Supervised risk predictor of breast cancer based on intrinsic subtypes

The Identification of Novel Prognostic and Predictive Biomarkers in Breast Cancer via the Elucidation of Tumor Ecotypes Using Ecotyper

Background

Breast cancer is a highly heterogeneous disease, characterized by tumor and nontumor cells at various cell states. Ecotyper is an innovative machine learning framework that quantifies the tumor microenvironment and delineates the tumor ecosystem, demonstrating clinical significance. However, further validation is needed in breast cancer.

Methods

Ecotyper was applied to identify multiple cellular states and tumor ecotypes using large-scale breast cancer bulk sequencing data, followed by a detailed analysis of their associations with clinical classification, molecular subtypes, survival prognosis, and immunotherapy response. Identified subtypes were further characterized using single-cell and spatial data sets to reveal molecular profiles.

Results

In a comprehensive analysis of 6578 breast cancer samples from four data sets, Ecotyper identified 69 cellular states and 10 tumor ecotypes. Of these, 37 cellular states significantly correlated with overall survival. Notably, specific states within epithelial cells, macrophages/monocytes, and fibroblasts were linked to a worse prognosis. CE2 abundance was identified as the most significant marker indicating unfavorable prognosis and was further validated in an additional data set of 116 HER2-negative patients. These biomarkers also indicated the efficacy of neoadjuvant immunotherapy in breast cancer. CE2-high cancers were characterized by an abundance of basal-like epithelial cells, scant lymphocytic infiltration, and activation of hypoxia signaling. Single-cell analysis showed that CE2-high areas were rich in SPP1-positive tumor-associated macrophages(TAM), basal-like epithelial cells, and hypoxic cancer-associated fibroblasts(CAF). Spatially, these regions were often peripheral in triple-negative breast cancer, adjacent to fibrotic/necrotic zones. Multiplex immunofluorescence confirmed the enrichment of SPP1+CD68+TAM and HIF1A+SMA+CAF in hypoxic triple-negative breast cancer (TNBC) regions.

Conclusions

Ecotyper identified novel biomarkers for breast cancer prognosis and treatment prediction. The CE2-high region may represent a hypoxic immune-suppressive niche.

Molecular characterisation of the residual disease after neoadjuvant endocrine therapy in ER+/HER2- breast cancer uncovers biomarkers of tumour response

BACKGROUND

Neoadjuvant endocrine therapy (NET) in oestrogen receptor-positive /HER2-negative breast cancer (ER+/HER2- BC) allows real-time evaluation of treatment sensitivity by monitoring tumour response and offers the opportunity of personalised therapy. However, the lack of reproducible biomarkers to assess response and long-term prognosis after NET is a significant barrier to increase its indications.

METHODS

In this study we searched for clinically relevant molecular reporters of response to NET in a multicentre population of ER+/HER2- BC patients (n = 87) by using: PAM50 gene expression panel and immunohistochemical evaluation of key proteins involved in tumorigenesis.

RESULTS

Our PAM50 analyses show that tumours changing from luminal A to normal-like subtype after NET presented better radiological and pathological tumour responses, a significant larger decrease in Ki67 at surgery, lower preoperative endocrine prognostic index score (PEPI) and lower tumour cellularity size (TCS) than those with persistent luminal A status. Patients with the highest response to NET showed the largest decrease in PAM50-derived risk of recurrence (ROR) following NET. In addition, the percentage of p53 positive cells was associated with decreased response to NET.

CONCLUSIONS

Our findings highlight the change of intrinsic subtype from luminal A to normal-like after NET as a putative biomarker characterising the patient population that obtains the highest benefit from NET. Our study also suggests that changes in PAM50-derived ROR score and p53 evaluation could also help to identify those patients. Thus, this study uncovers potential biomarkers of response to NET and prognosis, which should be validated in independent cohorts, helping to the implementation of NET in the clinical practice.

Artificial intelligence approaches for tumor phenotype stratification from single-cell transcriptomic data

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumors. However, given the extent of intra-tumoral heterogeneity, it is challenging to assess the risk associated with individual cell subpopulations, primarily due to the complexity of the cancer phenotype space and the lack of clinical annotations associated with tumor scRNA-seq studies. To this end, we introduce SCellBOW, a scRNA-seq analysis framework inspired by document embedding techniques from the domain of Natural Language Processing (NLP). SCellBOW is a novel computational approach that facilitates effective identification and high-quality visualization of single-cell subpopulations. We compared SCellBOW with existing best practice methods for its ability to precisely represent phenotypically divergent cell types across multiple scRNA-seq datasets, including our in-house generated human splenocyte and matched peripheral blood mononuclear cell (PBMC) dataset. For tumor cells, SCellBOW estimates the relative risk associated with each cluster and stratifies them based on their aggressiveness. This is achieved by simulating how the presence or absence of a specific cell subpopulation influences disease prognosis. Using SCellBOW, we identified a hitherto unknown and pervasive AR-/NElow (androgen-receptor-negative, neuroendocrine-low) malignant subpopulation in metastatic prostate cancer with conspicuously high aggressiveness. Overall, the risk-stratification capabilities of SCellBOW hold promise for formulating tailored therapeutic interventions by identifying clinically relevant tumor subpopulations and their impact on prognosis.

Refining housekeeping genes and demonstrating their potential for clinical and experimental applications

BACKGROUND

Housekeeping genes (HKGs) are crucial for maintaining basic cellular functions and are consistently expressed across various tissues and cell types, making them essential for normalizing gene expression. Their application is crucial in both basic research and clinical settings, such as breast cancer, where they help in accurate gene expression measurement and tumor subtype classification such as the PAM50 system. However, HKGs are often used without thorough assessment of their variability across different conditions, which may affect the reliability of normalization.

METHODS AND FINDINGS

We identified 16 candidate HKGs in breast tissue using TCGA RNA-seq data. These genes, along with previously known HKGs such as GAPDH, were evaluated across several breast cancer cell lines and experimental conditions that mimic clinical cancer treatment using quantitative real-time PCR (qPCR). The candidate HKGs were further validated using additional bulk and single cell RNA-seq datasets from the Gene Expression Omnibus (GEO) and by performing droplet digital PCR (ddPCR). We finally concluded that our candidate HKGs, especially EIF4H, GHITM, ATP5F1B, BRK1, and OS9, demonstrated greater stability than GAPDH and RPLP0. These genes were subsequently tested within the PAM50 breast cancer subtyping system, where they improved normalization performance over GAPDH.

CONCLUSION

We have identified novel HKGs useful in breast cancer research and proved that they exhibit more stable expression compared to previously known HKGs. These findings may offer researchers and clinicians a more reliable normalization standard for gene expression analysis, potentially enhancing the accuracy of breast cancer diagnosis and the selection of personalized treatments.

IL6 mediated cFLIP downregulation increases the migratory and invasive potential of triple negative breast cancer cell

c-FLIP (cellular FLICE-Like Inhibitor of Apoptotic protein) alias CFLAR (Cellular FADD-like apoptosis regulator) is an inhibitor of Caspase 8 and thus plays a key role in the regulation of extrinsic apoptotic pathway. However, the mechanisms of cFLIP regulation during the course of progression of cancer and it's involvement in tumour cell migration and invasion is yet to be known. Our TCGA data analysis has shown that cFLIP is downregulated in many cancers, including breast cancer, especially at the later stages. Next, we have analysed the role of cFLIP in breast cancer progression in In-vitro study. In doing so, we have used luminal breast cancer cell line MCF7 as non-aggressive and non-invasive breast cancer model and triple negative breast cancer cell lines MDA-MB-231, MDA-MB-468 and MDA-MB-453 as highly aggressive and invasive breast cancer cell model. When, we analysed and compared MCF7 and triple negative cell lines, we found a negative correlation between cFLIP expression pattern and metastasis which supported our In-silico study. Moreover, we found that Il6, one of the most prominent cytokines inside tumour microenvironment, helped in cFLIP downregulation via activation of p38 in MDA-MB-231 cell line. Not only that we have shown that cFLIP negatively regulated autophagy and this autophagy down-regulation resulted in decrease in metastasis. Thus, we have shown in an In-vitro model, for the first time, a complete interconnecting pathway in which IL6 mediated p38 activation directly influences metastasis by regulating autophagy via cFLIP downregulation.

BenchXAI: Comprehensive benchmarking of post-hoc explainable AI methods on multi-modal biomedical data

The increasing digitalization of multi-modal data in medicine and novel artificial intelligence (AI) algorithms opens up a large number of opportunities for predictive models. In particular, deep learning models show great performance in the medical field. A major limitation of such powerful but complex models originates from their 'black-box' nature. Recently, a variety of explainable AI (XAI) methods have been introduced to address this lack of transparency and trust in medical AI. However, the majority of such methods have solely been evaluated on single data modalities. Meanwhile, with the increasing number of XAI methods, integrative XAI frameworks and benchmarks are essential to compare their performance on different tasks. For that reason, we developed BenchXAI, a novel XAI benchmarking package supporting comprehensive evaluation of fifteen XAI methods, investigating their robustness, suitability, and limitations in biomedical data. We employed BenchXAI to validate these methods in three common biomedical tasks, namely clinical data, medical image and signal data, and biomolecular data. Our newly designed sample-wise normalization approach for post-hoc XAI methods enables the statistical evaluation and visualization of performance and robustness. We found that the XAI methods Integrated Gradients, DeepLift, DeepLiftShap, and GradientShap performed well over all three tasks, while methods like Deconvolution, Guided Backpropagation, and LRP-α1-β0 struggled for some tasks. With acts such as the EU AI Act the application of XAI in the biomedical domain becomes more and more essential. Our evaluation study represents a first step towards verifying the suitability of different XAI methods for various medical domains.

