PURPOSE: The aim of this study is to characterize the patterns of medication use by early-stage breast cancer (ESBC) survivors from diagnosis to 1 year post-chemotherapy. METHODS: A single-center longitudinal study was conducted with ESBC patients diagnosed between December 2011 and June 2014. Data on the medication use of individual patients were retrieved from prescription databases, supplemented by records from the National Electronic Health Records. The data covered the period from ESBC diagnosis to 1 year post-chemotherapy. Medication types were classified according to the World Health Organizations Anatomical Therapeutic Chemical classification system, and medication for chronic diseases was created by adapting a list of 20 chronic diseases provided by the U.S. Department of Human and Health Services. RESULTS: Of the 107 patients involved in the study (mean age 51.1 ± 8.4 years; 78.5 % Chinese), 46.7 % manifested non-cancer comorbidities, of which hypertension (24.3 %) was the most prevalent, followed by hyperlipidemia (13.1 %) and diabetes (5.6 %). Calcium channel blockers (12.1 %) and lipid-modifying agents (11.2 %) were the most common chronic medication types used before chemotherapy, and their use persisted during chemotherapy (10.3 and 11.2 %, respectively) and after chemotherapy (11.2 and 13.1 %, respectively). Hormonal therapy was the predominant post-chemotherapy medication (77.6 %). A statistically significant increase (p < 0.0001) was observed in the mean number of chronic disease medication classes prescribed to patients between the pre-chemotherapy (0.53 ± 1.04) and chemotherapy (0.62 ± 1.08) periods and between the chemotherapy and post-chemotherapy (1.63 ± 1.35) periods. CONCLUSIONS: There is an increase in trend of chronic medication usage in breast cancer survivors after cancer treatment. This study provides important insights into the design of medication management programs tailored to this population. Future studies should incorporate a control population to improve the interpretation of study results.

Purpose

The aim of this study is to characterize the patterns of medication use by early-stage breast cancer (ESBC) survivors from diagnosis to 1 year post-chemotherapy.

Methods

A single-center longitudinal study was conducted with ESBC patients diagnosed between December 2011 and June 2014. Data on the medication use of individual patients were retrieved from prescription databases, supplemented by records from the National Electronic Health Records. The data covered the period from ESBC diagnosis to 1 year post-chemotherapy. Medication types were classified according to the World Health Organization's Anatomical Therapeutic Chemical classification system, and medication for chronic diseases was created by adapting a list of 20 chronic diseases provided by the U.S. Department of Human and Health Services.

Results

Of the 107 patients involved in the study (mean age 51.1 ± 8.4 years; 78.5 % Chinese), 46.7 % manifested non-cancer comorbidities, of which hypertension (24.3 %) was the most prevalent, followed by hyperlipidemia (13.1 %) and diabetes (5.6 %). Calcium channel blockers (12.1 %) and lipid-modifying agents (11.2 %) were the most common chronic medication types used before chemotherapy, and their use persisted during chemotherapy (10.3 and 11.2 %, respectively) and after chemotherapy (11.2 and 13.1 %, respectively). Hormonal therapy was the predominant post-chemotherapy medication (77.6 %). A statistically significant increase (p < 0.0001) was observed in the mean number of chronic disease medication classes prescribed to patients between the pre-chemotherapy (0.53 ± 1.04) and chemotherapy (0.62 ± 1.08) periods and between the chemotherapy and post-chemotherapy (1.63 ± 1.35) periods.

Conclusions

There is an increase in trend of chronic medication usage in breast cancer survivors after cancer treatment. This study provides important insights into the design of medication management programs tailored to this population. Future studies should incorporate a control population to improve the interpretation of study results.

Purpose

To determine the effects of bevacizumab on patient-reported outcomes (PROs; secondary end point) in the AURELIA trial.

