RESUMO
Cancer immunotherapy has transformed the clinical approach to patients with malignancies, as profound benefits can be seen in a subset of patients. To identify this subset, biomarker analyses increasingly focus on phenotypic and functional evaluation of the tumor microenvironment to determine if density, spatial distribution, and cellular composition of immune cell infiltrates can provide prognostic and/or predictive information. Attempts have been made to develop standardized methods to evaluate immune infiltrates in the routine assessment of certain tumor types; however, broad adoption of this approach in clinical decision-making is still missing. We developed approaches to categorize solid tumors into 'desert', 'excluded', and 'inflamed' types according to the spatial distribution of CD8+ immune effector cells to determine the prognostic and/or predictive implications of such labels. To overcome the limitations of this subjective approach, we incrementally developed four automated analysis pipelines of increasing granularity and complexity for density and pattern assessment of immune effector cells. We show that categorization based on 'manual' observation is predictive for clinical benefit from anti-programmed death ligand 1 therapy in two large cohorts of patients with non-small cell lung cancer or triple-negative breast cancer. For the automated analysis we demonstrate that a combined approach outperforms individual pipelines and successfully relates spatial features to pathologist-based readouts and the patient's response to therapy. Our findings suggest that tumor immunophenotype generated by automated analysis pipelines should be evaluated further as potential predictive biomarkers for cancer immunotherapy. © 2024 The Pathological Society of Great Britain and Ireland.
Assuntos
Automação , Antígeno B7-H1 , Biomarcadores Tumorais , Carcinoma Pulmonar de Células não Pequenas , Imunofenotipagem , Neoplasias de Mama Triplo Negativas , Humanos , Imunoterapia , Antígeno B7-H1/antagonistas & inibidores , Neoplasias/tratamento farmacológico , Neoplasias/imunologia , Neoplasias/patologia , Imunofenotipagem/métodos , Terapia de Alvo Molecular , Automação/métodos , Estudos de Coortes , Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Carcinoma Pulmonar de Células não Pequenas/imunologia , Carcinoma Pulmonar de Células não Pequenas/patologia , Neoplasias de Mama Triplo Negativas/tratamento farmacológico , Neoplasias de Mama Triplo Negativas/imunologia , Neoplasias de Mama Triplo Negativas/patologia , Biomarcadores Tumorais/análise , Resultado do TratamentoRESUMO
Nonalcoholic fatty liver disease (NAFLD), among the most common chronic liver diseases worldwide, affects approximately 25% of the global population. Its incidence is increasing owing to various risk factors, including genetic variation, metabolic health, dietary habits, and microbiota. Hepatic steatosis is a critical histological characteristic of NAFLD. Evaluating liver fat content is vital for identifying and following up with patients at risk of developing NAFLD. NAFLD includes simple liver steatosis and more severe forms such as inflammation, nonalcoholic steatohepatitis, fibrosis, and cirrhosis. The early assessment of fatty liver is important for reversing liver disease progression. Metabolic (dysfunction)-associated fatty liver disease recently replaced NAFLD as the most common hepatic disease worldwide. This article reviews the current state of noninvasive imaging, especially ultrasound, for liver fat quantification.