White-box validation of quantitative product lines by statistical model checking and process mining

We propose a novel methodology for validating software product line (PL) models by integrating Statistical Model Checking (SMC) with Process Mining (PM). Our approach focuses on the feature-oriented language QFLan in the PL engineering domain, allowing modeling of PLs with rich cross-tree and quantitative constraints, as well as aspects of dynamic PLs like staged configurations. This richness leads to models with infinite state-space, requiring simulation-based analysis techniques like SMC. For instance, we illustrate with a running example involving infinite state space. SMC involves generating samples of system dynamics to estimate properties such as event probabilities or expected values. On the other hand, PM uses data-driven techniques on execution logs to identify and reason about the underlying execution process. In this paper, we propose, for the first time, applying PM techniques to SMC simulations' byproducts to enhance the utility of SMC analyses. Typically, when SMC results are unexpected, modelers must determine whether they stem from actual system characteristics or model bugs in a black-box manner. We improve on this by using PM to provide a white-box perspective on the observed system dynamics. Samples from SMC are fed into PM tools, producing a compact graphical representation of observed dynamics. The mined PM model is then transformed into a QFLan model, accessible to PL engineers. Using two well-known PL models, we demonstrate the effectiveness and scalability of our methodology in pinpointing issues and suggesting fixes. Additionally, we show its generality by applying it to the security domain.Comment: Pre-print Special Issue on Managing Variability in Complex Software-Intensive Systems of the Journal of Systems and Softwar

Consolidative thoracic radiation therapy for extensive-stage small cell lung cancer in the era of first-line chemoimmunotherapy: preclinical data and a retrospective study in Southern Italy

BackgroundConsolidative thoracic radiotherapy (TRT) has been commonly used in the management of extensive-stage small cell lung cancer (ES-SCLC). Nevertheless, phase III trials exploring first-line chemoimmunotherapy have excluded this treatment approach. However, there is a strong biological rationale to support the use of radiotherapy (RT) as a boost to sustain anti-tumor immune responses. Currently, the benefit of TRT after chemoimmunotherapy remains unclear. The present report describes the real-world experiences of 120 patients with ES-SCLC treated with different chemoimmunotherapy combinations. Preclinical data supporting the hypothesis of anti-tumor immune responses induced by RT are also presented.MethodsA total of 120 ES-SCLC patients treated with chemoimmunotherapy since 2019 in the South of Italy were retrospectively analyzed. None of the patients included in the analysis experienced disease progression after undergoing first-line chemoimmunotherapy. Of these, 59 patients underwent TRT after a multidisciplinary decision by the treatment team. Patient characteristics, chemoimmunotherapy schedule, and timing of TRT onset were assessed. Safety served as the primary endpoint, while efficacy measured in terms of overall survival (OS) and progression-free survival (PFS) was used as the secondary endpoint. Immune pathway activation induced by RT in SCLC cells was explored to investigate the biological rationale for combining RT and immunotherapy.ResultsPreclinical data supported the activation of innate immune pathways, including the STimulator of INterferon pathway (STING), gamma-interferon-inducible protein (IFI-16), and mitochondrial antiviral-signaling protein (MAVS) related to DNA and RNA release. Clinical data showed that TRT was associated with a good safety profile. Of the 59 patients treated with TRT, only 10% experienced radiation toxicity, while no ≥ G3 radiation-induced adverse events occurred. The median time for TRT onset after cycles of chemoimmunotherapy was 62 days. Total radiation dose and fraction dose of TRT include from 30 Gy in 10 fractions, up to definitive dose in selected patients. Consolidative TRT was associated with a significantly longer PFS than systemic therapy alone (one-year PFS of 61% vs. 31%, p<0.001), with a trend toward improved OS (one-year OS of 80% vs. 61%, p=0.027).ConclusionMulti-center data from establishments in the South of Italy provide a general confidence in using TRT as a consolidative strategy after chemoimmunotherapy. Considering the limits of a restrospective analysis, these preliminary results support the feasibility of the approach and encourage a prospective evaluation

White-box validation of quantitative product lines by statistical model checking and process mining

