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  • 1
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 40, No. 16_suppl ( 2022-06-01), p. e20565-e20565
    Abstract: e20565 Background: Tumor heterogeneity and treatment resistance remains a continued threat to patients with advanced non-small cell lung cancer (NSCLC), despite the emergence of targeted medicines, e.g. against epidermal growth factor receptor (EGFR) alterations. We developed an in silico EGFR mutated lung adenocarcinoma (EGFR+ LUAD) model to predict the effect of known oncogenic EGFR mutations (common EGFR mutations, EGFR exon 20 insertions). The model provides mechanistic representation of tumor progression, including response to gefitinib and captures tumor heterogeneity, patient age, gender, initial clinical disease stage, and smoking status. Methods: 5-step in silico model development: Model building: biology of EGFR+LUAD was characterized by extracting biological features and their functional relationships from 〉 300 published papers and translating them into ordinary differential equations (ODEs). Mutational burden, EGFR-downstream-pathways, tumor growth and heterogeneity, gefitinib-PK/PD, treatment-induced resistance and clinical outcome were incorporated into a knowledge-based model containing 27-97 variables, 108-258 parameters and 13-83 ODEs reflecting intra-tumor clonal heterogeneity in a mechanistic manner. Calibration: published spheroid, xenograft and clinical data were used for stepwise calibration to find the correct parameter values. Relevant virtual populations (VPOPs) matching real patients baseline characteristics were generated for model benchmarking and validation. Benchmarking against a published data-based model: coverage of experimental interquartile range (IQR) with simulated IQR (precision) assesses model fit with experimental data, coverage of simulated IQR with experimental IQR (overlap) assesses model fit with experimental variability. Validation: a VPOP with comparable baseline characteristics was simulated and results compared against a published patient dataset that wasn’t used in any step during calibration. Matching of prediction over clinical data was done using both coverage and bootstrapped log-rank metrics. Results: Our model provides comparable outputs to the data-based model without having access to the exact original data (our model: precision of 62%, overlap of 91% VS data-based model: precision of 72% and an overlap of 86%). Besides, as a validation criterion, our model also successfully reproduces the time to progression observed in an independent clinical trial (coverage 〉 99%, negative log-rank tests 〉 98%). Conclusions: We simulated tumor growth and treatment response in advanced EGFR+ LUAD patients and successfully validated results both against an existing data-based model and a published clinical data. Our model highlights the potential of in silico modeling to better understand complex diseases progression and support efficient development of innovative therapies.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2022
    detail.hit.zdb_id: 2005181-5
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  • 2
    In: BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 24, No. 1 ( 2023-09-04)
    Abstract: Over the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life data. However, available validation methods are mostly designed for statistical regressions rather than for mechanistic models. To our knowledge, in the latter case, there are no consensus standards, for instance for the validation of predictions against real-world data given the variability and uncertainty of the data. In this work, we focus on the prediction of time-to-event curves using as an application example a mechanistic model of non-small cell lung cancer. We designed four empirical methods to assess both model performance and reliability of predictions: two methods based on bootstrapped versions of parametric statistical tests: log-rank and combined weighted log-ranks (MaxCombo); and two methods based on bootstrapped prediction intervals, referred to here as raw coverage and the juncture metric. We also introduced the notion of observation time uncertainty to take into consideration the real life delay between the moment when an event happens, and the moment when it is observed and reported. Results We highlight the advantages and disadvantages of these methods according to their application context. We have shown that the context of use of the model has an impact on the model validation process. Thanks to the use of several validation metrics we have highlighted the limit of the model to predict the evolution of the disease in the whole population of mutations at the same time, and that it was more efficient with specific predictions in the target mutation populations. The choice and use of a single metric could have led to an erroneous validation of the model and its context of use. Conclusions With this work, we stress the importance of making judicious choices for a metric, and how using a combination of metrics could be more relevant, with the objective of validating a given model and its predictions within a specific context of use. We also show how the reliability of the results depends both on the metric and on the statistical comparisons, and that the conditions of application and the type of available information need to be taken into account to choose the best validation strategy.
