Computational approaches to support comparative analysis of multiparametric tests: Modelling versus Training.

Multiparametric assays for risk stratification are widely used in the management of breast cancer, with applications being developed for a number of other cancer settings. Recent data from multiple sources suggests that different tests may provide different risk estimates at the individual patient level. There is an increasing need for robust methods to support cost effective comparisons of test performance in multiple settings. The derivation of similar risk classifications using genes comprising the following multi-parametric tests Oncotype DX® (Genomic Health.), Prosigna™ (NanoString Technologies, Inc.), MammaPrint® (Agendia Inc.) was performed using different computational approaches. Results were compared to the actual test results. Two widely used approaches were applied, firstly computational "modelling" of test results using published algorithms and secondly a "training" approach which used reference results from the commercially supplied tests. We demonstrate the potential for errors to arise when using a "modelling" approach without reference to real world test results. Simultaneously we show that a "training" approach can provide a highly cost-effective solution to the development of real-world comparisons between different multigene signatures. Comparisons between existing multiparametric tests is challenging, and evidence on discordance between tests in risk stratification presents further dilemmas. We present an approach, modelled in breast cancer, which can provide health care providers and researchers with the potential to perform robust and meaningful comparisons between multigene tests in a cost-effective manner. We demonstrate that whilst viable estimates of gene signatures can be derived from modelling approaches, in our study using a training approach allowed a close approximation to true signature results

DNA methylation in endometrial cancer

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Survival in Women with De Novo Metastatic Breast Cancer: A Comparison of Real-World Evidence from a Publicly-Funded Canadian Province and the United States by Insurance Status

Metastatic breast cancer (MBC) patient outcomes may vary according to distinct health care payers and different countries. We compared 291 Alberta (AB), Canada and 9429 US patients p = 0.365). Median OS was not reached for the US privately insured and AB groups, and was 11 months and 8 months for the US Medicaid and US uninsured groups, respectively. The 3-year OS rates were comparable between US privately insured and AB groups (53.28% (51.95–54.59) and 55.54% (49.49–61.16), respectively). Both groups had improved survival (p < 0.001) relative to the US Medicaid and US uninsured groups [39.32% (37.25–41.37) and 40.53% (36.20–44.81)]. Our study suggests that a universal health care system is not inferior to a private insurance-based model for de novo MBC

Identification of the SUMO E3 ligase PIAS1 as a potential survival biomarker in breast cancer.

Metastasis is the ultimate cause of breast cancer related mortality. Epithelial-mesenchymal transition (EMT) is thought to play a crucial role in the metastatic potential of breast cancer. Growing evidence has implicated the SUMO E3 ligase PIAS1 in the regulation of EMT in mammary epithelial cells and breast cancer metastasis. However, the relevance of PIAS1 in human cancer and mechanisms by which PIAS1 might regulate breast cancer metastasis remain to be elucidated. Using tissue-microarray analysis (TMA), we report that the protein abundance and subcellular localization of PIAS1 correlate with disease specific overall survival of a cohort of breast cancer patients. In mechanistic studies, we find that PIAS1 acts via sumoylation of the transcriptional regulator SnoN to suppress invasive growth of MDA-MB-231 human breast cancer cell-derived organoids. Our studies thus identify the SUMO E3 ligase PIAS1 as a prognostic biomarker in breast cancer, and suggest a potential role for the PIAS1-SnoN sumoylation pathway in controlling breast cancer metastasis

Anemia, leukocytosis and thrombocytosis as prognostic factors in patients with cervical cancer treated with radical chemoradiotherapy: A retrospective cohort study

Introduction: Anemia has long been associated with poor prognosis in patients with cervical cancer. Recently, additional hematologic parameters have emerged as potential indicators of worse outcome in this patient group. In a cohort of cervical cancer patients treated with chemoradiotherapy (CRT) and brachytherapy, we report on the prognostic significance of hematologic parameters including anemia, leukocytosis, neutrophil to lymphocyte ratio (NLR), and thrombocytosis, the effect of combining anemia with other hematologic parameters, and the effect of changes in hemoglobin levels during treatment. Materials and methods: Two-hundred fifty-seven cervical cancer patients were retrospectively identified from a single cancer institution’s database. Hematologic parameters were categorized as: anemia (hemoglobin ≤115 g/L), leukocytosis (white blood cell count >10 × 109/L), thrombocytosis (platelets >400 × 109/L), and NLR (ratio >5). The association between clinical factors and hematologic parameters on progression-free survival (PFS) and overall survival (OS) were assessed at 5 years. Results: At 5 years, both pre-treatment anemia (PFS: 60% vs 34%, p < 0.0001; OS: 68% vs 41%, p < 0.0001) and on-treatment anemia (PFS: 62% vs 40%, p < 0.0001; OS: 70% vs 48%, p < 0.0001) were significantly associated with worse survival. This adverse effect on 5-year PFS and OS was increased in patients with both pre-treatment anemia and leukocytosis (PFS: 72% vs 42%, p < 0.0001; OS: 68% vs 37%, p < 0.0001) and pre-treatment anemia and elevated NLR (PFS: 61% vs 30%, p < 0.0001; OS: 68% vs 37%, p < 0.0001). Five-year PFS (50% vs 31%) and OS (60% vs 36%) was better in patients whose pre-treatment anemia improved to normal hemoglobin levels on treatment vs those patients who were anemic both pre- and on-treatment. Conclusion: Pre-treatment and on-treatment anemia were significant, independent predictors of worse PFS and OS. Anemia and other hematologic parameters remain prognostic markers for cervical cancer patients. Improvement in PFS and OS was seen in patients with normalization of hemoglobin

Combining TMEM Doorway Score and Mena^Calc Score Improves the Prediction of Distant Recurrence Risk in HR+/HER2− Breast Cancer Patients

Purpose: to develop several digital pathology-based machine vision algorithms for combining TMEM and MenaCalc scores and determine if a combination of these biomarkers improves the ability to predict development of distant metastasis over and above that of either biomarker alone. Methods: This retrospective study included a subset of 130 patients (65 patients with no recurrence and 65 patients with a recurrence at 5 years) from the Calgary Tamoxifen cohort of breast cancer patients. Patients had confirmed invasive breast cancer and received adjuvant tamoxifen therapy. Of the 130 patients, 86 cases were suitable for analysis in this study. Sequential sections of formalin-fixed paraffin-embedded patient samples were stained for TMEM doorways (immunohistochemistry triple staining) and MenaCalc (immunofluorescence staining). Stained sections were imaged, aligned, and then scored for TMEM doorways and MenaCalc. Different ways of combining TMEM doorway and MenaCalc scores were evaluated and compared to identify the best performing combined marker by using the restricted mean survival time (RMST) difference method. Results: the best performing combined marker gave an RMST difference of 5.27 years (95% CI: 1.71–8.37), compared to 3.56 years (95% CI: 0.95–6.1) for the associated standalone TMEM doorway analysis and 2.94 years (95% CI: 0.25–5.87) for the associated standalone MenaCalc analysis. Conclusions: combining TMEM doorway and MenaCalc scores as a new biomarker improves prognostication over that observed with TMEM doorway or MenaCalc Score alone in this cohort of 86 patients