Why Do Certain Chemotherapies Increase the Likelihood of Blood Cancer?

Source: Memorial Sloan Kettering - On Cancer
Date: 10/26/2020
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In recent years, improvements in cancer therapy have led to a significant increase in cancer survivorship. Experts estimate that by 2022, the United States will have 18 million cancer survivors, but a subset of those survivors will have long-term health problems to be addressed.

One rare complication of cancer treatment is the development of a secondary blood cancer — therapy-related acute myeloid leukemia or myelodysplastic syndrome. These blood cancers are very aggressive and do not respond well to treatment. Historically, doctors thought that cancer treatments such as chemotherapy and radiation caused an accumulation of mutations in the blood that led to these therapy-related cancers.

In recent years, however, researchers have found that these mutations in the blood can also occur spontaneously with increasing age. This phenomenon is called clonal hematopoiesis (CH), and it’s found in 10 to 20% of all people over age 70. The presence of CH increases the risk of developing a blood cancer. Using data from MSK-IMPACTTM, Memorial Sloan Kettering’s clinical genomic sequencing test, researchers have shown that CH is also frequent in cancer patients.

In a study published in Nature Genetics on October 26, 2020, MSK investigators sought to understand the relationship between CH in cancer patients and the risk of later developing a treatment-related blood cancer. The study included data from 24,000 people treated at MSK. The researchers found CH in about one-third of them.

“Because many people treated at MSK have genetic testing done using MSK-IMPACT, we have this amazing resource that allows us to study CH in cancer patients at a scope that nobody else has been able to do,” says physician-scientist Kelly Bolton, lead author of the study.

Decoding Genetic Changes Specific to Cancer Treatment

Focusing on a subset of patients on whom they had more detailed data, the investigators observed increased rates of CH in people who had already received treatment. They made specific connections between cancer therapies such as radiation therapy and particular chemotherapies — for example certain platinum drugs or agents called topoisomerase II inhibitors — and the presence of CH.

Unlike the CH changes found in the general population, the team found that CH mutations after cancer treatment occur most frequently in the genes whose protein products protect the genome from damage. One of these genes is TP53which is frequently referred to as “the guardian of the genome.”

The work was supported by the Precision Interception and Prevention (PIP) program at MSK, a multidisciplinary research program focused on identifying people who have the highest risk for developing cancer and improving methods for screening, early detection, and risk assessment.

The authors embarked on a three-year study to understand the relationship between CH and cancer therapy. For this part of the research, more than 500 people were screened for CH when they first came to MSK and then at a later point during their treatment. One finding from the study was that people with pre-existing CH whose blood carried mutations related to DNA damage repair such as TP53, were more likely to have those mutations grow after receiving cancer therapies, when compared to people who did not receive treatment.

“This finding provides a direct link between mutation type, specific therapies, and how these cells progress towards becoming a blood cancer,” says Elli Papaemmanuil of MSK’s Center for Computational Oncology, one of the two senior authors of the study. “Our hope is that this research will help us to understand the implications of having CH, and to begin to develop models that predict who with CH is at higher risk for developing a blood cancer.”

For a subset of patients with CH who developed therapy-related blood cancers, the researchers showed that blood cells acquired further mutations with time and progressed to leukemia. “We are now routinely screening our patients for the presence of CH mutations,” adds computational biologist Ahmet Zehir, Director of Clinical Bioinformatics and the study’s co-senior author. “The ability to introduce real-time CH screening for our patient population has allowed us to establish a clinic dedicated to caring for cancer patients with CH. As we continue to study more patients in the clinic, we expect to learn more about how to use these findings to find ways to detect treatment-related blood cancers early when they may be more treatable.”

Applying Findings to Future Treatments

In the future, this research may help to guide therapy by indicating whether some chemotherapy drugs are more appropriate than others in people with CH. People who are at a high risk of developing a treatment-related leukemia also may benefit from a different treatment schedule. “We hope that this research will allow us to ultimately map which CH mutations a person has and use that information to tailor their primary care and also mitigate the long-term risk of developing blood cancer,” Dr. Papaemmanuil says.

“We explored this in collaboration with investigators from the National Cancer Institute, Dana-Farber Cancer Institute, Moffit Cancer Center, and MD Anderson, and showed that such risk-adapted treatment decisions could achieve significant reduction of leukemia risk, without affecting outcomes for the primary cancer,” Dr. Bolton adds.

