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Meera Hameed

Publications and source records attributed to Meera Hameed.

2 recordsLinked to original sources
PubMed2026

Pleomorphic Liposarcoma: Comprehensive Genomic Analysis of 39 Cases With Comparison to Other Genomically Complex Sarcomas.

Pleomorphic liposarcoma (PLPS) is an aggressive high-grade sarcoma that often shows diverse morphological features and can mimic high-grade undifferentiated pleomorphic sarcoma (UPS)/spindle cell sarcoma or myxofibrosarcoma (MFS), especially when pleomorphic lipoblasts are sparse. The molecular profile of PLPS is distinct from well differentiated/dedifferentiated liposarcoma and myxoid liposarcoma. In this study, we investigate 39 cases of PLPS by comprehensive genomic profiling, occurring in 32 patients with available molecular data. Cases were reviewed and morphologic parameters-lipoblastic component, UPS-like, and MFS-like areas were estimated. The genomic findings were collected and compared to UPS and MFS groups studied using the same platform. The cohort included 15 females and 17 males, with a median age of 56.5 (range, 34-78). The lower extremity (n = 17) was the most common site involved, followed by upper extremity (n = 5) and pelvis (n = 5). UPS-like and MFS-like patterns were the most common morphologic variants, ranging from 15% to 95% and 20% to 90%, respectively. TP53 (87%) and RB1 (51%) mutations and copy number alterations were the most common alterations seen, followed by ATRX (36%). Compared to UPS and MFS, TP53 and RB1 gene alterations were significantly more common in PLPS. Conversely, CDKN2A/B deletions were infrequent in PLPS. Survival analysis showed that MYC amplification was associated with significantly shorter overall survival in PLPS. Among histologic variants, CYSLTR2 alterations were found to be highest in cases with predominantly pleomorphic lipoblasts; additionally, strong correlations were found between gene alteration frequencies of MFS and MFS-like PLPS, and between UPS and UPS-like PLPS. RB1 allele-specific copy number analysis showed loss of heterozygosity in 82% of cases. Our cohort of PLPS showed a complex molecular landscape with distinct genetic alterations, histologic correlations, and clinical outcomes, highlighting its unique position among genomically complex sarcomas and providing insights that may inform future diagnostic and therapeutic approaches.

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 = 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↗