Experts have discovered the first mutation in the development of human evolution.

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For the first time, scientists have caught a glimpse of the earliest genetic mutations in human development.

Using whole genome sequencing, they wound back time on cell samples from adults and revealed what took place in the genome when they were still microscopic embryos. It turns out, our first two cells contribute to our development in very different ways.

Biology Reference

Mutations come in two forms: the hereditary ones we get from our parents, which can be found in virtually every cell of the body; and the acquired (or somatic) mutations that can occur at any stage of a person's life, including those very first days when the embryo is just starting to split into multiple cells.

Somatic mutations don't necessarily cause problems, but they can sometimes lead to cancer and other diseases. They also don't necessarily live in every cell (that's called mosaicism). 

We have a fairly murky understanding of the somatic mutations that happen during the earliest life stages, because we can't just watch that stuff happening in real time.

But now researchers have discovered a way to trace these mutations back to their first appearance.

Medical Xpress

"This is the first time that anyone has seen where mutations arise in the very early human development. It is like finding a needle in a haystack," says geneticist Young Seok Ju from the Wellcome Trust Sanger Institute in the UK and the Korea Advanced Institute of Science and Technology.


"There are just a handful of these mutations, compared with millions of inherited genetic variations, and finding them allowed us to track what happened during embryogenesis."

To find these mutations, the team analysed blood and tissue samples from 279 people with breast cancer. Using samples from cancer patients allowed them to test whether mutations were present in both normal blood and tissue, and in surgically removed tumour samples.

Since breast cancer tumours develop from a single cell, a somatic mutation would either be present in every tumour cell, or not at all, which gives a clue to its possible origins.

By tracking and comparing the spread of different mutations in these various tissue samples, the scientists verified a whopping 163 mutations that must have happened within the first few cell divisions of the persons' embryonic development.

University of South Florida

This gave them a unique insight into how early embryonic cells interact.

And that's not all - a statistical analysis revealed that when a fertilised egg divides for the first time, those two cells actually contribute building material for the rest of the body at different proportions.

It appears that one of the first two cells that make us up gives rise to 70 percent of the body tissue, while the other one chips in for the rest.

"We determined the relative contribution of the first embryonic cells to the adult blood cell pool and found one dominant cell - that led to 70 percent of the blood cells - and one minor cell," says molecular biologist Inigo Martincorena from the Sanger Institute.

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"This opens an unprecedented window into the earliest stages of human development."

That's exciting, because having that window will let us discover even more about how humans develop and acquire various mutations from the get-go.

Even though the vast majority of mutations are random and harmless, occasionally they can affect an important gene, causing a developmental disorder or a disease.

"Essentially, the mutations are archaeological traces of embryonic development left in our adult tissues, so if we can find and interpret them, we can understand human embryology better," says lead researcher Mike Stratton, director of the Wellcome Trust Sanger Institute.

The researchers hope their discovery is just the first of many steps that will help us gain a better understanding of what happens to humans in the earliest days, when we're all nothing more than just a clump of cells.

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Artificial Intelligence Used to ID Skin Cancer. Deep learning algorithm does as well as dermatologists in identifying skin cancer

A dermatologist using a dermatoscope, a type of handheld microscope, to look at skin. Computer scientists at Stanford have created an artificially intelligent diagnosis algorithm for skin cancer that matched the performance of board-certified dermatologists. Credit: Matt Young

It's scary enough making a doctor's appointment to see if a strange mole could be cancerous. Imagine, then, that you were in that situation while also living far away from the nearest doctor, unable to take time off work and unsure you had the money to cover the cost of the visit. In a scenario like this, an option to receive a diagnosis through your smartphone could be lifesaving.

Universal access to health care was on the minds of computer scientists at Stanford when they set out to create an artificially intelligent diagnosis algorithm for skin cancer. They made a database of nearly 130,000 skin disease images and trained their algorithm to visually diagnose potential cancer. From the very first test, it performed with inspiring accuracy.

"We realized it was feasible, not just to do something well, but as well as a human dermatologist," said Sebastian Thrun, an adjunct professor in the Stanford Artificial Intelligence Laboratory. "That's when our thinking changed. That's when we said, 'Look, this is not just a class project for students, this is an opportunity to do something great for humanity.'"

The final product, the subject of a paper in the Jan. 25 issue of Nature, was tested against 21 board-certified dermatologists. In its diagnoses of skin lesions, which represented the most common and deadliest skin cancers, the algorithm matched the performance of dermatologists.

