Breast Cancer Research

Background: Patients with ER-positive/HER2-negative early breast cancer of low or intermediate grade are generally considered to have a favourable prognosis, yet a number will recur. This risk is poorly captured by standard clinicopathological factors. AI-based medical devices for whole slide image (WSI) analyses used for risk-stratification often predict on slide-level thereby overlooking spatial localisation of the biomarkers. We hypothesise that the spatial distribution of a previously reported AI-based marker provides independent prognostic value beyond its global score. Patients and Methods: This Swedish multicentre cohort study includes 6106 patients with primary invasive breast cancer: 3078 from the Stockholm region (training) and 3028 from Skane (independent test). Tile-level high-grade morphology scores were generated with ViT_DG, a vision-transformer adaptation of DeepGrade, and the presence of clusters of high-risk tiles localised at the invasive tumour front was defined as a binary biomarker. Primary analyses were pre-specified in the ER-positive/HER2-negative/lymph-node-negative and Grade 1-2 subgroup. Association with breast-cancer recurrence-free interval (BCRFI) and distant metastasis were assessed using multivariable Cox proportional hazards models (HR). Results: In the primary subgroup (n=1536 training; n=1403 test), tumour front clusters were present in 37% and 39% of patients. Cluster-positive status was associated with a 2.30-fold increased risk in the external test. Cluster-negative patients achieved a 10-year BCRFI of 95.3%, equivalent to Grade 1 patients and better than Grade 2 (92.4%), whereas cluster-positive patients (90.2%) did worse than the Grade 3 reference. The association was retained in patients classified as low-risk by ViT_DG (test set multivariable HR 2.86), identifying high-risk patients not captured by the global score. Conclusions: The absence of high-risk morphology clusters at the tumour front is an independent prognostic indicator, identifying a low-risk group independently of the global AI biomarker classification. More broadly, preserving the spatial distribution of AI-derived predictions enables prognostic stratification beyond slide-level aggregation.

Background: Triple-negative breast cancer (TNBC) disproportionately affects women of African ancestry, yet the biological mechanisms underlying this disparity remain poorly understood. While recent genome-wide association studies (GWAS) have identified numerous TNBC risk variants, their functional consequences have not been systematically evaluated in African-ancestry populations. We hypothesized that ancestry-specific regulatory variants drive distinct transcriptomic and proteomic landscapes that contribute to TNBC aggressiveness. Methods: We extracted 2,849 unique TNBC-associated variants (p-value [≤] 0.0001) from the GWAS Catalog. Of these, 206 were excluded due to absence in our whole-genome sequencing (WGS) dataset or low minor allele frequency ([≤] 0.01). We prioritized the remaining 2,643 bi-allelic variants for analysis in the GENE-FORECAST cohort. We integrated WGS with whole-blood transcriptomic (n=242) and proteomic data (n=362) to perform cis-eQTL and cis-pQTL analyses in discovery and validation sets. Results: We identified 38 cis-eQTL associations and 5 cis-pQTL associations that were statistically replicated across independent discovery and validation datasets within our cohort. Both omics layers converged on a single gene, Hemoglobin Zeta (HBZ). We identified five functional variants (four SNPs and one INDEL) in strong linkage disequilibrium that consistently upregulate HBZ mRNA and the corresponding circulating embryonic zeta-globin protein. These variants were found almost exclusively in African and African-admixed populations across GENE-FORECAST, 1000 Genomes, BioVU, and All of Us datasets. Four of the five variants were also confirmed as significant HBZ eQTLs in GTEx whole blood. Conclusion: Our integrative analysis reveals variants of the embryonic HBZ gene as a novel, ancestry-specific TNBC-associated risk factor that is detectable at both transcriptomic and proteomic levels. This suggests that aberrant reactivation of this embryonic globin may provide a selective advantage in the hypoxic TNBC microenvironment. These results highlight a unique mechanism of tumorigenesis in women of African ancestry, offering a potential distinct biomarker and therapeutic target for this high-risk population.

