Machine learning predicts cancer subtypes and progression from blood immune signatures

Abstract

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.

Key candidate genes and pathways in T lymphoblastic leukemia/lymphoma identified by bioinformatics and serological analyses

“…In addition, the KIF11 interaction and co expression network are mainly involved in the regulation of cell cycle, cell division, p53 signaling pathway, DNA repair and recombination, chromatin tissue, antigen processing and presentation, and drug resistance. In addition, tumor related neutrophils can support tumor progression by stimulating tumor cell invasion, migration, and movement, promoting angiogenesis, and regulating other immune cells (47)(48)(49)(50). KIF11 may play multiple roles in tumors by influencing the infiltration of neutrophils.…”

Application of a Machine Learning-Based Classification Approach for Developing Host Protein Diagnostic Models for Infectious Disease

“…Machine learning (ML) is a subfield of artificial intelligence that has rapidly gained traction in recent years in several areas, including biology [ 15 , 16 , 17 ]. ML approaches have recently been utilized to untangle complex, interdependent features to elucidate new biomedical insights, particularly in the cancer and infectious disease fields [ 18 , 19 , 20 , 21 ]. The sophisticated algorithms employed have demonstrated the capability to discern subtle differences and detect correlations that might elude traditional statistical methods or human analysis; this is especially true with multivariate datasets.…”

Artificial intelligence-based immunoprofiling serves as a potentially predictive biomarker of nivolumab treatment for advanced hepatocellular carcinoma

“…This study encouraged us to enroll more aHCC subjects and re-analyze immunoprofiling from aHCC patients with disease control and disease progression responses after nivolumab treatment. Furthermore, by automatically discovering integrated patterns from sophisticated biomedical data ( 11 ), machine learning (ML) has been applied in identifying biomarkers for predictive drug responses and disease diagnoses such as cancers ( 12 , 13 ). Accordingly, the current study aimed to re-compare the immunoprofiling between aHCC patients with disease control and disease progression responses to determine the immune cell subsets regarding the efficacy of nivolumab.…”