PEOPLE (NTC03447678), a phase II trial to test pembrolizumab as first-line treatment in patients with advanced NSCLC with PD-L1 <50%: a multiomics analysis.

BACKGROUND: Chemoimmunotherapy represents the standard of care for patients with advanced non-small cell lung cancer (NSCLC) and programmed death-ligand 1 (PD-L1) <50%. Although single-agent pembrolizumab has also demonstrated some activity in this setting, no reliable biomarkers yet exist for selecting patients likely to respond to single-agent immunotherapy. The main purpose of the study was to identify potential new biomarkers associated with progression-free-survival (PFS) within a multiomics analysis. METHODS: PEOPLE (NTC03447678) was a prospective phase II trial evaluating first-line pembrolizumab in patients with advanced EGFR and ALK wild type treatment-naïve NSCLC with PD-L1 <50%. Circulating immune profiling was performed by determination of absolute cell counts with multiparametric flow cytometry on freshly isolated whole blood samples at baseline and at first radiological evaluation. Gene expression profiling was performed using nCounter PanCancer IO 360 Panel (NanoString) on baseline tissue. Gut bacterial taxonomic abundance was obtained by shotgun metagenomic sequencing of stool samples at baseline. Omics data were analyzed with sequential univariate Cox proportional hazards regression predicting PFS, with Benjamini-Hochberg multiple comparisons correction. Biological features significant with univariate analysis were analyzed with multivariate least absolute shrinkage and selection operator (LASSO). RESULTS: From May 2018 to October 2020, 65 patients were enrolled. Median follow-up and PFS were 26.4 and 2.9 months, respectively. LASSO integration analysis, with an optimal lambda of 0.28, showed that peripheral blood natural killer cells/CD56dimCD16+ (HR 0.56, 0.41-0.76, p=0.006) abundance at baseline and non-classical CD14dimCD16+monocytes (HR 0.52, 0.36-0.75, p=0.004), eosinophils (CD15+CD16-) (HR 0.62, 0.44-0.89, p=0.03) and lymphocytes (HR 0.32, 0.19-0.56, p=0.001) after first radiologic evaluation correlated with favorable PFS as well as high baseline expression levels of CD244 (HR 0.74, 0.62-0.87, p=0.05) protein tyrosine phosphatase receptor type C (HR 0.55, 0.38-0.81, p=0.098) and killer cell lectin like receptor B1 (HR 0.76, 0.66-0.89, p=0.05). Interferon-responsive factor 9 and cartilage oligomeric matrix protein genes correlated with unfavorable PFS (HR 3.03, 1.52-6.02, p 0.08 and HR 1.22, 1.08-1.37, p=0.06, corrected). No microbiome features were selected. CONCLUSIONS: This multiomics approach was able to identify immune cell subsets and expression levels of genes associated to PFS in patients with PD-L1 <50% NSCLC treated with first-line pembrolizumab. These preliminary data will be confirmed in the larger multicentric international I3LUNG trial (NCT05537922). TRIAL REGISTRATION NUMBER: 2017-002841-31.

Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment.

Recently, a wide spectrum of artificial intelligence (AI)-based applications in the broader categories of digital pathology, biomarker development, and treatment have been explored. In the domain of digital pathology, these have included novel analytical strategies for realizing new information derived from standard histology to guide treatment selection and biomarker development to predict treatment selection and response. In therapeutics, these have included AI-driven drug target discovery, drug design and repurposing, combination regimen optimization, modulated dosing, and beyond. Given the continued advances that are emerging, it is important to develop workflows that seamlessly combine the various segments of AI innovation to comprehensively augment the diagnostic and interventional arsenal of the clinical oncology community. To overcome challenges that remain with regard to the ideation, validation, and deployment of AI in clinical oncology, recommendations toward bringing this workflow to fruition are also provided from clinical, engineering, implementation, and health care economics considerations. Ultimately, this work proposes frameworks that can potentially integrate these domains toward the sustainable adoption of practice-changing AI by the clinical oncology community to drive improved patient outcomes.

A Deep Learning Image-to-Image Translation Approach for a More Accessible Estimator of the Healing Time of Burns.

OBJECTIVE: An accurate and timely diagnosis of burn severity is critical to ensure a positive outcome. Laser Doppler imaging (LDI) has become a very useful tool for this task. It measures the perfusion of the burn and estimates its potential healing time. LDIs generate a 6-color palette image, with each color representing a healing time. This technique has very high costs associated. In resource-limited areas, such as low- and middle-income countries or remote locations like space, where access to specialized burn care is inadequate, more affordable and portable tools are required. This study proposes a novel image-to-image translation approach to estimate burn healing times, using a digital image to approximate the LDI. METHODS: This approach consists of a U-net architecture with a VGG-based encoder and applies the concept of ordinal classification. Paired digital and LDI images of burns were collected. The performance was evaluated with 10-fold cross-validation, mean absolute error (MAE), and color distribution differences between the ground truth and the estimated LDI. RESULTS: Results showed a satisfactory performance in terms of low MAE ( 0.2370 ±0.0086). However, the unbalanced distribution of colors in the data affects this performance. SIGNIFICANCE: This novel and unique approach serves as a basis for developing more accessible support tools in the burn care environment in resource-limited areas.

