IMMUNE BASED CLASSIFICATION OF SOLID TUMOURS (ICST): AN ONLINE SOFTWARE PACKAGE

Gholamreza Rafiee, Nuala McCabe, Laura Knight, Andrena McCavigan, Kienan Savage, Richard Kennedy

Research output: Other contribution

Abstract

There is a clear unmet need in availability of a clinically applicable classifier for the prospective classification of tumour samples for immune checkpoint therapy, especially when applied to formalin-fixed, paraffin embedded (FFPE) material. To this end, we developed and analytically validated a novel cross-platform web-based classification model using a comprehensive machine learning approach. This classification model could be used prospectively in the clinic to stratify patients into six identified immune subtypes. In contrast to the traditional immune subgrouping provided by the PanCan Atlas consortium, which assigns all tumours to one of six immune subtypes, we provide an objective measure by which ‘predominant subgroups’ and ‘non-classifiable’ cases can be also identified. The developed model has achieved a performance of 96.2% balanced accuracy on more than 2000 solid tumours from different malignancies.
Original languageEnglish
TypeSoftware
Media of outputBioinformatics Tool
Publication statusEarly online date - 2019

Fingerprint

Software
Neoplasms
Atlases
Paraffin
Formaldehyde
Therapeutics

Cite this

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title = "IMMUNE BASED CLASSIFICATION OF SOLID TUMOURS (ICST): AN ONLINE SOFTWARE PACKAGE",
abstract = "There is a clear unmet need in availability of a clinically applicable classifier for the prospective classification of tumour samples for immune checkpoint therapy, especially when applied to formalin-fixed, paraffin embedded (FFPE) material. To this end, we developed and analytically validated a novel cross-platform web-based classification model using a comprehensive machine learning approach. This classification model could be used prospectively in the clinic to stratify patients into six identified immune subtypes. In contrast to the traditional immune subgrouping provided by the PanCan Atlas consortium, which assigns all tumours to one of six immune subtypes, we provide an objective measure by which ‘predominant subgroups’ and ‘non-classifiable’ cases can be also identified. The developed model has achieved a performance of 96.2{\%} balanced accuracy on more than 2000 solid tumours from different malignancies.",
author = "Gholamreza Rafiee and Nuala McCabe and Laura Knight and Andrena McCavigan and Kienan Savage and Richard Kennedy",
year = "2019",
language = "English",
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IMMUNE BASED CLASSIFICATION OF SOLID TUMOURS (ICST) : AN ONLINE SOFTWARE PACKAGE. / Rafiee, Gholamreza; McCabe, Nuala; Knight, Laura; McCavigan, Andrena; Savage, Kienan; Kennedy, Richard.

2019, Software.

Research output: Other contribution

TY - GEN

T1 - IMMUNE BASED CLASSIFICATION OF SOLID TUMOURS (ICST)

T2 - AN ONLINE SOFTWARE PACKAGE

AU - Rafiee, Gholamreza

AU - McCabe, Nuala

AU - Knight, Laura

AU - McCavigan, Andrena

AU - Savage, Kienan

AU - Kennedy, Richard

PY - 2019

Y1 - 2019

N2 - There is a clear unmet need in availability of a clinically applicable classifier for the prospective classification of tumour samples for immune checkpoint therapy, especially when applied to formalin-fixed, paraffin embedded (FFPE) material. To this end, we developed and analytically validated a novel cross-platform web-based classification model using a comprehensive machine learning approach. This classification model could be used prospectively in the clinic to stratify patients into six identified immune subtypes. In contrast to the traditional immune subgrouping provided by the PanCan Atlas consortium, which assigns all tumours to one of six immune subtypes, we provide an objective measure by which ‘predominant subgroups’ and ‘non-classifiable’ cases can be also identified. The developed model has achieved a performance of 96.2% balanced accuracy on more than 2000 solid tumours from different malignancies.

AB - There is a clear unmet need in availability of a clinically applicable classifier for the prospective classification of tumour samples for immune checkpoint therapy, especially when applied to formalin-fixed, paraffin embedded (FFPE) material. To this end, we developed and analytically validated a novel cross-platform web-based classification model using a comprehensive machine learning approach. This classification model could be used prospectively in the clinic to stratify patients into six identified immune subtypes. In contrast to the traditional immune subgrouping provided by the PanCan Atlas consortium, which assigns all tumours to one of six immune subtypes, we provide an objective measure by which ‘predominant subgroups’ and ‘non-classifiable’ cases can be also identified. The developed model has achieved a performance of 96.2% balanced accuracy on more than 2000 solid tumours from different malignancies.

M3 - Other contribution

ER -