Displaying publications 161 - 162 of 162 in total

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  1. Emerging strategies for sensitization of therapy resistant tumors toward cancer therapeutics by targeting the Bcl-2 family, TGF-β, Wnt/β-Catenin, RASSF and miRNA regulated signaling pathways
    Veerasamy T, Eugin Simon S, Tan KO
    Int J Biochem Cell Biol, 2021 08;137:106016.
    PMID: 34082133 DOI: 10.1016/j.biocel.2021.106016
    Conventional chemotherapy relies on the cytotoxicity of chemo-drugs to inflict destructive effects on tumor cells. However, as most tumor cells develop resistance to chemo-drugs, small doses of chemo-drugs are unlikely to provide significant clinical benefits in cancer treatment while high doses of chemo-drugs have been shown to impact normal human cells negatively due to the non-specific nature and cytotoxicity associated with chemo-drugs. To overcome this challenge, sensitizations of tumor cells with bioactive molecules that specifically target the pro-survival and pro-apoptosis signaling pathways of the tumor cells are likely to increase the therapeutic impacts and improve the clinical outcomes by reducing the dependency and adverse effects associated with using high doses of chemo-drugs in cancer treatment. This review focuses on emerging strategies to enhance the sensitization of tumor cells toward cancer therapies based on our understanding of tumor cell biology and underlying signaling pathways.
  2. Fulltext Incorporating progesterone receptor expression into the PREDICT breast prognostic model
    Grootes I, Keeman R, Blows FM, Milne RL, Giles GG, Swerdlow AJ, et al.
    Eur J Cancer, 2022 Sep;173:178-193.
    PMID: 35933885 DOI: 10.1016/j.ejca.2022.06.011
    BACKGROUND: Predict Breast (www.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2).

    METHOD: The prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance.

    RESULTS: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0.902 for patients with ER-positive tumours (p = 2.3 × 10-6) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted.

    CONCLUSION: The inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predictions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration.

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