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.

中文翻译：

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基于深度学习图像的乳腺肿瘤内在分子亚型分类器揭示了可能影响生存的肿瘤异质性。

背景 通过基于表达的 PAM50 检测分类的乳腺癌内在分子亚型 (IMS) 被认为是一个很强的预后特征，即使受年龄、分级和淋巴结状态等标准临床病理特征控制，但需要分子测试来阐明这些亚型不常规进行。此外，当进行RNA测序等批量检测时，可能会错过可能影响预后和治疗决策的肿瘤内异质性。方法 作为一种更简便、更容易获得的确定乳腺癌 IMS 的方法，我们开发了一种深度学习方法，仅使用 H&E 染色的乳腺活检组织切片的全玻片图像来近似 PAM50 内在亚型。该算法在来自 443 个肿瘤的图像上进行训练，这些肿瘤之前经过了 PAM50 亚型分类，将图像的小斑块分类为四种主要的分子亚型 - 类基底型、富含 HER2 的、Luminal A 和 Luminal B，以及基底型与非基底型。 -基础。该算法随后用于对 222 个肿瘤进行亚型分类。结果 这种基于深度学习图像的分类器正确地对保留的肿瘤组中的大多数样本进行了亚型分类。然而，在许多情况下，在单个全幻灯片图像内跨斑块的指定亚型中观察到显着的异质性。我们对异质性进行了进一步分析，重点是对比 Luminal A 和 Basal 样亚型，因为我们的深度学习算法（类似于 PAM50）的分类与这两种亚型之间的生存率显着差异相关。研究发现，患有异质性肿瘤的患者的生存率介于 Luminal A 和 Basal 患者之间，并且激素受体表达模式的水平也更加多样化。结论在这里，我们提出了一种方法，可以最大限度地减少在 H&E 染色 WSI 的所有多尺度斑块中识别富含癌症的斑块所需的手动工作，该方法可以推广到任何适应症。这些结果表明，仅使用常规收集的全切片图像的先进深度机器学习方法可以近似基于 RNA-seq 的分子测试，例如 PAM50，并且重要的是，可能会增加可能需要更详细的亚型分析的异质性肿瘤的检测。