The study, called EVERGREEN, analyzed outcomes for 207 women whose estrogen receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer had progressed after treatment with a CDK4/6 inhibitor. Of these patients, 150 received everolimus alongside endocrine therapy, while 57 received endocrine therapy without everolimus. After a median follow-up of 31.8 months, the median real-world progression-free survival was 5.0 months in the everolimus group, compared with 4.3 months among those given endocrine therapy alone. The adjusted hazard ratio for progression or death was 0.68, with a 95 percent confidence interval of 0.47 to 0.99.

That result means the everolimus-containing treatment was associated with an approximately 32 percent lower relative risk of progression or death during the study period after statistical adjustment. It does not mean that every patient gained a fixed additional 32 percent of survival, nor that the cancer was controlled for 32 percent longer. The absolute difference in median progression-free survival was only 0.7 months—roughly three weeks. The researchers therefore describe the benefit as modest. There was no statistically significant improvement in the time until chemotherapy was needed or in overall survival, the measure that most directly captures whether treatment helps patients live longer.

Everolimus attacks a different part of the machinery that cancer cells use to grow. It inhibits mammalian target of rapamycin, or mTOR, a central signaling protein that helps regulate protein production, cell growth, metabolism and survival. In hormone receptor-positive breast cancer, signaling through the estrogen receptor can cooperate with the PI3K–AKT–mTOR pathway to keep malignant cells dividing even when estrogen-driven growth is being suppressed. Laboratory studies have suggested that increased activity in this pathway can contribute to resistance against CDK4/6 inhibition. By blocking mTOR while continuing endocrine therapy, clinicians aim to shut down a bypass route that cancer cells may exploit after cell-cycle treatment stops working.

The biological rationale, however, does not guarantee a large clinical effect. Tumors that acquire resistance to CDK4/6 inhibitors are not uniform. Some develop alterations in genes such as ESR1, which encodes the estrogen receptor; others involve the PI3K–AKT–mTOR network, including PIK3CA, AKT1 or PTEN. Still others may become less dependent on estrogen signaling altogether, switch to alternative growth programs or contain several resistant subclones at once. Everolimus may be most useful when the mTOR pathway remains an important engine of tumor growth, but the EVERGREEN study did not establish a biomarker that could reliably identify such patients before treatment.

The study’s design is important for interpreting its findings. EVERGREEN was a multicentre, international, retrospective quasi-experimental study rather than a randomized clinical trial. The investigators compared women treated at centers where everolimus plus endocrine therapy was the standard approach with women treated at centers where endocrine therapy alone was standard. This approach can provide valuable evidence from routine oncology practice, especially when randomized trials have not answered a specific treatment question. It also introduces potential sources of bias: treatment policies differ between hospitals, physicians may select everolimus for particular types of patients, and medical records may not capture every factor influencing treatment choice or disease assessment.

The patient groups were broadly balanced at baseline, according to the researchers, but the everolimus cohort had received a greater number of previous lines of therapy. That imbalance matters because heavily pretreated disease can be more biologically resistant and patients may have poorer overall health or fewer remaining treatment options. The investigators used adjusted analyses to account for measured differences, but statistical methods cannot completely remove the effects of unknown or unrecorded factors. “Real-world progression-free survival” is also less tightly controlled than progression-free survival in a prospective trial, where imaging schedules, response assessments and follow-up procedures are standardized.

The safety findings were consistent with earlier reports of everolimus, but the abstract does not provide a detailed breakdown of adverse events. The drug can cause mouth inflammation, rash, fatigue, diarrhea, metabolic changes and suppression of blood-cell production; it can also produce noninfectious pneumonitis, an inflammatory lung complication. These risks are particularly relevant in advanced cancer, where maintaining quality of life is a central treatment goal. A therapy that delays progression by several weeks may be worthwhile for a carefully selected patient who wants to remain on oral treatment and has limited alternatives, but less attractive for someone vulnerable to complications or eligible for a better-matched molecular therapy.

The findings arrive in a rapidly changing treatment landscape. After CDK4/6 inhibitor progression, options may include endocrine drugs designed to target specific resistance mutations, inhibitors of PI3K or AKT signaling, antibody–drug conjugates and chemotherapy. For example, the presence of an ESR1 mutation or an alteration in PIK3CA, AKT1 or PTEN may influence the suitability of other targeted approaches, although the best sequence of therapies is still evolving. The EVERGREEN results do not show that everolimus should replace these options. Instead, they suggest that it remains a possible strategy for a subset of patients whose disease is still endocrine-sensitive and for whom the expected benefits outweigh the drug’s side effects.

The study also illustrates why treatment after CDK4/6 resistance cannot be reduced to a single universal prescription. A median benefit measured in weeks can conceal meaningful differences between individuals: some patients may experience little response, while others may achieve substantially longer disease control. Future trials will need to connect outcomes to tumor biology, circulating tumor DNA, prior endocrine exposure and the pattern of progression. For now, EVERGREEN provides a cautiously encouraging but not practice-revolutionizing message: everolimus can add a small amount of control after CDK4/6 inhibitors, but the decision to use it should be individualized rather than automatic.

Fatty acid synthase is an important enzyme in the biosynthesis of fatty acids, and its expression has been closely linked to cancer progression. Understanding the relationship between FASN and tumor biology is crucial for the development of targeted therapies. In breast cancer specifically, elevated levels of FASN have been associated with poor prognosis, highlighting its potential as a target for therapeutic intervention. This marks a significant milestone in cancer research, where the metabolic pathways of tumors are increasingly recognized as viable targets for defeating cancer’s resilience.

The study led by Chen et al. explores how inhibiting FASN can induce changes in breast cancer cells that not only impede their proliferation but also render them more susceptible to radiation therapy. This dual mechanism of action is crucial in improving the effectiveness of existing treatment modalities, as combining metabolic inhibition with traditional therapies like radiotherapy could overcome some of the limitations posed by tumor heterogeneity and resistance to treatment. By precisely targeting the metabolic processes that fuel tumor growth, researchers aim to provide a more comprehensive strategy in the fight against breast cancer.

The method utilized in this research involved the application of a FASN inhibitor, which was administered to breast cancer cell lines. The results indicated marked alterations in cellular behavior, particularly with respect to cell survival and apoptosis rates. These findings suggest that inhibiting FASN not only stalls the cancer cells’ growth but may also push them towards programmed cell death, a desirable outcome in cancer treatment. Furthermore, the study’s results reflect a growing body of evidence that metabolic pathways are not just secondary players in cancer but are fundamentally intertwined with cancer’s growth and resistance mechanisms.

In addition to enhancing radiosensitivity, targeting FASN could offer new avenues for combination therapies. For instance, researchers could potentially pair FASN inhibitors with other treatments such as chemotherapy or immunotherapy, which could amplify overall therapeutic efficacy. The approach taken by Chen and colleagues thus paves the way for novel combination strategies that capitalize on the vulnerabilities of cancer cells at multiple levels, further complicating the tumor’s ability to adapt and survive.

While the implications of these findings for clinical practice are yet to be fully realized, they could significantly shift the paradigm of how breast cancer is treated. As the understanding of FASN’s role in tumor biology deepens, it is likely that future clinical trials will seek to evaluate the safety and efficacy of FASN inhibitors in combination with standard therapies. Additionally, this could pave the way for biomarker-driven approaches, where patients with high FASN expression levels could be identified as candidates for targeted therapies.

Notably, the discourse surrounding FASN inhibiting strategies does not simply stop at treatment efficacy. Researchers are also tasked with exploring potential side effects and the impact on normal cellular metabolism. Careful consideration must be given to ensure that inhibiting this pathway does not adversely affect healthy tissues, which could complicate treatment outcomes. As researchers delve into this promising avenue, the balance between efficacy and safety will remain a key focus of future investigations.

Establishing the exact molecular mechanisms through which FASN inhibition affects breast cancer cells is essential for enhancing therapeutic outcomes. Further studies will likely investigate the signaling pathways involved in the responsiveness of cancer cells to FASN inhibition and how these pathways intersect with existing treatments. These discoveries could not only refine therapeutic strategies but also uncover additional targets within the metabolic landscape of breast cancer.

As the research continues to unfold, attention must be directed toward the broader implications of targeting metabolic pathways in cancer. The success of FASN inhibition in breast cancer could inspire similar investigations into other types of cancer where altered lipid metabolism is a hallmark of malignancy. This expanding focus on metabolic vulnerabilities could usher in a new era of cancer treatment, where metabolism is considered a core component of cancer therapy alongside traditional modalities.

In conclusion, the groundbreaking work by Chen, Chan, and Shen exemplifies a significant stride towards harnessing metabolic pathways in cancer treatment. Their findings not only illuminate the potential of targeting FASN to enhance the efficacy of existing therapies but also encourage a re-evaluation of how metabolic processes can be manipulated in the context of cancer progression. As research progresses, the potential for translating these findings into clinical applications could significantly reshape the therapeutic landscape, offering hope to countless individuals battling breast cancer.

The study emphasizes the importance of interdisciplinary approaches in modern oncology, where collaboration between biochemists, oncologists, and molecular biologists is essential for translating laboratory discoveries into clinical realities. The excitement generated by these findings is palpable, as the scientific community anticipates future trials and studies that will build upon this foundational work. In the ongoing fight against breast cancer, the pursuit of innovative strategies such as targeting fatty acid synthase represents a vital step toward more effective treatments and improved patient outcomes.

As we look to the future, the promise of research focused on the metabolic aspects of cancer signifies a paradigm shift in oncology. Emphasizing metabolic considerations could lead to a new generation of targeted therapies that are not only more effective in eradicating tumors but also possess fewer side effects, ultimately resulting in a better quality of life for patients. The pioneering study by Chen and colleagues stands as a testament to the transformative potential of integrating metabolic research into the broader field of cancer therapeutics.

Subject of Research: Targeting Fatty Acid Synthase in Breast Cancer Cells
Article Title: Targeting Fatty Acid Synthase to Halt Tumor Progression and Enhance Radiosensitivity in Breast Cancer Cells
Article References: Chen, CI., Chan, HW., Shen, CY. et al. Targeting Fatty Acid Synthase to Halt Tumor Progression and Enhance Radiosensitivity in Breast Cancer Cells. J. Med. Biol. Eng. 44, 903–913 (2024). https://doi.org/10.1007/s40846-024-00920-5
Image Credits: AI Generated
DOI: 10.1007/s40846-024-00920-5
Keywords: Fatty Acid Synthase, Breast Cancer, Radiosensitivity, Tumor Progression, Targeted Therapy.

Central to this discovery is the tumor microenvironment—the complex and dynamic “neighborhood” surrounding cancer cells that includes various types of immune cells, stromal components, and signaling molecules. Within this milieu, tumor-associated macrophages (TAMs) emerge as critical players. These immune cells, normally involved in tissue repair and defense, are co-opted by tumors to support malignant progression. Researchers focused on a specific TAM subtype characterized by the expression of CD163 and the immune checkpoint molecule PD-L1. PD-L1 is known for its role in helping cancer cells evade immune detection, famously targeted by immune checkpoint inhibitors in various cancers.

The Sylvester team found that these PD-L1-positive TAMs accumulate in greater numbers within tumors from patients that developed resistance to tamoxifen therapy. Acting like “bodyguards” shielding the cancer from immune attack and therapy-induced death, these macrophages create an immunosuppressive niche that fosters tumor survival and regrowth. Their recruitment is orchestrated by DLL1, a signaling ligand secreted by the cancer cells themselves. DLL1 initiates a chemotactic cascade, operating through the CCR3/CCL7 pathway, to draw these macrophages into the tumor microenvironment.

This macrophage infiltration not only supports cancer cell survival but also maintains a subpopulation of cancer stem cells—an inherently resilient fraction of tumor cells capable of self-renewal and fueling tumor recurrence. Moreover, the presence of PD-L1-positive TAMs induces exhaustion of cytotoxic CD8+ T cells, the immune system’s frontline soldiers against malignancy. The combination of immune evasion and sustained cancer stem cell populations underscores the complexity and resilience of tamoxifen-resistant breast tumors.

To dissect this resistance mechanism and explore therapeutic interventions, researchers developed two preclinical models of ER+ breast cancer that mimic endocrine therapy resistance. In these models, blocking DLL1 and PD-L1 simultaneously with targeted antibodies, in conjunction with low-dose tamoxifen, led to marked reduction in tumor size. Tumor burden was further diminished by a significant decrease in cancer stem cell populations. This triple-therapy approach not only disrupted the protective macrophage niche but also reactivated the immune response by revitalizing exhausted T cells, effectively tipping the scales back against the cancer.

What sets this approach apart from previous strategies is its multipronged attack—targeting tumor cell signaling, dismantling the supportive immune microenvironment, and applying conventional hormone therapy at subtherapeutic doses to minimize side effects. The findings were validated both in preclinical models and patient-derived explant cultures, underscoring translational potential.

Of particular clinical significance, high levels of DLL1 and PD-L1+ TAMs in human tumors correlated strongly with poor patient outcomes and resistance to both tamoxifen and fulvestrant. These data suggest that quantifying these markers could aid in patient stratification and therapeutic decision-making in the future. The implication is profound: by interrupting DLL1-mediated recruitment of immunosuppressive macrophages and blocking PD-L1 checkpoint signaling, we may overcome a major hurdle in endocrine therapy resistance.

Despite the excitement, Dr. Rumela Chakrabarti, senior author and co-director of the Sylvester Surgical Breast Cancer Research Group, emphasizes cautious optimism. Extensive in vivo validation and early-phase clinical trials are necessary before this strategy can be widely implemented. Human tumors exhibit heterogeneity and complexity beyond preclinical models, requiring thorough investigation of potential side effects and resistance mechanisms to the triple therapy.

The broader scientific significance of this work lies in shifting the focus from cancer cells in isolation to the intricate ecosystem in which they thrive. Tumors are not merely rogue cell populations but communities of diverse cells interacting dynamically. Understanding and targeting these interactions—especially how malignant cells exploit immune cells to evade destruction—open new frontiers for cancer treatment.

This research also contributes to the expanding narrative of cancer immunotherapy, demonstrating how traditional hormone therapies can be synergized with immune modulation to tackle resistant tumors. Such integrated approaches may herald a new era wherein cancers previously deemed untreatable with endocrine therapy become manageable chronic conditions.

In conclusion, the identification of DLL1-responsive PD-L1+ tumor-associated macrophages as key mediators of endocrine resistance offers a compelling target for therapy. The triple combination of anti-DLL1, anti-PD-L1, and low-dose tamoxifen holds remarkable promise in preclinical settings, illuminating a path toward improved outcomes for patients suffering from stubborn ER+ breast cancer. As research progresses, this strategy could redefine standards of care, illustrating the power of dissecting the tumor microenvironment to unlock innovative, life-saving therapies.

Subject of Research: Endocrine resistance in estrogen receptor-positive (ER+) breast cancer mediated by tumor-associated macrophages.

Article Title: DLL1-responsive PD-L1+ tumor-associated macrophages promote endocrine resistance in breast cancer

News Publication Date: November 5, 2025

Web References:

• Sylvester Comprehensive Cancer Center
• Science Translational Medicine Article DOI
• InventUM blog
• SylvesterCancer on X

Image Credits: Photo by Sylvester Comprehensive Cancer Center

Keywords: Breast cancer, tumor-associated macrophages, endocrine therapy resistance, estrogen receptor-positive, PD-L1, DLL1, cancer stem cells, tumor microenvironment, immune checkpoint inhibition, tamoxifen resistance, fulvestrant resistance, immunosuppression

SIGLEC15 is a transmembrane protein that has recently garnered attention for its immunosuppressive capabilities across diverse solid tumor types, including breast cancer. Despite its relatively nascent characterization, accumulating evidence suggests that SIGLEC15 functions as a pivotal immune checkpoint molecule, distinct from the classical PD-1/PD-L1 axis, and may orchestrate tumor immune evasion by modulating myeloid cells and T-cell activity. Given these insights, a comprehensive elucidation of SIGLEC15’s role in breast cancer biology could unveil novel avenues for therapeutic intervention and biomarker-driven treatment stratification.

A team of investigators from Chongqing Medical University undertook an integrative study employing multi-omics datasets—namely TCGA (The Cancer Genome Atlas), GTEx (Genotype-Tissue Expression), and GEO (Gene Expression Omnibus)—to dissect the clinical and molecular significance of SIGLEC15 in breast cancer. Their analyses revealed a paradoxical yet intriguing association: elevated SIGLEC15 expression correlated with improved overall survival and favorable five-year prognosis. This counterintuitive finding challenges the conventional notion of immune checkpoints merely facilitating tumor progression, suggesting a complex and context-dependent functional spectrum for SIGLEC15 within the tumor milieu.

Delving deeper through single-cell RNA sequencing (scRNA-seq) of breast cancer tissue samples, the researchers pinpointed SIGLEC15 expression predominantly in malignant epithelial cells. These SIGLEC15-positive populations were characterized by a notable reduction in infiltrating CD4⁺ and CD8⁺ T-lymphocytes along with diminished presence of M0 and M1 macrophage subsets. Conversely, there was an enrichment of dendritic cells and B cells, indicative of a shift toward humoral immune mechanisms and an immunosuppressive microenvironment less conducive to cytotoxic T-cell mediated tumor eradication. This immune landscape remodeling underscores SIGLEC15’s role in shaping cellular cross-talk within the TME to favor immune escape.

Beyond its immunomodulatory effects, SIGLEC15 emerged as a critical regulator of epithelial–mesenchymal transition (EMT), a key driver of tumor invasiveness and metastasis. Functional assays demonstrated that SIGLEC15 exerts suppressive control over EMT by downregulating ZEB1, a master transcriptional regulator of this process. Overexpression models in the aggressive breast cancer cell lines BT549 and MDA-MB-231 revealed marked decreases in ZEB1 protein levels alongside classical mesenchymal markers such as N-cadherin and vimentin. Correspondingly, these alterations translated into diminished migratory and invasive capabilities as evidenced by wound healing assays and transwell migration metrics.

Conversely, silencing SIGLEC15 in MDA-MB-231 cells elicited robust enhancement in EMT phenotypes, underpinning its tumor suppressor-like function with respect to metastatic potential. These reciprocal functional validations underscore SIGLEC15’s dualistic role, whereby it modulates both immune suppression and tumor cell plasticity — a nuanced interplay that challenges prevailing assumptions and invites reconsideration of its utility as a therapeutic target.

Importantly, their investigation extended to therapeutic vulnerability profiling, revealing that high SIGLEC15-expressing breast tumors exhibited lower sensitivity to conventional platinum-based chemotherapies and PARP inhibitors, agents typically efficacious in DNA damage response deficient malignancies. Intriguingly, these same tumors demonstrated pronounced susceptibility to Nutlin-3a, a small-molecule antagonist of MDM2 that stabilizes and activates p53 tumor suppressor pathways. This finding suggests that SIGLEC15 expression status might serve as a predictive biomarker for tailoring treatment regimens, prioritizing MDM2 inhibition in tumors less amenable to DNA-damaging agents.

In vivo xenograft studies corroborated these insights, with Nutlin-3a markedly suppressing tumor growth in SIGLEC15-overexpressing models while low-SIGLEC15 tumors were more responsive to carboplatin chemotherapy. This mechanistic synergy between SIGLEC15 expression and drug response highlights the potential for integrating molecular diagnostics into therapeutic decision-making, advancing the paradigm of personalized medicine in breast cancer care.

Collectively, this comprehensive work delineates SIGLEC15 as a multifaceted mediator within the breast cancer TME that simultaneously modulates immune architecture and tumor cell invasive behavior. Its dual capacity to suppress EMT and orchestrate an immunosuppressive microenvironment positions it uniquely at the crossroads of tumor progression and immune evasion, rendering it a compelling candidate for translational research and clinical exploitation.

The implications are profound: beyond serving as a prognostic biomarker, SIGLEC15 may guide therapeutic selection—steering patients toward MDM2 inhibitors when overexpressed, while identifying those poised to benefit from platinum-based regimens in its absence. Furthermore, targeting SIGLEC15 or its downstream pathways could potentiate novel immunotherapeutic strategies that circumvent immune checkpoint resistance and metastasis.

This study exemplifies the power of integrating genomic, transcriptomic, and functional data to unravel complex tumor biology and paves the way for future clinical trials assessing SIGLEC15-targeted approaches. As breast cancer treatment pivots toward increasingly sophisticated and individualized paradigms, deciphering the molecular underpinnings of players like SIGLEC15 will be indispensable in improving patient outcomes and quality of life.

Subject of Research: Breast cancer; tumor microenvironment; SIGLEC15; immunosuppression; epithelial–mesenchymal transition

Article Title: SIGLEC15 modulates the immunosuppressive microenvironment and suppresses malignant phenotypes in triple-negative breast cancer

Web References:
https://www.sciencedirect.com/journal/genes-and-diseases
http://dx.doi.org/10.1016/j.gendis.2025.101799

References:
ZhaoFu Tan, Hongbin Xin, Jian Chen, Ming Lei, Gang Tu, Lingfeng Tang. SIGLEC15 modulates the immunosuppressive microenvironment and suppresses malignant phenotypes in triple-negative breast cancer. Genes & Diseases. DOI: 10.1016/j.gendis.2025.101799

Image Credits: ZhaoFu Tan, Hongbin Xin, Jian Chen, Ming Lei, Gang Tu, Lingfeng Tang

Keywords: Breast cancer, SIGLEC15, tumor microenvironment, immunosuppression, epithelial–mesenchymal transition, MDM2 inhibitor, Nutlin-3a, chemoresistance, single-cell RNA sequencing, prognostic biomarker

The rationale behind this combination stems from the distinct yet complementary mechanisms of action these drugs possess. Metformin is well-documented for its antineoplastic properties, primarily through the activation of AMP-activated protein kinase (AMPK) pathways, leading to inhibition of mTOR signaling and subsequent reduction in cancer cell proliferation. Azacitidine, on the other hand, interrupts aberrant DNA methylation patterns characteristic of malignant cells, reactivating tumor suppressor genes and inducing differentiation or apoptosis. The union of these two drugs was posited to amplify therapeutic outcomes in breast cancer treatment by addressing multiple oncogenic pathways simultaneously.

Utilizing the GSE45827 dataset, the authors conducted an extensive bioinformatics analysis to identify differentially expressed genes (DEGs) associated with breast cancer progression. Sophisticated computational tools such as GEO2R and ShinyGO were employed to map out key molecular players, allowing the construction of protein-protein interaction networks through STITCH and Cytoscape platforms. The MCODE algorithm further refined this network to distinguish pivotal clusters that regulate tumorigenic processes, pinpointing critical genes such as CCND1, ELAVL1, and EIF4EBP1 as candidates most involved in the malignancy.

Comparative analyses of these genes’ expression levels in tumor tissues versus matched normal controls, drawn from the GTEx Portal and TNMPlot databases, revealed a distinct upregulation pattern correlating with aggressive breast cancer phenotypes. Such data underlined the biological significance of these targets and established a compelling foundation for investigating their modulation by the drug combination. Moreover, survival outcomes analyzed via Kaplan-Meier plots indicated that alterations in these gene expressions bear prognostic weight, further emphasizing their therapeutic relevance.

In vitro assays on the MDA-MB-231 triple-negative breast cancer cell line validated the bioinformatics predictions. Cell viability assessments using MTT assays demonstrated that metformin and azacitidine, when administered individually, caused a dose-dependent reduction in cancer cell survival. Remarkably, isobologram analyses elucidated that the simultaneous application of both agents resulted in a pronounced synergistic effect, suggesting that lower doses could achieve enhanced antitumor activity while potentially reducing toxic side effects.

Expounding beyond cytotoxicity, the researchers explored the combination’s impact on metastatic potential through wound-healing assays, a proxy for cell migration and invasion ability. Results revealed that co-treatment substantially impaired the motility of MDA-MB-231 cells, an insight with profound implications as metastasis remains the leading cause of mortality in breast cancer patients. This inhibition of migration underscores the potential of the metformin-azacitidine regimen to interfere with not only primary tumor growth but also metastatic dissemination.

At a molecular level, real-time quantitative PCR assays monitored the expression dynamics of CCND1, ELAVL1, and EIF4EBP1 in response to drug treatment. These genes are critically involved in cell cycle progression, mRNA stability, and translation initiation, respectively—fundamental processes commandeered by cancer cells to sustain unchecked proliferation. The combination therapy effectively downregulated these targets, providing mechanistic explanations for the observed phenotypic tumor suppression. This coordinated genetic modulation suggests a multi-layered approach to dismantling cancer cell survival strategies.

The implications of integrating metformin and azacitidine are profound, especially given their individual clinical use histories and safety profiles. Metformin’s extensive application as an anti-diabetic agent presents a low barrier for clinical translation, while azacitidine’s capacity to restore epigenetic normalcy offers a novel angle in cancer pharmacotherapy. By validating their synergistic efficacy in breast cancer cells, this study paves the way for repurposing existing drugs in innovative combinations, potentially expediting new therapeutic options without the prolonged delays often associated with novel drug development.

Such an approach sits at the intersection of precision medicine and drug repurposing, leveraging comprehensive genomic data and robust in vitro experimentation to target cancer hallmarks. Importantly, the study also highlights the value of integrative bioinformatics pipelines for accelerating drug discovery processes, reinforcing the utility of publicly available datasets and analytical tools to identify viable molecular targets with translational potential.

While these results are promising, further investigations are warranted to explore the pharmacodynamics and pharmacokinetics of the metformin-azacitidine duo in vivo, alongside assessments in clinically relevant animal models. Determining optimal dosing regimens, evaluating potential off-target effects, and understanding interactions with existing chemotherapeutics will be vital steps to advancing this therapy toward clinical trials.

Moreover, exploring patient stratification based on gene expression profiles could refine this combination treatment’s application, enabling a more personalized therapeutic strategy that maximizes benefit and minimizes harm. The modulation of CCND1, ELAVL1, and EIF4EBP1 may serve as valuable biomarkers to monitor treatment response and disease progression.

This study ultimately exemplifies the potential of combining metabolic modulators with epigenetic therapies to dismantle complex oncogenic networks in breast cancer. Through meticulous computational analysis and rigorous experimental validation, the authors offer a compelling narrative that reinforces the importance of multidimensional treatment frameworks against formidable cancers.

As breast cancer researchers and clinicians confront the ongoing challenge of treatment resistance and heterogeneous tumor biology, this innovative combination therapy shines as a beacon of hope. It encourages a paradigm shift toward interdisciplinary methods, where repurposed drugs transcend their original indications to deliver impactful anticancer effects.

Looking ahead, the therapeutic horizon appears ever more promising with such integrative approaches gaining momentum. Should subsequent studies confirm these findings in clinical settings, patients battling breast cancer might soon benefit from safer, more effective, and economically accessible treatment options emerging from the synergistic marriage of metformin and azacitidine.

In conclusion, the work by Hosseini and colleagues represents a significant stride in breast cancer therapeutics, binding empirical rigor with translational promise. By deciphering and exploiting the complex gene networks underpinning tumor survival and metastasis, the metformin-azacitidine combination therapy could redefine future oncological practices and improve patient outcomes substantially.

Subject of Research: Combined therapeutic effects of metformin and azacitidine on breast cancer cells, focusing on gene expression regulation and cellular behavior.

Article Title: Combined anti-tumor effects of metformin and azacitidine in breast cancer cells

Article References:
Hosseini, S.S., Askari, N. & Yaghoobi, M.M. Combined anti-tumor effects of metformin and azacitidine in breast cancer cells. BMC Cancer 25, 1487 (2025). https://doi.org/10.1186/s12885-025-14908-0

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12885-025-14908-0

Cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors, combined with hormone therapy, have transformed the therapeutic landscape for advanced HR+/HER2- breast cancer by targeting cell cycle regulatory proteins integral to tumor proliferation. These inhibitors act by halting the cell cycle’s progression from the G1 to the S phase, effectively restraining cancer cell division and tumor growth. Despite the efficacy of this dual treatment approach, resistance and variable patient responses remain significant clinical challenges, underscoring the urgent need for predictive biomarkers.

In a meticulous study involving almost one hundred patients treated at ICO Badalona under the CARE programme, the research team identified a distinctive transcriptomic signature named KIMA (Key Immune Activation). KIMA enables oncologists to forecast a patient’s likelihood of poor response to CDK4/6 inhibitors based on the expression profile of specific immune-related genes. This discovery not only holds potential for predicting therapeutic efficacy but also opens novel avenues for combinatorial treatments incorporating immunomodulation.

The clinical cohort revealed striking differences in treatment outcomes, with 57% of patients achieving durable responses exceeding two years without tumor progression, while 43% experienced early relapse within months. Detailed transcriptomic analyses demonstrated that those patients with adverse outcomes harbored tumors exhibiting aberrant immune activation. This immune signature paradoxically correlates with an immunosuppressive tumor microenvironment, facilitating therapeutic resistance rather than promoting tumor eradication.

KIMA is composed of nine genes, including pivotal immune regulators such as STAT1, FOXP3, and TIGIT. The collective overexpression of these genes in the tumor milieu predicts a significantly diminished prognosis, characterized by accelerated disease progression and poor overall survival. Quantitatively, patients with elevated KIMA expression exhibited a median progression-free survival of approximately 11 months, starkly contrasted with about 36 months in those with low KIMA levels, highlighting its robust prognostic value.

The validity of KIMA was further corroborated through an independent clinical study, which confirmed that non-responders to CDK4/6 inhibitors possess distinct, high-level expression profiles of this immune activation signature. This consistency across datasets underpins KIMA’s potential utility as a clinical decision-making tool, facilitating earlier intervention strategies tailored to the molecular intricacies of each patient’s tumor.

Intriguingly, the study challenges the conventional paradigm that immune activation equates to effective anti-tumor immunity. Instead, in HR+/HER2- breast cancer, hyperactivation of certain immune pathways appears to foster a tumor-supportive environment, possibly through immune checkpoint pathways and regulatory T cell-mediated suppression. This insight sheds light on the complex interplay between tumor biology and the immune system’s dualistic role in cancer progression and therapeutic resistance.

The authors highlight the translational impact of this research, suggesting that patients identified with a high KIMA signature might benefit from novel therapeutic combinations. These could include the addition of innovative immunomodulatory agents aiming to reprogram the tumor microenvironment, thereby restoring immune surveillance and enhancing CDK4/6 inhibitor efficacy. Such personalized approaches promise to optimize treatment regimens and improve patient survival.

Leading the investigation, Dr. Eudald Felip and Dr. Edurne Garcia-Vidal emphasize the importance of integrating immune profiling into routine clinical practice for HR+/HER2- breast cancer. The identification of non-responders through genomic signatures like KIMA could prevent ineffective treatments and unnecessary toxicity while sparing healthcare resources, marking a significant stride in precision oncology.

The research consortium, including Dr. Ester Ballana and Dr. Mireia Margelí, underscores that harnessing the immune system’s intricacies and understanding its regulatory networks within cancerous tissues is pivotal for future therapeutic innovations. This study exemplifies the synergy between molecular biology, oncology, and immunology, providing a template for investigating resistance mechanisms in other cancer types.

Moving forward, large-scale clinical trials incorporating KIMA stratification are planned to validate its predictive power further and assess the efficacy of combined CDK4/6 inhibitor and immunotherapy protocols. Such efforts will be crucial in translating this signature from bench to bedside, ultimately improving survival and quality of life for patients battling HR+/HER2- breast cancer.

The discovery of KIMA and its clinical implications heralds a new chapter in breast cancer treatment, emphasizing the necessity to delve deeper into tumor immunogenomics. Through understanding and overcoming therapeutic resistance, this landmark study brings hope that the era of truly personalized medicine for breast cancer patients is imminent.

Subject of Research: Cells
Article Title: Immune-based transcriptomic signature predicts CDK4/6 inhibitor efficacy in HR+/HER2– breast cancer
News Publication Date: 7-Aug-2025
Web References: http://dx.doi.org/10.1002/ctm2.70426
Image Credits: ICO-IrsiCaixa-IGTP
Keywords: Breast cancer, Cancer, Oncology, Biomarkers, Immunology

The study highlights the alarming rates of breast cancer recurrence, a poignant concern that resonates with a significant number of survivors. In Ethiopia, breast cancer is a leading cause of oncology-related morbidity and mortality. Given the disparity in healthcare resources and access, the findings of this research have the potential to inform healthcare policies directly. By focusing on the time to recurrence, the research posits vital questions on the underlying factors emphasizing the need for targeted interventions.

An essential aspect of this research is the identification of various predictors influencing the time to recurrence. Factors such as patient’s age at diagnosis, tumor stage, grade, and lymph node involvement were examined. Each of these elements serves as a cornerstone for understanding the prognosis and recurrence risk associated with breast cancer. For instance, patients diagnosed at a younger age typically exhibit more aggressive forms of the disease, which could correlate with a shorter time to recurrence.

Advanced tumor stages present another critical determinant; stage IIIB and IV cancers often exhibit more complex biological behavior. In contrast, early-stage cancers usually have better survival rates and longer periods before recurrence. Grading of tumors, which assesses how much cancer cells differ from normal cells, also plays a pivotal role in predicting outcomes. High-grade tumors tend to grow and spread more rapidly, potentially leading to an earlier recurrence. As researchers parse through this multi-faceted data, they highlight the need for tailored treatment plans that can accommodate the diverse characteristics of breast cancer presentations.

The study’s authors meticulously compiled data, taking a retrospective look at patient records spanning several years. This method offers valuable insights into long-term outcomes; however, it also poses inherent challenges. The retrospective nature means that data accuracy relies heavily on previous documentation practices. Nonetheless, these records provide a treasure trove of information that can be analyzed to uncover patterns and trends significant to patient care.

Another influence on the recurrence rates discussed in this research is the socio-economic and cultural context in which patients find themselves. Accessibility to healthcare facilities, education about the disease, and personal health-seeking behaviors can significantly impact patient outcomes. Public hospitals in Addis Ababa serve a diverse population, which means that factors such as literacy levels and socio-economic status will inevitably influence patient adherence to treatment regimens. This context is crucial when considering intervention strategies aimed at increasing patient compliance and, by extension, improving prognosis.

The paper also deliberates on the psychological impacts associated with a breast cancer diagnosis and its treatment. Psychological factors such as anxiety and depression have been shown to manifest in various ways, potentially affecting patients’ immune response and resilience during treatment. Mental health is an oft-overlooked component in cancer care, yet it can heavily influence outcomes. This research advocates for a holistic approach combining medical treatments with psychological support to address the multidimensional needs of breast cancer patients.

Moreover, the role of lifestyle factors, such as diet, exercise, and smoking habits, as correlates to breast cancer recurrence, cannot be discounted. Emerging evidence in oncological research suggests that lifestyle modifications may influence cancer progression and recurrence. By understanding the intricate relationship between these factors and recurrence, healthcare providers can promote lifestyle changes that empower patients while simultaneously mitigating risk factors associated with recurrence.

The study’s findings underline the necessity for continuous monitoring and follow-ups among women who have overcome breast cancer. Regular screenings and imaging can help catch recurrences early, leading to more favorable outcomes. The authors recommend the development of tailored monitoring programs that consider the specific predictors outlined in their findings. Instead of a one-size-fits-all approach, personalized care can help bridge the gaps in current treatment protocols.

A salient point made in the research is the potential for integrating these findings into broader public health strategies. Policymakers can leverage the study’s insights to allocate resources more efficiently, ensuring that women at higher risk receive the necessary interventions and follow-ups. This can mitigate the burden of breast cancer recurrence on public health systems, which are often strained by high patient volumes and limited resources.

The study not only brings attention to the unique context of breast cancer in Ethiopia but also serves as a call to action for further research into this critical area. Knowledge gaps remain, particularly in the realms of genetics and molecular biology concerning Ethiopian populations. Future studies should aim to explore the underlying biological mechanisms that contribute to the observed variations in recurrence rates.

Ultimately, Chala and colleagues have paved the way for enhanced understanding and improved treatment strategies by methodically examining the time to breast cancer recurrence and its associated predictors in a localized context. As the scientific community continues to unravel the complexities of breast cancer, it is crucial to embrace an interdisciplinary approach, encompassing clinical, psychological, and socio-economic factors. This holistic understanding will undoubtedly lead to innovations in treatment protocols, personalized care, and better overall outcomes for breast cancer survivors.

Subject of Research: Time to breast cancer recurrence and associated predictors.

Article Title: Time to breast cancer recurrence and associated predictors in Public Hospitals of Addis Ababa, Central Ethiopia: a retrospective Cohort Study.

Article References:

Chala, Y., Techane, T., Bekele, B. et al. Time to breast cancer recurrence and associated predictors in Public Hospitals of Addis Ababa, Central Ethiopia: a retrospective Cohort Study.
J Cancer Res Clin Oncol 151, 187 (2025). https://doi.org/10.1007/s00432-025-06181-2

Image Credits: AI Generated

DOI:

Keywords: Breast cancer recurrence, predictive factors, Ethiopia, patient care, socio-economic impact.

The role of imaging biomarkers in oncology has rapidly evolved, with functional imaging techniques such as PET/CT providing unparalleled insight into tumor metabolism and proliferation. [18F]FLT, a radiotracer used to assess cellular proliferation by tagging thymidine analog uptake, emerges as a promising candidate in this domain. By measuring TBR—the quotient of tumor SUV relative to the background parenchymal tissue—clinicians aspire to refine therapeutic decision-making with precision beyond conventional tumor size assessment.

Central to this study was the analysis of 90 breast cancer patients across 17 centers, each undergoing a regimented imaging protocol that involved three [18F]FLT PET/CT scans at distinct treatment stages: pre-treatment baseline, post-first NAC cycle, and post-chemotherapy completion. This temporal approach enabled researchers to meticulously track dynamic changes in tumor metabolism alongside volumetric adjustments, juxtaposing functional and anatomical parameters.

Surprisingly, when considered independently, classical metrics such as tumor size and TBR values—both mean and maximum uptake ratios—demonstrated limited sensitivity and specificity in foretelling pathological response. The highest area under curve (AUC) statistic achieved for these metrics individually hovered at a modest 0.682, signaling suboptimal predictive capacity and underscoring the complexity of tumor biology and response heterogeneity.

Delving deeper, the investigators innovatively combined PET-derived functional data with CT-based anatomical measurements, thereby forming an integrated diagnostic model. This hybrid approach significantly amplified prognostic accuracy, with the combined model yielding AUC scores of 0.731 and 0.833 for baseline and post-chemotherapy scans respectively. Notably, evaluating the percentage change between these scans realized an even more striking AUC of 0.875, heralding a new benchmark for predictive modeling in this context.

Intriguingly, mid-NAC imaging, a time point often presumed to be critically informative, did not showcase substantial diagnostic value in either standalone or combined models. The peak AUC at this interim stage was a mere 0.626, raising pivotal questions regarding optimal imaging windows and the biological underpinnings manifesting during chemotherapy.

These findings collectively illuminate the complementary nature of functional and structural imaging parameters in capturing the multifaceted response of tumors to systemic treatment. The nuclear medicine community has long speculated on the merit of combining metabolic indicators with anatomical changes, and this study offers compelling empirical support for this paradigm. Importantly, the tumor to background parenchymal ratio serves as a nuanced functional biomarker, reflecting proliferative activity relative to surrounding healthy tissue rather than absolute uptake values alone.

Further, the large multicenter design lends robust external validity to the results, suggesting their generalizability across diverse clinical environments. Harnessing prospective data, though analyzed retrospectively here, reduces the bias often inherent in smaller, single-institution studies. This bodes well for potential clinical translation, where standardized imaging protocols can be implemented to guide therapeutic personalization.

Enhanced predictive accuracy in NAC response assessment carries profound implications. For patients, it could mean earlier, more informed decisions to modify or escalate treatment regimens, avoiding ineffective chemotherapy cycles and attendant toxicities. For clinicians, these insights empower a more data-driven approach to patient management, balancing efficacy with quality of life considerations.

However, challenges persist in integrating advanced imaging biomarkers into routine clinical workflows. Factors such as cost, accessibility, and expertise in interpreting dynamic PET/CT metrics must be addressed to realize widespread adoption. Additionally, further prospective trials are warranted to validate these findings and explore their utility in conjunction with emerging molecular and genomic biomarkers.

Beyond breast cancer, the methodological principles elucidated here—leveraging TBR in a combined functional-anatomical model—may extend to other malignancies where neoadjuvant chemotherapy plays a pivotal role. The study’s innovative use of serial imaging time points offers a template for dynamic treatment monitoring adaptable to diverse oncologic contexts.

Moreover, the study contributes critical knowledge to the evolving field of personalized oncology. By delineating how complex tumor-host interactions manifest on advanced imaging, clinicians gain a window into the temporal biological landscape of treatment response, paving the way for adaptive precision medicine strategies.

In summation, this comprehensive study underscores the transformative potential of integrating tumor size with [18F]FLT PET/CT derived tumor to background parenchymal ratios to predict neoadjuvant chemotherapy efficacy in breast cancer accurately. Its findings set the stage for future research priorities and clinical applications aiming to optimize patient outcomes via sophisticated imaging biomarkers.

As the oncology community continues to harness technological advancements, studies like these exemplify the vital intersection of molecular imaging and therapeutic innovation. They bring hope for a future where cancer treatments are tailored with unprecedented accuracy, sparing patients unnecessary interventions and enhancing survival prospects.

The promising results here resonate with the broader quest for biomarkers that are not only precise and reproducible but also practical and minimally invasive. The integration of functional metrics with conventional imaging might well represent the next leap forward in oncological diagnostics and patient care management.

Ultimately, this impactful research conducted across multiple leading centers enriches the scientific dialogue surrounding breast cancer treatment and shines a spotlight on the indispensable role of multimodal imaging in contemporary oncology.

Subject of Research: The predictive value of tumor to background parenchymal ratio (TBR) in [18F]FLT PET/CT imaging for assessing breast cancer response to neoadjuvant chemotherapy.

Article Title: Exploring the role of tumor to background parenchymal ratio of the [18F]FLT PET/CT measures in determining response to neoadjuvant chemotherapy in breast cancer: a multicenter study.

Article References:
Mohebbi, A., Asli, F., Mohammadzadeh, S. et al. Exploring the role of tumor to background parenchymal ratio of the [18F]FLT PET/CT measures in determining response to neoadjuvant chemotherapy in breast cancer: a multicenter study. BMC Cancer 25, 1139 (2025). https://doi.org/10.1186/s12885-025-14534-w

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12885-025-14534-w

Survival analysis has long incorporated frailty models to account for unobserved heterogeneity — the individual differences in risk factors not directly measured but influencing survival time. Yet, choosing the most efficient frailty model becomes particularly intricate when researchers grapple with high-dimensional data, a common scenario in contemporary genomics and clinical datasets. This study rises to this challenge by evaluating seven different AFT frailty models — Weibull, Log-logistic, Gamma, Gompertz, Log-normal, Generalized Gamma, and Extreme Value — and coupling their performance with sophisticated regularization techniques such as LASSO, Ridge, and Elastic Net.

What distinguishes the Accelerated Failure Time framework is its direct interpretability, modeling how covariates accelerate or decelerate the time until an event, such as death or relapse, occurs. However, frailty models add an additional layer of complexity by allowing random effects to embody patient-specific risk factors that remain unobserved but significantly impact survival. The researchers measured model efficacy through multiple robust criteria — Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), as well as prediction errors quantified by Mean Absolute Error (MAE) and Mean Squared Error (MSE).

