Ratios of T-cell immune-effectors with tumour associated macrophages and PD-1/PD-L1 axis immune-checkpoint molecules, as prognosticators in diffuse large B cell lymphoma: A population-based study

Background Risk-stratification of diffuse large B-cell lymphoma (DLBCL) requires identification of patients with disease that is not cured despite initial R-CHOP. Although the prognostic importance of the tumour microenvironment (TME) is established, the optimal strategy to quantify it is unknown. Methods The relationship between immune-effector and inhibitory (checkpoint) genes was assessed by NanoString™ in 252 paraffin-embedded DLBCL tissues. A model to quantify net anti-tumoural immunity as an outcome predictor was tested in 158 R-CHOP treated patients, and validated in tissue/blood from two independent R-CHOP treated cohorts of 233 and 140 patients respectively. Findings T and NK-cell immune-effector molecule expression correlated with tumour associated macrophage and PD-1/PD-L1 axis markers consistent with malignant B-cells triggering a dynamic checkpoint response to adapt to and evade immune-surveillance. A tree-based survival model was performed to test if immune-effector to checkpoint ratios were prognostic. The CD4*CD8:(CD163/CD68)*PD-L1 ratio was better able to stratify overall survival than any single or combination of immune markers, distinguishing groups with disparate 4-year survivals (92% versus 47%). The immune ratio was independent of and added to the revised international prognostic index (R-IPI) and cell-of-origin (COO). Tissue findings were validated in 233 DLBCL R-CHOP treated patients. Furthermore, within the blood of 140 R-CHOP treated patients immune-effector:checkpoint ratios were associated with differential interim-PET/CT+ve/-ve expression.

Using multi-label classification for acoustic pattern detection and assisting bird species surveys

Acoustics is a rich source of environmental information that can reflect the ecological dynamics. To deal with the escalating acoustic data, a variety of automated classification techniques have been used for acoustic patterns or scene recognition, including urban soundscapes such as streets and restaurants; and natural soundscapes such as raining and thundering. It is common to classify acoustic patterns under the assumption that a single type of soundscapes present in an audio clip. This assumption is reasonable for some carefully selected audios. However, only few experiments have been focused on classifying simultaneous acoustic patterns in long-duration recordings. This paper proposes a binary relevance based multi-label classification approach to recognise simultaneous acoustic patterns in one-minute audio clips. By utilising acoustic indices as global features and multilayer perceptron as a base classifier, we achieve good classification performance on in-the-field data. Compared with single-label classification, multi-label classification approach provides more detailed information about the distributions of various acoustic patterns in long-duration recordings. These results will merit further biodiversity investigations, such as bird species surveys.