No description available.

A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine. / Background: The International Prognostic Scoring System (IPSS) for myelodysplastic syndrome (MDS) is commonly used to predict survival and assign treatment. We explored whether markers of frailty add prognostic information to the IPSS in a cohort of older patients. Design, Setting, Participants: Retrospective cohort study of 114 MDS patients ≥ age 65 who presented to Dana‐Farber Cancer Institute between 2006‐2011 and completed a baseline quality of life questionnaire. Measurements: We evaluated questions corresponding to frailty and extracted clinical‐ pathologic data from medical records. We used Kaplan‐Meier and Cox proportional hazards models to estimate survival. Results: 114 patients consented and were available for analysis. The median age was 72.5 years, and the majority of patients were white ( 94.7%), male ( 74.6%), and over half had a Charlson comorbidity score < 2. Few patients ( 23.7%) had an IPSS score consistent with low‐risk disease and the majority received chemotherapy. In addition to traditional prognostic factors (IPSS score and history of prior chemotherapy or radiation), significant univariate predictors of survival included low serum albumin, Charlson score, the ability to take a long walk, and interference of physical symptoms in family life. The multivariate model that best predicted mortality included low serum albumin (HR=2.3; 95%CI: 1.06‐5.14), previous chemotherapy or radiation (HR=2.1; 95%CI: 1.16‐4.24), IPSS score (HR=1.7; 95%CI: 1.14‐2.49), and ease taking a long walk (HR=0.44; 95%CI: 0.23‐0.90). Conclusions: In this study of older adults with MDS, we found that markers of nutritional status and self‐reported physical function added important prognostic information to the IPSS score. More comprehensive risk assessment tools for older patients with MDS that include markers of function and frailty are needed.

Gene expression profiles have identified five major molecular breast cancer subtypes (Luminal A, Luminal B, Basal-like, HER2+/estrogen receptor− , and Normal Breast-like) that show significant differences in survival. The cost and complexity of gene expression technology has impeded its clinical implementation. By comparison, immunohistochemistry is an economical technique applicable to the standard formalin-fixed, paraffin-embedded material commonly used in hospital labs, and has the advantage of simultaneously interpretation with histomorphology. In this thesis, I hypothesize that a surrogate panel of immunohistochemical biomarkers can be developed to discriminate the breast cancer biological subtypes. The main study cohort consists of over 4000 primary invasive breast tumors, assembled into tissue microarrays. These patients were referred to the British Columbia Cancer Agency between 1986-1992 and have staging, pathology, treatment and follow-up information. In summary, our results demonstrate that (1) the rabbit monoclonal antibody, SP1, is an improved standard for immunohistochemiscal estrogen receptor assessment in breast cancer; (2) the transcription factor, GATA-3, is almost exclusively expressed among estrogen receptor positive tumors but does not seem to predict for tamoxifen response among estrogen receptor positive patients; (3) the proliferation marker, Ki-67, together with HER2 can segregate Luminal A from Luminal B subtypes, which carry distinct risks for breast cancer relapse and death; and (4) the inclusion of the basal markers EGFR and ck5/6 to “triple negative” breast cancers provides a more specific definition of basal-like breast cancer that better predicts patient survival. These results consistently demonstrate that an immunopanel of six biomarkers (estrogen receptor, progesterone receptor, HER2, Ki-67, epidermal growth factor receptor and cytokeratin 5/6) can be readily applied to standard pathology specimens to subtype breast cancer samples based on their underlying molecular biology. These findings have been considered sufficient to justify application of this panel onto NCIC (MA5, MA12) and CALGB (9341 and 9741) clinical trials specimens. This followup work which is underway and will determine if the six marker immunopanel can guide decisions about which patients need aggressive systemic drug treatment, and thereby ensure patients get the most effective, individualized adjuvant systemic therapy for their breast tumor.

No description available.

Gene expression profiles have identified five major molecular breast cancer subtypes (Luminal A, Luminal B, Basal-like, HER2+/estrogen receptor− , and Normal Breast-like) that show significant differences in survival. The cost and complexity of gene expression technology has impeded its clinical implementation. By comparison, immunohistochemistry is an economical technique applicable to the standard formalin-fixed, paraffin-embedded material commonly used in hospital labs, and has the advantage of simultaneously interpretation with histomorphology. In this thesis, I hypothesize that a surrogate panel of immunohistochemical biomarkers can be developed to discriminate the breast cancer biological subtypes. The main study cohort consists of over 4000 primary invasive breast tumors, assembled into tissue microarrays. These patients were referred to the British Columbia Cancer Agency between 1986-1992 and have staging, pathology, treatment and follow-up information. In summary, our results demonstrate that (1) the rabbit monoclonal antibody, SP1, is an improved standard for immunohistochemiscal estrogen receptor assessment in breast cancer; (2) the transcription factor, GATA-3, is almost exclusively expressed among estrogen receptor positive tumors but does not seem to predict for tamoxifen response among estrogen receptor positive patients; (3) the proliferation marker, Ki-67, together with HER2 can segregate Luminal A from Luminal B subtypes, which carry distinct risks for breast cancer relapse and death; and (4) the inclusion of the basal markers EGFR and ck5/6 to “triple negative” breast cancers provides a more specific definition of basal-like breast cancer that better predicts patient survival. These results consistently demonstrate that an immunopanel of six biomarkers (estrogen receptor, progesterone receptor, HER2, Ki-67, epidermal growth factor receptor and cytokeratin 5/6) can be readily applied to standard pathology specimens to subtype breast cancer samples based on their underlying molecular biology. These findings have been considered sufficient to justify application of this panel onto NCIC (MA5, MA12) and CALGB (9341 and 9741) clinical trials specimens. This followup work which is underway and will determine if the six marker immunopanel can guide decisions about which patients need aggressive systemic drug treatment, and thereby ensure patients get the most effective, individualized adjuvant systemic therapy for their breast tumor.

