ABOVE: Researchers created detailed 3D maps of various types of human tumors. These tumor atlases could inform the development of targeted therapies for difficult-to-treat cancers. ©ISTOCK, koto_feja

Since the first surgical tumor excision in 1882, scientists have made strides in the development of new techniques and therapies to treat different types of tumors.1 Today, physicians can bombard these unwanted masses of cells with surgery, radiation, chemotherapy, immunotherapy, and more. Still, some cancer cells persist. An incomplete picture of how a tumor initiates, grows, and spreads to other organs limits the efficacy of these treatments. 

In the past decade, cancer research has benefited greatly from new multiomics approaches for characterizing tumors, including single-cell RNA sequencing, spatial transcriptomics, and proteomics.2 In 2018, the Human Tumor Atlas Network (HTAN) was launched to elucidate the structural, cellular and molecular processes driving cancer development and progression. Today (October 30), members of the HTAN released their latest findings in a collection of 12 papers published in Nature Portfolio journals.3,5-14 

The authors created detailed 3D renderings of tumors from more than 2,000 individuals, with breast, colon, pancreatic, kidney and uterine cancers, among others. From these detailed atlases, the researchers extracted information about tumor structure, cellular composition, and the molecular and genetic factors driving their formation.3 The research also provides new approaches for analyzing these rich sources of data, which could inform the development of novel treatments that target treatment-resistant tumors.

“These 3D maps of tumors are important because they finally let us see what, until now, we have only been able to infer about tumor structures and their complexity,” said Li Ding, a computational biologist at the Washington University School of Medicine and coauthor of one the papers, in a statement. “We now have the ability to see how regions of the tumor differ in 3D space and how the behavior changes in response to therapy or when the tumor spreads to other organs. These studies have opened a new era in cancer research with the potential to transform the way we understand and treat cancer in the future.”

The 3D maps led to several new discoveries, including some that overturn previous theories of cancer evolution. Previously, researchers thought that colorectal tumors develop from a single cell in the intestinal wall.4 Now, using a retrospective analysis that tracks specific, irreversible genetic changes associated with different stages of cancer, HTAN members showed that 15 to 30 percent of precancerous lesions originate from multiple mutant cells.5 These colorectal tumors grew much faster than those originating from single cells.6 The team followed the evolution of these tumors and observed an increase in the levels of proteins that help cells stick together at the early stages of cancer development, suggesting that the cells cooperate to promote tumor growth.7 In the future, researchers could develop treatments that target this cellular crosstalk and eliminate these aggressive tumors. 

A team of scientists at the Stanford School of Medicine also did a deep dive into colorectal tumors occurring in people with Familial adenomatous polyposis (FAP), a genetic disease that causes hundreds of precancerous polyps in the intestines.8 When they compared how transcription, metabolism, protein expression, and lipid levels differed between the cells from unaffected tissue, benign polyps, and cancerous polyps they observed extensive alterations in the cancerous cells. They also noted increased activity of the arachidonic acid signaling pathway—a target for aspirin, which is used as an experimental preventive treatment for individuals affected by FAP. These observations support the continued use of this prophylactic treatment.

The new data revealed that the analyzed tumors could be divided into different “neighborhoods” based on the genetic makeup of the cells residing in different regions. These neighborhoods can have “hot” and “cold” regions based on the presence of immune cells. Furthermore, the data suggests that tumors that develop resistance to once-effective treatments have “cold” regions that lack immune cell infiltration. The methods developed by the HTAN can help others identify these regions in their own samples.

The large collection of papers also showed that an active metabolic center was common across all the tumors studied. “This understanding of 3D cancer metabolism will affect how our current treatments work, and sometimes don’t work, and will lead to development of novel treatments in cancer,” said Ryan Fields, a surgical oncologist at the Washington University School of Medicine and coauthor of one the papers, in a statement. “It really is transformative.”

Researchers can use the techniques developed in these studies to tease apart the formation of other tumor types. The data included in these 3D maps provides new opportunities to understand how different cell populations in the tumor environment contribute to the evolution of cancer. The consortium hopes that a detailed temporal and spatial understanding of tumors will one day lead advances in cancer prognosis and treatment. 

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  3. Mo C, et al. Tumour evolution and microenvironment interactions in 2D and 3D space. Nature. 2024
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  9. Klughammer J, et al. A multi-modal single-cell and spatial expression map of metastatic breast cancer biopsies across clinicopathological features. Nat Med. 2024
  10. Iglesia MD, et al. Differential chromatin accessibility and transcriptional dynamics define breast cancer subtypes and their lineages. Nat Cancer. 2024
  11. Zhu Y, et al. Global loss of promoter–enhancer connectivity and rebalancing of gene expression during early colorectal cancer carcinogenesis. Nat Cancer. 2024
  12. Kaur H, et al. Consensus tissue domain detection in spatial omics data using multiplex image labeling with regional morphology (MILWRM). Commun Biol. 2024
  13. Baker GJ, et al. Quality control for single-cell analysis of high-plex tissue profiles using CyLinter. Nat Methods. 2024
  14. Ma C, et al. Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics. Nat Methods. 2024