Breast Cancer Research

BackgroundThe management of residual axillary disease after neoadjuvant therapy (NAT) remains controversial, as current recommendations often treat ypN1 breast cancer as a homogeneous entity despite potential prognostic heterogeneity. Evidence supporting uniform axillary surgical strategies across different levels of residual nodal burden is limited. We investigated whether survival associations related to axillary surgical evaluation differ according to residual nodal burden in ypN1 disease, using an adjuvant cohort to validate a SEER-based proxy for surgical extent. MethodsPatients with 1-3 positive lymph nodes were identified in the SEER database (2000-2022) and stratified into neoadjuvant (NAT; n=30,560) and adjuvant (AT; n=197,586) cohorts. Axillary surgical evaluation was categorized as limited (2-3 examined nodes) or extensive ([≥]10 examined nodes). Survival was analyzed using Kaplan-Meier methods and log-logistic accelerated failure-time models, adjusted with inverse probability of treatment weighting. ResultsIn the ypN1 cohort, limited axillary evaluation was not associated with inferior overall survival among patients with a single residual positive node (IPTW-adjusted HR: 1.15, p=0.134; time ratio [TR]: 0.86, p=0.184). In contrast, limited evaluation was associated with worse survival in patients with two positive nodes (HR: 1.70, 95%CI 1.54-1.87; TR: 0.58, 95%CI 0.53-0.64). The findings were similar when using breast cancer-specific survival as the endpoint. ConclusionsSurvival associations related to axillary surgical evaluation after NAT vary according to residual nodal burden. Axillary de-escalation appears feasible in patients with a single residual positive node but cannot be extrapolated to those with multiple residual nodes, underscoring heterogeneity within ypN1 disease.

BackgroundThe genetic architecture of Breast Cancer (BC) in Arab populations remains largely understudied, limiting the precision of current prevention and screening programs. The Emirati Genome Program (EGP), one of the worlds first nation-wide sequencing initiatives, offers an unprecedented opportunity to delineate inherited BC risk across an entire population. MethodsWe analyzed 436,780 EGP individuals, including 229,309 women, integrating whole-genome sequencing (WGS) with electronic health records (EHRs). We quantified the prevalence and penetrance of pathogenic and likely pathogenic (P/LP) variants across 13 NCCN-recommended BC genes, evaluated the performance of established polygenic risk scores (PRS), and reconstructed >48,000 pedigrees to measure familial aggregation. ResultsP/LP variants were identified in 0.84% of women, accounting for 5.2% of BC cases (mean age of 45.9{+/-}11.1 years). Highly penetrant BRCA1 c.4065_4068del (p.Asn1355fs) and BRCA2 c.2808_2811del (p.Ala938Profs) variants showed age-specific cumulative risks of 37.6% and 31% by age 60, respectively, and allele frequencies up to tenfold higher in the Emirati population than in global reference datasets. The European-derived PRS model (PGS000004) demonstrated strong performance, advancing 10-year BC risk onset by a decade for women in the top decile. Family-based PRS discriminated affected from unaffected individuals, revealing higher polygenic risk even within sister pairs. Integration of monogenic, polygenic, and familial data defined a national framework for risk stratification, identifying disease-free women potentially eligible for targeted prevention. ConclusionsNation-scale genome sequencing reveals, for the first time, the comprehensive landscape of inherited BC susceptibility within a Middle Eastern population. The integration of monogenic, polygenic, and familial data establishes a national framework for genomic risk stratification--transforming population genomics into a foundation for precision prevention and early detection in the UAE and beyond.

BackgroundAlthough DCIS has a relatively low recurrence rate, many patients still receive adjuvant radiotherapy or endocrine therapy, raising concerns about overtreatment. Reliable biomarkers are therefore needed to predict an individual patients risk and guide treatment decisions. Recent studies suggest that the composition of the tumour-associated stroma (TAS) affects progression and outcome, highlighting TAS-derived biomarkers as promising candidates for further investigation. MethodsWe trained AI models for cell and tumour segmentation using whole slide digital pathology images acquired as part of a retrospective cohort study. We investigated the effects of cell density within both the tumour and the TAS to determine how they correlated with recurrence in the ipsilateral breast. ResultsWe found that the concentration of DCIS lesions on the slide and the density of mitotic figures inside the TAS region were significantly associated with recurrence risk. Additionally, we found some predictive value in the lymphocyte and red blood cell densities in different tumour regions. Stromal composition was shown to associate with recurrence risk, and density-based biomarkers were identified and used to cluster patients into phenotypes with significantly different risk profiles. ConclusionOur findings highlight the prognostic relevance of stromal composition in DCIS, and we identify novel density-based biomarkers that can be used to identify patients who are more likely to experience a local recurrence after breast-conserving surgery alone. These results may aid in developing future risk-stratification tools for breast cancer patients, thereby reducing overtreatment and improving patient care.

