More research into accelerated aging may lead to new clinical laboratory and anatomic pathology testing biomarkers for early-onset cancer
Could accelerated aging be contributing to the rise in early-onset cancer rates among younger individuals? A recent study conducted at the Washington University School of Medicine in St. Louis (WUSTL) claims the condition may be partially to blame for the increase in cancer diagnoses among young people. But what is accelerated aging, and what tests will clinical laboratories be required to perform to help physicians diagnose early-onset cancer in that age group?
“Accelerated aging—when someone’s biological age [how old one’s cells are] is greater than their chronological age [how long one has existed]—could increase the risk of cancer tumors,” Fox News reported.
In their presentation at the 2024 American Association for Cancer Research (AACR) annual meeting, the WUSTL researchers noted that “individuals born in or after 1965 had a 17% higher likelihood of accelerated aging than those born between 1950 and 1954,” according to an AACR news release.
The scientists studied “the association between accelerated aging and the risk of early-onset cancers,” and found that “each standard deviation increase in accelerated aging was associated with a 42% increased risk of early-onset lung cancer, a 22% increased risk of early-onset gastrointestinal cancer, and a 36% increased risk of early-onset uterine cancer.”
“Multiple cancer types are becoming increasingly common among younger adults in the United States and globally,” said Ruiyi Tian, MPH, a PhD candidate at WUSTL, in the news release. “Understanding the factors driving this increase will be key to improve the prevention or early detection of cancers in younger and future generations.”
Tian was part of the team conducting the study at the Cao Lab at WUSTL. The primary function of this lab is to uncover risk factors for various cancers and develop precision medicine protocols for cancer prevention and treatment.
“Historically, both cancer and aging have been viewed primarily as concerns for older populations,” Ruiyi Tian, MPH (above), a graduate student at Washington University School of Medicine in St. Louis and one of the study’s researchers, told Fox News. “The realization that cancer, and now aging, are becoming significant issues for younger demographics over the past decades was unexpected.” Clinical laboratories and anatomic pathologists will likely be performing cancer testing on younger populations as incidences of early-onset cancer increase. (Photo copyright: Washington University School of Medicine in St. Louis.)
The WUSTL researchers set out to prove that both chronological age and biological age could be determining factors in early-onset cancers. Chronological age refers to the amount of time an individual has been alive, while biological age refers to the age of cells and tissues based on physiological evidence.
“We all know cancer is an aging disease. However, it is really coming to a younger population,” said Yin Cao, MPH, Associate Professor of Surgery at WUSTL and senior author of the study, told CNN. “So, whether we can use the well-developed concept of biological aging to apply that to the younger generation is a really untouched area.”
To perform the research, the scientists examined data of 148,724 individuals between the ages of 37 and 54 located in the UK Biobank database. They calculated each person’s biological age by examining nine biomarkers found in blood:
They then input the data into the PhenoAge algorithm which estimated the biological age of each person.
“Individuals whose biological age was higher than their chronological age were defined as having accelerated aging,” the AACR news release noted.
The next step was to calculate each person’s level of accelerated aging by comparing biological and chronological ages. They then looked at how many of the individuals studied had been diagnosed with early-onset cancers.
For the WUSTL study, early-onset cancers were defined as cancers that were diagnosed before age 55. The researchers found 3,200 cases where such cancers had been discovered.
Faster Agers Twice as Likely to Develop Early-onset Cancer
The scientists then compared the data of people who showed slower aging to those showing faster aging based on the biobank samples. They found that individuals who had the highest accelerated aging were twice as likely to be diagnosed with early-onset lung cancer, had a 60% higher risk of gastrointestinal tumors, and had a more than 80% higher risk of uterine cancer.
“By examining the relationship between accelerating aging and the risk of early-onset cancers, we provide a fresh perspective on the shared etiology of early-onset cancers,” Tian said in the news release. “If validated, our findings suggest that interventions to slow biological aging could be a new avenue for cancer prevention, and screening efforts tailored to younger individuals with signs of accelerated aging could help detect cancers early.”