We propose a novel methodology to validate software product line (PL) models by integrating Statistical Model Checking (SMC) with Process Mining (PM). We consider the feature-oriented language QFLan from the PL engineering domain. QFLan allows to model PL equipped with rich cross-tree and quantitative constraints, as well as aspects of dynamic PLs such as the staged configurations. This richness allows us to easily obtain models with infinite state-space, calling for simulation-based analysis techniques, like SMC. For example, we use a running example with infinite state space. SMC is a family of analysis techniques based on the generation of samples of the dynamics of a system. SMC aims at estimating properties of a system like the probability of a given event (e.g., installing a feature), or the expected value of quantities in it (e.g., the average price of products from the studied family). Instead, PM is a family of data-driven techniques that uses logs collected on the execution of an information system to identify and reason about its underlying execution process. This often regards identifying and reasoning about process patterns, bottlenecks, and possibilities for improvement. In this paper, to the best of our knowledge, we propose, for the first time, the application of Process Mining (PM) techniques to the byproducts of Statistical Model Checking (SMC) simulations. This aims to enhance the utility of SMC analyses. Typically, if SMC gives unexpected results, the modeler has to discover whether these come from actual characteristics of the system, or from bugs in the model. This is done in a black-box manner, only based on the obtained numerical values. We improve on this by using PM to get a white-box perspective on the dynamics of the system observed by SMC. Roughly speaking, we feed the samples generated by SMC to PM tools, obtaining a compact graphical representation of the observed dynamics. This mined PM model is then transformed into a mined QFLan model, making it accessible to PL engineers. Using two well-known PL models, we show that our methodology is effective (helps in pinpointing issues in models, and in suggesting fixes), and that it scales to complex models. We also show that it is general, by applying it to the security domain.<br/

Overcoming resistance to targeted therapies in NSCLC: current approaches and clinical application

The discovery that a number of aberrant tumorigenic processes and signal transduction pathways are mediated by druggable protein kinases has led to a revolutionary change in nonsmall cell lung cancer (NSCLC) treatment. Epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) are the targets of several tyrosine kinase inhibitors (TKIs), some of them approved for treatment and others currently in clinical development. First-generation agents offer, in target populations, a substantial improvement of outcomes compared with standard chemotherapy in the treatment of advanced NSCLC. Unfortunately, drug resistance develops after initial benefit through a variety of mechanisms. Novel generation EGFR and ALK inhibitors are currently in advanced clinical development and are producing encouraging results in patients with acquired resistance to previous generation agents. The search for new drugs or strategies to overcome the TKI resistance in patients with EGFR mutations or ALK rearrangements is to be considered a priority for the improvement of outcomes in the treatment of advanced NSCLC

Predictive biomarkers of immunotherapy for non-small cell lung cancer: Results from an Experts Panel Meeting of the Italian Association of Thoracic Oncology

Unleashing the potential of immune system to fight cancer has become one of the main promising treatment modalities for advanced non-small cell lung cancer (NSCLC). The knowledge of numerous factors that come into play in the cancer-immunity cycle provide a wide range of potential therapeutic targets, including monoclonal antibodies that inhibits the programmed death-1 (PD-1) checkpoint pathway. Over the last two years, nivolumab, pembrolizumab and atezolizumab received approval for treatment of pretreated advanced NSCLC, and more recently, immunotherapy with pembrolizumab is the new standard of care as first-line in patients with high levels of programmed death-ligand 1 (PD-L1) expression. Selection of patients is mandatory and PD-L1 is the only biomarker currently available in clinical practice. However, PD-L1 staining is an imperfect marker, whose negativity does not exclude a response to immunotherapy, as well as the roughly half of patients are "not-responders" despite high tumor PD-L1 levels. The right cut-off, the differences among various immune checkpoint inhibitors and among various antibody clones, and a not trivial activity reported even in PD-L1 negative tumors are questions still open. New biomarkers beyond to PD-L1 assays as well as new strategies, including combination of immune checkpoint inhibitors are under investigation

Anti PD-1 and PDL-1 immunotherapy in the treatment of advanced non-small cell lung cancer (NSCLC): a review on toxicity profile and its management

The better understanding of immunology and antitumor immune responses have prompted the development of novel immunotherapy agents like PD-1 checkpoint inhibitors (anti-PD-1 and anti-PDL-1 antibodies) that improve the capacity of the immune system to acknowledge and delete tumors, including lung cancer. Currently, two anti-PD-1 (nivolumab and pembrolizumab) and one anti- PD-L1 (MPDL-3280A) agents are in advanced stages of development in advanced or metastatic non-small cell lung cancer (NSCLC). Among these, nivolumab demonstrated a survival benefit versus docetaxel in refractory squamous NSCLC, reporting 41% reduction in risk of death (median overall survival: 9.2 versus 6.0 months; objective response rate: 20% versus 9%), and better safety profile than standard-of-care chemotherapy (grade 3-4 adverse events: 7% versus 55%). However, the enhancement of immune response to cancer targeting specific immune regulatory checkpoints is associated with a toxicity profile different from that related to traditional chemotherapeutic agents and molecularly targeted therapies. The success of immunotherapy is related to ongoing evaluation/identification and treatment of these immune-related side effects. Herein, first clinical results of PD-1 agents in lung cancer are reviewed, focusing on toxicity profile and its management