A novel machine learning-based workflow to capture intra-patient heterogeneity through transcriptional multi-label characterization and clinically relevant classification

OBJECTIVES

Patient classification into specific molecular subtypes is paramount in biomedical research and clinical practice to face complex, heterogeneous diseases. Existing methods, especially for gene expression-based cancer subtyping, often simplify patient molecular portraits, neglecting the potential co-occurrence of traits from multiple subtypes. Yet, recognizing intra-sample heterogeneity is essential for more precise patient characterization and improved personalized treatments.

METHODS

We developed a novel computational workflow, named MULTI-STAR, which addresses current limitations and provides tailored solutions for reliable multi-label patient subtyping. MULTI-STAR uses state-of-the-art subtyping methods to obtain promising machine learning-based multi-label classifiers, leveraging gene expression profiles. It modifies standard single-label similarity-based techniques to obtain multi-label patient characterizations. Then, it employs these characterizations to train single-sample predictors using different multi-label strategies and find the best-performing classifiers.

RESULTS

MULTI-STAR classifiers offer advanced multi-label recognition of all the subtypes contributing to the molecular and clinical traits of a patient, also distinguishing the primary from the additional relevant secondary subtype(s). The efficacy was demonstrated by developing multi-label solutions for breast and colorectal cancer subtyping that outperform existing methods in terms of prognostic value, primarily for overall survival predictions, and ability to work on a single sample at a time, as required in clinical practice.

CONCLUSIONS

This work emphasizes the importance of moving to multi-label subtyping to capture all the molecular traits of individual patients, considering also previously overlooked secondary assignments and paving the way for improved clinical decision-making processes in diverse heterogeneous disease contexts. Indeed, MULTI-STAR novel, reproducible and generalizable approach provides comprehensive representations of patient inner heterogeneity and clinically relevant insights, contributing to precision medicine and personalized treatments.

Therapeutic potential of GNRHR analogs and SRC/FAK inhibitors to counteract tumor growth and metastasis in breast cancer

Breast cancer (BC) is the leading cause of cancer death in women, with hormone-dependent BC accounting for about 80 % of cases, primarily affecting postmenopausal women with gonadotropins, luteinizing hormone (LH), and follicle-stimulating hormone (FSH) elevated. Treatments targeting the gonadotropin-releasing hormone receptor (GnRHR), such as the agonist leuprorelin (LEU) and antagonist degarelix (DEGA), are used for hormone-dependent tumors. While the functional role of gonadotropin receptors in extragonadal tissues remains uncertain, recent studies suggest LH contributes to tumor development and progression. Tumor progression involves reorganization in the actin cytoskeleton, induction of adhesion, and cell migration, driven by proteins such as Src and the focal adhesion kinase (FAK), which are related to invasive behaviors. The overexpression of both protein kinases generates an invasive and metastatic phenotype, then inhibitors targeting Src (PP2) and FAK (FAKi) have been developed to counteract this effect. This study combined GnRH analogs with Src and FAK inhibitors to target BC progression. We found that LH treatment influenced gene expression linked to tumor development. Examining the GnRHR-LEU and GnRHR-DEGA complexes revealed structural differences affecting ligand binding. In an orthotopic tumor model, DEGA reduced tumor growth, while LEU had the opposite effect. Combining DEGA with PP2 or FAKi enhanced tumor inhibition, improving mice survival. These findings provide valuable insights into the essential regulatory role of gonadotropins in genes involved in tumorigenic processes, highlighting the potential of GnRHR antagonists combined with Src or FAK inhibitors as a promising strategy to develop new drugs that interfere with the ability of breast tumor progression.

Ki-67 and 21-gene recurrence score assay in decision making for adjuvant chemotherapy in breast cancer patients

Although significant advances have been made in the molecular subtyping of breast cancers, identification of patients who do not benefit from the chemotherapy is a major challenge. Pioneer studies have examined the predictive value of the clinicopathological factors, such as tumor size, disease stage, the expression levels of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) molecules and more importantly tumor cells proliferation index (Ki-67) to help guide patients' treatment and predict their outcome in the adjuvant chemotherapy setting. However, despite their clinical importance, no consensus is reached on their validity for chemotherapy decision. These challenges have ignited researchers to evaluate genomic signatures, which has led to the introduction of several genomic tests that can now help oncologists to include/exclude chemotherapy from the treatment regimen with more confidence. The present review aims to look back over the literature on the clinical significance of Ki-67 as well as the 21-gene recurrence score assay in identification of breast cancer patients who may benefit from the adjuvant chemotherapy.

Clinicopathological and molecular characterization of inflammatory breast cancer, the prospective INFLAME registry study

Inflammatory breast cancer (IBC) is rare, with challenging diagnostics and unfavorable outcomes. Therefore, more molecular insight into IBC is needed. The comprehensive Dutch prospective INFLAME registry related IBC follow-up and treatment to histopathology and molecular analysis. Of consecutive patients, nationwide identified with newly diagnosed IBC, clinicopathological, treatment and outcome data were collected. Histopathology and RNA-sequencing were related to outcome. 125 IBC patients were enrolled. Forty-one (34%) patients had HER2 + , and 31 (25%) had triple-negative IBC. The estimated 3-year OS was 78% in M0 IBC and 29% in M1. PFS was worst in triple-negative IBC (median 7.9 vs 16.3 and 15.8 months in M1 HER2+ and HR + /HER2- IBC). DFS and OS in M0 IBC were better with guideline-concordant trimodal therapy than without (HR 0.15 and 0.15; p = 0.000005 and 0.00038). The unique prospective INFLAME confirms unfavorable IBC characteristics and outcomes. International efforts may support guideline adherence and identify IBC-specific targets.

Molecular mechanisms of the anticancer action of fustin isolated from Cotinus coggygria Scop. in MDA-MB-231 triple-negative breast cancer cell line

The aim of the present work was to investigate some of the molecular mechanisms and targets of the anticancer action of the bioflavonoid fustin isolated from the heartwood of Cotinus coggygria Scop. in the triple-negative breast cancer cell line MDA-MB-231. For this purpose, we applied fluorescence microscopy analysis to evaluate apoptosis, necrosis, and mitochondrial integrity, wound healing assay to study fustin antimigratory potential and quantitative reverse transcription-polymerase chain reaction to analyze the expression of genes associated with cell cycle control, programmed cell death, metastasis, and epigenetic alterations. A complex network-based bioinformatic analysis was also employed for protein-protein network construction, hub genes identification, and functional enrichment. The results revealed a significant induction of early and late apoptotic and necrotic events, a slight alteration of the mitochondria-related fluorescence, and marked antimotility effect after fustin treatment. Of 34 analyzed genes, seven fustin targets were identified, of which CDKN1A, ATM, and MYC were significantly enriched in pathways such as cell cycle, intrinsic apoptotic signaling pathway in response to DNA damage and generic transcription pathway. Our findings outline some molecular mechanisms of the anticancer action of fustin pointing it out as a potential oncotherapeutic agent and provide directions for future in vivo research.

B7-H3 and CSPG4 co-targeting as Pan-CAR-T cell treatment of triple-negative breast cancer

PURPOSE

Chimeric antigen receptor T (CAR-T) cell therapy is under clinical investigation in patients with metastatic triple-negative breast cancer (TNBC). However, the identification of targetable antigens remains a high priority to avoid toxicity and prevent tumor escape.

EXPERIMENTAL DESIGN

Here we analyzed the gene expression of B7-H3 (CD276) and chondroitin sulfate proteoglycan 4 (CSPG4) in 98 TNBC samples identified in the AURORA US Network and Rapid Autopsy RNA sequencing data set at University of North Carolina (UNC). We then performed immunohistochemistry analysis for B7-H3 and CSPG4 protein expression in 151 TNBC samples collected at UNC. Finally, the validity of the proposed B7-H3 and CSGP4 co-targeting was tested in clinically relevant TNBC patient derived xenograft (PDX) models.

RESULTS

We observed that CD276 and CSPG4 genes are broadly and comparably expressed in TNBC samples, and gene expression is generally conserved in tumor metastases. None of the TNBC analyzed met the criteria for simultaneous low expression of CSPG4 and CD276 genes. Immunohistochemistry analysis showed a median H-score of 138 (105-168, lower and upper quartile, respectively) for B7-H3 expression and a median H-score of 33 (14-78 lower and upper quartile, respectively) for CSPG4 expression. Notably, 49% of the TNBC cores with B7-H3 H-score ≤105 exhibited a CSPG4 H-score exceeding its median value, and 37% and 18% of the TNBC cores with low B7-H3 expression scored CSPG4 expression above its median H-score or exceeded its upper quartile, respectively, confirming that at least one of these two proteins is expressed in 94% of the analyzed tumors. Finally, optimized dual-specific B7-H3 and CSPG4 CAR-T cells eradicated tumors with mixed antigen expression in TNBC PDX models.

CONCLUSIONS

These data highlight the clinical potential of the proposed approach that could be applicable to the great majority of patients with TNBC as well as most of patients with breast cancer in general.

Intra-tumoral spatial heterogeneity in breast cancer quantified using high-dimensional protein multiplexing and single cell phenotyping

BACKGROUND

Breast cancer is a highly heterogeneous disease where variations of biomarker expression may exist between individual foci of a cancer (intra-tumoral heterogeneity). The extent of variation of biomarker expression in the cancer cells, distribution of cell types in the local tumor microenvironment and their spatial arrangement could impact on diagnosis, treatment planning and subsequent response to treatment.