More clinical studies and research are needed to determine if accelerated aging truly is causing a rise in early-onset cancers. The fact that all of the participants in this study were from the United Kingdom indicates that future studies should include more diverse populations.
Studying accelerated aging’s influence on early-onset cancer may lead to new biomarkers that clinical laboratories and anatomic pathologists can use to help physicians diagnose the condition. Laboratory scientists and pathologists will want to follow any ongoing research and studies on the trend, as ‘accelerated aging’ might be identified as a new disorder to look for when diagnosing and treating cancers.
Speedy DNA sequencing and on-the-spot digital imaging may change the future of anatomic pathology procedures during surgery
Researchers at the Center for Molecular Medicine (CMM) at UMC Utrecht, a leading international university medical center in the Netherlands, have paired artificial intelligence (AI) and machine learning with DNA sequencing to develop a diagnostic tool cancer surgeons can use during surgeries to determine in minutes—while the patient is still on the operating table—whether they have fully removed all the cancerous tissue.
The method, “involves a computer scanning segments of a tumor’s DNA and alighting on certain chemical modifications that can yield a detailed diagnosis of the type and even subtype of the brain tumor,” according to The New York Times, which added, “That diagnosis, generated during the early stages of an hours-long surgery, can help surgeons decide how aggressively to operate, … In the future, the method may also help steer doctors toward treatments tailored for a specific subtype of tumor.”
This technology has the potential to reduce the need for frozen sections, should additional development and studies confirm that it accurately and reliably shows surgeons that all cancerous cells were fully removed. Many anatomic pathologists would welcome such a development because of the time pressure and stress associated with this procedure. Pathologists know that the patient is still in surgery and the surgeons are waiting for the results of the frozen section. Most pathologists would consider fewer frozen sections—with better patient outcomes—to be an improvement in patient care.
“It’s imperative that the tumor subtype is known at the time of surgery,” Jeroen de Ridder, PhD (above), associate professor in the Center for Molecular Medicine at UMC Utrecht and one of the study leaders, told The New York Times. “What we have now uniquely enabled is to allow this very fine-grained, robust, detailed diagnosis to be performed already during the surgery. It can figure out itself what it’s looking at and make a robust classification,” he added. How this discovery affects the role of anatomic pathologists and pathology laboratories during cancer surgeries remains to be seen. (Photo copyright: UMC Utrecht.)
Rapid DNA Sequencing Impacts Brain Tumor Surgeries
The UMC Utrecht scientists employed Oxford Nanopore’s “real-time DNA sequencing technology to address the challenges posed by central nervous system (CNS) tumors, one of the most lethal type of tumor, especially among children,” according to an Oxford Nanopore news release.
The researchers called their new machine learning AI application the “Sturgeon.”
According to The New York Times, “The new method uses a faster genetic sequencing technique and applies it only to a small slice of the cellular genome, allowing it to return results before a surgeon has started operating on the edges of a tumor.”
Jeroen de Ridder, PhD, an associate professor in the Center for Molecular Medicine at UMC Utrecht, told The New York Times that Sturgeon is “powerful enough to deliver a diagnosis with sparse genetic data, akin to someone recognizing an image based on only 1% of its pixels, and from an unknown portion of the image.” Ridder is also a principal investigator at the Oncode Institute, an independent research center in the Netherlands.
The researchers tested Sturgeon during 25 live brain surgeries and compared the results to an anatomic pathologist’s standard method of microscope tissue examination. “The new approach delivered 18 correct diagnoses and failed to reach the needed confidence threshold in the other seven cases. It turned around its diagnoses in less than 90 minutes, the study reported—short enough for it to inform decisions during an operation,” The New York Times reported.
