensitive PCR Approaches for EGFR-Thr790Met Mutation Detection in Pretreatment FFPE Samples and in Liquid Biopsies from Non-Small Cell Lung Cancer Patients

In pretreatment tumor samples of EGFR-mutated non-small cell lung cancer (NSCLC) patients, EGFR-Thr790Met mutation has been detected in a variable prevalence by different ultrasensitive assays with controversial prognostic value. Furthermore, its detection in liquid biopsy (LB) samples remains challenging, being hampered by the shortage of circulating tumor DNA (ctDNA). Here, we describe the technical validation and clinical implications of a real-time PCR with peptide nucleic acid (PNA-Clamp) and digital droplet PCR (ddPCR) for EGFR-Thr790Met detection in diagnosis FFPE samples and in LB. Limit of blank (LOB) and limit of detection (LOD) were established by analyzing negative and low variant allele frequency (VAF) FFPE and LB specimens. In a cohort of 78 FFPE samples, both techniques showed an overall agreement (OA) of 94.20%. EGFR-Thr790Met was detected in 26.47% of cases and was associated with better progression-free survival (PFS) (16.83 ± 7.76 vs. 11.47 ± 1.83 months; p = 0.047). In LB, ddPCR was implemented in routine diagnostics under UNE-EN ISO 15189:2013 accreditation, increasing the detection rate of 32.43% by conventional methods up to 45.95%. During follow-up, ddPCR detected EGFR-Thr790Met up to 7 months before radiological progression. Extensively validated ultrasensitive assays might decipher the utility of pretreatment EGFR-Thr790Met and improve its detection rate in LB studies, even anticipating radiological progression.

Prognostic impact of pretreatment T790M mutation on outcomes for patients with resected, EGFR-mutated, non-small cell lung cancer

Background

Many previous studies have demonstrated that minor-frequency pretreatment T790M mutation (preT790M) could be detected by ultrasensitive methods in a considerable number of treatment-naïve, epidermal growth factor receptor (EGFR)-mutated, non-small cell lung cancer (NSCLC) cases. However, the impact of preT790M in resected cases on prognosis remains unclear.

Methods

We previously reported that preT790M could be detected in 298 (79.9%) of 373 surgically resected, EGFR-mutated NSCLC patients. Therefore, we investigated the impact of preT790M on recurrence-free survival (RFS) and overall survival (OS) in this cohort by multivariate analysis. All patients were enrolled from July 2012 to December 2013, with follow-up until November 30, 2017.

Results

The median follow-up time was 48.6 months. Using a cutoff value of the median preT790M allele frequency, the high-preT790M group (n = 151) had significantly shorter RFS (hazard ratio [HR] = 1.51, 95% confidence interval [CI]: 1.01–2.25, P = 0.045) and a tendency for a shorter OS (HR = 1.87, 95% CI: 0.99–3.55, P = 0.055) than the low-preT790M group (n = 222). On multivariate analysis, higher preT790M was independently associated with shorter RFS (high vs low, HR = 1.56, 95% CI: 1.03–2.36, P = 0.035), irrespective of advanced stage, older age, and male sex, and was also associated with shorter OS (high vs low, HR = 2.16, 95% CI: 1.11–4.20, P = 0.024) irrespective of advanced stage, older age, EGFR mutation subtype, and history of adjuvant chemotherapy.

Conclusions

Minor-frequency, especially high-abundance of, preT790M was an independent factor associated with a poor prognosis in patients with surgically resected, EGFR-mutated NSCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09869-7.

Measuring competitive exclusion in non-small cell lung cancer

In this study, we experimentally measure the frequency-dependent interactions between a gefitinib-resistant non-small cell lung cancer population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. Using frequency-dependent growth rate data, we then show that gefitinib treatment results in competitive exclusion of the ancestor, while the absence of treatment results in a likely, but not guaranteed, exclusion of the resistant strain. Then, using simulations, we demonstrate that incorporating ecological growth effects can influence the predicted extinction time. In addition, we show that higher drug concentrations may not lead to the optimal reduction in tumor burden. Together, these results highlight the potential importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and as we translate adaptive therapy regimens to the clinic.

Comparison of T790M Acquisition After Treatment With First- and Second-Generation Tyrosine-Kinase Inhibitors: A Systematic Review and Network Meta-Analysis

Background

Lung adenocarcinoma is a common disease with a high mortality rate. Epidermal growth factor receptor (EGFR) mutations are found in adenocarcinomas, and oral EGFR-tyrosine kinase inhibitors (EGFR-TKIs) show good responses. EGFR-TKI therapy eventually results in resistance, with the most common being T790M. T790M is also a biomarker for predicting resistance to first- and second-generation EGFR-TKIs and is sensitive to osimertinib. The prognosis was better for patients with acquired T790M who were treated with osimertinib than for those treated with chemotherapy. Therefore, T790M mutation is important for deciding further treatment and prognosis. Previous studies based on small sample sizes have reported very different T790 mutation rates. We conducted a meta-analysis to evaluate the T790M mutation rate after EGFR-TKI treatment.

