Citations

AbstractAutomated methods are needed to facilitate high‐throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large‐scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37–0.87) and study (kappa range = 0.39–0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p‐value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000–4,500 cells: kappa = 0.78) than those with lower counts (50–500 cells: kappa = 0.41; p‐value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre‐ and post‐analytical quality control procedures are necessary in order to ensure satisfactory performance.

Abstract As anticancer therapies designed to target specific molecular pathways have been developed, it has become critical to develop methods to assess the response induced by such agents. Although traditional, anatomic CT, and MRI examinations are useful in many settings, increasing evidence suggests that these methods cannot answer the fundamental biologic and physiologic questions essential for assessment and, eventually, prediction of treatment response in the clinical trial setting, especially in the critical period soon after treatment is initiated. To optimally apply advances in quantitative imaging methods to trials of targeted cancer therapy, new infrastructure improvements are needed that incorporate these emerging techniques into the settings where they are most likely to have impact. In this review, we first elucidate the needs for therapeutic response assessment in the era of molecularly targeted therapy and describe how quantitative imaging can most effectively provide scientifically and clinically relevant data. We then describe the tools and methods required to apply quantitative imaging and provide concrete examples of work making these advances practically available for routine application in clinical trials. We conclude by proposing strategies to surmount barriers to wider incorporation of these quantitative imaging methods into clinical trials and, eventually, clinical practice. Our goal is to encourage and guide the oncology community to deploy standardized quantitative imaging techniques in clinical trials to further personalize care for cancer patients and to provide a more efficient path for the development of improved targeted therapies. Clin Cancer Res; 22(2); 284–90. ©2016 AACR.

ABSTRACTEpithelial‐mesenchymal transition (EMT) is a process whereby epithelial cells assume mesenchymal characteristics to facilitate cancer metastasis. However, EMT also contributes to the initiation and development of primary tumors. Prior studies that explored the hypothesis that EMT gene variants contribute to epithelial ovarian carcinoma (EOC) risk have been based on small sample sizes and none have sought replication in an independent population. We screened 15,816 single‐nucleotide polymorphisms (SNPs) in 296 genes in a discovery phase using data from a genome‐wide association study of EOC among women of European ancestry (1,947 cases and 2,009 controls) and identified 793 variants in 278 EMT‐related genes that were nominally (P < 0.05) associated with invasive EOC. These SNPs were then genotyped in a larger study of 14,525 invasive‐cancer patients and 23,447 controls. A P‐value <0.05 and a false discovery rate (FDR) <0.2 were considered statistically significant. In the larger dataset, GPC6/GPC5 rs17702471 was associated with the endometrioid subtype among Caucasians (odds ratio (OR) = 1.16, 95% CI = 1.07–1.25, P = 0.0003, FDR = 0.19), whereas F8 rs7053448 (OR = 1.69, 95% CI = 1.27–2.24, P = 0.0003, FDR = 0.12), F8 rs7058826 (OR = 1.69, 95% CI = 1.27–2.24, P = 0.0003, FDR = 0.12), and CAPN13 rs1983383 (OR = 0.79, 95% CI = 0.69–0.90, P = 0.0005, FDR = 0.12) were associated with combined invasive EOC among Asians. In silico functional analyses revealed that GPC6/GPC5 rs17702471 coincided with DNA regulatory elements. These results suggest that EMT gene variants do not appear to play a significant role in the susceptibility to EOC.

Abstract We have developed the use of reovirus as a systemically delivered oncolytic agent in both pre-clinical models and in early Phase clinical trials. Reovirus has direct oncolytic activity against many human/murine tumor cells, partly because of disruption of the PKR-mediated anti-viral response in malignant cells. In addition however, we have shown that anti-tumor therapy is directly associated with immune activation by virus replication in tumors. The immune mechanisms of therapy include both innate immune activation against virally infected tumor cells, as well as the generation of adaptive anti tumor immune responses as a result of in vivo priming against tumor associated antigens released during that killing. Therefore, to exploit the immune components of reovirus anti-tumor therapy, we hypothesized that the combination of reovirus therapy with systemic checkpoint inhibition would augment therapeutic efficacy. To test the hypothesis, we used C57Bl/6 mice, an immune-competent murine model, with established subcutaneous (s.c.) B16 melanomas. In this model, intra-tumoral injection of reovirus into s.c. tumors generated moderate therapy. Provision of systemic anti-PD-1 antibody along with i.t. reovirus, significantly enhanced survival compared to i.t. reovirus alone (p&lt;0.01) and led to &gt;40% of mice being cured long term. Immune analysis suggested that the enhanced therapeutic benefit of reovirus plus checkpoint inhibition is contributed by at least two factors. First, blockade of PD-1 significantly enhanced the ability of NK cells to recognize (TNF-α secretion), and kill, reovirus-infected target tumor cells. Second, anti PD-1 antibody led to a significant reduction in Treg activity in reovirus-treated mice, with the overall effect of increasing the adaptive CD8+ anti-tumor T cell response. Furthermore, in vivo depletion studies demonstrated that NK cells had a dramatic effect in reducing the therapeutic efficacy of reovirus plus anti-PD-1 therapy. Overall, the results indicate that combination therapy of reovirus with PD-1 blockade confers significant survival benefit, by augmenting tumor-specific NK responses and specifically attenuating tumor-specific immunosuppression. These data also suggest that combination of PD-1 inhibition therapy with reovirus oncolytic/immunotherapy represents a readily translatable method to enhance the therapeutic efficacy. Citation Format: Karishma Rajani, Christopher Parrish, Kevin Shim, Liz Ilett, Fiona Errington-Mais, Jill Thompson, Tim Kottke, Rosa Maria-Diaz, Peter Selby, Hardev Pandha, Kevin Harrington, Alan Melcher, Matt Coffey, Shane Zaidi, Richard Vile. Combination therapy of reovirus and PD-1 blockade effectively establishes tumor control via innate and adaptive immune responses. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1360. doi:10.1158/1538-7445.AM2015-1360

The Case Study Interpretation (CSI) cases presented at the 2014 International Clinical Cytometry Society (ICCS) meeting in Seattle illustrate the utility of state‐of‐the art multiparameter flow cytometry in the diagnosis of hematolymphoid neoplasms. Download the listmode files (Supporting Information) and test your analysis skills before reading the case reports, keeping in mind the following questions. How many separate abnormal mature B‐cell populations can you identify, and how many of these represent different subtypes of B‐cell neoplasm? How many separate abnormal mature T‐cell populations can you identify, and do these represent different subtypes of T‐cell neoplasm or phenotypic heterogeneity in one neoplasm? How many separate immature/blastic cell populations can you identify, and do they meet criteria for mixed phenotype leukemia? Is there a population of blasts that lacks T‐cell, B‐cell, and myeloid lineage defining antigens and if so, what entities should you consider and what additional antigens should you assess for? © 2015 International Clinical Cytometry Society

AbstractBreast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large‐scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65–70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa = 0.76), followed by human epidermal growth factor receptor 2 (Kappa = 0.69) and progesterone receptor (Kappa = 0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose‐response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96–98%), but yielded many false positives (positive predictive value = 30–32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large‐scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker‐specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results.