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Through extensive single-cell analysis, researchers at The University of Texas MD Anderson Cancer Center have created a spatial map of tumor-infiltrating B cells and plasma cells in early-stage lung cancers, highlighting previously unappreciated roles these immune cells play in tumor development and treatment outcomes.
The study, published today in Cancer Discovery, represents the largest and most comprehensive single-cell atlas on tumor-infiltrating B cells and plasma cells to date, which can be used to develop novel immunotherapy strategies.
“We know the tumor microenvironment plays an important role in regulating tumor growth and metastasis, but we have an incomplete understanding of these interactions. So far, most of the focus has been on T cells,” said co-corresponding author Linghua Wang, M.D., Ph.D., associate professor of Genomic Medicine. “Our study provides much-needed understanding of the phenotypes of B cells and plasma cells, which also play critical roles in early lung cancer development.”
Improved screening approaches have increased the proportion of lung cancers diagnosed at early stages. Surgery is curative for some patients, but new treatment approaches are needed because many still experience a recurrence of their disease. Understanding the early interactions between cancer cells and immune cells could reveal opportunities to block cancer growth or boost the anti-tumor immune response.
Previous research co-led by Wang and her colleagues discovered that B lineage cells are critical for responses to immunotherapy in patients with melanoma. Additionally, a study jointly led by Wang and Humam Kadara, Ph.D., associate professor of Translational Molecular Pathology, found that B cells and plasma cells were enriched in early-stage lung cancers relative to normal lung tissue. Plasma cells are terminally differentiated B cells responsible for antibody production.
To better understand the roles of these cells in early lung cancer development, the researchers performed single-cell analysis on 16 tumors and 47 matched normal lung tissues. The analysis was led by Dapeng Hao, Ph.D., and Guangchun Han, Ph.D., in the Wang laboratory, together with Ansam Sinjab, Ph.D., in the Kadara laboratory.

An artificial intelligence (AI) tool is highly effective at detecting pancreatic cancer on CT, according to a study published in Radiology, a journal of the Radiological Society of North America (RSNA).
Pancreatic cancer has the lowest five-year survival rate among cancers. It is projected to become the second leading cause of cancer death in the United States by 2030. Early detection is the best way to improve the dismal outlook, as prognosis worsens significantly once the tumor grows beyond 2 centimeters.
CT is the key imaging method for detection of pancreatic cancer, but it misses about 40% of tumors under 2 centimeters. There is an urgent need for an effective tool to help radiologists in improving pancreatic cancer detection.
Researchers in Taiwan have been studying a computer-aided detection (CAD) tool that uses a type of AI called deep learning to detect pancreatic cancer. They previously showed that the tool could accurately distinguish pancreatic cancer from noncancerous pancreas. However, that study relied on radiologists manually identifying the pancreas on imaging — a labor-intensive process known as segmentation. In the new study, the AI tool identified the pancreas automatically. This is an important advance considering that the pancreas borders multiple organs and structures and varies widely in shape and size.
The researchers developed the tool with an internal test set consisting of 546 patients with pancreatic cancer and 733 control participants. The tool achieved 90% sensitivity and 96% specificity in the internal test set.
Validation followed with a set of 1,473 individual CT exams from institutions throughout Taiwan. The tool achieved 90% sensitivity and 93% specificity in distinguishing pancreatic cancer from controls in that set. Sensitivity for detecting pancreatic cancers less than 2 centimeters was 75%.
“The performance of the deep learning tool seemed on par with that of radiologists,” said study senior author Weichung Wang, Ph.D., professor at National Taiwan University and director of the university’s MeDA Lab. “Specifically, in this study, the sensitivity of the deep learning computer-aided detection tool for pancreatic cancer was comparable with that of radiologists in a tertiary referral center regardless of tumor size and stage.”
The CAD tool has the potential to provide a wealth of information to assist clinicians, Dr. Wang said. It could indicate the region of suspicion to speed radiologist interpretation.
“The CAD tool may serve as a supplement for radiologists to enhance the detection of pancreatic cancer,” said the study’s co-senior author, Wei-Chi Liao, M.D., Ph.D., from National Taiwan University and National Taiwan University Hospital.
The researchers are planning further studies. In particular, they want to look at the tool’s performance in more diverse populations. Since the current study was retrospective, they want to see how it performs going forward in real-world clinical settings.
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Gut bacteria have a profound impact on health by aiding digestion, providing nutrients and metabolites, and working with the immune system to fend off pathogens. Some gut bacteria, however, have been implicated in progression of cancers of the gut and associated organs.