Methods

We systematic reviewed the electronic databases to evaluate the T790M mutation rate after treatment with first-generation (gefitinib, erlotinib, and icotinib) and second-generation (afatinib and dacomitinib) EGFR-TKIs. Random-effects network meta-analysis and single-arm meta-analysis were conducted to estimate the T790M mutation rate of the target EGFR-TKIs.

Results

A total of 518 studies were identified, of which 29 were included. Compared with afatinib, a higher odds ratio (OR) of the T790M mutation rate was observed after erlotinib [OR = 1.48; 95% confidence interval (CI):1.09–2.00] and gefitinib (OR = 1.45; 95% CI: 1.11–1.90) treatments. An even OR of the T790M mutation rate was noted after icotinib treatment (OR = 0.91, 95% CI: 0.46–1.79) compared with that after afatinib. The T790M mutation rate was significantly lower with afatinib (33%) than that with gefitinib (49%) and erlotinib treatments (47%) (p < 0.001). The acquired T790M mutation rate in all participants was slightly lower in Asians (43%) than that in Caucasians (47%).

Conclusions

Erlotinib and gefitinib had a higher OR for the T790M mutation than afatinib. The T790M mutation rate was significantly lower in afatinib than in gefitinib and erlotinib. T790M is of great significance because osimertinib shows a good prognosis in patients with T790M mutation.

Systematic Review Registration

PROSPERO, identifier CRD42021257824.

Early Steps of Resistance to Targeted Therapies in Non-Small-Cell Lung Cancer

Simple Summary

Patients with lung cancer benefit from more effective treatments, such as targeted therapies, and the overall survival has increased in the past decade. However, the efficacy of targeted therapies is limited due to the emergence of resistance. Growing evidence suggests that resistances may arise from a small population of drug-tolerant persister (DTP) cells. Understanding the mechanisms underlying DTP survival is therefore crucial to develop therapeutic strategies to prevent the development of resistance. Herein, we propose an overview of the current scientific knowledge about the characterisation of DTP, and summarise the new therapeutic strategies that are tested to target these cells.

Abstract

Lung cancer is the leading cause of cancer-related deaths among men and women worldwide. Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are effective therapies for advanced non-small-cell lung cancer (NSCLC) patients harbouring EGFR-activating mutations, but are not curative due to the inevitable emergence of resistances. Recent in vitro studies suggest that resistance to EGFR-TKI may arise from a small population of drug-tolerant persister cells (DTP) through non-genetic reprogramming, by entering a reversible slow-to-non-proliferative state, before developing genetically derived resistances. Deciphering the molecular mechanisms governing the dynamics of the drug-tolerant state is therefore a priority to provide sustainable therapeutic solutions for patients. An increasing number of molecular mechanisms underlying DTP survival are being described, such as chromatin and epigenetic remodelling, the reactivation of anti-apoptotic/survival pathways, metabolic reprogramming, and interactions with their micro-environment. Here, we review and discuss the existing proposed mechanisms involved in the DTP state. We describe their biological features, molecular mechanisms of tolerance, and the therapeutic strategies that are tested to target the DTP.

Optimal Strategy and Benefit of Pulsed Therapy Depend On Tumor Heterogeneity and Aggressiveness at Time of Treatment Initiation

Therapeutic resistance is a fundamental obstacle in cancer treatment. Tumors that initially respond to treatment may have a preexisting resistant subclone or acquire resistance during treatment, making relapse theoretically inevitable. Here, we investigate treatment strategies that may delay relapse using mathematical modeling. We find that for a single-drug therapy, pulse treatment-short, elevated doses followed by a complete break from treatment-delays relapse compared with continuous treatment with the same total dose over a length of time. For tumors treated with more than one drug, continuous combination treatment is only sometimes better than sequential treatment, while pulsed combination treatment or simply alternating between the two therapies at defined intervals delays relapse the longest. These results are independent of the fitness cost or benefit of resistance, and are robust to noise. Machine-learning analysis of simulations shows that the initial tumor response and heterogeneity at the start of treatment suffice to determine the benefit of pulsed or alternating treatment strategies over continuous treatment. Analysis of eight tumor burden trajectories of breast cancer patients treated at Memorial Sloan Kettering Cancer Center shows the model can predict time to resistance using initial responses to treatment and estimated preexisting resistant populations. The model calculated that pulse treatment would delay relapse in all eight cases. Overall, our results support that pulsed treatments optimized by mathematical models could delay therapeutic resistance.

