Biology subjects

Evans, L. K.

Publications and source records attributed to Evans, L. K..

3 recordsLinked to original sources

bioRxiv2026

A Bioprinted Head and Neck Cancer Organoid-Based Platform for Evaluating Multimodal Therapies

Treatment of advanced head and neck squamous cell carcinoma (HNSCC) often involves radiotherapy combined with chemotherapy, targeted therapy, or immunotherapy. However, due to its anatomical and molecular heterogeneity, identifying the most effective treatment for each patient remains a major clinical challenge. To address this need, we developed a high-throughput organoid-based drug screening platform that uses patient-derived organoids to assess candidate treatment regimens. We validated the platform by establishing bioprinted 3D organoids of human HNSCC cell lines and exposing them to X-ray radiation in combination with various small-molecule drugs and biologics. We quantified viability using ATP release assays and assessed extracellular matrix (ECM) invasion with a machine learning-based brightfield image analysis pipeline. Proof-of-concept experiments with HPV-negative HNSCC lines (HN30 and HN31, established from primary and metastatic disease from the same patient) and HPV-positive HNSCC cells (SCC154) revealed different therapy agents that can radiosensitize each cell line. Image analysis showed that copanlisib, afatinib, and ibrutinib could limit ECM invasion of HN31, while the AKT inhibitor ipatasertib promotes invasion of HN30 cells, consistent with previous studies. Application of the platform to patient-derived HPV+ oropharyngeal tumor organoids showed that they shared sensitivity to several agents while also exhibiting differences against certain therapies. Cetuximab, sorafenib, and nedisertib significantly radiosensitized organoids from two clinical samples. This work demonstrates the feasibility of performing sensitivity screening by integrating bioprinting, conventional viability assays, and advanced image analysis techniques. This platform has the potential to enable a personalized therapeutic pipeline for patients with advanced HNSCC, optimizing responses to radiotherapy and targeted agents to improve clinical outcomes while avoiding modulators that may promote tumor invasion.

cancer biology↗

bioRxiv2025

Machine Learning-Based Tumor Segmentation and Classification Using Dynamic Optical Contrast Imaging (DOCI) for Thyroid Cancer

Thyroid cancer presents significant diagnostic challenges due to its complex anatomy and diverse tissue types. This study leverages Dynamic Optical Contrast Imaging (DOCI), a label-free, real-time imaging technology, with machine learning to enhance tumor detection and segmentation. Using 23 DOCI filters, we applied Principal Component Analysis (PCA) for dimensionality reduction, k-nearest neighbors (k-NN) for classification, and U-Net models for segmentation. The approach achieved high accuracy in distinguishing tissue types, with PCA enabling clear clustering, k-NN classifying normal, follicular, and papillary tissues, and U-Net models achieving 96.34% and 92.02% accuracy for papillary and follicular segmentation, respectively. Filter importance analysis reduced input dimensionality without significantly compromising performance, highlighting the potential for optimized imaging protocols. These findings demonstrate DOCIs utility in improving diagnostic accuracy and tumor characterization in thyroid cancer and beyond, offering a foundation for personalized treatment planning and surgical precision.

cancer biology↗

bioRxiv2023

ICG-Functionalized Gold Nanostars As An Effective Contrast Agent For Real-time Tumor Localization with Dynamic Optical Contrast Imaging (DOCI) and Enhanced Radiation Therapy

The primary management of head and neck squamous cell carcinoma relies on complete surgical resection of the tumor. However, the establishment of negative margin complete resection is often difficult given the devastating side effects of aggressive surgery and the anatomic proximity to vital structures. Positive margin status is associated with significantly decreased survival. Currently, surgeons determine where the tumor cuts are made, by palpating the edges of the tumor and using prior imaging. After a tumor is presumed to be removed in its entirety, the surrounding tissues are sampled by frozen section histologic pathology to ensure that no microscopic disease is left behind, the efficacy of which varies and is subject to sampling error. The methodology by which frozen sections are collected whether tumor bed driven or specimen driven can also alter margin outcome. Thus, improving intraoperative detection of tumor margins is key to optimizing treatment and outcomes. Our group has developed a possible solution for this unmet clinical need. We have previously designed Dynamic Optical Contrast Imaging (DOCI), a novel imaging modality that acquires temporally dependent measurements of tissue autofluorescence. Furthermore, we demonstrated that DOCI can distinguish HNSCC from adjacent healthy tissue with a high degree of accuracy. DOCI images are captured in real time and offer an operatively wide field of view. With the addition of ICG conjugated gold nanostars (GNS) we can improve DOCI image contrast between tumors vs normal tissues as well as use the GNS for CT imaging and radiotherapeutic treatment.

cancer biology↗