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A Versatile and Upgraded Version of the LundTax Classification Algorithm Applied to Independent Cohorts

Cotillas, Elena Aramendía LU ; Bernardo, Carina LU orcid ; Veerla, Srinivas LU orcid ; Liedberg, Fredrik LU ; Sjödahl, Gottfrid LU orcid and Eriksson, Pontus LU (2024) In Journal of Molecular Diagnostics 26(12). p.1081-1101
Abstract

Stratification of cancer into biologically and molecularly similar subgroups is a cornerstone of precision medicine. The Lund Taxonomy classification system for urothelial carcinoma aims to be applicable across the whole disease spectrum including both non–muscle-invasive and invasive bladder cancer. A successful classification system is one that can be robustly and reproducibly applied to new samples. However, transcriptomic methods used for subtype classification are affected by analytic platform, data preprocessing, cohort composition, and tumor purity. Furthermore, only limited data have been published evaluating the transferability of existing classification algorithms to external data sets. In this study, a single sample... (More)

Stratification of cancer into biologically and molecularly similar subgroups is a cornerstone of precision medicine. The Lund Taxonomy classification system for urothelial carcinoma aims to be applicable across the whole disease spectrum including both non–muscle-invasive and invasive bladder cancer. A successful classification system is one that can be robustly and reproducibly applied to new samples. However, transcriptomic methods used for subtype classification are affected by analytic platform, data preprocessing, cohort composition, and tumor purity. Furthermore, only limited data have been published evaluating the transferability of existing classification algorithms to external data sets. In this study, a single sample classifier was developed based on in-house microarray and RNA-sequencing data, intended to be broadly applicable across studies and platforms. The new classification algorithm was applied to 10 published external bladder cancer cohorts (n = 2560 cases) to evaluate its ability to capture characteristic subtype-associated gene expression signatures and complementary data such as mutations, clinical outcomes, treatment response, or histologic subtypes. The effect of sample purity on the classification results was evaluated by generating low-purity versions of samples in silico. The classifier was robustly applicable across different gene expression profiling platforms and preprocessing methods and was less sensitive to variations in sample purity.

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Please use this url to cite or link to this publication:
author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Molecular Diagnostics
volume
26
issue
12
pages
21 pages
publisher
Elsevier
external identifiers
  • pmid:39326668
  • scopus:85207807855
ISSN
1525-1578
DOI
10.1016/j.jmoldx.2024.08.005
language
English
LU publication?
yes
id
4e28bf5b-f6c0-4da4-bd89-70cec4f719f1
date added to LUP
2024-12-11 13:22:04
date last changed
2025-08-21 10:03:11
@article{4e28bf5b-f6c0-4da4-bd89-70cec4f719f1,
  abstract     = {{<p>Stratification of cancer into biologically and molecularly similar subgroups is a cornerstone of precision medicine. The Lund Taxonomy classification system for urothelial carcinoma aims to be applicable across the whole disease spectrum including both non–muscle-invasive and invasive bladder cancer. A successful classification system is one that can be robustly and reproducibly applied to new samples. However, transcriptomic methods used for subtype classification are affected by analytic platform, data preprocessing, cohort composition, and tumor purity. Furthermore, only limited data have been published evaluating the transferability of existing classification algorithms to external data sets. In this study, a single sample classifier was developed based on in-house microarray and RNA-sequencing data, intended to be broadly applicable across studies and platforms. The new classification algorithm was applied to 10 published external bladder cancer cohorts (n = 2560 cases) to evaluate its ability to capture characteristic subtype-associated gene expression signatures and complementary data such as mutations, clinical outcomes, treatment response, or histologic subtypes. The effect of sample purity on the classification results was evaluated by generating low-purity versions of samples in silico. The classifier was robustly applicable across different gene expression profiling platforms and preprocessing methods and was less sensitive to variations in sample purity.</p>}},
  author       = {{Cotillas, Elena Aramendía and Bernardo, Carina and Veerla, Srinivas and Liedberg, Fredrik and Sjödahl, Gottfrid and Eriksson, Pontus}},
  issn         = {{1525-1578}},
  language     = {{eng}},
  number       = {{12}},
  pages        = {{1081--1101}},
  publisher    = {{Elsevier}},
  series       = {{Journal of Molecular Diagnostics}},
  title        = {{A Versatile and Upgraded Version of the LundTax Classification Algorithm Applied to Independent Cohorts}},
  url          = {{http://dx.doi.org/10.1016/j.jmoldx.2024.08.005}},
  doi          = {{10.1016/j.jmoldx.2024.08.005}},
  volume       = {{26}},
  year         = {{2024}},
}