NantOmics LLC, 9920 Jefferson Blvd., Culver City, CA, 90232, USA.
ImmunityBio, 9920 Jefferson Blvd., Culver City, CA, 90232, USA.
Breast Cancer Res. 2020 Jan 28;22(1):12. doi: 10.1186/s13058-020-1248-3.
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.
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.
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.
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.
基于表达的 PAM50 检测法对乳腺癌内在分子亚型（IMS）进行分类，被认为是一个强有力的预后特征，即使在控制了年龄、分级和淋巴结状态等标准临床病理特征后也是如此，但阐明这些亚型所需的分子检测并未常规进行。此外，当进行 RNA 测序等批量检测时，可能影响预后和治疗决策的肿瘤内异质性可能会被忽略。
为了更简便、更便捷地确定乳腺癌的 IMS，我们开发了一种深度学习方法，仅使用 H&E 染色的乳腺癌活检组织切片的全切片图像来近似 PAM50 内在分型。该算法是在先前经过 PAM50 分型的 443 个肿瘤的图像上进行训练的，将小的图像块分类为四个主要的分子亚型——基底样、HER2 富集型、Luminal A 型和 Luminal B 型，以及基底样与非基底样。然后，该算法用于 222 个肿瘤的保留集的亚型分类。
该深度学习图像分类器正确地对保留集肿瘤中的大多数样本进行了亚型分类。然而，在许多情况下，在单个全切片图像内的各个斑块中观察到分配的亚型存在显著异质性。我们进一步分析了异质性，重点关注对比的 Luminal A 型和基底样亚型，因为我们的深度学习算法的分类-类似于 PAM50-与这两种亚型之间的生存差异显著相关。发现被归类为异质性的肿瘤患者的生存情况介于 Luminal A 和基底患者之间，并且激素受体表达模式的变化程度也更大。
在这里，我们提出了一种方法，可以最大限度地减少在 H&E 染色的 WSIs 中识别所有多尺度斑块中富含癌症的斑块所需的人工工作，该方法可以推广到任何适应症。这些结果表明，仅使用常规采集的全切片图像的先进深度学习方法可以近似基于 RNA-seq 的分子检测，如 PAM50，并且重要的是，可能会增加对可能需要更详细亚型分析的异质性肿瘤的检测。
