Systems, apparatuses, and methods are described herein for disease detection using an analyte-agnostic approach. Such systems, apparatuses, and methods can include using an array with hydrogels disposed on a substrate, where the hydrogels include one or more polymerized monomers and one or more photoinitiators or photocleavage products thereof. One or more samples including one or more unlabeled analytes can be contacted with an array of polymers. The samples disposed on the array can be incubated for a first predetermined period of time, and heated at a predetermined temperature for a second predetermined period of time. An imaging device (e.g., flatbed scanner) can be used to measure an amount of one or more colorimetric or luminescence signals produced by the array after the incubating and heating. A neural network trained using the samples can then be used to predict a diagnostic or disease class for the sample.

The disclosure provides a method of detecting heterogeneity of disease in a cancer patient comprising (a) performing a direct analysis comprising immunofluorescent staining and morphological characteristization of nucleated cells in a blood sample obtained from the patient to identify and enumerate circulating tumor cells (CTC); (b) isolating the CTCs from the sample; (c) individually characterizing genomic parameters to generate a genomic profile for each of the CTCs, and (d) determining heterogeneity of disease in the cancer patient based on the profile. In some embodiments, the cancer is prostate cancer. In some embodiments, the prostate cancer is hormone refractory.

Provided herein are methods for predicting whether a cancer patient will respond to treatment with bevacizumab based on determining a level of small extracellular vesicle (sEV)-associated VEGF in the patient. Also provided are methods of treating patients with either bevacizumab or a VEGFR tyrosine kinase inhibitor, a VEGFR neutralizing antibody, or a VEGF ligand trap based on the level of small extracellular vesicle (sEV)-associated VEGF in the patient.

The present invention relates to a method for predicting susceptibility to sorafenib treatment by using an SULF2 gene, and a composition for treatment of sorafenib-resistant cancer using the SULF2 expression inhibition. The method for predicting susceptibility to sorafenib treatment by using the SULF2 gene according to the present invention can enable achievement of an optimal therapeutic effect by administering a drug suitable for cancer patients, and the composition for treatment of sorafenib-resistant cancer using the SULF2 inhibition has a very excellent anticancer treatment effect.

According to an embodiment of the present invention, there is provided a method of screening a prostate cancer patient by optical image analysis of a circulating tumor cell marker and a prostate-specific membrane antigen.

Methods of monitoring progression of multiple myeloma or plasmacytoma, particularly relapsed or refractory multiple myeloma, are described. Also described are methods of treating or determining response to a treatment for multiple myeloma or plasmacytoma in a subject.

A method comprises using an atomic-force microscope, acquiring a set of images associated with surfaces, and, using a machine-learning algorithm applied to the images, classifying the surfaces. As a particular example, the classification can be done in a way that relies on surface parameters derived from the images rather than using the images directly.

The present disclosure relates to the field of cancer. More particularly, the invention relates to methods of diagnosing and treating cancer, including determining a cancer type thereof. These methods involve the detection of markers in an exosome sample of the subject.

A method for detecting a tumor cell surface marker molecule PD-L1, comprising the following steps: providing a capture screen that has antibodies capable of specifically binding to tumor cells; making a sample to be tested flow through the capture screen, such that tumor cells in the sample to be tested bind to the capture screen; fixing captured tumor cells on the capture screen; and successively using a PD-L1 primary antibody solution, a PD-L1 secondary antibody solution labeled with a fluorophore AlexaFluor 647, a pan-CK-AlexaFluor 488 primary antibody solution, a CD45 primary antibody solution and a CD45 secondary antibody solution labeled with a fluorophore AlexaFluor 568, to incubate the cells fixed on the capture screen, and then labeling all cells on the capture screen with a nuclear fluorescent dye.

The present disclosure relates generally to methods and compositions for cancer immunotherapy, and more specifically, liquid markers for predicting effectiveness of cancer therapies. The disclosure features compositions and methods that are useful for predicting the efficacy of cancer treatment (e.g., a checkpoint inhibitor immunotherapy) and, in some embodiments, administering the cancer treatment such as immunotherapy.