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Aho, T.

Publications and source records attributed to Aho, T..

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↗

DNA Methylation-based Signatures Classify Sporadic Pituitary Tumors According to Clinicopathological Features.

BackgroundDistinct genome-wide methylation patterns have consistently clustered pituitary neuroendocrine tumors (PT) into molecular groups associated with specific clinicopathological features. Here we aim to identify, characterize and validate the methylation signatures that objectively classify PT into those molecular groups. MethodsCombining in-house and publicly available data, we conducted an analysis of the methylome profile of a comprehensive cohort of 177 tumor and 20 non-tumor specimens from the pituitary gland. We also retrieved methylome data from an independent pituitary tumor (PT) cohort (N=86) to validate our findings. ResultsWe identified three methylation clusters associated with functional status and adenohypophyseal cell lineages using an unsupervised approach. We also identified signatures based on differentially methylated CpG probes (DMP), some of which overlapped with pituitary-specific transcription factors genes (SF1 and Tpit), that significantly distinguished pairs of clusters related to functional status and adenohypophyseal cell lineage. These findings were reproduced in an independent cohort, validating these methylation signatures. The DMPs were mainly annotated in enhancer regions associated with pathways and genes involved in cell identity and tumorigenesis. ConclusionsWe identified and validated methylation signatures that distinguished PT by distinct functional status and adenohypophyseal cell lineages. These signatures, annotated in enhancer regions, indicate the importance of these elements in pituitary tumorigenesis. They also provide an unbiased approach to classify pituitary tumors according to the most recent classification recommended by the WHO 2017 using methylation profiling. Key-pointsO_LIDistinct methylation landscapes define PT groups with specific functional status/subtypes and adenohypophyseal lineages subtypes. C_LIO_LIMethylation abnormalities in each cluster mainly occur in CpG annotated in distal regions overlapping predicted enhancers regions associated with pathways and genes involved in cell identity and tumorigenesis. C_LIO_LIDNA methylation signatures provide an unbiased approach to classify PT. C_LI Importance of the studyThis study harnessed the largest methylome data to date from a comprehensive cohort of pituitary specimens obtained from four different institutions. We identified and validated methylation signatures that distinguished pituitary tumors into molecular groups that reflect the functionality and adenohypophyseal cell lineages of these tumors. These signatures, mainly located in enhancers, are associated with pathways and genes involved in cell identity and tumorigenesis. Our results show that methylome profiling provides an objective approach to classify PT according to the most recent classification of PT recommended by the 2017 WHO.

cancer biology↗