Category Archives: Cancer

Exposure to WIDESPREAD ENDOCRINE-DISRUPTING CHEMICAL during pregnancy may reduce protection against breast cancer

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Propylparaben is widespread, authors fear no way to avoid exposure

Low doses of propylparaben – a chemical preservative found in food, drugs and cosmetics – can alter pregnancy-related changes in the breast in ways that may lessen the protection against breast cancer that pregnancy hormones normally convey, according to a new study published by researchers at University of Massachusetts Amherst.

The findings, published March 16 in the journal Endocrinology, suggest that propylparaben is an endocrine-disrupting chemical that interferes with the actions of hormones, says environmental health scientist Laura Vandenberg, the study’s senior author. Endocrine disruptors can affect organs sensitive to hormones, including the mammary gland in the breast that produces milk.

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Photo by Anna Shvets

“We found that propylparaben disrupts the mammary gland of mice at exposure levels that have previously been considered safe based on results from industry-sponsored studies. We also saw effects of propylparaben after doses many times lower, which are more reflective of human intake,” Vandenberg says. “Although our study did not evaluate breast cancer risk, these changes in the mammary tissue are involved in mitigating cancer risk in women.”

Hormones produced during pregnancy not only allow breast tissue to produce milk for the infant, but also are partly responsible for a reduced risk of breast cancer in women who give birth at a younger age.

The researchers, including co-lead author Joshua Mogus, a Ph.D. student in Vandenberg’s lab, tested whether propylparaben exposure during the vulnerable period of pregnancy and breastfeeding adversely alters the reorganization of the mammary gland. They examined the mothers’ mammary glands five weeks after they exposed the female mice to environmentally doses of propylparaben during pregnancy and breastfeeding.

Compared with pregnant mice that had not received propylparaben, the exposed mice had mammary gland changes not typical of pregnancy, the researchers report. These mice had increased rates of cell proliferation, which Vandenberg says is a possible risk factor for breast cancer. They also had less-dense epithelial structures, fewer immune cell types and thinner periductal collagen, the connective tissue in the mammary gland.

“Some of these changes may be consistent with a loss of the protective effects that are typically associated with pregnancy,” says Mogus, who was chosen to present the research, deemed “particularly newsworthy” by the Endocrine Society, at the international group’s virtual annual meeting, ENDO 2021, beginning March 20.

Mogus says future studies should address whether pregnant females exposed to propylparaben are actually more susceptible to breast cancer. “Because pregnant women are exposed to propylparaben in many personal care products and foods, it is possible that they are at risk,” Mogus says, adding that pregnant and breastfeeding women should try to avoid using products containing propylparaben and other parabens.

“This chemical is so widely used, it may be impossible to avoid entirely,” Mogus adds. “It is critical that relevant public health agencies address endocrine-disrupting chemicals as a matter of policy.”

SOURCE: UMASS EDU Credit: Patty Shillington

Study compares discrimination claims of younger and older Americans with cancer

David Strauser – professor, Department of Kinesiology and Community Health (Photo by L. Brian Stauffer

Kinesiology and community health scientists found that younger and older adults with cancer differ in their experiences of employment discrimination.

CHAMPAIGN, Ill. — Researchers assessed the employment discrimination claims made by younger and older American adults with cancer and found substantial differences in the nature – and outcomes – of their claims.

Reported in the Journal of Cancer Survivorship, the research focused on Title I complaints made to the U.S. Equal Employment Opportunity Commission from 2009 to 2016. This included 1,001 claims from cancer survivors up to age 35 and 8,874 claims by adults over 35 with a history of cancer.

The Americans with Disabilities Act originally recognized that people with cancer and undergoing cancer treatment could experience declines in their physical and cognitive functioning. But these difficulties were thought to disappear at the end of treatment or when the cancer was in remission. The ADA was amended in 2009 to allow for the fact that even after treatment ends, people with a history of cancer and cancer treatment often experience lingering difficulties.

“Fatigue is the most common issue that people with cancer experience,” said David Strauser, a professor of kinesiology and community health at the University of Illinois Urbana-Champaign who led the new research. “Also, chemotherapy can affect their ability to concentrate, to focus on details or to process information as fast as they used to.”

Previous studies have found that “adult cancer survivors experience discrimination at a similar rate as other groups with disabilities,” Strauser said. While several studies have focused on older adults with cancer in the workplace, the employment discrimination experiences of younger adults with cancer have been overlooked, he said.

 A recent analysis of dozens of studies found that younger adult survivors of childhood cancer were nearly twice as likely to be unemployed as their healthy peers. Those with cancers of the central nervous system were nearly five times as likely to be unemployed.

All of the complaints that Strauser and his colleagues analyzed had been resolved by the EEOC – either by a finding of merit or with a determination that there was not enough evidence to proceed. The EEOC found that 26.6% of younger cancer survivors’ claims had merit. Older adults with a history of cancer had a higher success rate; 31.4% of their claims were found to have merit.

The primary complaints of older and younger adults with cancer involved what they saw as unfair working terms and conditions, harassment, discipline, failure to accommodate their disabilities and wrongful termination of their employment.

But younger cancer survivors were more likely than their older peers to claim discriminatory treatment in regard to opportunities for training and promotion. They also brought significantly more claims relating to reinstatement – being allowed to return to their jobs after taking leave for treatment – and the writing of references to potential future employers.