The investigators also hope to use the data from this study to develop better methods for detecting CH-related blood cancers when they first begin to form — and potentially to develop new interventions that could prevent CH from ever progressing to cancer. “We’re excited about the idea of continuing to grow and expand the CH clinic as part of the integrated vision of PIP,” says physician-scientist Ross Levine, who leads MSK’s CH clinic and is a member of the Human Oncology and Pathogenesis Program.

“In addition to continuing to follow people who are at the highest risk of developing a secondary cancer, we want to continue to use the clinic as a vehicle for studies like this,” he adds. “Our long-term goal is to move toward therapeutic interventions and preventing disease in a way that we’ve never been able to do before.”

One by One: Single-Cell Analysis Helps Map the Cancer Landscape

Source: Memorial Sloan Kettering - On Cancer
Date: 06/28/2018
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The composition of malignant tumors is incredibly complex. They contain not only cancer cells but also dozens of other cell types, such as supportive tissues, fat, and many kinds of immune cells. These other cells interact with the cancer cells and one another to influence how tumors behave.

To get to the bottom of what drives a tumor’s growth and to find ways to stop it, scientists need to be able to figure out what all these types of cells are and figure out how they work together. Two new studies from Sloan Kettering Institute investigators published today in Cell represent important steps in that direction. One classified the different kinds of immune cells found in breast cancer tumors. It was the largest study of its kind so far. The other took a more fundamental tack. It established a new mathematical framework for extracting as much information as possible from a tumor’s makeup.

“These studies are focused on efforts to look at tumors at the level of each individual cell,” says Dana Pe’er, Chair of SKI’s Computational and Systems Biology Program and senior author of both papers. “Without going down to the level of single cells, we aren’t fully able to understand what’s going on and what is driving a particular cancer.”

A Growing Field Seeks to Map Cancer

The field of single-cell analysis has expanded greatly in recent years. This is due in large part to rapid technological advances. Mathematical and computational techniques now make it possible to sort the huge quantities of data that are generated by these analyses.  

The Human Cell Atlas brings together investigators from all over the world to create a comprehensive reference map of all human cells. Dr. Pe’er, who co-chairs one of this project’s analysis working groups, says the effort has the potential to impact the understanding of many diseases, not just cancer. Collaborative endeavors like this, she notes, can answer fundamental questions about human development.

One of the primary tools in this field is a genomic analysis technique called single-cell ribonucleic acid sequencing (RNA-seq). This system looks at RNA rather than DNA. It enables investigators to determine which genes are expressed, or “turned on,” in particular cells, rather than just which genes are present in the DNA. Because every cell in the body contains the same DNA, RNA analysis provides much-needed detail about cell function and activity.

Characterizing the Immune Landscape of Breast Cancer

One of the new papers is a collaboration among SKI computational biologists and immunologists as well as Memorial Sloan Kettering’s breast cancer team. The study looked at cells taken from human breast tumors. The team also considered normal breast tissue, blood, and lymph node tissue. The investigators analyzed 45,000 immune cells taken from eight tumors and 27,000 additional immune cells.

Identifying such a large number of immune cells could explain why immunotherapy doesn’t always work the same way in each person. Immunotherapy harnesses the power of the body’s natural immune response to fight cancer. Some types of immune cells attack cancer, while others protect tumor cells from harm.

“One of our major findings was that there was a much greater diversity in the states of immune cells found in tumors compared with what we found in normal tissues,” says Alexander Rudensky, Chair of SKI’s Immunology Program and co-senior author of the breast cancer study. A cell’s state is based on not only what type of cell it is but also other factors, such as its location, size, and structure.

“We were surprised to find that, rather than distinct differences between tumor and nontumor tissue, there was a gradient in the levels of different immune cell states,” he says.

In other words, they found a range of differences in the immune makeup of these tissues, not a clear line between the immune cells present in cancer and normal tissue. “This helps explain why tumors have a range of behaviors — not all tumors respond in the same way to immunotherapy,” adds Dr. Rudensky, who is also a Howard Hughes Medical Institute investigator. “But at the same time, we saw common features among the breast cancer samples that were not seen in the normal tissues. From this work, we can start to think about how to develop immunotherapy that’s custom-built for people based on their individual tumor microenvironment.”

Dr. Rudensky explains that the focus on breast cancer was only a starting point to see if the approach would work. The researchers have plans to expand this research to many other types of cancer. “Until very recently, analyzing the data from thousands of cells at the same time would have been a major undertaking,” he says. “But thanks to the transformative work that’s been done by Dr. Pe’er’s team, we can start to grow this area of immunology research.”

