New AI Model Offers Cellular-Level View of Cancerous Tumors
Researchers studying cancer unveiled a new AI model that provides cellular-level mapping and visualizations of cancer cells, which scientists hope can shed light on how—and why—certain inter-cellular relationships triggers cancers to grow.
BioTuring
, a San Diego-based startup,
announced
an AI model that can quickly create detailed visualizations of cancerous tumors—at single-cell resolution. This type of granular data shows a cell’s size, shape, which genes are activated, and, crucially, the relative spatial position of millions of different cells within a tissue sample.
Unlike traditional, far slower biological computation methods, the new model provides real-time, high-resolution insights into tumor dynamics and how cancerous and immune cells interact.
“There are around 30 trillion cells in the human body, and if you look at a large tumor biopsy, it has a few million cells,” said Son Pham, BioTuring’s CEO. “The analogy would be—imagine you’re analyzing satellite imagery with a very high resolution, trying to understand how a city works. What our model does, in the context of biology, is show you each house, what’s inside those houses, who’s talking to whom, and what they’re saying.”
“Similarly, our models let you see which cells are talking to which other cells, which groups are forming and talking amongst themselves, and what kind of relationships they’re forming—which can answer some of the most complex challenges in clinical oncological research.”
BioTuring, a member of the
NVIDIA Inception
program for startups, is pursuing its research in single-cell spatial omics, a subfield of biology which examines biological molecules—like messenger RNA and proteins—in their original spatial context in tissue.
Video 1. Demo of BioTuring’s SpatialX deep learning platform for unified multi-technology spatial data analysis.
To create its high-resolution mapping, or “disease cell atlases”, including of ovarian cancer cells, the team used
NVIDIA H100 Tensor Core GPUs
, and
NVIDIA cuBLAS
and
NVIDIA cuSPARSE
libararies to accelerate matrix operations in optimized analyses like the more traditional machine learning algorithm, the
Weighted Gene Co-expression Network Analysis
and CellChat.
Knowing how cancer cells develop and metastasize within a human body—and specifically, within a microenvironment within an organ—could improve screening methods for early cancer detection. Additionally, researchers can use the model’s cellular insights to better understand tumor heterogeneity—or cancerous tumors in the same patient with cells that materially differ from one another.
The new model’s enhanced visual granularity means researchers and drug developers have a far better chance at discovering molecular markers that can more accurately target cancerous cells.
The model can see, for instance, how a person’s killer T cells—humans’ disease-fighting cells—can change shape to engage cancers. By knowing how a person’s immune system morphs to fight a specific cancer, a drug developer could create synthetic therapies that support the patient’s immune system.
“We’re helping uncover biological discoveries that researchers can use to drive therapeutic strategies,” said Rujuta Narurkar, BioTuring’s COO. “Understanding the tumor’s microenvironment through various stages will help map the trajectory of cancer and potentially reveal the source of cancer itself. This new level of cancer tissue resolution has never been possible before. But new technologies are now bringing it within reach.”
新的 AI 模型提供癌症细胞级视图
研究癌症的研究人员推出了一种新的 AI 模型，该模型提供了癌细胞的细胞级映射和可视化，科学家希望该模型能够揭示某些细胞间关系如何以及为什么会引发癌症生长。
位于圣地亚哥的初创公司 BioTuring
宣布推出
一种 AI 模型，该模型可以以单细胞分辨率快速创建癌症肿瘤的详细可视化图像。这种类型的颗粒数据显示了细胞的大小、形状、哪些基因被激活，关键是显示了组织样本中数百万个不同细胞的相对空间位置。
与速度慢得多的传统生物计算方法不同，新模型提供了有关肿瘤动力学以及癌细胞和免疫细胞相互作用的实时高分辨率见解。
BioTuring 首席执行官 Son Pham 表示：“人体大约有 30 万亿个细胞，如果进行大规模的肿瘤活检，就会发现有几百万个细胞。“类比是 – 想象一下，您正在以非常高分辨率分析卫星图像，试图了解城市的运作方式。在生物学领域，我们的模型所做的是向您展示每栋房子、这些房子里的东西、谁在和谁说话，以及他们在说什么。”
“同样，我们的模型可让您了解哪些细胞正在与哪些其他细胞对话，哪些细胞群正在形成并相互交流，以及它们正在形成何种关系，从而应对临床肿瘤研究中一些非常复杂的挑战。”
BioTuring 是
NVIDIA Inception
计划的成员，正在开展单细胞空间组学的研究。单细胞空间组学是生物学的一个分支领域，用于在组织中的原始空间环境中检查生物分子 (例如信使 RNA 和蛋白质)。
1
BioTuring SpatialX
为了创建高分辨率映射 (或“疾病细胞图谱”) (包括卵巢癌细胞)，该团队使用
NVIDIA H100 Tensor Core GPUs
，以及
NVIDIA cuBLAS
和
NVIDIA cuSPARSE
库来加速优化分析中的矩阵运算，例如更传统的机器学习算法、
Weighted Gene Co-expression Network Analysis
和 CellChat。
了解癌细胞在人体内部（尤其是在器官内的微环境中）的发展和转移情况，可以改进癌症早期检测的筛查方法。此外，研究人员可以利用该模型的细胞见解来更好地了解肿瘤异构性，或了解同一患者的恶性肿瘤，其细胞之间存在重大差异。
新模型增强的视觉粒度意味着研究人员和药物开发者有更大的机会发现能够更准确地向癌细胞的分子标记物。
例如，该模型可以看到一个人的杀手 T 细胞（即人类的抗病细胞）如何改变形状以致癌。通过了解人体的免疫系统如何演变以对抗特定癌症，药物开发者可以开发出支持患者免疫系统的合成疗法。
BioTuring 的首席运营官 Rujuta Narurkar 说：“我们正在帮助发现生物学发现，研究人员可以利用这些发现来推动治疗策略。“通过各个阶段了解肿瘤的微环境有助于绘制出癌症的轨迹，并可能揭示癌症本身的来源。这种更高水平的癌症组织分辨率在以前是不可能实现的。但现在，新技术使其触手可及。”