A method for identifying a kidney transplant recipient at an increased risk of developing interstitial fibrosis or tubular atrophy which comprises obtaining a post-transplant urine sample from the kidney transplant recipient; measuring the level of clusterin in the urine sample; comparing the level of clusterin in the patient sample to the level of clusterin in a control sample from the urine of a non-fibrotic kidney transplant recipient; diagnosing a kidney transplant recipient with a clusterin level that is significantly higher than the clusterin level in the control as being at an increased risk of developing interstitial fibrosis or tubular atrophy.

The subject disclosure presents systems and computer-implemented methods for providing reliable risk stratification for early-stage cancer patients by predicting a recurrence risk of the patient and to categorize the patient into a high or low risk group. A series of slides depicting serial sections of cancerous tissue are automatically analyzed by a digital pathology system, a score for the sections is calculated, and a Cox proportional hazards regression model is used to stratify the patient into a low or high risk group. The Cox proportional hazards regression model may be used to determine a whole-slide scoring algorithm based on training data comprising survival data for a plurality of patients and their respective tissue sections. The coefficients may differ based on different types of image analysis operations applied to either whole-tumor regions or specified regions within a slide.

The invention provides methods of measuring and/or quantifying the presence and/or amount of Her-3 and/or Her-3 in a complex and the presence and/or amount of p95 in a sample. The invention also provides antibodies specific for Her-3.

Provided herein are compositions for treatment of Acute Lymphoblastic Leukemia (ALL) and methods of their use, including inhibiting ALL relapse. Further provided herein are systems of treatment that are directed by a health care provider, and which combine prognostic methods for determining ALL relapse and the described treatments.

Embodiments may provide capability to predict the 10-year risk of local recurrence of ductal carcinoma in situ (DCIS) conditions or other non-invasive carcinomas in situ. Embodiments may evaluate the severity and frequency of numerical and structural CA present within DCIS, and may assign a quantitative centrosomal amplification score (CAS) to each sample. For example, a method of determining the risk profile of a carcinoma in situ in a patient may comprise determining severity and frequency of numerical and structural centrosome amplification present within a sample of a carcinoma in situ from the patient and determining at least one centrosome amplification score (CAS) value for the sample based on the determined severity and frequency of numerical and structural centrosome amplification in the sample, wherein the determined at least one CAS value provides a measure of a level of a 10-year risk of local recurrence associated with the carcinoma in situ.

Provided are a method for diagnosing cancer, a diagnosis kit and compositions useful for measurement of NK cell activity. The incidence of cancer may be diagnosed by monitoring changes in the in vivo immune system through measurement of NK cell activity in blood. Thus, the incidence of cancer may be readily predicted as described herein using a blood sample from a subject.

Heterogeneity for biomarkers in a tissue sample can be calculated. A heterogeneity score can be combined with an immunohistochemistry combination score to provide breast cancer recurrence prognosis. Heterogeneity can be based on percent positivity determinations for a plurality of biomarkers according to how many cells in the sample stain positive. An immunohistochemistry combination score can be calculated. An imaging tool can support a digital pathologist workflow that includes designating fields of view in an image of the tissue sample. Based on the fields of view, a heterogeneity metric can be calculated and combined with an immunohistochemistry combination score to generate a breast cancer recurrence prognosis score.

A protein antigen combination for detecting Alzheimer's disease autoantibodies in human serum sample and an application thereof are disclosed. The protein antigen combination includes at least two protein fragments selected from the following proteins: MAPT, ADARB1, P21, DNAJC8, RAGE, ASXL1, and JMJD2D. Based on the protein antigen composition, a kit for diagnosis, especially for early diagnosis of Alzheimer's disease or its related risks, can be prepared. By using the protein antigen composition provided by the invention, early Alzheimer's disease can be quickly and accurately diagnosed, which has important practical significance.

The present disclosure provides gene expression profiles that are associated with cancer, including certain gene expression profiles that differentiate between cancer that is at a high risk of recurrence. The gene expression profiles can be measured at the nucleic acid or protein level. The gene expression profiles can also be used to identify a subject for cancer treatment. Also provided are kits for use in predicting cancer recurrence and/or prognosing cancer and an array comprising probes for detecting the unique gene expression profiles associated with cancer.

Disclosed herein are biomarkers and methods of using the same for determining the risk of recurrence of cancer in a subject. The methods may include determining the level of expression of at least one MHCII gene, determining the level of expression of at least one TIL gene, and determining an Immune Activation Score for the subject. Further provided are methods of diagnosing a subject with triple-negative breast cancer (TNBC) as having TNBC Basal-like subtype, and methods of treating cancer in a subject.