Uncovering Hidden Data with MAGIC

The other Cell paper concentrates on identifying the differences among cancer cells themselves, rather than looking at immune cells in their midst. A culmination of five years of work from Dr. Pe’er, the study focuses on cutting-edge ways to reduce the high levels of noise and highlight the biological trends that come from such large quantities of data.

Dr. Pe’er compares the challenge to a common event on crime shows like CSI, in which an image from a photograph is too blurry to make out. “The detectives bring in an expert who has created a computer algorithm to analyze the pixels, allowing them to clearly read the letters and numbers on a license plate,” she says. “In the same way, we have developed a way to reduce the fuzziness in our data and see clear patterns.”

Dr. Pe’er’s collaborators included Smita Krishnaswamy, a former postdoctoral fellow in her lab who now leads her own lab at Yale University. The team dubbed the technique MAGIC for Markov affinity-based graph imputation of cells. The algorithm can recover gene expression data that may be missing from an individual cell if not all of the RNA has been captured.

“Cancer cells have a range of activities. The ones that have the ability to outsmart drugs or to spread to another part of the body are actually quite rare,” she concludes. “Only by looking at the tumor with single-cell resolution are we able to identify them and study them, enabling us to get to the bottom of what gives them these capabilities.”

How an Altered Gatekeeping Protein Can Cause Cancer

Source: Memorial Sloan Kettering - On Cancer
Date: 09/16/2020
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Cancer is caused by gene mutations, but sometimes it’s hard to figure out which mutations actually drive tumor growth and which are just along for the ride. One way to determine this is to look for so-called mutational hot spots. These areas of the genome are mutated in tumors more frequently than would be expected by chance. The frequency suggests that they may play an active role in cancer.

MSK-IMPACTTM, Memorial Sloan Kettering’s diagnostic test that looks for genetic changes in more than 400 genes in patients’ tumors, has proven quite useful in the discovery of new hots pots. Researchers in the Human Oncology and Pathogenesis Program (HOPP) have used the identification of one particular hot spot to explain why a gene called XPO1 causes cancer. The findings were published online July 8 in Cancer Discovery.

“Researchers already knew that XPO1 regulates which proteins are located in a cell’s nucleus and which get moved to the cytoplasm. This is a basic function for any cell,” says senior author Omar Abdel-Wahab, a physician-scientist in HOPP. “But until now, nobody has ever shown how the alteration of the XPO1 protein could cause cancer. This study shows how this happens.”

Decoding the Function of an Important Protein

XPO1 is often mutated in blood cancers — especially many types of lymphomaXPO1 mutations are also found in some solid tumors. A drug that targets these mutations, called selinexor (Xpovio®), was recently approved by the US Food and Drug Administration for the treatment of multiple myeloma. It is being tested in clinical trials for other cancers at several institutions, including MSK. But researchers weren’t sure which patients were most likely to respond to the drug.

An analysis of MSK-IMPACT data led by study co-author Barry Taylor, a computational oncologist and Associate Director of the Marie-Josée and Henry R. Kravis Center for Molecular Oncology, suggested that a specific mutation in XPO1, called E571, was common in cancer. To study the function of that particular mutation, Dr. Abdel-Wahab’s team put a version of the gene with the E571 mutation into mice. The mice developed cancer at a high rate. The researchers then did additional studies in the mice and in human cancer cells to find out how the mutation causes cancer.

“We found that the mutant form of XPO1 promoted excessive cell growth,” says first author Justin Taylor, an MSK medical oncologist and member of Dr. Abdel-Wahab’s lab. “Further analysis showed that this occurs because of XPO1’s role as a gatekeeper that regulates which proteins can exit the nucleus.” The researchers found that mutations affect the function of this gate, changing which proteins stay in the nucleus and which leave and go to the cytoplasm.

Dr. Taylor adds that the research also revealed why the drug is effective. “The mutation changes the electrical charge of the XPO1 protein, which makes selinexor bind to it more strongly. This makes the cancer cells more prone to die.”

Developing a More Personalized Approach

Clinical trials for selinexor are ongoing. Currently, MSK is participating in a trial for people with liposarcoma. The results of a trial for people with myelodysplastic syndrome were presented at a recent meeting of the American Society of Hematology.