But there were issues. Where the minute samples contain healthy brain tissue, identifying an adequate number of tumor markers could become problematic. Under those conditions, surgeons can ask an anatomic pathologist to “flag the [tissue samples] with the most tumor for sequencing, said PhD candidate Marc Pagès-Gallego, a bioinformatician at UMC Utrecht and a co-author of the study,” The New York Times noted.
“Implementation itself is less straightforward than often suggested,” Sebastian Brandner, MD, a professor of neuropathology at University College London, told The Times. “Sequencing and classifying tumor cells often still required significant expertise in bioinformatics as well as workers who are able to run, troubleshoot, and repair the technology,” he added.
“Brain tumors are also the most well-suited to being classified by the chemical modifications that the new method analyzes; not all cancers can be diagnosed that way,” The Times pointed out.
Thus, the research continues. The new method is being applied to other surgical samples as well. The study authors said other facilities are utilizing the method on their own surgical tissue samples, “suggesting that it can work in other people’s hands.” But more work is needed, The Times reported.
UMC Utrecht Researchers Receive Hanarth Grant
To expand their research into the Sturgeon’s capabilities, the UMC Utrecht research team recently received funds from the Hanarth Fonds, which was founded in 2018 to “promote and enhance the use of artificial intelligence and machine learning to improve the diagnosis, treatment, and outcome of patients with cancer,” according to the organization’s website.
The researchers will investigate ways the Sturgeon AI algorithm can be used to identify tumors of the central nervous system during surgery, a UMC Utrecht news release states. These type of tumors, according to the researchers, are difficult to examine without surgery.
“This poses a challenge for neurosurgeons. They have to operate on a tumor without knowing what type of tumor it is. As a result, there is a chance that the patient will need another operation,” said de Ridder in the news release.
The Sturgeon application solves this problem. It identifies the “exact type of tumor during surgery. This allows the appropriate surgical strategy to be applied immediately,” the news release notes.
The Hanarth funds will enable Jeroen and his team to develop a variant of the Sturgeon that uses “cerebrospinal fluid instead of (part of) the tumor. This will allow the type of tumor to be determined already before surgery. The main challenge is that cerebrospinal fluid contains a mixture of tumor and normal DNA. AI models will be trained to take this into account.”
The UMC Utrecht scientists’ breakthrough is another example of how organizations and research groups are working to shorten time to answer, compared to standard anatomic pathology methods. They are combining developing technologies in ways that achieve these goals.
New artificial intelligence model agrees with interpretations of human medical technologists and microbiologists with extraordinary accuracy
Microbiology laboratories will be interested in news from Brescia University in Italy, where researchers reportedly have developed a deep learning model that can visually identify and analyze bacterial species in culture plates with a high level of agreement with interpretations made by medical technologists.
They initially trained and tested the system to digitally identify pathogens associated with urinary tract infections (UTIs). UTIs are the source for a large volume of clinical laboratory microbiological testing.
The system, known as DeepColony, uses hierarchical artificial intelligence technology. The researchers say hierarchical AI is better suited to complex decision-making than other approaches, such as generative AI.
In their Nature paper, the researchers explained that microbiologists use conventional methods to visually examine culture plates that contain bacterial colonies. The scientists hypothesize which species of bacteria are present, after which they test their hypothesis “by regrowing samples from each colony separately and then employing mass spectroscopy techniques,” to confirm their hypotheses.
However, DeepColony—which was designed for use with clinical laboratory automation systems—looks at high-resolution digital scans of cultured plates and attempts to identify the bacterial strains and analyze them in much the same way a microbiologist would. For example, it can identify species based on their appearance and determine which colonies are suitable for analysis, the researchers explained.
“Working on a large stream of clinical data, and a complete set of 32 pathogens, the proposed system is capable of effectively assisting plate interpretation with a surprising degree of accuracy in the widespread and demanding framework of urinary tract infections,” the study authors wrote. “Moreover, thanks to the rich species-related generated information, DeepColony can be used for developing trustworthy clinical decision support services in laboratory automation ecosystems from local to global scale.”
