The next frontier in breast cancer: genomic co-alteration and its impact on biology and treatment planning

Typically, carcinogenesis results from the acquisition of sequential genetic alterations in multiple genes, resulting in extensive genomic heterogeneity (1). An area of ongoing research in breast cancer is the complex interplay between genetic alterations and environmental influences. Environmental factors, such as radiation exposure, diet, and hormone levels, interact with an individual’s genetic predisposition, influencing cancer risk and progression. For example, women with BRCA1 or BRCA2 mutations are at heightened risk of developing breast cancer, with certain environmental factors, such as prolonged exposure to estrogen, exacerbating this risk. In addition, genomic alterations within tumour cells can influence the surrounding stromal cells, immune cells, and extracellular matrix, creating a microenvironment that supports tumour growth and metastasis. This is thought to be a critical factor in breast cancer progression and treatment response (2). Historically, systemic therapy of breast cancer has been empirical, with treatment targeting the estrogen receptor (ER) or the human epidermal growth factor receptor 2 (HER2) supplementing untargeted cytotoxic chemotherapy. More recently, the armamentarium has expanded to include drugs targeting single genetic [e.g., germline or somatic BRCA1/2 mutations (3,4)] or other genomic alterations [e.g., PIK3CA, AKT1 mutations or PTEN alterations (5,6)]. However, there has been little evidence to support the importance of co-occurring mutually exclusive changes and the impact this co-alteration may have on the natural history of the disease or its response to treatment.

Research has explored non-random genetic alterations in multiple genes in various tumours (7). In these cohorts, mutual exclusivity was linked to specific tumour types and co-occurring alterations were associated with differing clinical outcomes and tumour microenvironment compositions. Complex analyses with clustered, regularly interspaced, short palindromic repeats and compound screening have demonstrated the potential interplay between specific oncogenic alterations. However, despite these advances, the clinical implications of co-occurrence and mutual exclusivity remained uncertain.

In their important study, by leveraging large-scale multi-omics and clinical sequencing data (8), Lin et al. characterized the interplay of co-occurring genomic alterations on breast cancer biology and treatment efficacy. In order to identify co-occurrence and mutual exclusivity of genomic alterations, Lin et al. incorporated data from two real-world cohorts. FUSCC-BRCA is a multi-omics cohort comprising 873 breast cancer patients at Fundan University Shanghai Cancer Center (FUSCC) and FUSCC-ClinSeq is a prospective targeted sequencing cohort of 4,405 consecutive patients treated at FUSCC. This analysis comprised a network of genomic co-occurrence and mutual exclusivity across different treatment stages (neoadjuvant, adjuvant, and metastatic). Although these cohorts are representative of Chinese patients, Lin et al. validated their findings in seven external global cohorts from publicly available repositories. The authors then evaluated them on drug-testing platforms that included patient-derived organoids, tumour fragments, and in vivo xenografts or isografts. These important steps provided valuable functional validation of the genomic network not previously performed in other similar studies. As expected, TP53, PIK3CA, and MYC amplifications were identified most commonly. By comparing to Caucasian patients within The Cancer Genome Atlas (TCGA) Program cohort, Lin et al. were able to note racial disparities: in ER-positive HER2-negative breast cancer, rates of TP53 and AKT1 mutation as well as PIK3CA amplification were more frequent in Asian patients.

To infer the relationship between genetic alterations present in the FUSCC-BRCA and FUSCC-ClinSeq cohorts, Lin et al. performed a Selected Events Linked By Evolutionary Conditions Across Human Tumours (SELECT) analysis, an algorithm that systematically identifies evolutionary dependencies from alteration patterns. The result was the identification of 50 co-occurring and 30 mutually exclusive events, including the previously established TP53 co-mutation with MYC amplification and the mutual exclusivity of PIK3CA and AKT1 mutation. Remarkably, Lin et al. also identified a previously unreported co-occurring TP53 mutation and MYB amplification. It is important to highlight that some co-alterations may be dynamic. There is increased mutational burden and diversity in metastatic breast cancer compared to early breast cancer. Therefore, it is difficult to ascertain at what point in carcinogenesis these co-alterations occurred. However, the co-alterations reported by Lin et al. are predominantly truncal and therefore, unlikely to change with the natural evolution of breast cancer. Interestingly, the authors showed how these co-alterations influence downstream pathways and metabolic reprogramming, offering new perspectives on tumour biology and potential resistance mechanisms. Of note, specific co-alterations like the TP53 mutation and AURKA amplification, TP53 mutation and MYB amplification, and germline BRCA1 mutation and MYC amplification were linked to different treatment responses. Specifically, the first two of these co-alterations were associated with relative resistance to endocrine therapy and immunotherapy respectively, while the last showed increased sensitivity to poly ADP ribose polymerase (PARP) inhibition. This suggests a possible role of considering the interactions of genomic alterations in treatment decision-making.