BackgroundTamoxifen is a cornerstone of endocrine treatment for hormone receptor-positive breast cancer, reducing recurrence and breast cancer-specific mortality. However, its use is associated with a small, yet clinically relevant, increase in uterine cancer. As diagnosis of this cancer remains symptom-triggered, it is essential for patients to be aware of this risk and report symptoms promptly for optimal outcomes. We therefore assessed risk awareness among breast cancer survivors while exploring their attitudes towards potential future endometrial surveillance strategies. MethodsOver a 10-month period, a web-based survey was conducted among breast cancer survivors with/without tamoxifen treatment. The mixed-format questionnaire included closed-ended questions and optional free-text comments. Quantitative data were summarized descriptively and analyzed statistically; qualitative responses were reviewed thematically to contextualize survey findings. ResultsOf 163 respondents, 154 breast cancer survivors were included in the analysis, 128 of whom had received tamoxifen. Among tamoxifen-associated participants, 60% reported insufficient awareness of the associated uterine cancer risk, and half expressed uncertainty about the adequacy of the current symptom-triggered endometrial evaluation. Despite this, acceptance of tamoxifen therapy was high; only one patient declined treatment over concerns about side effects. Almost all participants (96%) were willing to adopt endometrial surveillance methods, if developed and validated. ConclusionAs evaluation of tamoxifen-associated uterine pathology is symptom-triggered, our data highlight the need for improved and standardized risk communication to promote timely symptom recognition, reporting, and diagnostic evaluation. Moreover, our findings support incorporating patient-reported preferences into the development of future endometrial detection strategies to improve survivorship care.

Clinical interpretation of breast cancer sequencing is constrained not by a lack of data but by the absence of an organising framework that translates constellations of co-occurring mutations and copy-number alterations into tumour-level biology with prognostic and therapeutic meaning. This challenge is exemplified by PIK3CA, a clinically actionable alteration often treated as a single-label biomarker despite context-dependent associations with outcome. We analysed >5,000 breast tumours across multiple cohorts using integrated multi-omics (somatic mutations, copy-number, transcriptomic, proteomic and phosphoproteomic profiles) and quantified the directionality of downstream molecular consequences of recurrent alterations relative to TP53-associated trends to infer dominant tumour programmes. This revealed a robust functional organisation comprising (i) a canonical proliferative/replicative programme, enriched for cell-cycle, DNA replication and E2F signalling, and encompassing TP53 mutations and most recurrent CNAs, and (ii) a non-canonical signalling/cell-state programme marked by recurrent mutations including PIK3CA, CDH1, GATA3, MAP3K1 and AKT1, with opposing transcriptomic/proteomic directionality, comparatively lower proliferative output and a systematic tendency towards mutual exclusivity with TP53, consistent with alternative evolutionary routes. To operationalise these findings for clinical use, we developed T-OMICS (Tiered OMICS Classification System), which layers complementary readouts to deliver a single interpretable tumour profile: Tier 1 provides a continuous genomic-risk backbone via a DNA-anchored prognostic RNA signature capturing canonical proliferative/replicative output; Tier 2 assigns programme identity based on the dominant genomic context; Tier 3 quantifies within-programme activity along a continuum; and Tier 4 overlays non-redundant modifier mutations that refine phenotype, vulnerabilities and resistance liabilities, supported by orthogonal proteomic/phosphoproteomic pathway signals. In ER+/HER2- disease, T-OMICS resolves the prognostic ambiguity of PIK3CA by showing that "PIK3CA-mutant" is not a single biological entity: in a predominant low-genomic-score context, PIK3CA aligns with buffered luminal biology and favourable outcomes, whereas in high-score contexts--conditioned by TP53 background and modifier events--PIK3CA can mark adverse biology with distinct dependencies not captured by proliferation-centric readouts; notably, low-score PIK3CA tumours with CDH1 co-mutation shift to significantly worse outcomes. Together, these results establish a programme- and state-aware framework that converts sequencing reports into clinically legible tumour biology to support risk calibration, therapeutic prioritisation and evolution-aware sampling decisions from early-stage through metastatic ER+/HER2- breast cancer. Lay SummaryBreast cancer tumours often carry many genetic changes at the same time. While modern sequencing can identify these changes in detail, the results are frequently presented as long lists of mutations and DNA alterations that are difficult to interpret in terms of how a tumour behaves or how it should be treated. A well-known example is the PIK3CA gene: although it can be targeted with specific drugs, studies have reported mixed results on whether PIK3CA mutations are associated with better or worse outcomes, making it challenging to use this information confidently in clinical care. To address this problem, we analysed genomic (DNA-wide), RNA, and protein data from more than 5,000 breast tumours. We found that many common genomic changes cluster into two main biological "programmes" that reflect distinct ways tumours grow and survive. One programme is driven by rapid cell division and DNA replication and includes TP53 mutations and many common DNA copy-number changes; tumours following this programme tend to be more aggressive. The second programme is less focused on rapid growth and is defined by mutations such as PIK3CA, CDH1, GATA3, MAP3K1, and AKT1, which influence signalling and cell identity rather than directly accelerating proliferation. These programmes reflect broader tumour behaviours rather than the effects of single genes. Importantly, mutations in the second programme are usually not found alongside TP53 mutations, suggesting that breast cancers can develop through distinct biological routes--with some tumours following an alternative pathway (not overtly proliferation-dependent) that shapes their behaviour and may influence which treatments are most appropriate. Based on these findings, we developed a practical classification system, T-OMICS, for ER-positive, HER2-negative breast cancer. T-OMICS summarises which biological programme a tumour follows, how active or aggressive it is within that programme, and whether additional mutations are present that may influence treatment response or resistance. Using this framework, we show that PIK3CA mutations most often occur in a biologically buffered context associated with more favourable outcomes, but when they occur in more aggressive tumours--shaped by other key genetic changes--they can signal a higher-risk disease with different treatment needs. These findings indicate that treatment decisions should be based on the tumours overall biological pattern, not just the presence of a single mutation. By placing sequencing results in this broader context, T-OMICS supports more accurate risk assessment, better treatment planning, and more informed decisions about when to intensify therapy, from early-stage through advanced breast cancer. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/26346715v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@a602e7org.highwire.dtl.DTLVardef@108a6b1org.highwire.dtl.DTLVardef@f7ef9forg.highwire.dtl.DTLVardef@194b86d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical SummaryC_FLOATNO C_FIG