“Compared to the most common solutions based on single convolutional neural networks (CNN), multi-network architectures are attractive in our case because of their ability to fit into contexts where decision-making processes are stratified into a complex structure,” wrote the study’s lead author Alberto Signoroni, PhD (above), Associate Professor of Computer Science, University of Brescia, and his researcher team in their Nature paper. “The system must be designed to generate useful and easily interpretable information and to support expert decisions according to safety-by-design and human-in-the-loop policies, aiming at achieving cost-effectiveness and skill-empowerment respectively.” Microbiologists and clinical laboratory managers will want to follow the further development of this technology. (Photo copyright: University of Brescia.)
How Hierarchical AI Works
Writing in LinkedIn, patent attorney and self-described technology expert David Cain, JD, of Hauptman Ham, LLP, explained that hierarchical AI systems “are structured in layers, each with its own distinct role yet interconnected in a way that forms a cohesive whole. These systems are significant because they mirror the complexity of human decision-making processes, incorporating multiple levels of analysis and action. This multi-tiered approach allows for nuanced problem-solving and decision-making, akin to a seasoned explorer deftly navigating through a multifaceted terrain.”
DeepColony, the researchers wrote, consists of multiple convolutional neural networks (CNNs) that exchange information and cooperate with one another. The system is structured into five levels—labeled 0 through 4—each handling a different part of the analysis:
At level 0, the system determines the number of bacterial colonies and their locations on the plate.
At level 1, the system identifies “good colonies,” meaning those suitable for further identification and analysis.
At level 2, the system assigns each good colony to a bacterial species “based on visual appearance and growth characteristics,” the researchers wrote, referring to the determination as being “pathogen aware, similarity agnostic.”
The CNN used at this stage was trained by using images of 26,213 isolated colonies comprising 32 bacterial species, the researchers wrote in their paper. Most came from clinical laboratories, but some were obtained from the American Type Culture Collection (ATCC), a repository of biological materials and information resources available to researchers.
At level 3, the system attempts to improve accuracy by looking at the larger context of the plate. The goal here is to “determine if observed colonies are similar (pure culture) or different (mixed cultures),” the researchers wrote, describing this step as “similarity aware, pathogen agnostic.” This enables the system to recognize variants of the same strain, the researchers noted, and has the effect of reducing the number of strains identified by the system.
At this level, the system uses two “Siamese CNNs,” which were trained with a dataset of 200,000 image pairs.
Then, at level 4, the system “assesses the clinical significance of the entire plate,” the researchers added. Each plate is labeled as:
“Positive” (significant bacterial growth),
“No significant growth” (negative), or
“Contaminated,” meaning it has three or more “different colony morphologies without a particular pathogen that is prevalent over the others,” the researchers wrote.
If a plate is labeled as “positive,” it can be “further evaluated for possible downstream steps,” using MALDI-TOF mass spectrometry or tests to determine susceptibility to antimicrobial measures, the researchers stated.
“This decision-making process takes into account not only the identification results but also adheres to the specific laboratory guidelines to ensure a proper supportive interpretation in the context of use,” the researchers wrote.
Nearly 100% Agreement with Medical Technologists
To gauge DeepColony’s accuracy, the researchers tested it on a dataset of more than 5,000 urine cultures from a US laboratory. They then compared its analyses with those of human medical technologists who had analyzed the same samples.
Agreement was 99.2% for no-growth cultures, 95.6% for positive cultures, and 77.1% for contaminated or mixed growth cultures, the researchers wrote.
The lower agreement for contaminated cultures was due to “a deliberately precautionary behavior, which is related to ‘safety by design’ criteria,” the researchers noted.
Lead study author Alberto Signoroni, PhD, Associate Professor of Computer Science, University of Brescia, wrote in Nature that many of the plates identified by medical technologists as “contaminated” were labeled as “positive” by DeepColony. “We maximized true negatives while allowing for some false positives, so that DeepColony [can] focus on the most relevant or critical cases,” he said.
