Biomedical subjects

Svitlana Tyekucheva

Publications and source records attributed to Svitlana Tyekucheva.

5 recordsLinked to original sources

PubMed2026

Prognostic role of thymidine kinase 1 activity in hormone receptor positive metastatic breast cancer. A systematic review and meta-analysis.

INTRODUCTION: Circulating thymidine kinase 1 activity (TKa) is a potential prognostic biomarker in patients with hormone receptor-positive (HR+) metastatic breast cancer (MBC); however, results are heterogeneous. In this study we aimed to summarize the current evidence on the prognostic role of circulating TKa in women with HR+ MBC. METHODS: We conducted a systematic review of PubMed, Embase, and Cochrane CENTRAL databases and abstracts from main international oncology meetings. Phase II-IV clinical trials and prospective observational studies in patients with MBC assessing circulating TK1 levels or TKa and reporting hazard ratios (HRs) for progression-free survival (PFS) and/or overall survival (OS) were included. HRs were pooled using random-effects models (restricted maximum likelihood with Hartung-Knapp adjustment), with heterogeneity quantified by I2 and prediction intervals. The primary study outcome was the association of baseline and on-treatment TKa with PFS and OS. Secondary analyses aimed at exploring the source of heterogeneity. RESULTS: Eighteen studies, reporting data from nearly 3000 women, were included in the systematic review and 15 studies were meta-analyzed. Patients with HR+ MBC and high baseline TKa showed a significantly higher risk of progression (PFS: HR 1.90; 95% CI 1.57-2.30; p < 0.001) and death (OS: HR 2.48; 95% CI 1.94-3.17; p < 0.001) than those with low TKa. TKa at 2 and 4 weeks on-treatment was also prognostic (2 weeks, PFS: HR 2.76; 95% CI 2.34-3.26; p < 0.001; 4 weeks, HR 2.26; 95% CI 1.93-2.66; p < 0.001). Similar pooled effects were obtained when accounting for different cut-offs, TKa assessment, and sample-type. CONCLUSION: Pre-treatment high TKa is an adverse prognostic factor in women with HR+ MBC. High on-treatment TKa is also associated with worse outcome, potentially serving as an early signal of treatment resistance. We provide a comprehensive summary of the currently available evidence on the prognostic value of circulating TKa.

Breast cancer↗

PubMed2026

Follicular Lymphoma Transformation is Characterized by Cytokine-associated Remodeling of Stromal and Macrophage Compartments.

Across cancer, one of the most frequent examples of histologic transformation is the evolution of follicular lymphoma (FL) to an aggressive large cell lymphoma. Despite recent progress, understanding of the molecular and cellular underpinnings of transformation remains incomplete. Here, we dissect the interplay of tumor and microenvironment cell populations across transformation through a multimodal investigation of 95 FL and transformed FL (tFL) samples, including single-cell and bulk RNA-sequencing alongside spatial transcriptomics and proteomics, and validate findings across independent FL-tFL pairs. Upon transformation, fibroblasts and GPNMB+ macrophages increase while lymph-node organizing follicular dendritic and CCL21+ fibroblastic reticular cells were lost, resulting in an altered spatial distribution of cytokines that impacts T cell infiltration and macrophage differentiation and function. Secreted stromal and macrophage signals were further evident by non-invasive plasma proteomics. Taken together, our data reveal expansion of macrophages and fibroblasts as key features of transformation with potential diagnostic and therapeutic implications.

Journal Article↗

PubMed2006

ESPERR: learning strong and weak signals in genomic sequence alignments to identify functional elements.