IntroductionBRCA1/2 alterations are increasingly recognized as biologically and clinically relevant features in prostate cancer, yet the prognostic and therapeutic significance of zygosity status remains uncertain. Understanding differences between monoallelic and biallelic inactivation may refine risk stratification and guide therapeutic decision-making. Materials and MethodsA retrospective, desk-based observational analysis was performed using publicly accessible datasets from TCGA-PRAD (primary disease) and SU2C/PCF (metastatic disease). BRCA1/2 status was categorized as wild-type, monoallelic, or biallelic based on mutation, copy-number, and loss-of-heterozygosity profiles. Overall survival was evaluated using Kaplan-Meier estimates and Cox models. Systemic therapy outcomes were assessed by treatment class, incorporating exploratory interaction tests. ResultsIn TCGA-PRAD (n=300), OS did not significantly differ by zygosity (global log-rank p=0.45), with median OS of 80.0 months (wild-type), 78.0 months (monoallelic), and 55.0 months (biallelic). In SU2C/PCF (n=200), zygosity stratified outcomes significantly (global log-rank p=0.04): median OS was 22.0 months (wild-type), 14.0 months (monoallelic), and 16.0 months (biallelic). Treatment analyses showed ARSI exposure improved OS in wild-type disease (HR 0.60; 95% CI 0.38-0.95), while interaction testing suggested potential heterogeneity without statistical confirmation (interaction p=0.092). PARP inhibitor exposure showed directionally favorable HRs in wild-type and monoallelic groups but no significant interaction (interaction p=0.757). No therapy class demonstrated consistent effect modification by zygosity. ConclusionBRCA1/2 zygosity shows prognostic relevance in metastatic prostate cancer but not clearly in primary disease. While zygosity did not consistently modify systemic therapy associations in this dataset, findings support zygosity-aware reporting as a practical tool for molecular stratification and future research design.

BackgroundQuality-of-life (QoL) assessment is essential in breast cancer care, yet limited evidence describes how interrelated QoL domains change during pharmacotherapy. This study aimed to evaluate correlations among functional and symptom scales using the EORTC QLQ-C30 and QLQ-BR23, highlighting their ability to reveal multidimensional QoL patterns. MethodsA prospective observational study was conducted in two second-referral hospitals in Indonesia, enrolling 106 female breast cancer patients. QoL was assessed before and after pharmacotherapy using QLQ-C30 and QLQ-BR23. Changes in scores ({Delta}) were computed, and interdomain relationships were analyzed using Spearmans rho. ResultsPhysical functioning correlated with role functioning ({rho} = 0.55, p <0.001), emotial functioning ({rho} = 0.33, p <0.001), and social functioning ({rho} = 0.31, p = 0.002). Role and social functioning were likewise correlated ({rho} = 0.32, p = 0.001), indicating that improvements across functional domains tended to occur in parallel. Symptom scales showed strong positive clustering, including fatigue with pain ({rho} = 0.37, p <0.001), insomnia ({rho} = 0.35, p <0.001), and systemic side effects ({rho} = 0.48, p <0.001). Functional and symptom domains generally exhibited inverse relationships: physical functioning negatively correlated with fatigue ({rho} = -0.40), pain ({rho} = -0.43), both p <0.001, and systemic side effects ({rho} = -0.26; p = 0.01). ConclusionThe QLQ-C30 and QLQ-BR23 instruments effectively captured structured, clinically meaningful interdependencies. Functional improvements consistently aligned with symptom reductions, revealing coherent functional-symptom clustering. These findings underscore the sensitivity of QoL instruments to detect multidimensional patient-reported changes during breast cancer pharmacotherapy.

