Background

When studying the genetics of a human trait, we typically have to manage both genome-wide and targeted genotype data. There can be overlap of both people and markers from different genotyping experiments; the overlap can introduce several kinds of problems. Most times the overlapping genotypes are the same, but sometimes they are different. Occasionally, the lab will return genotypes using a different allele labeling scheme (for example 1/2 vs A/C). Sometimes, the genotype for a person/marker index is unreliable or missing. Further, over time some markers are merged and bad samples are re-run under a different sample name. We need a consistent picture of the subset of data we have chosen to work with even though there might possibly be conflicting measurements from multiple data sources.

Results

We have developed the dbVOR database, which is designed to hold data efficiently for both genome-wide and targeted experiments. The data are indexed for fast retrieval by person and marker. In addition, we store pedigree and phenotype data for our subjects. The dbVOR database allows us to select subsets of the data by several different criteria and to merge their results into a coherent and consistent whole. Data may be filtered by: family, person, trait value, markers, chromosomes, and chromosome ranges. The results can be presented in columnar, Mega2, or PLINK format.

Conclusions

dbVOR serves our needs well. It is freely available from https://watson.hgen.pitt.edu/register . Documentation for dbVOR can be found at https://watson.hgen.pitt.edu/register/docs/dbvor.html .

Multiple genome-wide scans involving sib-pairs or limited pedigrees have been extensively used for a wide number of complex genetic conditions. Comparing data from two or more scans, as well as combining data, require an understanding of the sources of genotyping errors and data discrepancies. We have conducted two genome-wide scans for age-related maculopathy using the Center for Inherited Disease Research (CIDR) and the Mammalian Genotyping Service (MGS). Thirty individuals were typed in common, in order to allow for the alignment of alleles and comparison of the data sets. The analysis of these 8914 genotypes distributed over 321 markers in common demonstrated excellent agreement between these two laboratories, which have low rates of internal errors. Under the assumption that within each genotype, the smaller MGS allele should correspond to the smaller CIDR allele, the alleles align well between the two centers, with only a small fraction (less than 0.65%) of the aligned alleles showing large differences in sizes. However, since called allele sizes are integer "labels" which may not directly reflect the true underlying allele sizes, it is important to carefully prepare in advance if one wishes to merge data from different laboratories. In particular, it would not suffice to attempt to align alleles by typing only one or two controls in common. Fortunately, for the purposes of linkage analysis, one can avoid merging difficulties by simply carrying out linkage analyses using laboratory-specific allele labels and allele frequencies for each laboratory-specific subset of the data.

Persistent arm pain is a common problem after breast cancer surgery. Little is known about genetic factors that contribute to this type of postsurgical pain. Study purpose was to explore associations between persistent arm pain phenotypes and genetic polymorphisms among 15 genes involved in catecholaminergic and serotonergic neurotransmission. Women (n = 398) rated the presence and intensity of arm pain monthly for 6 months after breast cancer surgery. Three distinct latent classes of patients were identified (ie, no arm pain [41.6%], mild arm pain (23.6%), and moderate arm pain (34.8%). Logistic regression analyses were used to evaluate for differences between genotype or haplotype frequencies and the persistent arm pain classes. Compared with the no arm pain class, 3 single nucleotide polymorphisms and 1 haplotype, in 4 genes, were associated with membership in the mild arm pain class: COMT rs4633, HTR2A haplotype B02 (composed of rs1923886 and rs7330636), HTR3A rs1985242, and TH rs2070762. Compared with the no arm pain class, 4 single nucleotide polymorphisms in 3 genes were associated with membership in the moderate arm pain class: COMT rs165656, HTR2A rs2770298 and rs9534511, and HTR3A rs1985242. Findings suggest that variations in catecholaminergic and serotonergic genes play a role in the development of persistent arm pain. PERSPECTIVE: Limited information is available on genetic factors that contribute to persistent arm pain after breast cancer surgery. Genetic polymorphisms in genes involved in catecholaminergic and serotonergic neurotransmission were associated with 2 persistent arm pain phenotypes. Findings may be used to identify patients are higher risk for this common pain condition.

Background and purpose

Since 2001, symptom cluster research has grown considerably. However, because multiple methodological considerations remain, ongoing synthesis of the literature is needed to identify gaps in this area of symptom science. This systematic review evaluated the progress in symptom clusters research in adults receiving primary or adjuvant chemotherapy since 2016.