Background: Neoadjuvant chemotherapy (NACT) facilitates de-escalation of breast and axillary treatment in breast cancer, but pathological responses may differ between the primary tumour and axillary lymph nodes. Understanding differential response is crucial for de-escalation strategies. Methods: This single university hospital retrospective study analysed operable breast cancers (cT1-cT3, cN0-cN3) treated with NACT (January 2013-November 2020). Two-tailed Fishers exact test or the Chi-squared test (for 2x2 comparisons) analysed concordance between breast and axillary pathological responses; Cox proportional hazards regression ascertained predictors of distant recurrence and survival; and logistic regression ascertained predictors of complete axillary response. Results: Among 413 patients (cN0=229; cN+=184) with a median age of 51, pathological response concordance between the breast and axilla was 81% (both pCR, 22.2%; concurrent residual disease, 58.8%). The cN0 group had a significantly higher ypN0 rate in breast pCR versus non-pCR (97.1% vs 80.8%, p<0.001). cN+ had axillary pCR concordant with breast pCR, compared to only 16.5% with residual breast disease. Breast pCR was the most significant predictor of axillary pCR (OR=11.6, p<0.001). Axillary pCR was highest in ER-/HER2+(66.7%) and triple-negative(46.9%) subtypes. Residual axillary disease was independently associated with the worst breast cancer-specific (HR=7.13, p=0.006) and overall Survival (HR=3.32, p=0.029). Kaplan-Meier analyses showed a trend towards worse survival outcomes with axillary residual disease, even with a breast pCR. Conclusions: In this limited series, breast pCR predicts axillary pCR in cN0 and cN+, particularly in HER2-positives and triple-negatives, supporting axillary de-escalation strategies in selected patients stratified by careful assessment of disease at the end of NACT.

Backgroud Breast cancer is a leading cause of cancer-related mortality, and HR+/HER2-; is the predominant subtype. Molecular pathology, including PIK3CA and ESR1 mutation, informs prognosis and treatment selection. Circulating tumor DNA (ctDNA) offers a non-invasive approach for cancer monitoring, but its clinical utility is limited by tissue plasma discordance, reflecting heterogeneous ctDNA release. The cellular and spatial determinants of this release remain poorly understood, particularly in HR+/HER2-; subtype, where heterogeneity across subclones complicates liquid biopsy interpretation. Results To identify the cellular and spatial determinants of ctDNA release, we developed scMASTER, integrating single-cell transcriptomes and mutations, spatial transcriptomics and matched tumor and plasma DNA sequencing. In ten patients with non-metastatic HR+/HER2- breast cancer, eight recurrent malignant epithelial states showed distinct ctDNA release potential. Antigen-presentation, stress, mitosis and mesenchymal-like states showed high release. PIK3CA and ESR1 mutations were linked to distinct functional states and altered ctDNA detectability. Three spatial architectures, EMT-enriched regions, intraductal luminal progenitors and avascular hypoxic zones, showed high release. Tumor-microenvironment interactions were bidirectional: anti-tumoral immunity correlated with reduced release, whereas immunomodulatory components correlated with increased release. A stratification model based on these features identified high-risk, immunosuppressed patients with poor prognosis, validated across external cohorts. Conclusions Our study establishes a single-cell spatial framework for decoding ctDNA release heterogeneity, linking tumor states, somatic mutations, spatial architecture, and immune interactions to ctDNA release. These findings provide mechanistic insights into tissue-plasma discordance, offer a biological rationale for interpreting ctDNA dynamics, refine patient risk stratification and liquid biopsy-based monitoring in HR+/HER2-; breast cancer.

Stromal cell composition has been proposed as a potential biomarker in ductal carcinoma in situ (DCIS) for assessing the risk of subsequent recurrence or progression to invasive breast cancer (IBC). However, the characteristics of the tumor microenvironment within DCIS associated with these outcomes remain poorly understood. Here, we perform spatial transcriptomic analyses of 49 primary DCIS specimens from patients with no recurrence, DCIS recurrence, or recurrence as IBC. Cell mapping reveals spatially organized stromal cell gradients within DCIS, with enrichment of TREM2+ macrophages and myofibroblastic cancer-associated fibroblasts (myCAFs) in tumor-proximal niches. Notably, the abundance of TREM2+ macrophages near tumor nests is associated with subsequent recurrence as IBC. Moreover, ligand-receptor pair analysis identifies GRN--SORT1, SPP1--CD44, and LGALS9--CD44 interactions as spatially plausible signaling interactions between TREM2+ macrophages and epithelial cells. Finally, these findings are validated at the protein level by multiplex immunostaining of serial sections. This study characterizes the spatial landscape of the DCIS tumor microenvironment associated with subsequent recurrence, providing a basis for developing biomarkers of disease progression and identifying potential therapeutic targets.