Cancer heterogeneity is traditionally attributed to multiple parallel signaling pathways. This belief is challenged here by proposing the ER/PR axis as the dominant pathway underlying the full spectrum of breast cancer. Absolutely quantitated ER, PR, Her2 and Ki67 protein levels were accumulated over 8 years from 1652 specimens collected non-selectively and measured with Quantitative Dot Blot (QDB) method over time. Cox analysis showed ER and Ki67 as independent adverse prognostic factors while PR was an independent favorable factor statistically. Their optimized stratification framework demonstrated that prognosis across all clinical subtypes was predominantly aligned along the ER/PR axis rather than being subtype-specific, including repeated identification of a subgroup with near-perfect 10-year survival probability from three independent cohorts to be proposed as the biological basis of the ultra-safe group in MINDACT trial. A parsimonious model is proposed where the ER/PR signaling hierarchy supersedes current prevailing clinical subtyping, with its balance essential for survival until ER levels become uncontrollable. This concept of pathway hierarchy may also exist in other major cancer types, and cannot be addressed without clinical epidemiology.

PurposePrognosis in metastatic non-seminomatous germ cell tumors (mNSGCT) is currently guided by the IGCCCG classification, which incorporates tumor markers, organs involved with metastatic disease, and primary site but not histologic subtype. We aimed to evaluate whether specific histological components provide additional prognostic information in a large international mNSGCT cohort. Patient and MethodsWe analyzed clinical, pathologic, and outcome data from 662 patients with mNSGCT across multiple international centers. Cox regression and multivariable stepwise models were used to evaluate the impact of age, tumor histology, serum markers, primary site of disease, chemotherapy, IGCCCG, and post-chemotherapy surgery on overall survival. Analyses were performed using both complete-case and imputed datasets to account for missing values. ResultsThe presence of any percentage of embryonal carcinoma (EC) was independently associated with improved overall survival HR 0.603 (95% CI: 0.37-0.98, p=0.040), whereas yolk sac tumor (YST) predicted worse prognosis in complete-case analysis HR 2.27 (95% CI: 1.43 - 3.61 p = 0.001). Choriocarcinoma was also associated with a HR 1.58 (95% CI: 1.08 - 2.32 p= 0.019) adverse outcomes. IGCCCG risk classification remained a strong predictor of mortality HR up to 8.9 for Poor vs Good risk, (95% CI: 4.63 - 17.09 p < 0.001), but histologic components added significant independent prognostic value. Post-chemotherapy retroperitoneal lymph node dissection (RPLND) conferred a substantial survival benefit HR 0.44 (95% CI: 0.258 - 0.754 p=0.003). Interestingly, teratoma was not associated with mortality but was linked to younger age, testicular primaries, and higher likelihood of residual disease requiring surgery. ConclusionsHistological composition, particularly the presence of EC or YST, has a significant and independent impact on survival in mNSGCT, beyond established risk classifications. Integration of histological subtypes may enhance prognostic accuracy and guide individualized treatment strategies in advanced germ cell tumors.

Guideline-based recommendations for metastatic lines of therapy (mLoTs), especially second lines and beyond, are comparatively sparse due to challenges in later-line treatment efficacy quantification. Scalable real-world evidence that captures the interaction between treatment and disease progression is therefore especially valuable, as regimens become increasingly individualized, confounding intensifies, and progression is rarely recorded as a structured EHR endpoint. We present a framework to (i) reconstruct clinically coherent mLoTs from longitudinal EHR using radiology-anchored progression evidence and (ii) generate individualized progression-free survival (PFS) estimates from a line-start multimodal snapshot in a highly heterogeneous cohort. In 2,881 patients contributing 8,791 metastatic mLoTs, the selected model shows strong discrimination over a 2-year horizon (Antolinis C = 0.680 {+/-} 0.006; cumulative/dynamic AUC at 1 year = 0.824 {+/-} 0.006). Predicted risk strata closely track Kaplan-Meier trends across line number and tumor subtypes, enabling calibrated risk stratification even in smaller sub-cohorts. Model prediction primarily relies on clinically plausible signals of recent metastatic burden and tumor markers, with limited dependence on surveillance cadence or subtype labels, and is robust to missingness. Together, this framework supports scalable evidence generation and interpretable, calibrated prognostication to inform risk assessment and care planning in heterogeneous metastatic practice.