Why skin cancer

Every year there are about 5.4 million new cases of skin cancer in the United States, and while the five-year survival rate for melanoma detected in its earliest states is around 97 percent, that drops to approximately 14 percent if it's detected in its latest stages. Early detection could likely have an enormous impact on skin cancer outcomes.

Diagnosing skin cancer begins with a visual examination. A dermatologist usually looks at the suspicious lesion with the naked eye and with the aid of a dermatoscope, which is a handheld microscope that provides low-level magnification of the skin. If these methods are inconclusive or lead the dermatologist to believe the lesion is cancerous, a biopsy is the next step.

Bringing this algorithm into the examination process follows a trend in computing that combines visual processing with deep learning, a type of artificial intelligence modeled after neural networks in the brain. Deep learning has a decades-long history in computer science but it only recently has been applied to visual processing tasks, with great success. The essence of machine learning, including deep learning, is that a computer is trained to figure out a problem rather than having the answers programmed into it.

"We made a very powerful machine learning algorithm that learns from data," said Andre Esteva, co-lead author of the paper and a graduate student in the Thrun lab. "Instead of writing into computer code exactly what to look for, you let the algorithm figure it out."

The algorithm was fed each image as raw pixels with an associated disease label. Compared to other methods for training algorithms, this one requires very little processing or sorting of the images prior to classification, allowing the algorithm to work off a wider variety of data.

From cats and dogs to melanomas and carcinomas

Rather than building an algorithm from scratch, the researchers began with an algorithm developed by Google that was already trained to identify 1.28 million images from 1,000 object categories. While it was primed to be able to differentiate cats from dogs, the researchers needed it to know a malignant carcinoma from a benign seborrheic keratosis.

"There's no huge dataset of skin cancer that we can just train our algorithms on, so we had to make our own," said Brett Kuprel, co-lead author of the paper and a graduate student in the Thrun lab. "We gathered images from the internet and worked with the medical school to create a nice taxonomy out of data that was very messy -- the labels alone were in several languages, including German, Arabic and Latin."

After going through the necessary translations, the researchers collaborated with dermatologists at Stanford Medicine, as well as Helen M. Blau, professor of microbiology and immunology at Stanford and co-author of the paper. Together, this interdisciplinary team worked to classify the hodgepodge of internet images. Many of these, unlike those taken by medical professionals, were varied in terms of angle, zoom and lighting. In the end, they amassed about 130,000 images of skin lesions representing over 2,000 different diseases.

During testing, the researchers used only high-quality, biopsy-confirmed images provided by the University of Edinburgh and the International Skin Imaging Collaboration Project that represented the most common and deadliest skin cancers -- malignant carcinomas and malignant melanomas. The 21 dermatologists were asked whether, based on each image, they would proceed with biopsy or treatment, or reassure the patient. The researchers evaluated success by how well the dermatologists were able to correctly diagnose both cancerous and non-cancerous lesions in over 370 images.

The algorithm's performance was measured through the creation of a sensitivity-specificity curve, where sensitivity represented its ability to correctly identify malignant lesions and specificity represented its ability to correctly identify benign lesions. It was assessed through three key diagnostic tasks: keratinocyte carcinoma classification, melanoma classification, and melanoma classification when viewed using dermoscopy. In all three tasks, the algorithm matched the performance of the dermatologists with the area under the sensitivity-specificity curve amounting to at least 91 percent of the total area of the graph.

An added advantage of the algorithm is that, unlike a person, the algorithm can be made more or less sensitive, allowing the researchers to tune its response depending on what they want it to assess. This ability to alter the sensitivity hints at the depth and complexity of this algorithm. The underlying architecture of seemingly irrelevant photos -- including cats and dogs -- helps it better evaluate the skin lesion images.

Health care by smartphone

Although this algorithm currently exists on a computer, the team would like to make it smartphone compatible in the near future, bringing reliable skin cancer diagnoses to our fingertips.

"My main eureka moment was when I realized just how ubiquitous smartphones will be," said Esteva. "Everyone will have a supercomputer in their pockets with a number of sensors in it, including a camera. What if we could use it to visually screen for skin cancer? Or other ailments?"

The team believes it will be relatively easy to transition the algorithm to mobile devices but there still needs to be further testing in a real-world clinical setting.

"Advances in computer-aided classification of benign versus malignant skin lesions could greatly assist dermatologists in improved diagnosis for challenging lesions and provide better management options for patients," said Susan Swetter, professor of dermatology and director of the Pigmented Lesion and Melanoma Program at the Stanford Cancer Institute, and co-author of the paper. "However, rigorous prospective validation of the algorithm is necessary before it can be implemented in clinical practice, by practitioners and patients alike."

