By Hanae Armitage

由Hanae Armitage

For any tumor that’s accessible by scalpel, physicians often take what’s called a biopsy, snipping away a tiny portion of the mass.

对于手术刀可触及的任何肿瘤，医生通常会进行所谓的活检，即切除一小部分肿块。

From that scrap, doctors glean as much information about the tumor as they can through two distinct methods. Imaging captures the physical likeness of the tumor cells, which can help scientists understand disease severity, among other things. And through molecular analysis, scientists can analyze the tissue to find drivers of disease on the cellular level.

医生可以通过两种截然不同的方法从这些废料中收集有关肿瘤的尽可能多的信息。 成像可以捕获肿瘤细胞的物理相似性，这可以帮助科学家了解疾病的严重程度。 通过分子分析，科学家可以分析组织以在细胞水平上寻找疾病的驱动因素。

These two modes of investigation are usually conducted separately, but James Zou, PhD, an assistant professor of biomedical data science at Stanford, has laid the groundwork for doctors to learn a tumor’s spatial and genomic properties simultaneously.

这两种调查模式通常是分开进行的，但斯坦福大学生物医学数据科学助理教授James Zou博士为医生同时学习肿瘤的空间和基因组特性奠定了基础。

“We thought, ‘How can we use machine learning to combine both of these, so that we make the most out of these biopsies?’” Zou told me.

“我们以为，'我们如何利用机器学习将两者结合起来，以便从这些活检中获得最大收益？'”邹告诉我。

“病理学” (“Insta-pathology”)

He and his team, led by graduate student Bryan He, have created an algorithm for a concept he’s dubbed “insta-pathology.” In an image of an individual tumor, a computer recognizes and labels the likely genomic activity of groups of cells based on their appearance.

由研究生Bryan He领导的他和他的团队为他被称为“ insta-pathology”的概念创建了一种算法。 在单个肿瘤的图像中，计算机根据其外观识别并标记细胞组可能的基因组活性。

Zou likens this idea to an Instagram filter. But instead of overlaying a photo with a location tag or puppy dog features, the “filter” adds information about which genes are turned on — and where — in a biopsy image.

Zou将此想法比作Instagram过滤器。 但是，“过滤器”不是在照片上贴上位置标签或小狗的特征，而是在活检图像中添加有关打开哪些基因以及在何处打开基因的信息。

So for instance, on an image with 1,000 cancer cells, the algorithm would annotate the photo based on the shapes, sizes and density of the cells, labeling regions to indicate which genes are likely causing specific physical characteristics of groups of cells. For example, cells with a smaller-than-normal nucleus are linked to activation of the gene AEBP1.

因此，例如，在具有1,000个癌细胞的图像上，该算法将基于细胞的形状，大小和密度对照片进行注释，并标记区域以指示哪些基因可能引起细胞组的特定物理特征。 例如，具有比正常细胞核小的细胞的细胞与基因AEBP1的激活相关 。

A paper describing the study was recently published in Nature Biomedical Engineering. Joakim Lundeberg, PhD, a pioneer of spatial genomics at the KTH Royal Institute of Technology, Stockholm, Sweden, co-led the research.

描述该研究的论文最近发表在《 自然生物医学工程》上 。 瑞典斯德哥尔摩KTH皇家技术学院空间基因组学的先驱Joakim Lundeberg博士共同领导了这项研究。

教学算法 (Teaching the algorithm)

In developing the algorithm, Zou and his team used a dataset of breast cancer cells already labeled with genomic information to “teach” their new algorithm what genes encode specific morphologic features. After many training iterations, the algorithm learned that certain shapes or physical features correspond to a gene or sets of genes.

在开发算法时，Zou和他的团队使用了已经用基因组信息标记的乳腺癌细胞数据集，以“教导”他们的新算法，哪些基因编码了特定的形态特征。 经过多次训练迭代后，该算法得知某些形状或物理特征对应于一个基因或一组基因。

This information can be extremely helpful for clinicians as they seek to understand a patient’s cancer, Zou said.

邹说，这些信息对于临床医生寻求了解患者的癌症非常有用。

“The different morphologies and the architecture of cells, and how those actually are related with expression changes, is valuable for understanding how cancer cells interact with its surroundings, which can tell doctors how aggressive the cancer is, or help them decide what drugs to prescribe,” he told me.

“不同的细胞形态和结构，以及它们实际上与表达变化之间的关系，对于理解癌细胞如何与其周围环境相互作用非常重要，它可以告诉医生癌症的侵袭性如何，或帮助他们决定开哪些药，“ 他告诉我。

It’s also important for understanding the immune response to cancer, as the image and algorithm can provide data about the amount of immune cells likely infiltrating the tumor, exactly where they go, and the immune-modulating genes that are active.

这对于理解对癌症的免疫React也很重要，因为图像和算法可以提供有关可能渗入肿瘤的免疫细胞数量，确切的去向以及活跃的免疫调节基因的数据。

更快，更容易获得的信息 (Faster, more accessible information)

Technically, there are other ways to obtain this sort of spatial genomic information, but it’s far more time-intensive and expensive, said Zou, as other techniques require scientists to painstakingly parse the genomic activity in single cells. Using an algorithm to “read” the cellular morphologies in a sample of tumor tissue would ideally provide doctors with faster, more accessible information to understand patients’ cancers and treat them.

Zou说，从技术上讲，还有其他方法可以获取这种空间基因组信息，但是这要花费大量的时间和金钱，因为其他技术需要科学家努力地分析单细胞中的基因组活性。 使用一种算法来“读取”肿瘤组织样本中的细胞形态，理想地将为医生提供更快，更容易获得的信息，以了解患者的癌症并进行治疗。

The algorithm is still early in its development, said Zou, but the team is excited at the prospects of how it could be used in the clinic. When tested on a preliminary dataset (none of which were used to train the algorithm), the algorithm accurately labeled 102 genes for at least 20 of 23 patients.

Zou说，该算法仍处于开发初期，但研究团队对如何将其用于临床前景感到兴奋。 在初步数据集(没有一个用于训练该算法)上进行测试时，该算法为23位患者中的至少20位准确标记了102个基因。

In the future, Zou sees this technique as broadly applicable to many types of cancer. What’s more, he hopes that, beyond accurately showing which cancer-causing genes are active in a tumor sample, the algorithm will also help scientists discover new genes that are contributing to the disease by providing information about the genes active in a given cancer sample.

将来，邹认为这项技术广泛适用于多种类型的癌症。 此外，他希望，除了准确显示出肿瘤样本中哪些致癌基因活跃之外，该算法还将通过提供有关特定癌症样本中活跃基因的信息，帮助科学家发现导致该疾病的新基因。

“Ultimately, our goal is to have this become a computer filter that clinicians can use to get spatial genomics data in real time,” said Zou.

“最终，我们的目标是使它成为一种计算机过滤器，临床医生可以用来实时获取空间基因组学数据，” Zou说。

Photo by James Zou

摄影：James Zou

Originally published at https://scopeblog.stanford.edu on July 7, 2020.

最初于 2020年7月7日 发布在 https://scopeblog.stanford.edu 。