Researchers have cured cervical cancer in mice using CRISPR gene-editing technology

Dr Luqman Jubair and Professor Nigel McMillan from Menzies Health Institute Queensland.

Gene-editing breakthrough in battle against cancer

In what is believed to be a world first, Griffith University researchers have cured cervical cancer in mice using CRISPR gene-editing technology.

“This is the first cure for any cancer using this technology,’’ said lead researcher Professor Nigel McMillan from Menzies Health Institute Queensland. 

The scientists used CRISPR-Cas9 to successfully target and treat cervical cancer tumours in vivo (via injection into live and tumour-bearing mice) using “stealth” nanoparticles.

“The nanoparticles search out the cancer-causing gene in cancer cells and “edit it’’ by introducing some extra DNA that causes the gene to be misread and stop being made,’’ Professor McMillan said.

“This is like adding a few extra letters into a word, so the spell checker doesn’t recognise it “anyTTmore’’. Because the cancer must have this gene to produce, once edited the cancer dies.

“In our study, the treated mice have 100% survival and no tumours. The mice showed no other clinical signs such as inflammation from treatment but there may be other gene changes we haven’t measured yet.

“Other cancers can be treated once we know the right genes.”

Nearly all cervical cancers are caused by a human papillomavirus infection (HPV), with more than 250* women in Australia dying from the disease each year. *source: Cancer Australia

“Persistent infection with high-risk HPV is responsible for 99.7% of cervical cancer cases,’’ Professor McMillan said.

“After infection, the HPV integrates the E6 and E7 oncogene (genes with the potential to cause cancer), into the human genome which drive and sustain cervical cancer.”

The Griffith University scientists are working towards human trials of the gene therapy in the next five years.

Learn more: Gene-editing breakthrough in battle against cancer


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Revolutionizing cervical cancer screening using artificial intelligence

via MEDICA – World Forum for Medicine

A research team led by investigators from the National Institutes of Health and Global Good has developed a computer algorithm that can analyze digital images of a woman’s cervix and accurately identify precancerous changes that require medical attention.

This artificial intelligence (AI) approach, called automated visual evaluation, has the potential to revolutionize cervical cancer screening, particularly in low-resource settings.

To develop the method, researchers used comprehensive datasets to “train” a deep, or machine, learning algorithm to recognize patterns in complex visual inputs, such as medical images. The approach was created collaboratively by investigators at the National Cancer Institute (NCI) and Global Good, a fund at Intellectual Ventures, and the findings were confirmed independently by experts at the National Library of Medicine (NLM). The results appeared in the Journal of the National Cancer Institute on January 10, 2019. NCI and NLM are parts of NIH.

“Our findings show that a deep learning algorithm can use images collected during routine cervical cancer screening to identify precancerous changes that, if left untreated, may develop into cancer,” said Mark Schiffman, M.D., M.P.H., of NCI’s Division of Cancer Epidemiology and Genetics, and senior author of the study. “In fact, the computer analysis of the images was better at identifying precancer than a human expert reviewer of Pap tests under the microscope (cytology).”

The new method has the potential to be of particular value in low-resource settings. Health care workers in such settings currently use a screening method called visual inspection with acetic acid (VIA). In this approach, a health worker applies dilute acetic acid to the cervix and inspects the cervix with the naked eye, looking for “aceto whitening,” which indicates possible disease. Because of its convenience and low cost, VIA is widely used where more advanced screening methods are not available. However, it is known to be inaccurate and needs improvement.

Automated visual evaluation is similarly easy to perform. Health workers can use a cell phone or similar camera device for cervical screening and treatment during a single visit. In addition, this approach can be performed with minimal training, making it ideal for countries with limited health care resources, where cervical cancer is a leading cause of illness and death among women.

To create the algorithm, the research team used more than 60,000 cervical images from an NCI archive of photos collected during a cervical cancer screening study that was carried out in Costa Rica in the 1990s. More than 9,400 women participated in that population study, with follow up that lasted up to 18 years. Because of the prospective nature of the study, the researchers gained nearly complete information on which cervical changes became precancers and which did not. The photos were digitized and then used to train a deep learning algorithm so that it could distinguish cervical conditions requiring treatment from those not requiring treatment.

Overall, the algorithm performed better than all standard screening tests at predicting all cases diagnosed during the Costa Rica study. Automated visual evaluation identified precancer with greater accuracy (AUC=0.91) than a human expert review (AUC=0.69) or conventional cytology (AUC=0.71). An AUC of 0.5 indicates a test that is no better than chance, whereas an AUC of 1.0 represents a test with perfect accuracy in identifying disease.

