Gene Expression Profiling in Leiomyosarcomas and Undifferentiated Pleomorphic Sarcomas: SRC as a New Diagnostic Marker

Background

Undifferentiated Pleomorphic Sarcoma (UPS) and high-grade Leiomyosarcoma (LMS) are soft tissue tumors with an aggressive clinical behavior, frequently developing local recurrence and distant metastases. Despite several gene expression studies involving soft tissue sarcomas, the potential to identify molecular markers has been limited, mostly due to small sample size, in-group heterogeneity and absence of detailed clinical data.

Materials and Methods

Gene expression profiling was performed for 22 LMS and 22 UPS obtained from untreated patients. To assess the relevance of the gene signature, a meta-analysis was performed using five published studies. Four genes (BAD, MYOCD, SRF and SRC) selected from the gene signature, meta-analysis and functional in silico analysis were further validated by quantitative PCR. In addition, protein-protein interaction analysis was applied to validate the data. SRC protein immunolabeling was assessed in 38 UPS and 52 LMS.

Results

We identified 587 differentially expressed genes between LMS and UPS, of which 193 corroborated with other studies. Cluster analysis of the data failed to discriminate LMS from UPS, although it did reveal a distinct molecular profile for retroperitoneal LMS, which was characterized by the over-expression of smooth muscle-specific genes. Significantly higher levels of expression for BAD, SRC, SRF, and MYOCD were confirmed in LMS when compared with UPS. SRC was the most value discriminator to distinguish both sarcomas and presented the highest number of interaction in the in silico protein-protein analysis. SRC protein labeling showed high specificity and a positive predictive value therefore making it a candidate for use as a diagnostic marker in LMS.

Conclusions

Retroperitoneal LMS presented a unique gene signature. SRC is a putative diagnostic marker to differentiate LMS from UPS.     

Pharmacodynamic Consequences of Administration of VLA-4 Antagonist CDP323 to Multiple Sclerosis Subjects: A Randomized,Double-Blind Phase 1/2 Study

Background

Lymphocyte inhibition by antagonism of α4 integrins is a validated therapeutic approach for relapsing multiple sclerosis (RMS).

Objective

Investigate the effect of CDP323, an oral α₄-integrin inhibitor, on lymphocyte biomarkers in RMS.

Methods

Seventy-one RMS subjects aged 18–65 years with Expanded Disability Status Scale scores ≤6.5 were randomized to 28-day treatment with CDP323 100 mg twice daily (bid), 500 mg bid, 1000 mg once daily (qd), 1000 mg bid, or placebo.

Results

Relative to placebo, all dosages of CDP323 significantly decreased the capacity of lymphocytes to bind vascular adhesion molecule-1 (VCAM-1) and the expression of α₄-integrin on VCAM-1–binding cells. All but the 100-mg bid dosage significantly increased total lymphocytes and naive B cells, memory B cells, and T cells in peripheral blood compared with placebo, and the dose-response relationship was shown to be linear. Marked increases were also observed in natural killer cells and hematopoietic progenitor cells, but only with the 500-mg bid and 1000-mg bid dosages. There were no significant changes in monocytes. The number of samples for regulator and inflammatory T cells was too small to draw any definitive conclusions.

Conclusions

CDP323 at daily doses of 1000 or 2000 mg induced significant increases in total lymphocyte count and suppressed VCAM-1 binding by reducing unbound very late antigen-4 expression on lymphocytes.

Trial Registration

ClinicalTrials.gov NCT00726648.     

Whole-Exome Sequencing to Identify a Novel LMNA Gene Mutation Associated with Inherited Cardiac Conduction Disease

Background

Inherited cardiac conduction diseases (CCD) are rare but are caused by mutations in a myriad of genes. Recently, whole-exome sequencing has successfully led to the identification of causal mutations for rare monogenic Mendelian diseases.

Objective

To investigate the genetic background of a family affected by inherited CCD.