Gene expression profiles have identified five major molecular breast cancer subtypes (Luminal A, Luminal B, Basal-like, HER2+/estrogen receptor− , and Normal Breast-like) that show significant differences in survival. The cost and complexity of gene expression technology has impeded its clinical implementation. By comparison, immunohistochemistry is an economical technique applicable to the standard formalin-fixed, paraffin-embedded material commonly used in hospital labs, and has the advantage of simultaneously interpretation with histomorphology. In this thesis, I hypothesize that a surrogate panel of immunohistochemical biomarkers can be developed to discriminate the breast cancer biological subtypes. The main study cohort consists of over 4000 primary invasive breast tumors, assembled into tissue microarrays. These patients were referred to the British Columbia Cancer Agency between 1986-1992 and have staging, pathology, treatment and follow-up information. In summary, our results demonstrate that (1) the rabbit monoclonal antibody, SP1, is an improved standard for immunohistochemiscal estrogen receptor assessment in breast cancer; (2) the transcription factor, GATA-3, is almost exclusively expressed among estrogen receptor positive tumors but does not seem to predict for tamoxifen response among estrogen receptor positive patients; (3) the proliferation marker, Ki-67, together with HER2 can segregate Luminal A from Luminal B subtypes, which carry distinct risks for breast cancer relapse and death; and (4) the inclusion of the basal markers EGFR and ck5/6 to “triple negative” breast cancers provides a more specific definition of basal-like breast cancer that better predicts patient survival. These results consistently demonstrate that an immunopanel of six biomarkers (estrogen receptor, progesterone receptor, HER2, Ki-67, epidermal growth factor receptor and cytokeratin 5/6) can be readily applied to standard pathology specimens to subtype breast cancer samples based on their underlying molecular biology. These findings have been considered sufficient to justify application of this panel onto NCIC (MA5, MA12) and CALGB (9341 and 9741) clinical trials specimens. This followup work which is underway and will determine if the six marker immunopanel can guide decisions about which patients need aggressive systemic drug treatment, and thereby ensure patients get the most effective, individualized adjuvant systemic therapy for their breast tumor. / Medicine, Faculty of / Pathology and Laboratory Medicine, Department of / Graduate

Kidney Cancer has numerous subtypes with Clear Cell Renal Cell Carcinoma (ccRCC) being the most common. Pre-existing prognostic models have not been validated in Canadian patients for recurrence free survival (RFS) and other outcomes. We conducted four studies: 1) externally validated pre-existing RCC prognostic models; 2) assessed the impact of baseline hazard function miscalibration on model assessment; 3) created new models and risk groups for RFS in non-metastatic ccRCC patients; 4) compared new risk groups to existing Canadian guidelines and created new imaging schedules. Pre-existing model performance varied considerably with some models performing well. The effect of baseline hazard function miscalibration varied across distribution shapes but the calibration slope was useful in relatively ranking prognostic model performance. The CKCis prognostic model and risk groups performed better than the existing CUA risk groups. Based on CKCis risk groups fewer scans are recommended in low-risk patients and more scans are recommended in higher risk patients. External validation of the CKCis model is required to assess clinical utility in different populations.

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No description available.

Each cancer is unique: it reflects the underlying genetic make-up of the patient and the stochastic mutational processes that occur within the tumour. This uniqueness suggests that each patient should receive a personalized type of therapy. Current predictions of a cancer patient’s outcome or prognosis are highly inaccurate. To aid in the prediction of patient prognosis based on highthroughput molecular datasets I have worked to optimize each step of the experimental pipeline: platform annotation, experimental design, consideration of tumour heterogeneity, data pre-processing and statistical analysis, and feature selection. First, a 12k CpG Island clone library was sequenced and annotated using a BLAT analysis. Second, microarrays built using this library were used in a fully-saturated study to evaluate the importance of ChIP-chip experimental design parameters. Third, intra-tumour heterogeneity was shown to influence specific pathways in a large fraction of genes. Fourth, a systematic empirical evaluation of 19,446 combinations of microarray analysis methods identified key steps of the analysis process and provided insight into their optimization. Finally, the combination of a two-stage experimental design and a novel semi-supervised algorithm yielded a six-gene, mRNA abundance-based classifier that could divide non-small cell lung cancer patients into two groups with significantly different outcomes in four independent validation cohorts. Further, a permutation study showed that millions of six-gene markers exist, but that ours ranked amongst the top 99.98% of all six-gene markers. The knowledge gained from these studies provides a key foundation for the development of personalized therapies for cancer patients.