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.

Circulating platelet-neutrophil aggregates characterize the development of type 1 diabetes in humans and NOD mice.

Platelet-neutrophil aggregates (PNAs) facilitate neutrophil activation and migration and could underpin the recruitment of neutrophils to the pancreas during type 1 diabetes (T1D) pathogenesis. PNAs, measured by flow cytometry, were significantly elevated in the circulation of autoantibody-positive (Aab+) children and new-onset T1D children, as well as in pre-T1D (at 4 weeks and 10-12 weeks) and T1D-onset NOD mice, compared with relevant controls, and PNAs were characterized by activated P-selectin+ platelets. PNAs were similarly increased in pre-T1D and T1D-onset NOD isolated islets/insulitis, and immunofluorescence staining revealed increased islet-associated neutrophil extracellular trap (NET) products (myeloperoxidase [MPO] and citrullinated histones [CitH3]) in NOD pancreata. In vitro, cell-free histones and NETs induced islet cell damage, which was prevented by the small polyanionic drug methyl cellobiose sulfate (mCBS) that binds to histones and neutralizes their pathological effects. Elevated circulating PNAs could, therefore, act as an innate immune and pathogenic biomarker of T1D autoimmunity. Platelet hyperreactivity within PNAs appears to represent a previously unrecognized hematological abnormality that precedes T1D onset. In summary, PNAs could contribute to the pathogenesis of T1D and potentially function as a pre-T1D diagnostic.

Structural, Functional, and Immunological Characterization of Profilin Panallergens Amb a 8, Art v 4, and Bet v 2.

Ragweed allergens affect several million people in the United States and Canada. To date, only two ragweed allergens, Amb t 5 and Amb a 11, have their structures determined and deposited to the Protein Data Bank. Here, we present structures of methylated ragweed allergen Amb a 8, Amb a 8 in the presence of poly(l-proline), and Art v 4 (mugwort allergen). Amb a 8 and Art v 4 are panallergens belonging to the profilin family of proteins. They share significant sequence and structural similarities, which results in cross-recognition by IgE antibodies. Molecular and immunological properties of Amb a 8 and Art v 4 are compared with those of Bet v 2 (birch pollen allergen) as well as with other allergenic profilins. We purified recombinant allergens that are recognized by patient IgE and are highly cross-reactive. It was determined that the analyzed allergens are relatively unstable. Structures of Amb a 8 in complex with poly(l-proline)10 or poly(l-proline)14 are the first structures of the plant profilin in complex with proline-rich peptides. Amb a 8 binds the poly(l-proline) in a mode similar to that observed in human, mouse, and P. falciparum profilin·peptide complexes. However, only some of the residues that form the peptide binding site are conserved.

Career Development From Adolescence Through Emerging Adulthood Insights From Information Technology Occupations.

Career development theories suggest that social-contextual experiences are influential in individuals' career interests, aspirations, and skill development and may be a source of gender and ethnic differences in certain career fields. In this mixed methods study, we examine the supportive and obstructive career-related experiences of 13 men and 13 women (modal age 25). Interviews focused primarily on the pathway toward or away from an information technology (IT) career. Thematic coding indicated that parents were mostly supportive, while experiences in school and work occasionally made individuals reconsider their career plans. Social influences often changed developmentally as participants entered full-time jobs. Gendered participation in IT was often attributed to women's perception that it is a male-oriented field.