Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types
Homologous recombination deficiency (HRD) is recognized as a pan-cancer predictive biomarker that potentially indicates who could benefit from treatment with PARP inhibitors (PARPi). Despite its clinical significance, HRD testing is highly complex. Here, we investigated in a proof-of-concept study w...
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| Hauptverfasser: | , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
08 October 2024
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| In: |
BMC biology
Year: 2024, Jahrgang: 22, Heft: 1, Pages: 1-14 |
| ISSN: | 1741-7007 |
| DOI: | 10.1186/s12915-024-02022-9 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s12915-024-02022-9 Verlag, kostenfrei, Volltext: https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-024-02022-9 
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| Verfasserangaben: | Chiara Maria Lavinia Loeffler, Omar S.M. El Nahhas, Hannah Sophie Muti, Zunamys I. Carrero, Tobias Seibel, Marko van Treeck, Didem Cifci, Marco Gustav, Kevin Bretz, Nadine T. Gaisa, Kjong-Van Lehmann, Alexandra Leary, Pier Selenica, Jorge S. Reis-Filho, Nadina Ortiz-Bruechle and Jakob Nikolas Kather |
MARC
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| 245 | 1 | 0 | |a Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types |c Chiara Maria Lavinia Loeffler, Omar S.M. El Nahhas, Hannah Sophie Muti, Zunamys I. Carrero, Tobias Seibel, Marko van Treeck, Didem Cifci, Marco Gustav, Kevin Bretz, Nadine T. Gaisa, Kjong-Van Lehmann, Alexandra Leary, Pier Selenica, Jorge S. Reis-Filho, Nadina Ortiz-Bruechle and Jakob Nikolas Kather |
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| 520 | |a Homologous recombination deficiency (HRD) is recognized as a pan-cancer predictive biomarker that potentially indicates who could benefit from treatment with PARP inhibitors (PARPi). Despite its clinical significance, HRD testing is highly complex. Here, we investigated in a proof-of-concept study whether Deep Learning (DL) can predict HRD status solely based on routine hematoxylin & eosin (H&E) histology images across nine different cancer types. We developed a deep learning pipeline with attention-weighted multiple instance learning (attMIL) to predict HRD status from histology images. As part of our approach, we calculated a genomic scar HRD score by combining loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large-scale state transitions (LST) from whole genome sequencing (WGS) data of n = 5209 patients across two independent cohorts. The model’s effectiveness was evaluated using the area under the receiver operating characteristic curve (AUROC), focusing on its accuracy in predicting genomic HRD against a clinically recognized cutoff value. Our study demonstrated the predictability of genomic HRD status in endometrial, pancreatic, and lung cancers reaching cross-validated AUROCs of 0.79, 0.58, and 0.66, respectively. These predictions generalized well to an external cohort, with AUROCs of 0.93, 0.81, and 0.73. Moreover, a breast cancer-trained image-based HRD classifier yielded an AUROC of 0.78 in the internal validation cohort and was able to predict HRD in endometrial, prostate, and pancreatic cancer with AUROCs of 0.87, 0.84, and 0.67, indicating that a shared HRD-like phenotype occurs across these tumor entities. This study establishes that HRD can be directly predicted from H&E slides using attMIL, demonstrating its applicability across nine different tumor types. | ||
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