METHODS

Using quantitative multiplex immunofluorescence (MxIF) imaging, we assessed the level of variations in biomarker expression levels among individual cells, density of cell cluster groups and spatial arrangement of immune subsets from regions sampled from 38 multi-focal breast cancers that were processed using whole-mount histopathology techniques. Molecular profiling was conducted to determine the intrinsic molecular subtype of each analysed region.

RESULTS

A subset of cancers (34.2%) showed intra-tumoral regions with more than one molecular subtype classification. High levels of intra-tumoral variations in biomarker expression levels were observed in the majority of cancers studied, particularly in Luminal A cancers. HER2 expression quantified with MxIF did not correlate well with HER2 gene expression, nor with clinical HER2 scores. Unsupervised clustering revealed the presence of various cell clusters with unique IHC4 protein co-expression patterns and the composition of these clusters were mostly similar among intra-tumoral regions. MxIF with immune markers and image patch analysis classified immune niche phenotypes and the prevalence of each phenotype in breast cancer subtypes was illustrated.

CONCLUSIONS

Our work illustrates the extent of spatial heterogeneity in biomarker expression and immune phenotypes, and highlights the importance of a comprehensive spatial assessment of the disease for prognosis and treatment planning.

Gene expression and agent-based modeling improve precision prognosis in breast cancer

Breast cancer survival is hard to predict because of the complex ways genes and cells interact. This study offers a new method to improve these predictions by combining gene expression profiling (GEP) with agent-based modeling (ABM). First, GEP will pinpoint genes that are important in breast cancer development. Then, a mathematical model will be built to show how these genes influence cell behavior. This data will be used in ABM to simulate tumor growth and treatment response. The ABM allows us to virtually test different treatments and see how they might affect patient survival. Finally, the model's accuracy will be checked against real patient data and compared to other models. By combining the strengths of GEP and ABM, this research could significantly improve breast cancer survival prediction. ABM's ability to analyze interactions mathematically could pave the way for more personalized and effective treatments.

Genomic and immune profiling of breast cancer brain metastases

BACKGROUND

Brain metastases (BrM) arising from breast cancer (BC) are an increasing consequence of advanced disease, with up to half of patients with metastatic HER2 + or triple negative BC experiencing central nervous system (CNS) recurrence. The genomic alterations driving CNS recurrence, along with contributions of the immune microenvironment, particularly by intrinsic subtype, remain unclear.

METHODS

We characterized the genomic and immune landscape of BCBrM from a cohort of 42 patients by sequencing whole-exome DNA (WES) and total RNA libraries from frozen and FFPE BrM and FFPE extracranial tumors (ECT). Analyses included PAM50 intrinsic subtypes, somatic mutations, copy number variations (CNV), pathway alterations, immune cell type deconvolution, and associations with clinical outcomes RESULTS: Intrinsic subtype calls were concordant for the majority of BrM-ECT pairs (60%). Across all BrM and ECT samples, the most common somatic gene mutation was TP53 (64%, 30/47). For patients with matched FFPE BrM-FFPE ECT, alterations tended to be conserved across tissue type, although differential somatic mutations and CNV in specific genes were observed. Several genomic pathways were differentially expressed between patient-matched BrM-ECT; MYC targets, DNA damage repair, cholesterol homeostasis, and oxidative phosphorylation were higher in BrM, while immune-related pathways were lower in BrM. Deconvolution of immune populations between BrM-ECT demonstrated activated dendritic cell populations were higher in BrM compared to ECT. Increased expression of several oncogenic preselected pathways in BrM were associated with inferior survival, including DNA damage repair, inflammatory response, and oxidative phosphorylation CONCLUSIONS: Collectively, this study illustrates that while some genomic alterations are shared between BrM and ECT, there are also unique aspects of BrM including somatic mutations, CNV, pathway alterations, and immune landscape. A deeper understanding of differences inherent to BrM will contribute to the development of BrM-tailored therapeutic strategies. Additional analyses are warranted in larger cohorts, particularly with additional matched BrM-ECT.

Mechanical Conditioning (MeCo) Score Progressively Increases Through the Metastatic Cascade in Breast Cancer via Circulating Tumor Cells

BACKGROUND

The mechanical conditioning (MeCo) score is a multigene expression signature that is acquired by cancer cells in the primary breast tumor and is reflective of their responsiveness to ECM stiffness caused by tumor fibrosis. Chromatin remodeling downstream of mechanotransduction allows cancer cells to retain these acquired aggressive features even in the absence of mechanical stimulation from the primary tumor microenvironment, for instance, after dissemination through systemic circulation during metastasis. Importantly, patients who have high MeCo score tumors are at higher risk of developing metastatic breast cancer, compared to those with low MeCo scores. Moreover, circulating tumor cells (CTCs) are associated with a higher rate of metastatic dissemination, making CTC detection in the circulation of patients with breast cancer a significant prognostic biomarker for breast cancer metastasis. Beyond their enumeration per blood volume units, specific prognostic features of CTCs are not fully explored. We sought to determine whether MeCo scores increase stepwise along the metastatic cascade, from primary tumors to CTCs to distant metastatic colonization, using patient-matched biopsies.

METHODS

CTCs were isolated from the peripheral blood of two patient cohorts: patients with early-stage breast cancer using immunomagnetic enrichment/FACS methodology; and patients with late-stage breast cancer using the ANGLE Parsortix microfluidics system. Gene expression profiling using RNA-seq was performed on CTCs and matched primary tumors (PTs) in the early-stage cohort, and on CTCs and matched metastases (METs) for the late-stage cohorts. A quantile normalization approach was used to allow comparison across cohorts and MeCo scores were computed for all samples. The Wilcoxon matched-pairs signed rank test was performed for the comparison of MeCo scores from matching samples within each cohort; the Mann-Whitney unpaired test was used to compare MeCo scores of CTCs across cohorts.

RESULTS

In 12 pairs of patients with early-stage breast cancer, MeCo scores in CTCs were significantly higher than in their matched PTs (p = 0.026). Additionally, in 26 pairs of metastatic patient CTCs and METs, MeCo scores were significantly higher in METs compared to matched CTCs (p = 0.0004). MeCo scores of CTCs were similar between patients with early- and late-stage breast cancers, despite differing CTC isolation strategies (epitope-dependent and microfluidics size gradient). Notably, 98% of the genes in the MeCo score were present across evaluable CTC, MET, and PT samples.

CONCLUSIONS

Our results show that the MeCo score is higher in CTCs than in PTs, and higher in METs compared to CTCs, in early- and late-stage breast cancer, respectively (i.e., PT < CTC < MET). Therefore, the MeCo score is progressively higher throughout the metastatic cascade in breast cancer. These findings demonstrate that mechanical conditioning from primary tumors is retained during metastatic progression, after mechanical induction by ECM stiffness is lost, as cancer cells disseminate through systemic circulation. Additionally, these findings support that cancer cells with higher MeCo scores are more competent with-and potentially selected for-metastatic progression. Importantly, these findings provide a novel feature of CTCs, mechanical conditioning (MeCo), which is associated with higher capacity for metastasis. Furthermore, since the CTC MeCo score is elevated even in early-stage breast cancer, it could provide, in addition to CTC enumeration, a potential prognostic indicator to improve metastatic risk assessment in early disease.

Facing the Challenge to Mimic Breast Cancer Heterogeneity: Established and Emerging Experimental Preclinical Models Integrated with Omics Technologies

Breast cancer (BC) is among the most common neoplasms globally and is the leading cause of cancer-related mortality in women. Despite significant advancements in prevention, early diagnosis, and treatment strategies made over the past two decades, breast cancer continues to pose a significant global health challenge. One of the major obstacles in the clinical management of breast cancer patients is the high intertumoral and intratumoral heterogeneity that influences disease progression and therapeutic outcomes. The inability of preclinical experimental models to replicate this diversity has hindered the comprehensive understanding of BC pathogenesis and the development of new therapeutic strategies. An ideal experimental model must recapitulate every aspect of human BC to maintain the highest predictive validity. Therefore, a thorough understanding of each model's inherent characteristics and limitations is essential to bridging the gap between basic research and translational medicine. In this context, omics technologies serve as powerful tools for establishing comparisons between experimental models and human tumors, which may help address BC heterogeneity and vulnerabilities. This review examines the BC models currently used in preclinical research, including cell lines, patient-derived organoids (PDOs), organ-on-chip technologies, carcinogen-induced mouse models, genetically engineered mouse models (GEMMs), and xenograft mouse models. We emphasize the advantages and disadvantages of each model and outline the most important applications of omics techniques to aid researchers in selecting the most relevant model to address their specific research questions.

Exploring the efficacy of extended endocrine therapy in pure mucinous breast carcinoma

BACKGROUND

This study aims to compare the efficacy of 5- versus 10-year endocrine therapy in pure mucinous breast carcinoma (PMBC), focusing on late recurrence and related factors for personalized treatment.

METHODS

Patients with PMBC who underwent surgery from 1996 to 2014 at Asan Medical Center were included. Recurrence was categorized as early (<5 years) or late (≥5 years). The primary endpoint was disease-free survival in the 5- and 10- year endocrine groups. Subgroup analysis was performed focused on clinically high-risk patients (tumor ≥2 cm, nodal metastasis, or high histologic grade).

RESULTS

A total of 489 patients with PMBC were identified. During a follow-up time of 126 months, 35 (7.2 %) patients had an early recurrence, 25 (5.1 %) patients had a late recurrence, and 394 (87.7 %) patients had no recurrence. High histologic grade was the only factor significantly correlated to late recurrence (hazard ratio 6.92, 95 % confidence interval 1.53-31.3). Among the 5-year disease-free survivors (N = 416), 340 (81.7 %) and 76 (18.3 %) patients underwent 5-year and 10-year endocrine therapy, respectively. Endocrine therapy duration did not impact the 10-year disease-free survival rate (5-year [95.4 %] vs. 10-year [97.3 %] endocrine therapy, log-rank test p = 0.504). Subgroup analysis with clinically high-risk patients revealed no survival difference based on the endocrine therapy duration, too.

