This entry is part of 16 in the series Grand Challenge
To beat cancer we have to be bold and tackle the toughest questions.
Two years ago we put some of those questions to researchers in the form of wide-ranging Grand Challenges. The response was amazing, and we’re now funding 4 international teams with more than £70 million over the next 5 years.
But we can’t stand still and wait to see what they find. We’ve made great progress against cancer but there’s still so much we don’t know.
So while our teams and others inspired by the first set of Grand Challenges are sinking their teeth into them, we’re now on the hunt for answers to a new collection.
Today we’re launching phase two of our global Grand Challenge. And these are the questions we’re putting to researchers to see if they can answer them.
Can we unpick exactly how immunotherapies work so more people can benefit?
The body’s immune system is a powerful force that is constantly fighting disease to keep us healthy. But cancers often work out a way to get around it.
Treatments that reveal cancer to the immune system so that it can recognise, target and kill cancer cells have been showing promise. But they don’t work in all patients, or against all cancers.
That’s because right now we don’t completely understand how the many different parts of the immune system work together, or how they interact with a tumour.
This Grand Challenge aims to improve our understanding of the immune system and its role in cancer so that new immunotherapies can be developed, and those already available can be used more effectively.
This Grand Challenge is to: create novel tumour vaccinology approaches that establish or enhance successful immune responses beyond what is revealed by current checkpoint therapy.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
Can we work out why certain faulty genes only cause cancer in specific parts of the body?
Different mistakes in DNA can cause different types of cancer. Faulty versions of the BRCA1 or BRCA2 genes are well-known for their links to breast cancer. And mistakes in the APC gene can cause bowel cancer, for example.
What scientists don’t know is why these errors cause cancer in those specific organs and not others. The faulty genes can be found in cells throughout the body, so this Grand Challenge aims to find out why only certain tissues are affected.
If scientists can figure out how certain cancer genes cause particular types of cancer, they may be able to find ways to prevent these cancers, and new ways to treat them.
This Grand Challenge is to: devise approaches to prevent or treat cancer based on mechanisms that determine tissue specificity of some cancer genes.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
Can we set rules for how best to combine treatments?
Credit: Flickr/limowreck666 via CC-BY-NC-ND 2.0
While some cancer cells are killed by treatment, others can survive. This is why some cancers can come back. The cancer cells that aren’t destroyed can continue to grow, forming a tumour again.
To get around this, doctors often give different treatments in combination. But it’s not always clear which combination is best, or how much of each treatment to give, or when the different treatments should be given.
We need to find clear answers to those questions so we can improve our understanding of combination treatments. If we better understand how treatments work together, we can make sure patients get the best combinations for their disease. And we might be able to predict how a patient’s cancer will respond to particular treatments in advance, so we can tailor the approach and stay one step ahead of the disease.
This Grand Challenge is to: define mechanistic rules for combinatorial treatments to overcome resistance and avoid toxicity.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
Can we work out which cancers are potentially lethal and need treatment and which aren’t?
Image by Pfree2014 via Wikimedia Commons under CC-BY-4.0
Not all cancers are created equal, even among people with the same type of cancer. In some cases a tumour can be slow-growing, meaning if it went undetected it would never cause the person any harm. But in others the same type of cancer can be fast-growing, meaning it should be treated immediately.
The problem is that right now, doctors can’t always tell the difference between cancers that need treating and cancers that don’t.
We want to find a way to tell these tumours apart. This will help doctors identify potentially life-threatening cancers earlier, helping them decide which patients need treatment and which patients can be spared it.
This Grand Challenge is to: distinguish between lethal cancers which need treating, and non-lethal cancers that don’t.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
Can we work out how some cancers come back a long time after being treated?
Sometimes, patients who seem to have been successfully treated for cancer can have the disease come back years or even decades later, often without any warning.
It’s believed that the cancer cells that weren’t killed by initial treatment can go to sleep, lying dormant before a new chance to grow kicks in. But what causes some cancer cells to go sleep, or where they hide when they’re asleep, is unknown. Scientists also don’t know for sure what wakes these cells up years later.
We need to shed light on these unknowns. This could lead to new ways to find these cells and eliminate them. Or, if we could predict when they’re about to rise from their slumber, keep them sleeping permanently.
This Grand Challenge is to: Identify and target tumour cells that remain dormant for many years after seemingly effective treatment.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
Can we use data and artificial intelligence to detect cancer earlier?
When cancer is detected early, treatment is more likely to be successful. But too often, cancers are diagnosed at a late stage when they’re much harder to treat.
If there were unknown hidden clues in people’s lives that point to cancer, could we find a way to gather this information and help detect cancer earlier?
