Physiologically Based Pharmacokinetic Model of Cefotaxime in Patients with Impaired Renal Function.

Background: Cefotaxime is a widely prescribed cephalosporin antibiotic used to treat various infections. It is mainly eliminated unchanged by the kidney through tubular secretion and glomerular filtration. Therefore, a reduction of kidney function may increase exposure to the drug and induce toxic side effects.

Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma.

We employed a mechanistic learning approach, integrating on-treatment tumor kinetics (TK) modeling with various machine learning (ML) models to address the challenge of predicting post-progression survival (PPS)-the duration from the time of documented disease progression to death-and overall survival (OS) in Head and Neck Squamous Cell Carcinoma (HNSCC). We compared the predictive power of model-derived TK parameters versus RECIST and assessed the efficacy of nine TK-OS ML models against conventional survival models. Data from 526 advanced HNSCC patients treated with chemotherapy and cetuximab in the TPExtreme trial were analyzed using a double-exponential model.

A beginner's guide to supervised analysis for mass cytometry data in cancer biology.

Mass cytometry enables deep profiling of biological samples at single-cell resolution. This technology is more than relevant in cancer research due to high cellular heterogeneity and complexity. Downstream analysis of high-dimensional datasets increasingly relies on machine learning (ML) to extract clinically relevant information, including supervised algorithms for classification and regression purposes.

Predicting Survival in Patients with Advanced NSCLC Treated with Atezolizumab Using Pre- and on-Treatment Prognostic Biomarkers.

Existing survival prediction models rely only on baseline or tumor kinetics data and lack machine learning integration. We introduce a novel kinetics-machine learning (kML) model that integrates baseline markers, tumor kinetics, and four on-treatment simple blood markers (albumin, C-reactive protein, lactate dehydrogenase, and neutrophils). Developed for immune-checkpoint inhibition (ICI) in non-small cell lung cancer on three phase II trials (533 patients), kML was validated on the two arms of a phase III trial (ICI and chemotherapy, 377 and 354 patients).

A Senescence-Mimicking (Senomimetic) VEGFR TKI Side Effect Primes Tumor Immune Responses via IFN/STING Signaling.

Tyrosine kinase inhibitors (TKIs) that block the vascular endothelial growth factor receptors (VEGFRs) not only disrupt tumor angiogenesis but also have many unexpected side effects that impact tumor cells directly. This includes the induction of molecular markers associated with senescence, a form of cellular aging that typically involves growth arrest. We have shown that VEGFR TKIs can hijack these aging programs by transiently inducting senescence markers (SMs) in tumor cells to activate senescence-associated secretory programs that fuel drug resistance.

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment.

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI).

Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases.

Intracranial progression after curative treatment of early-stage non-small cell lung cancer (NSCLC) occurs from 10 to 50% and is difficult to manage, given the heterogeneity of clinical presentations and the variability of treatments available. The objective of this study was to develop a mechanistic model of intracranial progression to predict survival following a first brain metastasis (BM) event occurring at a time [Formula: see text]. Data included early-stage NSCLC patients treated with a curative intent who had a BM as the first and single relapse site (N = 31).

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.

Peripheral location of lung cancer is associated with higher local disease recurrence.

Objectives: Our goal was to evaluate the association between the distance of the tumour to the visceral pleura and the rate of local recurrence in patients surgically treated for stage pI lung cancer.

Methods: We conducted a single-centre retrospective review of 578 consecutive patients with clinical stage IA lung cancer who underwent a lobectomy or segmentectomy from January 2010 to December 2019. We excluded 107 patients with positive margins, previous lung cancer, neoadjuvant treatment and pathological stage II or higher status or for whom preoperative computed tomography (CT) scans were not available at the time of the study.

Immune biology of NSCLC revealed by single-cell technologies: implications for the development of biomarkers in patients treated with immunotherapy.

First-line immunotherapy in non-small-cell lung cancer largely improved patients' survival. PD-L1 testing is required before immune checkpoint inhibitor initiation. However, this biomarker fails to accurately predict patients' response.

Pharmacokinetics of intact lipid nanocapsules using new quantitative FRET technique.

The present study investigated the pharmacokinetics of intact lipid nanocapsules (LNCs) after intravenous administration in rats. Six different Förster resonance energy transfer LNCs (FRET-LNCs) have been studied with 2 sizes (50 and 85 nm) and 3 coating types (none, DSPE-mPEG 2000 or stearylamine). A FRET-LNCs blood extraction method was developed to retain an accurate FRET signal.

Mass Cytometry Reveals Classical Monocytes, NK Cells, and ICOS+ CD4+ T Cells Associated with Pembrolizumab Efficacy in Patients with Lung Cancer.

Purpose: Immune checkpoint inhibitors (ICI) have revolutionized the treatment of non-small cell lung cancer (NSCLC), but predictive biomarkers of their efficacy are imperfect. The primary objective is to evaluate circulating immune predictors of pembrolizumab efficacy in patients with advanced NSCLC.

Experimental Design: We used high-dimensional mass cytometry (CyTOF) in baseline blood samples of patients with advanced NSCLC treated with pembrolizumab.

CD47-SIRPα Controls ADCC Killing of Primary T Cells by PMN Through a Combination of Trogocytosis and NADPH Oxidase Activation.

Immunotherapies targeting the "don't eat me" myeloid checkpoint constituted by CD47 SIRPα interaction have promising clinical potential but are limited by toxicities associated with the destruction of non-tumor cells. These dose-limiting toxicities demonstrate the need to highlight the mechanisms of anti-CD47-SIRPα therapy effects on non-tumor CD47-bearing cells. Given the increased incidence of lymphopenia in patients receiving anti-CD47 antibodies and the strong ADCC (antibody-dependent cellular cytotoxicity) effector function of polymorphonuclear cells (), we investigated the behavior of primary PMNs cocultured with primary T cells in the presence of anti-CD47 mAbs.

Machine Learning for Prediction of Immunotherapy Efficacy in Non-Small Cell Lung Cancer from Simple Clinical and Biological Data.

Background: Immune checkpoint inhibitors (ICIs) are now a therapeutic standard in advanced non-small cell lung cancer (NSCLC), but strong predictive markers for ICIs efficacy are still lacking. We evaluated machine learning models built on simple clinical and biological data to individually predict response to ICIs.

Methods: Patients with metastatic NSCLC who received ICI in second line or later were included.

Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression.

Background: Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy.

Cannabinoid and planar cell polarity signaling converges to direct placentation.

Directed trophoblast migration toward the maternal mesometrial pole is critical for placentation and pregnancy success. Trophoblasts replace maternal arterial endothelial cells to increase blood supply to the placenta. Inferior trophoblast invasion results in pregnancy complications including preeclampsia, intrauterine growth restriction, miscarriage, and preterm delivery.