A new study by researchers from the University of Chicago shows that some commensal bacteria promote the development of leukemia caused by the murine leukemia virus (MuLV) by suppressing the animal’s adaptive anti-tumor immune response. When both the virus and commensal bacteria are present in mice, three genes known as negative immune regulators are expressed more, or upregulated, which in turn tamps down the immune response that would otherwise kill the tumor cells. Two of these three negative immune regulators are also known to be indicators of poor prognosis for humans with some forms of cancer.
“These two negative immune regulators have been really well documented to be poor prognostic factors in some human cancers, but nobody knew why,” said Tatyana Golovkina, PhD, Professor of Microbiology at UChicago and senior author of the study. “Using a mouse model of leukemia, we found that the bacteria contribute to upregulation of these negative immune regulators, allowing developing tumors to escape recognition by the immune system.”
Results of the research, “Gut commensal bacteria enhance pathogenesis of a tumorigenic murine retrovirus,” were published September 13 in Cell Reports.
Cancer is usually thought to be the result of spontaneous mutations that cause cells to grow and multiply out of control, forming tumors. In 1910, a pathologist named Peyton Rous took a sample from cancerous tumor in a chicken and injected it into a healthy bird, which developed cancer as well. His discovery was dismissed at the time, but researchers later discovered that the cancer was transmitted by a retrovirus. This discovery prompted more research and subsequent identification of numerous retroviruses causing various types of cancer.
Some cancer-causing retroviruses take advantage of gut microbes to spread and replicate. For example, in a 2011 study, Golovkina and her team found that a virus that causes mammary tumors in mice depends on gut bacteria, enabling the virus to block the immune responses from recognizing and eliminating infected cells. Thus, the microbes help the virus replicate and as a result, tumors develop.
In the new study, the researchers wanted to see if commensal bacteria affected the development of a virus-induced cancer in another way besides assisting its replication. They used germ-free (GF) mice that had been raised in a special facility so they had no microbes, and specific pathogen free (SPF) mice that don’t have any pathogenic microbes that could causes disease but do have common commensal microbes, including bacteria that normally populate the gut. GF and SPF mice were both infected with the murine leukemia virus (MuLV). While the virus infected and replicated equally well in both types of mice, only SPF mice developed high-frequency tumors.
Virally induced cancer cells all express viral antigens, or molecules that mark them as foreign to the host and make them the targets for the immune attack. For the virally induced tumor cells to continue to replicate, they must be protected from the immune system’s attack, so, Golovkina’s team searched for a microbe-dependent immune evasion mechanism that enabled virally induced cancer cells to survive in the host.
The team performed a series of experiments with immunodeficient mice that were specially engineered, so they lacked the adaptive immune system. In the germ-free setting, these mice developed tumors when exposed to the virus with the same frequency as immunosufficient SPF mice with intact immune systems. So, the anti-tumor immune response was being counteracted by microorganisms, which were subsequently identified as commensal bacteria.
The researchers then found that commensal bacteria induced three genes known as negative immune regulators in infected mice. These genes normally act to shut down the immune system after it dealt with a pathogen, but in this case, they held back an immune response directed against cancer cells. Two of the three upregulated negative immune regulators — Serpinb9b and Rnf128 — are also known to be indicators of poor prognosis for humans with some spontaneous cancers. Not all commensal bacteria had tumor promoting properties, so Golovkina and her team are continuing to research more on why this immune suppressing capability only comes into play when both virus and bacteria are present.
“Now we have to figure out what’s so special about bacteria which have these properties,” she said.
The study, “Gut commensal bacteria enhance pathogenesis of a tumorigenic murine retrovirus,” was supported by the National Institutes of Health and National Institute of Diabetes and Digestive and Kidney Diseases Digestive Disease Research Core Center. Additional authors include Jessica Spring, Aly A. Khan, Sophie Lara, Kelly O’Grady, Jessica Wilks, Sandeep Gurbuxani, Steven Erickson, and Alexander Chervonsky from the University of Chicago; and Michael Fischbach and Amy Jacobson from Stanford University.
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Materials provided by University of Chicago. Original written by Matt Wood. Note: Content may be edited for style and length.

Biomedical engineers and medical researchers at UNSW Sydney have independently made discoveries about embryonic blood stem cell creation that could one day eliminate the need for blood stem cell donors.
The achievements are part of a move in regenerative medicine towards the use of ‘induced pluripotent stem cells’ to treat disease, where stem cells are reverse engineered from adult tissue cells rather than using live human or animal embryos.
But while we have known about induced pluripotent stem cells since 2006, scientists still have plenty to learn about how cell differentiation in the human body can be mimicked artificially and safely in the lab for the purposes of delivering targeted medical treatment.