Methods and Results

We used whole-exome sequencing to study a Chinese family with multiple family members affected by CCD. Using the pedigree information, we proposed a heterozygous missense mutation (c.G695T, Gly232Val) in the lamin A/C (LMNA) gene as a candidate mutation for susceptibility to CCD in this family. The mutation is novel and is expected to affect the conformation of the coiled-coil rod domain of LMNA according to a structural model prediction. Its pathogenicity in lamina instability was further verified by expressing the mutation in a cellular model.

Conclusions

Our results suggest that whole-exome sequencing is a feasible approach to identifying the candidate genes underlying inherited conduction diseases.     

Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

Background

This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches.

Methods and Materials

We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared.

Results

Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses.

Conclusion

The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset.     

Identification of GRB2 and GAB1 Coexpression as an Unfavorable Prognostic Factor for Hepatocellular Carcinoma by a Combination of Expression Profile and Network Analysis

Aim

To screen novel markers for hepatocellular carcinoma (HCC) by a combination of expression profile, interaction network analysis and clinical validation.

Methods

HCC significant molecules which are differentially expressed or had genetic variations in HCC tissues were obtained from five existing HCC related databases (OncoDB.HCC, HCC.net, dbHCCvar, EHCO and Liverome). Then, the protein-protein interaction (PPI) network of these molecules was constructed. Three topological features of the network (''Degree'', ''Betweenness'', and ''Closeness'') and the k-core algorithm were used to screen candidate HCC markers which play crucial roles in tumorigenesis of HCC. Furthermore, the clinical significance of two candidate HCC markers growth factor receptor-bound 2 (GRB2) and GRB2-associated-binding protein 1 (GAB1) was validated.

Results

In total, 6179 HCC significant genes and 977 HCC significant proteins were collected from existing HCC related databases. After network analysis, 331 candidate HCC markers were identified. Especially, GAB1 has the highest k-coreness suggesting its central localization in HCC related network, and the interaction between GRB2 and GAB1 has the largest edge-betweenness implying it may be biologically important to the function of HCC related network. As the results of clinical validation, the expression levels of both GRB2 and GAB1 proteins were significantly higher in HCC tissues than those in their adjacent nonneoplastic tissues. More importantly, the combined GRB2 and GAB1 protein expression was significantly associated with aggressive tumor progression and poor prognosis in patients with HCC.

Conclusion

This study provided an integrative analysis by combining expression profile and interaction network analysis to identify a list of biologically significant HCC related markers and pathways. Further experimental validation indicated that the aberrant expression of GRB2 and GAB1 proteins may be strongly related to tumor progression and prognosis in patients with HCC. The overexpression of GRB2 in combination with upregulation of GAB1 may be an unfavorable prognostic factor for HCC.     

Can K-ras Gene Mutation Be Utilized as Prognostic Biomarker for Colorectal Cancer Patients Receiving Chemotherapy? A Meta-Analysis and Systematic Review

Introduction

K-ras gene mutations were common in colorectal patients, but their relationship with prognosis was unclear.

Objective

Verify prognostic differences between patient with and without mutant K-ras genes by reviewing the published evidence.

Method

Systematic reviews and data bases were searched for cohort/case-control studies of prognosis of colorectal cancer patients with detected K-ras mutations versus those without mutant K-ras genes, both of whom received chemotherapy. Number of patients, regimens of chemotherapy, and short-term or long-term survival rate (disease-free or overall) were extracted. Quality of studies was also evaluated.

Principal Findings

7 studies of comparisons with a control group were identified. No association between K-ras gene status with neither short-term disease free-survival (OR=1.01, 95% CI, 0.73-1.38, P=0.97) nor overall survival (OR=1.06, 95% CI, 0.82-1.36, P=0.66) in CRC patients who received chemotherapy was indicated. Comparison of long-term survival between two groups also indicated no significant difference after heterogeneity was eliminated (OR=1.09, 95% CI, 0.85-1.40, P=0.49).

Conclusions

K-ras gene mutations may not be a prognostic index for colorectal cancer patients who received chemotherapy.     

Response-Predictive Gene Expression Profiling of Glioma Progenitor Cells In Vitro

Background

High-grade gliomas are amongst the most deadly human tumors. Treatment results are disappointing. Still, in several trials around 20% of patients respond to therapy. To date, diagnostic strategies to identify patients that will profit from a specific therapy do not exist.