“What we’re seeing here is that younger cancer survivors have different needs related to employment than their older counterparts,” Strauser said. “Their discrimination claims tend to be related to issues around their career advancement.”

This finding suggests that employers may not be familiar with laws protecting the rights of people with disabilities that stem from chronic illness, Strauser said.

“I think employers get a lot of training and support on how to handle affirmative action issues and family leave for parents,” he said. “But when it comes to disability in relation to chronic illness, they tend to be less versed, and we don’t do a lot of training in that area. These results suggest we need to do more.”

The paper “The employment discrimination experiences of younger and older Americans with cancer under Title I of the Americans with Disabilities Act” is available online and from the U. of I. News Bureau.

DOI: 10.1007/s11764-020-00867-x

SOURCE: news.illinois.edu Credit: Diana Yates

Deep learning may help doctors choose better lung cancer treatments

Deep learning, a powerful machine learning model, could guide doctors and healthcare workers in weighing treatment and care options, according to a team of Great Valley researchers.

MALVERN, Pa. — Doctors and healthcare workers may one day use a machine learning model, called deep learning, to guide their treatment decisions for lung cancer patients, according to a team of Penn State Great Valley researchers.

In a study, the researchers report that they developed a deep learning model that, in certain conditions, was more than 71% accurate in predicting survival expectancy of lung cancer patients, significantly better than traditional machine learning models that the team tested. The other machine learning models the team tested had about a 61% accuracy rate.

Information on a patient’s survival expectancy could help guide doctors and caregivers in making better decisions on using medicines, allocating resources and determining the intensity of care for patients, according to Youakim Badr, associate professor of data analytics.

“This is a high-performance system that is highly accurate and is aimed at helping doctors make these important decisions about providing care to their patients,” said Badr. “Of course, this tool can’t be used as a substitute for a doctor in making decisions on lung cancer treatments.”

According to Robin G. Qiu, professor of information science and engineering and an affiliate of the Institute for Computational and Data Sciences, the model can analyze a large amount of data — typically called features in machine learning — that describe the patients and the disease to understand how a combination of factors affect lung cancer survival periods. Features can include information such as types of cancer, size of tumors, the speed of tumor growth, and demographic data.

What is deep learning?

Deep learning may be uniquely suited to tackle lung cancer prognosis because the model can provide the robust analysis necessary in cancer research, according to the researchers, who report their findings in International Journal of Medical Informatics. Deep learning is a type of machine learning that is based on artificial neural networks, which are generally modeled on how the human brain’s own neural network functions.

In deep learning, however, developers apply a sophisticated structure of multiple layers of these artificial neurons, which is why the model is referred to as “deep.” The learning aspect of deep learning comes from how the system learns from connections between data and labels, said Badr.

“Deep learning is a machine-learning algorithm that makes associations between the data, itself, and the labels that we use to describe the data examples,” said Badr. “By making these associations, it learns from the data.”

Qiu added that deep learning’s structure offers several advantages for many data science tasks, especially when confronted with data sets that have a large number of records — in this case, patients — as well as a large number of features.

“It improves performance tremendously,” said Qiu. “In deep learning we can go deeper, which is why they call it that. In traditional machine learning, you have a simple structure of layers of neural networks. In each layer, you have a group of cells. In deep learning, there are many layers of these cells that can be architected into a sophisticated structure to perform better feature transformation and extraction, which gives you the ability to further improve the accuracy of any model.”

In the future, the researchers would like to improve the model and test its ability to analyze other types of cancers and medical conditions.

“The accuracy rate is good, but it’s not perfect, so part of our future work is to improve the model,” said Qiu.

To further improve their deep learning model, the researchers would also need to connect with domain experts, who are people who have specific knowledge. In this case, the researchers would like to connect with experts on specific cancers and medical conditions.

“In a lot of cases, we might not know a lot of features that should go into the model,” said Qiu. “But, by collaborating with domain experts, they could help us collect important features about patients that we might not be aware of and that would further improve the model.”

The researchers analyzed data from the Surveillance, Epidemiology, and End Results (SEER) program. The SEER dataset is one of the biggest and most comprehensive databases on the early diagnosis information for cancer patients in the United States, according to Shreyesh Doppalapudi, a graduate-student research assistant and first author of the paper. The program’s cancer registries cover almost 35% of U.S. cancer patients.

“One of the really good things about this data is that it covers a large section of the population and it’s really diverse,” said Doppalapudi. “Another good thing is that it covers a lot of different features, which you can use for many different purposes. This becomes very valuable, especially when using machine learning approaches.”

Doppalapudi added that the team compared several deep learning approaches, including artificial neural networks, convolutional neural networks and recurrent neural networks, to traditional machine learning models. The deep learning approaches performed much better than the traditional machine learning methods, he said.

Deep learning architecture is better suited to processing such large, diverse datasets, such as the SEER program, according to Doppalapudi. Working on these types of datasets requires robust computational capacity. In this study, the researchers relied on ICDS’s Roar supercomputer.

With about 800,000 to 900,000 entries in the SEER dataset, the researchers said that manually finding these associations in the data with an entire team of medical researchers would be extremely difficult without assistance from machine learning.

If it were only three fields I would say it would be impossible — and we had about 150 fields,” said Doppalapudi. “Understanding all of those different fields and then reading and learning from that information, would be impossible.”

SOURCE: NEWS.PSU.EDU Credit: Matt Swayne