Genomic sequence signals - such as base composition, presence of particular motifs, or evolutionary constraint - have been used effectively to identify functional elements. However, approaches based only on specific signals known to correlate with function can be quite limiting. When training data are available, application of computational learning algorithms to multispecies alignments has the potential to capture broader and more informative sequence and evolutionary patterns that better characterize a class of elements. However, effective exploitation of patterns in multispecies alignments is impeded by the vast number of possible alignment columns and by a limited understanding of which particular strings of columns may characterize a given class. We have developed a computational method, called ESPERR (evolutionary and sequence pattern extraction through reduced representations), which uses training examples to learn encodings of multispecies alignments into reduced forms tailored for the prediction of chosen classes of functional elements. ESPERR produces a greatly improved Regulatory Potential score, which can discriminate regulatory regions from neutral sites with excellent accuracy ( approximately 94%). This score captures strong signals (GC content and conservation), as well as subtler signals (with small contributions from many different alignment patterns) that characterize the regulatory elements in our training set. ESPERR is also effective for predicting other classes of functional elements, as we show for DNaseI hypersensitive sites and highly conserved regions with developmental enhancer activity. Our software, training data, and genome-wide predictions are available from our Web site (http://www.bx.psu.edu/projects/esperr).

Algorithms↗

PubMed2006

Genomic environment predicts expression patterns on the human inactive X chromosome.

What genomic landmarks render most genes silent while leaving others expressed on the inactive X chromosome in mammalian females? To date, signals determining expression status of genes on the inactive X remain enigmatic despite the availability of complete genomic sequences. Long interspersed repeats (L1s), particularly abundant on the X, are hypothesized to spread the inactivation signal and are enriched in the vicinity of inactive genes. However, both L1s and inactive genes are also more prevalent in ancient evolutionary strata. Did L1s accumulate there because of their role in inactivation or simply because they spent more time on the rarely recombining X? Here we utilize an experimentally derived inactivation profile of the entire human X chromosome to uncover sequences important for its inactivation, and to predict expression status of individual genes. Focusing on Xp22, where both inactive and active genes reside within evolutionarily young strata, we compare neighborhoods of genes with different inactivation states to identify enriched oligomers. Occurrences of such oligomers are then used as features to train a linear discriminant analysis classifier. Remarkably, expression status is correctly predicted for 84% and 91% of active and inactive genes, respectively, on the entire X, suggesting that oligomers enriched in Xp22 capture most of the genomic signal determining inactivation. To our surprise, the majority of oligomers associated with inactivated genes fall within L1 elements, even though L1 frequency in Xp22 is low. Moreover, these oligomers are enriched in parts of L1 sequences that are usually underrepresented in the genome. Thus, our results strongly support the role of L1s in X inactivation, yet indicate that a chromatin microenvironment composed of multiple genomic sequence elements determines expression status of X chromosome genes.

Algorithms↗

PubMed2005

Strong and weak male mutation bias at different sites in the primate genomes: insights from the human-chimpanzee comparison.

Male mutation bias is a higher mutation rate in males than in females thought to result from the greater number of germ line cell divisions in males. If errors in DNA replication cause most mutations, then the magnitude of male mutation bias, measured as the male-to-female mutation rate ratio (alpha), should reflect the relative excess of male versus female germ line cell divisions. Evolutionary rates averaged among all sites in a sequence and compared between mammalian sex chromosomes were shown to be indeed higher in males than in females. However, it is presently unknown whether individual classes of substitutions exhibit such bias. To address this issue, we investigated male mutation bias separately at non-CpG and CpG sites using human-chimpanzee whole-genome alignments. We observed strong male mutation bias at non-CpG sites: alpha in the X-autosome comparison was approximately 6-7, which was similar to the male-to-female ratio in the number of germ line cell divisions. In contrast, mutations at CpG sites exhibited weak male mutation bias: alpha in the X-autosome comparison was only approximately 2-3. This is consistent with the methylation-induced and replication-independent mechanism of CpG transitions, which constitute the majority of mutations at CpG sites. Interestingly, our study also indicated weak male mutation bias for transversions at CpG sites, implying a spontaneous mechanism largely not associated with replication. Male mutation bias was equally strong at CpG and non-CpG sites located within unmethylated "CpG islands," suggesting the replication-dependent origin of these mutations. Thus, we found that the strength of male mutation bias is nonuniform in the primate genomes. Importantly, we discovered that male mutation bias depends on the proportion of CpG sites in the loci compared. This might explain the differences in the magnitude of primate male mutation bias observed among studies.

Animals↗