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Armitage, J. N.

Publications and source records attributed to Armitage, J. N..

2 recordsLinked to original sources

Multi-regional characterisation of renal cell carcinoma and microenvironment at single cell resolution

Tumour behaviour is dependent on the oncogenic properties of cancer cells and their multicellular interactions. These dependencies were examined through 270,000 single cell transcriptomes and 100 micro-dissected whole exomes obtained from 12 patients with kidney tumours. Tissue was sampled from multiple regions of tumour core, tumour-normal interface, normal surrounding tissues, and peripheral blood. We found the principal spatial location of CD8+ T cell clonotypes largely defined exhaustion state, with clonotypic heterogeneity not explained by somatic intra-tumoural heterogeneity. De novo mutation calling from single cell RNA sequencing data allows us to lineagetrace and infer clonality of cells. We discovered six meta-programmes that distinguish tumour cell function. An epithelial-mesenchymal transition meta-programme, enriched at the tumour-normal interface appears modulated through macrophage expressed IL1B, potentially forming a therapeutic target. Single sentence summaryKidney cancer evolution, prognosis and therapy are revealed by a single cell multi-regional study of the microenvironment.

genomics↗

Comprehensive characterisation of cell-free tumour DNA in plasma and urine of patients with renal tumours

Cell-free tumour-derived DNA (ctDNA) allows non-invasive monitoring of cancers but its utility in renal cell cancer (RCC) has not been established. Here, untargeted and targeted sequencing methods, applied to two independent cohorts of renal tumour patients (n=90), were used to determine ctDNA content in plasma and urine. Our data revealed lower plasma ctDNA levels in RCC relative to other cancers, with untargeted detection of [~]33%. A sensitive personalised approach, applied to plasma and urine from select patients improved detection to [~]50%, including in patients with early-stage and even benign lesions.\n\nA machine-learning based model predicted detection, potentially offering a means of triaging samples for personalised analysis. In addition, with limited data we observed that plasma, and for the first time, urine ctDNA may better represent tumour heterogeneity than tissue biopsy. Furthermore, longitudinal sampling of >200 plasma samples revealed that ctDNA can track disease course. Additional datasets will be required to validate these findings.\n\nOverall, our data highlight RCC as a ctDNA-low malignancy, but indicate potential clinical utility provided improvement in detection approaches.\n\nOne sentence summaryComplementary sequencing methods show that cell-free tumour DNA levels are low in renal cancer though, via various strategies, may still be informative.

cancer biology↗