Methods

Eligible studies were published in English between 1 January 2017 and 17 May 2021; evaluated for and identified symptom clusters 'de novo;' and included only adults being treated with primary or adjuvant chemotherapy. Studies were excluded if patients had advanced cancer or were receiving palliative chemotherapy; symptoms were measured after treatment; symptom clusters were pre-specified or a patient-centred analytic approach was used. For each study, symptom instrument(s); statistical methods and symptom dimension(s) used to create the clusters; whether symptoms were allowed to load on more than one factor; method used to assess for stability of symptom clusters and associations with secondary outcomes and biomarkers were extracted.

Results

Twenty-three studies were included. Memorial Symptom Assessment Scale was the most common instrument and exploratory factor analysis was the most common statistical method used to identify symptom clusters. Psychological, gastrointestinal, and nutritional clusters were the most commonly identified clusters. Only the psychological cluster remained relatively stable over time. Only five studies evaluated for secondary outcomes.

Discussion

While symptom cluster research has evolved, clear criteria to evaluate the stability of symptom clusters and standardised nomenclature for naming clusters are needed. Additional research is needed to evaluate the biological mechanism(s) for symptom clusters.

Prospero registration number

CRD42021240216.

BACKGROUND: Morning and evening fatigue are distinct and distressing symptoms experienced during chemotherapy that demonstrate a large amount of interindividual variability. OBJECTIVES: The objectives of this study were to identify subgroups of patients with distinct morning and evening fatigue co-occurrence profiles and evaluate for differences among these subgroups in demographic, clinical, and symptom characteristics and quality of life. METHODS: Oncology patients ( n = 1,334) completed the Lee Fatigue Scale to self-report morning and evening fatigue, six times over two cycles of chemotherapy. Latent profile analysis was used to identify subgroups of patients with distinct morning and evening physical fatigue profiles. RESULTS: Four distinct morning and evening fatigue profiles were identified (i.e., Both Low, Low Morning + Moderate Evening, Both Moderate, and Both High). Compared to the Both Low profile, the Both High profile was significantly younger, less likely to be married or partnered, more likely to live alone, had a higher comorbidity burden, and lower functional status. The Both High profile had higher levels of anxiety, depressive symptoms, sleep disturbance, and pain and lower levels of quality of life. DISCUSSION: The variability in the morning and evening severity scores among the four profiles supports the hypothesis that morning and evening fatigue are distinct but related symptoms. Clinically meaningful levels of both morning and evening fatigue were reported by 50.4% of our sample, which suggests that the co-occurrence of these two symptoms is relatively common. Patients in Both Moderate and Both High profiles experienced an extremely high symptom burden that warrants ongoing assessments and aggressive symptom management interventions.

Background

Age-related maculopathy (ARM) is a common cause of visual impairment in the elderly populations of industrialized countries and significantly affects the quality of life of those suffering from the disease. Variants within two genes, the complement factor H (CFH) and the poorly characterized LOC387715 (ARMS2), are widely recognized as ARM risk factors. CFH is important in regulation of the alternative complement pathway suggesting this pathway is involved in ARM pathogenesis. Two other complement pathway genes, the closely linked complement component receptor (C2) and complement factor B (CFB), were recently shown to harbor variants associated with ARM.

Methods/principal findings

We investigated two SNPs in C2 and two in CFB in independent case-control and family cohorts of white subjects and found rs547154, an intronic SNP in C2, to be significantly associated with ARM in both our case-control (P-value 0.00007) and family data (P-value 0.00001). Logistic regression analysis suggested that accounting for the effect at this locus significantly (P-value 0.002) improves the fit of a genetic risk model of CFH and LOC387715 effects only. Modeling with the generalized multifactor dimensionality reduction method showed that adding C2 to the two-factor model of CFH and LOC387715 increases the sensitivity (from 63% to 73%). However, the balanced accuracy increases only from 71% to 72%, and the specificity decreases from 80% to 72%.

Conclusions/significance

C2/CFB significantly influences AMD susceptibility and although accounting for effects at this locus does not dramatically increase the overall accuracy of the genetic risk model, the improvement over the CFH-LOC387715 model is statistically significant.

Purpose

Approximately 20 % of patients develop lymphedema (LE) following breast cancer (BC) surgery. An evaluation of distinct trajectories of volume change may improve our ability to diagnose LE sooner. The purposes of this study were to identify subgroups of women with distinct trajectories of limb volume changes following BC surgery and to evaluate for phenotypic differences among these classes.

