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Unlocking the secrets of ovarian cancer.
Credit: Johns Hopkins Medicine
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In what is believed to be the largest study of its kind, scientists at the Johns Hopkins University and the Pacific Northwest National Laboratory led a study that examined the proteomes of 169 ovarian cancer patients to identify critical proteins expressed by their tumors.
By integrating their findings with the tumors’ genetic data — the cancer genome — the investigators report the potential for new insights into the progress of the most malignant form of the disease.
“Correlating our data with clinical outcomes is the first step toward the eventual ability to predict outcomes that reflect patient survival, with potential applications for precision medicine and new targets for pharmaceutical interventions,” says Daniel W. Chan, Ph.D., a professor of pathology and oncology who led the team at the Johns Hopkins University School of Medicine. “But just like anything in medicine, clinical validation will be a long and rigorous process.”
In a summary of the work published in the advance online edition of the journal Cell on June 29, the researchers say their achievement illustrates the power of combining genomic and proteomic data to potentially yield a more complete picture of the biology of a cancer that accounts for 3 percent of all cancers in women and is the fifth-leading cause of cancer deaths among women in the United States.
“With this knowledge, researchers expect to be better able to identify the biological factors defining the 70 percent of ovarian cancer patients who suffer from the most malignant form of ovarian cancer, called high-grade serous carcinoma,” says Chan, a member of the Johns Hopkins Kimmel Cancer Center. Currently, only one in six patients lives five or more years beyond diagnosis.
“Historically, cancer’s been looked at as a disease of the genome,” says Karin Rodland, Ph.D., chief scientist for biomedical research at the Pacific Northwest National Laboratory. “But that genome has to express itself in functional outcomes, and that’s what the proteomic data add, because proteins are what get the actual work of the genome done.”
That work starts, Rodland says, when genes are transcribed into RNA, the genetic material that makes protein, the full set of which is referred to as the transcriptome.
The biological complexity of cancer is due in significant part to the fact that not every part of the transcriptome leads to proteins, and not every protein stays stable, with many undergoing changes that affect their impact and interactions with other proteins.
In their effort to better explain ovarian cancer biology, each team studied subsets of 169 high-grade serous carcinoma tissue samples and accompanying genomic and clinical data drawn from The Cancer Genome Atlas (TCGA), a collaborative effort to map cancer’s genetic mutations. The task for ovarian cancer was completed in 2011.
The Johns Hopkins team initially selected 122 of the samples based on those tumors’ ability to repair damaged DNA — known as homologous recombination deficiency — and characterized by changes in genes, including BRCA1, BRCA2 and PTEN, mutations long linked to increased cancer risk and severity.
“We chose to examine these samples because patients with changes in these genes already are benefiting from a specific drug regimen for breast cancer, so if we could find similar changes in ovarian cancer genomes and proteomes, those patients would likely benefit from the same regimen,” says Chan.
The Pacific Northwest team initially selected 84 samples based on overall patient survival times. “We examined the data for the shortest-surviving patients and the longest-surviving patients, hoping to pinpoint biological factors associated with extremely short survival or better-than-average, longer survival,” says Rodland.
Then, through their participation in the Clinical Proteomic Tumor Analysis Consortium (CPTAC), a program of the U.S. National Cancer Institute that funded both teams, the two teams combined their efforts.
Using protein measurement and identification techniques, such as mass spectrometry, the teams identified 9,600 proteins in all the tumors and pursued study on 3,586 proteins common to all 169 tumor samples.
A hallmark of cancer, and particularly high-grade serous carcinoma, is the appearance of more copies of certain regions of the genome. These so-called copy number alterations can lead to changes in protein abundance. When the researchers compared known regions of copy number alterations, they found that parts of chromosomes 2, 7, 20 and 22 led to changes in abundance of more than 200 proteins. A more careful study of those 200 proteins revealed that many are involved in cell movement and immune system function, both processes implicated in cancer progression, the researchers say.
“By comparing data for overlapping patient samples and finding comparable measurements of protein analysis at both institutions, we think our findings indicate excellent scientific rigor and reproducibility,” says Rodland.
This work should give researchers everywhere a wealth of credible information, she continues, because of the number of samples investigated, the number of proteins investigated in each sample, and the ability to cross-reference and correlate the new protein data with the TCGA genome and transcriptome data. “In brief, adding the proteome on top of the genome — what we call proteogenomics — provided a new dimension of information that enabled discovery of new biological insights into ovarian cancer while creating a valuable resource for the scientific community,” says Rodland.
“High-grade serous carcinoma is such a challenging disease, requiring complex clinical care to achieve long-term survival. This new knowledge gives us new directions to test in the lab and clinic,” says Douglas A. Levine, M.D., director of gynecologic oncology at the Laura and Isaac Perlmutter Cancer Center at NYU Langone Medical Center. “This proteogenomic analysis will help us improve patient outcomes and quality of life.”
The ovarian cancer teams were co-led by Chan, Hui Zhang, Zhen Zhang (The Johns Hopkins University), and by Rodland and Richard Smith (Pacific Northwest National Laboratory). Co-authors of the study included Bai Zhang, Jian-Ying Zhou, Li Chen, Shisheng Sun, Lijun Chen, Punit Shah, Paul Aiyetan, Yuan Tian, Caitlin Choi, Stefani Thomas, Lori Sokoll, Ie-Ming Shih, Leslie Cope, Heng Zhu and Akhilesh Pandey of The Johns Hopkins University; Tao Liu, Samuel H. Payne, Jason E. McDermott, Vladislav A Petyuk, Debjit Ray, Feng Yang, Marina A. Gritsenko, Therese R. Clauss, Matthew E. Monroe, Song Nie, Chaochao Wu and Ronald J. Moore of the Pacific Northwest National Laboratory; Douglas A. Levine of the Laura and Isaac Perlmutter Cancer Center at NYU Langone Medical Center; and Michael P. Snyder of Stanford University School of Medicine. Other contributors included colleagues from Vanderbilt University School of Medicine, University of California, San Diego, New York University School of Medicine, Virginia Polytechnic Institute and State University, the National Cancer Institute’s Office of Cancer Clinical Proteomics Research, and the CPTAC investigators.
This work was supported by the National Cancer Institute: U24CA160019 and U24CA160036, by the National Institutes of Health P41GM103493 and by the Department of Energy DE-AC05-76RL01830.