Medical investigators are theorizing that a combination of two treatments that activate myeloid cells may effectively treat a recalcitrant form of pancreatic cancer that thwarts conventional immunotherapy.
Mobilizing the immune system to destroy cancers has been one of the breakthrough treatments of the past decade, spurring the defeat of cancers via immunotherapies that marshal T cells to wage war against malignancies. The problem facing oncologists and their patients is that T cell-based immunotherapy works well for some cancers, but not all forms of the disease.
Activated T cells are extraordinary killers in immunotherapies, and while these lymphoid cells are effective when mobilized against some cancers, there apparently is a role for new strategies, scientists now say.
A research team, affiliated with UNIST has unveiled a novel method to produce a selective anticancer precursor substance that targets and eliminates cancer cells. This groundbreaking method, previously existing only in theory, has now been experimentally proven for the first time, opening up new possibilities in the development of innovative drugs through extensive research on the effects of anticancer precursors on the human body.
Led by Professor Jaeheung Cho of the Department of Chemistry at UNIST, the research team has successfully demonstrated that the synthesis of hydroxymato cobalt (III), a potential candidate substance for anticancer precursors, involves the reaction of metal-active oxygen species with nitrile. Unlike previous studies that relied on expensive heavy metals, this new method utilizes cost-effective metals and operates at lower temperatures.
Using a new technology developed at MIT, diagnosing lung cancer could become as easy as inhaling nanoparticle sensors and then taking a urine test that reveals whether a tumor is present.
The new diagnostic is based on nanosensors that can be delivered by an inhaler or a nebulizer. If the sensors encounter cancer-linked proteins in the lungs, they produce a signal that accumulates in the urine, where it can be detected with a simple paper test strip.
This approach could potentially replace or supplement the current gold standard for diagnosing lung cancer, low-dose computed tomography (CT). It could have an especially significant impact in low- and middle-income countries that don't have widespread availability of CT scanners, the researchers say.
Wellcome Sanger Institute: Cancer Drug Discovery Accelerated as Hundreds of Overlooked Targets Prioritised January 11, 2024
Introduction:
(Eurekalert) A new, systematic analysis of cancer cells identifies 370 candidate priority drug targets across 27 cancer types, including breast, lung and ovarian cancers.
By looking at multiple layers of functional and genomic information, researchers were able to create an unbiased, panoramic view of what enables cancer cells to grow and survive. They identify new opportunities for cancer therapies in a significant leap towards a new generation of smarter, more effective cancer treatments.
In the most comprehensive study of its kind, researchers from the Wellcome Sanger Institute, Open Targets and their collaborators, pooled together data from 930 cancer cell lines. They then used machine learning methods to find the drug targets that show the most promise for developing new treatments, and the patients who would most benefit from such treatments. This involved assessing the occurrence of these targets in actual patient tumours and linking them to specific biological markers and genetic and molecular features found in the tumours.
The findings, published today (11 January) in Cancer Cell, not only bring researchers one step closer to producing a full Cancer Dependency Map of every vulnerability in every type of cancer, but help guide focused efforts to accelerate the development of targeted cancer treatments.
There are many types of cancer that currently lack effective treatments, such as liver and ovarian cancers. Chemotherapy and radiotherapy are effective treatments, but unable to distinguish normal cells from cancerous ones, so can cause damage throughout the entire body with harsh side effects, such as extreme fatigue, nausea and hair loss.
Breast cancer is the most common cancer in women. The development of breast cancer often originates from epithelial cells in the mammary gland—the very cells that specialize in milk production during and after pregnancy.
A team of researchers from Friedrich Schiller University Jena (Germany), the university in Shenzhen (China) and Jena University Hospital (Germany) has taken a closer look at this specialization process and deciphered a molecular mechanism that also appears to play an important role in cancer development.
It may be possible to develop new diagnostic procedures and treatment methods for breast cancer based on these research findings now published in Cell Reports.
Researchers Seek to Improve Blood Tests’ Ability to Detect and Monitor Cancer January 18, 2024
Introduction:
(Eurekalert) CAMBRIDGE, MA -- Tumors constantly shed DNA from dying cells, which briefly circulates in the patient’s bloodstream before it is quickly broken down. Many companies have created blood tests that can pick out this tumor DNA, potentially helping doctors diagnose or monitor cancer or choose a treatment.
The amount of tumor DNA circulating at any given time, however, is extremely small, so it has been challenging to develop tests sensitive enough to pick up that tiny signal. A team of researchers from MIT and the Broad Institute of MIT and Harvard has now come up with a way to significantly boost that signal, by temporarily slowing the clearance of tumor DNA circulating in the bloodstream.
The researchers developed two different types of injectable molecules that they call “priming agents,” which can transiently interfere with the body’s ability to remove circulating tumor DNA from the bloodstream. In a study of mice, they showed that these agents could boost DNA levels enough that the percentage of detectable early-stage lung metastases leapt from less than 10 percent to above 75 percent.
This approach could enable not only earlier diagnosis of cancer, but also more sensitive detection of tumor mutations that could be used to guide treatment. It could also help improve detection of cancer recurrence.
“You can give one of these agents an hour before the blood draw, and it makes things visible that previously wouldn’t have been. The implication is that we should be able to give everybody who’s doing liquid biopsies, for any purpose, more molecules to work with,” says Sangeeta Bhatia, the John and Dorothy Wilson Professor of Health Sciences and Technology and of Electrical Engineering and Computer Science at MIT, and a member of MIT’s Koch Institute for Integrative Cancer Research and the Institute for Medical Engineering and Science.
AI Harnesses Tumor Genetics to Predict Treatment Response January 18, 2024
Introduction:
(Eurekalert) In a groundbreaking study published on January 18, 2024, in Cancer Discovery, scientists at University of California San Diego School of Medicine leveraged a machine learning algorithm to tackle one of the biggest challenges facing cancer researchers: predicting when cancer will resist chemotherapy.
All cells, including cancer cells, rely on complex molecular machinery to replicate DNA as part of normal cell division. Most chemotherapies work by disrupting this DNA replication machinery in rapidly dividing tumor cells. While scientists recognize that a tumor's genetic composition heavily influences its specific drug response, the vast multitude of mutations found within tumors has made prediction of drug resistance a challenging prospect.
The new algorithm overcomes this barrier by exploring how numerous genetic mutations collectively influence a tumor's reaction to drugs that impede DNA replication. Specifically, they tested their model on cervical cancer tumors, successfully forecasting responses to cisplatin, one of the most common chemotherapy drugs. The model was able to identify tumors at most risk for treatment resistance and was also able to identify much of the underlying molecular machinery driving treatment resistance.
"Clinicians were previously aware of a few individual mutations that are associated with treatment resistance, but these isolated mutations tended to lack significant predictive value. The reason is that a much larger number of mutations can shape a tumor's treatment response than previously appreciated," Trey Ideker, PhD, professor in Department of Medicine at UC San Diego of Medicine, explained. "Artificial intelligence bridges that gap in our understanding, enabling us to analyze a complex array of thousands of mutations at once."
Cancer vaccine with minimal side effects nearing Phase 3 clinical trials.
Source: ABC news, via AOL
Dr. Thomas Wagner, founder of the biotech company Orbis Health Solutions and cancer researcher, has made it his life's mission to find a way to treat cancer without the dreaded side effects that, for some, can become worse than the cancer itself or may even lead to an earlier death.
"The tragedy of cancer is not just that person, the diagnosis, but it's also the fear of the therapy," Wagner told ABC News.
Many traditional cancer treatments, such as chemotherapy, work by killing off cancer cells but also kill off non-cancerous cells throughout the body. This can cause a range of side effects including hair loss, nausea, vomiting, or may knock out a person's immune system putting them at risk of life-threatening infections, Wagner said.
After seeing cancer patients suffer from debilitating side effects of their treatment, Wagner began his mission to develop a cancer treatment that harnessed the power of a person's immune system instead of eliminating it. This treatment was developed as a vaccine that has now been studied for decades, and each shot is completely personalized to each patient.
Scientists Unravel Key Steps in the Road to DNA Repair January 20, 2024
Extract:
(Eurekalert) As we go about our daily lives, our DNA is subjected to all kinds of environmental and internal stress, some of which can lead to breakage of both strands in the double helix. This can be disastrous, and lead to imminent cell death. Luckily, processes like HR (homologous recombination) are continuously repairing this damage.
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HR enables accurate repair of double-strand breaks, as well as the exchange of genetic information, making it a key part of biodiversity. But the exact biochemical picture of HR, including what happens when (the) RecA (protein) carries both the single and double strands, is not yet clear.
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The team, in cooperation with a team from the Tokyo Metropolitan Institute of Medical Science, adopted two approaches to tackle which of these actually happens. In the first, they used a mutant of RecA which cannot separate the double strands i.e. cannot unwind the strand, to see whether this affected DNA repair. It turns out that this has minimal effect. In the second, they tried to measure how much torsion was created in the strand at different stages of the process. They found that the only torsion (twisting – caltrek) due to unwinding they could detect occurred after the homology search was complete i.e. when strand invasion occurred. For the first time, the team clearly showed that the second model was correct.
Detailed insights into homologous recombination are vital to understanding what happens when things go wrong. For example, factors implicated in breast cancer (BRCA1 and BRCA2) are also responsible for the correct loading of single-stranded DNA onto RAD51, the human version of RecA. This suggests that problems with HR might underlie high incidences of breast cancer in patients with hereditary defects in BRCA1 or BRCA2. The team hopes findings like theirs will lead to new directions for research into cancer.