Somatic mutations and the tumor immune microenvironment in breast tumors are important predictors of treatment response and survival, yet data for Hispanic/Latina (H/L) women are limited. Here we analyzed whole exome sequencing data from tumor/normal pairs and RNAseq data from 748 H/L women and 388 non-Hispanic White (NHW) women. Overall, the somatic profiles in tumors from H/L women were similar to NHW women. However, somatic mutations in genome organizer CTCF were significantly more common in H/L women. We also found that tumor microenvironment immune ecotypes CE9 and CE10, characterized by increased lymphocyte infiltration and more favorable prognosis, were more common among women with higher Indigenous American ancestry. Finally, we found that a germline APOBEC3A/B copy-number deletion was more prevalent in H/L than in NHW and was associated with the COSMIC APOBEC mutational signatures and with CE10 ecotype. Overall, these results suggest that ancestry differences may provide insights into specific mutation and immune profiles.

Multi-cancer early detection (MCED) tests can detect several cancer types and stages. We previously developed a methylation and protein (MP V1) MCED classifier. In this study, we present a refined MP V2 classifier, developed by evaluating model architectures that improved performance in prospectively enrolled case-control cohorts under standard testing conditions. The newly developed MP V2 classifier was trained to be more generalizable and achieve increased early-stage sensitivity at a target specificity of [&ge;]97.0%. MP V1 and MP V2 classifier performances were compared using a previously described test set, and MP V2 performance was also evaluated in a new independent clinical validation set. Compared to MP V1, the MP V2 classifier demonstrated a 7.3% increase in overall sensitivity, with sensitivity increases of 7.6%, 9.2%, and 8.3% for stages I, II, and stages I/II, respectively, in the intended use (breast and prostate cancers excluded) test set. In an independent validation intended use set, the MP V2 classifier showed an overall sensitivity of 55.6%, with sensitivities of 26.8%, 42.9%, and 34.8% for stages I, II, and stages I/II, respectively. In a case-control setting, the MP V2 classifier offered improved sensitivity for early-stage cancers at a lower specificity target.

BackgroundThe FACE-Q Skin Cancer Module is a condition-specific patient-reported outcome measure for facial skin cancer. While its psychometric properties have been established, normative reference values that enable score interpretation in clinical practice and research are lacking. ObjectiveTo establish normative reference values for the FACE-Q Skin Cancer Module using preoperative patient data and to validate these values by comparison with a demographically matched cohort of healthy partners. MethodsTwo cohorts were analyzed: 287 patients with facial skin cancer (preoperative scores) and 82 healthy partners of skin cancer patients (same-age population without facial skin cancer). Both cohorts completed the Appearance (9 items) and Psychosocial Distress (8 items) scales. Patients additionally completed the Cancer Worry scale (10 items) and Sun Protection scale (5 items). Scores were transformed to a 0-100 scale. Normative values were expressed as percentiles overall and stratified by sex and age group. Group comparisons used independent t-tests, Mann-Whitney U tests, and Cohens d. Internal consistency was assessed with Cronbachs alpha. ResultsPatient and partner cohorts were well matched for age (68.6{+/-}11.9 vs 68.4{+/-}13.0, p=0.902) and sex (46.7% vs 41.5% female, p=0.476). Surprisingly, preoperative facial appearance scores were virtually identical between patients and partners (55.6{+/-}14.0 vs 56.6{+/-}13.6, p=0.590, d=-0.08), as were psychosocial distress scores (14.3{+/-}12.0 vs 14.4{+/-}13.3, p=0.942, d=-0.01). This equivalence held across age groups. A significant sex interaction was identified: female patients scored lower on appearance than female partners (54.3 vs 59.9, p=0.048, d=-0.40), whereas no difference existed among males (56.9 vs 53.1, p=0.168). Internal consistency was excellent in both cohorts (Cronbachs 0.82-0.93). Patients reported marginally higher sun protection behaviors than partners (38.0 vs 33.6, p=0.050). ConclusionsPreoperative FACE-Q Skin Cancer scores in patients are equivalent to those of demographically matched healthy individuals, confirming that these scores serve as valid normative references. The established percentile norms enable clinicians and researchers to interpret individual patient scores in context. The sex-specific difference in appearance scores warrants further investigation.