Will DeepColony replace medical technologists in clinical laboratories any time soon? Not likely. But the Brescia University study indicates the direction AI in healthcare is headed, with high accuracy and increasing speed. The day may not be far off when pathologists and microbiologists regularly employ AI algorithms to diagnose disease.
According to an EADV press release, the AI software demonstrated a “100% (59/59 cases identified) sensitivity for detecting melanoma—the most serious form of skin cancer.” The AI software also “correctly detected 99.5% (189/190) of all skin cancers and 92.5% (541/585) of pre-cancerous lesions.”
“Of the basal cell carcinoma cases, a single case was missed out of 190, which was later identified at a second read by a dermatologist ‘safety net.’ This further demonstrates the need to have appropriate clinical oversight of the AI,” the press release noted.
AI is being utilized more frequently within the healthcare industry to diagnose and treat a plethora of illnesses. This recent study performed by scientists in the United Kingdom demonstrates that new AI models can be used to accurately diagnose some skin cancers, but that “AI should not be used as a standalone detection tool without the support of a consultant dermatologist,” the press release noted.
“The role of AI in dermatology and the most appropriate pathway are debated,” said Kashini Andrew, MBBS, MSc (above), Specialist Registrar at University Hospitals Birmingham NHS Foundation Trust. “Further research with appropriate clinical oversight may allow the deployment of AI as a triage tool. However, any pathway must demonstrate cost-effectiveness, and AI is currently not a stand-alone tool in dermatology. Our data shows the great promise of AI in future provision of healthcare.” Clinical laboratories and dermatopathologists in the United States will want to watch the further development of this AI application. (Photo copyright: LinkedIn.)
How the NHS Scientists Conducted Their Study
Researchers tested their algorithm for almost three years to determine its ability to detect cancerous and pre-cancerous growths. A group of dermatologists and medical photographers entered patient information into their algorithm and trained it how to detect abnormalities. The collected data came from 22,356 patients with suspected skin cancers and included photos of known cancers.
The scientists then repeatedly recalibrated the software to ensure it could distinguish between non-cancerous lesions and potential cancers or malignancies. Dermatologists then reviewed the final data from the algorithm and compared it to diagnoses from health professionals.
“This study has demonstrated how AI is rapidly improving and learning, with the high accuracy directly attributable to improvements in AI training techniques and the quality of data used to train the AI,” said Kashini Andrew, MBBS, MSc, Specialist Registrar at University Hospitals Birmingham NHS Foundation Trust, and co-author of the study, in EADV press release.
Freeing Up Physician Time
The EADV Congress where the NHS researchers presented their findings took place in October in Berlin. The first model of their AI software was tested in 2021 and that version was able to detect:
85.9% (195 out of 227) of melanoma cases,
83.8% (903 out of 1078) of all skin cancers, and
54.1% (496 out of 917) of pre-cancerous lesions.
After fine-tuning, the latest version of the algorithm was even more promising, with results that included the detection of:
100% (59 out of 59) cases of melanoma,
99.5% (189 out of 190) of all skin cancers, and
92.5% (541 out of 585) pre-cancerous lesions.
“The latest version of the software has saved over 1,000 face-to-face consultations in the secondary care setting between April 2022 and January 2023, freeing up more time for patients that need urgent attention,” Andrew said in the press release.
Still, the researchers admit that AI should not be used as the only detection method for skin cancers.
“We would like to stress that AI should not be used as a standalone tool in skin cancer detection and that AI is not a substitute for consultant dermatologists,” stated Irshad Zaki, B Med Sci (Hons), Consultant Dermatologist at University Hospitals Birmingham NHS Foundation Trust and one of the authors of the study, in the press release.