PurposeMen with pathogenic BRCA1 and BRCA2 variants (BRCA+) have an elevated lifetime risk of male breast cancer (MBC) yet data regarding mammography in this population remain limited. MethodsWe performed a retrospective cohort study of BRCA+ assigned male at birth individuals (males) at Penn (1/1/2008-5/1/2024) and the Veterans Administration (VA, 10/1/2002-1/1/2026) for mammography uptake. Mammography results and MBC characteristics were compared between BRCA+ and BRCA-males. ResultsAmong 810 BRCA+ males, 19% had a documented mammogram for screening or symptoms. Among 562 BRCA+ males age-eligible for mammogram without prior MBC, most (63%) had documentation of a mammography-related discussion and 16% completed mammogram, of whom 25% were BRCA1+ and 75% were BRCA2+. Among mammograms obtained for screening of 97 BRCA+ males, there were few BI-RADS 4/5 (N=1, 1%) and no MBC was detected. Symptom-indicated mammograms in 59 BRCA+ compared to 228 BRCA-males yielded BI-RADS 4/5 findings in 35% versus 25%, p<0.0001, and MBC diagnoses in 29% versus 21%, p=0.219. The positive predictive value (PPV) of BI-RADS 4/5 lesions was similar in BRCA+ vs BRCA-males (71% vs 74% and 100% vs 93%, respectively). Among 22 BRCA+ and 57 BRCA-MBC, 55% and 42% were node positive and 82% and 86% were ER+Her2-, respectively. ConclusionScreening mammography utilization among BRCA+ males was low despite elevated hereditary risk. Regardless of BRCA status, nearly all MBC were diagnosed based on symptoms, and a high proportion of MBC were diagnosed at advanced stages. Our findings identify a critical need for both improved identification of males at high risk for breast cancer and improved screening practices of high-risk men.

Background. Brain metastasis affects 15-30% of patients with breast cancer, yet brain-specific biology underlying central nervous system colonisation is poorly captured by available clinical measures. The choroid plexus, an MRI biomarker of neuroinflammation and a primary immune-cell gateway implicated in brain metastasis, has not been examined in this setting. Methods. In a real-world, cross-sectional study of 1,000 women with breast cancer (190 with brain metastasis), bilateral choroid plexus volumes on contrast-enhanced brain MRI were quantified using a deep-learning pipeline. Guided by the principle that hemispheric asymmetry indices are sensitive to focal pathology, this study derived from an exploratory bilateral volume comparison a parameter-free Choroid Plexus Asymmetry Index (CPAI = |L-R|/(L+R)). Multivariable logistic regression, propensity-score matching, laterality and grade-stratified analyses assessed robustness. Results. The results show that total choroid plexus volume does not differ between groups, whereas left and right volumes change asymmetrically and CPAI is elevated in patients with metastasis (median 0.088 vs 0.059; Cliff's delta = 0.20, 95% CI 0.10-0.29). CPAI is minimally correlated with 10 peripheral markers and with histological grade. The association is robust under multivariable adjustment (adjusted OR 1.58 per +1 SD, 95% CI 1.32-1.88) and propensity-score matching, with |CPAI| scaling with metastatic burden, independent of metastasis side. Conclusions. Choroid plexus morphological asymmetry on routine brain MRI is associated with breast cancer brain metastasis independently of peripheral inflammation, tumour grade and conventional markers.