Methods

In this study, we used serum-free short-term treated in vitro cell cultures to predict treatment response in vitro. This approach allowed us (a) to enrich specimens for brain tumor initiating cells and (b) to confront cells with a therapeutic agent before expression profiling.

Results

As a proof of principle we analyzed gene expression in 18 short-term serum-free cultures of high-grade gliomas enhanced for brain tumor initiating cells (BTIC) before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed to predict therapy-induced impairment of proliferation in vitro.

Conclusion

For the tyrosine kinase inhibitor Sunitinib used in this dataset, the approach revealed additional predictive information in comparison to the evaluation of classical signaling analysis.     

Random-Effects,Fixed-Effects and the within-between Specification for Clustered Data in Observational Health Studies: A Simulation Study

Background

When unaccounted-for group-level characteristics affect an outcome variable, traditional linear regression is inefficient and can be biased. The random- and fixed-effects estimators (RE and FE, respectively) are two competing methods that address these problems. While each estimator controls for otherwise unaccounted-for effects, the two estimators require different assumptions. Health researchers tend to favor RE estimation, while researchers from some other disciplines tend to favor FE estimation. In addition to RE and FE, an alternative method called within-between (WB) was suggested by Mundlak in 1978, although is utilized infrequently.

Methods

We conduct a simulation study to compare RE, FE, and WB estimation across 16,200 scenarios. The scenarios vary in the number of groups, the size of the groups, within-group variation, goodness-of-fit of the model, and the degree to which the model is correctly specified. Estimator preference is determined by lowest mean squared error of the estimated marginal effect and root mean squared error of fitted values.

Results

Although there are scenarios when each estimator is most appropriate, the cases in which traditional RE estimation is preferred are less common. In finite samples, the WB approach outperforms both traditional estimators. The Hausman test guides the practitioner to the estimator with the smallest absolute error only 61% of the time, and in many sample sizes simply applying the WB approach produces smaller absolute errors than following the suggestion of the test.

Conclusions

Specification and estimation should be carefully considered and ultimately guided by the objective of the analysis and characteristics of the data. The WB approach has been underutilized, particularly for inference on marginal effects in small samples. Blindly applying any estimator can lead to bias, inefficiency, and flawed inference.     

The Impact of Psychological Capital on Job Burnout of Chinese Nurses: The Mediator Role of Organizational Commitment

Background

Nursing has a high risk of job burnout, but only a few studies have explored its influencing factors from an organizational perspective.

Objective

The present study explores the impact of psychological capital on job burnout by investigating the mediating effect of organizational commitment on this relationship.

Methods

A total of 473 female nurses from four large general hospitals in Xi’an City of China were selected as participants. Data were collected via the Psychological Capital Questionnaire, the Maslach Burnout Inventory-General Survey, and the Organizational Commitment Scale.

Results

Both psychological capital and organizational commitment were significantly correlated to job burnout. Structural equation modelling indicated that organizational commitment partially mediated the relationship between psychological capital and job burnout.

Conclusion

The final model revealed a significant path from psychological capital to job burnout through organizational commitment. These findings extended prior reports and shed some light on the influence of psychological capital on job burnout.     

Relationship between the COMT-Val158Met and BDNF-Val66Met Polymorphisms,Childhood Trauma and Psychotic Experiences in an Adolescent General Population Sample

Objective

Psychotic experiences occur at a much greater prevalence in the population than psychotic disorders. There has been little research to date, however, on genetic risk for this extended psychosis phenotype. We examined whether COMT or BDNF genotypes were associated with psychotic experiences or interacted with childhood trauma in predicting psychotic experiences.

Method

Psychiatric interviews and genotyping for COMT-Val158Met and BDNF-Val66Met were carried out on two population-based samples of 237 individuals aged 11-15 years. Logistic regression was used to examine for main effects by genotype and childhood trauma, controlling for important covariates. This was then compared to a model with a term for interaction between genotype and childhood trauma. Where a possible interaction was detected, this was further explored in stratified analyses.

