Mathematical optimisation of the cisplatin plus etoposide combination for managing extensive-stage small-cell lung cancer patients

Convergent evolution of allele-specific gene expression that leads to non-small cell lung cancer in different human populations

Phenotypical innovations during evolution are caused by novel mutations, which are usually heterozygous at the beginning. The gene expressions on two alleles of these mutation sites are not necessarily identical, leading to flexible allele-specific regulation in cell systems. We retrieve the transcriptome data of normal and non-small cell lung cancer (NSCLC) tissues from 47 African Americans (AA) and 50 European Americans (EA). We analyze the differentially expressed genes (DEGs) in NSCLC as well as the tumor-specific mutations. Expression and mutation profiles show convergent evolution in AA and EA populations. The tumor-specific mutations are poorly overlapped, but many of them are located in the same genes, mainly oncogenes and tumor suppressor genes. The DEGs in tumors are majorly caused by the mutated alleles rather than normal alleles. The relative expressions of mutated alleles are highly correlated between AA and EA. The differential expression in NSCLC is predominantly mediated by the mutated alleles on heterozygous sites. This molecular mechanism underlying NSCLC oncogenesis is conserved across different human populations, exhibiting convergent evolution. We present this novel angle that differential expression analysis should be performed separately for different alleles. Our ideas should greatly benefit the cancer community.

The functional significance of circRNA/miRNA/mRNA interactions as a regulatory network in lung cancer biology

Lung cancer, the leading cause of cancer-related deaths, presents significant challenges to patients due to its poor prognosis. Recent research has increasingly implicated circular RNAs in the development and progression of lung cancer. These circular RNAs have been found to impact various aspects of tumor behavior, including proliferation, metastasis, cell cycle regulation, apoptosis, cancer stem cells, therapy response, and the tumor microenvironment. One of the key mechanisms by which circular RNAs exert their influence is through their ability to act as miRNA sponges, sequestering microRNAs and preventing them from targeting other RNA molecules. Accumulating evidence suggests that circular RNAs can function as competing endogenous RNAs, affecting the expression of target mRNAs by sequestering microRNAs. Dysregulation of competing endogenous RNAs networks involving circular RNAs, microRNAs, and mRNAs leads to the aberrant expression of oncogenes and tumor suppressors involved in lung cancer pathogenesis. Understanding the dynamic interplay and molecular mechanisms among circular RNAs, microRNAs, and mRNAs holds great promise for advancing early diagnosis, personalized therapeutic interventions, and improved patient outcomes in lung cancer. Therefore, this study aims to provide an in-depth exploration of the executive roles of circular RNAs/microRNAs/ mRNAs interactions in lung cancer pathogenesis and their potential utility for diagnosing lung cancer, predicting patient prognosis, and guiding targeted therapies. By offering a comprehensive overview of the dysregulation of the axes as driving factors in lung cancer, we aim to pave the way for their translation into clinical practice in the future.

Hsa_circ_0092856 Promoted the Proliferation, Migration, and Invasion of NSCLC Cells by Up-Regulating the Expression of eIF3a

Circular RNA (circRNA) plays a very important regulatory role in a variety of human malignancies such as non-small-cell lung cancer (NSCLC). In the current study, we explored the role of hsa_circ_0092856 in the progression of NSCLC. We screened CircRNA from the eIF3a gene in the Circbase database. The biological functions of hsa_circ_0092856 in NSCLC were analyzed via qRT-PCR, a CCK-8 assay, a plate cloning experiment, scratch testing, a transwell chamber experiment, an RNA nuclear mass separation experiment, an RIP experiment, and a Western blot test. The results showed that hsa_circ_0092856 was highly expressed in NSCLC cells, and the knockdown of hsa_circ_0092856 could inhibit the proliferation, migration, and invasion of NSCLC cells. The overexpression of hsa_circ_0092856 has the opposite effect. The expression of eIF3a also changed with the change in hsa_circ_0092856. These results suggest that hsa_circ_0092856 may play a key role in the progression of NSCLC by regulating the expression of eIF3a.

