Molecular Pathology
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Item Topographic analysis of pancreatic cancer by TMA and digital spatial profiling reveals biological complexity with potential therapeutic implications.(NATURE PORTFOLIO, 2024-05-18) Bingham, V; Harewood, L; McQuaid, S; Craig, SG; Revolta, JF; Kim, CS; Srivastava, S; Quezada-MarĂn, J; Humphries, MP; Salto-Tellez, M; Salto-Tellez, ManuelPancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human malignancies. Tissue microarrays (TMA) are an established method of high throughput biomarker interrogation in tissues but may not capture histological features of cancer with potential biological relevance. Topographic TMAs (T-TMAs) representing pathophysiological hallmarks of cancer were constructed from representative, retrospective PDAC diagnostic material, including 72 individual core tissue samples. The T-TMA was interrogated with tissue hybridization-based experiments to confirm the accuracy of the topographic sampling, expression of pro-tumourigenic and immune mediators of cancer, totalling more than 750 individual biomarker analyses. A custom designed Next Generation Sequencing (NGS) panel and a spatial distribution-specific transcriptomic evaluation were also employed. The morphological choice of the pathophysiological hallmarks of cancer was confirmed by protein-specific expression. Quantitative analysis identified topography-specific patterns of expression in the IDO/TGF-β axis; with a heterogeneous relationship of inflammation and desmoplasia across hallmark areas and a general but variable protein and gene expression of c-MET. NGS results highlighted underlying genetic heterogeneity within samples, which may have a confounding influence on the expression of a particular biomarker. T-TMAs, integrated with quantitative biomarker digital scoring, are useful tools to identify hallmark specific expression of biomarkers in pancreatic cancer.Item Proteomic features of soft tissue tumours in adolescents and young adults.(SPRINGERNATURE, 2024-05-18) Tam, YB; Low, K; Ps, H; Chadha, M; Burns, J; Wilding, CP; Arthur, A; Chen, TW; Thway, K; Sadanandam, A; Jones, RL; Huang, PH; Tam, Yuen Bun; Chadha, Madhumeeta; Huang, PaulBACKGROUND: Adolescents and young adult (AYA) patients with soft tissue tumours including sarcomas are an underserved group with disparities in treatment outcomes. METHODS: To define the molecular features between AYA and older adult (OA) patients, we analysed the proteomic profiles of a large cohort of soft tissue tumours across 10 histological subtypes (AYA n = 66, OA n = 243), and also analysed publicly available functional genomic data from soft tissue tumour cell lines (AYA n = 5, OA n = 8). RESULTS: Biological hallmarks analysis demonstrates that OA tumours are significantly enriched in MYC targets compared to AYA tumours. By comparing the patient-level proteomic data with functional genomic profiles from sarcoma cell lines, we show that the mRNA splicing pathway is an intrinsic vulnerability in cell lines from OA patients and that components of the spliceosome complex are independent prognostic factors for metastasis free survival in AYA patients. CONCLUSIONS: Our study highlights the importance of performing age-specific molecular profiling studies to identify risk stratification tools and targeted agents tailored for the clinical management of AYA patients.Item Mass spectrometry strategies to understand treatment effects and heterogeneity in soft tissue sarcomas(Institute of Cancer Research (University Of London), 2024-07-25) Chadha, M; Huang P; Huang, P; Chadha, MadhumeetaSoft Tissue sarcoma are rare and heterogenous mesenchymal malignancies. Optimal disease management in STS is surgical excision as the primary treatment modality. Despite negative surgical margins, ~50% of patients relapse. Neoadjuvant therapy (NAT) is given prior to surgery for locally advanced and/metastatic disease. However, its use as a standard treatment is contentious due to conflicting results from clinical trials. Additionally, there remains a limited understanding of the underlying biology governing NAT response and resistance. There is an urgent need for improved prognostication to identify patients who would derive substantial benefits from NAT, as well as the identification of predictive biomarkers for NAT response. Proteomic analysis of Sarculator-nomogram risk group, a prognostic tool, rationalized the effectiveness of anthracycline therapy in high-risk patients. This has translated into enhanced overall survival outcomes among high-risk patients, attributed to elevated levels of MCM proteins, integral components of the MCM complex responsible for initiating DNA replication prior to the G1 phase of the cell cycle. Notably, this study marks the first integration of proteomics data with the Sarculator nomogram to develop advanced prognostic tools. Furthermore, both transcriptomics and proteomics data identified the presence of two distinct molecular subtypes within synovial sarcoma (SS), each characterized by unique biological features. The analysis has also unveiled pathways associated with NAT response, including epithelial-mesenchymal transition, MYC targets, and heightened immune signalling. Moreover, through comparative analysis of transcriptomic and proteomic datasets, distinct prognostic biomarkers associated with each omics platform have been identified, emphasizing the valuable and distinct information contained within each dataset. By integrating these two datasets, a panel of 58 biomarkers (comprising 45 proteins and 13 genes) have been identified, effectively segregating NAT-treated SS samples from treatment-naĂŻve samples. In summary, this project offers valuable insights into the biology underlying NAT and demonstrates the potential of integrating proteomic signatures with nomograms to create advanced prognostic tools for the future.Item Utilising Quantitative Imaging And Radiomics To Unravel Intra-tumoural Heterogeneity In Soft Tissue Sarcoma(Institute of Cancer Research (University Of London), 2024-05-09) Arthur, A; Messiou C; Messiou, C; Arthur, AmaniSoft tissue sarcomas (STS) are rare mesenchymal tumours that