Dr. Abdel-Wahab says that the E571K mutation may prove to be an effective way to determine who is likely to benefit from selinexor. This could influence future clinical trials of the drug and lead to a more personalized approach to who gets the drug.

The researchers also plan to study the role of other XPO1 defects in cancer, including cases where the gene is overexpressed but not necessarily mutated.

Machine Learning May Help Classify Cancers of Unknown Primary

Source: Memorial Sloan Kettering - On Cancer
Date: 11/14/2019
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Experts estimate that between 2 and 5% of all cancers are classified as cancer of unknown primary (CUP), also called occult primary cancer. This means that the place in the body where the cancer began cannot be determined. Despite many advances in diagnostic technologies, the original site of some cancers will never be found. However, characteristic patterns of genetic changes occur in cancers of each primary site, and these patterns can be used to infer the origin of individual cases of CUP.

In a study published November 14, a team from Memorial Sloan Kettering reports that they have harnessed data from MSK-IMPACT to develop a machine-learning algorithm to help determine where a tumor originates. MSK-IMPACT is a test to detect mutations and other critical changes in the genes of tumors. When combined with other pathology tests, the algorithm may be a valuable addition to the tool kit used to make more-accurate diagnoses. The findings were reported in JAMA Oncology.

“This tool will provide additional support for our pathologists to diagnose tumor types,” says geneticist Michael Berger, one of the senior authors of the new study. “We’ve learned through clinical experience that it’s still important to identify a tumor’s origin, even when conducting basket trials involving therapies targeting genes that are mutated across many cancers.”

Basket trials are designed to take advantage of targeted treatments by assigning drugs to people based on the mutations found in their tumors rather than where in the body the cancer originated. Yet doctors who prescribe these treatments have learned that, in many cases, the tissue or organ in which the tumor started is still an important factor in how well targeted therapies work. Vemurafenib (Zelboraf®) is one drug where this is the case. It is effective at treating melanoma with a certain mutation but doesn’t provide the same benefit in colon cancer, even when it’s driven by the same mutation.Cancer of Unknown Primary Origin

If it is unclear where in the body a cancer started, it is called cancer of unknown primary (CUP) or occult primary cancer.

Harnessing Valuable Data

Since MSK-IMPACT launched in 2014, more than 40,000 people have had their tumors tested. The test is now offered to all people treated for advanced cancer at MSK.

In addition to providing detailed information about thousands of patients’ tumors, the test has led to a wealth of genomic data about cancers. It has become a major research tool for learning more about cancer’s origins.

The primary way that pathologists diagnose tumors is to look through a microscope at tissue samples. They also examine the specific proteins expressed by cancers, which can help predict a cancer’s origin. But these tests do not always allow a definitive conclusion.

“However, there are occasionally cases where we think we know the diagnosis based on the conventional pathology analysis, but the molecular pattern we observe with MSK-IMPACT suggests that the tumor is something different,” Dr. Berger explains. “This new tool is a way to computationally formalize the process that our molecular pathologists have been performing based on their experience and knowledge of genomics. Going forward, it can help them confirm these diagnoses.”

“Because cancers that have spread usually retain the same pattern of genetic alterations as the primary tumor, we can leverage the specific genetic changes to suggest a cancer site that was not apparent by imaging or conventional pathologic testing,” says co-author David Klimstra, Chair of MSK’s Department of Pathology.

“Usually the first question from patients and doctors alike is: ‘Where did this cancer start?’ ” says study co-author Anna Varghese, a medical oncologist who treats many people with CUP. “Although even with MSK-IMPACT we can’t always determine where the cancer originated, the MSK-IMPACT results can point us in a certain direction with respect to further diagnostic tests to conduct or targeted therapies or immunotherapies to use.”

Collecting Data on Common Cancers

In the current study, the investigators used data from nearly 7,800 tumors representing 22 cancer types to train the algorithm. The researchers excluded rare cancers, for which not enough data were available at the time. But all the most common types are represented, including lung cancerbreast cancerprostate cancer, and colorectal cancer.

The analysis incorporated not only individual gene mutations but more complex genomic changes. These included chromosomal gains and losses, changes in gene copy numbers, structural rearrangements, and broader mutational signatures.

“The type of machine learning we use in this study requires a lot of data to train it to perform accurately,” says computational oncologist Barry Taylor, the study’s other senior author. “It would not have been possible without the large data set that we have already generated and continue to generate with MSK-IMPACT.”

