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\"The Art of music is a handsomely illustrated and rich interdisciplinary look at the mutual influence between music and the visual arts across cultures and eras. The book sheds new light on more familiar artists at the intersection of the visual and the musical, such as Wassily Kandinsky and Arnold Schoenberg, and presents new scholarship on less well-known examples in the arts of Asia, Africa, the Americas, and Europe, from antique pottery to contemporary video and sound art. Essays consider key works and themes such as synesthesia and other formal and theoretical crossovers, motifs of musicians, and performative and ritual functions of music, musical instruments, and art. With more than 250 color images illustrating works of art in diverse traditions, The Art of music offers enriching reading for scholars and general audiences alike\"-- Provided by publisher.

In this study, involving melanoma patients and a mouse model for melanoma, an optimal anti-tumour response was induced by using a combination of radiation with anti-CTLA4 and anti-PD-L1 antibody therapies, each attacking the tumour from a different angle. Three-way treatment of melanoma This study, involving melanoma patients and a mouse model for melanoma, demonstrates that an optimal anti-tumour response involves a combination of the three tested treatment modalities: high-dose radiation, together with two different types of immune checkpoint inhibitors (anti-CTLA4 and anti PD-L1), each attacking the tumour from a different angle. Immune checkpoint inhibitors 1 result in impressive clinical responses 2 , 3 , 4 , 5 , but optimal results will require combination with each other 6 and other therapies. This raises fundamental questions about mechanisms of non-redundancy and resistance. Here we report major tumour regressions in a subset of patients with metastatic melanoma treated with an anti-CTLA4 antibody (anti-CTLA4) and radiation, and reproduced this effect in mouse models. Although combined treatment improved responses in irradiated and unirradiated tumours, resistance was common. Unbiased analyses of mice revealed that resistance was due to upregulation of PD-L1 on melanoma cells and associated with T-cell exhaustion. Accordingly, optimal response in melanoma and other cancer types requires radiation, anti-CTLA4 and anti-PD-L1/PD-1. Anti-CTLA4 predominantly inhibits T-regulatory cells (T reg cells), thereby increasing the CD8 T-cell to T reg (CD8/T reg ) ratio. Radiation enhances the diversity of the T-cell receptor (TCR) repertoire of intratumoral T cells. Together, anti-CTLA4 promotes expansion of T cells, while radiation shapes the TCR repertoire of the expanded peripheral clones. Addition of PD-L1 blockade reverses T-cell exhaustion to mitigate depression in the CD8/T reg ratio and further encourages oligoclonal T-cell expansion. Similarly to results from mice, patients on our clinical trial with melanoma showing high PD-L1 did not respond to radiation plus anti-CTLA4, demonstrated persistent T-cell exhaustion, and rapidly progressed. Thus, PD-L1 on melanoma cells allows tumours to escape anti-CTLA4-based therapy, and the combination of radiation, anti-CTLA4 and anti-PD-L1 promotes response and immunity through distinct mechanisms.

Alterations to the natural microbiome are linked to different diseases, and the presence or absence of specific microbes is directly related to disease outcomes. We performed a comprehensive analysis with unique cohorts of the four subtypes of breast cancer (BC) characterized by their microbial signatures, using a pan-pathogen microarray strategy. The signature (includes viruses, bacteria, fungi, and parasites) of each tumor subtype was correlated with clinical data to identify microbes with prognostic potential. The subtypes of BC had specific viromes and microbiomes, with ER+ and TN tumors showing the most and least diverse microbiome, respectively. The specific microbial signatures allowed discrimination between different BC subtypes. Furthermore, we demonstrated correlations between the presence and absence of specific microbes in BC subtypes with the clinical outcomes. This study provides a comprehensive map of the oncobiome of BC subtypes, with insights into disease prognosis that can be critical for precision therapeutic intervention strategies.

