An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics.

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

A comprehensive analysis of clinical and polygenic germline influences on somatic mutational burden.

Tumor mutational burden (TMB), the total number of somatic mutations in the tumor, and copy number burden (CNB), the corresponding measure of aneuploidy, are established fundamental somatic features and emerging biomarkers for immunotherapy. However, the genetic and non-genetic influences on TMB/CNB and, critically, the manner by which they influence patient outcomes remain poorly understood. Here, we present a large germline-somatic study of TMB/CNB with >23,000 individuals across 17 cancer types, of which 12,000 also have extensive clinical, treatment, and overall survival (OS) measurements available. We report dozens of clinical associations with TMB/CNB, observing older age and male sex to have a strong effect on TMB and weaker impact on CNB. We additionally identified significant germline influences on TMB/CNB, including fine-scale European ancestry and germline polygenic risk scores (PRSs) for smoking, tanning, white blood cell counts, and educational attainment. We quantify the causal effect of exposures on somatic mutational processes using Mendelian randomization. Many of the identified features associated with TMB/CNB were additionally associated with OS for individuals treated at a single tertiary cancer center. For individuals receiving immunotherapy, we observed a complex relationship between PRSs for educational attainment, self-reported college attainment, TMB, and survival, suggesting that the influence of this biomarker may be substantially modified by socioeconomic status. While the accumulation of somatic alterations is a stochastic process, our work demonstrates that it can be shaped by host characteristics including germline genetics.

Brachytherapy: An overview for clinicians.

Brachytherapy is a specific form of radiotherapy consisting of the precise placement of radioactive sources directly into or next to the tumor. This technique is indicated for patients affected by various types of cancers. It is an optimal tool for delivering very high doses to the tumor focally while minimizing the probability of normal tissue complications. Physicians from a wide range of specialties may be involved in either the referral to or the placement of brachytherapy. Many patients require brachytherapy as either primary treatment or as part of their oncologic care. On the basis of high-level evidence from randomized controlled trials, brachytherapy is mainly indicated: 1) as standard in combination with chemoradiation in patients with locally advanced cervical cancer; 2) in surgically treated patients with uterine endometrial cancer for decreasing the risk of vaginal vault recurrence; 3) in patients with high-risk prostate cancer to perform dose escalation and improve progression-free survival; and 4) in patients with breast cancer as adjuvant, accelerated partial breast irradiation or to boost the tumor bed. In this review, the authors discuss the clinical relevance of brachytherapy with a focus on indications, levels of evidence, and results in the overall context of radiation use for patients with cancer.

A Mixed-Effect Kernel Machine Regression Model for Integrative Analysis of Alpha Diversity in Microbiome Studies.

Increasing evidence suggests that human microbiota plays a crucial role in many diseases. Alpha diversity, a commonly used summary statistic that captures the richness and/or evenness of the microbial community, has been associated with many clinical conditions. However, individual studies that assess the association between alpha diversity and clinical conditions often provide inconsistent results due to insufficient sample size, heterogeneous study populations and technical variability. In practice, meta-analysis tools have been applied to integrate data from multiple studies. However, these methods do not consider the heterogeneity caused by sequencing protocols, and the contribution of each study to the final model depends mainly on its sample size (or variance estimate). To combine studies with distinct sequencing protocols, a robust statistical framework for integrative analysis of microbiome datasets is needed. Here, we propose a mixed-effect kernel machine regression model to assess the association of alpha diversity with a phenotype of interest. Our approach readily incorporates the study-specific characteristics (including sequencing protocols) to allow for flexible modeling of microbiome effect via a kernel similarity matrix. Within the proposed framework, we provide three hypothesis testing approaches to answer different questions that are of interest to researchers. We evaluate the model performance through extensive simulations based on two distinct data generation mechanisms. We also apply our framework to data from HIV reanalysis consortium to investigate gut dysbiosis in HIV infection.

Biological and clinical relevance of correlated expression levels of coding and long noncoding RNAs in HPV16 positive cervical cancers.

