Medical Biology - Research Publications
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ItemNo Preview AvailableApplications of dielectric pads, novel materials and resonators in 1.5T and 3T MRI(IOP Publishing, 2020-04-23)Abstract In order to boost the performance of magnetic resonance imaging without increasing the static magnetic field, it is necessary to increase its intrinsic sensitivity. This allows a reduction in the scanning time, increased spatial resolution, and can enable low-field strength systems (which are much cheaper and can be used to scan patients with metallic implants) to have a higher signal-to-noise ratio (SNR) so that they are comparable to more expensive higher field strength systems. In this contribution, we demonstrate radiofrequency field enhancing and shaping devices based on novel materials, such as high permittivity dielectric structures and metamaterials. These materials can substantially enhance SNR, thus potentially increasing image resolution or allowing faster examinations.
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ItemCD8+TISSUE-RESIDENT MEMORY T CELLS ARE TUMOUR REACTIVE AND INCREASE AFTER IMMUNOTHERAPY IN A CASE OF METASTATIC MUCOSAL MELANOMA(BMJ PUBLISHING GROUP, 2020-11-01)Background Mucosal melanoma is a rare subtype of melanoma originating from mucosal tissues (1), metastases are very aggressive and respond poorly to therapy, including immune checkpoint inhibitors (ICI) such as anti-CTLA4 and anti-PD1 antibodies (2–5). CD8+ T cells constitute the most abundant immune infiltrate in metastatic melanoma, of which the Tissue Resident Memory subset (TRM) is of particular interest (6). CD8+ TRM cells express the highest levels of immune checkpoint receptors, proliferate in response to ICI and correlate with longer disease-free and overall survival (6–8). The immune landscape in mucosal melanoma remains poorly characterized. We aimed to: 1) phenotype CD8+ T cells and TRM infiltrating metastatic mucosal melanoma, 2) characterize the clonality of TRM in relation to other CD8+ T cell subsets and 3) define the capacity of CD8+ T cells and TRM to respond to melanoma cells and to in vivo and in vitro anti-PD1 treatment. Methods We investigated the CD8+ T and TRM cells infiltrating two temporally- and spatially-distant subcutaneous metastases, these originated from a primary vaginal mucosal melanoma. One metastasis was excised prior to anti-PD1 treatment and one was anti-PD1 refractory, having progressed on treatment. We used mass cytometry and single-cell RNA and TCR sequencing to characterise the phenotype and clonality of the T cells, multiplex immunohistochemistry to define their spatial relationship with tumour cells and other T cells, and functional assays to determine TRM response to tumour cells (figure 1). Results CD8+ TRM frequency increased with time and anti-PD1 treatment, forming clusters at the tumour margin. T cells in the anti-PD1 refractory lesion were more activated than T cells in the first tumour and were bound by anti-PD1 antibody in vivo. T cells could not be stimulated by anti-PD1 directly ex vivo. Both metastatic lesions shared common T cell clusters including TRM. Furthermore, TRM in each tumour shared T cell clones, suggesting the presence of common antigens between metastatic sites. Indeed, the two metastases had a similar mutational profile. In vitro expanded tumour infiltrating lymphocytes from both lesions recognized tumour cells from both lesions and the same neoantigen generated from a single point mutation in the gene CDKN1C. Finally, tumour cells stimulated TRM cells more robustly than other T cells subsets. Abstract 548 Figure 1Graphical depiction of the methods used to characterise T cells in mucosal metastatic melanoma Conclusions In this patient with vaginal mucosal melanoma, subsequent melanoma metastases of clonal origin attracted CD8+ T cells of similar specificity, among which TRM cells responded more vigorously to tumour cells than other T cells subsets. Acknowledgements The authors would like to acknowledge imCORE La Hoffmann- Roche Ltd. for funding. Ethics Approval Patients diagnosed with stage 3 or 4 metastatic melanoma and undergoing clinically indicated surgery were