Human cells tightly control their dimensions, but in some cancers, normal cell size control is lost. In this study we measure cell volumes of epithelial cells from human lung adenocarcinoma progression in situ. By leveraging artificial intelligence (AI), we reconstruct tumor cell shapes in three dimensions (3D) and find airway type 2 cells display up to 10-fold increases in volume. Surprisingly, cell size increase is not caused by altered ploidy, and up to 80% of near-euploid tumor cells show abnormal sizes. Size dysregulation is not explained by cell swelling or senescence because cells maintain cytoplasmic density and proper organelle size scaling, but is correlated with changes in tissue organization and loss of a novel network of processes that appear to connect alveolar type 2 cells. To validate size dysregulation in near-euploid cells, we sorted cells from tumor single-cell suspensions on the basis of size. Our study provides data of unprecedented detail for cell volume dysregulation in a human cancer. Broadly, loss of size control may be a common feature of lung adenocarcinomas in humans and mice that is relevant to disease and identification of these cells provides a useful model for investigating cell size control and consequences of cell size dysregulation.

Quantitative histomorphometry (QH) refers to the application of advanced computational image analysis to reproducibly describe disease appearance on digitized histopathology images. QH thus could serve as an important complementary tool for pathologists in interrogating and interpreting cancer morphology and malignancy. In the US, annually, over 60,000 prostate cancer patients undergo radical prostatectomy treatment. Around 10,000 of these men experience biochemical recurrence within 5 years of surgery, a marker for local or distant disease recurrence. The ability to predict the risk of biochemical recurrence soon after surgery could allow for adjuvant therapies to be prescribed as necessary to improve long term treatment outcomes. The underlying hypothesis with our approach, co-occurring gland angularity (CGA), is that in benign or less aggressive prostate cancer, gland orientations within local neighborhoods are similar to each other but are more chaotically arranged in aggressive disease. By modeling the extent of the disorder, we can differentiate surgically removed prostate tissue sections from (a) benign and malignant regions and (b) more and less aggressive prostate cancer. For a cohort of 40 intermediate-risk (mostly Gleason sum 7) surgically cured prostate cancer patients where half suffered biochemical recurrence, the CGA features were able to predict biochemical recurrence with 73% accuracy. Additionally, for 80 regions of interest chosen from the 40 studies, corresponding to both normal and cancerous cases, the CGA features yielded a 99% accuracy. CGAs were shown to be statistically signicantly ([Formula: see text]) better at predicting BCR compared to state-of-the-art QH methods and postoperative prostate cancer nomograms.

Background Automated classification of histopathology involves identification of multiple classes, including benign, cancerous, and confounder categories. The confounder tissue classes can often mimic and share attributes with both the diseased and normal tissue classes, and can be particularly difficult to identify, both manually and by automated classifiers. In the case of prostate cancer, they may be several confounding tissue types present in a biopsy sample, posing as major sources of diagnostic error for pathologists. Two common multi-class approaches are one-shot classification (OSC), where all classes are identified simultaneously, and one-versus-all (OVA), where a \"target” class is distinguished from all \"non-target” classes. OSC is typically unable to handle discrimination of classes of varying similarity (e.g. with images of prostate atrophy and high grade cancer), while OVA forces several heterogeneous classes into a single \"non-target” class. In this work, we present a cascaded (CAS) approach to classifying prostate biopsy tissue samples, where images from different classes are grouped to maximize intra-group homogeneity while maximizing inter-group heterogeneity. Results We apply the CAS approach to categorize 2000 tissue samples taken from 214 patient studies into seven classes: epithelium, stroma, atrophy, prostatic intraepithelial neoplasia (PIN), and prostate cancer Gleason grades 3, 4, and 5. A series of increasingly granular binary classifiers are used to split the different tissue classes until the images have been categorized into a single unique class. Our automatically-extracted image feature set includes architectural features based on location of the nuclei within the tissue sample as well as texture features extracted on a per-pixel level. The CAS strategy yields a positive predictive value (PPV) of 0.86 in classifying the 2000 tissue images into one of 7 classes, compared with the OVA (0.77 PPV) and OSC approaches (0.76 PPV). Conclusions Use of the CAS strategy increases the PPV for a multi-category classification system over two common alternative strategies. In classification problems such as histopathology, where multiple class groups exist with varying degrees of heterogeneity, the CAS system can intelligently assign class labels to objects by performing multiple binary classifications according to domain knowledge.

