A new model of the causes of breast cancer, created by a team led by researchers at UC San Francisco, Genentech and Stanford University, is designed to capture the complex interrelationships between dozens of primary and secondary breast cancer causes and stimulate further research.

Breast cancer is the most common cancer in women, with an estimated 268,600 new cases and 41,760 deaths in the United States in 2019.

“Even though breast cancer can be treated successfully if caught early, we’re looking for the best ways to prevent it in the first place,” said lead author Robert A. Hiatt, MD, Ph.D., UCSF professor of epidemiology and biostatistics and associate director for population science at the UCSF Helen Diller Family Comprehensive Cancer Center.

“This model weighs what we currently know about the relationships and potential interactions between factors that individual women can control—such as drinking alcohol, hormone replacement therapy and overweight and obesity—and areas of public health policy that affect how people are influenced by social factors,” Hiatt said. “It illustrates the dynamic and sometimes unexpected ways in which the many different causes of breast cancer interact with and influence one another.”

The model, published on July 8, 2020 in Cancer Epidemiology, Biomarkers and Prevention, a journal of the American Association for Cancer Research, includes biological variables such as age, breast density and genetics as well as physical risk factors such as exposure to carcinogenic chemicals, radiation and second-hand smoke.

Overall, the model, called Paradigm II, illustrates 96 potential interactions between biological, behavioral, social and physical domains of breast cancer causation.

The model

A patient and family walk into a doctor’s office. They hope that the latest tests will reveal what is causing the patient’s illness and end the diagnostic odyssey they have been going through for years. Having an accurate diagnosis also means that maybe there is a treatment that at least can alleviate the patient’s condition.

At Baylor College of Medicine, Dr. Sarah Elsea and her colleagues have been working on improving their ability to identify the genetic cause of undiagnosed conditions. Their study appears in the journal Genetics in Medicine.

“About nine of every 10 patients that are referred to us have neurological conditions, such as developmental delay and intellectual disability, for which they don’t have a diagnosis,” said Elsea, professor of molecular and human genetics at Baylor and corresponding author of the work.

To identify the genetic cause of undiagnosed conditions, the researchers look for potentially defective genes in the patient’s genome. They use whole-exome sequencing, which analyzes all the genes that encode proteins. A gene may have many variants that encode slightly different versions of the same protein that still carry their function normally. But some variants may encode defective proteins that can cause disease. The challenging part is determining whether the variant of a particular gene that is found in a patient is causing the disease.

“In some cases, the variant is missing all or a large portion of the gene, which results in a non-functional protein. This suggests that the variant is involved in the disease. However, most genetic variants involve changes in a single building block of the DNA. That one ‘misspelled’ gene sequence may or may not result in a defective or less functional protein, and we need