    Type of Medium: Online Resource
    ISSN: 1471-2105
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2041484-5
    SSG: 12
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  • 3
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 41, No. 16_suppl ( 2023-06-01), p. e21190-e21190
    Abstract: e21190 Background: 16,4% of lung adenocarcinomas (LUAD) are presenting a mutation in the epidermal growth factor receptor (EGFR), resulting in its constitutive activation and leading to uncontrolled cell proliferation [1]. Tyrosine kinase inhibitors (TKI) have been developed to inhibit EGFR activity but the presence of metastases and resistance mutations explain the lack of durable response to the treatmen t [2]. Knowledge-based mechanistic models reproducing existing clinical trials, based on population characteristics, can be used to help the design of future clinical trials. In particular, they can inform on the best responders to given treatments. Methods: We developed a physiologically based pharmacokinetic model of osimertinib, a 3rd generation TKI, to account for the distribution of the drug in the primary tumor and metastases after oral administration. This model was then combined to a pathophysiological mechanistic model of EGFR-mutant LUAD to represent the impact of osimertinib on the signals arising from EGFR activation. The combined model outputs the evolution of the primary tumor and each metastasis to allow the evaluation of the patient progression according to the RECIST criteria. Furthermore, each tumor in the model is composed of several subclones each possessing their own set of mutations and therefore responding differently to the treatment. Data from the clinical trials FLAURA and AURA3, in which osimertinib was given respectively as first and second line, were used to calibrate the model. Visual predictive checks as well as statistical tests were performed to ensure the proper behavior of the model. Results: The model successfully reproduced the time to progression in an EGFR mutant LUAD population treated with osimertinib as first line or as second line. In addition, the model reproduced the causes of progression according to the RECIST criteria and the sites of apparition of new metastases (in lung, brain, liver and bone). Conclusions: Reproduction of real world data brings credibility to the model. This is essential to use the model as an investigational tool to provide relevant insights, potentially on the best responders to osimertinib. After validation with additional clinical patient level data, the model could be used to create synthetic control arms in upcoming clinical trials. This would grant an improved analysis of covariate relationships with the comparison of an investigational treatment to the standard of care osimertinib administered as first or second line. It would also reduce the number of patients needed in the trial. References: [1] DOI: 10.1007/s11523-021-00848-9 [2] DOI: 10.3389/fonc.2020.602762 Acknowledgments: The authors would like to thank the novadiscovery team associated with this project and Janssen-Cilag France for their support.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2023
    detail.hit.zdb_id: 2005181-5
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  • 4
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-05-19)
    Abstract: Primary infection with herpes simplex type 1 (HSV-1) occurring around the mouth and nose switches rapidly to lifelong latent infection in sensitive trigeminal ganglia (TG) neurons. Sporadic reactivation of these latent reservoirs later in life is the cause of acute infections of the corneal epithelium, which can cause potentially blinding herpes simplex keratitis (HSK). There is no effective vaccine to protect against HSK, and antiviral drugs provide only partial protection against recurrences. We previously engendered an acute disease-free, non-reactivating latent state in mice when challenged with virulent HSV-1 in orofacial mucosa, by priming with non-neurovirulent HSV-1 (TK del ) before the challenge. Herein, we define the local immune infiltration and inflammatory chemokine production changes after virulent HSV-1 challenge, which were elicited by TK del prime. Heightened immunosurveillance before virulent challenge, and early enhanced lymphocyte-enriched infiltration of the challenged lip were induced, which corresponded to attenuation of inflammation in the TG and enhanced viral control. Furthermore, classical latent-phase T cell persistence around latent HSV-1 reservoirs were severely reduced. These findings identify the immune processes that are likely to be responsible for establishing non-reactivating latent HSV-1 reservoirs. Stopping reactivation is essential for development of efficient vaccine strategies against HSV-1.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2615211-3
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  • 5
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 8, No. 1 ( 2018-02-15)
    Abstract: Understanding the innate immune response to vaccination is critical in vaccine design. Here, we studied blood innate myeloid cells after first and second immunization of cynomolgus macaques with the modified vaccinia virus Ankara. The inflammation at the injection site was moderate and resolved faster after the boost. The blood concentration of inflammation markers increased after both injections but was lower after the boost. The numbers of neutrophils, monocytes, and dendritic cells were transiently affected by vaccination, but without any major difference between prime and boost. However, phenotyping deeper those cells with mass cytometry unveiled their high phenotypic diversity with subsets responding differently after each injection, some enriched only after the primary injection and others only after the boost. Actually, the composition in subphenotype already differed just before the boost as compared to just before the prime. Multivariate analysis identified the key features that contributed to these differences. Cell subpopulations best characterizing the post-boost response were more activated, with a stronger expression of markers involved in phagocytosis, antigen presentation, costimulation, chemotaxis, and inflammation. This study revisits innate immunity by demonstrating that, like adaptive immunity, innate myeloid responses differ after one or two immunizations.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