Human papillomavirus (HPV) drives cervical cancer (CaCx) pathogenesis and viral oncoproteins jeopardize global gene expression in such cancers. In this study, our aim was to identify differentially expressed coding (DEcGs) and long noncoding RNA genes (DElncGs) specifically sense intronic and Natural Antisense Transcripts as they are located in the genic regions and may have a direct influence on the expression pattern of their neighbouring coding genes. We compared HPV16-positive CaCx patients (N = 44) with HPV-negative normal individuals (N = 34) by employing strand-specific RNA-seq and determined the relationships between DEcGs and DElncGs and their clinical implications. By performing Gene set enrichment and protein-protein interaction (PPI) analyses of DEcGs, we identified enrichment of processes crucial for abortive virus life cycle and cancer progression. The DEcGs formed 16 gene clusters which we identified through Molecular Complex Detection (MCODE) plugin of Cytoscape. All the gene clusters portrayed cancer-related functions. We recorded significantly correlated expression levels of 79 DElncGs with DEcGs at proximal genomic loci based on Pearson's Correlation coefficients. Of these gene pairs, 24 pairs portrayed significantly altered correlation coefficients among patients, compared to normal individuals. Of these, 6 DEcGs of 6 such gene pairs, belonged to 5 of the identified gene clusters, one of which was survival-associated. Out of the 24 correlated DEcG: DElncG pairs, we identified 3 pairs, where expression of both members was significantly associated with patient overall survival. The findings justify the cooperative roles of these gene pairs, in patient prognostication, thereby bearing immense potential for translation. Thus, elucidation of correlative strengths between paired DElncGs and DEcGs in patient and normal samples, could serve as a foundation for identification of therapeutic and prognostic targets of HPV16-positive CaCx.

A radiomics model to predict γδ T-cell abundance and overall survival in head and neck squamous cell carcinoma.

γδ T cells are becoming increasingly popular because of their attractive potential for antitumor immunotherapy. However, the role and assessment of γδ T cells in head and neck squamous cell carcinoma (HNSCC) are not well understood. We aimed to explore the prognostic value of γδ T cell and predict its abundance using a radiomics model. Computer tomography images with corresponding gene expression data and clinicopathological data were obtained from online databases. After outlining the volumes of interest manually, the radiomic features were screened using maximum melevance minimum redundancy and recursive feature elimination algorithms. A radiomics model was developed to predict γδ T-cell abundance using gradient boosting machine. Kaplan-Meier survival curves and univariate and multivariate Cox regression analyses were used for the survival analysis. In this study, we confirmed that γδ T-cell abundance was an independent predictor of favorable overall survival (OS) in patients with HNSCC. Moreover, a radiomics model was built to predict the γδ T-cell abundance level (the areas under the operating characteristic curves of 0.847 and 0.798 in the training and validation sets, respectively). The calibration and decision curves analysis demonstrated the fitness of the model. The high radiomic score was an independent protective factor for OS. Our results indicated that γδ T-cell abundance was a promising prognostic predictor in HNSCC, and the radiomics model could discriminate its abundance levels and predict OS. The noninvasive radiomics model provided a potentially powerful prediction tool to aid clinical judgment and antitumor immunotherapy.

High-value decisions are fast and accurate, inconsistent with diminishing value sensitivity.

It is a widely held belief that people's choices are less sensitive to changes in value as value increases. For example, the subjective difference between $11 and $12 is believed to be smaller than between $1 and $2. This idea is consistent with applications of the Weber-Fechner Law and divisive normalization to value-based choice and with psychological interpretations of diminishing marginal utility. According to random utility theory in economics, smaller subjective differences predict less accurate choices. Meanwhile, in the context of sequential sampling models in psychology, smaller subjective differences also predict longer response times. Based on these models, we would predict decisions between high-value options to be slower and less accurate. In contrast, some have argued on normative grounds that choices between high-value options should be made with less caution, leading to faster and less accurate choices. Here, we model the dynamics of the choice process across three different choice domains, accounting for both discriminability and response caution. Contrary to predictions, we mostly observe faster and more accurate decisions (i.e., higher drift rates) between high-value options. We also observe that when participants are alerted about incoming high-value decisions, they exert more caution and not less. We rule out several explanations for these results, using tasks with both subjective and objective values. These results cast doubt on the notion that increasing value reduces discriminability.