The Sponge Interaction Between Circular RNA and microRNA Serves as a Fast-Evolving Mechanism That Suppresses Non-small Cell Lung Cancer (NSCLC) in Humans

Non-small cell lung cancer (NSCLC) is one of the most lethal cancer types in the world. Currently, the molecular mechanisms and pathways underlying NSCLC oncogenesis are poorly understood. Using multiple Omics data, we systematically explored the differentially expressed circular RNAs (circRNAs) in NSCLC. We also investigated potential microRNA sponges (that absorb circRNAs) in NSCLC and downstream target genes with experimental verifications. hsa_circ_0003497 was down-regulated in NSCLC and played an inhibitory role in tumorigenesis. In contrast, miR-197-3p was up-regulated in NSCLC. hsa_circ_0003497 directly interacts with miR-197-3p and releases a target gene of miR-197-3p termed CTNND1 (a known tumor suppressor gene). Evolutionary analysis reveals fast evolution of this hsa_circ_0003497-miR-197-3p-CTNND1-NSCLC axis in mammals. This work clarified the biological functions and molecular mechanisms of how hsa_circ_0003497 suppresses NSCLC through miR-197-3p and CTNND1. We discovered molecular markers for the prognosis of NSCLC and provided potential intervention targets for its treatment.

Individual prediction of thrombocytopenia at next chemotherapy cycle: Evaluation of dynamic model performances

AIMS

Thrombocytopenia is a common major side-effect of cytotoxic cancer therapies. A clinically relevant problem is to predict an individual's thrombotoxicity in the next planned chemotherapy cycle in order to decide on treatment adaptation. To support this task, 2 dynamic mathematical models of thrombopoiesis under chemotherapy were proposed, a simple semimechanistic model and a comprehensive mechanistic model. In this study, we assess the performance of these models with respect to existing thrombocytopenia grading schemes.

METHODS

We consider close-meshed individual time series data of 135 non-Hodgkin's lymphoma patients treated with 6 cycles of CHOP/CHOEP chemotherapies. Individual parameter estimates were derived on the basis of these data considering a varying number of cycles per patient. Parsimony assumptions were applied to optimize parameter identifiability. Models' predictability are assessed by determining deviations of predicted and observed degrees of thrombocytopenia in the next cycles.

RESULTS

The mechanistic model results in better agreement of model prediction and individual time series data. Prediction accuracy of future cycle toxicities by the mechanistic model is higher even if the semimechanistic model is provided with data of more cycles for calibration.

CONCLUSION

We successfully established a quantitative and clinically relevant method for assessing prediction performances of biomathematical models of thrombopoiesis under chemotherapy. We showed that the more comprehensive mechanistic model outperforms the semimechanistic model. We aim at implementing the mechanistic model into clinical practice to assess its utility in real life clinical decision-making.

Prognostic value of neuron-specific enolase for small cell lung cancer: a systematic review and meta-analysis

BACKGROUND

Neuron-specific enolase (NSE) has become a widely used and easily attainable laboratory assay of small cell lung cancer (SCLC). However, the prognostic value of NSE for SCLC patients remains controversial. The aim of the study was to evaluate the correlation between elevated serum NSE before therapy and survival of SCLC patients.

METHODS

We performed a systematic review and meta-analysis. A systematic literature search was conducted in PubMed, Embase, and the Cochrane Central Register from the inception dates to December 2019. Eligible articles were included according to inclusion and exclusion criteria; then, data extraction and quality assessment were performed. The primary outcome was overall survival (OS), and the secondary endpoint was progression-free survival (PFS).

RESULTS

We identified 18 studies comprising 2981 patients. Pooled results revealed that elevated NSE was associated with worse OS (HR = 1.78, 95% CI 1.55-2.06, p < 0.001) and PFS (HR = 1.50, 95% CI 1.16-1.93, p = 0.002). In subgroup analysis, elevated NSE did not predict worse OS in patients who received only chemotherapy (HR 1.22, 95% CI 0.96-1.55, p = 0.10) or part of whom received surgical resection before chemotherapy and radiotherapy (HR = 2.16, 95% CI 0.82-5.69, p = 0.12).

CONCLUSION

Elevated serum NSE before any therapy of SCLC patients may be a negative prognostic factor for OS and PFS. The prognostic value of NSE for OS was particularly observed in patients treated by standard management.

Circular RNA in Lung Cancer Research: Biogenesis, Functions, and Roles

Lung cancer is one of the most common and deadly malignancies worldwide, in spite of advances in targeted therapy in recent years. An effective strategy for lung cancer prevention remains a major problem. Advances in next-generation sequencing have helped in understanding the RNA and identifying novel circular RNAs (circRNAs) that may have a broad impact on the early diagnosis and treatment of lung cancer. The circRNAs, exhibiting spatiotemporal-specific expression, are stable and conserved and present diverse biological functions in the normal and diseased states, including cancer. In this review, we summarize the recent advances in elucidating the functional role of circRNAs in lung cancer pathogenesis and discuss their potential mechanisms.

Modeling individual time courses of thrombopoiesis during multi-cyclic chemotherapy

Background

Thrombocytopenia is a major side-effect of cytotoxic cancer therapies. The aim of precision medicine is to develop therapy modifications accounting for the individual’s risk.

Methodology/Principle findings

To solve this task, we develop an individualized bio-mechanistic model of the dynamics of bone marrow thrombopoiesis, circulating platelets and therapy effects thereon. Comprehensive biological knowledge regarding cell differentiation, amplification, apoptosis rates, transition times and corresponding regulations are translated into ordinary differential equations. A model of osteoblast/osteoclast interactions was incorporated to mechanistically describe bone marrow support of quiescent cell stages. Thrombopoietin (TPO) as a major regulator is explicitly modelled including pharmacokinetics and–dynamics of TPO injections. Effects of cytotoxic drugs are modelled by transient depletions of proliferating cells.

To calibrate the model, we used population data from the literature and close-meshed individual data of N = 135 high-grade non-Hodgkin’s lymphoma patients treated with CHOP-like chemotherapies. To limit the number of free parameters, several parsimony assumptions were derived from biological data and tested via Likelihood methods. Heterogeneity of patients was explained by a few model parameters. The over-fitting issue of individual parameter estimation was successfully dealt with a virtual participation of each patient in population-based experiments.

The model qualitatively and quantitatively explains a number of biological observations such as the role of osteoblasts in explaining long-term toxic effects, megakaryocyte-mediated feedback on stem cells, bi-phasic stimulation of thrombopoiesis by TPO, dynamics of megakaryocyte ploidies and non-exponential platelet degradation. Almost all individual time series could be described with high precision. We demonstrated how the model can be used to provide predictions regarding individual therapy adaptations.

Conclusions

We propose a mechanistic thrombopoiesis model of unprecedented comprehensiveness in both, biological mechanisms considered and experimental data sets explained. Our innovative method of parameter estimation allows robust determinations of individual parameter settings facilitating the development of individual treatment adaptations during chemotherapy.

Chemotherapy is ubiquitously used to treat cancer diseases. Due to general toxicity of the drugs, chemotherapy results in a number of side effects especially with respect to blood formation. Here we study the loss of platelets during chemotherapy which is dose limiting in many situations. However, this side-effect greatly varies between patients with respect to both, severity and necessity of clinical countermeasures.We therefore developed a mathematical model to predict the time course of platelets of patients under chemotherapy and to propose possible treatment adaptations in cases of intolerable toxicity. The model is based on available biological knowledge and data of platelet formation and therapeutic effects thereon. As a major result, we could describe individual time series data of 135 patients under chemotherapy. Conversely, the model can be used to make predictions regarding alternative therapy schedules such as postponement of therapy or chemotherapy dose reductions. Our model is intended to support clinical decision making on an individual patient level.

Administration of temozolomide: Comparison of conventional and metronomic chemotherapy regimens

PURPOSE

We compare the Maximum Tolerated Dose (MTD) and Metronomic Chemotherapy (MC) protocols for temozolomide administration. We develop an innovative methodology for characterizing optimal chemotherapy regimens.

METHODS

We use a PK/PD model based on Faivre et al. (2013) for the pharmacokinetics of temozolomide, as well as the pharmacodynamics of its efficacy. For toxicity, which is measured by the nadir of the normalized absolute neutrophil count, we formalize the myelosuppression effect of temozolomide with the physiological model of Panetta et al. (2003b). We introduce a multi-criteria tool for comparing protocols along their efficacy and toxicity dimensions.

RESULTS

We show that the toxicity of the MC regimen proposed by Faivre et al. (2013) can greatly be reduced without affecting its efficacy, while the standard MTD protocol efficacy cannot be improved without impairing its toxicity. We also show that for any acceptable toxicity level, the optimal protocol remains closely related to standard MTD.

CONCLUSIONS

Overall, our new method enables a rich comparison between protocols along multiple dimensions. We can rank protocols for temozolomide administration. It is a first step toward building optimal individual protocols.

Dose- and time-dependence of the host-mediated response to paclitaxel therapy: a mathematical modeling approach

It has recently been suggested that pro-tumorigenic host-mediated processes induced in response to chemotherapy counteract the anti-tumor activity of therapy, and thereby decrease net therapeutic outcome. Here we use experimental data to formulate a mathematical model describing the host response to different doses of paclitaxel (PTX) chemotherapy as well as the duration of the response. Three previously described host-mediated effects are used as readouts for the host response to therapy. These include the levels of circulating endothelial progenitor cells in peripheral blood and the effect of plasma derived from PTX-treated mice on migratory and invasive properties of tumor cells in vitro. A first set of mathematical models, based on basic principles of pharmacokinetics/pharmacodynamics, did not appropriately describe the dose-dependence and duration of the host response regarding the effects on invasion. We therefore provide an alternative mathematical model with a dose-dependent threshold, instead of a concentration-dependent one, that describes better the data. This model is integrated into a global model defining all three host-mediated effects. It not only precisely describes the data, but also correctly predicts host-mediated effects at different doses as well as the duration of the host response. This mathematical model may serve as a tool to predict the host response to chemotherapy in cancer patients, and therefore may be used to design chemotherapy regimens with improved therapeutic outcome by minimizing host mediated effects.

The prognostic value of the serum neuron specific enolase and lactate dehydrogenase in small cell lung cancer patients receiving first-line platinum-based chemotherapy

The aim of this study was to investigate the associations of serum levels of neuron-specific enolase (NSE), pro-gastrin releasing peptide (ProGRP), and lactate dehydrogenase (LDH) with clinical response and survival in small cell lung cancer (SCLC) patients receiving first-line platinum-based chemotherapy.One hundred thirty-six patients with SCLC were recruited in this study. All the patients received first-line platinum-based chemotherapy. Clinical efficacy was assessed according to Response Evaluation Criteria in Solid Tumors v1.1 criteria. Serum samples were collected from SCLC patients before chemotherapy. NSE, ProGRP, and LDH levels were measured by commercial electrochemiluminescence immunoassay, enzyme-linked immune sorbent assay, and kinetic spectrophotometric method, respectively.Overall response rate was 71.3% with 97 patients who achieved complete response (CR) +  partial response (PR). NSE and LDH level declined in patients who achieved CR + PR compared with patients in stable disease (SD) and progress disease (PD). Multivariate logistic regression analysis revealed that NSE > 50.324 ng/mL, stage ED, and distant metastases were independent risk factors for patients achieving CR + PR, and chemotherapy > 4 cycles was an independent protective factor in predicting CR + PR. Receiver operating characteristic (ROC) curves presented that expression of NSE, ProGRP, and LDH are of good predicting value for patients achieving CR + PR. Patients with a higher level of NSE and LDH presented worse progression-free survival and overall survival. In addition, multivariate Cox regression analysis showed that NSE level > 50.324 ng/mL and distant metastasis were independently correlated with worse OS.Serum NSE and LDH could be promising biomarkers for predicting therapy response and survival of SCLC patients receiving first-line platinum-based chemotherapy.