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  • 1.
    Flodbring Larsson, Per
    et al.
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Karlsson, Richard
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). Division of Experimental Cancer Research, Department of Translational Medicine, Clinical Research Centre, Lund University, Malmö, Sweden.
    Sarwar, Martuza
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Miftakhova, Regina R.
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Wang, Tianyan
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Khaja, Azharuddin Sajid Syed
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Semenas, Julius
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Chen, Sa
    Umeå University, Faculty of Science and Technology, Department of Molecular Biology (Faculty of Science and Technology).
    Hedblom, Andreas
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). Division of Experimental Cancer Research, Department of Translational Medicine, Clinical Research Centre, Lund University, Malmö, Sweden.
    Amjad, Ali
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Ekström-Holka, Kristina
    Simoulis, Athanasios
    Kumar, Anjani
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Gjörloff Wingren, Anette
    Robinson, Brian
    Wai, Sun Nyunt
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR).
    Mongan, Nigel P.
    Heery, David M.
    Öhlund, Daniel
    Umeå University, Faculty of Medicine, Wallenberg Centre for Molecular Medicine at Umeå University (WCMM). Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Grundström, Thomas
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Ødum, Niels
    Persson, Jenny L.
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). Division of Experimental Cancer Research, Department of Translational Medicine, Clinical Research Centre, Lund University, Malmö, Sweden; Department of Biomedical Sciences, Malmö University, Malmö, Sweden.
    FcγRIIIa receptor interacts with androgen receptor and PIP5K1α to promote growth and metastasis of prostate cancer2022In: Molecular Oncology, ISSN 1574-7891, E-ISSN 1878-0261Article in journal (Refereed)
    Abstract [en]

    Low-affinity immunoglobulin gamma Fc region receptor III-A (FcγRIIIa) is a cell surface protein that belongs to a family of Fc receptors that facilitate the protective function of the immune system against pathogens. However, the role of FcγRIIIa in prostate cancer (PCa) progression remained unknown. In this study, we found that FcγRIIIa expression was present in PCa cells and its level was significantly higher in metastatic lesions than in primary tumors from the PCa cohort (P = 0.006). PCa patients with an elevated level of FcγRIIIa expression had poorer biochemical recurrence (BCR)-free survival compared with those with lower FcγRIIIa expression, suggesting that FcγRIIIa is of clinical importance in PCa. We demonstrated that overexpression of FcγRIIIa increased the proliferative ability of PCa cell line C4-2 cells, which was accompanied by the upregulation of androgen receptor (AR) and phosphatidylinositol-4-phosphate 5-kinase alpha (PIP5Kα), which are the key players in controlling PCa progression. Conversely, targeted inhibition of FcγRIIIa via siRNA-mediated knockdown or using its inhibitory antibody suppressed growth of xenograft PC-3 and PC-3M prostate tumors and reduced distant metastasis in xenograft mouse models. We further showed that elevated expression of AR enhanced FcγRIIIa expression, whereas inhibition of AR activity using enzalutamide led to a significant downregulation of FcγRIIIa protein expression. Similarly, inhibition of PIP5K1α decreased FcγRIIIa expression in PCa cells. FcγRIIIa physically interacted with PIP5K1α and AR via formation of protein-protein complexes, suggesting that FcγRIIIa is functionally associated with AR and PIP5K1α in PCa cells. Our study identified FcγRIIIa as an important factor in promoting PCa growth and invasion. Further, the elevated activation of FcγRIII and AR and PIP5K1α pathways may cooperatively promote PCa growth and invasion. Thus, FcγRIIIa may serve as a potential new target for improved treatment of metastatic and castration-resistant PCa.

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  • 2.
    Johnson, Heather
    et al.
    Olympia Diagnostics, Inc., CA, Sunnyvale, United States.
    Amjad, Ali
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Zhang, Xuhui
    Department of Bio-Diagnosis, Institute of Basic Medical Sciences, Beijing, China.
    Wang, Tianyan
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Simoulis, Athanasios
    Department of Clinical Pathology and Cytology, Skåne University Hospital, Malmö, Sweden.
    Gjörloff Wingren, Anette
    Department of Biomedical Sciences, Malmö University, Malmö, Sweden.
    Persson, Jenny L.
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). Department of Biomedical Sciences, Malmö University, Malmö, Sweden.
    K-RAS associated gene-mutation-based algorithm for prediction of treatment response of patients with subtypes of breast cancer and especially triple-negative cancer2022In: Cancers, ISSN 2072-6694, Vol. 14, no 21, article id 5322Article in journal (Refereed)
    Abstract [en]