Results

While childhood trauma showed a borderline association with psychotic experiences, COMT-Val158Met and BDNF-Val66Met genotypes were not directly associated with psychotic experiences in the population. Testing for gene x environment interaction was borderline significant in the case of COMT-Val158Met with individuals with the COMT-Val158Met Val-Val genotype, who had been exposed to childhood trauma borderline significantly more likely to report psychotic experiences than those with Val-Met or Met-Met genotypes. There was no similar interaction by BDNF-Val66Met genotype.

Conclusion

The COMT-Val158Met Val-Val genotype may be a genetic moderator of risk for psychotic experiences in individuals exposed to childhood traumatic experiences.     

Risk of Narcolepsy Associated with Inactivated Adjuvanted (AS03) A/H1N1 (2009) Pandemic Influenza Vaccine in Quebec

Context

An association between an adjuvanted (AS03) A/H1N1 pandemic vaccine and narcolepsy has been reported in Europe.

Objective

To assess narcolepsy risk following administration of a similar vaccine in Quebec.

Design

Retrospective population-based study.

Setting

Neurologists and lung specialists in the province were invited to report narcolepsy cases to a single reference centre.

Population

Patients were interviewed by two sleep experts and standard diagnostic tests were performed. Immunization status was verified in the provincial pandemic influenza vaccination registry.

Main Outcome Measures

Confirmed narcolepsy with or without cataplexy with onset of excessive daytime sleepiness between January 1^st, 2009, and December 31^st, 2010. Relative risks (RRs) were calculated using a Poisson model in a cohort analysis, by a self-controlled case series (SCCS) and a case-control method.

Results

A total of 24 cases were included and overall incidence rate was 1.5 per million person-years. A cluster of 7 cases was observed among vaccinated persons in the winter 2009–2010. In the primary cohort analysis, 16-week post-vaccination RR was 4.32 (95% CI: 1.50–11.12). RR was 2.07 (0.70–6.17) in the SCCS, and 1.48 (0.37–7.03) using the case-control method. Estimates were lower when observation was restricted to the period of pandemic influenza circulation, and tended to be higher in persons <20 years old and for cataplexy cases.

Conclusions

Results are compatible with an excess risk of approximately one case per million vaccine doses, mainly in persons less than 20 years of age. However, a confounding effect of the influenza infection cannot be ruled out.     

Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery

Background

Since most published articles comparing the performance of artificial neural network (ANN) models and logistic regression (LR) models for predicting hepatocellular carcinoma (HCC) outcomes used only a single dataset, the essential issue of internal validity (reproducibility) of the models has not been addressed. The study purposes to validate the use of ANN model for predicting in-hospital mortality in HCC surgery patients in Taiwan and to compare the predictive accuracy of ANN with that of LR model.

Methodology/Principal Findings

Patients who underwent a HCC surgery during the period from 1998 to 2009 were included in the study. This study retrospectively compared 1,000 pairs of LR and ANN models based on initial clinical data for 22,926 HCC surgery patients. For each pair of ANN and LR models, the area under the receiver operating characteristic (AUROC) curves, Hosmer-Lemeshow (H-L) statistics and accuracy rate were calculated and compared using paired T-tests. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and the relative importance of variables. Compared to the LR models, the ANN models had a better accuracy rate in 97.28% of cases, a better H-L statistic in 41.18% of cases, and a better AUROC curve in 84.67% of cases. Surgeon volume was the most influential (sensitive) parameter affecting in-hospital mortality followed by age and lengths of stay.

Conclusions/Significance

In comparison with the conventional LR model, the ANN model in the study was more accurate in predicting in-hospital mortality and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.     

Microarray Gene Expression Analysis of Tumorigenesis and Regional Lymph Node Metastasis in Laryngeal Squamous Cell Carcinoma

Background

Laryngeal squamous cell carcinoma (LSCC) is the most common type in head and neck squamous cell carcinoma (HNSCC), and the development and progression of LSCC are multistep processes accompanied by changes of molecular biology.

Objective

The purpose of this study was to investigate the molecular basis of tumorigenesis and regional lymph node metastasis in LSCC, and provide a set of genes that may be useful for the development of novel diagnostic markers and/or more effective therapeutic strategies.