Two studies have emerged from UNSW researchers in this area that shine new light on not only how the precursors to blood stem cells occur in animals and humans, but how they may be induced artificially.
In a study published today in Cell Reports, researchers from UNSW School of Biomedical Engineering demonstrated how a simulation of an embryo’s beating heart using a microfluidic device in the lab led to the development of human blood stem cell ‘precursors’, which are stem cells on the verge of becoming blood stem cells.
And in an article published in Nature Cell Biology recently, researchers from UNSW Medicine & Health revealed the identity of cells in mice embryos responsible for blood stem cell creation.

Abnormally high blood sugar (glucose) levels can result in Type 2 diabetes when things go awry with the body’s skeletal muscle, which plays a key role in regulating glucose.
Scientists are using in vitro (in a dish) skeletal muscle engineering to gain a better understanding of the complex genetic and environmental factors underlying diabetes. This involves putting lab-grown, healthy skeletal muscle tissues in a state resembling diabetes — high glucose and high insulin — or growing skeletal muscle from diabetic patients’ muscle stem cells.
In Biophysics Reviews, from AIP Publishing, researchers from Georgia Institute of Technology and Emory University School of Medicine describe how skeletal muscle engineering has advanced significantly during the past few decades. The recent development of using human muscle stem cells to grow 3D skeletal muscle makes it easier to explore diabetes in humans.
“We can use skeletal muscle grown within the lab to capture and study diabetes disease characteristics,” said co-author Christina Sheng. “Moreover, these so-called in vitro models enable researchers to discover new medicines for diabetes. Many scientists are also trying to better understand how exercise is beneficial to patients with diabetes via these models.”
In vitro skeletal muscle models are also leading to personalized medicine, in which an individual patient’s muscle can be grown, studied, and tested to determine if new medicines will work for them.
Generally, muscle stem cells are harvested, expanded in number, and seeded together. Then they fuse together to form multinucleated muscle fibers, creating lab-grown skeletal muscle.
To mimic diabetes, the researchers treat lab-grown skeletal muscle with excess sugar, insulin, fats, or cells that promote inflammation and study how these factors affect skeletal muscle health.
“Our Biohybrid System Lab grows and exercises skeletal muscle to study healthy protein factors secreted by muscles during exercise,” said co-author Sung Jin Park. “These protein factors have the potential to treat diabetes, which poses a growing social, economic, and medical burden worldwide.”
“Most of the skeletal muscle in vitro models for exercise and diabetes studies are two-dimensional,” said Park. “More efforts are needed to build 3D models to better mimic human 3D skeletal muscle structure, allowing for more translatable studies.”
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Materials provided by American Institute of Physics. Note: Content may be edited for style and length.

Maintaining a regular workout routine can help you look and feel great — but did you know that exercise also helps maintain your muscles and bones? People who are unable to engage in physical activity experience weakening of the muscles and bones, a condition known as locomotor frailty. Recently, researchers in Japan have identified a new drug that may aid in the treatment of locomotor frailty by inducing similar effects as exercise.
Physical inactivity can result in a weakening of the muscles (known as sarcopenia) and bones (known as osteoporosis). Exercise dispels this frailty, increasing muscle strength and promoting bone formation while suppressing bone resorption. However, exercise therapy cannot be applied to all clinical cases. Drug therapy may be helpful in treating sarcopenia and osteoporosis, especially when patients have cerebrovascular disease, dementia, or when they have already become bedridden. However, there is no single drug that addresses both tissues simultaneously.
In a new study published in Bone Research, researchers from Tokyo Medical and Dental University(TMDU) developed a novel drug screening system to identify a compound that mimics the changes in muscle and bone that occur as a result of exercise. Using the screening system, the researchers identified the aminoindazole derivative locamidazole (LAMZ). LAMZ was capable of stimulating the growth of muscle cells and bone-forming cells, osteoblasts, while suppressing the growth of bone-resorbing cells, osteoclasts.
When LAMZ was administrated to mice orally, it was successfully transmitted into the blood, with no obvious side effects. “We were pleased to find that LAMZ-treated mice exhibited larger muscle fiber width, greater maximal muscle strength, a higher rate of bone formation, and lower bone resorption activity,” says lead author of the study Takehito Ono.
The research team further addressed the mode of function of LAMZ and found that LAMZ mimics calcium and PGC-1α signaling pathways. These pathways are activated during exercise and stimulate expression of downstream molecules that are involved in the maintenance of muscle and bone.
To investigate whether LAMZ can treat locomotor frailty, LAMZ was administrated to an animal model of sarcopenia and osteoporosis. “Both oral and subcutaneous administration of the drug improved the muscle and bone of mice with locomotor frailty,” says senior author Tomoki Nakashima.
