New Findings Could Lead To Good News For Cancer Research, Prevention And Treatment
Rayshell Clapper for redOrbit.com – Your Universe Online
Researchers released some pretty incredible findings last week about cancer. In a multiple institute study including researchers and authors from the University of California at Santa Cruz (UCSC), the Buck Institute for Research on Aging, the University of California at San Francisco (UCSF), the University of North Carolina, Chapel Hill (UNC), and the Broad Institute of Harvard and MIT looked closely at how cancers are classified. The study is part of the Pan-Cancer Initiative of the Cancer Genome Atlas (TCGA).
According to a statement from the UCSC, at present “Cancers are classified primarily on the basis of where in the body the disease originates.” This means that cancers are classified based on the tissue: breast, lung, bladder, colon, et cetera. However, the researchers found what they believe will be a better way to classify cancers: based on cell type not just tissue type.
According to the Buck Institute for Research on Aging, “Scientists analyzed the DNA, RNA and protein from 12 different tumor types using six different TCGA “platform technologies” to see how the different tumor types compare to each other. The study showed that cancers are more likely to be molecularly and genetically similar based on their cell type of origin as opposed to their tissue type of origin (e.g. breast, kidney, bladder, etc.).”
Based on these findings, the researchers believe at least 10 percent of cancer patients would have their cancer reclassified. As UCSF points out, that means one out of every 10 cancer patients would have their cancers more accurately diagnosed.
Reclassification means that these patients would likely receive different and more accurate treatment thus hopefully leading to a higher likelihood of survival and remission. These findings could also lead to future research on cancer treatments to find more accurate medications and procedures according to news from UNC.
As UCSC explains, to find all this, “The research team used statistical analyses of the molecular data to divide the tumors into groups or “clusters,” first analyzing the data from each platform separately and then combining them in an integrated cross-platform analysis…All six platforms as well as the integrated analysis converged on the same divisions of the cancers into 11 major subtypes. Five of those subtypes were nearly identical to their tissue-of-origin counterparts. But some tissue-of-origin categories split into several different molecular subtypes, and some subtypes encompass tumors with several different tissues of origin.”
Specifically, the study researchers identified breast and bladder cancers as two areas where they found that the more cell-specific classification was more appropriate and accurate than the tissue-specific classification. UNC explained that in breast cancer, the breast is a very complex organ with many different cell types, which obviously leads to a variety of breast cancers: luminal, HER2-enriched, and basal-like. The Mayo Clinic defines the differences in these three types of breast cancers. First, though, it is important to understand the hormone status of breast cancer.
In breast cancer, a type may be estrogen receptor (ER) positive, progesterone positive (PR) positive, or hormone receptor (HR) negative. ER positive refers to a type of breast cancer that is sensitive to estrogen whereas PR positive means that type is sensitive to progesterone while HR negative refers to the fact that type of cancer does not have hormone receptors thus it will not be affected by treatments focusing on blocking hormones. Additionally, breast cancers consider the genetic makeup of breast cancer. Those that have too many copies of the HER-2 gene have too much of the growth-promoting protein HER-2, so treatments focus on slowing and killing these cancer cells.
To that end, luminal breast cancers can be ER positive, PR positive but HER-2 negative (called luminal A breast cancer) or ER positive, PR negative, and HER-2 positive (called luminal B breast cancer). HER2-enriched cancers are ER negative, PR negative, but HER-2 positive. And the basal-like cancers are ER, PR, and HER-2 negative. Basal-like cancers are also called triple-negative breast cancer. It is the basal-like breast cancers where the researchers found that a more accurate way of classifying them is via cell origin rather than tissue origin because the basal-like breast cancers looked more like ovarian cancer and cancers of the squamous-cell (skin) type.
Although breast cancer shows the clearest indicators supporting the idea that cancers can and should be classified by both tissue and cell type, other cancers supported this as well, namely bladder cancer. UCSC explains that bladder cancer split into three subtypes based on cell-origin classification: bladder cancer only, bladder cancers that clustered with lung adenocarcinomas, and bladder cancers that were squamous-like cancers.
In continuing research, these five institutes will broaden samples from tumors from the 12 tumor types used in this study to 21 tumor types. All expect that more cancers will be reclassified.
Not only does this study give greater understanding to cancers, but it can and likely will lead to better treatment options. More hopefully, this study and those that follow could also lead to better cancer prevention.
According to UCSC, the work was performed as part of the UCSC-Buck Institute Genome Data Analysis Center for the TCGA project led by Stuart, Benz, and David Haussler, director of the UC Santa Cruz Genomics Institute. The corresponding authors of the paper are Stuart, Benz, and Charles Perou of the University of North Carolina, Chapel Hill. The co-first authors are Katherine Hoadley of UNC; Christina Yau of the Buck Institute; Denise Wolf of UCSF; and Andrew Cherniack of the Broad Institute of Harvard and MIT. Stuart’s graduate students Sam Ng and Vladislav Uzunangelov also made significant contributions to the analysis.
Results of this research were published August 7 in the journal Cell.