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.)
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.
Device could pave the way for real-time, noninvasive breath analysis to detect and monitor diseases and be a new service medical laboratories can offer
Breathalyzer technology is not new, but until now human breath detection devices have not been comparable to clinical laboratory blood testing for disease detection and monitoring. That may soon change and there are implications for clinical laboratories, partly because breath samples are considered to be non-invasive for patients.
Medical laboratory scientists will understand the significance of this development. JILA’s enhanced breathalyzer device could pave the way for real-time, noninvasive breath analysis to detect and monitor diseases, and potentially eliminate the need for many blood-based clinical laboratory tests.
During their research, physicist Jun Ye, PhD, and David Nesbitt, PhD, both Fellows at JILA and professors at University of Colorado Boulder, detected and monitored four biomarkers in the breath of a volunteer:
“Determining the identity and concentration of the molecules present in breath is a powerful tool to assess the overall health of a person, analogous to blood testing in clinical medicine, but in a faster and less invasive manner,” the researchers wrote in PNAS.
“The presence of a particular molecule (or combination of molecules) can indicate the presence of a certain health condition or infection, facilitating a diagnosis. Monitoring the concentration of the molecules of interest over time can help track the development (or recurrence) of a condition, as well as the effectiveness of the administered treatment,” they added.
How the JILA Breathalyzer Detects Biomarkers
According to a 2008 NIST news release, JILA researchers had developed a prototype comb breathalyzer in that year. However, the research did not continue. But then the COVID-19 pandemic brought the JILA/NIST laboratories focus back to the breathalyzer with hopes that new research could lead to a breath test for detecting the SARS-CoV-2 coronavirus and other conditions.
“We are really quite optimistic and committed to pushing this technology to real medical applications,” Ye said in the 2021 NIST news release.
Analytical Scientist explained that JILA’s new and improved breathalyzer system “fingerprints” chemicals by measuring the amount of light absorbed as a laser frequency comb passes back and forth through breath samples loaded into a mirrored glass tube.
JILA’s original 13-year-old prototype comb analyzed colors and amounts of light in the near-infrared band. However, JILA’s recent improvements include advances in optical coatings and a shift to analyzing mid-infrared band light, allowing detection sensitivity up to parts-per-trillion level, a thousand-fold improvement over the prototype.
“By matching the frequency of the comb teeth with the cavity modes—the ‘standing modes’ of the cavity—we can increase the interaction path length between molecules inside the cavity and laser light by a factor of around 4000, equivalent to an effective path length of a few kilometers,” she added. “We then probe the light that leaks out of the cavity by sending it into an FTIR [Fourier-transform infrared] spectrometer to find out which exact comb teeth have been absorbed and by how much. In turn, this tells us which molecules are present in the breath sample and their concentration.”
Even Hippocrates Studied Breath
Ye noted in the NIST statement that JILA is the only institution that has published research on comb breathalyzers.
In their PNAS paper, the researchers wrote, “Breath analysis is an exceptionally promising and rapidly developing field of research, which examines the molecular composition of exhaled breath. … Despite its distinctive advantages of being a rapid, noninvasive technique and its long history dating back to Hippocrates, breath analysis has not yet been as widely deployed for routine diagnostics and monitoring as other methods, such as blood-based analysis.
“We have shown that this technique offers unique advantages and opportunities for the detection of light biomarkers in breath,” the researchers noted, “and it is poised to facilitate real-time, noninvasive monitoring of breath for clinical studies, as well as for early detection and long-term monitoring of temporary and permanent health conditions.”
Validation of these findings and further design research to make the system portable are required before JILA’s frequency comb breathalyzer can become a competitor to clinical laboratory blood tests for disease identification and monitoring. Nevertheless, JILA’s research brings breathalyzer technology a step closer to offering real-time, non-invasive analysis of human biomarkers for disease.
The technology is similar to the concept of a liquid biopsy, which uses blood specimens to identify cancer by capturing tumor cells circulating in the blood.