Laser-Induced Controllable Porosity in Additive Manufacturing Boosts Efficiency of Electrocatalytic Water Splitting.

In laser-based additive manufacturing (AM), porosity and unmelted metal powder are typically considered undesirable and harmful. Nevertheless in this work, precisely controlling laser parameters during printing can intentionally introduce controllable porosity, yielding a porous electrode with enhanced catalytic activity for the oxygen evolution reaction (OER). This study demonstrates that deliberate introduction of porosity, typically considered a defect, leads to improved gas molecule desorption, enhanced mass transfer, and increased catalytically active sites. The optimized P-93% electrode displays superior OER performance with an overpotential of 270 mV at 20 mA cm-2. Furthermore, it exhibits remarkable long-term stability, operating continuously for over 1000 h at 10 mA cm-2 and more than 500 h at 500 mA cm-2. This study not only provides a straightforward and mass-producible method for efficient, binder-free OER catalysts but also, if optimized, underscores the potential of laser-based AM driven defect engineering as a promising strategy for industrial water splitting.

Synthetic Leaves Based on Crystalline Olefin-Linked Covalent Organic Frameworks for Efficient CO2 Photoreduction with Water.

We report, for the first time, a new synthetic strategy for the preparation of crystalline two-dimensional olefin-linked covalent organic frameworks (COFs) based on aldol condensation between benzodifurandione and aromatic aldehydes. Olefin-linked COFs can be facilely crystallized through either a pyridine-promoted solvothermal process or a benzoic anhydride-mediated organic flux synthesis. The resultant COF leaf with high in-plane π-conjugation exhibits efficient visible-light-driven photoreduction of carbon dioxide (CO2) with water (H2O) in the absence of any photosensitizer, sacrificial agents, or cocatalysts. The production rate of carbon monoxide (CO) reaches as high as 158.1 µmol g-1 h-1 with near 100% CO selectivity, which is accompanied by the oxidation of H2O to oxygen. Both theoretical and experimental results confirm that the key lies in achieving exceptional photoinduced charge separation and low exciton binding. We anticipate that our findings will facilitate new possibilities for the development of semiconducting COFs with structural diversity and functional variability.

Engrailed: Pathological and physiological effects of a multifunctional developmental gene.

Engrailed-1 (EN1) is a developmental gene that encodes En1, a highly conserved transcription factor involved in regionalization during early embryogenesis and in the later maintenance of normal neurons. After birth, EN1 still plays a role in the development and physiology of the body; for example, it exerts a protective effect on midbrain dopaminergic (mDA) neurons, and loss of EN1 causes mDA neurons in the ventral midbrain to gradually die approximately 6 weeks after birth, resulting in motor and nonmotor symptoms similar to those observed in Parkinson's disease. Notably, EN1 has been identified as a possible susceptibility gene for idiopathic Parkinson's disease in humans. EN1 is involved in the processes of wound-healing scar production and tissue and organ fibrosis. Additionally, EN1 can lead to tumorigenesis and thus provides a target for the treatment of some tumors. In this review, we summarize the effects of EN1 on embryonic organ development, describe the consequences of the deletion or overexpression of the EN1 gene, and discuss the pathways in which EN1 is involved. We hope to clarify the role of EN1 as a developmental gene and present potential therapeutic targets for diseases involving the EN1 gene.

Proteomic profiling of ovarian clear cell carcinomas identifies prognostic biomarkers for chemotherapy.