Patients and methods

Patients with platinum-resistant ovarian cancer were randomly assigned to chemotherapy alone (CT) or with bevacizumab (BEV-CT). PROs were assessed using the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Ovarian Cancer Module 28 (EORTC QLQ-OV28) and Functional Assessment of Cancer Therapy-Ovarian Cancer symptom index (FOSI) at baseline and every two or three cycles (8/9 weeks) until disease progression. The primary PRO hypothesis was that more patients receiving BEV-CT than CT would achieve at least a 15% (≥ 15-point) absolute improvement on the QLQ-OV28 abdominal/GI symptom subscale (items 31-36) at week 8/9. Patients with missing week 8/9 questionnaires were included as unimproved. Questionnaires from all assessments until disease progression were analyzed using mixed-model repeated-measures (MMRM) analysis. Sensitivity analyses were used to determine the effects of differing assumptions and methods for missing data.

Results

Baseline questionnaires were available from 89% of 361 randomly assigned patients. More BEV-CT than CT patients achieved a ≥ 15% improvement in abdominal/GI symptoms at week 8/9 (primary PRO end point, 21.9% v 9.3%; difference, 12.7%; 95% CI, 4.4 to 20.9; P = .002). MMRM analysis covering all time points also favored BEV-CT (difference, 6.4 points; 95% CI, 1.3 to 11.6; P = .015). More BEV-CT than CT patients achieved ≥ 15% improvement in FOSI at week 8/9 (12.2% v 3.1%; difference, 9.0%; 95% CI, 2.9% to 15.2%; P = .003). Sensitivity analyses gave similar results and conclusions.

Conclusion

Bevacizumab increased the proportion of patients achieving a 15% improvement in patient-reported abdominal/GI symptoms during chemotherapy for platinum-resistant ovarian cancer.

Epidermal growth factor receptor exon 20 insertion mutations (EGFRexon20ins) are detected in approximately 2% of patients with non-small cell lung cancer (NSCLC). Due to a lack of effective therapy, the prognosis of these patients is typically poor. Sunvozertinib (DZD9008) was designed as an oral, potent, irreversible, and selective EGFR tyrosine kinase inhibitor, showing activity against EGFRexon20ins and other mutations. In both cell lines and xenograft models, sunvozertinib shows potent antitumor activity. In the two ongoing phase I clinical studies, sunvozertinib was tolerated up to 400 mg once daily. The most common drug-related adverse events included diarrhea and skin rash. Antitumor efficacy was observed at the doses of 100 mg and above in patients with EGFRexon20ins NSCLC across different subtypes, with prior amivantamab treatment as well as with baseline brain metastasis. The median duration of response has not been reached.

Significance

We report the discovery and early clinical development of sunvozertinib, a potential treatment option for the unmet medical need of EGFRexon20ins NSCLC. This article is highlighted in the In This Issue feature, p. 1599.

Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg(2)) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg(2) from imputed SNPs (5.1× enrichment; p = 3.7 × 10(-17)) and 38% (SE = 4%) of hg(2) from genotyped SNPs (1.6× enrichment, p = 1.0 × 10(-4)). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained <10% of hg(2) despite having the highest enrichment. We replicated these findings but found no significant contribution from rare coding variants in independent schizophrenia cohorts genotyped on GWAS and exome chips. Our results highlight the value of analyzing components of heritability to unravel the functional architecture of common disease.

Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia.

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Schizophrenia and bipolar disorder are two distinct diagnoses that share symptomology. Understanding the genetic factors contributing to the shared and disorder-specific symptoms will be crucial for improving diagnosis and treatment. In genetic data consisting of 53,555 cases (20,129 bipolar disorder [BD], 33,426 schizophrenia [SCZ]) and 54,065 controls, we identified 114 genome-wide significant loci implicating synaptic and neuronal pathways shared between disorders. Comparing SCZ to BD (23,585 SCZ, 15,270 BD) identified four genomic regions including one with disorder-independent causal variants and potassium ion response genes as contributing to differences in biology between the disorders. Polygenic risk score (PRS) analyses identified several significant correlations within case-only phenotypes including SCZ PRS with psychotic features and age of onset in BD. For the first time, we discover specific loci that distinguish between BD and SCZ and identify polygenic components underlying multiple symptom dimensions. These results point to the utility of genetics to inform symptomology and potential treatment.

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.