It is important to highlight a number of limitations of the analysis performed by Lin et al. First, only 65% of co-alterations were validated in at least one independent external cohort. As Lin et al. acknowledged, some cohorts employed targeted sequencing panels that limit validation of a number of genes included in the panel. However, this does result in some uncertainty. Lin et al. also indicated appropriately that some of these co-alterations occur at low frequencies and thus may not be detectable in smaller cohorts. However, the observation of co-occurring alterations and the discovery that they may have prognostic implication is notable. Whether these changes are predictive of treatment benefit will need validation in appropriately powered randomized trials of specific systemic therapy. This may be challenging as some of the co-alterations are uncommon. For example, less than 5% of ER-positive patients carry both TP53 mutation and AURKA amplification. While data suggest this co-alteration was associated with poor outcomes in tamoxifen-treated patients, the lack of a control group not treated with tamoxifen means that predictive value cannot be assumed. An important question which has yet to be answered is to what extent co-alteration impacts outcomes beyond consideration of each alteration independently. It also remains unclear whether the impact of these co-alterations can be measured using established surrogate markers of prognosis (e.g., genomic grade) or response to treatment (e.g., expression of programed death ligand 1).

Nevertheless, the ability to identify patients who may or may not respond to targeted therapy by accounting for co-alterations beyond the single driver mutation is novel. For example, it is known that individuals with BRCA pathogenic variants are sensitive to PARP inhibitors but even in this subpopulation, there is variability in response (9). The potential for co-alterations to refine the population most likely to respond would be very valuable; patients with BRCA1 mutation and MYC amplification may be preferentially treated with a PARP inhibitor over other chemotherapy agents.

It is important to emphasize that outcomes in early-stage breast cancer are favorable overall with some subgroups of stage III disease reaching a 5-year disease-specific survival rate greater than 90%, especially if low/intermediate grade, ER-positive, or HER2-positive and treated with contemporary systemic therapy (10). In such patients, even the presence of co-alteration may not translate to a large absolute effect on prognosis. In such patients, an interesting clinical question may be whether absence of such co-alterations can be used to support de-escalation strategies.

In summary, Lin et al. should be commended for their in-depth characterization and functional validation of genomic co-alteration in breast cancer. They report on provocative work questioning the value of co-occurring or mutually exclusive genomic alterations and the potential impact of these co-alterations in informing genome-based treatment decisions beyond single driver alterations. While the aim is to potentially improve the effectiveness of precision oncology, this will require data from larger cohorts and deeper mechanistic studies to validate and understand the functional implications of these genomic interactions. Researchers will also need to leverage bioinformatics tools such as TCGA and cBioPortal and supplement them with large-scale data repositories such as the Genomic Data Commons (GDC). Integrative multi-omics approaches can also be used to explore co-alteration of copy number variations (CNVs), DNA methylation, and gene expression. These multi-modal data, with inputs from prospective longitudinal studies or leveraged real-world data from shared electronic medical record systems, can be analyzed by machine learning to build predictive models that estimate patient outcomes based on their genomic profiles and treatment histories. Such data could pave the way for establishing a bench-to-bedside translational platform in the ongoing pursuit for precision oncology.

Acknowledgments

Funding: None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Translational Cancer Research. The article has undergone external peer review.

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-952/prf

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-952/coif). E.A. declares honoraria from Gilead, Novartis and Pfizer. The other author has no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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