Optimizing the future: how mathematical models inform treatment schedules for cancer

For decades, mathematical models have influenced how we schedule chemotherapeutics. More recently, mathematical models have leveraged lessons from ecology, evolution, and game theory to advance predictions of optimal treatment schedules, often in a personalized medicine manner. We discuss both established and emerging therapeutic strategies that deviate from canonical standard-of-care regimens, and how mathematical models have contributed to the design of such schedules. We first examine scheduling options for single therapies and review the advantages and disadvantages of various treatment plans. We then consider the challenge of scheduling multiple therapies, and review the mathematical and clinical support for various conflicting treatment schedules. Finally, we propose how a consilience of mathematical and clinical knowledge can best determine the optimal treatment schedules for patients.

The Role of TP53 Mutations in EGFR-Mutated Non-Small-Cell Lung Cancer: Clinical Significance and Implications for Therapy

Simple Summary

Non-Small-Cell Lung Cancer (NSCLC) is the primary cause of cancer-related death worldwide. Patients carrying Epidermal Growth Factor Receptor (EGFR) mutations usually benefit from targeted therapy treatment. Nonetheless, primary or acquired resistance mechanisms lead to treatment discontinuation and disease progression. Tumor protein 53 (TP53) mutations are the most common mutations in NSCLC, and several reports highlighted a role for these mutations in influencing prognosis and responsiveness to EGFR targeted therapy. In this review, we discuss the emerging data about the role of TP53 in predicting EGFR mutated NSCLC patients’ prognosis and responsiveness to targeted therapy.

Abstract

Non-Small-Cell Lung Cancer (NSCLC) is the primary cause of cancer-related death worldwide. Oncogene-addicted patients usually benefit from targeted therapy, but primary and acquired resistance mechanisms inevitably occur. Tumor protein 53 (TP53) gene is the most frequently mutated gene in cancer, including NSCLC. TP53 mutations are able to induce carcinogenesis, tumor development and resistance to therapy, influencing patient prognosis and responsiveness to therapy. TP53 mutants present in different forms, suggesting that different gene alterations confer specific acquired protein functions. In recent years, many associations between different TP53 mutations and responses to Epidermal Growth Factor Receptor (EGFR) targeted therapy in NSCLC patients have been found. In this review, we discuss the current landscape concerning the role of TP53 mutants to guide primary and acquired resistance to Tyrosine-Kinase Inhibitors (TKIs) EGFR-directed, investigating the possible mechanisms of TP53 mutants within the cellular compartments. We also discuss the role of the TP53 mutations in predicting the response to targeted therapy with EGFR-TKIs, as a possible biomarker to guide patient stratification for treatment.

Discovery of a Series of 1,2,3-Triazole-Containing Erlotinib Derivatives With Potent Anti-Tumor Activities Against Non-Small Cell Lung Cancer

Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are emerging at the vanguard of therapy for non-small-cell lung cancer (NSCLC) patients with EGFR-activating mutations. However, the increasing therapeutic resistance caused by novel mutations or activated bypass pathways has impaired their performance. In this study, we link one of the commercial EGFR-TKIs, Erlotinib, to different azide compounds to synthesize a novel class of 1,2,3-triazole ring-containing Erlotinib derivatives. We discovered that several new compounds show robust antiproliferation activity against diverse NSCLC cells in vitro including PC-9, H460, H1975 and A549. Two of the most potent compounds, e4 and e12 have been found to be more efficient than Erlotinib in all NSCLC cell lines except PC-9. They significantly induce apoptosis and cell cycle arrest in PC-9 and H460 cells. The antitumor efficacy of compound e4 in vivo is close to that of Erlotinib in a PC-9 xenograft mouse model. Most Erlotinib-1,2,3-triazole compounds exhibit moderate to good inhibitory activities toward wild-type EGFR as indicated by enzyme-linked immunosorbent assay (ELISA), and the EGFR phosphorylation was inhibited in H460 and PC-9 cells exposed to e4 or e12. These data suggest that these Erlotinib-1,2,3-triazole compounds are suitable candidates for use against NSCLC and more unknown mechanisms merit further investigation.

Overexpression of Reactive Oxygen Species Modulator 1 Predicts Unfavorable Clinical Outcome in EGFR-Mutant Lung Adenocarcinomas Treated With Targeted Therapy

Purpose

Reactive oxygen species modulator 1 (Romo1) is a novel protein that regulates the production of intracellular reactive oxygen species. Romo1 has been shown to be associated with poor survival in various clinical settings for the treatment of lung cancer. In this study, we evaluated whether tissue Romo1 expression was associated with clinical outcomes in epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma treated with tyrosine kinase inhibitors (TKIs).