Methods

A total number of 10 patients who underwent surgery for primary laryngeal squamous cell carcinoma were recruited for microarray analysis. LSCC tissues compared with corresponding adjacent non-neoplastic tissues were analysed by Illumina mRNA microarrays, and LSCC tissues with regional lymph node metastasis and LSCC tissues without regional lymph node metastasis were analyzed in the same manner. The most frequently differently expressed genes screened by microarrays were also validated by qRT-PCR in another 42 patients diagnosed for LSCC.

Results

Analysed by Illumina mRNA microarrays, there were 361 genes significantly related to tumorigenesis while 246 genes significantly related to regional lymph node metastasis in LSCC. We found that the six genes (CDK1, CDK2, CDK4, MCM2, MCM3, MCM4) were most frequently differently expressed functional genes related to tumorigenesis while eIF3a and RPN2 were most frequently differently expressed functional genes related to regional lymph node metastasis in LSCC. The expressions of these genes were also validated by qRT-PCR.

Conclusions

The research revealed a gene expression signature of tumorigenesis and regional lymph node metastasis in laryngeal squamous cell carcinoma. Of the total, the deregulation of several genes (CDK1, CDK2, CDK4, MCM2, MCM3, MCM4, EIF3a and RPN2) were potentially associated with disease development and progression. The result will contribute to the understanding of the molecular basis of LSCC and help to improve diagnosis and treatment.     

Drosophila cbl is essential for control of cell death and cell differentiation during eye development

Background

Activation of cell surface receptors transduces extracellular signals into cellular responses such as proliferation, differentiation and survival. However, as important as the activation of these receptors is their appropriate spatial and temporal down-regulation for normal development and tissue homeostasis. The Cbl family of E3-ubiquitin ligases plays a major role for the ligand-dependent inactivation of receptor tyrosine kinases (RTKs), most notably the Epidermal Growth Factor Receptor (EGFR) through ubiquitin-mediated endocytosis and lysosomal degradation.

Methodology/Principal Findings

Here, we report the mutant phenotypes of Drosophila cbl (D-cbl) during eye development. D-cbl mutants display overgrowth, inhibition of apoptosis, differentiation defects and increased ommatidial spacing. Using genetic interaction and molecular markers, we show that most of these phenotypes are caused by increased activity of the Drosophila EGFR. Our genetic data also indicate a critical role of ubiquitination for D-cbl function, consistent with biochemical models.

Conclusions/Significance

These data may provide a mechanistic model for the understanding of the oncogenic activity of mammalian cbl genes.     

Analysis of Serum Inflammatory Mediators Identifies Unique Dynamic Networks Associated with Death and Spontaneous Survival in Pediatric Acute Liver Failure

Background

Tools to predict death or spontaneous survival are necessary to inform liver transplantation (LTx) decisions in pediatric acute liver failure (PALF), but such tools are not available. Recent data suggest that immune/inflammatory dysregulation occurs in the setting of acute liver failure. We hypothesized that specific, dynamic, and measurable patterns of immune/inflammatory dysregulation will correlate with outcomes in PALF.

Methods

We assayed 26 inflammatory mediators on stored serum samples obtained from a convenience sample of 49 children in the PALF study group (PALFSG) collected within 7 days after enrollment. Outcomes were assessed within 21 days of enrollment consisting of spontaneous survivors, non-survivors, and LTx recipients. Data were subjected to statistical analysis, patient-specific Principal Component Analysis (PCA), and Dynamic Bayesian Network (DBN) inference.

Findings

Raw inflammatory mediator levels assessed over time did not distinguish among PALF outcomes. However, DBN analysis did reveal distinct interferon-gamma-related networks that distinguished spontaneous survivors from those who died. The network identified in LTx patients pre-transplant was more like that seen in spontaneous survivors than in those who died, a finding supported by PCA.

Interpretation

The application of DBN analysis of inflammatory mediators in this small patient sample appears to differentiate survivors from non-survivors in PALF. Patterns associated with LTx pre-transplant were more like those seen in spontaneous survivors than in those who died. DBN-based analyses might lead to a better prediction of outcome in PALF, and could also have more general utility in other complex diseases with an inflammatory etiology.     