“When this algorithm is combined with advances in HPV vaccination, emerging HPV detection technologies, and improvements in treatment, it is conceivable that cervical cancer could be brought under control, even in low-resource settings,” said Maurizio Vecchione, executive vice president of Global Good.

The researchers plan to further train the algorithm on a sample of representative images of cervical precancers and normal cervical tissue from women in communities around the world, using a variety of cameras and other imaging options. This step is necessary because of subtle variations in the appearance of the cervix among women in different geographic regions. The ultimate goal of the project is to create the best possible algorithm for common, open use.

Learn more: AI approach outperformed human experts in identifying cervical precancer



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A new epigenetics-based test for cervical cancer with a 100 percent detection rate

A cytologic smear under the microscope

A new test for cervical cancer was found to detect all of the cancers in a randomised clinical screening trial of 15,744 women, outperforming both the current Pap smear and human papillomavirus (HPV) test at a reduced cost, according to a study led by Queen Mary University of London.

The study, published in the International Journal of Cancer, compared a new ‘epigenetics-based’ cervical cancer test with Pap smear and HPV tests, and investigated how well it predicted the development of cervical cancer up to five years in advance in a large study of women aged 25-65 in Canada.

As opposed to checking for patterns in the DNA genetic code itself that are indicative of the HPV virus, the new test looks at the naturally-occurring chemical markers that appear on top of the DNA, making up its ‘epigenetic profile’.

‘An enormous development’

Lead researcher Professor Attila Lorincz from Queen Mary’s Wolfson Institute of Preventive Medicine who also helped develop the world’s first test for HPV in 1988, said: “This is an enormous development. We’re not only astounded by how well this test detects cervical cancer, but it is the first time that anyone has proven the key role of epigenetics in the development of a major solid cancer using data from patients in the clinic. Epigenetic changes are what this cervical cancer test picks up and is exactly why it works so well.

“In contrast to what most researchers and clinicians are saying, we are seeing more and more evidence that it is in fact epigenetics, and not DNA mutations, that drives a whole range of early cancers, including cervical, anal, oropharyngeal, colon, and prostate.”

Screening to prevent cervical cancer is typically done through the Pap smear, which involves the collection, staining and microscopic examination of cells from the cervix. Unfortunately, the Pap smear can detect only around 50 per cent of cervical pre-cancers.

A much more accurate cervical screening method involves testing for the presence of DNA from the human papillomavirus (HPV) – the primary but indirect cause of cervical cancer. There are estimated to be around 10 million women in the UK who are infected by HPV.

However, the HPV test only identifies whether or not women are infected with a cancer-causing HPV, but not their actual risks of cancer, which remain quite low. This causes unnecessary worry for the majority of HPV-infected women who receive a positive result but will eventually clear the virus and not develop the disease.

Predicting a person’s risk of cervical cancer

The new test was significantly better than either the Pap smear or HPV test. It detected 100 per cent of the eight invasive cervical cancers that developed in the 15,744 women during the trial. In comparison, the Pap smear only detected 25 per cent of the cancers, and the HPV test detected 50 per cent.

The study also looked more closely at a subset of 257 HPV-positive women which were representatively selected from the large study. The new test detected 93 per cent of pre-cancerous lesions in those women, compared to 86 per cent detected using a combination of the Pap smear and HPV test, and 61 per cent detected using the Pap smear on its own.

Reducing the number of screening appointments needed

Professor Lorincz added: “This really is a huge advance in how to deal with HPV-infected women and men, numbering in the billions worldwide, and it is going to revolutionise screening.

“We were surprised by how well this new test can detect and predict early cervical cancers years in advance, with 100 per cent of cancers detected, including adenocarcinomas, which is a type of cervical cancer that is very difficult to detect. The new test is much better than anything offered in the UK at present but could take at least five years to be established.”

The authors say that using this test in the clinic would reduce the number of visits to the doctor and screening appointments, as high-grade disease would be detected from the start. They also say that if it was fully implemented, it would be cheaper than the Pap smear.

Learn more: New cervical cancer test has 100 per cent detection rate



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Newest HPV vaccine is highly effective at preventing HPV infection and disease

via Medscape

Long-term study show the nine-valent HPV vaccine greatly reduces the risk of HPV infection and HPV-associated diseases

Cervical cancer is the second most common cause of cancer-related death worldwide, with almost 300,000 deaths occurring each year. More than 80 percent of these deaths occur in developing nations. The advent of human papillomavirus (HPV) vaccines has significantly reduced the number of those who develop and die from cervical cancer. And thanks to an international effort to improve the vaccine, the medical community is one step closer to preventing more HPV-associated diseases. The researchers, including those from Moffitt Cancer Center, published the final results of a study showing the newest vaccine is highly effective at preventing HPV infection and disease. The study was published this week in The Lancet.

