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Biology subjects

Lajer, P.

Publications and source records attributed to Lajer, P..

2 recordsLinked to original sources

scIRES-seq reveals multimodal molecular heterogeneity in CTCs from small cell cervical cancer

Circulating tumor cells (CTCs) can provide a window into cellular and molecular mechanisms of cancer dissemination. However, the rarity and heterogeneity of CTCs complicate reliable identification and molecular characterization. Here, we introduce scIRES-seq, a framework linking immunophenotype, genetic variation, epigenomic profiles and gene expression within individual cells. In this proof-of-concept study, we coupled scIRES-seq to real-time imaging spectral flow cytometry and applied it to CTCs from a patient with metastatic cervical small cell neuroendocrine carcinoma. Tumor-matched copy-number alterations and targeted somatic variants established CTC identity, supported by DNA methylation profiles concordant with metastatic tissue and transcriptional features consistent with neuroendocrine differentiation. Genetic and DNA methylation analyses identified two largely concordant CTC subpopulations that were not clearly distinguished by their transcriptomes, highlighting the complementary information gained through single-cell multi-omics profiling. These findings demonstrate the feasibility of integrated CTC identification and characterization and provide a framework for investigating relationships between genetic diversity and molecular states in rare, heterogeneous cell populations.

cancer biology↗

Hierarchical classification of hematologic malignancies using epigenetic and genetic information

Molecular testing in hematology requires different assays for disease subgroup identification, risk stratification and selection of appropriate treatment regimens. Yet, molecular tests are not necessarily standardized between diagnostic laboratories, resulting in varying turnaround times and potentially divergent results. To resolve this issue and enable single-assay molecular testing, we have developed a hierarchical classification framework that combines epigenetic and genetic data from whole genome nanopore sequencing (WGNS) with machine learning to determine disease entities, epigenetic subgroups (epitypes) and genetic aberrations in hematopoietic neoplasms. We curated DNA methylation data from 5,420 samples and trained a classifier allowing entity-level diagnostics featuring 21 conditions, including healthy controls, acute and chronic myeloid and lymphoid neoplasms. This classifier was subsequently combined with entity-specific epitype classifiers predicting 44 therapeutically or prognostically relevant states, followed by integration of genetic data. Benchmarking of the combined (epi-)genetic testing strategy using WGNS confirmed high accuracy in the detection of diagnostic groups and risk stratification, and identified diagnosis-defining molecular alterations that were not reported by standard-of-care work-up.

cancer biology↗