Abstract Rationale The etiology of sarcoidosis is unknown, but microbial agents are suspected as triggers. Objectives We sought to identify bacterial, fungal, or viral lineages in specimens from patients with sarcoidosis enriched relative to control subjects using metagenomic DNA sequencing. Because DNA from environmental contamination contributes disproportionately to samples with low authentic microbial content, we developed improved methods for filtering environmental contamination. Methods We analyzed specimens from subjects with sarcoidosis (n = 93), control subjects without sarcoidosis (n = 72), and various environmental controls (n = 150). Sarcoidosis specimens consisted of two independent sets of formalin-fixed, paraffin-embedded lymph node biopsies, BAL, Kveim reagent, and fresh granulomatous spleen from a patient with sarcoidosis. All specimens were analyzed by bacterial 16S and fungal internal transcribed spacer ribosomal RNA gene sequencing. In addition, BAL was analyzed by shotgun sequencing of fractions enriched for viral particles, and Kveim and spleen were subjected to whole-genome shotgun sequencing. Measurements and Main Results In one tissue set, fungi in the Cladosporiaceae family were enriched in sarcoidosis compared with nonsarcoidosis tissues; in the other tissue set, we detected enrichment of several bacterial lineages in sarcoidosis but not Cladosporiaceae. BAL showed limited enrichment of Aspergillus fungi. Several microbial lineages were detected in Kveim and spleen, including Cladosporium. No microbial lineage was enriched in more than one sample type after correction for multiple comparisons. Conclusions Metagenomic sequencing revealed enrichment of microbes in single types of sarcoidosis samples but limited concordance across sample types. Statistical analysis accounting for environmental contamination was essential to avoiding false positives.

Background Mammography screening has improved early breast cancer detection, leading to reduced mortality and lower rates of advanced breast cancer. However, mammography has a high false positive rate that results in over a million invasive breast biopsies of benign lesions in the US each year. Therefore, there is a need for noninvasive, blood-based diagnostics that can accurately assess risk of malignancy for women with indeterminate lesions identified by mammography, such as BI-RADS category 4 breast lesions. The aim of this study is to identify biomarkers from multiplexed extracellular vesicle liquid biopsy that can accurately classify mammographically detected BI-RADS 4 lesions. Methods We analyzed plasma from 113 prospectively enrolled subjects with BI-RADS 4 breast lesions, including 86 women with benign lesions and 27 women with malignant lesions (including 12 with stage I invasive carcinoma and 14 with ductal carcinoma in situ ). None of the invasive carcinomas were metastatic. From each plasma sample, we used track etched magnetic nanopore technology to separately isolate HER2 and CD24 expressing extracellular vesicles (EVs) and measured their miRNA cargo using next-generation sequencing. We evaluated the performance of EV-miRNA biomarkers for classifying malignancy and applied LASSO classification to identify a panel of four complementary EV miRNA biomarkers that we validated by qPCR. Results We identified 19 differentially enriched miRNA from HER2+ EVs and 11 differentially enriched miRNA from CD24+ EVs of women with malignant lesions compared to benign lesions. We observed individual miRNA with an AUC of up to 0.87 for miR-340-5p from HER2+ EVs and 0.75 for miR-223-3p from CD24+ EVs. LASSO classification selected a panel of four complementary EV miRNA for classifying breast cancer: miR-340-5p (HER2+ EVs), miR-598-3p (CD24+), miR-15b-5p (HER2+), and miR-126-3p (CD24+). Conclusions HER2+ and CD24+ EV subpopulations contain complementary biomarkers suitable for validation in larger studies that can accurately detect early-stage breast cancer among women with BI-RADS category 4 breast lesions.

The microbiome is fundamentally one of the most unique organs in the human body. Dysbiosis can result in critical inflammatory responses and result in pathogenesis contributing to neoplastic events. We used a pan-pathogen array technology (PathoChip) coupled with next-generation sequencing to establish microbial signatures unique to human oral and oropharyngeal squamous cell carcinomas (OCSCC/OPSCC). Signatures for DNA and RNA viruses including oncogenic viruses, gram positive and negative bacteria, fungi and parasites were detected. Cluster and topological analyses identified 2 distinct groups of microbial signatures related to OCSCCs/OPSCCs. Results were validated by probe capture next generation sequencing; the data from which also provided a comprehensive map of integration sites and chromosomal hotspots for micro-organism genomic insertions. Identification of these microbial signatures and their integration sites may provide biomarkers for OCSCC/OPSCC diagnosis and prognosis as well as novel avenues for study of their potential role in OCSCCs/OPSCCs.