Set forth herein are glycopeptide biomarkers useful for diagnosing diseases and conditions, such as but not limited to, cancer (e.g., ovarian), an autoimmune disease, fibrosis and aging conditions. Also set forth herein are methods of generating glycopeptide biomarkers and methods of analyzing glycopeptides using mass spectroscopy. Also set forth herein are methods of analyzing glycopeptides using machine learning algorithms.

The present invention provides markers for judging the efficacy of therapy with a PD-1 signal inhibitor before or at an early stage of the therapy. As biomarkers for predicting or judging the efficacy of therapy with a PD-1 signal inhibitor, surrogate indicators of metabolic changes relating to mitochondrial activity in T cells and/or T cell activation in a subject are used. As such indicators, intestinal flora-related metabolites in the serum or plasma, energy metabolism-related metabolites in the serum or plasma, amino acid metabolism-related metabolites and/or derivatives thereof in the serum of plasma, oxygen consumption rate and/or ATP turnover in peripheral blood CD8.sup.+ cells, amino acids in T cells, and T-bet in peripheral blood CD8.sup.+ cells may be used.

A method of treating a cancer in a mammalian subject, where the cancer has multi-therapy resistance, comprises administering to the mammalian subject a therapeutically effective amount of a pentaaza macrocyclic ring complex corresponding to the Formula (I) below, optionally with administration of a further anti-cancer therapy.

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Provided is a method for evaluating the progression of glioma in an individual comprising monitoring the levels of circulating exosomes that are positive for survivin and a glial marker (such as glial fibrillary acidic protein). An increase in the level of such exosomes is indicative of poor prognosis. Levels of circulating exosomes that are positive for survivin and glial marker can also be used for evaluating the efficacy of a therapy for glioma in an individual, and modifying the therapy or introducing additional therapy if levels of such exosomes are found to be increasing.

This disclosure provides methods and systems for classifying biological particles, e.g., blood cells, microbes, circulating tumor cells (CTCs). Using impedance flow cytometry, such as multi-frequency impedance cytometry, in conjunction with supervised machine learning, the disclosed methods and systems demonstrated improved accuracy in classifying biological particles.

The present application provides compositions and methods for determining a disease or condition in a subject. The method comprises contacting a body fluid with a molecule comprising a reporter thereof and the reported is cleaved by an agent in the body fluid. Diseases and conditions that can be determined by the method are also described.

The present invention relates to a method for discontinuing a treatment with a TKI by determining the number and/or frequency of innate CD8(+) T-cells in subject and concluding that the treatment with a TKI should be discontinued when the number and/or frequency of innate CD8 T-cells is higher than the predetermined reference value. Inventors evaluated whether innate CD8(+) T-cells are an early target of CML therapy success. Among peripheral blood effector CD8(+) T-cells, inventors shown that both number nd/or frequency and functional signatures of innate CD8(+) T-cells are enhanced as early as 3 months of therapy. Strikingly, they observe that patients with high innate CD8(+) T-cell number and/or frequency at 3 months and/or diagnosis achieve a DMR earlier than patients with low innate CD8(+) T-cell number. Furthermore, a higher number and/or frequency of high innate CD8(+) T-cell patients achieved a stable DMR for over 2 years. They have also observed that the success of TKI therapy cessation is associated with higher proportion of innate CD8 T-cells expressing perforin. Collectively, these findings highlight innate CD8(+) T-cells as a potential marker for both CML therapy success and successful long-term treatment-free remission (TFR), and thus therapy discontinuation eligibility.

In one embodiment, an object of the present invention is to provide a biomarker for predicting the prognosis of a cancer patient such as a breast cancer patient. In one embodiment, the present invention relates to use of a CK2α protein or a fragment thereof in a nucleolus as a marker for predicting the prognosis of a cancer patient, a method for predicting the prognosis of a cancer patient using the marker, or a kit comprising a reagent for measuring the marker.

The present disclosure relates to methods of monitoring bladder cancer patients and analyzing patient samples for presence of methylated DNA and optionally particular gene mutations. In some embodiments, analysis results are correlated with clinical outcome measures such as risk of bladder cancer recurrence.

A method that provides a graphical indication of whether a patient will have cancer recurrence uses univariate and bivariate prognostic features that were generated as part of a minimal spanning tree (MST). The method determines the values of first and second features. A first value is measured by detecting objects in an image of tissue from the cancer patient stained with a protein-specific IHC biomarker. A second value is measured using objects marked with an mRNA-specific probe biomarker detected in the tissue. The first feature is the univariate prognostic feature for cancer recurrence in a cohort of cancer patients. A combination of the first and second features is the bivariate prognostic feature for cancer recurrence in the cohort. The first and second features are elements of the MST. Nodes of the MST represent the univariate features, edges represent the bivariate features, and edge weights represent prognostic significance of bivariate features.