Biology subjects

Lalchungnunga, H.

Publications and source records attributed to Lalchungnunga, H..

2 recordsLinked to original sources

bioRxiv2025

Path2Omics: Enhanced transcriptomic and methylation prediction accuracy from tumor histopathology

Precision oncology is becoming increasingly integral to clinical practice, demonstrating notable improvements in treatment outcomes. While molecular data provide comprehensive insights, obtaining such data remains costly and time-consuming. To address this challenge, we developed Path2Omics, a deep learning model that predicts gene expression and methylation from histopathology for 23 cancer types. Path2Omics was trained on 20,497 slides (9,456 formalin-fixed and paraffin-embedded (FFPE) and 11,041 fresh frozen (FF)) from 8,007 patients across 23 The Cancer Genome Atlas cohorts. When tested on FFPE slides, the most readily available format in clinical pathology practice, the integrated model outperformed its individual FF and FFPE components, robustly predicting nearly 5,000 genes on average, approximately five times more than our recently published DeepPT model. Externally evaluated on seven independent cohorts, Path2Omics robustly predicted the expression of approximately 4,400 genes, yielding a 30% increase over the FFPE model alone. Finally, we demonstrate that the inferred gene expression is nearly as effective as the actual values in predicting patient survival and treatment response. These results lay the basis for using Path2Omics to advance precision oncology from histopathology slides in a speedy and cost-effective manner. Statement of significancePath2Omics is a deep learning model that accurately predicts gene expression and methylation from histopathology slides across 23 cancer types. Unlike existing approaches that rely solely on FFPE slides for training, Path2Omics leverages both FFPE and FF slides by constructing two separate models and integrating them. Downstream analyses show that the inferred values from Path2Omics are nearly as effective as actual values in predicting patient survival and treatment response.

bioinformatics↗

bioRxiv2024

Deconvolution of cancer methylation patterns determines that altered methylation in cancer is dominated by a non-disease associated proliferation signal

All cancers are associated with massive reorganisation of cellular epigenetic patterns, including extensive changes in the genomic patterns of DNA methylation. However, the huge scale of these changes has made it very challenging to identify key DNA methylation changes responsible for driving cancer development. Here, we present a novel approach to address this problem called methylation mapping. Through comparison of multiple types of B-lymphocyte derived malignancies and normal cell populations, this approach can define the origins of methylation changes as proliferation-driven, differentiation-driven and disease-driven (including both cancer-specific changes and cancer absent changes). Each of these categories of methylation change were found to occur at genomic regions that vary in sequence context, chromatin structure and associated transcription factors, implying underlying mechanistic differences behind the acquisition of methylation at each category. This analysis determined that only a very small fraction (about 3%) of DNA methylation changes in B-cell cancers are disease related, with the overwhelming majority (97%) being driven by normal biological processes, predominantly cell proliferation. Furthermore, the low level of true disease-specific changes can potentially simplify identification of functionally relevant DNA methylation changes, allowing identification of previously unappreciated candidate drivers of cancer development, as illustrated here by the identification and functional confirmation of SLC22A15 as a novel tumour suppressor candidate in acute lymphoblastic leukaemia. Overall, this approach should lead to a clearer understanding of the role of altered DNA methylation in cancer development, facilitate the identification of DNA methylation targeted genes with genuine functional roles in cancer development and thus identify novel therapeutic targets.

cancer biology↗