Purpose: Cyclin-dependent kinase 4/6 inhibitors in combination with endocrine therapy have become widely adopted as a first-line treatment approach for hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer (ABC). Through a collaboration between Novartis and the US Food and Drug Administration, we integrated multimodal data from phase III MONALEESA trials to identify novel prognostic factors for HR+/HER2- ABC. Experimental Design: Baseline clinical and genomics data were collected from patients in the MONALEESA-2, -3, and -7 trials. Circulating tumor DNA (ctDNA) from plasma samples and gene expression in archival tumor biopsies were analyzed. Machine learning models were trained (n = 1,355) on progression-free survival (PFS) using multimodal features via fusion models. In a test set (n = 382), prognostic performances were evaluated using the Kaplan-Meier, Cox, and Weibull models. Results: The late fusion model achieved a prognostic performance (C-index: 0.727, 95% CI: 0.641-0.799) slightly outperforming early fusion (C-index: 0.718, 95% CI: 0.631-0.791) and single-modality models. Model-based risk stratification identified distinct low- and high-risk groups (PFS HR, 0.28; 95% CI: 0.20-0.38). Among the top 40 features for progression risk (18 clinical, 20 gene expression, and 2 ctDNA), these were associated with higher risk: liver involvement, higher histological grades, early relapse; lower STC2 and RBBP8 expressions and higher FBX05 expression; and higher tumor mutational burden and ctDNA purity. Conclusions: Integrating clinical and genomic modalities improved progression risk prediction compared with single-modality models, while highlighting salient features that inform the underlying biology and support hypothesis generation.

Background Chemotherapy (CT) de-escalation strategies based on dual HER2 blockade with trastuzumab and pertuzumab (HP) have shown promising efficacy in HER2-positive (HER2+) early breast cancer (EBC), but biomarkers to guide patient selection are lacking. The Ataraxis Breast platform (ATX) uses artificial intelligence (AI) models integrating hematoxylin-eosin (H&E) whole-slide images and clinical variables to predict pathological complete response (pCR) and long-term outcomes. We evaluated ATX in the phase II PHERGain (NCT03161353) and PHERGain-2 (NCT04733118) trials. Methods The ATX NEO model was assessed for pCR prediction in PHERGain (n=306 evaluable patients) and PHERGain-2 (n=383 evaluable patients). The ATX RISK model was evaluated for prognosis in PHERGain (n=309 evaluable patients). Prespecified cutoffs classified patients into low-, medium-, and high-probability groups for pCR and low- or high-risk groups for recurrence. Associations with pCR were assessed using logistic regression. Prognostic performance was evaluated using Cox proportional hazards models, Kaplan-Meier estimates, and Harrell's C-index. Results Overall pCR rates were 40.2% in PHERGain and 60.3% in PHERGain-2. Continuous NEO scores were significantly associated with pCR in both PHERGain (odds ratio [OR], 1.28; 95% CI, 1.11-1.48; P<0.001) and PHERGain-2 (OR, 1.30; 95% CI, 1.13-1.52; P<0.001). Using prespecified cutoffs, pCR rates increased across low-, medium-, and high-probability groups (19.2%, 41.3%, and 48.3% in PHERGain; 29.5%, 61.6%, and 70.1% in PHERGain-2). In PHERGain, the RISK score was significantly associated with event-free survival (hazard ratio per 1-SD increase, 1.56; 95% CI, 1.17-2.08; P=0.002; C-index, 0.66). Five-year event-free survival was 93.9% in the low-risk group and 83.1% in the high-risk group. Conclusions NEO score was consistently associated with pCR across two independent HER2+ EBC trials, while RISK score provided prognostic information for long-term outcomes in PHERGain. Prespecified score categories identified clinically distinct response and risk groups. These findings support further evaluation of AI-based biomarkers to refine patient selection for CT de-escalation strategies in HER2+ EBC.

Genetic background can alter the latency and behavior of cancers initiated by the same oncogenic driver, but how inherited variation reshapes the regulatory landscape that permits tumor development remains poorly understood. The PWK genetic background accelerates mammary tumor onset in the NeuT model. We compared PWK NeuT and BALB NeuT tumors using tumor-level RNA-seq, histone ChIP-seq, and single-cell multiome profiling. PWK tumors exhibited broad transcriptional reprogramming characterized by reduced immune-associated expression and increased epithelial differentiation and neuronal-like programs. Chromatin remodeling was spatially redistributed rather than uniformly increased or decreased. PWK tumors showed expansion of H3K9me3- and H4K20me3-associated chromatin, while both marks were depleted across many expressed genes and concentrated within selected intergenic and repeat-rich territories, revealing coordinated redistribution of constitutive heterochromatin. Single-cell multiome profiling showed that broad epithelial representation remained similar between strains, yet specific malignant states were strongly strain biased. Integrated RNA and accessibility analyses identified concordant strain-selective regulatory programs and candidate loci, while trajectory analysis revealed distinct epithelial state transitions between PWK NeuT and BALB NeuT tumors. Together, these findings indicate that inherited genetic background reshapes transcriptional programs, heterochromatin organization, and malignant epithelial state trajectories in NeuT mammary tumors, providing a regulatory framework for understanding strain-dependent differences in tumor onset.

