Leland Stanford Junior University

Other

Robert Tibshirani

Ronald Levy

Gilbert Chu

Izidore Lossos

David Botstein

Mark A Wechser

Ash A Alizadeh

Virginia Goss Tusher

Michael P Snyder

Dean W Felsher

Jean Tang

Wan-Jen Hong

Liang Liang

Kerri Elyse Rieger

Meital Ryan

Rajanala Samyak

Trevor Hastie

Methods to compute gestational age and time to delivery utilizing temporal alignment methods and applications thereof are described. Generally, systems utilize analyte measurements collected at one or more time points to determine a gestational age and time to delivery, which can be used as a basis to perform interventions and treat individuals. Computational models trained utilizing temporal alignment of analyte measurements can be used to determine gestational age and time to delivery.

Systems and temporal alignment methods for evaluation of gestational age and time to delivery

Through genomic analysis a 12-gene signature comprising up-regulated genes and down-regulated genes has been identified that is highly predictive of metastasis and overall survival in human patients with HCC. Another aspect of the disclosure methods of determining the metastatic status of an hepatocellular carcinoma of a patient, comprising obtaining a first differential gene expression profile from a carcinoma sample from a subject having an hepatocellular carcinoma and creating a report summarizing the normalized data obtained by the first gene expression analysis and including a determination of the metastatic status of the hepatic carcinoma.

Method of determining the prognosis of hepatocellular carcinomas using a multigene signature associated with metastasis

Methods and kits for classifying patients having diffuse large B-cell lymphoma (DLBCL) based upon expression of a plurality of genes are disclosed. Real-time quantitative RT-PCR can be used to measure expression values. Correlating expression values of the plurality of genes in a tumor sample from the patient to reference expression values obtained from DLBCL patients can stratify patients in the classification groups. The methods and kits can be used to predict overall patient survival.

Classification of patients having diffuse large b-cell lymphoma based upon gene expression

Methods are provided for determining whether a patient treated with an anti-proliferative agent is susceptible to toxicity. In practicing the subject methods, an expression profile for the transcriptional response to a therapy is obtained from the patient and compared to a reference profile to determine whether the patient is susceptible to toxicity. In addition, reagents and kits thereof that find use in practicing the subject methods are provided.

Methods and compositions for determining risk of treatment toxicity

Microarrays can measure the expression of thousands of genes and thus identify changes in expression between different biological states. Methods are needed to determine the significance of these changes, while accounting for the enormous number of genes. We describe a new method, Significance Analysis of Microarrays (SAM), that assigns a score to each gene based on the change in gene expression relative to the standard deviation of repeated measurements. For genes with scores greater than an adjustable threshold, SAM uses permutations of the repeated measurements to estimate the percentage of such genes identified by chance, the false discovery rate (FDR). When the transcriptional response of human cells to ionizing radiation was measured by microarrays, SAM identified 34 genes that changed at least 1.5-fold with an estimated FDR of 12%, compared to FDRs of 60% and 84% using conventional methods of analysis. Of the 34 genes, 19 were involved in cell cycle regulation, and 3 in apoptosis. Surprisingly, 4 nucleotide excision repair genes were induced, suggesting that this repair pathway for UV-damaged DNA might play a heretofore unrecognized role in repairing DNA damaged by ionizing radiation.

Significance analysis of microarrays

Classification of patients having diffuse large B-cell lymphoma based upon gene expression

Growing community of inventors

Palo Alto, CA, United States of America

Robert Tibshirani