CONCLUSION

Extended endocrine therapy did not significantly reduce late recurrence in PMBC, even in high-risk groups, underscoring the importance of personalized strategies for sustained outcomes.

Single-cell and spatial RNA sequencing identify divergent microenvironments and progression signatures in early- versus late-onset prostate cancer

The clinical and pathological outcomes differ between early-onset (diagnosed in men ≤55 years of age) and late-onset prostate cancer, potentially attributed to the changes in hormone levels and immune activities associated with aging. Exploring the heterogeneity therein holds potential for developing age-specific precision interventions. Here, through single-cell and spatial transcriptomic analyses of prostate cancer tissues, we identified that an androgen response-related transcriptional meta-program (AR-MP) might underlie the age-related heterogeneity of tumor cells and microenvironment. APOE+ tumor-associated macrophages infiltrated AR-MP-activated tumor cells in early-onset prostate cancer, potentially facilitating tumor progression and immunosuppression. By contrast, inflammatory cancer-associated fibroblasts in late-onset prostate cancer correlated with downregulation of AR-MP of tumor cells and increased epithelial-to-mesenchymal transition and pre-existing castration resistance, which may also be linked to smoking. This study provides potential insights for tailoring precision treatments by age groups, emphasizing interventions that include targeting AR and tumor-associated macrophages in young patients but anchoring epithelial-to-mesenchymal transition and inflammatory cancer-associated fibroblasts in old counterparts.

IRSN-23 gene diagnosis enhances breast cancer subtype classification and predicts response to neoadjuvant chemotherapy: new validation analyses

BACKGROUND

This study evaluates the reproducibility of the IRSN-23 model, which classifies patients into highly chemotherapy-sensitive (Gp-R) or less-sensitive (Gp-NR) groups based on immune-related gene expression using DNA microarray analysis, and its impact on breast cancer subtype classification.

METHODS

Tumor tissues from 146 breast cancer patients receiving neoadjuvant chemotherapy (paclitaxel-FEC) ± trastuzumab at Osaka University Hospital (OUH) were used to classify patients into Gp-R or Gp-NR using IRSN-23. The ability to predict a pathological complete response (pCR) was assessed and the results were validated with independent public datasets (N = 1282).

RESULTS

In the OUH dataset, the pCR rate was significantly higher in the Gp-R group than in the Gp-NR group without trastuzumab (29 versus 1%, P = 1.70E-5). In all validation sets without anti-HER2 therapy, the pCR rate in the Gp-R group was significantly higher than that in the Gp-NR group. The pooled analysis of the validation set showed higher pCR rates in the Gp-R group than in the Gp-NR group, both without (N = 1103, 40 versus 12%, P = 2.02E-26) and with (N = 304, 49 versus 35%, P = 0.017) anti-HER2 therapy. Collaboration analyses of IRSN-23 and Oncotype Dx or PAM50 could identify highly chemotherapy-sensitive groups and refine breast cancer subtype classification based on the tumor microenvironment (offensive factor-PAM50 and defensive factor-IRSN-23), and the immune subtype was correlated with a better prognosis after NAC.

CONCLUSIONS

This study offers new validation analyses of IRSN-23 in predicting chemotherapy efficacy, showing high reproducibility. The findings indicate the clinical value of using IRSN-23 for refining breast cancer subtype classification, with implications for personalized treatment strategies and improved patient outcomes.

Radiomic Parenchymal Phenotypes of Breast Texture from Mammography and Association with Risk of Breast Cancer

Background Parenchymal phenotypes reflect the intrinsic heterogeneity of both tissue structure and distribution on mammograms. Purpose To define parenchymal phenotypes on the basis of radiomic texture features derived from full-field digital mammography (FFDM) in breast screening populations and assess associations of parenchymal phenotypes with future risk of breast cancer and masking (false-negative [FN] findings or interval cancers), beyond breast density, and by race and ethnicity Materials and Methods A two-stage study design included a retrospective cross-sectional study of 30 000 randomly selected women with four-view FFDM (mean age, 57.4 years) and a nested case-control study of 1055 women with invasive breast cancer (151 Black and 893 White women) matched to 2764 women without breast cancer (411 Black and 2345 White women) (mean age, 60.4 years) sampled from April 2008 to September 2019 from three diverse breast screening practices. Radiomic features (n = 390) were extracted and standardized using an automated pipeline and adjusted for age and practice. Variation was classified using hierarchical clustering and principal component (PC) analysis. The resulting clusters and PCs were examined for association with invasive breast cancer risk, FN findings on mammograms, and symptomatic interval cancers beyond radiologist-reported Breast Imaging Reporting and Data System (BI-RADS) breast density using conditional logistic regression and likelihood ratio tests. Discrimination for breast cancer was assessed with area under the receiver operating characteristic curve (AUC). Results Six clusters and six PCs were defined, replicated, and associated with a higher risk of invasive breast cancer (P = .01 and P < .001, respectively) after adjustment for age, body mass index (calculated as weight in kilograms divided by height in meters squared), and BI-RADS breast density. PCs showed similar associations among Black and White women (P = .23). PCs were also positively associated with FN findings (P = .004) and symptomatic interval cancers (P = .006). AUC improved for all breast cancer end points when incorporating PCs, with the greatest improvement shown in prediction of FN findings (AUC with vs without PCs, 0.73 [95% CI: 0.68, 0.78] vs 0.66 [95% CI: 0.61, 0.71] , respectively; P = .004) and symptomatic interval cancers (AUC with vs without PCs, 0.77 [95% CI: 0.71, 0.82] vs 0.68 [95% CI: 0.62, 0.74], respectively; P = .006). Conclusion Parenchymal phenotypes based on radiomic features extracted from FFDM were associated with a higher risk of invasive breast cancer, specifically for FN findings and symptomatic interval cancer. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Mesurolle and El Khoury in this issue.

Genomic Characteristics Related to Histology-Based Immune Features in Breast Cancer

The immune cell component of the tumor microenvironment is an important modulator of tumor progression. In patients with breast cancer, tumor-infiltrating lymphocytes (TILs) and tertiary lymphoid structures (TLS) represent core aspects of antitumor immunity, both increasingly recognized for clinical relevance. In this study, we evaluated immune-related histology features using whole-slide hematoxylin and eosin (H&E) images of The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) data set (n = 1035) and analyzed these distinct features relative to gene expression, PAM50 subtypes, and patient survival. H&E images were evaluated for TILs, plasma cells (PCs), high-endothelial venule-associated lymphoid aggregates (HALA), and mature TLS. For HALA and TLS, location relative to the tumor (nontumor, peritumor, and intratumor) was determined. HER2-enriched (HER2E) and basal-like breast tumors exhibited the highest mean TILs and the presence of PCs. HALA were present in 35.1% of cases and TLS in 6.5% of cases, also predominantly in HER2E and basal-like tumors. We derived gene expression signatures for 10 histologically defined immune features and tested their clinical significance using transcriptomic and survival data from the Sweden Cancerome Analysis Network - Breast (SCAN-B) cohort. Signatures related to TILs, PCs, HALA/TLS, TLS, and specifically intratumor HALA and TLS were associated with better survival in HER2E and basal-like tumors. Peritumor HALA/TLS and nontumor signatures were nonsignificant or associated with worse outcomes. Furthermore, we compared the immune microenvironment of high-TIL (TILs > 10%) tumors from TCGA-BRCA by PAM50 subtype through supervised analyses of 200+ immune gene expression signatures, and unique immune features were identified for each subtype. In high-TIL luminal tumors, enriched immune signatures had little relation to prognosis. High-TIL HER2E and basal-like tumors had distinct immune signatures linked to improved survival, related to B and T cells, respectively. Overall, PAM50 subtypes of breast cancer exhibit distinct immune microenvironments, both histologically and molecularly. These differences in immune properties should be considered when developing precise treatment strategies to achieve optimal therapeutic efficacy for patients.

Single-cell and multi-omics integration reveals cholesterol biosynthesis as a synergistic target with HER2 in aggressive breast cancer

Breast cancer stands as one of the most prevalent malignancies affecting women. Alterations in molecular pathways in cancer cells represent key regulatory disruptions that drive malignancy, influencing cancer cell survival, proliferation, and potentially modulating therapeutic responsiveness. Therefore, decoding the intricate molecular mechanisms and identifying novel therapeutic targets through systematic computational approaches are essential steps toward advancing effective breast cancer treatments. In this study, we developed an integrative computational framework that combines single-cell RNA sequencing (scRNA-seq) and multi-omics analyses to delineate the functional characteristics of malignant cell subsets in breast cancer patients. Our analyses revealed a significant correlation between cholesterol biosynthesis and HER2 expression in malignant breast cancer cells, supported by proteomics data, gene expression profiles, drug treatment scores, and cell-surface HER2 intensity measurements. Given previous evidence linking cholesterol biosynthesis to HER2 membrane dynamics, we proposed a combinatorial strategy targeting both pathways. Experimental validation through clonogenic and viability assays demonstrated that simultaneous inhibition of cholesterol biosynthesis (via statins) and HER2 (via Neratinib) synergistically reduced malignant breast cancer cells, even in HER2-negative contexts. Through systematic analysis of scRNA-seq and multi-omics data, our study computationally identified and experimentally validated cholesterol biosynthesis and HER2 as novel combinatorial therapeutic targets in breast cancer. This data-driven approach highlights the potential of leveraging multiple molecular profiling techniques to uncover previously unexplored treatment strategies.