We’re generating, collecting and sharing more information than ever before, and this includes information that may be tied to our health. Security and privacy are a priority as technology and personal data are combined, but what if there was a way to link this information and improve cancer detection?
If we could use information in this way, who would do it? And how? Could doctors use hints from prescription records, online searches or social media activity to detect cancer earlier?
All of these questions need exploring, and we want to understand the possibility of examining medical and non-medical databases to spot patterns that could point to cancer. If it works, this could eventually lead to new ways to detect cancer earlier.
This Grand Challenge is to: detect cancer earlier by interrogating medical and non-medical data sets using machine and deep-learning.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
Can we use the body’s microbes to improve cancer treatment?
Every part of your body is home to millions of different microorganisms. Together, they form a community called a microbiota, which differ from organ to organ and person to person.
These microbiota have important roles in maintaining people’s health and in the development of different diseases – including cancer.
Scientists have shown that these microbes can play a role in how cancers develop, by damaging DNA or altering how the immune system responds to cancer cells. They’ve also shown that the microbiota might affect how patients respond to certain treatments.
But it’s not clear exactly how these microbes do this. If we can better understand the mechanisms by which microbiota affect how a patient responds to treatment, we might be able to control this and improve treatments so more patients benefit.
This Grand Challenge is to: improve treatment responses by manipulating the composition and status of the microbiota.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
Can we work out how lifestyle factors, like obesity, cause cancer?
Being overweight or obese is linked to 13 types of cancer. But unlike some other causes of cancer, we’re still in the dark about exactly how excess bodyweight causes these diseases. Similarly, we know that being physically active can reduce the risk of developing cancer, but we don’t fully understand the mechanics of why this is.
If scientists and doctors can fully understand how obesity and other lifestyle factors cause different types of cancer, we could help prevent, diagnose and treat more of these cases in the future.
This Grand Challenge is to: determine the mechanisms that cause cancer without known mutagenesis, such as obesity, in order to devise novel interventions.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
These are the questions that make up the next stage of our Grand Challenge. The search for answers begins now.
Michael and Aine
You can find out more about our current Grand Challenge teams in our blog post series.
from Cancer Research UK – Science blog http://ift.tt/2rTqkmp
This entry is part of 16 in the series Grand Challenge
To beat cancer we have to be bold and tackle the toughest questions.
Two years ago we put some of those questions to researchers in the form of wide-ranging Grand Challenges. The response was amazing, and we’re now funding 4 international teams with more than £70 million over the next 5 years.
But we can’t stand still and wait to see what they find. We’ve made great progress against cancer but there’s still so much we don’t know.
So while our teams and others inspired by the first set of Grand Challenges are sinking their teeth into them, we’re now on the hunt for answers to a new collection.
Today we’re launching phase two of our global Grand Challenge. And these are the questions we’re putting to researchers to see if they can answer them.
Can we unpick exactly how immunotherapies work so more people can benefit?
The body’s immune system is a powerful force that is constantly fighting disease to keep us healthy. But cancers often work out a way to get around it.
Treatments that reveal cancer to the immune system so that it can recognise, target and kill cancer cells have been showing promise. But they don’t work in all patients, or against all cancers.
That’s because right now we don’t completely understand how the many different parts of the immune system work together, or how they interact with a tumour.
This Grand Challenge aims to improve our understanding of the immune system and its role in cancer so that new immunotherapies can be developed, and those already available can be used more effectively.
This Grand Challenge is to: create novel tumour vaccinology approaches that establish or enhance successful immune responses beyond what is revealed by current checkpoint therapy.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
Can we work out why certain faulty genes only cause cancer in specific parts of the body?
Different mistakes in DNA can cause different types of cancer. Faulty versions of the BRCA1 or BRCA2 genes are well-known for their links to breast cancer. And mistakes in the APC gene can cause bowel cancer, for example.
What scientists don’t know is why these errors cause cancer in those specific organs and not others. The faulty genes can be found in cells throughout the body, so this Grand Challenge aims to find out why only certain tissues are affected.
If scientists can figure out how certain cancer genes cause particular types of cancer, they may be able to find ways to prevent these cancers, and new ways to treat them.
This Grand Challenge is to: devise approaches to prevent or treat cancer based on mechanisms that determine tissue specificity of some cancer genes.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
Can we set rules for how best to combine treatments?
Credit: Flickr/limowreck666 via CC-BY-NC-ND 2.0
While some cancer cells are killed by treatment, others can survive. This is why some cancers can come back. The cancer cells that aren’t destroyed can continue to grow, forming a tumour again.
