Nutriovia

Scientists have long studied the ins and outs of cancer cells to find out more about the disease, but they find more and more that non -cancer cells near cancer cells exert a powerful influence on the trajectory of a tumor.

“Not all cells of a tumor are cancer cells – they are not even the most dominant type of cell,” said Sylvia Plevritis, PHD, president of Stanford Medicine’s biomedical data science. “There are many other types of cells that support tumors.”

To better capture all the locations and interactions of the cells, the Plevrite and a team of researchers have developed something they call the “roommate” (pronounced co-Locate-ome). Modeled after the nomenclature which describes other classes of molecules and facets of human biology (collective information on genes is called the genome; proteins, proteo; metabolites, metabolome, etc.) The roommate documents the details of malignant cells on their neighbors – what these cells are and how many them are.

“We have been studying cancer cells for so long, but the image is still incomplete,” said Gina Bouchard, PHD, an instructor of biomedical data science. “Understanding tumor biology does not only concern cancer cells; there is a whole ecosystem that must be studied. Cancer cells need help to survive, resist, prosper and even sometimes die.”

A study describing the results was published in Nature communications last month. Bouchard is the main author and Plevritis is the main author.

Cartography influence

Cancer cells surprisingly depend on their environment. Depending on the location, the type and quantity of non -cancer cells surrounding the tumor, the behavior of cells can change, whether by faster growth, reduced sensitivity to drugs or increased cell metabolism.

“The questions we ask are very simple. We want to know who the neighbors are for each cell. Who likes who? Who does not like who? It is what cells tend to be together, and which are rarely found together,” said Bouchard. Cells that attract each other are described as “roommate” while those that seem to be repelled form “anti-collection”. These roommalizations are then linked to the state of cancer – aggressive, resistant, sensitive to drugs – and recorded in the roommate.

The team developed experimental models of lung cancer in the laboratory, then used artificial intelligence to analyze them, identifying non -cancer cells and how they organized inside and around tumor cells. They then compared the roommalizations with those of the patient’s tumor biopsies. After having mapped hundreds of cellular configurations, they confirmed that the majority of roommalizations in primary patient tumors are observed in experimental models. (This overlap is the key, said Bouchard. This means that models are a precious and precise representation of what is happening in someone who has lung cancer.)

Previous research of plevrite and others has shown strong interactions between fibroblasts and cancer cells, but exactly how fibroblasts interact with cancer cells is not clear. In an experience, the pleavity has shown that the cancer cells of the lung die when they are sprayed with a type of anti-tumor medication that ransives cell growth. But throw fibroblasts in the mixture, and the entire landscape changes – literally. The prontent has mapped the tumor models treated and saw that post-processing, cancer cells and fibroblasts were generally left intact in the same quantity. But they had reorganized.

“This space reorganization seems to have given rise to resistance to drugs,” said Pleurité, Professor William M. Hume at the School of Medicine. “It was like changing the furniture in the room, then finding the outings are blocked.”

Chase new tracks

While the team continues to record the space cards of the tumor treated and untreated, they hope to unlock more configurations that help encourage doctors to know why certain cancers persist after treatment. Ideally, the researchers said that the roommate could provide information that guide the treatment of patient cancer: if a specific colocation gives resistance to a current medication, for example, doctors can look for another who could have a better chance of working. They also hope that the collaboration cards will generate testable hypotheses to describe the aspects of the biology of cancer which remain vague.

As they collect more data, the team plans to use AI to identify specific spatial patterns and create cards catalogs that correspond to different cellular states for a variety of cancers. “Then we can start to see if certain space motifs are shared between the types of cancer, no matter where they come from the body. This could reveal universal rules of tumor behavior and guide the design of more widely effective treatment,” said pleavity. “It’s something that really fascinates me.”

A researcher from the University of Oxford contributed to this research.

This study was funded by the National Institute of Health (subsidies R25CA1CA180993, U54CA274511 and K99CA255586) and the Quebec Reherche Funds.

Stanford’s Biomedical Data Sciences also supported the work.

