Breast Neoplasms; Immunohistochemistry; Medical Oncology; Melanoma; Pathology; Biomarkers, Pharmacological
Skin Diseases Research Center, Yale
My lab has a strong translational theme and has two main directions: 1) development of new quantitative approaches to pathology and their use to classify tumors by prognosis or predict response to cancer therapy (cancer tissue biomarker research); and 2) the molecular analysis of growth factor receptors and signaling. Studies fall into 3 groups. Group 1: translational studies using tissue microarray technology and AQUA (automated quantitative analysis) applying basic molecular observations and tools to diagnostic problems in pathology. Main topics include predicting response to therapy in breast cancer and predicting metastasis in breast cancer and melanoma. Group 2: the examination of mechanisms of signaling by Met, (the HGF/SF receptor), ErbB family members and other receptor tyrosine kinases (RTK) in epithelial tumors. We are particularly interested in translocation of these receptors to the nucleus. Finally, Group 3: the use of spectral/spatial analysis tools to improve diagnostic accuracy in cytopathology.
Specialized Terms: Quantitative Pathology; Cancer Tissue Biomarkers; Melanoma; Breast Cancer; Cell-cell adhesion in cancer; Translation of molecular techniques to diagnostic cytopathology; General Cytopathology; Immunohistochemistry; thyroid pathology
Extensive Research Description
Nearly 100% of Dr. Rimm’s lab efforts are related to cancer. He has largely focused on tissue biomarker research. His most innovative research has involved construction of patient cohorts using the tissue microarray format and the development of methods for quantitative analysis of protein expression on tissue microarrays and whole tissue sections. He was the lead author on a recent paper in Journal of Clinical Oncology that sets forth guidelines for construction of tissue microarrays from cooperative group clinical trial samples. This expertise has landed him positions on correlative science committees in the ALTTO and TEACH breast cancer clinical trials. He has also published extensively in the field of biospecimen science including a series of papers published in Laboratory Investigation, the most popular being cited over 500 times. He is a regular invited speaker at the Biospecimen Research Network annual meeting and is supported by a large contract from the Office of Biospecimen and Biorepository Research. However, his most innovative efforts have been related to automated quantitative analysis of formalin fixed, paraffin embedded tissue. He and his lab developed the AQUA method of quantitative immunofluorescence that was published in 2002 in Nature Medicine (over 350 citations). This technology attempted to remove the subjectivity from the analysis of immunohistochemistry specimens by using co-localization to define regions of interest, rather than feature extraction of pathologist defined subregions. There are over 100 publications in the literature from labs in the US and around the world that use this technology, including many in high impact journals (NEJM, Nature, Cancer Cell, JCO, etc). The technology has been patented and was the founding intellectual property of HistoRx in 2004. The company has largely used the technology to assist pharmaceutical companies in development of companion diagnostics. Last year, the technology was licensed to Genoptix, a CLIA lab in California that now delivers AQUA read, standardized measurements of ER, PR and HER2 on patient specimens. This effort represents a pathway from Dr. Rimm’s lab to the clinic.
Proinvasion metastasis drivers in early-stage melanoma are oncogenes
Scott KL, Nogueira C, Heffernan TP, van Doorn R, Dhakal S, Hanna JA, Min C, Jaskelioff M, Xiao Y, Wu CJ, Cameron LA, Perry SR, Zeid R, Feinberg T, Kim M, Vande Woude G, Granter SR, Bosenberg M, Chu GC, Depinho RA, Rimm DL, Chin L. (2011) Proinvasion metastasis drivers in early-stage melanoma are oncogenes, Cancer Cell 20:92-103
Standardization of Estrogen Receptor Measurement in Breast Cancer Suggests False-Negative Results Are a Function of Threshold Intensity Rather Than Percentage of Positive Cells
Welsh AW, Moeder CB, Kumar S, Gershkovich P, Alarid ET, Harigopal M, Haffty BG, Rimm DL: (2011) Standardization of Estrogen Receptor Measurement in Breast Cancer Suggests False-Negative Results Are a Function of Threshold Intensity Rather Than Percentage of Positive Cells, J Clin Oncol 29, 2978-2984
Cancer and leukemia group B pathology committee guidelines for tissue microarray construction representing multicenter prospective clinical trial tissues
Rimm DL, Nielsen TO, Jewell SD, Rohrer DC, Broadwater G, Waldman F, Mitchell KA, Singh B, Tsongalis GJ, Frankel WL, Magliocco AM, Lara JF, Hsi ED, Bleiweiss IJ, Badve SS, Chen B, Ravdin PM, Schilsky RL, Thor A, Berry DA: (2011) Cancer and leukemia group B pathology committee guidelines for tissue microarray construction representing multicenter prospective clinical trial tissues, J Clin Oncol 2011, 29:2282-2290
Tissue Biomarkers for Prognosis in Cutaneous Melanoma: A Systematic Review and Meta-analysis
Rothberg BE, Bracken MB, Rimm DL (2009).Tissue Biomarkers for Prognosis in Cutaneous Melanoma: A Systematic Review and Meta-analysis. J Natl Cancer Inst. 101(7):452-474
Automated quantitative analysis (AQUA) of in situ protein expression, antibody concentration, and prognosis
McCabe, A., Dolled-Filhart, M., Camp, R.L., and Rimm, D.L. (2005). Automated quantitative analysis (AQUA) of in situ protein expression, antibody concentration, and prognosis. J. Natl. Cancer Inst. 97:1808-1815.
What brown cannot do for you
Rimm, D.L.. (2006) What brown cannot do for you (commentary) Nat Biotechnol 24, 914-916.
- Algorithms for Automated Tissue Microarray Analysis Reveal Novel Disease Sub-classifications. Camp, R.L. Chung, G.G., and Rimm, D.L. (2002) Algorithms for Automated Tissue Microarray Analysis Reveal Novel Disease Sub-classifications. Nature Medicine 8(11):1323-8