Distant metastasis is the most consequential stage of breast cancer progression because tumor cells that travel to organs such as the bone, liver, lungs, or brain are responsible for most breast cancer-related deaths. Clinicians already use tumor stage, hormone-receptor status, HER2 expression, genomic tests, and other clinical factors to estimate risk, but these measures do not fully explain why patients with seemingly similar disease can follow very different trajectories. The new analysis adds to growing evidence that cancer prognosis may depend on molecular relationships operating within tumor cells, not simply on the level of one isolated gene.

The researchers focused on SUCLA2 and USP10 because both have biological links to cancer-related processes, although they operate in different molecular contexts. SUCLA2 encodes the beta subunit of succinyl-CoA ligase, an enzyme involved in mitochondrial energy metabolism and the tricarboxylic acid cycle. Mitochondria do more than generate energy: they also influence redox balance, biosynthesis, cell death, and the ability of cancer cells to adapt to stressful environments. USP10 encodes ubiquitin-specific peptidase 10, a deubiquitinating enzyme that removes ubiquitin tags from proteins. By regulating protein stability and signaling, deubiquitinating enzymes can affect pathways involved in DNA damage responses, cell survival, growth, and immune regulation.

Rather than examining the genes independently, the study assessed their combined expression patterns in four independent breast cancer cohorts containing information on distant metastasis-free survival. Patients were grouped according to the relationship between SUCLA2 and USP10 expression, allowing the researchers to test whether a molecular combination could distinguish risk more effectively than either biomarker alone. Survival differences were evaluated using Kaplan–Meier analyses, with patients divided according to an optimal cutoff determined through receiver operating characteristic, or ROC, analysis. Statistical significance was assessed with the log-rank test.

The strongest signal emerged in patients who had low SUCLA2 expression together with high USP10 expression. Among patients who had not received treatment, this molecular pattern was associated with significantly poorer DMFS, indicating a greater likelihood of developing distant metastases during follow-up. The result was notable because the risk pattern was not reproduced consistently when SUCLA2 or USP10 was considered separately. In other words, the prognostic information appeared to reside in the relationship between the genes rather than in the absolute expression of either one.

Treatment status substantially changed the association. In patients who received treatment, the elevated metastatic risk linked to low SUCLA2 and high USP10 was no longer observed. This finding does not establish that therapy directly neutralizes the biological effects of the gene interaction, because the analysis was observational and treatment decisions may have been influenced by clinical features that were not fully captured. However, it raises the possibility that treatment modifies the relationship between tumor metabolism, protein regulation, and metastatic behavior. It also suggests that a biomarker can perform differently in treated and untreated populations, an issue that is critical when developing clinically useful prediction tools.

The researchers further examined the product of SUCLA2 and USP10 expression, a mathematical representation of their combined activity. Kaplan–Meier curves for this interaction measure supported the idea that the joint signal could separate patients with different metastatic outcomes. Such interaction-based models are designed to capture situations in which the effect of one molecular factor depends on the level of another. This is biologically plausible in cancer, where metabolic pathways, protein turnover, stress responses, and treatment resistance are tightly interconnected. A gene that appears weakly informative on its own may become clinically meaningful when interpreted in the context of another pathway.

“Our results indicate that molecular interactions may provide more informative biomarkers than single-gene measurements,” the researchers noted. The conclusion reflects a broader shift in precision oncology. Many current approaches focus on identifying mutations or expression changes in individual genes, but tumors function as dynamic networks. A metabolic enzyme and a deubiquitinating enzyme may influence overlapping cellular systems without being part of a simple linear pathway. Their combined expression could therefore act as a proxy for a tumor state characterized by altered energy use, protein stability, stress tolerance, or metastatic capacity.

The findings could eventually help identify breast cancer patients who require closer surveillance or more intensive treatment, but substantial validation is still needed. The study relied on retrospective gene-expression and clinical datasets, and the cohorts may differ in tumor subtypes, treatment regimens, follow-up duration, and methods of molecular measurement. Laboratory experiments will be necessary to determine whether SUCLA2 and USP10 directly regulate one another or instead reflect a third biological process. Prospective clinical studies must also test whether the interaction remains predictive when adjusted for tumor stage, age, receptor status, chemotherapy, endocrine therapy, HER2-targeted treatment, and other established factors. If these results are confirmed, the SUCLA2–USP10 relationship could become both a prognostic tool and a starting point for investigating new strategies to limit metastatic progression.

Subject of Research: Not applicable

Article Title: SUCLA2-USP10 interaction rather than SUCLA2 alone correlates with metastasis in breast cancer patients

Web References: https://doi.org/10.70401/cbm.2026.0022

References: He X, Shao Y, Sun X. “SUCLA2-USP10 interaction rather than SUCLA2 alone correlates with metastasis in breast cancer patients.” Computational Biomedicine. DOI: 10.70401/cbm.2026.0022.

Image Credits: © He X, Shao Y, Sun X, 2026. Creative Commons Attribution 4.0 International License.

Keywords: breast cancer, distant metastasis-free survival, SUCLA2, USP10, gene interaction, biomarkers, precision oncology, tumor metabolism, deubiquitinating enzymes, Kaplan–Meier analysis

The research team collected comprehensive data from 682 breast cancer patients diagnosed between 2014 and 2022 across four major hospitals in China. They compiled preoperative grayscale ultrasound (US), color Doppler flow imaging (CDFI), and contrast-enhanced ultrasound (CEUS) scans alongside critical clinical information. This rich dataset laid the groundwork for developing a multifaceted radiomics model aimed at detecting ALNM—a vital prognostic indicator that directly influences treatment decisions and survival outcomes in breast cancer patients.

Axillary lymph node involvement remains a pivotal determinant in breast cancer staging and therapy planning. Conventionally, detecting metastasis relies on invasive procedures like sentinel lymph node biopsy, often fraught with complications and patient burden. The advent of noninvasive radiomics models that extract quantitative imaging features from ultrasound modalities introduces a paradigm shift—enabling precise preoperative prediction and potentially reducing unnecessary surgical interventions.

Central to the study’s methodology was the application of Least Absolute Shrinkage and Selection Operator (LASSO) regression to distill a vast array of radiomic features embedded within ultrasound images. This statistical technique refined feature selection by eliminating redundancies and identifying those most predictive of ALNM. Subsequently, eight distinct machine learning algorithms were utilized to construct radiomics models drawing on US, CDFI, and CEUS datasets individually and in combination, providing a robust framework to evaluate predictive performance.

The results demonstrated that the combined US + CDFI + CEUS model significantly outperformed ultrasound-only assessments in predicting the presence and extent of metastatic axillary lymph nodes. Specifically, the integrated radiomics model achieved Areas Under the Curve (AUCs) of 0.88 in the training set and maintained strong predictive accuracy in both internal and external validation cohorts, with AUCs exceeding 0.75. These statistics underscore the reliability and generalizability of the approach across diverse clinical environments.

Beyond binary classification of lymph node status (N0 versus N+), the model adeptly distinguished between varying metastatic burdens—a crucial clinical nuance. It achieved high AUCs when differentiating patients with 1–2 positive nodes from those with three or more, highlighting its ability to stratify patients based on metastatic extent. This granularity facilitates more tailored therapeutic regimens, optimizing outcomes while minimizing overtreatment.

Importantly, the study also explored the prognostic implications of integrating radiomics with systemic immunoinflammatory markers, including platelet count and neutrophil-to-lymphocyte ratio (NLR). Such markers reflect tumor-host interactions and the underlying inflammatory milieu, which are increasingly recognized as key determinants of cancer progression. By combining the radiomic signature (Radscore) with these clinical biomarkers, the researchers developed a composite model predicting disease-free survival (DFS) with notable accuracy.

This composite clinical-radiomics model exhibited C-indices ranging from 0.73 to 0.80 across different cohorts, outperforming models based solely on clinical data. In external validations, it demonstrated superior AUCs for predicting 2-, 3-, and 5-year DFS outcomes compared to clinical models alone, with improvements reaching statistical significance. Such advances enable oncologists not only to forecast metastatic spread but also to anticipate patient prognosis, facilitating more informed counseling and individualized surveillance strategies.

The utility of the model was further bolstered by rigorous calibration and decision curve analyses, which confirmed its clinical applicability and net benefit. Good agreement between observed and predicted outcomes indicated that the model’s predictions closely mirrored real-world patient trajectories. Decision curve analysis demonstrated tangible clinical benefits over existing tools, suggesting that adopting this radiomics-based approach could enhance decision-making in breast cancer management.

At the core of the innovation lies the incorporation of contrast-enhanced ultrasound, which augments traditional imaging by highlighting microvascular perfusion characteristics within suspicious lesions and nodes. CEUS provides dynamic functional information beyond morphological assessment, enabling extraction of texture and intensity-based radiomic features deeply linked to tumor biology and metastatic potential. This multimodal imaging strategy thus enriches data granularity and model robustness.

