دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival.
المؤلفون: Jaber MI; NantOmics LLC, 9920 Jefferson Blvd., Culver City, CA, 90232, USA., Song B; ImmunityBio, 9920 Jefferson Blvd., Culver City, CA, 90232, USA., Taylor C; Department of Pathology, Keck School of Medicine, University of Southern California, HMR 2011 Zonal Ave., Health Sciences Campus, Los Angeles, CA, 90033, USA., Vaske CJ; ImmunityBio, 2901 Mission St. Ext., Santa Cruz, CA, 95066, USA., Benz SC; ImmunityBio, 2901 Mission St. Ext., Santa Cruz, CA, 95066, USA., Rabizadeh S; NantOmics LLC, 9920 Jefferson Blvd., Culver City, CA, 90232, USA.; ImmunityBio, 9920 Jefferson Blvd., Culver City, CA, 90232, USA., Soon-Shiong P; ImmunityBio, 9920 Jefferson Blvd., Culver City, CA, 90232, USA., Szeto CW; ImmunityBio, 2901 Mission St. Ext., Santa Cruz, CA, 95066, USA. Christopher.Szeto@ImmunityBio.com.
المصدر: Breast cancer research : BCR [Breast Cancer Res] 2020 Jan 28; Vol. 22 (1), pp. 12. Date of Electronic Publication: 2020 Jan 28.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 100927353 Publication Model: Electronic Cited Medium: Internet ISSN: 1465-542X (Electronic) Linking ISSN: 14655411 NLM ISO Abbreviation: Breast Cancer Res Subsets: MEDLINE
أسماء مطبوعة: Publication: London, UK : BioMed Central Ltd
Original Publication: London, UK : Current Science, c1999-
مواضيع طبية MeSH: Deep Learning* , Gene Expression Regulation, Neoplastic*, Biomarkers, Tumor/*genetics , Breast Neoplasms/*mortality , Breast Neoplasms/*pathology , Image Processing, Computer-Assisted/*methods , Molecular Typing/*methods, Breast Neoplasms/classification ; Breast Neoplasms/genetics ; Female ; Humans ; Neoplasm Grading ; Receptor, ErbB-2/metabolism ; Survival Rate
مستخلص: 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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فهرسة مساهمة: Keywords: Breast cancer; Deep learning algorithm; Intrinsic molecular subtype (IMS); Whole-slide imaging (WSI)
المشرفين على المادة: 0 (Biomarkers, Tumor)
EC 2.7.10.1 (ERBB2 protein, human)
EC 2.7.10.1 (Receptor, ErbB-2)
تواريخ الأحداث: Date Created: 20200130 Date Completed: 20201001 Latest Revision: 20231113
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC6988279
DOI: 10.1186/s13058-020-1248-3
PMID: 31992350
قاعدة البيانات: MEDLINE
الوصف
تدمد:1465-542X
DOI:10.1186/s13058-020-1248-3