A Multimodal Deep Learning Model for the Classification of Breast Cancer Subtypes

Background: Breast cancer is a heterogeneous disease with distinct molecular subtypes, each requiring tailored therapeutic strategies. Accurate classification of these subtypes is crucial for optimizing treatment and improving patient outcomes. While immunohistochemistry remains the gold standard for subtyping, it is invasive and may not fully capture tumor heterogeneity. Artificial Intelligence (AI), particularly Deep Learning (DL), offers a promising non-invasive alternative by analyzing medical imaging data. Methods: In this study, we propose a multimodal DL model that integrates mammography images with clinical metadata to classify breast lesions into five categories: benign, luminal A, luminal B, HER2-enriched, and triple-negative. Using the publicly available Chinese Mammography Database (CMMD), our model was trained and evaluated on a dataset of 4056 images from 1775 patients. Results: The proposed multimodal approach significantly outperformed a unimodal model based solely on mammography images, achieving an AUC of 88.87% for multiclass classification of these five categories, compared to 61.3% AUC for the unimodal model. Conclusions: These findings highlight the potential of multimodal AI-driven approaches for non-invasive breast cancer subtype classification, paving the way for improved diagnostic precision and personalized treatment strategies.

Risk Assessment Using Gene Expression Profiling Correlates with Clinical Prognosis Estimation in Hormone Receptor-Positive/HER2-Negative Early Breast Cancer

Background

Gene expression profiles (GEPs) are recommended for tailoring adjuvant treatment in patients with hormone receptor (HR)-positive/HER2-negative breast cancer (BC) with intermediate clinical and pathological risk. This single-center retrospective study aimed at evaluating the clinical relevance of the additive information provided by GEPs in clinical routine at a tertiary care center.

Methods

From 03/2010 to 07/2019, GEPs by either MammaPrint (MP) or PAM50 of HR-positive/HER2-negative early-stage BC were retrospectively included in the study. Pseudonymized data were processed for statistical analysis. Correlations between clinical and molecular risk markers were calculated. Survival was estimated using the Kaplan-Meier method.

Results

Clinical and molecular risk data were available for 213 patients; complete follow-up data were available for 189 patients. According to GEPs by either MP (n = 69) or PAM50 (n = 144), 67 patients (31.5%) had low, 58 (27.2%) intermediate only in PAM50, and 88 (41.3%) high-risk BC. The MP group showed a higher rate of molecular low-risk tumors, while tumors analyzed by PAM50 were more frequent in a molecular high-risk situation. A significant correlation of proliferation rate and grading with the molecular risk score was observed (p < 0.001 each). Adjuvant chemotherapy was recommended in 87.5% of molecular high-risk tumors but administered in 64.8% only. Interestingly, a worse DFS was detected in the molecular low-risk group compared to the high-risk group (p = 0.55). It may be assumed that this is associated with an advanced tumor stage in these patients.

Conclusion

In HR-positive/HER2-negative BC, proliferation rate as well as tumor grade correlated significantly with risk assessment by GEPs. Despite a high-risk result, chemotherapy is often omitted due to patient-specific factors such as age, comorbidities, or patients' preference. On the other hand, survival with genomic low-risk tumors is likely to be compromised to more advanced stage, questioning the clinical validity of GEPs in these cases.

Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for Predicting the Prognosis of Neoadjuvant Chemotherapy in Breast Cancer

Breast cancer (BRCA) continues to pose a serious risk to women's health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Nevertheless, the heterogeneity in treatment outcomes necessitates the identification of reliable biomarkers and prognostic models. Programmed cell death (PCD) pathways serve as a critical factor in tumor development and treatment response. However, the relationship between the diverse patterns of PCD and NAC in BRCA remains unclear. We integrated machine learning and multiple bioinformatics tools to explore the association between 19 PCD patterns and the prognosis of NAC within a cohort of 921 BRCA patients treated with NAC from seven multicenter cohorts. A prognostic risk model based on PCD-related genes (PRGs) was constructed and evaluated using a combination of 117 machine learning algorithms. Immune infiltration analysis, mutation analysis, pharmacological analysis, and single-cell RNA sequencing (scRNA-seq) were conducted to explore the genomic profile and clinical significance of these model genes in BRCA. Immunohistochemistry (IHC) was employed to validate the expression of select model genes (UGCG, BTG22, TNFRSF21, and MYB) in BRCA tissues. We constructed a PRGs prognostic risk model by using a signature comprising 20 PCD-related DEGs to forecast the clinical outcomes of NAC in BRCA patients. The prognostic model demonstrated excellent predictive accuracy, with a high concordance index (C-index) of 0.772, and was validated across multiple independent datasets. Our results demonstrated a strong association between the developed model and the survival prognosis, clinical pathological features, immune infiltration, tumor microenvironment (TME), gene mutations, and drug sensitivity of NAC for BRCA patients. Moreover, IHC studies further demonstrated that the expression of certain model genes in BRCA tissues was significantly associated with the efficacy of NAC and emerged as an autonomous predictor of outcomes influencing the outcome of patients. We are the first to integrate machine learning and bulk and scRNA-seq to decode various cell death mechanisms for the prognosis of NAC in BRCA. The developed unique prognostic model, based on PRGs, provides a novel and comprehensive strategy for predicting the NAC outcomes of BRCA patients. This model not only aids in understanding the mechanisms underlying NAC efficacy but also offers insights into personalized treatment strategies, potentially improving patient outcomes.

Goistrat: gene-of-interest-based sample stratification for the evaluation of functional differences

PURPOSE

Understanding the impact of gene expression in pathological processes, such as carcinogenesis, is crucial for understanding the biology of cancer and advancing personalised medicine. Yet, current methods lack biologically-informed-omics approaches to stratify cancer patients effectively, limiting our ability to dissect the underlying molecular mechanisms.

RESULTS

To address this gap, we present a novel workflow for the stratification and further analysis of multi-omics samples with matched RNA-Seq data that relies on MSigDB curated gene sets, graph machine learning and ensemble clustering. We compared the performance of our workflow in the top 8 TCGA datasets and showed its clear superiority in separating samples for the study of biological differences. We also applied our workflow to analyse nearly a thousand prostate cancer samples, focusing on the varying expression of the FOLH1 gene, and identified specific pathways such as the PI3K-AKT-mTOR gene sets as well as signatures linked to prostate tumour aggressiveness.

CONCLUSION

Our comprehensive approach provides a novel tool to identify disease-relevant functions of genes of interest (GOI) in large datasets. This integrated approach offers a valuable framework for understanding the role of the expression variation of a GOI in complex diseases and for informing on targeted therapeutic strategies.

Elacestrant in Women with Estrogen Receptor-Positive and HER2-Negative Early Breast Cancer: Results from the Preoperative Window-of-Opportunity ELIPSE Trial

PURPOSE

Elacestrant has shown significantly prolonged progression-free survival compared with standard-of-care endocrine therapy in estrogen receptor-positive (ER-positive), HER2-negative metastatic breast cancer, whereas potential benefit in early-stage disease requires further exploration. The SOLTI-ELIPSE window-of-opportunity trial investigated the biological changes induced by a short course of preoperative elacestrant in postmenopausal women with early breast cancer.

PATIENTS AND METHODS

Eligible patients with untreated T1c (≥1.5 cm)-T3, N0, ER-positive/HER2-negative breast cancer with locally assessed Ki67 ≥10% received elacestrant at a daily dose of 345 mg for 4 weeks. The primary efficacy endpoint was complete cell cycle arrest, defined as Ki67 ≤2.7%, on day 28.

RESULTS

Overall, 22 patients were evaluable for the primary endpoint. Elacestrant was associated with a complete cell cycle arrest rate of 27.3% and a statistically significant Ki67 geometric mean change of -52.9% (P = 0.007; 95% confidence interval, -67.4 to -32.1). Notably, the treatment with elacestrant led to a shift toward a more endocrine-sensitive and less proliferative tumor phenotype based on PAM50-based gene signatures. Elacestrant increased the expression of immune-response genes (GZMB, CD4, and CD8A) and suppressed proliferation and estrogen-signaling genes (MKI67, ESR1, and AR). These biological changes were independent of the levels of Ki67 suppression on day 28. The most common adverse events were grade 1 anemia (21.7%), hot flushes (8.7%), constipation (8.7%), and abdominal pain (8.7%). One patient experienced a grade 3 cutaneous rash, leading to treatment discontinuation. No other serious adverse events were reported.

CONCLUSIONS

Preoperative treatment with elacestrant in early breast cancer demonstrated relevant biological and molecular responses and exhibited a manageable safety profile. These findings support further investigation of elacestrant in the early setting.

Interplay between gut microbial communities and metabolites modulates pan-cancer immunotherapy responses

Immune checkpoint blockade (ICB) therapy has revolutionized cancer treatment but remains effective in only a subset of patients. Emerging evidence suggests that the gut microbiome and its metabolites critically influence ICB efficacy. In this study, we performed a multi-omics analysis of fecal microbiomes and metabolomes from 165 patients undergoing anti-programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) therapy, identifying microbial and metabolic entities associated with treatment response. Integration of data from four public metagenomic datasets (n = 568) uncovered cross-cohort microbial and metabolic signatures, validated in an independent cohort (n = 138). An integrated predictive model incorporating these features demonstrated robust performance. Notably, we characterized five response-associated enterotypes, each linked to specific bacterial taxa and metabolites. Among these, the metabolite phenylacetylglutamine (PAGln) was negatively correlated with response and shown to attenuate anti-PD-1 efficacy in vivo. This study sheds light on the interplay among the gut microbiome, the gut metabolome, and immunotherapy response, identifying potential biomarkers to improve treatment outcomes.