PurposeCastration-resistant prostate cancer (CRPC) is characterized by marked clinical heterogeneity and poor long-term survival, underscoring the need for tools that can rapidly and reliably individualize patient risk. While several prognostic models exist, their complexity has limited routine clinical use. Here, we developed and validated PROGRESS (PROstate cancer Global Risk Evaluation and Stratification Score), a simplified prognostic score, derived through machine learning-guided feature selection, to enhance risk stratification and support individualized, risk-informed clinical decision-making. MethodsPROGRESS was developed using baseline data from 2,035 metastatic CRPC patients enrolled in four different phase III trials. An unsupervised machine-learning approach was applied to identify latent patient subgroups with distinct survival outcomes irrespectively of allocated treatment arm, followed by classical multivariable modelling to derive a simple and straight-forward prognostic score based on routinely available objective laboratory variables. External validation was performed in three independent datasets comprising metastatic CRPC patients treated across different therapeutic settings (n=1,239) and non-metastatic CRPC patients managed with standard care (n=660). Overall survival was assessed using Kaplan-Meier and Cox regression analyses. ResultsUnsupervised modelling identified two patient risk subpopulations with significantly different overall survival rates (median 27.4 vs 17.7 months; hazard ratio [HR] 2.20, 95% CI 1.91-2.54; p<.00001). Feature contribution analysis yielded three independent predictors -PSA, ALP, and AST-used to build PROGRESS. In the training cohort, PROGRESS demonstrated strong discrimination (AUC 0.89). Using a prespecified cut-off, patients classified as increased risk had significantly shorter survival than low-risk patients (median 18.3 vs 25.6 months; HR 1.72, 95% CI 1.50-1.97; p<.0001). PROGRESS prognostic performance was consistent across all validation cohorts, including metastatic and non-metastatic disease, with HRs ranging from 1.74 to 3.46 (all p<.0001). ConclusionsBy integrating machine-learning-based pattern discovery with classical statistical modelling, PROGRESS provides a simple, objective, and clinically accessible approach for individual risk stratification in CRPC. Its reliance on three inexpensive, routinely measured laboratory parameters would facilitate practical implementation in clinical settings, enhancing visibility of underlying disease aggressiveness for individual clinical decision-making. PROGRESS could represent a pragmatic first step toward improving patient selection for clinical trials while identifying regulatory meaningful endpoints achievable in a sizeable patient population; further validation in prospective clinical studies and real-world datasets would allow to confirm its clinical utility and generalizability. PROGRESS can be freely accessed for research use only at the following link: https://dev.ai.topazium.com.

BackgroundPancreatic ductal adenocarcinoma is one of the most aggressive and lethal malignancies of the gastrointestinal tract. The poor prognosis is largely attributed to late-stage diagnosis, pronounced tumor heterogeneity, and limited therapeutic efficacy. These challenges underscore the urgent need for the identification of robust molecular biomarkers and novel therapeutic targets. MethodsGene expression data from a total of 146 pancreatic tissue samples, comprising 72 normal and 74 tumor specimens obtained from the Pan-Cancer Atlas(TCGA) were analyzed. Differential gene expression analysis was conducted using the DESeq2 package, followed by functional enrichment analysis based on GO and KEGG. A classification model was developed using the XGBoost algorithm and evaluated through 500 bootstrapping iterations and 5-fold cross-validation to ensure robustness and generalizability. Model interpretability was assessed using SHAP (SHapley Additive exPlanations) values to identify genes with the highest predictive contribution. ResultsA comprehensive transcriptomic analysis revealed significant dysregulation of multiple genes between normal and tumor pancreatic tissues. Genes such as GJB3, S100A2, MSLN, and SLC2A1 were notably overexpressed, whereas DEFA6, APOB, and RBP2 exhibited marked downregulation, indicative of impaired exocrine function and aberrant epithelial reprogramming. The XGBoost classification model achieved an average area under the curve (AUC) of 0.9868 and an overall accuracy of 98.6%. SHAP (SHapley Additive exPlanations) analysis identified GJB3, LINC02086, and TSPAN1 as key predictive features. Six genes were concurrently identified as differentially expressed and highly influential within the model, supporting their potential utility as robust biomarkers for pancreatic tumor characterization. ConclusionsPancreatic ductal adenocarcinoma is marked by extensive transcriptomic reprogramming. The integration of differential gene expression analysis with interpretable machine learning enabled the identification of a molecular signature with potential diagnostic and therapeutic relevance.