    detail.hit.zdb_id: 2615211-3
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  • 6
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2022-12-07)
    Abstract: T cell exhaustion is a hallmark of hepatitis C virus (HCV) infection and limits protective immunity in chronic viral infections and cancer. Limited knowledge exists of the initial viral and immune dynamics that characterise exhaustion in humans. We studied longitudinal blood samples from a unique cohort of individuals with primary infection using single-cell multi-omics to identify the functions and phenotypes of HCV-specific CD8 + T cells. Early elevated IFN-γ response against the transmitted virus is associated with the rate of immune escape, larger clonal expansion, and early onset of exhaustion. Irrespective of disease outcome, we find heterogeneous subsets of progenitors of exhaustion, based on the level of PD-1 expression and loss of AP-1 transcription factors. Intra-clonal analysis shows distinct trajectories with multiple fates and evolutionary plasticity of precursor cells. These findings challenge the current paradigm on the contribution of CD8 + T cells to HCV disease outcome and provide data for future studies on T cell differentiation in human infections.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2553671-0
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  • 7
    In: European Journal of Immunology, Wiley, Vol. 51, No. 7 ( 2021-07), p. 1732-1747
    Abstract: Long‐lived T‐memory stem cells (T SCM ) are key to both naturally occurring and vaccine‐conferred protection against infection. These cells are characterized by the CD45RA + CCR7 + CD95 + phenotype. Significant heterogeneity within the T SCM population is recognized, but distinguishing surface markers and functional characterization of potential subsets are lacking. Human CD8 T SCM subsets were identified in healthy subjects who had been previously exposed to CMV or Influenza (Flu) virus in flow cytometry by expression of CD122 or CXCR3, and then characterized in proliferation, multipotency, self‐renewal, and intracellular cytokine production (TNF‐α, IL‐2, IFN‐γ), together with transcriptomic profiles. The T SCM CD122 hi ‐expressing subset (versus CD122 lo ) demonstrated greater proliferation, greater multipotency, and enhanced polyfunctionality with higher frequencies of triple positive (TNF‐α, IL‐2, IFN‐γ) cytokine‐producing cells upon exposure to recall antigen. The T SCM CXCR3 lo subpopulation also had increased proliferation and polyfunctional cytokine production. Transcriptomic analysis further showed that the T SCM CD122 hi population had increased expression of activation and homing molecules, such as Ccr6 , Cxcr6 , Il12rb , and Il18rap , and downregulated cell proliferation inhibitors, S100A8 and S100A9. These data reveal that the T SCM CD122 hi phenotype is associated with increased proliferation, enhanced multipotency and polyfunctionality with an activated memory‐cell like transcriptional profile, and hence, may be favored for induction by immunization and for adoptive immunotherapy.
    Type of Medium: Online Resource
    ISSN: 0014-2980 , 1521-4141
    URL: Issue
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    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 1491907-2
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  • 8
    In: Frontiers in Immunology, Frontiers Media SA, Vol. 12 ( 2022-1-4)
    Abstract: Innate immunity modulates adaptive immunity and defines the magnitude, quality, and longevity of antigen-specific T- and B- cell immune memory. Various vaccine and administration factors influence the immune response to vaccination, including the route of vaccine delivery. We studied the dynamics of innate cell responses in blood using a preclinical model of non-human primates immunized with a live attenuated vaccinia virus, a recombinant Modified vaccinia virus Ankara (MVA) expressing a gag-pol-nef fusion of HIV-1, and mass cytometry. We previously showed that it induces a strong, early, and transient innate response, but also late phenotypic modifications of blood myeloid cells after two months when injected subcutaneously. Here, we show that the early innate effector cell responses and plasma inflammatory cytokine profiles differ between subcutaneous and intradermal vaccine injection. Additionally, we show that the intradermal administration fails to induce more highly activated/mature neutrophils long after immunization, in contrast to subcutaneous administration. Different batches of antibodies, staining protocols and generations of mass cytometers were used to generate the two datasets. Mass cytometry data were analyzed in parallel using the same analytical pipeline based on three successive clustering steps, including SPADE, and categorical heatmaps were compared using the Manhattan distance to measure the similarity between cell cluster phenotypes. Overall, we show that the vaccine per se is not sufficient for the late phenotypic modifications of innate myeloid cells, which are evocative of innate immune training. Its route of administration is also crucial, likely by influencing the early innate response, and systemic inflammation, and vaccine biodistribution.