Association of Androgen Receptor Expression With Tumor Immune Landscape and Treatment Outcomes of Patients With Breast Cancer

PURPOSE

Although estrogen receptor is well studied in breast cancer (BC), the role of androgen receptor (AR) in prognosis and therapy response is less understood. Here, we characterized the clinicopathologic and molecular features of AR gene expression in BC subtypes.

METHODS

Ten thousand seven hundred twenty-eight BC samples were tested by next-generation DNA sequencing, whole-transcriptome sequencing, and immunohistochemistry at Caris Life Sciences (Phoenix, AZ). Tumors with AR-high and AR-low RNA expression were stratified by top and bottom quartiles, respectively. Treatment-associated survival was obtained from insurance claims and calculated from treatment start to last contact using Kaplan-Meier estimates. Statistical significance was determined by chi-square and Mann-Whitney U test with P values adjusted for multiple comparisons (q < .05).

RESULTS

AR-low was associated with basal-like tumors. AR-high tumors were associated with increased mutation rates in several genes-namely PIK3CA and CDH1-across all subtypes, while other associations such as RB1 and MAP3K1 were subtype-dependent. The immune landscape was differentially affected by AR expression in each subtype, but these differences did not correspond to differential responses to immune checkpoint blockade. Patients with AR-high tumors had a longer therapy response for most subtypes, but those with AR-high tumors that were human epidermal growth factor receptor 2-enriched and luminal B trended toward worse chemotherapy or hormone therapy response, respectively.

CONCLUSION

Our data suggest a unique molecular profile of AR-high BC that is subtype-specific and generally associated with improved outcomes. Exploration of specific mutations and immune-oncology markers associated with AR-high may aid in molecularly selected clinical trial design for patients with advanced BC.

Multimodal data integration in early-stage breast cancer

The use of biomarkers in breast cancer has significantly improved patient outcomes through targeted therapies, such as hormone therapy anti-Her2 therapy and CDK4/6 or PARP inhibitors. However, existing knowledge does not fully encompass the diverse nature of breast cancer, particularly in triple-negative tumors. The integration of multi-omics and multimodal data has the potential to provide new insights into biological processes, to improve breast cancer patient stratification, enhance prognosis and response prediction, and identify new biomarkers. This review presents a comprehensive overview of the state-of-the-art multimodal (including molecular and image) data integration algorithms developed and with applicability to breast cancer stratification, prognosis, or biomarker identification. We examined the primary challenges and opportunities of these multimodal data integration algorithms, including their advantages, limitations, and critical considerations for future research. We aimed to describe models that are not only academically and preclinically relevant, but also applicable to clinical settings.

First versus second-generation molecular profiling tests: How both can guide decision-making in early-stage hormone-receptor positive breast cancers?

Hormone receptor-positive (HR+) and human epidermal growth factor receptor 2-negative (HER2-) tumors represent the most common types of early-stage breast cancer. However, their response to adjuvant systemic treatments varies widely due to tumor heterogeneity. Current decisions for adjuvant treatment rely heavily on clinical and pathological characteristics, which can sometimes lead to overtreatment. Accurately identifying patients who will benefit from adjuvant chemotherapy at an individual level remains a challenge. Multigene profiling assays are now widely used in clinics to better assess recurrence risk and chemotherapy response for HR+ disease. In this report, we examine the advantages and limitations of two widely used molecular profiling tests-Oncotype DX and Prosigna. Both Oncotype DX and Prosigna have been demonstrated to be effective prognostic tools in early breast cancer, with Oncotype DX also being validated as a predictive tool to guide chemotherapy decisions. We focus on studies that directly compare these molecular tests and discuss how their strengths can be leveraged to improve clinical decision-making for early-stage HR+ breast cancers. Finally, we highlight remaining knowledge gaps and propose directions for future research.

Super-enhancer profiling reveals ThPOK/ZBTB7B, a CD4+ cell lineage commitment factor, as a master regulator that restricts breast cancer cells to a luminal non-migratory phenotype

Despite efforts to understand breast cancer biology, metastatic disease remains a clinical challenge. Identifying suppressors of breast cancer progression and mechanisms of transition to more invasive phenotypes could provide game changing therapeutic opportunities. Transcriptional deregulation is central to all malignancies, highlighted by the extensive reprogramming of regulatory elements that underlie oncogenic programs. Among these, super-enhancers (SEs) stand out due to their enrichment in genes controlling cancer hallmarks. To reveal novel breast cancer dependencies, we integrated the analysis of the SE landscape with master regulator activity inference for a series of breast cancer cell lines. As a result, we identified T-helper-inducing Poxviruses and Zinc-finger (POZ)/Krüppel-like factor (ThPOK, ZBTB7B), a CD4+ cell lineage commitment factor, as a breast cancer master regulator that is recurrently associated with a SE. ThPOK expression is highest in luminal breast cancer but is significantly reduced in the basal subtype. Manipulation of ThPOK levels in cell lines shows that its repressive function restricts breast cancer cells to an epithelial phenotype by suppressing the expression of genes involved in the epithelial-mesenchymal transition (EMT), WNT/b-catenin target genes, and the pro-metastatic TGFb pathway. Our study reveals ThPOK as a master transcription factor that restricts the acquisition of metastatic features in breast cancer cells.

Proteogenomic discovery of RB1-defective phenocopy in cancer predicts disease outcome, response to treatment, and therapeutic targets

Genomic defects caused by truncating mutations or deletions in the Retinoblastoma tumor suppressor gene (RB1) are frequently observed in many cancer types leading to dysregulation of the RB pathway. Here, we propose an integrative proteogenomic approach that predicts cancers with dysregulation in the RB pathway. A subset of these cancers, which we term as "RBness," lack RB1 genomic defects and yet phenocopy the transcriptional profile of RB1-defective cancers. We report RBness as a pan-cancer phenomenon, associated with patient outcome and chemotherapy response in multiple cancer types, and predictive of CDK4/6 inhibitor response in estrogen-positive breast cancer. Using RNA interference and a CRISPR-Cas9 screen in isogenic models, we find that RBness cancers also phenocopy synthetic lethal vulnerabilities of cells with RB1 genomic defects. In summary, our findings suggest that dysregulation of the RB pathway in cancers lacking RB1 genomic defects provides a molecular rationale for how these cancers could be treated.

The DNA methylation landscape of primary triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a clinically challenging and molecularly heterogenous breast cancer subgroup. Here, we investigate the DNA methylation landscape of TNBC. By analyzing tumor methylome profiles and accounting for the genomic context of CpG methylation, we divide TNBC into two epigenetic subtypes corresponding to a Basal and a non-Basal group, in which characteristic transcriptional patterns are correlated with DNA methylation of distal regulatory elements and epigenetic regulation of key steroid response genes and developmental transcription factors. Further subdivision of the Basal and non-Basal subtypes identifies subgroups transcending genetic and proposed TNBC mRNA subtypes, demonstrating widely differing immunological microenvironments, putative epigenetically-mediated immune evasion strategies, and a specific metabolic gene network in older patients that may be epigenetically regulated. Our study attempts to target the epigenetic backbone of TNBC, an approach that may inform future studies regarding tumor origins and the role of the microenvironment in shaping the cancer epigenome.

Identification of mitochondrial permeability transition-related lncRNAs as quantitative biomarkers for the prognosis and therapy of breast cancer

Breast cancer (BC) continues to pose a global health threat and presents challenges for treatment due to its high heterogeneity. Recent advancements in the understanding of mitochondrial permeability transition (MPT) and the regulatory roles of long non-coding RNAs (lncRNAs) offer potential insights for the stratification and personalized treatment of BC. Although the association between MPT and lncRNAs has not been widely studied, a few research studies have indicated a regulatory impact of lncRNAs on MPT, further deepening the understanding of the tumor. To identify reliable biomarkers associated with MPT for managing BC, bulk RNA-seq data of MPT-related lncRNAs acquired from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project were utilized to assess BC patients. A scoring system, termed the MPT-related score (MPTRscore), was developed using LASSO-Cox regression on data from 1,029 BC patients from TCGA-BRCA. Meanwhile, the superior prognostic accuracy of the MPTRscore was demonstrated by comparing it with biomarkers, including PAM50 subtyping for standardization. Subsequently, a clinical prediction model was created by incorporating the MPTRscore and clinical variables. This analysis revealed two distinct MPTRscore groups characterized by different biomolecular processes, tumor microenvironment (TME) patterns, and clinical outcomes. The MPTRscore was further investigated through unsupervised consensus clustering of TCGA-BRCA based on MPTRscore-related prognostic genes. Additionally, the MPTRscore was identified as an independent prognostic factor for BC and showed guiding utility in immunotherapy and chemotherapy response. Specifically, patients with a low MPTRscore exhibited better prognosis and treatment responses compared to those with a high MPTRscore. Significantly, the relevance of clustering results and MPTRscore was found to be mediated by lncRNA transcript RP11-573D15.8-018. In conclusion, MPTRscore-related clusters were identified in BC, and an integrative score was developed as a biomarker for predicting BC prognosis and therapeutic response. Additionally, molecular interactions underlying the relationship between MPTRscore-related clusters and MPTRscore were uncovered, proving insights for BC stratification. These findings may aid in prognosis determination and therapeutic decision-making for BC patients.

Integrated multi-omics unveils novel immune signature for predicting prognosis in colon cancer patients

Colon cancer, a prevalent malignancy, is subject to intricate immune modulation, which substantially affects both treatment efficacy and prognostic outcomes. Furthermore, colon cancer is highly heterogeneous, and our comprehensive understanding of its immune microenvironment has not yet been fully realized. There is still ample opportunity for in-depth investigation into the composition and interactions of immune cells within colon cancer, as well as their implications for disease prognosis. In this study, we employed single-cell data from colon cancer to distinguish immune cells from non-immune cells through cluster analysis. Furthermore, we conducted an in-depth analysis of myeloid and T cells, which were categorized into 20 distinct cell subpopulations. Functional enrichment analysis revealed T cells' active involvement in the Fatty Acid Metabolism and Adipogenesis pathways, while immune checkpoint-associated genes (ICGs) were notably upregulated in CD8+ T cells. Subsequent analysis involved calculating gene scores to characterize cell subpopulations, which, when combined with patient survival time analysis, revealed a significant association between the gene characterization score (referred to as "imm-score") and the survival of colon cancer patients. Specifically, the presence of CD8+-ANXA1hi-T cells was linked to shortened overall survival in the high imm-score subgroup. Subsequently, combined with genomic analysis, patients in the high imm-score subgroup exhibited elevated tumor mutation burden (TMB) and heightened activity in both the epithelial-mesenchymal transition (EMT) and Notch signaling pathway. Finally, according to our new algorithm, scores calculated predicted the effectiveness of immunotherapy for patients. The results revealed that patients with lower scores could achieve better therapeutic outcomes with immunotherapy. This study offers an extensive analysis of the interplay between T cells and myeloid cells within colon cancer tissues, exploring their impact on the survival and prognosis of colon cancer patients. Additionally, it unveils the potential significance of the imm-score in colon cancer, potentially indicating a poor prognosis and providing novel insights into the immune-regulatory mechanisms underlying the disease.

A novel method for subgroup discovery in precision medicine based on topological data analysis

BACKGROUND

The Mapper algorithm is a data mining topological tool that can help us to obtain higher level understanding of disease by visualising the structure of patient data as a similarity graph. It has been successfully applied for exploratory analysis of cancer data in the past, delivering several significant subgroup discoveries. Using the Mapper algorithm in practice requires setting up multiple parameters. The graph then needs to be manually analysed according to a research question at hand. It has been highlighted in the literature that Mapper's parameters have significant impact on the output graph shape and there is no established way to select their optimal values. Hence while using the Mapper algorithm, different parameter values and consequently different output graphs need to be studied. This prevents routine application of the Mapper algorithm in real world settings.

METHODS

We propose a new algorithm for subgroup discovery within the Mapper graph. We refer to the task as hotspot detection as it is designed to identify homogenous and geometrically compact subsets of patients, which are distinct with respect to their clinical or molecular profiles (e.g. survival). Furthermore, we propose to include the existence of a hotspot as a criterion while searching the parameter space, addressing one of the key limitations of the Mapper algorithm (i.e. parameter selection).

RESULTS

Two experiments were performed to demonstrate the efficacy of the algorithm, including an artificial hotspot in the Two Circles dataset and a real world case study of subgroup discovery in oestrogen receptor-positive breast cancer. Our hotspot detection algorithm successfully identified graphs containing homogenous communities of nodes within the Two Circles dataset. When applied to gene expression data of ER+ breast cancer patients, appropriate parameters were identified to generate a Mapper graph revealing a hotspot of ER+ patients with poor prognosis and characteristic patterns of gene expression. This was subsequently confirmed in an independent breast cancer dataset.

CONCLUSIONS

Our proposed method can be effectively applied for subgroup discovery with pathology data. It allows us to find optimal parameters of the Mapper algorithm, bridging the gap between its potential and the translational research.

Role of PARP Inhibitors: A New Hope for Breast Cancer Therapy

Tumors formed by the unchecked growth of breast cells are known as breast cancer. The second most frequent cancer in the world is breast cancer. It is the most common cancer among females. In 2022, 2,296,840 women were diagnosed with breast cancer. The therapy of breast cancer is evolving through the development of Poly (ADP-ribose) polymerase (PARP) inhibitors, which are offering people with specific genetic profiles new hope as research into the disease continues. It focuses on patients with BRCA1 and BRCA2 mutations. This review summarizes the most recent research on the mechanisms of action of PARP inhibitors and their implications for breast cancer therapy. We review how therapeutic applications are developing and highlight recent studies showing the effectiveness of these medicines whether used alone or in combination. Furthermore, the significance of customized therapy is highlighted in enhancing patient outcomes as we address the function of genetic testing in identifying candidates for PARP inhibition. Recommendations for future research areas to maximize the therapeutic potential of PARP inhibitors are also included, along with challenges and limits in their clinical usage. The objective of this review is to improve our comprehension of the complex interaction between breast cancer biology and PARP inhibition. This knowledge will help to guide screening approaches, improve clinical practice, and support preventive initiatives for people at risk.

[Apocrine lesions of the breast]

Apocrine breast lesions encompass a spectrum of histopathological abnormalities, ranging from benign apocrine metaplasia to invasive apocrine carcinomas. Their defining feature lies in cells with abundant eosinophilic cytoplasm and round nuclei with prominent nucleoli. These cells strongly express the androgen receptor while lacking estrogen receptor-alpha and progesterone receptor expression. Benign lesions, frequently associated with mammary cysts or papillomas, lack nuclear and architectural atypia. In contrast, atypical apocrine lesions exhibit significant nuclear and structural abnormalities, posing diagnostic challenges when distinguishing them from apocrine ductal or lobular carcinoma in situ. Diagnosis relies on the extent of atypia and the presence of tumor necrosis. Invasive apocrine carcinomas are rare, accounting for less than 1% of all breast cancers, and predominantly occur in postmenopausal women. Histologically, they are often grade 1 or 2 tumors. Approximately 50% exhibit HER2 amplification and overexpression. Immunohistochemically, they are characterized by positivity for FOXA1 and GATA3, and negativity for FOXC1 and SOX10, and variable expression of TRPS1. These carcinomas belong to the molecular apocrine carcinoma family, which includes HER2-enriched tumors driven by HER2 addiction and androgen receptor-positive luminal tumors, a subtype of triple-negative breast cancers. The latter are defined by androgen receptor pathway activation and are frequently associated with PI3K pathway alterations and cell cycle dysregulation, suggesting potential therapeutic targets.

VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data

Summary

Effective clustering of biomedical data is crucial in precision medicine, enabling accurate stratification of patients or samples. However, the growth in availability of high-dimensional categorical data, including 'omics data, necessitates computationally efficient clustering algorithms. We present VICatMix, a variational Bayesian finite mixture model designed for the clustering of categorical data. The use of variational inference (VI) in its training allows the model to outperform competitors in terms of computational time and scalability, while maintaining high accuracy. VICatMix furthermore performs variable selection, enhancing its performance on high-dimensional, noisy data. The proposed model incorporates summarization and model averaging to mitigate poor local optima in VI, allowing for improved estimation of the true number of clusters simultaneously with feature saliency. We demonstrate the performance of VICatMix with both simulated and real-world data, including applications to datasets from The Cancer Genome Atlas, showing its use in cancer subtyping and driver gene discovery. We demonstrate VICatMix's potential utility in integrative cluster analysis with different 'omics datasets, enabling the discovery of novel disease subtypes.

Availability and implementation

VICatMix is freely available as an R package via CRAN, incorporating C++ for faster computation, at https://CRAN.R-project.org/package=VICatMix.

Prosigna Risk of Recurrence score and intrinsic subtypes are associated with adjuvant anthracycline chemotherapy benefit in high-risk breast cancer

NCIC-CTG MA.5 and DBCG 89D are symmetrically designed randomized trials comparing adjuvant cyclophosphamide, epirubicin, and fluorouracil with cyclophosphamide, methotrexate, and fluorouracil in high-risk breast cancer patients. In a joint analysis we evaluate the predictive value in terms of anthracycline benefit of molecular subtyping by PAM50. A statistically significant interaction (P = 0.008) between continuous Risk of Recurrence (ROR) score and treatment regimen is evident, translating into a clear distinct treatment effect according to ROR score category with HR 0.51 for ROR score ≥ 72 and HR 1.10 for ROR score < 52 (Pinteraction = 0.004). The analysis provides evidence of the benefit from anthracycline in HER2-enriched subtype; for patients with discordance of HER2 subtype and clinical HER2 status, HER2-enriched subtype was predictive of anthracycline benefit whereas clinical HER2 positive status was not. Anthracycline-based adjuvant chemotherapy may safely be withheld for patients with a low ROR score while the benefit increases with increasing ROR score.

Prognostic and molecular multi-platform analysis of CALGB 40603 (Alliance) and public triple-negative breast cancer datasets

Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous disease that remains challenging to target with traditional therapies and to predict risk. We provide a comprehensive characterization of 238 stage II-III TNBC tumors with paired RNA and DNA sequencing data from the CALGB 40603 (Alliance) clinical trial, along with 448 stage II-III TNBC tumors with paired RNA and DNA data from three additional datasets. We identify DNA mutations associated with RNA-based subtypes, specific TP53 missense mutations compatible with potential neoantigen activity, and a consistently highly altered copy number landscape. We train exploratory multi-modal elastic net models of TNBC patient overall survival to determine the added impact of DNA-based features to RNA and clinical features. We find that mutations and copy number show little to no prognostic value, while RNA expression features, including signatures of T cell and B cell activity, along with stage, improve stratification of TNBC survival risk.