ImportancePersistent racial and ethnic disparities in breast and prostate cancer mortality are well documented. Most prior studies emphasize between-group differences and rely on population averages or single composite measures of social disadvantage, which can obscure high-need communities within groups. How socio-behavioral determinants of health vary within groups across local gradients of cancer mortality remains incompletely characterized. A framework that combines race- and cancer-specific mortality with local, domain-level socio-behavioral profiles may help identify where burden is greatest and which specific barriers warrant prioritization. ObjectiveTo determine how socio-behavioral risk relates to breast and prostate cancer mortality within racial and ethnic groups and to characterize domain-specific behavioral profiles across low-, moderate- and high-mortality counties to inform targeted, equity-oriented cancer control strategies. DesignCross-sectional study of U.S. counties. Setting United States, county-level analysis. Participants3,141 U.S. counties, stratified within Non-Hispanic White, Non-Hispanic Black, and Hispanic populations. ExposuresCounty-level socio-behavioral determinants of health measured using a composite index comprising seven domains: community solidarity; education, health literacy, and digital connectivity; quality of care; housing and environmental risk; economic livelihoods; lifestyle behaviors; and touchpoints with care. Main outcomes and measuresRace/ethnicity-specific, age-adjusted breast and prostate cancer mortality rates (2018-2022) and county-level socio-behavioral risk scores. Counties were grouped into mortality tertiles within each race/ethnicity-by-cancer-stratum. ResultsAcross groups, higher socio-behavioral risk was associated with higher breast and prostate cancer mortality. For breast cancer, socio-behavioral risk increased monotonically across mortality tertiles for all groups, with the largest within-group increases among Hispanic and Non-Hispanic Black women. For prostate cancer, risk generally increased across mortality tertiles for all groups. Although Hispanic populations had lower population-average mortality, high-mortality Hispanic counties exhibited pronounced risk in lifestyle behaviors, economic livelihoods, and touchpoints with care. Domain patterns associated with high mortality varied by race, ethnicity, and cancer type, with touchpoints with care and economic livelihoods consistently prominent. Conclusions and relevanceWithin-group heterogeneity in socio-behavioral risk is substantial across U.S. counties. Linking population-specific, domain-level socio-behavioral profiles to cancer mortality may support more precise and equity-oriented cancer control strategies than reliance on group averages or composite indices. Key pointsO_ST_ABSQuestionC_ST_ABSWithin racial and ethnic groups, how do socio-behavioral determinants of health vary across US counties with low, moderate, and high breast and prostate cancer mortality? FindingsIn this cross-sectional study, higher county-level socio-behavioral risk was associated with higher breast and prostate cancer mortality across racial and ethnic groups. Race/ethnicity-specific, domain-level profiles revealed within-group heterogeneity, including persistently elevated risk among Non-Hispanic Black populations and pronounced domain-specific gaps in high-mortality Hispanic counties. MeaningLinking population-specific socio-behavioral profiles to local cancer mortality can guide more precise and equity-oriented prioritization of intervention domains and geographies than reliance on group averages or composite indices.

IntroductionDespite advancements in non-small cell lung cancer (NSCLC) management through the use of molecular biomarkers, the recently introduced 9th edition of the TNM staging system remains based exclusively on anatomic descriptors, with no consistently demonstrated improvement in risk stratification for early-stage disease. This study explores the integration of a molecular prognostic classifier into the conventional TNM staging system. MethodsWe analyzed 502 patients with stage I-III lung adenocarcinoma (LUAD) who underwent surgical resection with tumor-based gene expression profiling at the Quebec Heart and Lung Institute. A molecular prognostic classifier was developed and integrated into the 9th edition TNM staging system to generate a novel model (TNMEx). Prognostic performance was compared with the 8th and 9th TNM editions using prognostic discrimination and reclassification metrics. External validation of the molecular classifier was performed in 271 LUAD cases from The Cancer Genome Atlas (TCGA). An independent cohort of 606 resected LUAD patients from the National Cancer Center Hospital (Tokyo) was used to externally compare the prognostic performance of the 8th and 9th TNM staging systems in the absence of molecular data. ResultsThe molecular prognostic classifier was developed based on the expression levels of 26 prognosis-associated genes, weighted by their corresponding coefficients. The classifier was subsequently integrated into the 9th edition TNM staging to generate the TNMEx model. The TNMEx system demonstrated superior prognostic performance, achieving a higher concordance index (C-index = 0.72) compared to the 9th edition TNM (C-index = 0.65, p=0.006). Moreover, TNMEx significantly improved patient risk reclassification compared to both the 8th (net reclassification improvement [NRI] = 0.27, integrated discrimination improvement [IDI] = 0.04) and 9th editions (NRI = 0.40, IDI = 0.05), underscoring its superior ability to stratify outcomes. The 8th and 9th editions showed only limited improvement in overall prognostic accuracy and risk stratification, as reflected by their relatively modest C-index values (0.62 and 0.65, respectively) and minimal reclassification gains (NRI = -0.06, IDI = 0.003). ConclusionsIncorporating a molecular-based prognostic model significantly enhanced the ability to recognize patients at high risk and to predict their survival outcomes more accurately than traditional TNM staging systems.