According to the American Cancer Society, lung cancer is responsible for approximately 25% of cancer deaths in the US and is the leading cause of cancer deaths in both men and women. The ACS estimates there will be about 236,740 new cases of lung cancer diagnosed in the US this year, and about 130,180 deaths due to the disease.
Early-stage lung cancer is typically asymptomatic which leads to later stage diagnoses and lowers survival rates, largely due to a lack of early disease detection tools. The current method used to detect early lung cancer lesions is low-dose spiral CT imaging, which is costly and can be risky due to the radiation hazards of repeated screenings, the news release noted.
MGH’s newly developed diagnostic tool detects lung cancer from alterations in blood metabolites and may lead to clinical laboratory tests that could dramatically improve survival rates of the deadly disease, the MGH scientist noted in a news release.
Detecting Lung Cancer in Blood Metabolomic Profiles
The MGH scientists created their lung-cancer predictive model based on magnetic resonance spectroscopy which can detect the presence of lung cancer from alterations in blood metabolites.
The researchers screened tens of thousands of stored blood specimens and found 25 patients who had been diagnosed with non-small-cell lung carcinoma (NSCLC), and who had blood specimens collected both at the time of their diagnosis and at least six months prior to the diagnosis. They then matched these individuals with 25 healthy controls.
The scientists first trained their statistical model to recognize lung cancer by measuring metabolomic profiles in the blood samples obtained from the patients when they were first diagnosed with lung cancer. They then compared those samples to those of the healthy controls and validated their model by comparing the samples that had been obtained from the same patients prior to the lung cancer diagnosis.
The predictive model yielded values between the healthy controls and the patients at the time of their diagnoses.
“This was very encouraging, because screening for early disease should detect changes in blood metabolomic profiles that are intermediate between healthy and disease states,” Cheng noted.
The MGH scientists then tested their model with a different group of 54 patients who had been diagnosed with NSCLC using blood samples collected before their diagnosis. The second test confirmed the accuracy of their model.
Predicting Five-Year Survival Rates for Lung Cancer Patients
Values derived from the MGH predictive model measured from blood samples obtained prior to a lung cancer diagnosis also could enable oncologists to predict five-year survival rates for patients. This discovery could prove to be useful in determining clinical strategies and personalized treatment decisions.
The researchers plan to analyze the metabolomic profiles of the clinical characteristics of lung cancer to understand the entire metabolic spectrum of the disease. They hope to create similar models for other illnesses and have already created a model that can distinguish aggressive prostate cancer by measuring the metabolomics profiles of more than 400 patients with that disease.
In addition, they are working on a similar model to screen for Alzheimer’s disease using blood samples and cerebrospinal fluid.
More research and clinical studies are needed to validate the utilization of blood metabolomics models as early screening tools in clinical practice. However, this technology might provide pathologists and clinical laboratories with diagnostic tests for the screening of early-stage lung cancer that could save thousands of lives each year.
By training a computer to analyze blood samples, and then automating the expert assessment process, the AI processed months’ worth of blood samples in a single day
New technologies and techniques for acquiring and transporting biological samples for clinical laboratory testing receive much attention. But what of the quality of the samples themselves? Blood products are expensive, as hospital medical laboratories that manage blood banks know all too well. Thus, any improvement to how labs store blood products and confidently determine their viability for transfusion is useful.
Improving Blood Diagnostics through Precision Medicine and Deep Learning
“This project is an excellent example of how we are using our world-class expertise in precision health to contribute to the interdisciplinary work required to make fundamental changes in blood diagnostics,” said Jason Acker, PhD, a senior scientist at Canadian Blood Services’ Centre for Innovation, Professor of Laboratory Medicine and Pathology at the University of Alberta, and one of the lead authors of the study, in the Folio article.
The research took more than three years to complete and involved 19 experts from 12 academic institutions and blood collection facilities located in Canada, Germany, Switzerland, the United Kingdom, and the US.