The study’s multicenter design and large patient cohort contribute to the robustness and external validity of findings. Data partitioning ensured rigorous training, internal validation, and external validation phases, mitigating overfitting risks—a common pitfall in radiomics research. The diverse population sampled from geographically and institutionally distinct centers strengthens the generalizability of conclusions, paving the way for broader clinical adoption.

Nonetheless, the authors acknowledge that further prospective studies are warranted to validate the model’s performance in routine clinical workflows and to assess integration feasibility with existing diagnostic protocols. Future research may also explore combining ultrasound radiomics with genomic or molecular biomarkers, fostering an integrative oncology paradigm blending imaging phenotypes with biological insights.

Moreover, the technology holds promise for guiding neoadjuvant therapy decisions, surgical planning, and personalized follow-up schedules. By accurately delineating lymph node status preoperatively, the model may reduce the need for invasive biopsies, lowering patient morbidity and healthcare costs. Predicting prognosis through combined imaging and inflammatory markers offers a noninvasive avenue to identify high-risk patients who may benefit from intensified treatments.

This advancement epitomizes the transformative potential of artificial intelligence and machine learning in oncology, leveraging routinely acquired imaging data to extract clinically actionable insights. As ultrasound is widely available, radiation-free, and cost-effective, deploying enhanced radiomics models such as this could democratize access to precision diagnostics worldwide, especially in resource-limited settings.

The study epitomizes a confluence of medical imaging, computational analysis, and clinical oncology, showcasing how interdisciplinary efforts can unlock novel diagnostic and prognostic capabilities. By converting complex imaging data into predictive models that inform real-world treatment pathways, research like this accelerates the transition from traditional medicine to precision oncology.

In summary, the novel multimodal ultrasound radiomics model combining grayscale US, CDFI, and CEUS represents a formidable tool in the battle against breast cancer. It not only elevates the accuracy of axillary lymph node metastasis detection but also synergizes with immunoinflammatory biomarkers to enhance prognosis prediction. Such integrative models promise to optimize patient stratification, personalize therapy, and ultimately improve survival and quality of life for countless breast cancer patients worldwide.

As this technology matures and is incorporated into clinical practice, it may redefine breast cancer care pathways, reducing diagnostic uncertainties and empowering clinicians with nuanced risk assessments. The synergistic blend of advanced imaging, machine learning, and biological markers portends a new era of precision oncology where treatment decisions are increasingly guided by data-driven insights.

This landmark study stands as a testament to the potential of radiomics combined with immunology to revolutionize cancer prognostication. It paves the way for future endeavors harnessing artificial intelligence to unravel complex disease patterns from noninvasive imaging, transforming clinical oncology from reactive to proactive, and setting new standards in patient-centered care.

Subject of Research:
Prediction of axillary lymph node metastasis and prognosis in breast cancer using multimodal ultrasound radiomics combined with immunoinflammatory markers.

Article Title:
Contrast-enhanced ultrasound radiomics model for predicting axillary lymph node metastasis and prognosis in breast cancer: a multicenter study.

Article References:
Li, S.Y., Li, Y.M., Fang, Y.Q. et al. Contrast-enhanced ultrasound radiomics model for predicting axillary lymph node metastasis and prognosis in breast cancer: a multicenter study. BMC Cancer 25, 1315 (2025). https://doi.org/10.1186/s12885-025-14632-9

Image Credits: Scienmag.com

DOI:
https://doi.org/10.1186/s12885-025-14632-9

The implications of the study are profound, as the researchers have uncovered a correlation between the adhesive properties of tumor cells and the aggressiveness of breast cancer. During testing, it was evident that cells derived from patients exhibiting less aggressive forms of breast cancer exhibited strong adherence, while those sourced from patients with advanced or aggressive cancer types displayed a significantly weaker adhesive profile. This dichotomy highlights the potential of adhesion strength as a biomarker for assessing the metastatic potential of tumors.

The microfluidic device instrumental to this study consists of precisely designed chambers that mimic the physiological environment of breast tissue. The device’s chambers are lined with adhesive proteins, such as fibronectin, which facilitate the adhesion of the tumor cells. As fluid flows through these chambers, tumor cells are subjected to varying levels of shear stress. Researchers meticulously observe how these cells detach from the chamber walls, thereby classifying them based on their adhesion strength. This groundbreaking method opens a new avenue for predicting tumor behavior and progression.

In previous work, the same research group had established that less adherent cancer cells were more likely to invade adjacent tissues. This earlier finding has now been corroborated through the analysis of tumor samples from patients at various stages of breast cancer. In particular, this new research focused heavily on ductal carcinoma in situ (DCIS), a non-invasive form of breast cancer that is often considered stage zero. One of the ongoing challenges in treating DCIS lies in determining which cases may progress to invasive cancer, a question that has eluded clinicians for years.

The current criteria for making clinical decisions regarding the treatment of DCIS often rely on lesion size and histological grade. However, these metrics are not always reliable indicators of cancer behavior. The study’s proponents argue that the introduction of adhesion strength as a parameter for assessment could revolutionize how clinicians classify and treat early-stage breast cancer. Identifying patients at higher risk will allow for more tailored therapeutic interventions, minimizing the chances of over-treatment in lower-risk cases.

The research findings were published in the journal Cell Reports on March 5, 2025, highlighting the collaboration between bioengineering and clinical medicine. Senior author Adam Engler underscored the potential impact of their findings, emphasizing that improved diagnostic capabilities could significantly enhance personalized treatment strategies based on tumor characteristics. As the clinical landscape continues to evolve, there is a pressing need to incorporate more nuanced metrics such as adhesion strength in routine breast cancer diagnostics.

During their study, the research team analyzed samples from a diverse group of 16 patients, collecting normal breast tissues as well as tumors from non-invasive DCIS to more aggressive forms of breast cancer. The results were illuminating; the aggressive cancer samples consistently demonstrated weakly adherent cells, marking a clear distinction from normal tissue, which showed strong adherence. These findings underscore the heterogeneous nature of breast cancer, suggesting that even within single disease subtypes, there can be vast differences in tumor biology among patients.

Madison Kane, a co-first author of the study, expressed excitement over the variability seen in adhesion strength among DCIS patients. Some exhibited strong adherence, while others had weakly adherent cells, leading the researchers to hypothesize that those with minimally adherent cells are more likely to experience aggressive disease progression. Tracking these patients over the next five years could yield critical insights into the relationship between adhesion properties and metastatic behavior.

The research team positions the microfluidic device as a transformative diagnostic tool that could empower oncologists with greater foresight. By detecting irregular adhesion patterns in tumor cells early on, the device may facilitate timely interventions before the onset of metastasis, ultimately improving patient outcomes and survival rates. The potential for a critical advancement in breast cancer care is extraordinary, as it promises a shift from a reactive to a proactive treatment paradigm.

Interdisciplinary collaboration has emerged as a cornerstone of this research effort, bringing together bioengineers, oncologists, and clinical researchers. By working closely with Moores Cancer Center, which provided vital patient samples and clinical insights, the team has been able to bridge the gap between laboratory discoveries and real-world patient care. Such partnerships are essential for translating scientific discoveries into tangible benefits for patients facing this challenging disease.

The development and clinical evaluation of the microfluidic device were supported by funding from various prestigious institutions, including the National Institutes of Health and the National Science Foundation. As research funding plays a critical role in such innovative studies, the collaboration exemplifies how shared resources can amplify the impact of scientific inquiry through rigorous inquiry and comprehensive support for train students and researchers.

With promising preliminary data in hand, the research group is ambitious regarding the future direction of their work. Expanding the patient base and refining the microfluidic device’s design will be next steps toward a validated diagnostic tool for breast cancer. The ability to predict aggressive disease based on cellular adhesion strength can potentially change the clinical landscape, empowering physicians to make informed decisions that enhance patient care.

In summary, the work emerging from UC San Diego not only elucidates a new aspect of tumor biology but also establishes a compelling rationale for developing advanced diagnostic techniques in oncology. By harnessing the physical properties of tumor cells, researchers are poised to make groundbreaking contributions that could profoundly affect breast cancer treatment and management strategies.

Subject of Research: Tumor cell adhesion in breast cancer prognosis
Article Title: Adhesion Strength of Tumor Cells Predicts Metastatic Disease in vivo
News Publication Date: 5-Mar-2025
Web References: Link to article
References: Published in Cell Reports
Image Credits: David Baillot/UC San Diego Jacobs School of Engineering

Keywords: Breast cancer, tumor cells, adhesion strength, metastasis, microfluidic device, personalized medicine, DCIS, oncology, cancer prognosis, UC San Diego.