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Matveeva, A.

Publications and source records attributed to Matveeva, A..

2 recordsLinked to original sources

Analysis of single nucleotide substitution in CYP21A2 gene using Alphagenome

Context: CYP21A2 encodes steroid 21-hydroxylase, which supplies precursors for adrenal glucocorticoid and mineralocorticoid synthesis. Interpretation of its variants is complicated by a homologous pseudogene, complex alleles, heterogeneous functional assays and uncertain noncoding effects. We integrated observed variation with a reference-locus saturation analysis to distinguish transcriptional, splicing and protein-level mechanisms. Methods: The NM_000500.9 MANE transcript was reconstructed from GRCh38 genomic sequence and verified against RefSeq and UniProt P08686. ClinVar, gnomAD and UniProt records were retrieved with provenance, and a cutoff-limited PubMed search was reconciled against Entrez. AlphaGenome Atlas scores were obtained for every possible single nucleotide substitution across the 3,230-bp transcript span. Reported non-SNV alleles were analyzed by direct AlphaGenome inference. Adrenal, testis, ovary, placenta, liver and kidney expression predictions were calculated. Results: The results include 483 ClinVar records, 3,208 gnomAD records and 82 UniProt natural-variants. All 9,690 locus-wide substitutions were scored, comprising 6,018 exonic and 3,672 intronic substitutions. The coding set contained 3,201 missense, 1,098 synonymous, 147 stop-gained, nine start-lost, eight stop-lost and one stop-retained substitution. Direct inference was completed for 413 additional reported alleles and four context controls. Established splice defects, including c.293-13C>G and c.738+75C>T, showed appreciable splicing scores without corresponding large reductions in predicted total RNA. Clinical classification groups differed in AVI score distributions. Conclusions: A sequence-verified CYP21A2 catalogue connects clinical annotations, population observations, splicing predictions and measured enzyme function. Complete single-base coverage of the defined CYP21A2 transcript was achievable. Regulatory predictions should guide mechanistic experiments and remain separate from enzyme activity, tissue function and clinical classification.

genetics↗

Spatial Effects of Infiltrating T cells on Neighbouring Cancer Cells and Prognosis in Stage III CRC patients

Colorectal cancer (CRC) is one of the most frequently occurring cancers, but prognostic biomarkers identifying patients at risk of recurrence are still lacking. In this study, we aimed to investigate in more detail the spatial relationship between intratumoural T cells, cancer cells, and cancer cell hallmarks, as prognostic biomarkers in stage III colorectal cancer patients. We conducted multiplexed imaging of 56 protein markers at single cell resolution on resected fixed tissue from stage III CRC patients who received adjuvant 5-fluorouracil-based chemotherapy. Images underwent segmentation for tumour, stroma and immune cells, and cancer cell state protein marker expression was quantified at a cellular level. We developed a Python package for estimation of spatial proximity, nearest neighbour analysis focusing on cancer cell - T cell interactions at single-cell level. In our discovery cohort (MSK), we processed 462 core samples (total number of cells: 1,669,228) from 221 adjuvant 5FU-treated stage III patients. The validation cohort (HV) consisted of 272 samples (total number of cells: 853,398) from 98 stage III CRC patients. While there were trends for an association between percentage of cytotoxic T cells (across the whole cancer core), it did not reach significance (Discovery cohort: p = 0.07, Validation cohort: p = 0.19). We next utilized our region-based nearest neighbourhood approach to determine the spatial relationships between cytotoxic T cells, helper T cells and cancer cell clusters. In the both cohorts, we found that lower distance between cytotoxic T cells, T helper cells and cancer cells was significantly associated with increased disease-free survival. An unsupervised trained model that clustered patients based on the median distance between immune cells and cancer cells, as well as protein expression profiles, successfully classified patients into low-risk and high-risk groups (Discovery cohort: p = 0.01, Validation cohort: p = 0.003).

cancer biology↗