HPV is an extremely common virus. It is estimated that by age 50, four out of five women have been infected with the virus at one point throughout their lifetimes. HPV causes ailments such as genital and anal warts and, in some instances, continued infection can lead to the development of benign or cancerous growths of the cervix, vulva, vagina, anus, penis, tonsils, and base of the tongue. There are more than 100 types of HPV, but only approximately 13 types are associated with cancer development. HPV 16 and 18 alone are estimated to cause 70 percent of all cervical cancers.

Two existing HPV vaccines, Cervarix® and Gardasil®, are effective at preventing disease caused by HPV types 16 and 18, while Gardasil also protects against genital warts caused by HPV 6 and 11. However, these vaccines do not protect against all HPV types that are associated with cancer.  Scientists developed an improved vaccine called 9vHPV that targets HPV 16, 18, 6, and 11, and an additional 5 HPV types that are the next most commonly associated with cervical cancer (HPV 31, 33, 45, 52 and 58).

“Based on epidemiological studies, the 9vHPV vaccine could prevent approximately 90 percent of cervical cancer, 90 percent of HPV-related vulvar and vaginal cancer, 70 to 85 percent of high-grade cervical disease in females, and approximately 90 percent of HPV-related anal cancer and genital warts in males and females worldwide,” explained Anna R. Giuliano, Ph.D., Director of the Center for Infection Research in Cancer at Moffitt.

Researchers from 18 countries and 105 study sites conducted a phase 3 study to compare the activity of the new 9vHPV vaccine against the older vaccine that protected against four HPV types (Gardasil). The study randomized 14,215 women 16 to 26 years of age to either 9vHPV or Gardasil, and the study participants were medically followed for 6 years after vaccination.

The study found that the 9vHPV vaccine has long-term activity against HPV infection and disease.  The 9vHPV vaccine reduced the risk of developing HPV 31/33/45/52/58-related cervical, vulvar, and vaginal disease by 97.7 percent when compared to Gardasil®, and the two vaccines had similar activity at preventing HPV 6/11/16/18-associated disease. The 9vHPV vaccine was also highly effective at reducing the risk of having HPV 31/33/45/52/58-associated cervical cell abnormalities, biopsies, and definitive therapies.

9vHPV, known as Gardasil 9, became available in 2015 to protect females and males ages 9 through 26 years against HPV-associated cancers and genital warts. Scientists hope its continued use will greatly reduce the incidence and mortality of HPV-associated diseases.

“The 9vHPV vaccine is licensed in over 40 countries for the prevention of HPV-related anogenital cancers and pre-cancer, and genital warts. The results of this study support comprehensive vaccination programs and inform public health decision related to implementation,” said Giuliano.

Learn more: New Study Finds Improved Vaccine That Protects against Nine Types of HPV is Highly Effective


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Low-cost artificial intelligence image detection method for cervical cancer

via Medical Design Technology

An artificial intelligence image detection method has the potential to outperform PAP and HPV tests in screening for cervical cancer; Low-cost technique could be used in less-developed countries, where 80 percent of cervical cancer deaths occur.

Artificial intelligence–commonly known as A.I.–is already exceeding human abilities. Self-driving cars use A.I. to perform some tasks more safely than people. E-commerce companies use A.I. to tailor product ads to customers’ tastes quicker and with more precision than any breathing marketing analyst.

And, soon, A.I. will be used to “read” biomedical images more accurately than medical personnel alone–providing better early cervical cancer detection at lower cost than current methods.

However, this does not necessarily mean radiologists will soon be out of business.

“Humans and computers are very complementary,” says Sharon Xiaolei Huang, associate professor of computer science and engineering at Lehigh University in Bethlehem, PA. “That’s what A.I. is all about.”

Huang directs the Image Data Emulation & Analysis Laboratory at Lehigh where she works on artificial intelligence related to vision and graphics, or, as she says: “creating techniques that enable computers to understand images the way humans do.” Among Huang’s primary interests is training computers to understand biomedical images.

Now, as a result of 10 years work, Huang and her team have created a cervical cancer screening technique that, based on an analysis of a very large dataset, has the potential to perform as well or better than human interpretation on other traditional screening results, such as Pap tests and HPV tests–at a much lower cost. The technique could be used in less-developed countries, where 80% of deaths from cervical cancer occur.