Taken together, the research team’s findings show that LAMZ represents a potential therapeutic method for the treatment of locomotor frailty by mimicking exercise.
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Materials provided by Tokyo Medical and Dental University. Note: Content may be edited for style and length.

A new mechanism has been identified through which very small pollutant particles in the air may trigger lung cancer in people who have never smoked, paving the way to new prevention approaches and development of therapies, according to late-breaking data [to be] reported at the ESMO Congress 2022 by scientists of the Francis Crick Institute and University College London, funded by Cancer Research UK (1). The particles, which are typically found in vehicle exhaust and smoke from fossil fuels, are associated with non-small cell lung cancer (NSCLC) risk, accounting for over 250,000 lung cancer deaths globally per year (2,3).
“The same particles in the air that derive from the combustion of fossil fuels, exacerbating climate change, are directly impacting human health via an important and previously overlooked cancer-causing mechanism in lung cells. The risk of lung cancer from air pollution is lower than from smoking, but we have no control over what we all breathe. Globally, more people are exposed to unsafe levels of air pollution than to toxic chemicals in cigarette smoke, and these new data link the importance of addressing climate health to improving human health,” said Charles Swanton, the Francis Crick Institute and Cancer Research UK Chief Clinician, London, UK, who will present the research results at the ESMO 2022 Presidential Symposium on Saturday, 10 September.
The new findings are based on human and laboratory research on mutations in a gene called EGFR which are seen in about half of people with lung cancer who have never smoked. In a study of nearly half a million people living in England, South Korea and Taiwan, exposure to increasing concentrations of airborne particulate matter (PM) 2.5 micrometres (μm) in diameter was linked to increased risk of NSCLC with EGFR mutations.
In the laboratory studies, the Francis Crick Institute scientists showed that the same pollutant particles (PM2.5) promoted rapid changes in airway cells which had mutations in EGFR and in another gene linked to lung cancer called KRAS, driving them towards a cancer stem cell like state. They also found that air pollution drives the influx of macrophages which release the inflammatory mediator, interleukin-1β, driving the expansion of cells with the EGFR mutations in response to exposure to PM2.5, and that blockade of interleukin-1β inhibited lung cancer initiation. These findings were consistent with data from a previous large clinical trial showing a dose dependent reduction in lung cancer incidence when people were treated with the anti-IL1β antibody, canakinumab (4).
In a final series of experiments, the Francis Crick team used state-of-the-art, ultradeep mutational profiling of small samples of normal lung tissue and found EGFR and KRAS driver mutations in 18% and 33% of normal lung samples, respectively.
“We found that driver mutations in EGFR and KRAS genes, commonly found in lung cancers, are actually present in normal lung tissue and are a likely consequence of ageing. In our research, these mutations alone only weakly potentiated cancer in laboratory models. However, when lung cells with these mutations were exposed to air pollutants, we saw more cancers and these occurred more quickly than when lung cells with these mutations were not exposed to pollutants, suggesting that air pollution promotes the initiation of lung cancer in cells harbouring driver gene mutations. The next step is to discover why some lung cells with mutations become cancerous when exposed to pollutants while others don’t,” said Swanton.

Published1 day agoSharecloseShare pageCopy linkAbout sharingImage source, Getty ImagesHungary’s government has tightened its abortion rules, which will make the process of pursuing a termination more bureaucratic for pregnant women.From Thursday onwards, pregnant women will have to listen to the fetus’s heartbeat before having an abortion.Doctors will have to submit a report confirming that this has been done.Hungary’s nationalist government recently blamed increased rates of women in higher education for lower birth rates and a shrinking economy.In a decree issued on Monday, Hungary’s interior ministry urges gynaecologists, obstetricians, and other pre-natal healthcare providers to present pregnant women with a fetus’s vital functions in a “clearly identifiable way” from 15 September onwards. According to medical practice, the sign of a fetus’s vital functions can be a heartbeat. Far-right politician Dora Duro welcomed the decree, calling it a step towards “protecting all fetuses from conception”.Amnesty International Hungary said the amended decree would make it “harder to access legal and safe abortion”.Abortion has been legal in Hungary since 1953. The charity’s spokesman, Aron Demeter, told AFP the announcement was “definitely a worrying step back, a bad sign”. Hungary mums-of-four to pay no income taxHungarians warned education becoming ‘too feminine’Hungarian Prime Minister Viktor Orban has long sought to boost Hungary’s flagging birth rate and his right-wing government prides itself in standing for traditional family values.In 2019, Mr Orban announced that women with four children would be exempt from paying income tax for life.Hungary has faced criticism for its gender inequality for some time. After a visit in 2019, Council of Europe Commissioner for Human Rights Dunja Mijatovic accused the country of backsliding in gender equality and women’s rights. More on this storyHungary mums-of-four to pay no income tax11 February 2019Hungarians warned education becoming ‘too feminine’26 August

Starting twice daily flushing of the mucus-lined nasal cavity with a mild saline solution soon after testing positive for COVID-19 can significantly reduce hospitalization and death, investigators report.
They say the technique that can be used at home by mixing a half teaspoon each of salt and baking soda in a cup of boiled or distilled water then putting it into a sinus rinse bottle is a safe, effective and inexpensive way to reduce the risk of severe illness and death from coronavirus infection that could have a vital public health impact.
“What we say in the emergency room and surgery is the solution to pollution is dilution,” says Dr. Amy Baxter, emergency medicine physician at the Medical College of Georgia at Augusta University and corresponding author of the study in Ear, Nose & Throat Journal.
“By giving extra hydration to your sinuses, it makes them function better.
If you have a contaminant, the more you flush it out, the better you are able to get rid of dirt, viruses and anything else,” says Baxter.
“We found an 8.5-fold reduction in hospitalizations and no fatalities compared to our controls,” says senior author Dr. Richard Schwartz, chair of the MCG Department of Emergency Medicine. “Both of those are pretty significant endpoints.”
The study appears to be the largest, prospective clinical trial of its kind and the older, high-risk population they studied — many of whom had preexisting conditions like obesity and hypertension — may benefit most from the easy, inexpensive practice, the investigators say.

An international team of researchers, writing in the journal Nature Medicine, advises that strong care needs to be taken not to misuse or overuse machine learning (ML) in healthcare research.
“I absolutely believe in the power of ML but it has to be a relevant addition,” said neurosurgeon-in-training and statistics editor Dr Victor Volovici, first author of the comment, from Erasmus MC University Medical Center, The Netherlands. “Sometimes ML algorithms do not perform better than traditional statistical methods, leading to the publication of papers that lack clinical or scientific value.”
Real world examples have shown that the misuse of algorithms in healthcare could perpetuate human prejudices or inadvertently cause harm when the machines are trained on biased datasets.
“Many believe ML will revolutionise healthcare because machines make choices more objectively than humans. But without proper oversight, ML models may do more harm than good,” said Associate Professor Nan Liu, senior author of the comment, from the Centre for Quantitative Medicine and Health Services & Systems Research Programme at Duke-NUS Medical School, Singapore.
“If, through ML, we uncover patterns that we otherwise would not see — like in radiology and pathology images — we should be able to explain how the algorithms got there, to allow for checks and balances.”
Together with a group of scientists from the UK and Singapore, the researchers highlight that although guidelines have been formulated to regulate the use of ML in clinical research, these guidelines are only applicable once a decision to use ML has been made and do not ask whether or when its use is appropriate in the first place.
For example, companies have successfully trained ML algorithms to recognise faces and road objects using billions of images and videos. But when it comes to their use in healthcare settings, they are often trained on data in the tens, hundreds or thousands. “This underscores the relative poverty of big data in healthcare and the importance of working towards achieving sample sizes that have been attained in other industries, as well as the importance of a concerted, international big data sharing effort for health data,” the researchers write.
Another issue is that most ML and deep learning algorithms (that do not receive explicit instructions regarding the outcome) are often still regarded as a ‘black box’. For example, at the start of the COVID-19 pandemic, scientists published an algorithm that could predict coronavirus infections from lung photos. Afterwards, it turned out that the algorithm had drawn conclusions based on the imprint of the letter ‘R’ (for ‘Right Lung’) in the photos, which was always found in a slightly different spot on the scans.
“We have to get rid of the idea that ML can discover patterns in data that we cannot understand,” said Dr Volovici about the incident. “ML can very well discover patterns that we cannot see directly, but then you have to be able to explain how you came to that conclusion. In order to do that, the algorithm has to be able to show what steps it took, and that requires innovation.”
The researchers advise that ML algorithms should be evaluated against traditional statistical approaches (when applicable) before they are used in clinical research. And when deemed appropriate, they should complement clinician decision-making, rather than replace it. “ML researchers should recognise the limits of their algorithms and models in order to prevent their overuse and misuse, which could otherwise sow distrust and cause patient harm,” the researchers write.
The team is working on organising an international effort to provide guidance on the use of ML and traditional statistics, and also to set up a large database of anonymised clinical data that can harness the power of ML algorithms.
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