    Type of Medium: Online Resource
    ISSN: 1664-3224
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2606827-8
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  • 9
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 856-856
    Abstract: Introduction: 16,4% of lung adenocarcinomas (LUAD) are presenting a mutation in the Epidermal Growth Factor Receptor (EGFR), as reported in the Epidemiological Strategy and Medical Economics database[1], resulting in its constitutive activation and leading to uncontrolled cell proliferation. While some tyrosine kinase inhibitors (TKIs) have been developed to target EGFR mutations, their efficacy is not long-lasting, due to the emergence of resistance mutations[2] . Based on in silico approaches, we investigate and compare the impact of two TKIs (1st and 3rd generation) on tumor size evolution and clinical outcome, depending on the target population. Materials and Methods: We developed in Novadiscovery's jinkō platform a detailed mechanistic disease model of EGFR-mutant LUAD that predicts patients’ disease progression, based on their characteristics. We added on top of this disease model, a mechanistic physiologically-based pharmaco-kinetics model for each TKI drug, integrating their mechanisms of action. Publicly available data were used to calibrate the drug models and assess their credibility.We used the combination of the disease model with the two drug models to simulate clinical trials to compare the impact of both drugs on the course of the disease. Results:Both the 1st and 3rd generation TKI drug models can reproduce the pharmacokinetics in mice and humans. Combination of these models with the EGFR-mutant LUAD disease model is used to predict the tumor evolution in mice and the clinical outcome in humans. Differences in disease progression between treatments are observed according to the patients’ tumor mutational profiles. Discussion and Conclusion: The knowledge based construction of this EGFR mutant LUAD disease and treatment model successfully reproduced publicly available real-world data and will be challenged to reproduce the results from the FLAURA trial for an additional step of validation. The credibility of the model thereby acquired is a first step in the use of the model to compare existing treatments to investigational treatments and further support innovative therapies development. As such, in silico approaches are a complementary and valuable tool to existing in vitro or in animal experiments, alongside with clinical trials performance. References: [1] Chouaid et al, TargOnc, 2021, https://doi.org/10.1007/s11523-021-00848-9. [2] Vyse et al, Signal Transduction and Targeted Therapy, 2019 https://doi.org/10.1038/s41392-019-0038-9. Citation Format: Hippolyte Darré, Bastien Martin, Firas Hammami, Arnaud Nativel, Diane Lefaudeux, Raphaël Toueg, Michaël Duruisseaux, Jean-Louis Palgen, Perrine Masson, Adèle L'Hostis, Nicoletta Ceres, Claudio Monteiro. Comparison of the effect of two EGFR-TKI in patients with EGFR-mutant lung adenocarcinoma using in silico clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 856.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 10
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Vol. 83, No. 7_Supplement ( 2023-04-04), p. 857-857
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 857-857
    Abstract: Introduction The in silico epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma (ISELA) model predicts tumor progression in patients with advanced EGFR-mutated lung adenocarcinoma [1]. To investigate model credibility, we dug into a simplifying assumption made in the ISELA model and in other models [2] [3], namely the assumption that tumors have a spherical shape. The validity of the spherical assumption (SA) is assessed by analyzing two lung cancer datasets, and its impact on the model is assessed by comparing predictions of the ISELA model with different shape hypotheses. Material and Methods To evaluate the impact of tumor shape assumptions on the estimated tumor volume with respect to real-world data, two lung cancer datasets [4][5] were analyzed to appraise the sphericity of lung tumors. As individual longest tumor radii were available for each tumor, the estimated spherical volume was computed under the SA and compared to the reported tumor volume. The ellipsoid assumption (EA) was also explored as an alternative -less simplifying- shape assumption. As the three tumor axes are rarely reported and were unavailable in these datasets, the three tumor axes in the EA were defined with proportionality relationships to the longest available radius. The estimated elliptical volume was then compared to the measured volume. To quantify the impact of the SA on the model’s primary output, time to progression (TTP), an alternative ISELA model assuming ellipsoid tumors was implemented. Two clinical trial simulations -one under the SA and the other under the EA- were performed on the same virtual patients with only the sphericity parameter differing, thus allowing a patient per patient comparison. Results The datasets analysis revealed that under the SA the tumor volume was overestimated in most cases, whereas under the EA the tumor volume was better approximated. On average, tumors had their shortest axis equal to 0.7 times their longest radius. Comparison of predictions between simulations showed that only 5% of the virtual patients changed their treatment response status (non-responder/responder). In patients classified as responding to the treatment in both simulations, the median TTP difference was 14 days. Conclusion Both real data and in silico simulations enabled us to better understand to which extent the SA is a simplifying but yet credible assumption in modeling lung cancer progression. Considering ellipsoid tumors is nevertheless a promising alternative as it better predicts tumor volume. Additional studies are needed to further explore its use as support to clinical decision making. References [1] Jacob, et al. bioRxiv 2022. [2] Thomlinson, et al. British journal of cancer 1955. [3] Greenspan, Studies in Applied Mathematics 1972. [4] Dmitry, et al. The Cancer Imaging Archive 2017. [5] Jamal-Hanjani, et al. New England Journal of Medicine 2017. Citation Format: Julie Kleine-Schultjann, Germán Gómez, Adèle L'Hostis, Jean-Louis Palgen, Claudio Monteiro, Perrine Masson. Assuming tumors have a spherical shape in modeling EGFR mutant lung adenocarcinoma: Impact on modeled clinical outcome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 857.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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