The hormone receptor-positive luminal subtype accounts for approximately 70% of all diagnosed breast cancer cases, with endocrine regimens serving as the first-line therapy. However, 30% to 50% of luminal breast cancer patients acquire therapeutic resistance with five to ten years of treatment. Understanding the mechanisms of endocrine resistance and identifying new therapeutic targets remain urgent priorities. Here, we demonstrate that expression of the alpha subunit of prolyl 4-hydroxylase (P4HA1), a key collagen-modifying enzyme, is significantly upregulated in endocrine therapy-resistant luminal breast cancer cells compared to parental endocrine therapy-sensitive cells. Clinically, elevated P4HA1 expression correlates with shortened relapse-free survival in patients with luminal breast cancer. Genetically knocking down P4HA1 or pharmacologically inhibiting its activity successfully restores the sensitivity of endocrine-resistant luminal breast cancer cells to hormone therapy. Mechanistically, we reveal that combining P4HA1 inactivation with endocrine treatment dramatically enhances endoplasmic reticulum (ER) stress-related gene expression. Consistently, treating resistant cells with an exogenous ER stress inducer similarly enhances sensitivity to hormone therapy. Furthermore, we demonstrate that ER stress induced by P4HA1 inactivation is molecularly mediated by an accumulation of reactive oxygen species (ROS). Together, these findings uncover a novel, non-canonical role for P4HA1 as a critical mediator linking endocrine resistance to cellular stress adaptations, positioning P4HA1 as a promising therapeutic vulnerability in endocrine-resistant breast cancer. P4HA1 serves as a necessary role in maintenance resistant state, making it a viable target for combination therapies to overcome endocrine resistance.

The immunomodulatory effects of chemotherapy (chemoimmunomodulation; CIM) are clinically consequential and heterogeneous, yet no systematic framework exists for classifying the immunomodulatory trajectory a tumor follows in response to treatment (CIM trajectory). Here, we present the CIM Induction Classifier (CIMIC), an unsupervised clustering pipeline leveraging delta gene expression across 3,189 CIM-related genes to classify specimens chemoimmunomodulatory trajectory. Applied to two pre- and post-chemotherapy breast cancer (BC) datasets (NKI/SMC, N = 36; NEO, N = 19) and nine epirubicin-perturbed triple-negative BC (TNBC) cell lines, CIMIC identified two divergent CIM trajectories: a functional CIM (Fun-CIM) trajectory, broadly conserved across tumors and cell lines and characterized by induction of inflammatory cell death, antigen presentation, viral mimicry, and adaptive immune activation programs, and a dysfunctional CIM (Dys-CIM) trajectory, characterized by induction of proteostatic and metabolic stress-adaptation programs, reduced immune cell abundances and cytotoxic activity, and enrichment of aggressive BC subtypes. Using survival and longitudinal transcriptomic data in NKI/SMC (N = 20), treatment-induced increases in Fun-CIM-associated genes and ssGSEA scores were associated with reduced recurrence, whereas Dys-CIM-associated genes and scores were associated with increased recurrence. In multivariable analyses within independent chemotherapy-treated BC cohorts (METABRIC, N = 412; SCAN-B, N = 2,462), higher baseline Fun-CIM ssGSEA scores were associated with better outcomes, whereas higher baseline Dys-CIM ssGSEA scores were associated with worse outcomes. These findings establish CIM as a dynamic, trajectory-level process and position CIMIC as a framework for defining CIM trajectories and supporting future efforts to identify predictors, mechanisms, and therapeutic strategies that maximize beneficial CIM.

Breast cancer affects approximately one in eight US women during their lifetime, and about 10% of new cases occur before age 45. Polygenic risk scores (PRS) are associated with breast cancer risk, and previous studies have evaluated PRS alongside pathogenic-variant carrier status. Whether a continuous, gene-effect-weighted rare-variant burden adds information beyond PRS, and whether the two genetic measures relate similarly to diagnosis age, remain unclear. We studied these questions using whole-genome sequencing and electronic health record data from roughly 8,000 breast cancer cases and 134,727 controls of genetically inferred European ancestry in the All of Us Research Program. In joint case-status models, increase in PRS and rare-variant burden (Burdenrare) was associated with higher odds of breast cancer (odds ratios, 1.55 and 1.27, respectively). Adding both scores and their interaction to covariates increased the cross-validated area under the receiver operating characteristic curve from 0.745 to 0.782. The PRS odds ratio differed between noncarriers and carriers (1.56 versus 1.25), suggesting that their effects are not simply multiplicative. We then examined whether the same genetic measures were associated with recorded age at diagnosis among cases. In eligible cases, each standard deviation increase in Burdenrare was associated with diagnosis 1.82 years earlier, compared with 0.43 years earlier for PRS. The proportion carrying a qualifying rare variant decreased from 26.5% among cases diagnosed before age 35 to 3.2% among those diagnosed at age 70 or older. Higher breast cancer risk can itself produce an earlier mean diagnosis age among cases, so we compared observed age associations with those expected from age-scale risk models under age-constant hazard ratios. Notably, the PRS association was consistent with its risk-based expectation, whereas the Burdenrare association was stronger than expected. In this common whole-genome sequencing framework, the weighted rare-variant burden adds information beyond PRS for breast cancer case status and shows a more pronounced association with early diagnosis, guiding future studies of risk and diagnosis timing.

The FORESEE trial (NCT04450706) was a prospective study to evaluate the feasibility of integrating organoid-based drug testing with comprehensive genomic characterization to inform treatment selection in patients with HER2-negative metastatic breast cancer. The primary objective of the study was to determine the feasibility of providing personalized therapy recommendations using a combination of genomic vulnerability information with drug testing in patient-derived organoids (PDOs) in a clinically relevant time frame. Of the 15 patients enrolled, 10 cases had biopsies with adequate tumor content, and PDOs were successfully established in 4 of 10 cases. Drug profiling and genomic testing was completed in all established organoids, yielding an overall success rate of 40% in cases of biopsies with adequate tumor content. Drug sensitivity and genomic results were returned within 12 weeks and informed subsequent treatment selection in all evaluable cases. The primary endpoint - demonstrating that PDO-based drug screening combined with genomic characterization would identify clinically actionable outcomes in at least 46% of patients based on prior data - was not met. In conclusion, successful PDO establishment represents a limiting step in the implementation of FPO for metastatic breast cancer; however when PDO were successful, drug responses were always obtained and physicians chose to use the information in 100% of cases. These findings highlight the need for improved strategies optimizing use of patient biopsy material for drug testing in order to fully realize the clinical potential of FPO.

Circulating tumor cells (CTCs) are key drivers of distant metastasis, while platelets facilitate this process by protecting them from immune surveillance. However, the detection of CTCs exhibiting platelet markers and their communication with monocytes has not been thoroughly investigated. This study seeks to identify the intercellular communication of CTCs that have acquired platelet traits by integrating single-cell RNA-seq data of 377 CTCs and 2,634 white blood cells. Trajectory analysis identified a subpopulation of CTCs with enhanced platelet-related functions, including platelet aggregation and resistance to NK cell-mediated cytotoxicity, termed Platelet-Programmed CTCs. The Dampened Weighted Least Squares method was implemented to estimate the platelet-programmed tumor cell proportion in 1,102 bulk RNA-seq samples from primary breast cancer tumors. A strong inverse association was found between platelet-programmed tumor cell proportion and overall survival (Cox-PH p-value =0.00419, HR=6 per 10% increase in proportion, 95% CI:1.76-20.7). However, this association did not differ significantly between early- and late-stage patients. Cell-cell communication analyses and molecular docking revealed the CD40LG-ITGA5:ITGB1 pair as a potential immune checkpoint candidate for monocytes in cancer (Hex score=-656.83; TNF-TNFR reference complex=-515). However, further experimental and clinical validation is required to translate these findings. In conclusion, these findings deepen our understanding of the immune evasion mechanisms of CTCs by acquiring platelet characteristics. This immune checkpoint axis offers a novel avenue for further experimental validation and future intervention.

