Identifying cancer-associated leukocyte profiles using high-resolution flow cytometry screening and machine learning.

Background: Machine learning (ML) is a valuable tool with the potential to aid clinical decision making. Adoption of ML to this end requires data that reliably correlates with the clinical outcome of interest; the advantage of ML is that it can model these correlations from complex multiparameter data sets that can be difficult to interpret conventionally. While currently available clinical data can be used in ML for this purpose, there exists the potential to discover new "biomarkers" that will enhance the effectiveness of ML in clinical decision making. Since the interaction of the immune system and cancer is a hallmark of tumor establishment and progression, one potential area for cancer biomarker discovery is through the investigation of cancer-related immune cell signatures. Hence, we hypothesize that blood immune cell signatures can act as a biomarker for cancer progression. Methods: To probe this, we have developed and tested a multiparameter cell-surface marker screening pipeline, using flow cytometry to obtain high-resolution systemic leukocyte population profiles that correlate with detection and characterization of several cancers in murine syngeneic tumor models. Results: We discovered a signature of several blood leukocyte subsets, the most notable of which were monocyte subsets, that could be used to train CATboost ML models to predict the presence and type of cancer present in the animals. Conclusions: Our findings highlight the potential utility of a screening approach to identify robust leukocyte biomarkers for cancer detection and characterization. This pipeline can easily be adapted to screen for cancer specific leukocyte markers from the blood of cancer patient.

Irradiation immunity interactions.

The immune system can influence cancer development by both impeding and/or facilitating tumour growth and spread. A better understanding of this complex relationship is fundamental to optimise current and future cancer therapeutic strategies. Although typically regarded as a localised and immunosuppressive anti-cancer treatment modality, radiation therapy has been associated with generating profound systemic effects beyond the intended target volume. These systemic effects are immune-driven suggesting radiation therapy can enhance anti-tumour immunosurveillance in some instances. In this review, we summarise how radiation therapy can positively and negatively affect local and systemic anti-tumour immune responses, how co-administration of immunotherapy with radiation therapy may help promote anti-tumour immunity, and how the use of immune biomarkers may help steer radiation therapy-immunotherapy personalisation to optimise clinical outcomes.

Machine learning predicts cancer subtypes and progression from blood immune signatures.

Clinical adoption of immune checkpoint inhibitors in cancer management has highlighted the interconnection between carcinogenesis and the immune system. Immune cells are integral to the tumour microenvironment and can influence the outcome of therapies. Better understanding of an individual's immune landscape may play an important role in treatment personalisation. Peripheral blood is a readily accessible source of information to study an individual's immune landscape compared to more complex and invasive tumour bioipsies, and may hold immense diagnostic and prognostic potential. Identifying the critical components of these immune signatures in peripheral blood presents an attractive alternative to tumour biopsy-based immune phenotyping strategies. We used two syngeneic solid tumour models, a 4T1 breast cancer model and a CT26 colorectal cancer model, in a longitudinal study of the peripheral blood immune landscape. Our strategy combined two highly accessible approaches, blood leukocyte immune phenotyping and plasma soluble immune factor characterisation, to identify distinguishing immune signatures of the CT26 and 4T1 tumour models using machine learning. Myeloid cells, specifically neutrophils and PD-L1-expressing myeloid cells, were found to correlate with tumour size in both the models. Elevated levels of G-CSF, IL-6 and CXCL13, and B cell counts were associated with 4T1 growth, whereas CCL17, CXCL10, total myeloid cells, CCL2, IL-10, CXCL1, and Ly6Cintermediate monocytes were associated with CT26 tumour development. Peripheral blood appears to be an accessible means to interrogate tumour-dependent changes to the host immune landscape, and to identify blood immune phenotypes for future treatment stratification.

Ghrelin restoration of function in vitro in somatotropes from male mice lacking the Janus kinase (JAK)-binding site of the leptin receptor.

Deletion of the signaling domain of leptin receptors selectively in somatotropes, with Cre-loxP technology, reduced the percentage of immunolabeled GH cells and serum GH. We hypothesized that the deficit occurred when leptin's postnatal surge failed to stimulate an expansion in the cell population. To learn more about the deficiency in GH cells, we tested their expression of GHRH receptors and GH mRNA and the restorative potential of secretagogue stimulation in vitro. In freshly plated dissociated pituitary cells from control male mice, GHRH alone (0.3 nM) increased the percentage of immunolabeled GH cells from 27 ± 0.05% (vehicle) to 42 ± 1.8% (P < .002) and the secretion of GH 1.8-3×. Deletion mutant pituitary cells showed a 40% reduction in percentages of immunolabeled GH cells (16.7 ± 0.4%), which correlated with a 47% reduction in basal GH levels (50 ng/mL control; 26.7 ng/mL mutants P = .01). A 50% reduction in the percentage of mutant cells expressing GHRH receptors (to 12%) correlated with no or reduced responses to GHRH. Ghrelin alone (10 nM) stimulated more GH cells in mutants (from 16.7-23%). When added with 1-3 nM GHRH, ghrelin restored GH cell percentages and GH secretion to levels similar to those of stimulated controls. Counts of somatotropes labeled for GH mRNA confirmed normal percentages of somatotropes in the population. These discoveries suggest that leptin may optimize somatotrope function by facilitating expression of membrane GHRH receptors and the production or maintenance of GH stores.