Comprehensive peripheral blood immunoprofiling reveals five immunotypes with immunotherapy response characteristics in patients with cancer.

The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient's immune system has hindered a wider adoption of immunoprofiling for treatment monitoring and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types and gene expression profiles. An independent analysis of 17,800 open-source transcriptomes with the same approach corroborated these findings. Continuous immunotype-based signature scores were developed to correlate systemic immunity with patient responses to different cancer treatments, including immunotherapy, prognostically and predictively. Our approach and findings illustrate the potential utility of a simple blood test as a flexible tool for stratifying cancer patients into therapy response groups based on systemic immunoprofiling.

Procrustes is a machine-learning approach that removes cross-platform batch effects from clinical RNA sequencing data.

With the increased use of gene expression profiling for personalized oncology, optimized RNA sequencing (RNA-seq) protocols and algorithms are necessary to provide comparable expression measurements between exome capture (EC)-based and poly-A RNA-seq. Here, we developed and optimized an EC-based protocol for processing formalin-fixed, paraffin-embedded samples and a machine-learning algorithm, Procrustes, to overcome batch effects across RNA-seq data obtained using different sample preparation protocols like EC-based or poly-A RNA-seq protocols. Applying Procrustes to samples processed using EC and poly-A RNA-seq protocols showed the expression of 61% of genes (N = 20,062) to correlate across both protocols (concordance correlation coefficient > 0.8, versus 26% before transformation by Procrustes), including 84% of cancer-specific and cancer microenvironment-related genes (versus 36% before applying Procrustes; N = 1,438). Benchmarking analyses also showed Procrustes to outperform other batch correction methods. Finally, we showed that Procrustes can project RNA-seq data for a single sample to a larger cohort of RNA-seq data. Future application of Procrustes will enable direct gene expression analysis for single tumor samples to support gene expression-based treatment decisions.

Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes.

Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. We present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of >9,400 tissue and blood sorted cell RNA profiles incorporated into millions of artificial transcriptomes to accurately reconstruct the tumor microenvironment (TME). Bioinformatics correction for technical and biological variability, aberrant cancer cell expression inclusion, and accurate quantification and normalization of transcript expression increased Kassandra stability and robustness. Performance was validated on 4,000 H&E slides and 1,000 tissues by comparison with cytometric, immunohistochemical, or single-cell RNA-seq measurements. Kassandra accurately deconvolved TME elements, showing the role of these populations in tumor pathogenesis and other biological processes. Digital TME reconstruction revealed that the presence of PD-1-positive CD8+ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.

Substrain specific behavioral responses in male C57BL/6N and C57BL/6J mice to a shortened 21-hour day and high-fat diet.

Altered circadian rhythms have negative consequences on health and behavior. Emerging evidence suggests genetics influences the physiological and behavioral responses to circadian disruption. We investigated the effects of a 21 h day (T = 21 cycle), with high-fat diet consumption, on locomotor activity, explorative behaviors, and health in male C57BL/6J and C57BL/6N mice. Mice were exposed to either a T = 24 or T = 21 cycle and given standard rodent chow (RC) or a 60% high-fat diet (HFD) followed by behavioral assays and physiological measures. We uncovered numerous strain differences within the behavioral and physiological assays, mainly that C57BL/6J mice exhibit reduced susceptibility to the obesogenic effects of (HFD) and anxiety-like behavior as well as increased circadian and novelty-induced locomotor activity compared to C57BL/6N mice. There were also substrain-specific differences in behavioral responses to the T = 21 cycle, including exploratory behaviors and circadian locomotor activity. Under the 21-h day, mice consuming RC displayed entrainment, while mice exposed to HFD exhibited a lengthening of activity rhythms. In the open-field and light-dark box, mice exposed to the T = 21 cycle had increased novelty-induced locomotor activity with no further effects of diet, suggesting daylength may affect mood-related behaviors. These results indicate that different circadian cycles impact metabolic and behavioral responses depending on genetic background, and despite circadian entrainment.

Constant light alters serum hormone levels related to thyroid function in male CD-1 mice.

Disruptions to the circadian rhythm can lead to altered metabolism. Modification of thyroid function may be a reason why circadian misalignment may contribute to future metabolic disorders. We investigated whether circadian disruption through constant light (LL) can lead to variations in hormone levels associated with thyroid function. Mice were exposed to LL or a 12:12 Light:Dark (LD) cycle for 6 weeks; then glucose tolerance and thyroid hormone levels were measured at ZT 6 and ZT 18. There was day/night variation in glucose tolerance, but LL had no effect. LL reduced TSH, increased fT4, and abolished day/night variation in fT3 and leptin. These findings illustrate that LL alters thyroid-related hormones, providing evidence of a link between circadian disruption and thyroid function.