“The role of AI in dermatology and the most appropriate pathway are debated. Further research with appropriate clinical oversight may allow the deployment of AI as a triage tool,” said Andrew in the press release. “However, any pathway must demonstrate cost-effectiveness, and AI is currently not a stand-alone tool in dermatology. Our data shows the great promise of AI in future provision of healthcare.”
Two People in the US Die of Skin Cancer Every Hour
According to the Skin Cancer Foundation, skin cancer is the most common cancer in the United States as well as the rest of the world. More people in the US are diagnosed with skin cancer every year than all other cancers combined.
When detected early, the five-year survival rate for melanoma is 99%, but more than two people in the US die of skin cancer every hour. At least one in five Americans will develop skin cancer by the age of 70 and more than 9,500 people are diagnosed with the disease every day in the US.
The annual cost of treating skin cancers in the United States is estimated at $8.1 billion annually, with approximately $3.3 billion of that amount being for melanoma and the remaining $4.8 billion for non-melanoma skin cancers.
More research is needed before University Hospitals Birmingham’s new AI model can be used clinically in the diagnoses of skin cancers. However, its level of accuracy is unprecedented in AI diagnostics. This is a noteworthy step forward in the field of AI for diagnostic purposes that can be used by clinical laboratories and dermatopathologists.
If validated, study findings may result in new biomarkers for clinical laboratory cholesterol tests and for diagnosing dementia
Researchers continue to find new associations between biomarkers commonly tested by clinical laboratories and certain health conditions and diseases. One recent example comes from research conducted by the University of California San Francisco. The UCSF study connected cholesterol biomarkers generally used for managing cardiovascular disease with an increased risk for dementia as well.
The researchers found that both high and low levels of high-density lipoprotein (HDL)—often referred to as “good” cholesterol—was associated with dementia in older adults, according to a news release from the American Academy of Neurology (AAN).
UCSF’s large, longitudinal study incorporated data from 184,367 people in the Kaiser Permanente Northern California health plan. How the findings may alter cholesterol biomarker use in future diagnostics has not been determined.
“The elevation in dementia risk with both high and low levels of HDL cholesterol was unexpected, but these increases are small, and their clinical significance is uncertain,” said epidemiologist Maria Glymour, ScD (above), study author and Professor of Epidemiology and Biostatistics at UCSF School of Medicine, in a news release. This is another example of how researchers are associating common biomarkers tested regularly by clinical laboratories with additional health conditions and disease states. (Photo copyright: University of California San Francisco.)
HDL Levels Link to Dementia Risk
The UCSF researchers used cholesterol measurements and health behavior questions as they tracked Kaiser Permanente Northern California health plan members who were at least 55 years old between 2002 and 2007, and who did not have dementia at the time of the study’s launch.
The researchers then followed up with the study participants through December 2020 to find out if they had developed dementia, Medical News Today reported.
“Previous studies on this topic have been inconclusive, and this study is especially informative because of the large number of participants and long follow-up,” said epidemiologist Maria Glymour, ScD, study author and Professor of Epidemiology and Biostatistics at UCSF School of Medicine, in the AAN news release. “This information allowed us to study the links with dementia across the range of cholesterol levels and achieve precise estimates even for people with cholesterol levels that are quite high or quite low.”
According to HealthDay, UCSF’s study findings included the following:
More than 25,000 people developed dementia over about nine years. They were divided into five groups.
53.7 milligrams per deciliter (mg/dL) was the average HDL cholesterol level, amid an optimal range of above 40 mg/dL for men and above 50 mg/dL for women.
A 15% rate of dementia was found in participants with HDL of 65 mg/dL or above.
A 7% rate of dementia was found in participants with HDL of 11 mg/dL to 41 mg/dL.
“We found a U-shaped relationship between HDL and dementia risk, such that people with either lower or higher HDL had a slightly elevated risk of dementia,” Erin Ferguson, PhD student of Epidemiology at UCSF, the study’s lead study author, told Medical News Today.
What about LDL?
