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Cekan, P.

Publications and source records attributed to Cekan, P..

2 recordsLinked to original sources

Fluorescently guided workflow with rationally engineered 5' ligation adapters for high-sensitivity and low-bias small RNA sequencing

MicroRNAs (miRNAs) act as key regulators of gene expression across diverse cellular processes, and their precise quantification can provide unique insight into disease pathogenesis. High-throughput sequencing allows for comprehensive small RNA profiling; however, standard commercial library preparation workflows are challenged by issues of low sensitivity and representational bias, limiting reliable profiling, especially in scenarios where samples are scarce. Several structural studies have shown that this bias primarily arises due to sequence and secondary structure variations between miRNAs and adapters during enzyme-catalyzed biochemical reactions. In this work, we propose a new approach to ligation adapter engineering using a bioinformatic analysis of the human miRNome to rationally design structure-forcing 5 adapters, that physically override localized, unpredictable structural variations during the intermediate ligation state. We show that this approach combined with a practical fluorescence-guided workflow, utilizing a fluorescently-labeled 3 adapter and novel Fluorescent Ligation Rulers (FLRs) to guide precise band excision, can minimize representational bias and increase the sensitivity of small RNA sequencing from low-input biological matrices. In comprehensive benchmarks using a synthetic panel, this method significantly reduced bias and outperformed alternative commercial protocols. Finally, we demonstrate that this workflow enhances biomarker detection and library quality in challenging clinical matrices, especially in cerebrospinal fluid. Overall, this protocol enables highly accurate miRNome characterization and is well-suited for biomarker discovery in challenging sample types.

molecular biology↗

Multimodal histopathologic models stratify hormone receptor-positive early breast cancer

For patients with hormone receptor-positive, early breast cancer without HER2 amplification, multigene expression assays including Oncotype DX (R) recurrence score (RS) have been clinically validated to identify patients who stand to derive added benefit from adjuvant cytotoxic chemotherapy. However, cost and turnaround time have limited its global adoption despite recommendation by practice guidelines. We investigated if routinely available hematoxylin and eosin (H&E)-stained pathology slides could act as a surrogate triaging data substrate by predicting RS using machine learning methods. We trained and validated a multimodal transformer model, Orpheus, using 6,203 patients across three independent cohorts, taking both H&E images and their corresponding synoptic text reports as input. We showed accurate inference of recurrence score from whole-slide images (r = 0.63 (95% C.I. 0.58 - 0.68); n = 1,029), the raw text of their corresponding reports (r = 0.58 (95% C.I. 0.51 - 0.64); n = 972), and their combination (r = 0.68 (95% C.I. 0.64 - 0.73); n = 964) as measured by Pearsons correlation. To predict high-risk disease (RS>25), our model achieved an area under the receiver operating characteristic curve (AUROC) of 0.89 (95% C.I. 0.83 - 0.94), and area under the precision recall curve (AUPRC) of 0.64 (95% C.I. 0.60 - 0.82), compared to 0.49 (95% C.I. 0.36 - 0.64) for an existing nomogram based on clinical and pathologic features. Moreover, our model generalizes well to external international cohorts, effectively identifying recurrence risk (r = 0.61, p < 10-4, n = 452; r = 0.60, p < 10-4, n = 575) and high-risk status (AUROC = 0.80, p < 10-4, AUPRC = 0.68, p < 10-4, n = 452; AUROC = 0.83, p < 10-4, AUPRC = 0.73, p < 10-4, n = 575) from whole-slide images. Probing the biologic underpinnings of the model decisions uncovered tumor cell size heterogeneity, immune cell infiltration, a proliferative transcription program, and stromal fraction as correlates of higher-risk predictions. We conclude that at an operating point of 94.4% precision and 33.3% recall, this model could help increase global adoption and shorten lag between resection and adjuvant therapy.

pathology↗