enrolled in prospective studies approved by the Peter MacCallum Cancer Centre human ethics research committee (13/141). All experimental protocols have been approved and clinical data has been collected prospectively. References Carvajal RD, Hamid O, Ariyan C. Mucosal Melanoma. [cited 2020 Apr 1]; Available from: https://www.uptodate.com/contents/mucosal-melanoma Del Vecchio M, Di Guardo L, Ascierto PA, Grimaldi AM, Sileni VC, Pigozzo J, et al. Efficacy and safety of ipilimumab 3 mg/kg in patients with pretreated, metastatic, mucosal melanoma. Eur J Cancer Oxf Engl 1990; 2014 Jan;50(1):121–7. Postow MA, Luke JJ, Bluth MJ, Ramaiya N, Panageas KS, Lawrence DP, et al. Ipilimumab for patients with advanced mucosal melanoma. The Oncologist 2013 Jun;18(6):726–32. D’Angelo SP, Larkin J, Sosman JA, Lebbé C, Brady B, Neyns B, et al. Efficacy and safety of nivolumab alone or in combination with ipilimumab in patients with mucosal melanoma: a pooled analysis. J Clin Oncol Off J Am Soc Clin Oncol. 2017 Jan 10;35(2):226–35. Hamid O, Robert C, Ribas A, Hodi FS, Walpole E, Daud A, et al. Antitumour activity of pembrolizumab in advanced mucosal melanoma: a post-hoc analysis of KEYNOTE-001, 002, 006. Br J Cancer 2018;119(6):670–4. Boddupalli CS, Bar N, Kadaveru K, Krauthammer M, Pornputtapong N, Mai Z, et al. Interlesional diversity of T cell receptors in melanoma with immune checkpoints enriched in tissue-resident memory T cells. JCI Insight [Internet]. 2016 Dec 22 [cited 2019 Apr 24];1(21). Available from: https://insight.jci.org/articles/view/88955 Edwards J, Wilmott JS, Madore J, Gide TN, Quek C, Tasker A, et al. CD103+ Tumor-resident CD8+ T cells are associated with improved survival in immunotherapy-naïve melanoma patients and expand significantly during anti-PD-1 treatment. Clin Cancer Res Off J Am Assoc Cancer Res 2018 Jul 1;24(13):3036–45. Savas P, Virassamy B, Ye C, Salim A, Mintoff CP, Caramia F, et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat Med 2018 Jul;24(7):986–93.
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ItemNo Preview AvailableSynthesis and antiplasmodial activity of 3-furyl and 3-thienylquinoxaline-2-carbonitrile 1,4-di-N-oxide derivatives.(MDPI AG, 2008-01-17)The aim of this study was to identify new compounds active against Plasmodium falciparum based on our previous research carried out on 3-phenyl-quinoxaline-2-carbonitrile 1,4-di-N-oxide derivatives. Twelve compounds were synthesized and evaluated for antimalarial activity. Eight of them showed an IC(50) less than 1 microM against the 3D7 strain. Derivative 1 demonstrated high potency (IC(50)= 0.63 microM) and good selectivity (SI=10.35), thereby becoming a new lead-compound.
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ItemPredicting disordered regions in proteins using the profiles of amino acid indices.(Springer Science and Business Media LLC, 2009-01-30)BACKGROUND: Intrinsically unstructured or disordered proteins are common and functionally important. Prediction of disordered regions in proteins can provide useful information for understanding protein function and for high-throughput determination of protein structures. RESULTS: In this paper, algorithms are presented to predict long and short disordered regions in proteins, namely the long disordered region prediction algorithm DRaai-L and the short disordered region prediction algorithm DRaai-S. These algorithms are developed based on the Random Forest machine learning model and the profiles of amino acid indices representing various physiochemical and biochemical properties of the 20 amino acids. CONCLUSION: Experiments on DisProt3.6 and CASP7 demonstrate that some sets of the amino acid indices have strong association with the ordered and disordered status of residues. Our algorithms based on the profiles of these amino acid indices as input features to predict disordered regions in proteins outperform that based on amino acid composition and reduced amino acid composition, and also outperform many existing algorithms. Our studies suggest that the profiles of amino acid indices combined with the Random Forest learning model is an important complementary method for pinpointing disordered regions in proteins.