Device to release cancer cells for better analysis

A new device developed at the University of Michigan could provide a non-invasive way to monitor the progress of an advanced cancer treatment.

It can pick cancer cells out of a blood sample and let them go later, enabling further tests that can show whether the therapy is successfully ridding the patient of the most dangerous cancer cells.



Cells released into the bloodstream by tumors could be used to monitor cancer treatment, but they are very difficult to capture, accounting for roughly one in a billion cells, says Sunitha Nagrath, U-M assistant professor of chemical engineering.

Nagrath and her collaborators pioneered technologies for capturing these cells from blood samples. Their devices trapped the cells on chips made with graphene oxide, a single layer of carbon and oxygen atoms. But all analysis had to be done on the chip because the cells were firmly stuck.

"We could grow the cells on the chip or analyze them all together, but research has shown that cancer cells are not all the same," she said. "Hence, it is important to study cells individually, and our new device makes this possible."

The stem cell theory of cancer holds that relapses occur because chemotherapy and radiation therapy are not very effective at killing cancer stem cells, which can make up as much as 10 percent of a tumor. As a result, the cancer stem cells left behind are able to regrow the tumor or spread to other areas of the body.

New treatments in clinical trials attack the stem cells, but killing this smaller population does not immediately shrink the tumor. Doctors need a good way to monitor whether the cancer stem cells are on the decline. This may be possible through blood tests, but clinicians need to study captured cells individually, and that means removing them from the chip.

A press release can be found from University of Michigan website.

Microscope uses artificial intelligence to find cancer cells more efficiently

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.
 

In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological “labels” to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses.

There are other current techniques that don’t use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

The new technique images cells without destroying them and can identify 16 physical characteristics — including size, granularity and biomass — instead of just one. It combines two components that were invented at UCLA: a photonic time stretch microscope, which is capable of quickly imaging cells in blood samples, and a deep learning computer program that identifies cancer cells with over 95 percent accuracy.

Deep learning is a form of artificial intelligence that uses complex algorithms to extract meaning from data with the goal of achieving accurate decision making.

The new microscope overcomes those challenges using specially designed optics that boost the clarity of the images and simultaneously slow them enough to be detected and digitized at a rate of 36 million images per second. It then uses deep learning to distinguish cancer cells from healthy white blood cells.

The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.

News source: UCLA

First computer program to detect DNA mutations in single cancer cells

Researchers at The University of Texas MD Anderson Cancer Center have announced a new method for detecting DNA mutations in a single cancer cell versus current technology that analyzes millions of cells which they believe could have important applications for cancer diagnosis and treatment. The results are published in the April 18 online issue of Nature Methods.

Existing technology, known as next-generation sequencing (NGS), measures genomes derived from millions of cells versus the newer method for single-cell sequencing, called Monovar. Developed by MD Anderson researchers, Monovar allows scientists to examine data from multiple single cells. The study was, in part, funded by MD Anderson’s Moon Shots Program, an unprecedented effort to significantly reduce deaths from cancer.

This led to development of newer technology, called single cell sequencing (SCS), that has had a major impact in many areas of biology, including cancer research, neurobiology, microbiology, and immunology, and has greatly improved understanding of certain tumor characteristics in cancer. Monovar improves further on the new SCS’s computational tools which scientists found “lacking” by more accurately detecting slight alterations in DNA makeup known as single nucleotide variants (SNVs).

Full news coverage can be found from  The University of Texas MD Anderson Cancer Center Website.