Methods

In this prospective longitudinal study, 380 women were enrolled prior to unilateral BC surgery. Upper limb bioimpedance was measured preoperatively and serially for 1 year postoperatively. Resistance ratios (RRs) were calculated. A RR of >1 indicates affected limb volume > unaffected limb volume. Latent class growth analysis (LCGA) was used to identify classes of women with distinct postoperative RR trajectories. Differences among classes were evaluated using analyses of variance and chi-square analyses.

Results

Three distinct classes were identified as follows: RR <0.95 (37.9 %), RR ~1.00 (46.8 %), and RR >1.05 (15.3 %). Patients in the RR >1.05 class were more likely to have diabetes (p = 0.036), were more likely to have BC on their dominant side (p < 0.001), had higher RR ratios at the preoperative and 1-month assessments (p < 0.001), and were more likely to be diagnosed with LE (p < 0.001).

Conclusions

LCGA is a useful analytic technique to identify subgroups of women who may be at higher risk for the development of LE, based on trajectories of limb volume change after BC surgery.

Implications for cancer survivors

Assessment of preoperative and 1-month bioimpedance RRs may allow for the earlier identification of patients who are at higher risk for the development of LE.

Age-related maculopathy (ARM) is an important cause of visual impairment in the elderly population. It is of crucial importance to identify genetic factors and their interactions with environmental exposures for this disorder. This study was aimed at investigating the CFH, ELOVL4, PLEKHA1 and LOC387715 genes in independent cohorts collected using different ascertainment schemes. The study used a case-control design with subjects originally recruited through the Cardiovascular Health Study (CHS) and the Age-Related Eye Disease Study (AREDS). CFH was significantly associated with ARM in both cohorts (P

Purpose

Between 19 and 58% of oncology patients experience chemotherapy-induced nausea (CIN). In a sample of outpatients with breast, gastrointestinal (GI), gynecological, and lung cancer, the study purposes were to evaluate for inter-individual differences in the severity of CIN over two cycles of chemotherapy (CTX) and to determine which demographic and clinical characteristics and GI symptoms were associated with higher initial levels as well as with the trajectories of CIN severity.

Methods

Patients completed study questionnaires at six time points over two cycles of CTX. These questionnaires provided information on demographic and clinical characteristics, as well as the occurrence of twelve GI symptoms. Hierarchical linear modeling based on full maximum likelihood estimation was performed.

Results

Of the 1251 patients, 47.2% reported CIN. Across two cycles of CTX, lower functional status scores and higher levels of comorbidity were associated with higher initial levels of CIN. Younger age and emetogenicity of the CTX regimen were associated with higher initial levels as well as worse trajectories of CIN. The occurrence of five GI symptoms (i.e., vomiting, lack of appetite, constipation, feeling bloated, and difficulty swallowing) was associated with higher initial levels of CIN. The occurrence of mouth sores was associated with higher initial levels as well as with worst trajectories of CIN.

Conclusions

This study is the first to identify distinct demographic, clinical, and GI symptom characteristics associated with CIN severity. These findings suggest that the etiology of CIN is complex and may warrant interventions beyond standard antiemetics.

Background

Fatigue is the most common and debilitating symptom experienced by oncology patients undergoing chemotherapy. Little is known about patient characteristics that predict changes in fatigue severity over time.

Purpose

To predict the severity of evening fatigue in the week following the administration of chemotherapy using machine learning approaches.

Methods

Outpatients with breast, gastrointestinal, gynecological, or lung cancer (N=1217) completed questionnaires one week prior to and one week following administration of chemotherapy. Evening fatigue was measured with the Lee Fatigue Scale (LFS). Separate prediction models for evening fatigue severity were created using clinical, symptom, and psychosocial adjustment characteristics and either evening fatigue scores or individual fatigue item scores. Prediction models were created using two regression and three machine learning approaches.

Results

Random forest (RF) models provided the best fit across all models. For the RF model using individual LFS item scores, two of the 13 individual LFS items (i.e., "worn out", "exhausted") were the strongest predictors.

Conclusion

This study is the first to use machine learning techniques to predict evening fatigue severity in the week following chemotherapy from fatigue scores obtained in the week prior to chemotherapy. Our findings suggest that the language used to assess clinical fatigue in oncology patients is important and that two simple questions may be used to predict evening fatigue severity.