A standout finding emerged from the comparison: the Extreme Value Frailty AFT model consistently outperformed all other candidates across varying sample sizes (25%, 50%, and 75%). This model exhibited the lowest values of AIC and BIC, underscoring an optimal balance between model complexity and goodness-of-fit. Moreover, its predictive accuracy, as demonstrated through reduced MAE and MSE scores, confirmed its robustness. These quantitative markers point to the Extreme Value model as a superior statistical instrument to predict breast cancer outcomes effectively.

Model interpretability remains a critical priority, especially when translating analytical insights into clinical decisions. Here, regularization techniques provided a substantial boon. Specifically, LASSO (Least Absolute Shrinkage and Selection Operator) regularization refined the model structure by shrinking insignificant covariate coefficients to zero, thereby enhancing parsimony without sacrificing predictive fidelity. Non-informative variables like age, progesterone receptor status (PR), and hospitalization were systematically excluded, sharpening the focus on pivotal predictors that influence survival.

Among the variables retained by the LASSO-regularized Extreme Value model were competing risks, metastasis, cancer stage, and lymph node involvement. These factors stood out as the most critical determinants of prognosis. Intriguingly, the study quantified the survival advantage conferred by these parameters. For example, patients without metastasis enjoyed an expected survival time approximately two and a half times longer than those with metastatic disease. Similarly, those diagnosed at lower cancer stages experienced about a 26% increase in survival duration, while minimal lymph node involvement corresponded to a 16% improvement.

Further, molecular markers and tumor characteristics held independent prognostic weight. Patients with HER2-negative tumors showed a 20% longer expected survival compared to their positive counterparts. The absence of the aggressive Triple Negative breast cancer subtype also translated into a 15% survival extension. Tumor grade exhibited a parallel trend, where lower grades aligned with an 11% longer survival period. Likewise, the presence or absence of recurrence impacted survival, with recurrence associated with a 19% reduction.

Beyond statistical validation, the research illuminated clinically meaningful subgroup stratification. By classifying patients into Low, Medium, and High-risk cohorts based on their covariate profiles, the model revealed distinct survival trajectories. This stratification aligns seamlessly with Kaplan–Meier survival curves, which displayed pronounced survival declines linked to metastasis, lymph node status, tumor grade, HER2 status, and molecular subtypes. Such detailed risk categorization can empower oncologists to tailor treatment intensity and monitoring frequency more precisely.

The findings also highlighted the nuanced role of competing risks in survival analysis, especially risks related to hospitalization events. These competing risks significantly affect patient outcomes, suggesting that integrated treatment approaches addressing both cancer progression and comorbid conditions are vital. This dual focus underscores the necessity of holistic patient management strategies, which blend oncologic care with addressing ancillary health issues.

By contrast, traditional models frequently struggle with overfitting when applied to high-dimensional clinical data, diluting their generalizability. The rigorous application of LASSO and similar regularization techniques effectively counters this challenge by shrinking noisy or redundant predictors, thereby bolstering model stability. Through this dimensionality reduction, the Extreme Value Frailty AFT model achieves a powerful synergy of precision and interpretability.

A particularly illuminating aspect of the study involves the comparative performance metrics across sample sizes. Even when working with just 25% of the dataset, the Extreme Value model retained superiority, as evidenced by its AIC score of 100.41, outperforming the second-best Log-logistic model. This consistency across data scales validates the model’s adaptability and resilience, critical features for real-world applications where data availability can fluctuate.

The underlying theoretical appeal of the Extreme Value distribution in frailty modeling lies in its ability to accommodate heavy-tailed survival times and extreme observations, which standard distributions like Weibull or Gamma may inadequately capture. Such flexibility proves invaluable in oncology, where patient responses often exhibit significant variability. By properly modeling this heterogeneity, survival predictions become more accurate and clinically actionable.

Importantly, the meticulous forest plot analyses provided a graphical representation of the covariates’ hazard ratios and confidence intervals, visually substantiating the statistical claims. This visualization further highlighted the dominant influence of key clinical variables such as metastasis and lymph node involvement, reinforcing their prognostic significance.

Complementing the quantitative analysis, Kaplan–Meier survival curves offered intuitive illustrations of clinical subgroup differences. These plots revealed stark survival disparities across molecular subtypes, with Triple Negative and HER2-overexpressing breast cancers manifesting the poorest outcomes. This empirical evidence not only corroborates previous clinical observations but also magnifies the urgency for subtype-specific therapeutic innovations.

The study’s integrative framework demonstrates the power of combining advanced statistical methodologies with pragmatic model selection and validation. It charts a course toward personalized prognostic tools capable of guiding clinical decisions and optimizing patient outcomes. As data complexity in oncology escalates, such methodological rigor will become indispensable.

Ultimately, this research transcends the confines of breast cancer prognosis, indicating broader applicability across diverse medical conditions characterized by survival data with embedded heterogeneity. By harnessing regularized frailty models like the Extreme Value AFT, researchers and clinicians alike gain a potent toolkit for unmasking subtle predictors and refining risk assessments.

The study’s implications resonate deeply within the precision medicine movement — a paradigm that seeks to tailor diagnostics and therapeutics to individual patient profiles. Sophisticated survival models capable of identifying key prognostic variables while mitigating overfitting are essential ingredients in this transformative endeavor. With increasing computational power and richer datasets, such approaches will likely shape the future of medical research and personalized patient care.

In sum, the pioneering work by Bosson-Amedenu and colleagues underscores the critical impact of sophisticated statistical modeling in enhancing breast cancer survival predictions. Through systematic evaluation, the Extreme Value Frailty AFT model combined with LASSO regularization emerges as a formidable approach, offering refined interpretability, improved prediction accuracy, and valuable clinical insights. This advancement fortifies the armamentarium of oncologists, biostatisticians, and epidemiologists striving to decode the complexity of cancer progression and improve patient prognoses worldwide.

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Subject of Research: Breast cancer survival prediction using advanced Accelerated Failure Time frailty models enhanced by regularization techniques.

Article Title: Evaluating key predictors of breast cancer through survival: a comparison of AFT frailty models with LASSO, ridge, and elastic net regularization

Article References:
Bosson-Amedenu, S., Ayitey, E., Ayiah-Mensah, F. et al. Evaluating key predictors of breast cancer through survival: a comparison of AFT frailty models with LASSO, ridge, and elastic net regularization.
BMC Cancer 25, 665 (2025). https://doi.org/10.1186/s12885-025-14040-z

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12885-025-14040-z