The invention provides, inter alia, methods of prognosing a subject with, or suspected of having, multiple myeloma. In certain embodiments, the methods entail testing the gene expression levels of enolase 1 (ENO1), fatty acid binding protein 5 (FABP5), thyroid hormone receptor interactor 13 (TRIP 13), transgelin 2 (TAGLN2), and replication factor C (activator 1) 4 (RFC4) in a biological sample isolated from the subject. The invention also provides methods of treatment for multiple myeloma, as well as kits, oligonucleotides, and systems for performing the methods provided by the invention.

Early detection of tumors is a major determinant of survival of patients suffering from tumors, including gastric tumors. Members of the GTM gene family can be differentially expressed in gastric tumor tissue, and thus can be used as markers for the detection of gastric and other types of cancer. The present invention provides for novel GTMs for the detection of tumors, including gastric tumors, and in particular human zymogen granule protein 16 (ZG16). The GTMs can be used in isolation or together with other known GTMs to provide for novel signatures to be used in the detection of tumors, including gastric tumors.

Methods and systems for visualizing gene expression data in a way that permits the comparison of different patient groups to facilitate medical applications, including cancer diagnostics and treatment planning, particularly breast cancer. The method organises gene expression data for at least one patient into a plurality of windows of a specified size, calculates an average RSEM score for all of the genes in each window and presents the average RSEM scores in a two-dimensional array, wherein one axis organises the windows by patient and the other axis organises the windows by sequence.

The invention describes how to estimate delta-Cq values from measured (raw-)Cq values gained from PCR measurements and how to calculate confidence intervals for them. This is realized by the following processing steps: A noise model, which might be constructed on some training PCR data, calculates the distribution of the true target material concentration of a single well for an observed measurement results. Said distribution is calculated for all types of measurement results including “Numeric” raw-Cq values as well as Cq being “Undetected”, which denotes that no fluorescence signal was detected during all cycles and thus corresponds to no or very few target molecules.

This invention relates to a method of classification of cancer type or subtype in a subject by detecting glycosyltransferase gene expression in a sample from the subject. In particular, where the method is for classification of cancer subtype in the subject the glycosyltransferase genes may be survival associated glycosyltransferase genes. The invention further relates to a kit comprising glycosyltransferase gene biomarkers for use with the method, and a method of treatment of cancer in a subject with the use of the method or kit of the invention.

The invention provides methods for diagnosing eosinophilic esophagitis in a patient using a biomarker based assay directed to KCNJ2/Kir2.1 and related compositions, kits, and computer program products.

Methods for generating a prognostic and/or subtype signature for a subject with pancreatic ductal adenocarcinoma (PDAC) are provided. In some embodiments, the methods include determining expression levels for one or more genes listed in Tables 2-5, 9, 10, or 11, and/or the DE-S and/or DE-T subset of genes in PDAC cells obtained from the subject, wherein the determining provides a prognostic and/or subtype signature for the subject. Also provided are methods for classifying a subject diagnosed with pancreatic ductal adenocarcinoma (PDAC) as having an activated stroma subtype or a normal stroma subtype of PDAC and/or a basal subtype or a classical subtype of PDAC; and methods for identifying a differential treatment strategy for a subject diagnosed with pancreatic ductal adenocarcinoma (PDAC).

The invention encompasses products and methods relating to microRNAs involved in various cancers.

Methods, systems, devices and computer implemented methods of prognosing or classifying patients using a biomarker comprising a plurality of subnetwork modules are disclosed. In some embodiments, the method comprises determining an activity of a plurality of genes in a test sample of a patient, wherein the plurality of genes are associated with the plurality of subnetwork modules. An expression profile is constructed using the activity of the plurality of genes. The dysregulation of each of the plurality of subnetwork modules is determined by calculating a score proportional to a degree of dysregulation in each of the plurality of subnetwork modules from the expression profile. The patient is prognosed or classified by inputting each dysregulation score into a model for predicting patient outcomes for patients having a disease, and inputting a clinical indicator of the patient into the model, to obtain a risk associated with the disease.

Disclosed are methods for determining copy number variation (CNV) known or suspected to be associated with a variety of medical conditions. In some embodiments, methods are provided for determining copy number variation of fetuses using maternal samples comprising maternal and fetal cell free DNA. In some embodiments, methods are provided for determining CNVs known or suspected to be associated with a variety of medical conditions. Some embodiments disclosed herein provide methods to improve the sensitivity and/or specificity of sequence data analysis by deriving a fragment size parameter. In some implementations, information from fragments of different sizes are used to evaluate copy number variations. In some implementations, one or more t-statistics obtained from coverage information of the sequence of interest is used to evaluate copy number variations. In some implementations, one or more fetal fraction estimates are combined with one or more t-statistics to determine copy number variations.