Joachim Torhorst

Bio: Joachim Torhorst is an academic researcher from University of Basel. The author has contributed to research in topics: Breast cancer & Tissue microarray. The author has an hindex of 33, co-authored 81 publications receiving 9653 citations. Previous affiliations of Joachim Torhorst include University of Lausanne & University Hospital of Basel.

Tissue microarrays for high-throughput molecular profiling of tumor specimens

Abstract: Many genes and signalling pathways controlling cell proliferation, death and differentiation, as well as genomic integrity, are involved in cancer development. New techniques, such as serial analysis of gene expression and cDNA microarrays, have enabled measurement of the expression of thousands of genes in a single experiment, revealing many new, potentially important cancer genes. These genome screening tools can comprehensively survey one tumor at a time; however, analysis of hundreds of specimens from patients in different stages of disease is needed to establish the diagnostic, prognostic and therapeutic importance of each of the emerging cancer gene candidates. Here we have developed an array-based high-throughput technique that facilitates gene expression and copy number surveys of very large numbers of tumors. As many as 1000 cylindrical tissue biopsies from individual tumors can be distributed in a single tumor tissue microarray. Sections of the microarray provide targets for parallel in situ detection of DNA, RNA and protein targets in each specimen on the array, and consecutive sections allow the rapid analysis of hundreds of molecular markers in the same set of specimens. Our detection of six gene amplifications as well as p53 and estrogen receptor expression in breast cancer demonstrates the power of this technique for defining new subgroups of tumors.

Haematogenous metastastic patterns in colonic carcinoma: An analysis of 1541 necropsies

Abstract: The sequence of events in haematogenous metastasis from colonic carcinoma was analysed, using 1541 necropsy reports from 16 centres. The findings are consistent with the cascade hypothesis that metastases develop in discrete steps, first in the liver, next in the lungs and finally, in other sites. Deviations of necropsy findings from the cascade model are largely explained on the basis of false negative reports. In only 216 of 1194 cases was there suggestive evidence that metastatic patterns (excluding lymph nodes) were causally related to lymphatic or non-haematogenous pathways. The incidence of metastatic involvement in 'other' (quaternary) sites correlated with target organ blood-flow (ml min-) per g, only when bone marrow and thyroid were excluded. In the thyroid the incidence was lower than expected on the basis of blood flow per g tissue; this may indicate that the thyroid is an unfavourable site for metastatic growth of colonic carcinoma. In the bone marrow it is higher; the latter may be due to delivery of cancer cells via both arterial blood and the vertebral venous plexus. Recognition of this pattern of metastases in the bone marrow could be important with respect of diagnosis and therapy, in patients with colonic carcinoma.

Tissue microarrays for rapid linking of molecular changes to clinical endpoints.

Abstract: Advances in genomics and proteomics are dramatically increasing the need to evaluate large numbers of molecular targets for their diagnostic, predictive or prognostic value in clinical oncology. Conventional molecular pathology techniques are often tedious, time-consuming, and require a lot of tissue, thereby limiting both the number of tissues and the number of targets that can be evaluated. Here, we demonstrate the power of our recently described tissue microarray (TMA) technology in analyzing prognostic markers in a series of 553 breast carcinomas. Four independent TMAs were constructed by acquiring 0.6 mm biopsies from one central and from three peripheral regions of each of the formalin-fixed paraffin embedded tumors. Immunostaining of TMA sections and conventional "large" sections were performed for two well- established prognostic markers, estrogen receptor (ER) and progesterone receptor (PR), as well as for p53, another frequently examined protein for which the data on prognostic utility in breast cancer are less unequivocal. Compared with conventional large section analysis, a single sample from each tumor identified about 95% of the information for ER, 75 to 81% for PR, and 70 to 74% for p53. However, all 12 TMA analyses (three antibodies on four different arrays) yielded as significant or more significant associations with tumor-specific survival than large section analyses (p < 0.0015 for each of the 12 comparisons). A single sample from each tumor was sufficient to identify associations between molecular alterations and clinical outcome. It is concluded that, contrary to expectations, tissue heterogeneity did not negatively influence the predictive power of the TMA results. TMA technology will be of substantial value in rapidly translating genomic and proteomics information to clinical applications.