Accurate prediction of response to targeted anticancer therapy frequently depends on the expression level of the drug target. However, transcriptomic profiling requires tumor tissue that is often unavailable, and circulating cell-free DNA (cfDNA) sequencing carries no expression information. Here, in an in silico companion study to a trial of ROR1 antibody plus nab-paclitaxel in triple-negative breast cancer (TNBC), we transformed whole-exome sequencing (WES) data into somatic activity profiles of signaling pathways (sAPSPs) using the previously published DAGM framework, and used them together with matched expression data to develop regression model sets predicting ROR1 and CAV1 expression. The model sets were applied to 10 trial participants (4 with tumor tissue WES, 6 with plasma cfDNA WES). Patients were classified as predicted-sensitive only when both ROR1 and CAV1 were predicted to be highly expressed. The prediction matched the observed best response in 9 of 10 patients; all three predicted-sensitive patients achieved a partial response. These exploratory findings support the feasibility of WES-based expression prediction for non-invasive response stratification and warrant prospective validation.

BackgroundCancer-associated fibroblasts (CAF) are major regulators of the tumor microenvironment (TME), yet the pathways governing CAF function remain incompletely understood. Type 2 deiodinase (DIO2, D2), which converts thyroxine (T4) to the active thyroid hormone triiodothyronine (T3), has not been systematically investigated in CAF across different solid tumors. MethodsWe analyzed DIO2 expression using single-cell RNA sequencing (scRNA-seq) from primary thyroid tumors, a large integrated scRNASeq thyroid cancer atlas, and publicly available scRNA-seq datasets representing six additional solid tumor types. Bulk RNA sequencing data from the Oncology Research Information Exchange Network (ORIEN) database were used to examine correlations among DIO2, CAF subtype markers, and overall survival (OS). A genetically engineered TPO-CreERT2/BrafV600E/Trp53-deficient (TBP) mouse model was evaluated for CAF-specific Dio2 expression and pharmacologic inhibition of D2 activity. TBP mice were crossed with Dio2-/- (Dio2 KO) mice to investigate the role of Dio2 in cancer development and progression. ResultsDIO2 expression was highly restricted to CAF and was largely absent from epithelial-derived cancer, immune, and endothelial cells. Across thyroid cancer and six additional malignancies, DIO2 was consistently detected in CAF, demonstrating that CAF-specific DIO2 expression is a conserved feature of multiple tumor types. Within thyroid cancer, DIO2 was enriched in myofibroblastic CAF (myCAF) compared with inflammatory CAF (iCAF), and bulk transcriptomic analyses demonstrated stronger correlations between DIO2 expression and canonical myCAF markers than iCAF markers across numerous cancers. Elevated DIO2 expression was associated with worse OS in pancreatic and bladder cancers, paralleling the adverse prognostic impact of increased myCAF abundance. In contrast, thyroid cancer demonstrated an inverse association attributable to loss of epithelial DIO2 expression during tumor dedifferentiation. The TBP mouse model recapitulated human disease, with Dio2 expression restricted to CAF, robust stromal fibrosis, and measurable D2 enzymatic activity that was suppressed by cefuroxime and lenvatinib. Genetic loss of Dio2 in the TME resulted in significantly reduced tumor growth. Conclusions DIO2 is a conserved marker of CAF, and myCAF, across multiple solid tumors and is associated with adverse clinical outcomes in some cancers. The TBP mouse provides a robust preclinical model to define the mechanistic role of DIO2 in CAF biology and to evaluate DIO2-directed therapeutic strategies targeting the tumor microenvironment.

