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Biomedical subjects

Jane Houldsworth

Publications and source records attributed to Jane Houldsworth.

2 recordsLinked to original sources
PubMed2025

Clinicopathologic and Molecular Analysis of Colorectal Carcinomas With Spectrum of Neuroendocrine Carcinoma Components.

The genetics of colorectal carcinoma (CRC) with neuroendocrine differentiation remain poorly understood; recent studies focusing on pure neuroendocrine carcinomas (NECs) demonstrated mutation profiles closely resembling colorectal adenocarcinomas (ACAs) with more frequent BRAF mutations and Rb/p16 pathway dysregulation. However, pathogenesis of mixed neuroendocrine-non-neuroendocrine neoplasms (MiNENs) and ACAs with minor NEC component (AMiNECs) remains controversial. We aimed to define the behavior and molecular underpinnings of these tumors in comparison with conventional ACAs. In total, 20 NECs, 10 MiNENs, and 8 AMiNECs were compared with 100 controls with ACAs. Well-differentiated neuroendocrine tumors of any grade were excluded. CRCs with NEC components presented at a slightly earlier age (mean, 59 vs 65 years; P = .24) in a similar sex distribution (male:female, 1:1.11 vs 1.04:1; P = .97). The majority of cases arose either from a precursor adenoma (42%) or in the setting of inflammatory bowel disease (18%), whereas 5 of 10 cases (50%) originating from the rectum were human papillomavirus driven. Despite similarity in tumor size and depth of invasion among all groups, CRCs with NEC components showed more frequent lymph node and distant metastases (P < .001 each), leading to more advanced disease stage (stage III/IV; P < .001) and worse 5-year survival outcomes (35.4% for NECs, 30% for MiNENs, and 41.6% for AMiNECs vs 85.1% for ACAs; P < .001), compared with ACAs. Next-generation sequencing revealed more frequent BRAF (40% vs 3%; P < .001) and BRCA1 alterations (15% vs 1%; P = .001) in NECs compared with ACAs. Genomic alterations in RB1 were exclusively found in NECs (10%) and MiNENs (20%). In conclusion, the presence of any NEC component (from AMiNEC to pure NEC) in CRC carries a dismal prognosis. Yet, these tumors are more likely to harbor potentially targetable mutations such as BRAF p.V600E and alterations in BRCA1/2, which are of therapeutic value.

Humans↗
PubMed2025

Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection.

Artificial intelligence models using digital histopathology slides stained with hematoxylin and eosin offer promising, tissue-preserving diagnostic tools for patients with cancer. Despite their advantages, their clinical utility in real-world settings remains unproven. Assessing EGFR mutations in lung adenocarcinoma demands rapid, accurate and cost-effective tests that preserve tissue for genomic sequencing. PCR-based assays provide rapid results but with reduced accuracy compared with next-generation sequencing and require additional tissue. Computational biomarkers leveraging modern foundation models can address these limitations. Here we assembled a large international clinical dataset of digital lung adenocarcinoma slides (N&#x2009;=&#x2009;8,461) to develop a computational EGFR biomarker. Our model fine-tunes an open-source foundation model, improving task-specific performance with out-of-center generalization and clinical-grade accuracy on primary and metastatic specimens (mean area under the curve: internal 0.847, external 0.870). To evaluate real-world clinical translation, we conducted a prospective silent trial of the biomarker on primary samples, achieving an area under the curve of 0.890. The artificial-intelligence-assisted workflow reduced the number of rapid molecular tests needed by up to 43% while maintaining the current clinical standard performance. Our retrospective and prospective analyses demonstrate the real-world clinical utility of a computational pathology biomarker.

Humans↗