Generic Information Can Retrieve Known Biological Associations: Implications for Biomedical Knowledge Discovery

Motivation

Weighted semantic networks built from text-mined literature can be used to retrieve known protein-protein or gene-disease associations, and have been shown to anticipate associations years before they are explicitly stated in the literature. Our text-mining system recognizes over 640,000 biomedical concepts: some are specific (i.e., names of genes or proteins) others generic (e.g., ‘Homo sapiens’). Generic concepts may play important roles in automated information retrieval, extraction, and inference but may also result in concept overload and confound retrieval and reasoning with low-relevance or even spurious links. Here, we attempted to optimize the retrieval performance for protein-protein interactions (PPI) by filtering generic concepts (node filtering) or links to generic concepts (edge filtering) from a weighted semantic network. First, we defined metrics based on network properties that quantify the specificity of concepts. Then using these metrics, we systematically filtered generic information from the network while monitoring retrieval performance of known protein-protein interactions. We also systematically filtered specific information from the network (inverse filtering), and assessed the retrieval performance of networks composed of generic information alone.

Results

Filtering generic or specific information induced a two-phase response in retrieval performance: initially the effects of filtering were minimal but beyond a critical threshold network performance suddenly drops. Contrary to expectations, networks composed exclusively of generic information demonstrated retrieval performance comparable to unfiltered networks that also contain specific concepts. Furthermore, an analysis using individual generic concepts demonstrated that they can effectively support the retrieval of known protein-protein interactions. For instance the concept “binding” is indicative for PPI retrieval and the concept “mutation abnormality” is indicative for gene-disease associations.

Conclusion

Generic concepts are important for information retrieval and cannot be removed from semantic networks without negative impact on retrieval performance.     

A Stochastic Multi-Scale Model of HIV-1 Transmission for Decision-Making: Application to a MSM Population

Background

In the absence of an effective vaccine against HIV-1, the scientific community is presented with the challenge of developing alternative methods to curb its spread. Due to the complexity of the disease, however, our ability to predict the impact of various prevention and treatment strategies is limited. While ART has been widely accepted as the gold standard of modern care, its timing is debated.

Objectives

To evaluate the impact of medical interventions at the level of individuals on the spread of infection across the whole population. Specifically, we investigate the impact of ART initiation timing on HIV-1 spread in an MSM (Men who have Sex with Men) population.

Design and Methods

A stochastic multi-scale model of HIV-1 transmission that integrates within a single framework the in-host cellular dynamics and their outcomes, patient health states, and sexual contact networks. The model captures disease state and progression within individuals, and allows for simulation of therapeutic strategies.

Results

Early ART initiation may substantially affect disease spread through a population.

Conclusions

Our model provides a multi-scale, systems-based approach to evaluate the broader implications of therapeutic strategies.     

Comorbid Diseases Interact with Breast Cancer to Affect Mortality in the First Year after Diagnosis—A Danish Nationwide Matched Cohort Study

Background

Survival of breast cancer patients with comorbidity, compared to those without comorbidity, has been well characterized. The interaction between comorbid diseases and breast cancer, however, has not been well-studied.

Methods

From Danish nationwide medical registries, we identified all breast cancer patients between 45 and 85 years of age diagnosed from 1994 to 2008. Women without breast cancer were matched to the breast cancer patients on specific comorbid diseases included in the Charlson comorbidity Index (CCI). Interaction contrasts were calculated as a measure of synergistic effect on mortality between comorbidity and breast cancer.

Results

The study included 47,904 breast cancer patients and 237,938 matched comparison women. In the first year, the strongest interaction between comorbidity and breast cancer was observed in breast cancer patients with a CCI score of ≥4, which accounted for 29 deaths per 1000 person-years. Among individual comorbidities, dementia interacted strongly with breast cancer and accounted for 148 deaths per 1000 person-years within one year of follow-up. There was little interaction between comorbidity and breast cancer during one to five years of follow-up.

Conclusions

There was substantial interaction between comorbid diseases and breast cancer, affecting mortality. Successful treatment of the comorbid diseases or the breast cancer can delay mortality caused by this interaction in breast cancer patients.