Clear cell ovarian carcinoma (CCOC) is a relatively rare subtype of ovarian cancer (OC) with high degree of resistance to standard chemotherapy. Little is known about the underlying molecular mechanisms, and it remains a challenge to predict its prognosis after chemotherapy. Here, we first analyzed the proteome of 35 formalin-fixed paraffin-embedded (FFPE) CCOC tissue specimens from a cohort of 32 patients with CCOC (H1 cohort) and characterized 8697 proteins using data-independent acquisition mass spectrometry (DIA-MS). We then performed proteomic analysis of 28 fresh frozen (FF) CCOC tissue specimens from an independent cohort of 24 patients with CCOC (H2 cohort), leading to the identification of 9409 proteins with DIA-MS. After bioinformatics analysis, we narrowed our focus to 15 proteins significantly correlated with the recurrence free survival (RFS) in both cohorts. These proteins are mainly involved in DNA damage response, extracellular matrix (ECM), and mitochondrial metabolism. Parallel reaction monitoring (PRM)-MS was adopted to validate the prognostic potential of the 15 proteins in the H1 cohort and an independent confirmation cohort (H3 cohort). Interferon-inducible transmembrane protein 1 (IFITM1) was observed as a robust prognostic marker for CCOC in both PRM data and immunohistochemistry (IHC) data. Taken together, this study presents a CCOC proteomic data resource and a single promising protein, IFITM1, which could potentially predict the recurrence and survival of CCOC.

A novel approach to the analysis of Overall Survival (OS) as response with Progression-Free Interval (PFI) as condition based on the RNA-seq expression data in The Cancer Genome Atlas (TCGA).

BACKGROUND: Overall Survival (OS) and Progression-Free Interval (PFI) as survival times have been collected in The Cancer Genome Atlas (TCGA). It is of biomedical interest to consider their dependence in pathway detection and survival prediction. We intend to develop novel methods for integrating PFI as condition based on parametric survival models for identifying pathways associated with OS and predicting OS. RESULTS: Based on the framework of conditional probability, we developed a family of frailty-based parametric-models for this purpose, with exponential or Weibull distribution as baseline. We also considered two classes of existing methods with PFI as a covariate. We evaluated the performance of three approaches by analyzing RNA-seq expression data from TCGA for lung squamous cell carcinoma and lung adenocarcinoma (LUNG), brain lower grade glioma and glioblastoma multiforme (GBMLGG), as well as skin cutaneous melanoma (SKCM). Our focus was on fourteen general cancer-related pathways. The 10-fold cross-validation was employed for the evaluation of predictive accuracy. For LUNG, p53 signaling and cell cycle pathways were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. For GBMLGG, ten pathways (e.g., Wnt signaling, JAK-STAT signaling, ECM-receptor interaction, etc.) were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated better predictive performance compared to the approaches without the consideration of PFI. For SKCM, p53 signaling pathway was detected only by our Weibull-baseline-based model. And three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. CONCLUSIONS: Based on our study, it is necessary to incorporate PFI into the survival analysis of OS. Furthermore, PFI is a survival-type time, and improved results can be achieved by our conditional-probability-based approach.

Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.

Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melanoma remains limited. In this study, we utilized single-cell data from 92,521 cells to explore the tumour cell landscape. Through clustering analysis, we identified six distinct cell clusters and investigated their differentiation and metabolic heterogeneity using multi-omics approaches. Notably, cytotrace analysis and pseudotime trajectories revealed distinct stages of tumour cell differentiation, which have implications for patient survival. By leveraging markers from these clusters, we developed a tumour cell-specific machine learning model (TCM). This model not only predicts patient outcomes and responses to immunotherapy, but also distinguishes between genomically stable and unstable tumours and identifies inflamed ('hot') versus non-inflamed ('cold') tumours. Intriguingly, the TCM score showed a strong association with TOMM40, which we experimentally validated as an oncogene promoting tumour proliferation, invasion and migration. Overall, our findings introduce a novel biomarker score that aids in selecting melanoma patients for improved prognoses and targeted immunotherapy, thereby guiding clinical treatment decisions.

Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach.

Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mRNA, lncRNA, microRNA, methylation and mutation data from the TCGA database for LUAD. Utilizing ten clustering algorithms, we identified stable multi-omics consensus clusters (MOCs). These data were then amalgamated with ten machine learning approaches to develop a robust model capable of reliably identifying patient prognosis and predicting immunotherapy outcomes. Through ten clustering algorithms, two prognostically relevant MOCs were identified, with MOC2 showing more favourable outcomes. We subsequently constructed a MOCs-associated machine learning model (MOCM) based on eight MOCs-specific hub genes. Patients characterized by a lower MOCM score exhibited better overall survival and responses to immunotherapy. These findings were consistent across multiple datasets, and compared to many previously published LUAD biomarkers, our MOCM score demonstrated superior predictive performance. Notably, the low MOCM group was more inclined towards 'hot' tumours, characterized by higher levels of immune cell infiltration. Intriguingly, a significant positive correlation between GJB3 and the MOCM score (R = 0.77, p < 0.01) was discovered. Further experiments confirmed that GJB3 significantly enhances LUAD proliferation, invasion and migration, indicating its potential as a key target for LUAD treatment. Our developed MOCM score accurately predicts the prognosis of LUAD patients and identifies potential beneficiaries of immunotherapy, offering broad clinical applicability.

Risk modeling of single-cell transcriptomes reveals the heterogeneity of immune infiltration in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common primary hepatic malignancies. E2F transcription factors play an important role in the tumorigenesis and progression of HCC, mainly through the RB/E2F pathway. Prognostic models for HCC based on gene signatures have been developed rapidly in recent years; however, their discriminating ability at the single-cell level remains elusive, which could reflect the underlying mechanisms driving the sample bifurcation. In this study, we constructed and validated a predictive model based on E2F expression, successfully stratifying patients with HCC into two groups with different survival risks. Then we used a single-cell dataset to test the discriminating ability of the predictive model on infiltrating T cells, demonstrating remarkable cellular heterogeneity as well as altered cell fates. We identified distinct cell subpopulations with diverse molecular characteristics. We also found that the distribution of cell subpopulations varied considerably across onset stages among patients, providing a fundamental basis for patient-oriented precision evaluation. Moreover, single-sample gene set enrichment analysis revealed that subsets of CD8+ T cells with significantly different cell adhesion levels could be associated with different patterns of tumor cell dissemination. Therefore, our findings linked the conventional prognostic gene signature to the immune microenvironment and cellular heterogeneity at the single-cell level, thus providing deeper insights into the understanding of HCC tumorigenesis.

Phylogenomic analyses and reclassification of the Mesorhizobium complex: proposal for 9 novel genera and reclassification of 15 species.

BACKGROUD: The genus Mesorhizobium is shown by phylogenomics to be paraphyletic and forms part of a complex that includes the genera Aminobacter, Aquamicrobium, Pseudaminobacter and Tianweitania. The relationships for type strains belong to these genera need to be carefully re-evaluated. RESULTS: The relationships of Mesorhizobium complex are evaluated based on phylogenomic analyses and overall genome relatedness indices (OGRIs) of 61 type strains. According to the maximum likelihood phylogenetic tree based on concatenated sequences of 539 core proteins and the tree constructed using the bac120 bacterial marker set from Genome Taxonomy Database, 65 type strains were grouped into 9 clusters. Moreover, 10 subclusters were identified based on the OGRIs including average nucleotide identity (ANI), average amino acid identity (AAI) and core-proteome average amino acid identity (cAAI), with AAI and cAAI showing a clear intra- and inter-(sub)cluster gaps of 77.40-80.91% and 83.98-86.16%, respectively. Combined with the phylogenetic trees and OGRIs, the type strains were reclassified into 15 genera. This list includes five defined genera Mesorhizobium, Aquamicrobium, Pseudaminobacter, Aminobacterand Tianweitania, among which 40/41 Mesorhizobium species and one Aminobacter species are canonical legume microsymbionts. The other nine (sub)clusters are classified as novel genera. Cluster III, comprising symbiotic M. alhagi and M. camelthorni, is classified as Allomesorhizobium gen. nov. Cluster VI harbored a single symbiotic species M. albiziae and is classified as Neomesorhizobium gen. nov. The remaining seven non-symbiotic members were proposed as: Neoaquamicrobium gen. nov., Manganibacter gen. nov., Ollibium gen. nov., Terribium gen. nov., Kumtagia gen. nov., Borborobacter gen. nov., Aerobium gen. nov.. Furthermore, the genus Corticibacterium is restored and two species in Subcluster IX-1 are reclassified as the member of this genus. CONCLUSION: The Mesorhizobium complex are classified into 15 genera based on phylogenomic analyses and OGRIs of 65 type strains. This study resolved previously non-monophyletic genera in the Mesorhizobium complex.