Method

Romo1 expression in tumor tissues was examined by immunohistochemistry and evaluated by histologic score. Univariate and multivariate analyses were performed to identify the clinicopathologic parameters, including Romo1 expression, which may be associated with progression-free survival (PFS), overall survival (OS), and incidence of secondary T790M mutation.

Results

A total of 96 tumor specimens were analyzed. With the cut-off value of 200, 71 (74.0%) and 25 (26.0%) patients were classified into low and high Romo1 groups, respectively. The median PFS of the high Romo1 group was significantly shorter than that of the low Romo1 group (13.1 vs 19.9 months, p = 0.0165). The median OS of the high Romo1 group was also significantly shorter than that of the low Romo1 group (19.8 vs 37.0 months, p = 0.0006). Multivariate analyses showed that high Romo1 expression was independently associated with both poor PFS (hazard ratio [HR] = 2.48, 95% confidence interval [CI]: 1.35–4.56, p = 0.0034) and poor OS (HR = 3.17, 95% CI: 1.57–6.41, p = 0.0013). In addition, the rate of secondary T790M mutation after TKI failure was significantly lower in the high Romo1 group than the low Romo1 group (16.7% vs. 38.3%, p = 0.0369).

Conclusions

Romo1 overexpression was associated with poor response to treatment and short survival in patients treated with EGFR-TKIs, suggesting a distinct subgroup warranting active surveillance and tailored therapeutic approach. In addition, our data highlight that Romo1 could be a potential predictive and prognostic biomarker for this patient population.

Preclinical Models for the Study of Lung Cancer Pathogenesis and Therapy Development

Experimental preclinical models have been a cornerstone of lung cancer translational research. Work in these model systems has provided insights into the biology of lung cancer subtypes and their origins, contributed to our understanding of the mechanisms that underlie tumor progression, and revealed new therapeutic vulnerabilities. Initially patient-derived lung cancer cell lines were the main preclinical models available. The landscape is very different now with numerous preclinical models for research each with unique characteristics. These include genetically engineered mouse models (GEMMs), patient-derived xenografts (PDXs) and three-dimensional culture systems ("organoid" cultures). Here we review the development and applications of these models and describe their contributions to lung cancer research.

Improving cancer treatments via dynamical biophysical models

Despite significant advances in oncological research, cancer nowadays remains one of the main causes of mortality and morbidity worldwide. New treatment techniques, as a rule, have limited efficacy, target only a narrow range of oncological diseases, and have limited availability to the general public due their high cost. An important goal in oncology is thus the modification of the types of antitumor therapy and their combinations, that are already introduced into clinical practice, with the goal of increasing the overall treatment efficacy. One option to achieve this goal is optimization of the schedules of drugs administration or performing other medical actions. Several factors complicate such tasks: the adverse effects of treatments on healthy cell populations, which must be kept tolerable; the emergence of drug resistance due to the intrinsic plasticity of heterogeneous cancer cell populations; the interplay between different types of therapies administered simultaneously. Mathematical modeling, in which a tumor and its microenvironment are considered as a single complex system, can address this complexity and can indicate potentially effective protocols, that would require experimental verification. In this review, we consider classical methods, current trends and future prospects in the field of mathematical modeling of tumor growth and treatment. In particular, methods of treatment optimization are discussed with several examples of specific problems related to different types of treatment.

Molecular Mechanism of EGFR-TKI Resistance in EGFR-Mutated Non-Small Cell Lung Cancer: Application to Biological Diagnostic and Monitoring

Non-small cell lung cancer (NSCLC) is the most common cancer in the world. Activating epidermal growth factor receptor (EGFR) gene mutations are a positive predictive factor for EGFR tyrosine kinase inhibitors (TKIs). For common EGFR mutations (Del19, L858R), the standard first-line treatment is actually third-generation TKI, osimertinib. In the case of first-line treatment by first (erlotinib, gefitinib)- or second-generation (afatinib) TKIs, osimertinib is approved in second-line treatment for patients with T790M EGFR mutation. Despite the excellent disease control results with EGFR TKIs, acquired resistance inevitably occurs and remains a biological challenge. This leads to the discovery of novel biomarkers and possible drug targets, which vary among the generation/line of EGFR TKIs. Besides EGFR second/third mutations, alternative mechanisms could be involved, such as gene amplification or gene fusion, which could be detected by different molecular techniques on different types of biological samples. Histological transformation is another mechanism of resistance with some biological predictive factors that needs tumor biopsy. The place of liquid biopsy also depends on the generation/line of EGFR TKIs and should be a good candidate for molecular monitoring. This article is based on the literature and proposes actual and future directions in clinical and translational research.