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Biology subjects

Thway, K.

Publications and source records attributed to Thway, K..

2 recordsLinked to original sources

The adequacy of tissue microarrays in the assessment of inter- and intra-tumoural heterogeneity of infiltrating lymphocyte burden in leiomyosarcoma

The characterisation and clinical relevance of tumour-infiltrating lymphocytes (TILs) in leiomyosarcoma (LMS), a subtype of soft tissue sarcoma that exhibits histological heterogeneity, is not established. The use of tissue microarrays (TMA) in studies that profile TIL burden is attractive but given the potential for intra-tumoural heterogeneity to introduce sampling errors, the adequacy of this approach is undetermined. In this study, we assessed the histological inter-and intra-tumoural heterogeneity in TIL burden within a retrospective cohort of primary LMS specimens. Using a virtual TMA approach, we also analysed the optimal number of TMA cores required to provide an accurate representation of TIL burden in a full tissue section. We establish that LMS have generally low and spatially homogenous TIL burdens, although a small proportion exhibit higher levels and more heterogeneous distribution of TILs. We show that a conventional and practical number (1-3) of TMA cores is adequate for correct ordinal categorisation of tumours with high or low TIL burden, but that many more cores ([≥] 11) is required to accurately estimate absolute TIL numbers. Our findings provide a benchmark for the design of future studies aiming to define the clinical relevance of the immune microenvironments of LMS and other sarcoma subtypes.

cancer biology

Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

Cancer patients with advanced disease exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates sequencing data with functional assay data to develop patient-specific combination cancer treatments. This computational modeling approach addresses three major challenges in personalized cancer therapy, which we validate across multiple species via computationally-designed personalized synergistic drug combination predictions, identification of unifying therapeutic targets to overcome intra-tumor heterogeneity, and mitigation of cancer cell resistance and rewiring mechanisms. These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

bioinformatics