A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival

Background Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal status, yet the molecular testing required to elucid...

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Vydané v:Breast cancer research : BCR Ročník 22; číslo 1; s. 12 - 10
Hlavní autori: Jaber, Mustafa I., Song, Bing, Taylor, Clive, Vaske, Charles J., Benz, Stephen C., Rabizadeh, Shahrooz, Soon-Shiong, Patrick, Szeto, Christopher W.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 28.01.2020
BioMed Central Ltd
Springer Nature B.V
BMC
Predmet:
Algorithms
Biomedical and Life Sciences
Biomedicine
Biopsy
Breast cancer
Breast tumors
Cancer
Cancer Research
Classification
Data mining
Datasets
Decision making
Deep learning
Deep learning algorithm
Development and progression
ErbB-2 protein
Gene expression
Health aspects
Hormones
Image processing
Intrinsic molecular subtype (IMS)
Learning algorithms
Machine learning
Medical prognosis
Methods
Oncology
Patients
Principal components analysis
Prognosis
Research Article
Ribonucleic acid
RNA
RNA sequencing
Surgical Oncology
Survival
Tumors
Whole-slide imaging (WSI)
Breast cancer
Intrinsic molecular subtype (IMS)
Whole-slide imaging (WSI)
Deep learning algorithm
ISSN:1465-542X, 1465-5411, 1465-542X
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Shrnutí:Background Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal status, yet the molecular testing required to elucidate these subtypes is not routinely performed. Furthermore, when such bulk assays as RNA sequencing are performed, intratumoral heterogeneity that may affect prognosis and therapeutic decision-making can be missed. Methods As a more facile and readily available method for determining IMS in breast cancer, we developed a deep learning approach for approximating PAM50 intrinsic subtyping using only whole-slide images of H&E-stained breast biopsy tissue sections. This algorithm was trained on images from 443 tumors that had previously undergone PAM50 subtyping to classify small patches of the images into four major molecular subtypes—Basal-like, HER2-enriched, Luminal A, and Luminal B—as well as Basal vs. non-Basal. The algorithm was subsequently used for subtype classification of a held-out set of 222 tumors. Results This deep learning image-based classifier correctly subtyped the majority of samples in the held-out set of tumors. However, in many cases, significant heterogeneity was observed in assigned subtypes across patches from within a single whole-slide image. We performed further analysis of heterogeneity, focusing on contrasting Luminal A and Basal-like subtypes because classifications from our deep learning algorithm—similar to PAM50—are associated with significant differences in survival between these two subtypes. Patients with tumors classified as heterogeneous were found to have survival intermediate between Luminal A and Basal patients, as well as more varied levels of hormone receptor expression patterns. Conclusions Here, we present a method for minimizing manual work required to identify cancer-rich patches among all multiscale patches in H&E-stained WSIs that can be generalized to any indication. These results suggest that advanced deep machine learning methods that use only routinely collected whole-slide images can approximate RNA-seq-based molecular tests such as PAM50 and, importantly, may increase detection of heterogeneous tumors that may require more detailed subtype analysis.
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ISSN:1465-542X
1465-5411
1465-542X
DOI:10.1186/s13058-020-1248-3