The researchers are currently seeking funding for the next step in their project, which is to conduct clinical trials using this data-driven detection method.

A more accurate screening tool, at lower cost

Huang’s screening system is built on image-based classifiers (an algorithm that classifies data) constructed from a large number of Cervigram images. Cervigrams are images taken by digital cervicography, a noninvasive visual examination method that takes a photograph of the cervix. The images, when read, are designed to detect cervical intraepithelial neoplasia (CIN), which is the potentially precancerous change and abnormal growth of squamous cells on the surface of the cervix.

“Cervigrams have great potential as a screening tool in resource-poor regions where clinical tests such as Pap and HPV are too expensive to be made widely available,” says Huang. “However, there is concern about Cervigrams’ overall effectiveness due to reports of poor correlation between visual lesion recognition and high-grade disease, as well as disagreement among experts when grading visual findings.”

Huang thought that computer algorithms could help improve accuracy in grading lesions using visual information–a suspicion that, so far, is proving correct.

Because Huang’s technique has been shown, via an analysis of the very large dataset, to be both more sensitive–able to detect abnormality–as well as more specific (fewer false positives), it could be used to improve cervical cancer screening in developed countries like the U.S.

“Our method would be an effective low-cost addition to a battery of tests helping to lower the false positive rate since it provides 10% better sensitivity and specificity than any other screening method, including Pap and HPV tests,” says Huang.

Correlating visual features and patient data to cancer

To identify the characteristics that are most helpful in screening for cancer, the team created hand-crafted pyramid features (basic components of recognition systems)–as well as investigated the performance of a common deep learning framework known as convolutional neural networks (CNN) for cervical disease classification.

They describe their results in an article in the March issue of Pattern Recognition called: “Multi-feature base benchmark for cervical dysplasia classification.” The researchers have also released the multi-feature dataset and extensive evaluations using seven classic classifiers here.

To build the screening tool, Huang and her team used data from 1,112 patient visits, where 345 of the patients were found to have lesions that were positive for moderate or severe dysplasia (considered high-grade and likely to develop into cancer) and 767 had lesions that were negative (considered low-grade with mild dysplasia typically cleared by the immune system).

These data were selected from a large medical archive collected by the U.S. National Cancer Institute consisting of information from 10,000 anonymized women who were screened using multiple methods, including Cervigrams, over a number of visits. The data also contains the diagnosis and outcome for each patient.

“The program we’ve created automatically segments tissue regions seen in photos of the cervix, correlating visual features from the images to the development of precancerous lesions,” says Huang. “In practice, this could mean that medical staff analyzing a new patient’s Cervigram could retrieve data about similar cases–not only in terms of optics, but also pathology since the dataset contains information about the outcomes of women at various stages of pathology.”

From the study: “…with respect to accuracy and sensitivity, our hand-crafted PLBP-PLAB-PHOG feature descriptor with random forest classifier (RF.PLBP-PLAB-PHOG) outperforms every single Pap test or HPV test, when achieving a specificity of 90%. When not constrained by the 90% specificity requirement, our image-based classifier can achieve even better overall accuracy. For example, our fine-tuned CNN features with Softmax classifier can achieve an accuracy of 78.41% with 80.87% sensitivity and 75.94% specificity at the default probability threshold 0.5. Consequently, on this dataset, our lower-cost image-based classifiers can perform comparably or better than human interpretation based on widely-used Pap and HPV tests…”

According to the researchers, their classifiers achieve higher sensitivity in a particularly important area: detecting moderate and severe dysplasia–or cancer.

Exploring classification with improved imaging technique

Among Huang’s other projects is a collaboration with Chao Zhou, assistant professor of electrical and computer engineering at Lehigh. They are working on the use of an established medical imaging technique called optical coherence microscopy (OCM)–most commonly used in ophthalmology–to analyze breast tissue to produce computer-aided diagnoses. Their analysis is designed to help surgeons minimize the tissue removed while operating on cancer patients by providing highly accurate, real-time information about the health of the excised tissue.

They recently conducted a feasibility study with promising results that have been published in an article in Medical Image Analysis called: “Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy.”

Huang and Zhou used multi-scale and integrated image features to improve classification accuracy and were able to achieve high sensitivity (100%) and specificity (85.2%) for cancer detection using OCM images.

“Chao has done a lot of work in new instrumentation–improving the quality of biomedical images,” says Huang. “Since he works on the images–or data inputs–and I work on the results of the data analysis–or outputs, our collaboration is a natural fit.”

Learn more: Robot radiology: Low-cost AI could screen for cervical cancer better than humans



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