Quantifying the impact of the COVID-19 pandemic on cancer center clinical trial operations.

BACKGROUND: Oncology clinical trials are complex, and the COVID-19 pandemic caused major disruptions in 2020. METHODS: Using its networking and sharing of best practices, the Association of American Cancer Institutes, comprising 105 cancer centers, solicited a longitudinal series of voluntary surveys from members to assess how clinical trial office operations were affected. The surveys showed that centers were able to keep oncology trials available to patients while maintaining safety. Data were collected regarding interventional clinical trial accruals for the calendar years 2019, 2020, and 2021. RESULTS: Data demonstrated a sizeable decrease in interventional treatment trial accruals in both 2020 and 2021 compared with prepandemic figures in 2019. No cancer center reported an increase in interventional treatment trial accruals in 2020 compared with 2019, with most centers reporting a moderate decrease. In mid-2022, 15% of respondents reported an increasing trend, 31% reported no significant change, and 54% continued to report a decrease. CONCLUSIONS: The pandemic necessitated rapid adoption of trial operations, with the emergence of several best practices, including remote monitoring, remote consenting, electronic research charts, and work-from-home strategies for staff. The national infrastructure to conduct trials was significantly affected by the pandemic, with noteworthy resiliency, evidenced by improvements in efficiencies and patient-centered care delivery but with residual capacity challenges that will be evident for the foreseeable future.

Wnt signaling in triple negative breast cancer is associated with metastasis.

BACKGROUND: Triple Negative subset of (TN) Breast Cancers (BC), a close associate of the basal-like subtype (with limited discordance) is an aggressive form of the disease which convey unpredictable, and poor prognosis due to limited treatment options and lack of proven effective targeted therapies. METHODS: We conducted an expression study of 240 formalin-fixed, paraffin-embedded (FFPE) primary biopsies from two cohorts, including 130 TN tumors, to identify molecular mechanisms of TN disease. RESULTS: The annotation of differentially expressed genes in TN tumors contained an overrepresentation of canonical Wnt signaling components in our cohort and others. These observations were supported by upregulation of experimentally induced oncogenic Wnt/ß-catenin genes in TN tumors, recapitulated using targets induced by Wnt3A. A functional blockade of Wnt/ß-catenin pathway by either a pharmacological Wnt-antagonist, WntC59, sulidac sulfide, or ß-catenin (functional read out of Wnt/ß-catenin pathway) SiRNA mediated genetic manipulation demonstrated that a functional perturbation of the pathway is causal to the metastasis- associated phenotypes including fibronectin-directed migration, F-actin organization, and invasion in TNBC cells. A classifier, trained on microarray data from ß-catenin transfected mammary cells, identified a disproportionate number of TNBC breast tumors as compared to other breast cancer subtypes in a meta-analysis of 11 studies and 1,878 breast cancer patients, including the two cohorts published here. Patients identified by the Wnt/ß-catenin classifier had a greater risk of lung and brain, but not bone metastases. CONCLUSION: These data implicate transcriptional Wnt signaling as a hallmark of TNBC disease associated with specific metastatic pathways.

Linkage analysis of anti-CCP levels as dichotomized and quantitative traits using GAW15 single-nucleotide polymorphism scan of NARAC families.

Rheumatoid arthritis is a clinically and genetically heterogeneous disease. Anti-cyclic citrullinated (anti-CCP) antibodies have a high specificity for rheumatoid arthritis and levels correlate with disease severity. The focus of this study was to examine whether analyzing anti-CCP levels could increase the power of linkage analysis by identifying a more homogeneous subset of rheumatoid arthritis patients. We also wanted to compare linkage signals when analyzing anti-CCP levels as dichotomized (CCP_binary), categorical (CCP_cat), and continuous traits, with and without transformation (log_CCP and CCP_cont). Illumina single-nucleotide polymorphism scans of the North American Rheumatoid Arthritis Consortium families were analyzed for four chromosomes (6, 7, 11, 22) using nonparametric linkage (NPL) (rheumatoid arthritis and CCP_binary), regress (CCP_cat and Log_CCP), and deviates (CCP_cont) analysis options as implemented in Merlin. Similar linkage results were obtained from analyses of rheumatoid arthritis, CCP_binary, and CCP_cont. The only exception was that we observed improved linkage signals and a narrower region for CCP_binary as compared to a clinical diagnosis of rheumatoid arthritis alone on chromosome 7, a region which previously showed variation in linkage results with rheumatoid arthritis according to anti-CCP levels. Analyses of CCP_cat and Log_CCP had little power to detect linkage. Our data suggested that linkage analyses of anti-CCP levels may facilitate identification of rheumatoid arthritis genes but quantitative analyses did not further improve power. Our study also highlighted that quantitative trait linkage results are highly sensitive to phenotype transformation and analytic approaches.