    Purpose: There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision. Methods: Random Forest ML was applied to screen the algorithms of various combinations of gene mutation profiles of primary tumors at diagnosis using a TCGA Cohort (n = 399) with up to 150 months follow-up as a training set and validated in a MSK Cohort (n = 807) with up to 220 months follow-up. Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2−) were also assessed. The predictive performance of the candidate algorithms as classifiers was further assessed using logistic regression, Kaplan–Meier progression-free survival (PFS) plot, and univariate/multivariate Cox proportional hazard regression analyses. Results: A novel algorithm termed the 12-Gene Algorithm based on mutation profiles of KRAS, PIK3CA, MAP3K1, MAP2K4, PTEN, TP53, CDH1, GATA3, KMT2C, ARID1A, RunX1, and ESR1, was identified. The performance of this algorithm to distinguish non-progressed (responder) vs. progressed (non-responder) to treatment in the TCGA Cohort as determined using AUC was 0.96 (95% CI 0.94–0.98). It predicted progression-free survival (PFS) with hazard ratio (HR) of 21.6 (95% CI 11.3–41.5) (p < 0.0001) in all patients. The algorithm predicted PFS in the triple-negative subgroup with HR of 19.3 (95% CI 3.7–101.3) (n = 42, p = 0.000). The 12-Gene Algorithm was validated in the MSK Cohort with a similar AUC of 0.97 (95% CI 0.96–0.98) to distinguish responder vs. non-responder patients, and had a HR of 18.6 (95% CI 4.4–79.2) to predict PFS in the triple-negative subgroup (n = 75, p < 0.0001). Conclusions: The novel 12-Gene algorithm based on multitude gene-mutation profiles identified through ML has a potential to predict breast cancer treatment response to therapies, especially in triple-negative subgroups patients, which may assist personalized therapies and reduce mortality.

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  • 3. Johnson, Heather
    et al.
    El-Schich, Zahra
    Amjad, Ali
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Zhang, Xuhui
    Simoulis, Athanasios
    Gjörloff Wingren, Anette
    Persson, Jenny L.
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). Department of Biomedical Sciences, Malmö University, Malmö, Sweden.
    Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients2022In: Cancers, ISSN 2072-6694, Vol. 14, no 8, article id 2045Article in journal (Refereed)
    Abstract [en]

    PURPOSE: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors.

    METHODS: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters.

    RESULTS: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p &lt; 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p &lt; 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p &lt; 0.001).

    CONCLUSION: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies.

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  • 4.
    Wang, Tianyan
    et al.
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Sarwar, Martuza
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Whitchurch, Jonathan B.
    School of Pharmacy, University of Nottingham, Nottingham, United Kingdom.
    Collins, Hilary M.
    School of Pharmacy, University of Nottingham, Nottingham, United Kingdom.
    Green, Tami
    Umeå University, Faculty of Medicine, Umeå Centre for Molecular Medicine (UCMM).
    Semenas, Julius
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Amjad, Ali
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Roberts, Christopher J.
    School of Pharmacy, University of Nottingham, Nottingham, United Kingdom.
    Morris, Ryan D.
    School of Pharmacy, University of Nottingham, Nottingham, United Kingdom.
    Hubert, Madlen
    Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB). Department of Pharmacy, Uppsala University, Uppsala, Sweden.
    Chen, Sa
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    El-Schich, Zahra
    Department of Biomedical Science, Malmö University, Malmö, Sweden.
    Wingren, Anette G.
    Department of Biomedical Science, Malmö University, Malmö, Sweden.
    Grundström, Thomas
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
    Lundmark, Richard
    Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB).
    Mongan, Nigel P.
    School of Veterinary Medicine and Science, University of Nottingham, Nottingham, United Kingdom; Department of Pharmacology, Weill Cornell Medicine, New York, NY, United States.
    Gunhaga, Lena
    Umeå University, Faculty of Medicine, Umeå Centre for Molecular Medicine (UCMM).
    Heery, David M.
    School of Pharmacy, University of Nottingham, Nottingham, United Kingdom.
    Persson, Jenny L.
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). Department of Biomedical Science, Malmö University, Malmö, Sweden; Department of Translational Medicine, Lund University, Clinical Research Centre in Malmö, Malmö, Sweden.
    PIP5K1α is Required for Promoting Tumor Progression in Castration-Resistant Prostate Cancer2022In: Frontiers in Cell and Developmental Biology, E-ISSN 2296-634X, Vol. 10, article id 798590Article in journal (Refereed)
    Abstract [en]

    PIP5K1α has emerged as a promising drug target for the treatment of castration-resistant prostate cancer (CRPC), as it acts upstream of the PI3K/AKT signaling pathway to promote prostate cancer (PCa) growth, survival and invasion. However, little is known of the molecular actions of PIP5K1α in this process. Here, we show that siRNA-mediated knockdown of PIP5K1α and blockade of PIP5K1α action using its small molecule inhibitor ISA-2011B suppress growth and invasion of CRPC cells. We demonstrate that targeted deletion of the N-terminal domain of PIP5K1α in CRPC cells results in reduced growth and migratory ability of cancer cells. Further, the xenograft tumors lacking the N-terminal domain of PIP5K1α exhibited reduced tumor growth and aggressiveness in xenograft mice as compared to that of controls. The N-terminal domain of PIP5K1α is required for regulation of mRNA expression and protein stability of PIP5K1α. This suggests that the expression and oncogenic activity of PIP5K1α are in part dependent on its N-terminal domain. We further show that PIP5K1α acts as an upstream regulator of the androgen receptor (AR) and AR target genes including CDK1 and MMP9 that are key factors promoting growth, survival and invasion of PCa cells. ISA-2011B exhibited a significant inhibitory effect on AR target genes including CDK1 and MMP9 in CRPC cells with wild-type PIP5K1α and in CRPC cells lacking the N-terminal domain of PIP5K1α. These results indicate that the growth of PIP5K1α-dependent tumors is in part dependent on the integrity of the N-terminal sequence of this kinase. Our study identifies a novel functional mechanism involving PIP5K1α, confirming that PIP5K1α is an intriguing target for cancer treatment, especially for treatment of CRPC.

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