Scholarlink

Background <p>Tumor evolution, driven by the emergence of genetically and epigenetically distinct subclones, enables cancers to adapt to selective pressures and become more aggressive, posing a major challenge in oncology. Multi-regional sequencing has been the primary means of studying tumor evolution and the resultant intra-tumor heterogeneity (ITH), but its high cost, resource-intensiveness, and limited scalability have hindered clinical utility.</p> Methods <p>Here, we present MorphoITH, a novel framework that aims to infer molecular ITH from routinely collected histopathology slides by quantifying phenotypic diversity. MorphoITH integrates a task-agnostic, self-supervised deep learning similarity measure to capture phenotypic variation across multiple dimensions (cytology, architecture, and microenvironment) along with rigorous methods to eliminate spurious sources of variation.</p> Results <p>Applying MorphoITH to clear cell renal cell carcinoma (ccRCC), a disease notably shaped by ITH, we show that it captures clinically significant biological features such as vascular architecture and nuclear grade. MorphoITH also recognizes morphological changes associated with subclonal alterations in key driver genes (<i>BAP1</i>, <i>PBRM1</i>, <i>SETD2</i>). Finally, in a multi-regional sequencing dataset, we find that the morphological trajectories revealed by MorphoITH largely mirror underlying patterns of genetic evolution.</p> Conclusions <p>MorphoITH provides a scalable and rigorous approach to quantify morphological ITH, serving as a potential proxy for underlying genetic ITH and tumor evolution. By linking histopathology with genomic insights, it lays the foundation for more refined phenotypic profiling in support of precision oncology.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

MorphoITH: a framework for deconvolving intra-tumor heterogeneity using tissue morphology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月19日

查看全文
  • Aleksandra Weronika Nielsen,
  • Hafez Eslami Manoochehri,
  • Hua Zhong,
  • Vandana Panwar,
  • Vipul Jarmale,
  • Jay Jasti,
  • Mehrdad Nourani,
  • Dinesh Rakheja,
  • James Brugarolas,
  • Payal Kapur,
  • Satwik Rajaram

摘要

Background

Tumor evolution, driven by the emergence of genetically and epigenetically distinct subclones, enables cancers to adapt to selective pressures and become more aggressive, posing a major challenge in oncology. Multi-regional sequencing has been the primary means of studying tumor evolution and the resultant intra-tumor heterogeneity (ITH), but its high cost, resource-intensiveness, and limited scalability have hindered clinical utility.

Methods

Here, we present MorphoITH, a novel framework that aims to infer molecular ITH from routinely collected histopathology slides by quantifying phenotypic diversity. MorphoITH integrates a task-agnostic, self-supervised deep learning similarity measure to capture phenotypic variation across multiple dimensions (cytology, architecture, and microenvironment) along with rigorous methods to eliminate spurious sources of variation.

Results

Applying MorphoITH to clear cell renal cell carcinoma (ccRCC), a disease notably shaped by ITH, we show that it captures clinically significant biological features such as vascular architecture and nuclear grade. MorphoITH also recognizes morphological changes associated with subclonal alterations in key driver genes (BAP1, PBRM1, SETD2). Finally, in a multi-regional sequencing dataset, we find that the morphological trajectories revealed by MorphoITH largely mirror underlying patterns of genetic evolution.

Conclusions

MorphoITH provides a scalable and rigorous approach to quantify morphological ITH, serving as a potential proxy for underlying genetic ITH and tumor evolution. By linking histopathology with genomic insights, it lays the foundation for more refined phenotypic profiling in support of precision oncology.