The UCSF researchers found no correlation between low-density lipoprotein (LDL)—often referred to as “bad” cholesterol”—and increased risk for dementia. But the risk did increase slightly when use of statin lipid-lowering medications were included in the analysis.
“Higher LDL was not associated with dementia risk overall, but statin use qualitatively modified the association. Higher LDL was associated with a slightly greater risk of Alzheimer’s disease-related dementia for statin users,” the researchers wrote in Neurology.
“We found no association between LDL cholesterol and dementia risk in the overall study cohort. Our results add to evidence that HDL cholesterol has similarly complex associations with dementia as with heart disease and cancer,” Glymour noted in the AAN news release.
Australian Study also Links High HDL to Dementia
A separate study from Monash University in Melbourne, Victoria, Australia, found that “abnormally high levels” of HDL was also associated with increased risk for dementia, according to a Monash news release.
The Monash study—which was part of the ASPREE (ASPpirin in Reducing Events in the Elderly) trial of people taking daily aspirin—involved 16,703 Australians and 2,411 Americans during the years 2010 to 2014. The researchers found:
850 participants had developed dementia over about six years.
A 27% increased risk of dementia among people with HDL above 80 mg/dL and a 42% higher dementia risk for people 75 years and older with high HDL levels.
These findings, Newsweek pointed out, do not necessarily mean that high levels of HDL cause dementia.
“There might be additional factors that affect both these findings, such as a genetic link that we are currently unaware of,” Andrew Doig, PhD, Professor, Division of Neuroscience at University of Manchester, told Newsweek. Doig was not involved in the in the Monash University research.
Follow-up research could explore the possibility of diagnosing dementia earlier using blood tests and new biomarkers, Newsweek noted.
Cholesterol Lab Test Results of Value to Clinical Labs
If further studies validate new biomarkers for testing and diagnosis, a medical laboratory’s longitudinal record of cholesterol test results over many years may be useful in identifying people with an increased risk for dementia.
Clinical pathologists and laboratory managers will want to stay tuned as additional study insights and findings are validated and published. Existing laboratory testing reference ranges may need to be revised as well.
As well, the findings of this UCSF research demonstrate that, in this age of information, there will be plenty of opportunities for clinical lab scientists and pathologists to take their labs’ patient data and combine it with other sets of data. Digital tools like artificial intelligence (AI) and machine learning would then be used to assess that large pool of data and produce clinically actionable insights. In turn, that positions labs to add more value and be paid for that value.
If further research confirms these findings, clinical laboratory identification of cancer cells could lead to new treatments for certain childhood cancers
Can cancer cells be changed into normal healthy cells? According to molecular biologists at the Cold Spring Harbor Laboratory (CSHL) in Long Island the answer is, apparently, yes. At least for certain types of cancer. And clinical laboratories and anatomic pathologists may play a key role in identifying these specific cancer cells and then guiding physicians in selecting the most appropriate therapies.
The cancer cells in question are called rhabdomyosarcoma (RMS) and are “particularly aggressive,” according to ScienceAlert. Generally, and most sadly, the cancer primarily affects children below the age of 18. It begins in skeletal muscle, mutates throughout the body, and is often deadly.
“Treatment usually involves chemotherapy, surgery, and radiation procedures. Now, new research by scientists at Cold Spring Harbor Laboratory demonstrates differentiation therapy as a new treatment option for RMS,” Genetic Engineering and Biotechnology News (GEN) reported.
For those young cancer patients, this new research could become a lifesaving therapy as further studies validate the approach, which has been in development for six years.
“Every successful medicine has its origin story,” said Christopher Vakoc, MD, PhD (above), a molecular biologist at Cold Spring Harbor Laboratory, who led the team that develop the method for converting cancer cells into healthy cells. “And research like this is the soil from which new drugs are born.” As these findings are confirmed, it may be that clinical laboratories and anatomic pathologists will be needed to identify the specific cancer cells in patients once treatment is developed. (Photo copyright: Cold Spring Harbor Laboratory.)