Scientists have long studied the ins and outs of cancer cells to find out more about the disease, but they find more and more that non -cancer cells near cancer cells exert a powerful influence on the trajectory of a tumor.

“Not all cells of a tumor are cancer cells – they are not even the most dominant type of cell,” said Sylvia Plevritis, PHD, president of Stanford Medicine’s biomedical data science. “There are many other types of cells that support tumors.”

To better capture all the locations and interactions of the cells, the Plevrite and a team of researchers have developed something they call the “roommate” (pronounced co-Locate-ome). Modeled after the nomenclature which describes other classes of molecules and facets of human biology (collective information on genes is called the genome; proteins, proteo; metabolites, metabolome, etc.) The roommate documents the details of malignant cells on their neighbors – what these cells are and how many them are.

“We have been studying cancer cells for so long, but the image is still incomplete,” said Gina Bouchard, PHD, an instructor of biomedical data science. “Understanding tumor biology does not only concern cancer cells; there is a whole ecosystem that must be studied. Cancer cells need help to survive, resist, prosper and even sometimes die.”

A study describing the results was published in Nature communications last month. Bouchard is the main author and Plevritis is the main author.

Cartography influence

Cancer cells surprisingly depend on their environment. Depending on the location, the type and quantity of non -cancer cells surrounding the tumor, the behavior of cells can change, whether by faster growth, reduced sensitivity to drugs or increased cell metabolism.

“The questions we ask are very simple. We want to know who the neighbors are for each cell. Who likes who? Who does not like who? It is what cells tend to be together, and which are rarely found together,” said Bouchard. Cells that attract each other are described as “roommate” while those that seem to be repelled form “anti-collection”. These roommalizations are then linked to the state of cancer – aggressive, resistant, sensitive to drugs – and recorded in the roommate.

The team developed experimental models of lung cancer in the laboratory, then used artificial intelligence to analyze them, identifying non -cancer cells and how they organized inside and around tumor cells. They then compared the roommalizations with those of the patient’s tumor biopsies. After having mapped hundreds of cellular configurations, they confirmed that the majority of roommalizations in primary patient tumors are observed in experimental models. (This overlap is the key, said Bouchard. This means that models are a precious and precise representation of what is happening in someone who has lung cancer.)

Previous research of plevrite and others has shown strong interactions between fibroblasts and cancer cells, but exactly how fibroblasts interact with cancer cells is not clear. In an experience, the pleavity has shown that the cancer cells of the lung die when they are sprayed with a type of anti-tumor medication that ransives cell growth. But throw fibroblasts in the mixture, and the entire landscape changes – literally. The prontent has mapped the tumor models treated and saw that post-processing, cancer cells and fibroblasts were generally left intact in the same quantity. But they had reorganized.

“This space reorganization seems to have given rise to resistance to drugs,” said Pleurité, Professor William M. Hume at the School of Medicine. “It was like changing the furniture in the room, then finding the outings are blocked.”

Chase new tracks

While the team continues to record the space cards of the tumor treated and untreated, they hope to unlock more configurations that help encourage doctors to know why certain cancers persist after treatment. Ideally, the researchers said that the roommate could provide information that guide the treatment of patient cancer: if a specific colocation gives resistance to a current medication, for example, doctors can look for another who could have a better chance of working. They also hope that the collaboration cards will generate testable hypotheses to describe the aspects of the biology of cancer which remain vague.

As they collect more data, the team plans to use AI to identify specific spatial patterns and create cards catalogs that correspond to different cellular states for a variety of cancers. “Then we can start to see if certain space motifs are shared between the types of cancer, no matter where they come from the body. This could reveal universal rules of tumor behavior and guide the design of more widely effective treatment,” said pleavity. “It’s something that really fascinates me.”

A researcher from the University of Oxford contributed to this research.

This study was funded by the National Institute of Health (subsidies R25CA1CA180993, U54CA274511 and K99CA255586) and the Quebec Reherche Funds.

Stanford’s Biomedical Data Sciences also supported the work.