Both Drs. Berger and Taylor emphasize that this is still early research that will need to be validated with further studies. In addition, since the method was developed specifically using test results from MSK-IMPACT, it may not be as accurate for genomic tests made by companies or other institutions.

Improving Diagnosis for Cancer of Unknown Primary

MSK’s pathologists and other experts hope this tool will be particularly valuable in diagnosing tumors in people who have CUP. Up to 50,000 people in the United States are diagnosed with CUP every year. If validated for this purpose, MSK-IMPACT could make it easier to select the best therapies and to enroll people in clinical trials.

“This study emphasizes that the diagnosis and treatment of cancer is truly a multidisciplinary effort,” Dr. Taylor says. “We want to get all the data we can from each patient’s tumor so we can inform the diagnosis and select the best therapy for each person.”

This work was funded in part by Illumina, the Marie-Josée and Henry R. Kravis Center for Molecular OncologyCycle for Survival, National Institutes of Health grants (P30-CA008748, R01 CA204749, and R01 CA227534), an American Cancer Society grant (RSG-15-067-01-TBG), the Sontag Foundation, the Prostate Cancer Foundation, and the Robertson Foundation.

Dr. Varghese has received institutional research support from Eli Lilly and Company, Bristol-Myers Squibb, Verastem Oncology, BioMed Valley Discoveries, and Silenseed. Dr. Klimstra reports equity in Paige.AI, consulting activities with Paige.AI and Merck, and publication royalties from UpToDate and the American Registry of Pathology. Dr. Berger reports research funding from Illumina and advisory board activities with Roche. All stated activities were outside of the work described in this study.

Crowdsourcing Science: Using Competition to Drive Creativity

Source: National Institute of General Medical Sciences - Biomedical Beat Blog
Date: 02/05/2020
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Historically, crowdsourcing has played an important role in certain fields of scientific research. Wildlife biologists often rely on members of the public to monitor animal populations. Using backyard telescopes, amateur astronomers provide images and measurements that lead to important discoveries about the universe. And many meteorologists use data collected by citizen scientists to study weather conditions and patterns.

Now, thanks largely to advances in computing, researchers in computational biology and data science are harnessing the power of the masses and making discoveries that provide valuable insights into human health.

Tackling Data on Complex Diseases 

Trey Ideker, Ph.D. , a professor of medicine at the University of California, San Diego (UCSD), has used crowdsourcing in his graduate-level bioinformatics classes to analyze results from genome-wide association studies. These studies allow researchers to identify particular gene variations that are linked with a disease or another trait.

Some diseases are triggered by single gene mutations or changes, but most conditions are much more complex. A combination of gene variations as well as environmental and other factors influence disease development.

“Common diseases like diabetes, cancer, heart disease, and neurological and psychiatric disorders have hundreds or even thousands of genes that are contributing to them,” Dr. Ideker says. “When you have diseases that involve so many genes, and the massive amount of data that’s associated with those genes, it’s hard to find connections.”

Students in Dr. Ideker’s class applied a classroom approach to crowdsourcing for a project on schizophrenia. The challenge was to develop computer algorithms that could generate a ranked list of 100 genes associated with schizophrenia. Students were given data on gene variants from more than 51,000 individuals.

“Setting this up as a competition pushed the students to explore different methods,” says UCSD graduate student Samson Fong, the teaching assistant for the class. “In a regular lab setting, you might have one or two people working on a problem. In this case, we allowed the students to develop eight or nine different approaches, which we could compare side by side. We learned much more about which methods worked and which didn’t.”

Dr. Ideker adds that running a competition in a classroom setting, rather than within the scientific community, encouraged collaboration as well as competition.

Fong agrees, noting that students learned a lot from each other. Additionally, they were guided to take their projects in different directions, to ensure a wider range of solutions. He explains that because students worked on teams instead of individually, they tackled much more complex problems than they could have on their own.

Advancing Science with a Combination of Computational Methods

The winning method from the competition led to a computational approach, called Network-Assisted Genomic Association, published in iScience. This approach outperformed other methods in identifying known disease genes and in how well the results from the analysis could be replicated. Dr. Ideker has already used the competition framework in another class to develop algorithms related to computational challenges in structural biology.

“This is a field that’s evolving so quickly that for most problems, there is unlikely to be only one computational method that will work,” Dr. Ideker says. “With this classroom approach, we see how the research can benefit from sampling a bouquet of different ideas. You’re pushing science forward in a really nice way.”

Dr. Ideker’s work is supported by NIGMS grant P41GM103504.