Poly (ADP-ribose) polymerase inhibitors (PARPi) are the only targeted treatment available for early triple-negative breast cancer (TNBC), an aggressive subtype with limited treatment options. While PARPi are effective in patients with BRCA mutations, resistance to these inhibitors can arise. We previously developed BRCA-mutant cell lines, HCC1395, with acquired resistance to talazoparib and olaparib, and have now developed PARPi-resistant MX1 cell lines in an analogous manner. Ribosomal pathways were upregulated in 3/4 cell lines with acquired PARPi resistance. The resistant cell lines showed high and low basal nucleolar stress based on nucleolar morphology and nucleophosmin delocalization from nucleolus to nucleoplasm. Treatment of these cell lines with the combination of olaparib and a ribosome biogenesis inhibitor, BMH-21, demonstrated synergy in the PARPi-resistant cell lines, but not in the parental cells. Interestingly, HCC1395 cells, which had lower baseline nucleolar stress, showed greater nucleophosmin delocalization with combination treatment than MX1 cells, which had higher baseline nucleolar stress. All four cell lines demonstrated sustained DNA damage with the combination. We extended our investigation to 3 BRCA wild-type TNBC cell lines, BT549, Hs578T, and MDAMB231 and identified synergy in the context of intrinsic PARPi resistance. In MDAMB231 cells, combining olaparib with BMH-21 induced a senescence-like phenotype, increasing gH2AX-positive cells by up to 7.3-fold, senescence-associated B-galactosidase (SA-B-gal) staining by 5.6-fold, and p21 by 3.6-fold. These results were mirrored with a second ribosome biogenesis inhibitor, CX-5461. Finally, we identified distinct patterns of ribosomal pathway activity in a cohort of patients with treatment-resistant TNBC, and found correlations between ribosomal pathways with MYC, proliferation, DNA damage, and senescence. Together, these data identify ribosome biogenesis as a potential therapeutic vulnerability in PARPi-resistant TNBC and support its targeting in combination with PARPi, while demonstrating a broader for ribosome biogenesis in treatment-resistant TNBC.

Background: This study compared functional and cognitive trajectories in older women treated for early breast cancer with matched women without cancer. Methods: Data were derived from two Dutch cohorts: the CLIMB study (women aged 75 years and older with operable breast cancer) and the ISCOPE study (community-dwelling women aged 75 years and older without cancer). Breast cancer patients were matched 1:2 to controls on age and baseline functional or cognitive status. Functional status was assessed using the Groningen Activity Restriction Scale (GARS), and cognitive function using the Mini-Mental State Examination (MMSE). Longitudinal changes over 12 months were analyzed using both unadjusted and confounder-adjusted linear mixed models. Results: 168 breast cancer patients and 336 matched breast cancer-free controls were included with a mean age of 80 years. Most breast cancer patients had stage 0-I or II disease and received surgery and/or radiotherapy; 48% received adjuvant endocrine therapy, chemotherapy was rare (<1%). At baseline, both cohorts were comparable in age and functional or cognitive status, although there was a higher comorbidity burden in the control cohort. No significant differences in functional or cognitive decline were observed between the cohorts over 12 months. Conclusions: In older women, early breast cancer and its treatment did not lead to accelerated decline in functional status or cognitive function within the first year compared to matched older women without cancer. These findings suggest that, for relatively fit older patients, breast cancer treatment may not accelerate functional decline in the short term.

Blood-based metabolomic profiling has been widely investigated for breast cancer (BC) detection; however, clinical implementation remains limited due to variability in sample handling, analytical reproducibility, and overfitting during statistical analysis. We established a plasma GC/MS metabolomics workflow for discriminating BC from healthy controls (HC) using conventional machine-learning algorithms. Plasma samples (n = 360; BC = 180, HC = 180) were collected prospectively under standardized preanalytical conditions before surgery and the initiation of systematic anticancer therapy and analyzed using a quantitative GC/MS platform with automated derivatization. Feature selection and model development were conducted using three machine-learning (ML) algorithms (Lasso logistic regression (LR), random forest classifier (RFC), and support vector machine (SVM)). A total of 45 metabolite candidate biomarkers were identified, and the optimal number of metabolite features for each algorithm was estimated by a recursive feature elimination (RFE)-based strategy. The best-performing models achieved area under the ROC curve values (AUC) of 0.910 (LR), 0.893 (RFC), and 0.843 (SVM). We selected prioritizing candidate biomarkers consistently expressed across the multi-algorithm pipeline. A bagging ensemble model improved stability (AUC = 0.911) and reduced false-positive predictions in the independent HC dataset. In addition, model stability with respect to false-positive predictions was assessed using an independent HC cohort (n = 15) that was collected at a separate institution. These results indicate that a plasma metabolomics workflow combined with conventional multi-algorithm ML, algorithm-specific feature selection, and independent assessment provides stable discrimination between BC and HC in a moderately sized cohort.