Scientists have long studied the ins and outs of cancer cells to find out more about the disease, but they find more and more that non -cancer cells near cancer cells exert a powerful influence on the trajectory of a tumor.

“Not all cells of a tumor are cancer cells – they are not even the most dominant type of cell,” said Sylvia Plevritis, PHD, president of Stanford Medicine’s biomedical data science. “There are many other types of cells that support tumors.”

To better capture all the locations and interactions of the cells, the Plevrite and a team of researchers have developed something they call the “roommate” (pronounced co-Locate-ome). Modeled after the nomenclature which describes other classes of molecules and facets of human biology (collective information on genes is called the genome; proteins, proteo; metabolites, metabolome, etc.) The roommate documents the details of malignant cells on their neighbors – what these cells are and how many them are.

“We have been studying cancer cells for so long, but the image is still incomplete,” said Gina Bouchard, PHD, an instructor of biomedical data science. “Understanding tumor biology does not only concern cancer cells; there is a whole ecosystem that must be studied. Cancer cells need help to survive, resist, prosper and even sometimes die.”

A study describing the results was published in Nature communications last month. Bouchard is the main author and Plevritis is the main author.

Cartography influence

Cancer cells surprisingly depend on their environment. Depending on the location, the type and quantity of non -cancer cells surrounding the tumor, the behavior of cells can change, whether by faster growth, reduced sensitivity to drugs or increased cell metabolism.

“The questions we ask are very simple. We want to know who the neighbors are for each cell. Who likes who? Who does not like who? It is what cells tend to be together, and which are rarely found together,” said Bouchard. Cells that attract each other are described as “roommate” while those that seem to be repelled form “anti-collection”. These roommalizations are then linked to the state of cancer – aggressive, resistant, sensitive to drugs – and recorded in the roommate.

The team developed experimental models of lung cancer in the laboratory, then used artificial intelligence to analyze them, identifying non -cancer cells and how they organized inside and around tumor cells. They then compared the roommalizations with those of the patient’s tumor biopsies. After having mapped hundreds of cellular configurations, they confirmed that the majority of roommalizations in primary patient tumors are observed in experimental models. (This overlap is the key, said Bouchard. This means that models are a precious and precise representation of what is happening in someone who has lung cancer.)

Previous research of plevrite and others has shown strong interactions between fibroblasts and cancer cells, but exactly how fibroblasts interact with cancer cells is not clear. In an experience, the pleavity has shown that the cancer cells of the lung die when they are sprayed with a type of anti-tumor medication that ransives cell growth. But throw fibroblasts in the mixture, and the entire landscape changes – literally. The prontent has mapped the tumor models treated and saw that post-processing, cancer cells and fibroblasts were generally left intact in the same quantity. But they had reorganized.

“This space reorganization seems to have given rise to resistance to drugs,” said Pleurité, Professor William M. Hume at the School of Medicine. “It was like changing the furniture in the room, then finding the outings are blocked.”

Chase new tracks

While the team continues to record the space cards of the tumor treated and untreated, they hope to unlock more configurations that help encourage doctors to know why certain cancers persist after treatment. Ideally, the researchers said that the roommate could provide information that guide the treatment of patient cancer: if a specific colocation gives resistance to a current medication, for example, doctors can look for another who could have a better chance of working. They also hope that the collaboration cards will generate testable hypotheses to describe the aspects of the biology of cancer which remain vague.

As they collect more data, the team plans to use AI to identify specific spatial patterns and create cards catalogs that correspond to different cellular states for a variety of cancers. “Then we can start to see if certain space motifs are shared between the types of cancer, no matter where they come from the body. This could reveal universal rules of tumor behavior and guide the design of more widely effective treatment,” said pleavity. “It’s something that really fascinates me.”

A researcher from the University of Oxford contributed to this research.

This study was funded by the National Institute of Health (subsidies R25CA1CA180993, U54CA274511 and K99CA255586) and the Quebec Reherche Funds.

Stanford’s Biomedical Data Sciences also supported the work.