KIF2C/MCAK a prognostic biomarker and its oncogenic potential in malignant progression, and prognosis of cancer patients: a systematic review and meta-analysis as biomarker.

KIF2C/MCAK (KIF2C) is the most well-characterized member of the kinesin-13 family, which is critical in the regulation of microtubule (MT) dynamics during mitosis, as well as interphase. This systematic review briefly describes the important structural elements of KIF2C, its regulation by multiple molecular mechanisms, and its broad cellular functions. Furthermore, it systematically summarizes its oncogenic potential in malignant progression and performs a meta-analysis of its prognostic value in cancer patients. KIF2C was shown to be involved in multiple crucial cellular processes including cell migration and invasion, DNA repair, senescence induction and immune modulation, which are all known to be critical during the development of malignant tumors. Indeed, an increasing number of publications indicate that KIF2C is aberrantly expressed in multiple cancer entities. Consequently, we have highlighted its involvement in at least five hallmarks of cancer, namely: genome instability, resisting cell death, activating invasion and metastasis, avoiding immune destruction and cellular senescence. This was followed by a systematic search of KIF2C/MCAK's expression in various malignant tumor entities and its correlation with clinicopathologic features. Available data were pooled into multiple weighted meta-analyses for the correlation between KIF2Chigh protein or gene expression and the overall survival in breast cancer, non-small cell lung cancer and hepatocellular carcinoma patients. Furthermore, high expression of KIF2C was correlated to disease-free survival of hepatocellular carcinoma. All meta-analyses showed poor prognosis for cancer patients with KIF2Chigh expression, associated with a decreased overall survival and reduced disease-free survival, indicating KIF2C's oncogenic potential in malignant progression and as a prognostic marker. This work delineated the promising research perspective of KIF2C with modern in vivo and in vitro technologies to further decipher the function of KIF2C in malignant tumor development and progression. This might help to establish KIF2C as a biomarker for the diagnosis or evaluation of at least three cancer entities.

Increased risk of contralateral breast cancer for BRCA1/2 wild-type, high-risk Korean breast cancer patients: a retrospective cohort study.

BACKGROUND: This study aimed to investigate the contralateral breast cancer (CBC) recurrence rate in Korean breast cancer patients according to their BRCA1/2 germline mutation status, focusing particularly on the CBC recurrence risk in BRCA1/2 negative (BRCAx) patients. METHODS: We conducted a retrospective study on 13,107 primary breast cancer patients. The patients were divided into high-risk and low-risk groups for hereditary breast cancer based on the Korean National Health Insurance Service's eligibility criteria for BRCA1/2 germline mutation testing. The high-risk group was further categorized into the BRCA mutation group, the BRCAx group, and the not tested group. We evaluated the overall survival and cumulative risk of developing CBC in these patients. RESULTS: Among 4494 high-risk patients, 973 (21.7%) underwent genetic testing for BRCA1/2 germline mutation, revealing mutations in 158 patients (16.2%). We observed significant overall survival differences across all four groups, with the high-risk, not-tested group demonstrating notably worse overall survival (p < 0.001). However, when adjusted for other prognostic factors, there was no significant differences in hazard ratio of death between the four groups. The cumulative risk of CBC also varied among the groups. Patients with BRCA1/2 mutations showed a 7.3-fold increased risk of CBC compared to the low-risk group (95% CI 4.11-13.0, p < 0.001). Interestingly, BRCAx patients also demonstrated a significantly higher risk of CBC (HR 2.77, 95% CI 1.76-4.35, p < 0.001). The prognostic importance of the BRCAx for CBC recurrence persisted after adjusting for the age and subtype, but became insignificant when the family history of breast cancer was adjusted. CONCLUSION: Breast cancer patients who are at high risk of hereditary breast cancer but with wild-type BRCA 1/2 genes (BRCAx) have increased risk of developing contralateral breast cancer when compared to the low-risk patients. More careful surveillance and follow-up can be offered to these patients especially when they have family history of breast cancer.