U-Net based Mapping from Digital Images to Laser Doppler Imaging for Burn Assessment.

The incidence of burn injuries is higher in low-and middle-income countries, and particularly in remote areas where the access to specialized burn assessment, care and recovery is limited. Given the high costs associated with one of the most used techniques to evaluate the severity of a burn, namely laser Doppler imaging (LDI), an alternative approach could be beneficial for remote locations. This study proposes a novel approach to estimate the LDI from digital images of a burn. The approach is a pixel-wise regression model based on convolutional neural networks. To minimize the dependency on the conditions in which the images are taken, the effect of two image normalization techniques is also studied. Results indicate that the model performs satisfactorily on average, presenting low mean absolute and squared errors and high structural similarity index. While no significant differences are found when changing the normalization of the images, the performance is affected by their quality. This suggests that changes in the intensity of the images do not alter the relevant information about the wound, whereas changes in brightness, contrast and sharpness do.

Printability of Double Network Alginate-Based Hydrogel for 3D Bio-Printed Complex Structures.

Three-dimensional (3D) bio-printing has recently emerged as a crucial technology in tissue engineering, yet there are still challenges in selecting materials to obtain good print quality. Therefore, it is essential to study the influence of the chosen material (i.e., bio-ink) and the printing parameters on the final result. The "printability" of a bio-ink indicates its suitability for bio-printing. Hydrogels are a great choice because of their biocompatibility, but their printability is crucial for exploiting their properties and ensuring high printing accuracy. However, the printing settings are seldom addressed when printing hydrogels. In this context, this study explored the printability of double network (DN) hydrogels, from printing lines (1D structures) to lattices (2D structures) and 3D tubular structures, with a focus on printing accuracy. The DN hydrogel has two entangled cross-linked networks and a balanced mechanical performance combining high strength, toughness, and biocompatibility. The combination of poly (ethylene glycol)-diacrylate (PEDGA) and sodium alginate (SA) enables the qualities mentioned earlier to be met, as well as the use of UV to prevent filament collapse under gravity. Critical correlations between the printability and settings, such as velocity and viscosity of the ink, were identified. PEGDA/alginate-based double network hydrogels were explored and prepared, and printing conditions were improved to achieve 3D complex architectures, such as tubular structures. The DN solution ink was found to be unsuitable for extrudability; hence, glycerol was added to enhance the process. Different glycerol concentrations and flow rates were investigated. The solution containing 25% glycerol and a flow rate of 2 mm/s yielded the best printing accuracy. Thanks to these parameters, a line width of 1 mm and an angle printing inaccuracy of less than 1° were achieved, indicating good shape accuracy. Once the optimal parameters were identified, a tubular structure was achieved with a high printing accuracy. This study demonstrated a 3D printing hydrogel structure using a commercial 3D bio-printer (REGEMAT 3D BIO V1) by synchronizing all parameters, serving as a reference for future more complex 3D structures.

Thermotropic Liquid Crystals for Temperature Mapping.

Wound management in Space is an important factor to be considered in future Human Space Exploration. It demands the development of reliable wound monitoring systems that will facilitate the assessment and proper care of wounds in isolated environments, such as Space. One possible system could be developed using liquid crystal films, which have been a promising solution for real-time in-situ temperature monitoring in healthcare, but they are not yet implemented in clinical practice. To progress in the latter, the goal of this study is twofold. First, it provides a full characterization of a sensing element composed of thermotropic liquid crystals arrays embedded between two elastomer layers, and second, it discusses how such a system compares against non-local infrared measurements. The sensing element evaluated here has an operating temperature range of 34-38°C, and a quick response time of approximately 0.25 s. The temperature distribution of surfaces obtained using this system was compared to the one obtained using the infrared thermography, a technique commonly used to measure temperature distributions at the wound site. This comparison was done on a mimicked wound, and results indicate that the proposed sensing element can reproduce the temperature distributions, similar to the ones obtained using infrared imaging. Although there is a long way to go before implementing the liquid crystal sensing element into clinical practice, the results of this work demonstrate that such sensors can be suitable for future wound monitoring systems.

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients.

Introduction: Artificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. Methods: We prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. Results: Of 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. Conclusions: In this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients.