Abstract: The present invention provides a method for predicting the treatment response of a human bladder cancer patient, the method comprising: a) measuring the gene expression of at least 9, at least 10, at least 15, at least 20 or at least 30 of the genes from Group 1 in Table 10 and at least 1, at least 2, at least 3 or at least 5 of the genes from Groups 2-4 in Table 10 in a sample obtained from the bladder tumour of the patient to obtain a sample gene expression profile of at least said genes; and b) making a prediction of the treatment response and/or prognosis of the patient based on the sample gene expression profile. Related methods and systems are also described. The invention finds particular use in predicting whether a bladder cancer patient is likely to be sensitive to (chemo)radiation therapy.

Abstract: The present invention provides a method for predicting the treatment response of a human gastroesophageal cancer patient, the method comprising: a) measuring the gene expression of at least 3 of the following genes: CDH1, CDK6, COX2, ELOVL5, GATA4, EGFR, TBCEL, FGF7, CDH17, FNBP1, PIP5K1B, TWIST, CD44, MET, CEACAM1, TOX3, GLIPR2, GSTP1, RON, TMEM136, MYB, BRCA2, FGF1, POU5F1, EPR, DPYD, ABL2 and SH3RF1 in a sample obtained from the gastroesophageal tumour of the patient to obtain a sample gene expression profile of at least said genes; and b) making a prediction of the treatment response and/or prognosis of the patient based on the sample gene expression profile. Also provided are related computer-implemented methods and methods of treatment of gastroesophageal cancer.

Abstract: The present invention relates to methods for predicting prognosis and overall survival among tumour/cancer patients, and methods for classifying and stratifying these patients, particularly patients having pancreatic neuroendocrine tumors (PanNETs). The invention also relates to therapeutic methods for treating classified patients. Measuring gene expression levels of at least some of a selected group 198 genes is shown to be useful in the stratification of patients into groups with prognostic significance, and making a prediction of prognosis.

Abstract: The present invention provides a method for predicting the treatment response of a human gastroesophageal cancer patient, the method comprising: a) measuring the gene expression of at least 3 of the following genes: CDH1, CDK6, COX2, ELOVL5, GATA4, EGFR, TBCEL, FGF7, CDH17, FNBP1, PIP5K1B, TWIST, CD44, MET, CEACAM1, TOX3, GLIPR2, GSTP1, RON, TMEM136, MYB, BRCA2, FGF1, POU5F1, EPR, DPYD, ABL2 and SH3RF1 in a sample obtained from the gastroesophageal tumour of the patient to obtain a sample gene expression profile of at least said genes; and b) making a prediction of the treatment response and/or prognosis of the patient based on the sample gene expression profile. Also provided are related computer-implemented methods and methods of treatment of gastroesophageal cancer.

Abstract: Herein is described the use of a collection of 50 breast cancer cell lines to match responses to 77 conventional and experimental therapeutic agents with transcriptional, proteomic and genomic subtypes found in primary tumors. Almost all compounds produced strong differential responses across the cell lines produced responses that were associated with transcriptional and proteomic subtypes and produced responses that were associated with recurrent genome copy number abnormalities. These associations can now be incorporated into clinical trials that test subtype markers and clinical responses simultaneously.

Abstract: The present invention relates to gene sets, the expression levels of which are useful for classifying colorectal tumors and predicting disease-free prognosis and response of patients to specific therapies that are either novel or currently available in the clinics for colorectal cancer patients.

Abstract: Herein is described the use of a collection of 50 breast cancer cell lines to match responses to 77 conventional and experimental therapeutic agents with transcriptional, proteomic and genomic subtypes found in primary tumors. Almost all compounds produced strong differential responses across the cell lines produced responses that were associated with transcriptional and proteomic subtypes and produced responses that were associated with recurrent genome copy number abnormalities. These associations can now be incorporated into clinical trials that test subtype markers and clinical responses simultaneously.