IntroductionThe precise determination of diagnostic cut-off points is essential for the development of multimarker panels in oncology. In previous work on pulmonary nodules, it was observed that the standard two-parameter logistic fit could be insufficient for biomarkers with asymmetric distributions. Furthermore, the calculation of empirical cut-off points based on graphical visualization presented limitations in precision and reproducibility. ObjectiveThis study presents a methodological advancement in the data analysis phase (Stage 1), introducing new Python algorithms for the direct analytical calculation of empirical intersections and robust mathematical modeling using Dual Annealing with both two-parameter and four-parameter logistic functions. This improved methodology feeds into the ThresholdXpert 1.0 software tool for combinatorial optimization of biomarker panels (Stage 2), and is applied here to the diagnostic challenge of hepatocellular carcinoma (HCC). MethodsThe methodology was first validated by re-analyzing a dataset of patients with pulmonary nodules (N=895). It was subsequently applied to an HCC dataset derived from the cohort of Jang et al. (208 HCC, 193 cirrhosis, 401 total), randomly divided into a training set (280) and an independent test set (121). Scripts were developed to compare the previous two-parameter logistic fit with the new two- and four-parameter logistic models. Finally, ThresholdXpert 1.0 was used for multimarker panel optimization. ResultsThe integration of empirical calculation, logistic modeling, and combinatorial optimization through ThresholdXpert 1.0 provides a robust and coherent framework for the development of multimarker diagnostic panels. The four-parameter logistic model provided additional validation without substantially modifying cut-off values for most biomarkers, confirming the stability of the approach while offering greater flexibility for complex distributions. When applied to hepatocellular carcinoma, the framework identified a molecular panel composed of AFP, PIVKA-II, OPN, and DKK-1 with sensitivity of 0.77 and specificity of 0.72, and an optimized panel incorporating inverse MELD that achieved the best overall balance (sensitivity 0.73, specificity 0.75) in independent external validation. These results demonstrate the potential of this approach as a generalizable tool for the optimized design of binary diagnostic systems in oncology. ConclusionThe integration of complementary mathematical modeling enhances the capability of ThresholdXpert 1.0 to identify robust diagnostic panels, as in some cases a single biomarker may outperform biomarker combinations, and vice versa. This approach enabled the integration of molecular biomarkers and clinical variables under a unified mathematical framework. Contactroberto117343@gmail.com

Gastrointestinal (GI) polyposis is a major risk factor for colorectal cancer (CRC) and a defining feature of hereditary polyposis syndromes such as familial adenomatous polyposis (FAP). Therapy-associated polyposis (TAP), however, is a rare and incompletely characterized condition that develops decades after treatment for childhood or young adult cancers (CYAC), most often following abdominopelvic radiation or exposure to alkylating agents. As long-term CYAC survival improves, the burden of late GI toxicity, including markedly elevated risks of polyps, CRC, and secondary cancers, continues to rise, yet the molecular features of TAP remain poorly understood. Here, we present the largest clinicopathological and genomic study of TAP to date, comprising 29 patients diagnosed at a median age of 49 years and a median latency of 29 years after primary cancer therapy. Most patients (78%) had received alkylating agents and exhibited high rates of secondary malignancies. Histopathology revealed mixed polyp subtypes with a predominance of adenomas. Given these features and the presence of family history in a subset of patients, we investigated the possibility of Hereditary Mixed Polyposis Syndrome (HMPS). Whole-genome sequencing excluded HMPS by demonstrating absence of the canonical 40-kb GREM1 duplication and lack of consistent GREM1 overexpression. Comparative genomic analysis revealed that TAP adenomas exhibit more extensive genome fragmentation and a higher burden of large structural variants than FAP adenomas. Mutational signature profiling identified strong contributions from age-associated signatures (SBS1, SBS5) and a strong, pervasive contribution of the alkylating-agent signature SBS25, even in samples lacking matched normal tissue, whereas platinum-associated SBS31 was minimal. Patient-derived organoids from TAP adenomas showed impaired differentiation, suggesting persistent therapy-induced stem cell dysfunction. Together, these findings define TAP as a distinct polyposis syndrome marked by heterogeneous histology, long latency, profound structural genomic injury, and chemotherapy-specific mutational scars. This work supports early and tailored GI surveillance for CYAC survivors and provides mechanistic insight into the long-term consequences of cytotoxic therapy on intestinal epithelial homeostasis.

BackgroundGynecological cancers and their treatments can compromise fertility, with profound psychosocial consequences for women of reproductive age. Yet, womens lived experiences of cancer-related infertility remain underexplored in low-resource settings, including Ghana. This study examined the impact of gynecological cancers on fertility among reproductive-aged women receiving care at Ho Teaching Hospital, Ghana. MethodsA qualitative descriptive design was used. Fourteen women aged 15-49 years with gynecological cancers who had completed or were undergoing treatment were purposively recruited until saturation. Semi-structured interviews (30-45 minutes) were conducted face-to-face or by telephone in English, Twi, or Ewe, audio-recorded, transcribed verbatim, and analyzed using thematic analysis. Strategies to enhance rigor included independent coding, member checking, reflexivity, and peer debriefing. ResultsFive themes and eighteen subthemes emerged. Participants described infertility as a threat to womanhood and future life plans, expressed as a sense of incompleteness, fear of rejection, denial, and shattered aspirations. Social consequences included stigma and impaired intimate relationships. Treatment-related burdens, menstrual changes, pain, fatigue, and anxiety compounded distress. Economic hardship and educational disruption were common. Women also demonstrated resilience through adherence to treatment, dietary and lifestyle modifications, faith-based coping, and family support. ConclusionGynecological cancer-related infertility is a multidimensional survivorship burden. Integrating fertility counseling, psychosocial support, symptom management, and financial/social protection into cancer care is critical in Ghanaian settings.