Abstract:
Provided is a gene expression signature consisting of 12 biomarkers for use in prognosing or classifying a subject with lung squamous cell carcinoma into a poor survival group or a good survival group. The 12-gene signature specific for squamous cell carcinoma consists of the biomarkers RPL22, VEGFA, G0S2, NES, TNFRSF25, DKFZP586P0123, COL8A2, ZNF3, PJPK5, RNFT2, ARHGEF12 and PTPN20A.
Abstract:
The present disclosure describes methods and compositions for diagnosing or predicting likelihood of a OSCC recurrence in a subject having undergone OSCC resection comprising: a) determining an expression level of one or more biomarkers selected from Table 4, 5 and/or 7, optionally MMP1, COL4A1, THBS2 and/or P4HA2 in a test sample from the subject, the one or more biomarkers comprising at least one of THBS2 and P4HA2, and b) comparing the expression level of the one or more biomarkers with a control, wherein a difference or a similarity in the expression level of the one or more biomarkers between the test sample and the control is used to diagnose or predict the likelihood of OSCC recurrence in the subject In particular, the present disclosure describes methods and compositions using a four-gene biomarker signature that can predict recurrence of oral squamous cell carcinoma in subjects that have histologically normal surgical resection margins.
Abstract:
The application provides methods of prognosmg, diagnosing, screening and classifying lung cancer patients into poor survival groups or good survival groups. A number of altered genomic regions have been identified that distinguish subtype of lung adenocarcinoma (ADC), specifically between bronchioloalveolar carcinoma (BAC) and invasive ADC with BAC features (AWBF), and genes and biomarkers whose expression are altered in individuals with pulmonary ADC according to different survival outcomes. The amplification and/or deletion of these genomic regions, and/or the biomarker expression profiles can be used to classify patients with ADC into a BAC group with excellent survival outcome, or an invasive ADC with BAC features group with higher risk of developing metastatic recurrence and poorer survival outcome. The application also includes kits for use in the methods of the application.
Abstract:
Disclosed herein are methods and materials for prognosing survival of lung cancer patients, the methods comprising the detection of gains and losses of minimal common regions and/or genes associated with prognosis and benefit of chemotherapy.
Abstract:
The present application provides methods of diagnosing or detecting ovarian cancer. The present application also includes kits for use in the methods of the application.
Abstract:
The invention provides methods of prognosing and classifying non-small cell lung cancer (NSCLC) patients into poor survival groups or good survival groups based on the differential expression of biomarkers The invention also includes kits for use in the methods of the invention.
Abstract:
A method for determining prognosis in a subject having a hematological cancer comprising: a) determining an expression profile by measuring the gene expression levels of a set of genes selected from a leukemic stem cell (LSC) gene signature marker set or an hematopoietic stem cell (HSC) gene signature marker set, in a sample from a subject; and b) classifying the subject as having a good prognosis or a poor prognosis based on the expression profile; wherein a good prognosis predicts an increased likelihood of survival within a predetermined period after initial diagnosis and poor prognosis predicts a decreased likelihood of survival within the predetermined period after initial diagnosis.
Abstract:
There is provided a method for identifying a biomarker, such as a gene signature, associated with a biological parameter A 6-gene signature for non-small cell lung cancer (NSCLC) is also provided, as well as a method of prognosmg or classifying a subject with non-small cell lung cancer into a poor survival group or a good survival group, using said gene signature
Abstract:
The application provides methods of prognosing and classifying lung cancer patients into poor survival groups or good survival groups and for determining the benefit of adjuvant chemotherapy by way of a multigene signature. The application also includes kits and computer products for use in the methods of the application.