exhibit extensive biological and clinical heterogeneity. Our understanding of this heterogeneity is incomplete and has continued to be a barrier to clinical advancements, and as such, outcomes for STS patients remain poor. Across oncology, radiology has transformed into a data-driven specialty, where advanced imaging techniques and computational science such as quantitative imaging and radiomics have the potential to provide in-depth information about tumour biology. If clinically translated, these tools could provide tumour characterisation by probing biology in a non-invasive and global manner revolutionising STS diagnostics and prognostics. However, progression of these tools has been slow as we continue to lack an understanding of the biology underlying imaging outputs. Herein, my project aimed to utilise imaging to approach biological heterogeneity in retroperitoneal sarcoma (RPS) in two ways: direct radiological-pathological correlation and large-scale CT-based radiomics. Using a retrospective cohort of 21 patients with RPS, immunohistochemistry of multi-regional tissue matched to regions of interest on quantitative MRI (qMRI) were analysed. Specifically key immune cells were characterised and correlation analysis with qMRI and histopathological data was carried out. Expansion to patient level data was conducted to include clinical data for correlation. Key findings were intra-tumoural heterogeneity of immune cells, the correlation of MRI enhancing fraction with T cells and the correlation of Ki67 proliferation index with T cells and CD68+ cells at both block and patient levels. To interrogate further the capability of unravelling heterogeneity using imaging, radiomic and deep learning (DL-) radiomic data available for the same cohort was analysed. Correlation analysis revealed 32 correlations between immune and histopathological data with DL-radiomics whilst 5 correlations were found between histopathological and radiomic data, with absence of any correlations between immune and radiomic data. In addition, clustering of DL-radiomics data revealed a persistent cluster inclusive of all recurrence cases alluding to a possible DL-radiomic signature for RPS disease recurrence. Using large-scale CT-based radiomics, we developed and tested a classification model for the two most common RPS subtypes, leiomyosarcoma (LMS) and liposarcoma (LPS) and for histological grade. Our model utilised a novel feature selection pipeline, sub-segmentation to provide sub-region volumes, repeatability analysis and intense cross-validation to increase the explainability and stability of our final models. Selecting the best performing models for prediction of subtype and for grade, we successfully independently validated our models using international, multi-centre trial data. Final area under the receiver operator curves were 0.928 for subtype and 0.882 for low versus intermediate/high grade RPS. Compared with standard of care radiological and histopathological assessment of subtype and grade, our models outperformed with regards to accuracy: 0.843 (radiomics) versus 0.65 (clinical) for subtype and 0.823 (radiomics) versus 0.44 (clinical) for low versus intermediate/high grade. These powerful and validated radiomics models, if prospectively validated could revolutionise the upfront characterisation of RPS tumours, offering a complimentary tool for diagnosis and risk stratification. Overall, my project has provided an exciting foundation of using qMRI and radiomics to provide a virtual biopsy of underlying biology of RPS tumours. It qualifies as a rich resource to guide future work to ultimately encourage the clinical translation of cutting-edge tools that may be utilised in diagnosis, risk stratification, treatment planning and monitoring and prognosis of STS.Item FUME-TCRseq Enables Sensitive and Accurate Sequencing of the T-cell Receptor from Limited Input of Degraded RNA.(AMER ASSOC CANCER RESEARCH, 2024-05-15) Baker, A-M; Nageswaran, G; Nenclares, P; Ronel, T; Smith, K; Kimberley, C; LaclĂ©, MM; Bhide, S; Harrington, KJ; Melcher, A; Rodriguez-Justo, M; Chain, B; Graham, TA; Baker, Ann-Marie Clare; Nenclares, Pablo; Ronel, Tahel; Harrington, Kevin; Melcher, Alan; Graham, TrevorUNLABELLED: Genomic analysis of the T-cell receptor (TCR) reveals the strength, breadth, and clonal dynamics of the adaptive immune response to pathogens or cancer. The diversity of the TCR repertoire, however, means that sequencing is technically challenging, particularly for samples with low-quality, degraded nucleic acids. Here, we developed and validated FUME-TCRseq, a robust and sensitive RNA-based TCR sequencing methodology that is suitable for formalin-fixed paraffin-embedded samples and low amounts of input material. FUME-TCRseq incorporates unique molecular identifiers into each molecule of cDNA, allowing correction for sequencing errors and PCR bias. Using RNA extracted from colorectal and head and neck cancers to benchmark the accuracy and sensitivity of FUME-TCRseq against existing methods demonstrated excellent concordance between the datasets. Furthermore, FUME-TCRseq detected more clonotypes than a commercial RNA-based alternative, with shorter library preparation time and significantly lower cost. The high sensitivity and the ability to sequence RNA of poor quality and limited amount enabled quantitative analysis of small numbers of cells from archival tissue sections, which is not possible with other methods. Spatially resolved FUME-TCRseq analysis of colorectal cancers using macrodissected archival samples revealed the shifting T-cell landscapes at the transition to an invasive phenotype and between tumor subclones containing distinct driver alterations. In summary, FUME-TCRseq represents an accurate, sensitive, and low-cost tool for the characterization of T-cell repertoires, particularly in samples with low-quality RNA that have not been accessible using existing methodology. SIGNIFICANCE: FUME-TCRseq is a TCR sequencing methodology that supports sensitive and spatially resolved detection of TCR clones in archival clinical specimens, which can facilitate longitudinal tracking of immune responses through disease course and treatment.