To perform the study, the scientists first collected and manually categorized 52,000 red blood cell images. Those images were then used to train an algorithm that mimics the way a human mind works. The computer system was next tasked with analyzing the shape of RBCs for quality purposes.
Removing Human Bias from RBC Classification
“I was happy to collaborate with a group of people with diverse backgrounds and expertise,” said Tracey Turner, a senior research assistant in Acker’s laboratory and one of the authors of the study, in a Canadian Blood Services (CBS) article. “Annotating and reviewing over 52,000 images took a long time, however, it allowed me to see firsthand how much bias there is in manual classification of cell shape by humans and the benefit machine classification could bring.”
According to the CBS article, a red blood cell lasts about 115 days in the human body and the shape of the RBC reveals its age. Newer, healthier RBCs are shaped like discs with smooth edges. As they age, those edges become jagged and the cell eventually transforms into a sphere and loses the ability to perform its duty of transporting oxygen throughout the body.
Blood donations are processed, packed, and stored for later use. Once outside the body, the RBCs begin to change their shape and deteriorate. RBCs can only be stored for a maximum of 42 days before they lose the ability to function properly when transfused into a patient.
Scientists routinely examine the shape of RBCs to assess the quality of the cell units for transfusion to patients and, in some cases, diagnose and assess individuals with certain disorders and diseases. Typically, microscope examinations of red blood cells are performed by experts in medical laboratories to determine the quality of the stored blood. The RBCs are classified by shape and then assigned a morphology index score. This can be a complex, time-consuming, and laborious process.
“One of the amazing things about machine learning is that it allows us to see relationships we wouldn’t otherwise be able to see,” Acker said. “We categorize the cells into the buckets we’ve identified, but when we categorize, we take away information.”
Human analysis, apparently, is subjective and different professionals can arrive at different results after examining the same blood samples.
“Machines are naive of bias, and AI reveals some characteristics we wouldn’t have identified and is able to place red blood cells on a more nuanced spectrum of change in shape,” Acker explained.
The researchers discovered that the AI could accurately analyze and categorize the quality of the red blood cells. This ability to perform RBC morphology assessment could have critical implications for transfusion medicine.
“The computer actually did a better job than we could, and it was able to pick up subtle differences in a way that we can’t as humans,” Acker said.
“It’s not surprising that the red cells don’t just go from one shape to another. This computer showed that there’s actually a gradual progression of shape in samples from blood products, and it’s able to better classify these changes,” he added. “It radically changes the speed at which we can make these assessments of blood product quality.”
More Precision Matching Blood Donors to Recipients
According to the World Health Organization (WHO), approximately 118.5 million blood donations are collected globally each year. There is a considerable contrast in the level of access to blood products between high- and low-income nations, which makes accurate assessment of stored blood even more critical. About 40% of all blood donations are collected in high-income countries that home to only about 16% of the world’s population.
More studies and clinical trials will be necessary to determine if U of A’s approach to using AI to assess the quality of RBCs can safely transfer to clinical use. But these early results promise much in future precision medicine treatments.
“What this research is leading us to is the fact that we have the ability to be much more precise in how we match blood donors and recipients based on specific characteristics of blood cells,” Acker stated. “Through this study we have developed machine learning tools that are going to help inform how this change in clinical practice evolves.”
The AI tools being developed at the U of A could ultimately benefit patients as well as blood collection centers, and at hospitals where clinical laboratories typically manage the blood banking services, by making the process of matching transfusion recipients to donors more precise and ultimately safer.
New vaccine has potential to reduce volume of clinical laboratory testing for bacterial and viral infections
By now, nearly all pathologists and clinical laboratory scientists acknowledge that advances in molecular diagnostics and genetic testing are contributing to significant improvements in patient care. Now comes news of a comparable breakthrough in another field of medicine with the potential to protect many individuals from pneumonia and similar infectious diseases.
This cutting-edge pneumococcal vaccine allows Streptococcus pneumoniae to colonize and live inside the body as long as there is no risk to the host. When a threat is detected, the vaccine establishes an immune system response to annihilate the disease-causing bacteria. (more…)