To get around this, doctors often give different treatments in combination. But it’s not always clear which combination is best, or how much of each treatment to give, or when the different treatments should be given.
We need to find clear answers to those questions so we can improve our understanding of combination treatments. If we better understand how treatments work together, we can make sure patients get the best combinations for their disease. And we might be able to predict how a patient’s cancer will respond to particular treatments in advance, so we can tailor the approach and stay one step ahead of the disease.
This Grand Challenge is to: define mechanistic rules for combinatorial treatments to overcome resistance and avoid toxicity.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
Can we work out which cancers are potentially lethal and need treatment and which aren’t?
Image by Pfree2014 via Wikimedia Commons under CC-BY-4.0
Not all cancers are created equal, even among people with the same type of cancer. In some cases a tumour can be slow-growing, meaning if it went undetected it would never cause the person any harm. But in others the same type of cancer can be fast-growing, meaning it should be treated immediately.
The problem is that right now, doctors can’t always tell the difference between cancers that need treating and cancers that don’t.
We want to find a way to tell these tumours apart. This will help doctors identify potentially life-threatening cancers earlier, helping them decide which patients need treatment and which patients can be spared it.
This Grand Challenge is to: distinguish between lethal cancers which need treating, and non-lethal cancers that don’t.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
Can we work out how some cancers come back a long time after being treated?
Sometimes, patients who seem to have been successfully treated for cancer can have the disease come back years or even decades later, often without any warning.
It’s believed that the cancer cells that weren’t killed by initial treatment can go to sleep, lying dormant before a new chance to grow kicks in. But what causes some cancer cells to go sleep, or where they hide when they’re asleep, is unknown. Scientists also don’t know for sure what wakes these cells up years later.
We need to shed light on these unknowns. This could lead to new ways to find these cells and eliminate them. Or, if we could predict when they’re about to rise from their slumber, keep them sleeping permanently.
This Grand Challenge is to: Identify and target tumour cells that remain dormant for many years after seemingly effective treatment.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
Can we use data and artificial intelligence to detect cancer earlier?
When cancer is detected early, treatment is more likely to be successful. But too often, cancers are diagnosed at a late stage when they’re much harder to treat.
If there were unknown hidden clues in people’s lives that point to cancer, could we find a way to gather this information and help detect cancer earlier?
We’re generating, collecting and sharing more information than ever before, and this includes information that may be tied to our health. Security and privacy are a priority as technology and personal data are combined, but what if there was a way to link this information and improve cancer detection?
If we could use information in this way, who would do it? And how? Could doctors use hints from prescription records, online searches or social media activity to detect cancer earlier?
All of these questions need exploring, and we want to understand the possibility of examining medical and non-medical databases to spot patterns that could point to cancer. If it works, this could eventually lead to new ways to detect cancer earlier.
This Grand Challenge is to: detect cancer earlier by interrogating medical and non-medical data sets using machine and deep-learning.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
Can we use the body’s microbes to improve cancer treatment?
Every part of your body is home to millions of different microorganisms. Together, they form a community called a microbiota, which differ from organ to organ and person to person.
These microbiota have important roles in maintaining people’s health and in the development of different diseases – including cancer.
Scientists have shown that these microbes can play a role in how cancers develop, by damaging DNA or altering how the immune system responds to cancer cells. They’ve also shown that the microbiota might affect how patients respond to certain treatments.
But it’s not clear exactly how these microbes do this. If we can better understand the mechanisms by which microbiota affect how a patient responds to treatment, we might be able to control this and improve treatments so more patients benefit.
This Grand Challenge is to: improve treatment responses by manipulating the composition and status of the microbiota.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
Can we work out how lifestyle factors, like obesity, cause cancer?
Being overweight or obese is linked to 13 types of cancer. But unlike some other causes of cancer, we’re still in the dark about exactly how excess bodyweight causes these diseases. Similarly, we know that being physically active can reduce the risk of developing cancer, but we don’t fully understand the mechanics of why this is.
If scientists and doctors can fully understand how obesity and other lifestyle factors cause different types of cancer, we could help prevent, diagnose and treat more of these cases in the future.
This Grand Challenge is to: determine the mechanisms that cause cancer without known mutagenesis, such as obesity, in order to devise novel interventions.
If you’re a researcher and want to build a team to take on this challenge, visit our website to find out how you can apply.
These are the questions that make up the next stage of our Grand Challenge. The search for answers begins now.
Michael and Aine
You can find out more about our current Grand Challenge teams in our blog post series.
from Cancer Research UK – Science blog http://ift.tt/2rTqkmp
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