Even in light of the challenges ahead, the researchers are hopeful that deep learning could someday contribute to visual diagnosis in many medical fields.

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The above post is reprinted from materials provided by Sciencedaily . Note: Materials may be edited for content and length.

DOCTORS SUCCESSFULLY TREAT TWO BABIES WITH LEUKEMIA USING GENE-EDITED IMMUNE CELLS

Scientists are using gene-editing techniques to fight cancer.

IT’S A PROMISING APPROACH, BUT STILL NEEDS A LOT MORE RESEARCH

In a study out this week in the journal Science Translational Medicine, a group of British doctors reported that they had successfully “cured” two infants of the blood cancer leukemia using a treatment that involves genetically modified immune cells from a donor.

The study was incredibly small—just two babies—and the infants have only been free of leukemia for 16 and 18 months. Technically, that’s not long enough to say they are cured. Declaring someone who previously had cancer as “cured” usually doesn’t happen until that person has been free of the disease for a few years, at least. But what’s significant about this study is that it combines a promising, novel approach—CAR T cell therapy—with a relatively new gene-editing technique called TALENS, which enables the direct manipulation of genes within a person’s DNA.

In the cancer community, CAR T cell therapy is already touted as a promising immunotherapy treatment (which involves harnessing a person’s immune system to fight cancer on its own), but in preliminary trials, it’s had its limitations. Before it can become a universal cancer treatment, these kinks and logistics need to be worked out. And researchers in the field think that many of them can be solved using gene-editing techniques such as TALENS, the one used in this study, as well as CRISPR, supposedly the easiest such technique to date.


First, what is CAR T-cell treatment?

CAR T, which stands for chimeric antigen receptor T cell, is a new type of cancer treatment which is not yet publicly available, but is in active clinical trials in the United States as well as many other countries such as the United Kingdom and China. The therapy involves removing some T cells (specialized immune cells) from a patient's blood. Then those cells are genetically altered in a lab, giving them special receptors on their surface called CARs. Once the cells are ready, they are infused back into the patient’s blood, where the new (CAR) receptors seek out tumor cells, attach to them, and kill them.
CAR T-cell trials are currently in phase II clinical trials in the United States. A few drug companies, including Novartis, have plans to make the therapy available as early as this year.


How does gene-editing help?

This new treatment has worked really well for blood cancers like leukemia, especially in young children. The problem, as the researchers point out in their study, is that each set of T cells have to be custom made for each patient. That takes a lot of time, and a lot of money. Further, it’s not always feasible, or even possible, to harvest T cells from leukemia patients who simply don’t have enough healthy ones to begin with.
And that’s where gene-editing comes in. The researchers took T cells from donor recipients and made a total of four genetic changes. The two they made with TALENS enabled the T cells to become universal—allowing them to be used in any person without the risk of rejection (a phenomenon called graft-versus-host disease, where the recipient’s immune system creates such an overwhelming response to the foreign cells that the patient can die as a result). The other genetic alterations added that signature receptor to seek out and attack cancer.


What are the limitations of this study?

The two infants in the study—aged 11 and 18 months—both had an aggressive form of leukemia, and had already been subjected to other treatments like chemotherapy and stem cell transplants. And the fact that they have remained cancer free is extremely promising. But again, the study was small. Further, according to a report in MIT Technology Review, many CAR T experts argue that because the children also received other treatments simultaneously (one had a stem cell transplant soon after receiving the CAR T cells) it’s impossible to know for sure whether the CAR T cells were the sole reason the cancer cells stayed away. “There is a hint of efficacy but no proof,” Stephan Grupp, director of cancer immunotherapy at the Children’s Hospital of Philadelphia, told MIT Tech Review. “It would be great if it works, but that just hasn’t been shown yet.


What’s next?

The combination of CAR T cell immunotherapy with gene-editing remains an incredibly promising area of research. Not only to create a “universal donor” CAR T cell, but also to make the treatment more effective. Researchers at the University of Pennsylvania are currently researching using the the gene-editing technique CRISPR to edit out two genes—called checkpoint inhibitors—that prevent CAR T from working as well as it should. The trial, which could take place this year, would be the first case of a CRISPR-altered cell being used in a human patient in the United States. In November, a Chinese group tested their first CRISPR gene-edited T cells in a patient with lung cancer.
However, it’s important to remember that CAR T cell therapy is in its early stages, and CRISPR/TALEN gene edited CAR T is even newer. There’s still a lot more work to be done, including many, many more studies like this one, with a lot more patients, before it’s available for everyone.

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The above post is reprinted from materials provided by Popsci . Note: Materials may be edited for content and length.