Contemporary survival in metastatic bladder cancer patients: A population-based study.

The overall survival (OS) improvement after the advent of several novel systemic therapies, designed for treatment of metastatic urothelial carcinoma of the urinary bladder (mUCUB), is not conclusively studied in either contemporary UCUB patients and/or non-UCUB patients. Within the Surveillance, Epidemiology, and End Results database, contemporary (2017-2020) and historical (2000-2016) systemic therapy-exposed metastatic UCUB and, subsequently, non-UCUB patients were identified. Separate Kaplan-Meier and multivariable Cox regression (CRM) analyses first addressed OS in mUCUB and, subsequently, in metastatic non-UCUB (mn-UCUB). Of 3443 systemic therapy-exposed patients, 2725 (79%) harbored mUCUB versus 709 (21%) harbored mn-UCUB. Of 2725 mUCUB patients, 582 (21%) were contemporary (2017-2020) versus 2143 (79%) were historical (2000-2016). In mUCUB, median OS was 11 months in contemporary versus 8 months in historical patients (Δ = 3 months; p < .0001). After multivariable CRM, contemporary membership status (2017-2020) independently predicted lower overall mortality (OM; hazard ratio [HR] = 0.68, 95% confidence interval [CI] = 0.60-0.76; p < .001). Of 709 mn-UCUB patients, 167 (24%) were contemporary (2017-2020) and 542 (76%) were historical (2000-2016). In mn-UCUB, median OS was 8 months in contemporary versus 7 months in historical patients (Δ = 1 month; p = .034). After multivariable CRM, contemporary membership status (2017-2020) was associated with HR of 0.81 (95% CI = 0.66-1.01; p = .06). In conclusion, contemporary systemic therapy-exposed metastatic patients exhibited better OS in UCUB. However, the magnitude of survival benefit was threefold higher in mUCUB and approximated the survival benefits recorded in prospective randomized trials of novel systemic therapies.

Metabolomic landscape of overall and common cancers in the UK Biobank: A prospective cohort study.

Information about the NMR metabolomics landscape of overall, and common cancers is still limited. Based on a cohort of 83,290 participants from the UK Biobank, we used multivariate Cox regression to assess the associations between each of the 168 metabolites with the risks of overall cancer and 20 specific types of cancer. Then, we applied LASSO to identify important metabolites for overall cancer risk and obtained their associations using multivariate cox regression. We further conducted mediation analysis to evaluate the mediated role of metabolites in the effects of traditional factors on overall cancer risk. Finally, we included the 13 identified metabolites as predictors in prediction models, and compared the accuracies of our traditional models. We found that there were commonalities among the metabolic profiles of overall and specific types of cancer: the top 20 frequently identified metabolites for 20 specific types of cancer were all associated with overall cancer; most of the specific types of cancer had common identified metabolites. Meanwhile, the associations between the same metabolite with different types of cancer can vary based on the site of origin. We identified 13 metabolic biomarkers associated with overall cancer, and found that they mediated the effects of traditional factors. The accuracies of prediction models improved when we added 13 identified metabolites in models. This study is helpful to understand the metabolic mechanisms of overall and a wide range of cancers, and our results also indicate that NMR metabolites are potential biomarkers in cancer diagnosis and prevention.