DNA Methylation Signatures of Tumor-Associated Natural Killer Cells with Self-Functionalized Nanosensor Enable Colorectal Cancer Diagnosis.

Natural killer (NK) cells undergo multiple DNA genomic alterations, especially methylation-based modifications that affect activation and function. Several epigenetic modifier markers have been targeted for immunotherapy to date, but the possibility of cancer diagnosis using NK cell's DNA has been overlooked. Here, we investigated the potential use of NK cell DNA genome modifications as markers for the diagnosis of colorectal cancer (CRC) and validated their efficacy in CRC patients. Using Raman spectroscopy as the detection methodology, we identified CRC-specific methylation signatures by comparing CRC-interacted NK cells to healthy circulating NK cells. Subsequently, we identified methylation-dependent alterations in these NK cell populations. These markers were then utilized by a machine learning algorithm to develop a diagnostic model with predictive capabilities. The diagnostic prediction model accurately differentiated CRC patients from normal controls. Our findings demonstrated the utility of NK DNA markers in the diagnosis of CRC.

Minimally invasive detection of cancer using metabolic changes in tumor-associated natural killer cells with Oncoimmune probes.

Natural Killer (NK) cells, a subset of innate immune cells, undergo cancer-specific changes during tumor progression. Therefore, tracking NK cell activity in circulation has potential for cancer diagnosis. Identification of tumor associated NK cells remains a challenge as most of the cancer antigens are unknown. Here, we introduce tumor-associated circulating NK cell profiling (CNKP) as a stand-alone cancer diagnostic modality with a liquid biopsy. Metabolic profiles of NK cell activation as a result of tumor interaction are detected with a SERS functionalized OncoImmune probe platform. We show that the cancer stem cell-associated NK cell is of value in cancer diagnosis. Through machine learning, the features of NK cell activity in patient blood could identify cancer from non-cancer using 5uL of peripheral blood with 100% accuracy and localization of cancer with 93% accuracy. These results show the feasibility of minimally invasive cancer diagnostics using circulating NK cells.

Glioblastoma Associated Natural Killer Cell EVs Generating Tumour-Specific Signatures: Noninvasive GBM Liquid Biopsy with Self-Functionalized Quantum Probes.

Diagnosis of glioblastoma (GBM) poses a recurring struggle due to many factors, including the presence of the blood-brain barrier (BBB) in addition to the significant tumor heterogeneity. Natural killer (NK) cells of the innate immune system are the primary immune surveillance mechanism for GBM and identify GBM tumors without any previous sensitization. The metabolic reprogramming of NK cells during GBM association is expected to be reflected in its extracellular vesicles. Therefore, tracking the activity of NK cell vesicles in circulation (circulating immune vesicles, CIVs) has great potential for accurate GBM diagnosis. However, identification GBM associated CIVs in circulation is immensely challenging as there is no availability of clinically validated GBM-specific circulating biomarkers. Here, we present GBM associated CIV profiling for noninvasive GBM diagnosis. We investigated the feasibility of using the signals derived from GBM associated CIVs as a de novo methodology for GBM diagnosis. An ultrasensitive sensor and a marker-free approach were essential for the detection of rare signals of GBM associated CIVs. For this purpose, we designed GBM ImmunoProfiler platform using scalable ultrafast laser multiphoton ionization mechanism and adopted surface enhanced Raman spectroscopy (SERS) ensuring simultaneous detection of multiple CIV signals to identify GBM. We experimentally demonstrated that GBM associated CIVs carry unique, tumor-specific signals. The features of GBM associated CIVs were explored through machine learning identifying its similarity with GBM patient blood (without cell isolation) using a very small amount of peripheral blood (5 µL) with 96.82% sensitivity and 100% specificity. In addition, we demonstrated that a tumor associated CIV profile can classify between multiple brain cancer types (astrocytoma, oligodendroglioma, and glioblastoma). We also experimentally demonstrated significant variation in the immune checkpoint protein expression (PDL-1 and CTLA-4) between GBM associated CIVs and uninteracted CIVs. Preclinical analysis with serum specimens of GBM patients showed the possibility of using our technology for minimally invasive GBM diagnosis. With clinical validation, our technology has potential to improve GBM diagnostics with a useful, minimally invasive GBM liquid biopsy.