Expression of Cytokeratins 17 and 5 Identifies a Group of Breast Carcinomas with Poor Clinical Outcome

Abstract: While several prognostic factors have been identified in breast carcinoma, the clinical outcome remains hard to predict for individual patients. Better predictive markers are needed to help guide difficult treatment decisions. In a previous study of 78 breast carcinoma specimens, we noted an association between poor clinical outcome and the expression of cytokeratin 17 and/or cytokeratin 5 mRNAs. Here we describe the results of immunohistochemistry studies using monoclonal antibodies against these markers to analyze more than 600 paraffin-embedded breast tumors in tissue microarrays. We found that expression of cytokeratin 17 and/or cytokeratin 5/6 in tumor cells was associated with a poor clinical outcome. Moreover, multivariate analysis showed that in node-negative breast carcinoma, expression of these cytokeratins was a prognostic factor independent of tumor size and tumor grade.

Prognostic relevance of gene amplifications and coamplifications in breast cancer.

Abstract: Multiple different oncogenes have been described previously to be amplified in breast cancer including HER2, EGFR, MYC, CCND1, and MDM2. Gene amplification results in oncogene overexpression but may also serve as an indicator of genomic instability. As such, presence of one or several gene amplifications may have prognostic significance. To assess the prognostic importance of amplifications and coamplifications of HER2, EGFR, MYC, CCND1, and MDM2 in breast cancer, we analyzed a breast cancer tissue microarray containing samples from 2197 cancers with follow-up information. Fluorescence in situ hybridizations revealed amplifications of CCND1 in 20.1%, HER2 in 17.3%, MDM2 in 5.7%, MYC in 5.3%, and EGFR in 0.8% of the tumors. All gene amplifications were significantly associated with high grade. HER2 (P < 0.001) and MYC amplification (P < 0.001) were also linked to shortened survival. In case of HER2, this was independent of grade, pT, and pN categories. MYC amplification was almost 3 times more frequent in medullary cancer (15.9%), than in the histologic subtype with the second highest frequency (ductal; 5.6%; P = 0.0046). HER2 and MYC amplification were associated with estrogen receptor/progesterone receptor negativity (P < 0.001) whereas CCND1 amplification was linked to estrogen receptor/progesterone receptor positivity (P < 0.001). Coamplifications were more prevalent than expected based on the individual frequencies. Coamplifications of one or several other oncogenes occurred in 29.6% of CCND1, 43% of HER2, 55.7% of MDM2, 65% of MYC, and 72.8% of EGFR-amplified cancers. HER2/MYC-coamplified cancers had a worse prognosis than tumors with only one of these amplifications. Furthermore, a gradual decrease of survival was observed with increasing number of amplifications. In conclusion, these data support a major prognostic impact of genomic instability as determined by a broad gene amplification survey in breast cancer.

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

Abstract: Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

Initial sequencing and comparative analysis of the mouse genome.

Abstract: The sequence of the mouse genome is a key informational tool for understanding the contents of the human genome and a key experimental tool for biomedical research. Here, we report the results of an international collaboration to produce a high-quality draft sequence of the mouse genome. We also present an initial comparative analysis of the mouse and human genomes, describing some of the insights that can be gleaned from the two sequences. We discuss topics including the analysis of the evolutionary forces shaping the size, structure and sequence of the genomes; the conservation of large-scale synteny across most of the genomes; the much lower extent of sequence orthology covering less than half of the genomes; the proportions of the genomes under selection; the number of protein-coding genes; the expansion of gene families related to reproduction and immunity; the evolution of proteins; and the identification of intraspecies polymorphism.

The phosphatidylinositol 3-Kinase AKT pathway in human cancer.

Abstract: One signal that is overactivated in a wide range of tumour types is the production of a phospholipid, phosphatidylinositol (3,4,5) trisphosphate, by phosphatidylinositol 3-kinase (PI3K) This lipid and the protein kinase that is activated by it — AKT — trigger a cascade of responses, from cell growth and proliferation to survival and motility, that drive tumour progression Small-molecule therapeutics that block PI3K signalling might deal a severe blow to cancer cells by blocking many aspects of the tumour-cell phenotype

Repeated observation of breast tumor subtypes in independent gene expression data sets

Abstract: Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by hierarchical clustering based on patterns of expression of 534 "intrinsic" genes and shown to subdivide into one basal-like, one ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like subgroup. The genes used for classification were selected based on their similar expression levels between pairs of consecutive samples taken from the same tumor separated by 15 weeks of neoadjuvant treatment. Similar cluster analyses of two published, independent data sets representing different patient cohorts from different laboratories, uncovered some of the same breast cancer subtypes. In the one data set that included information on time to development of distant metastasis, subtypes were associated with significant differences in this clinical feature. By including a group of tumors from BRCA1 carriers in the analysis, we found that this genotype predisposes to the basal tumor subtype. Our results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities.