By Lina carolina 
Breast cancers can be classified into subgroups that hint at the aggressiveness of the cancer and the likelihood that the patient will experience a recurrence years after their initial diagnosis. Now, researchers at Stanford Medicine have demonstrated that these diverse subgroups can be consolidated into three overarching categories, distinguished by specific structural variations within their DNA. This groundbreaking classification, published in the prestigious journal Nature on January 8th, reveals that these variations, including amplifications of cancer-driving genes known as oncogenes and the presence of small, independent DNA circles called extrachromosomal DNA (ecDNA), are established early in tumor development and persist throughout the disease’s progression and metastasis.
This discovery holds profound implications for patient care, promising to refine diagnostic accuracy and pave the way for more personalized and effective therapeutic strategies. By understanding the fundamental role these genomic variations play in tumor evolution, physicians can better predict patient outcomes and tailor treatment plans, distinguishing between those who may benefit most from aggressive early interventions and those who could safely defer more intensive approaches.
Unraveling the Complexity of Breast Cancer Evolution
For decades, breast cancer classification has primarily relied on the presence or absence of specific protein receptors on cancer cells. Tumors are broadly categorized as hormone-receptor positive (including estrogen receptor-positive and progesterone receptor-positive), HER2-positive, or triple-negative. Hormone-receptor positive cancers, the most common type, often respond well to therapies that target estrogen production or receptor binding. HER2-positive cancers, while aggressive, can be managed with drugs that inhibit the HER2 protein. Triple-negative breast cancers, accounting for approximately 10% of new diagnoses, are generally considered the most challenging to treat, often exhibiting early recurrence and limited targeted therapy options.
However, this receptor-based classification has limitations. Even within these broad categories, significant variability in patient prognosis and treatment response exists. Christina Curtis, PhD, the RZ Cao Professor and a professor of oncology, of genetics, and of biomedical data science at Stanford Medicine, and the senior author of the study, has long been interested in understanding the origins of aggressive breast tumors, their resistance to therapy, and their propensity for distant recurrence. "My lab has had a long-standing interest in understanding how aggressive breast tumors arise, why they are resistant to therapy and why they are prone to recur in distant organs," Dr. Curtis stated. "This research shows that breast tumors develop key structural variants that set the tumor on its course very early in its development. In short, some are born to be bad. It emphasizes the importance of robust biomarkers and of intervening early in the course of the disease."
A Decade of Research Leading to New Insights
The current study builds upon a decade of meticulous research by Dr. Curtis and her team. In 2012, they employed machine-learning techniques to analyze DNA and RNA sequences from both healthy and cancerous breast tissues. This comprehensive molecular snapshot revealed 11 clinically significant subgroups of breast cancer, a far more granular classification than previously recognized based on receptor expression alone. While these subgroups demonstrated varied prognoses, the underlying mechanisms driving these differences remained elusive.
A subsequent study in 2019, analyzing data from 75,000 individuals with estrogen-receptor positive breast cancer, highlighted a persistent risk of recurrence, even years after initial diagnosis and treatment, particularly in patients considered low-risk. This finding prompted Dr. Curtis and her colleagues to investigate whether their defined subgroups could offer a more accurate prediction of this long-term recurrence risk. Their findings were striking: four of the eight estrogen-receptor positive subgroups exhibited a significantly higher likelihood of recurrence, even a decade or two after diagnosis. Alarmingly, this elevated risk in a quarter of women with hormone-receptor positive, HER2-negative breast cancer approached nearly 50%, a level comparable to HER2-positive breast cancers before the advent of targeted therapies like trastuzumab (Herceptin), and even surpassing that of some triple-negative breast cancers.
Furthermore, the research demonstrated the utility of this subgroup classification in identifying triple-negative breast cancer patients who were unlikely to experience recurrence beyond five years, as well as those at higher risk. This stratification is crucial for guiding treatment intensity and long-term monitoring, enabling physicians to identify individuals who might benefit from aggressive early intervention or more vigilant follow-up, while sparing others from potentially unnecessary or overly harsh treatments.
The Genomic Architecture: A New Framework for Classification
Despite these advances in predictive power, the fundamental drivers behind the observed differences among subgroups remained unclear. "We wanted to take a step back," Dr. Curtis explained. "Each of the four higher risk subgroups has copy number events — duplications or amplifications of specific oncogenes involving different regions of the genome. These patterns of genomic copy number change were similar to that seen in HER2-positive disease. If we look at these tumors in an unbiased way and deconstruct these different types of mutations, what could we learn about their processes that give rise to these characteristic events? Would we discover something different?"
This pivotal question led the Stanford team to examine the "genomic architecture"—the intricate landscape of mutations and structural variations within the DNA of nearly 2,000 breast cancers, spanning all stages from early-stage ductal carcinoma in situ (DCIS) to advanced metastatic disease. Their analysis revealed that these tumors could be systematically grouped into three distinct categories based on characteristic genomic anomalies.
The Three Fundamental Genomic Architectures
1. Amplification-Driven Tumors with Extrachromosomal DNA (ecDNA)
A significant finding was the strong overlap between the high-risk hormone-receptor positive subgroups and the HER2-positive subgroup. Both categories exhibited complex, yet localized, amplifications of cancer-associated genes. Crucially, they also harbored substantial amounts of ecDNA, small, circular DNA molecules that are independent of the main chromosomes and are often enriched with oncogenes. These ecDNAs, known to disregard normal cellular regulatory mechanisms, have been implicated in driving tumor growth and evolution in other cancers.
"Here we have two different molecular subtypes, which we treat differently in the clinic but that strongly overlap in their patterns of chromosomal instability," Dr. Curtis remarked, highlighting the potential for unified therapeutic strategies targeting this shared genomic vulnerability. The presence of these amplified oncogenes and ecDNA suggests a common mechanism of rapid proliferation and tumor progression.
2. Globally Unstable Genomes
The triple-negative breast cancers presented a distinct genomic profile characterized by global instability. These tumors accumulated alterations across their entire genome, with a subset also demonstrating deficiencies in their DNA repair mechanisms. "The whole genome shows scars," Dr. Curtis described. "It’s not limited to particular oncogenes." This widespread genomic damage indicates a fundamental breakdown in the cell’s ability to maintain genetic integrity, leading to a chaotic accumulation of mutations that fuel aggressive cancer behavior.
3. Stable Genomes with Typical Recurrence Risks
In contrast to the other two groups, the "garden-variety" hormone-receptor positive, HER2-negative breast cancers, those with the expected, lower risks of recurrence, exhibited relatively stable genomes. These tumors showed fewer and less significant structural variations, suggesting a more controlled and less aggressive evolutionary trajectory.
Early Origins and Persistent Influence
A critical insight from the study is that the structural variations defining these three groups are not acquired late in the disease but are established very early in tumor development. These foundational genomic alterations are maintained as the tumors grow, invade surrounding tissues, and metastasize to distant organs. This persistence underscores their fundamental role in dictating tumor behavior and clinical outcome.
Furthermore, these genomic architectures correlate with the tumor’s interaction with the immune system. The patterns of immune cell infiltration and response are influenced by the underlying genomic landscape, suggesting that therapies might need to consider both the tumor’s intrinsic genomic vulnerabilities and its microenvironment.
Implications for Targeted Therapies and Early Intervention
The discovery of these three fundamental genomic architectures opens up exciting avenues for developing novel targeted therapies. The researchers hypothesize that existing drugs designed to target DNA repair deficiencies, such as those used for BRCA-mutated breast cancers, could potentially benefit the approximately 13% of patients with DNA repair-deficient, estrogen-receptor positive breast cancers identified within the globally unstable group.
Tumors characterized by focal amplifications and ecDNA (the first group) may be particularly vulnerable to compounds that specifically inhibit their oncogenic drivers or target the replication stress they experience. Other therapeutic strategies could focus on directly interrupting the mutational processes that propagate these genomic events.
"These early, sometimes catastrophic mutational events happen decades prior to the diagnosis of the tumor, emphasizing opportunities for earlier interventions," Dr. Curtis emphasized. "Despite the complexity of their genomes, there are constraints and only so many evolutionary paths for a tumor to follow. We now have an understanding of how and when these complex alterations arise and their accompanying vulnerabilities."
This research, supported by grants from the National Institutes of Health (CA252457, CA261719) and the Breast Cancer Research Foundation, represents a significant leap forward in our understanding of breast cancer biology. By moving beyond receptor status to a more fundamental classification based on genomic architecture, the Stanford Medicine team has provided a powerful new framework for diagnosis, prognosis, and the development of precision therapies. This work promises to redefine how breast cancer is understood and treated, ultimately leading to improved outcomes and a more personalized approach to patient care. The implications extend beyond immediate treatment, offering the potential for early detection and preventative strategies by identifying individuals at high risk based on these early genomic signatures.