Differentiation Therapy
According to an article in the Chinese Journal of Cancer on the National Library of Medicine website, “Differentiation therapy is based on the concept that a neoplasm is a differentiation disorder [aka, differentiation syndrome] or a dedifferentiation disease. In response to the induction of differentiation, tumor cells can revert to normal or nearly normal cells, thereby altering their malignant phenotype and ultimately alleviating the tumor burden or curing the malignant disease without damaging normal cells.”
Vakoc and his team first pursued differentiation therapy to treat Ewing sarcoma, a pediatric cancer that forms in soft tissues or in bone. In January 2023, GEN reported that the researchers had discovered that “Ewing sarcoma could potentially be stopped by developing a drug that blocks the protein known as ETV6.”
“This protein is present in all cells. But when you perturb the protein, most normal cells don’t care,” Vakoc told GEN. “The process by which the sarcoma forms turns this ETV6 molecule—this relatively innocuous, harmless protein that isn’t doing very much—into something that’s now controlling a life-death decision of the tumor cell.”
The researchers discovered that when ETV6 was blocked in lab-grown Ewing sarcoma cells, the cells became normal, healthy cells. “The sarcoma cell reverts back into being a normal cell again,” they told GEN. “The shape of the cell changes. The behavior of the cells changes. A lot of the cells will arrest their growth. It’s really an explosive effect.”
The scientists then turned their attention on Rhabdomyosarcoma to see if they could elicit a similar response.
“In this study, we developed a high-throughput genetic screening method to identify genes that cause rhabdomyosarcoma cells to differentiate into normal muscle. We used this platform to discover the protein NF-Y as an important molecule that contributes to rhabdomyosarcoma biology. CRISPR-based genetic targeting of NF-Y converts rhabdomyosarcoma cells into differentiated muscle, and we reveal the mechanism by which this occurs,” they wrote in PNAS.
“Scientists have successfully induced rhabdomyosarcoma cells to transform into normal, healthy muscle cells. It’s a breakthrough that could see the development of new therapies for the cruel disease, and it could lead to similar breakthroughs for other types of human cancers,” ScienceAlert reported.
“The cells literally turn into muscle,” Vakoc told ScienceAlert. “The tumor loses all cancer attributes. They’re switching from a cell that just wants to make more of itself to cells devoted to contraction. Because all its energy and resources are now devoted to contraction, it can’t go back to this multiplying state,” he added.
Promising New Therapies for Multiple Cancers in Children
Differentiation therapy as a treatment option gained popularity when “scientists noticed that leukemia cells are not fully mature, similar to undifferentiated stem cells that haven’t yet fully developed into a specific cell type. Differentiation therapy forces those cells to continue their development and differentiate into specific mature cell types,” ScienceAlert noted.
Vakoc and his team had previously “effectively reversed the mutation of the cancer cells that emerge in Ewing sarcoma.” It was those promising results from differentiation therapy that inspired the team to push further and attempt success with rhabdomyosarcoma.
Their results are “a key step in the development of differentiation therapy for rhabdomyosarcoma and could accelerate the timeline for which such treatments are expected,” ScienceAlert commented.
Developing New Therapies for Deadly Cancers
Vakoc and his team are considering differentiation therapy’s potential effectiveness for other types of cancer as well. They note that “their technique, now demonstrated on two different types of sarcoma, could be applicable to other sarcomas and cancer types since it gives scientists the tools needed to find how to cause cancer cells to differentiate,” ScienceAlert reported.
“Since many forms of human sarcoma exhibit a defect in cell differentiation, the methodology described here might have broad relevance for the investigation of these tumors,” the researchers wrote in PNAS.
Clinical laboratories and anatomic pathologist play a critical role in identifying many types of cancers. And though any treatment that comes from the Cold Spring Harbor Laboratory research is years away, it illustrates how new insights into the basic dynamics of cancer cells is helping researchers develop effective therapies for attacking those cancers.