Genomic characterization of the HER2-enriched intrinsic molecular subtype in primary ER-positive HER2-negative breast cancer

ER-positive/HER2-negative (ERpHER2n) breast cancer classified as PAM50 HER2-enriched (ERpHER2n-HER2E) represents a small high-risk patient subgroup. In this study, we investigate genomic, transcriptomic, and clinical features of ERpHER2n-HER2E breast tumors using two primary ERpHER2n cohorts comprising a total of 5640 patients. We show that ERpHER2n-HER2E tumors exhibit aggressive clinical features and poorer clinical outcomes compared to Luminal A and Luminal B tumors. Furthermore, ERpHER2n-HER2E breast cancer does not consist of misclassified or HER2-low cases, has little impact of ERBB2, is highly proliferative and less ER dependent than other luminal subtypes. It is not an obvious biological entity but is nevertheless associated with potentially targetable molecular features, notably a high immune response and high FGFR4 expression. Strikingly, molecular features that define the HER2E subtype in luminal disease are also consistent in HER2-positive disease, including an epigenetic mechanism for high FGFR4 expression in breast cancer.

Determinants of response and molecular dynamics in HER2+ER+ breast cancers from the NA-PHER2 trial receiving HER2-targeted and endocrine therapies

Improved outcomes in HER2+ female breast cancer have resulted from chemotherapy and anti-HER2 therapies. However, HER2+ER+ cancers exhibit lower response rates. The phase 2 NA-PHER2 trial (NCT02530424) investigated chemo-free preoperative HER2 blockade (trastuzumab + pertuzumab) and CDK4/6 inhibition (palbociclib) with or without endocrine therapy (fulvestrant) in HER2+ER+ breast cancer. Clinical endpoints (i.e. Ki67 dynamics and pathological complete response) were previously reported. Here we report on the biomarker analysis, secondary objective of the study. Through RNA sequencing and tumour infiltrating lymphocytes (TIL) assessment in serial biopsies, we identified biomarkers predictive of pCR or Day14 Ki67 response and unveiled treatment-induced molecular changes. High immune infiltration and low ER signalling correlated with pCR, while TP53 mutations associated with high Day14 Ki67. Stratification based on Ki67 at Day14 and at surgery defined three response groups (Ki67 HighHigh, LowHigh, LowLow), with divergent tumour and stroma expression dynamics. The HighHigh group showed dysfunctional immune infiltration and overexpression of therapeutic targets like PAK4 at baseline. The LowLow group exhibited a Luminal A phenotype by the end of treatment. This study expands our understanding of drivers and dynamics of HER2+ER+ tumour response, towards treatment tailoring.

AJGM: joint learning of heterogeneous gene networks with adaptive graphical model

MOTIVATION

Inferring gene networks provides insights into biological pathways and functional relationships among genes. When gene expression samples exhibit heterogeneity, they may originate from unknown subtypes, prompting the utilization of mixture Gaussian graphical model (GGM) for simultaneous subclassification and gene network inference. However, this method overlooks the heterogeneity of network relationships across subtypes and does not sufficiently emphasize shared relationships. Additionally, GGM assumes data follows a multivariate Gaussian distribution, which is often not the case with zero-inflated scRNA-seq data.

RESULTS

We propose an Adaptive Joint Graphical Model (AJGM) for estimating multiple gene networks from single-cell or bulk data with unknown heterogeneity. In AJGM, an overall network is introduced to capture relationships shared by all samples. The model establishes connections between the subtype networks and the overall network through adaptive weights, enabling it to focus more effectively on gene relationships shared across all networks, thereby enhancing the accuracy of network estimation. On synthetic data, the proposed approach outperforms existing methods in terms of sample classification and network inference, particularly excelling in the identification of shared relationships. Applying this method to gene expression data from triple-negative breast cancer confirms known gene pathways and hub genes, while also revealing novel biological insights.

AVAILABILITY AND IMPLEMENTATION

The Python code and demonstrations of the proposed approaches are available at https://github.com/yyytim/AJGM, and the software is archived in Zenodo with DOI: 10.5281/zenodo.14740972.

Breast cancer prediction based on gene expression data using interpretable machine learning techniques

Breast cancer remains a global health burden, with an increase in deaths related to this particular cancer. Accurately predicting and diagnosing breast cancer is important for treatment development and survival of patients. This study aimed to accurately predict breast cancer using a dataset comprising 1208 observations and 3602 genes. The study employed feature selection techniques to identify the most influential predictive genes for breast cancer using machine learning (ML) models. The study used K-nearest Neighbors (KNN), random forests (RF), and a support vector machine (SVM). Furthermore, the study employed feature- and model-based importance and explainable ML methods, including Shapley values, Partial dependency (PDPS), and Accumulated Local Effects (ALE) plots, to explain the genes' importance ranking from the ML methods. Shapley values highlighted the significance of some of the genes in predicting cancer presence. Model-based feature ranking techniques, particularly the Leaving-One-Covariate-In (LOCI) method, identified the ten most critical genes for predicting tumor cases. The LOCI rankings from the SVM and RF methods were aligned. Additionally, visualization methods such as PDPS and ALE plots demonstrated how individual feature changes affect predictions and interactions with other genes. By combining feature selection techniques and explainable ML methods, this study has demonstrated the interpretability and reliability of machine learning models for breast cancer prediction, emphasizing the importance of incorporating explainable ML approaches for medical decision-making.

MELK as a Mediator of Stemness and Metastasis in Aggressive Subtypes of Breast Cancer

Triple-negative breast cancer (TNBC) is the breast cancer subtype with the poorest prognosis and lacks actionable molecular targets for treatment. Maternal embryonic leucine zipper kinase (MELK) is highly expressed in TNBC and has been implicated in poor clinical outcomes, though its mechanistic role in the aggressive biology of TNBC is poorly understood. Here, we demonstrate a role of MELK in TNBC progression and metastasis. Analysis of publicly available datasets revealed that high MELK expression correlates with worse overall survival, recurrence-free survival, and distant metastasis-free survival, and MELK is co-expressed with metastasis-related genes. Functional studies demonstrated that MELK inhibition, using genomic or pharmacologic inhibition, reduces mammosphere formation, migration, and invasion in high-MELK-expressing TNBC cell lines. Conversely, MELK overexpression in low-MELK-expressing cell lines significantly increased invasive capacity in vitro and metastatic potential in vivo, as evidenced by enhanced metastasis to the liver and lungs in a chorioallantoic membrane assay. These findings highlight MELK as a key regulator of TNBC aggressiveness and support its potential as a therapeutic target to mitigate metastasis and improve patient outcomes.

PRR13 expression as a prognostic biomarker in breast cancer: correlations with immune infiltration and clinical outcomes

Introduction

Breast cancer continues to be a primary cause of cancer-related mortality among women globally. Identifying novel biomarkers is essential for enhancing patient prognosis and informing therapeutic decisions. The PRR13 gene, associated with taxol resistance and the progression of various cancers, remains under-characterized in breast cancer. This study aimed to investigate the role of PRR13 in breast cancer and its potential as a prognostic biomarker.

Methods

We performed a comparative analysis of PRR13 gene expression utilizing the TCGA database against non-cancerous tissues and employed STRING to evaluate PRR13's protein-protein interactions and associated pathways. Additionally, we investigated the relationship between PRR13 mRNA expression and immune cell infiltration in breast cancer (BRCA) using two methodologies. Furthermore, a retrospective analysis of 160 patients was conducted, wherein clinical data were collected and PRR13 expression was evaluated through immunohistochemistry and qRT-PCR to determine its association with clinicopathological features and patient survival.

Results

Analysis of the TCGA database revealed significant upregulation of PRR13 expression across 12 different cancer types, including breast cancer. High PRR13 expression was positively correlated with various immune cells, including NK cells, eosinophils, Th17 cells, and mast cells, whereas a negative correlation was observed with B cells, macrophages, and other immune subsets. Enrichment analysis of PRR13 and its 50 interacting proteins revealed significant associations with biological processes such as cell adhesion and migration, and pathways including ECMreceptor interaction and PI3K-Akt signaling. Single-cell analysis demonstrated associations between PRR13 and pathways pertinent to inflammation and apoptosis. Validation studies confirmed elevated PRR13 expression in tumor tissue compared to adjacent non-cancerous tissue. Immunohistochemistry demonstrated high PRR13 expression in 55.6% of cancer cases, particularly associated with advanced clinical stage and lymph node metastasis. Moreover, high PRR13 expression significantly correlated with shorter overall survival and served as an independent prognostic factor. Subgroup analysis underscored the prognostic significance of PRR13 in aggressive tumor subtypes, with particularly strong associations observed in T3, N1-3, and moderately to poorly differentiated tumors.

Discussion

In conclusion, PRR13 expression is upregulated in breast cancer tissues and may serve as a valuable prognostic indicator for breast cancer patients, potentially impacting patient survival and therapeutic strategies.

Leveraging Multi-omics to Disentangle the Complexity of Ovarian Cancer

To better understand ovarian cancer lethality and treatment resistance, sophisticated computational approaches are required that address the complexity of the tumor microenvironment, genomic heterogeneity, and tumor evolution. The ovarian cancer tumor ecosystem consists of multiple tumors and cell types that support disease growth and progression. Over the last two decades, there has been a revolution in -omic methodologies to broadly define components and essential processes within the tumor microenvironment, including transcriptomics, metabolomics, proteomics, genome sequencing, and single-cell analyses. While most of these technologies comprehensively characterize a single biological process, there is a need to understand the biological and clinical impact of integrating multiple -omics platforms. Overall, multi-omics is an intriguing analytic framework that can better approximate biological complexity; however, data aggregation and integration pipelines are not yet sufficient to reliably glean insights that affect clinical outcomes.