Autoimmunity and susceptibility to Hodgkin lymphoma: a population-based case-control study in Scandinavia.

BACKGROUND: Personal history of autoimmune diseases is consistently associated with increased risk of non-Hodgkin lymphoma. In contrast, there are limited data on risk of Hodgkin lymphoma following autoimmune diseases and almost no data addressing whether there is a familial association between the conditions. METHODS: Using population-based linked registry data from Sweden and Denmark, 32 separate autoimmune and related conditions were identified from hospital diagnoses in 7476 case subjects with Hodgkin lymphoma, 18,573 matched control subjects, and more than 86,000 first-degree relatives of case and control subjects. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) as measures of relative risks for each condition using logistic regression and also applied multivariable hierarchical regression models. All P values are two-sided. RESULTS: We found statistically significantly increased risks of Hodgkin lymphoma associated with personal histories of several autoimmune conditions, including rheumatoid arthritis (OR = 2.7, 95% CI = 1.9 to 4.0), systemic lupus erythematosus (OR = 5.8, 95% CI = 2.2 to 15.1), sarcoidosis (OR = 14.1, 95% CI = 5.4 to 36.8), and immune thrombocytopenic purpura (OR = infinity, P = .002). A statistically significant increase in risk of Hodgkin lymphoma was associated with family histories of sarcoidosis (OR = 1.8, 95% CI = 1.01 to 3.1) and ulcerative colitis (OR = 1.6, 95% CI = 1.02 to 2.6). CONCLUSIONS: Personal or family history of certain autoimmune conditions was strongly associated with increased risk of Hodgkin lymphoma. The association between both personal and family histories of sarcoidosis and a statistically significantly increased risk of Hodgkin lymphoma suggests shared susceptibility for these conditions.

Identification of susceptibility loci for complex diseases in a case-control association study using the Genetic Analysis Workshop 14 dataset.

Although current methods in genetic epidemiology have been extremely successful in identifying genetic loci responsible for Mendelian traits, most common diseases do not follow simple Mendelian modes of inheritance. It is important to consider how our current methodologies function in the realm of complex diseases. The aim of this study was to determine the ability of conventional association methods to fine map a locus of interest. Six study populations were selected from 10 replicates (New York) from the Genetic Analysis Workshop 14 simulated dataset and analyzed for association between the disease trait and locus D2. Genotypes from 45 single-nucleotide polymorphisms in the telomeric region of chromosome 3 were analyzed by Pearson's chi-square tests for independence to test for association with the disease trait of interest. A significant association was detected within the region; however, it was found 3 cM from the documented location of the D2 disease locus. This result was most likely due to the method used for data simulation. In general, this study showed that conventional case-control association methods could detect disease loci responsible for the development of complex traits.

Linkage analysis of the GAW14 simulated dataset with microsatellite and single-nucleotide polymorphism markers in large pedigrees.

Recent studies have suggested that a high-density single nucleotide polymorphism (SNP) marker set could provide equivalent or even superior information compared with currently used microsatellite (STR) marker sets for gene mapping by linkage. The focus of this study was to compare results obtained from linkage analyses involving extended pedigrees with STR and single-nucleotide polymorphism (SNP) marker sets. We also wanted to compare the performance of current linkage programs in the presence of high marker density and extended pedigree structures. One replicate of the Genetic Analysis Workshop 14 (GAW14) simulated extended pedigrees (n = 50) from New York City was analyzed to identify the major gene D2. Four marker sets with varying information content and density on chromosome 3 (STR [7.5 cM]; SNP [3 cM, 1 cM, 0.3 cM]) were analyzed to detect two traits, the original affection status, and a redefined trait more closely correlated with D2. Multipoint parametric and nonparametric linkage analyses (NPL) were performed using programs GENEHUNTER, MERLIN, SIMWALK2, and S.A.G.E. SIBPAL. Our results suggested that the densest SNP map (0.3 cM) had the greatest power to detect linkage for the original trait (genetic heterogeneity), with the highest LOD score/NPL score and mapping precision. However, no significant improvement in linkage signals was observed with the densest SNP map compared with STR or SNP-1 cM maps for the redefined affection status (genetic homogeneity), possibly due to the extremely high information contents for all maps. Finally, our results suggested that each linkage program had limitations in handling the large, complex pedigrees as well as a high-density SNP marker set.

Linkage disequilibrium mapping in trisomic populations: analytical approaches and an application to congenital heart defects in Down syndrome.

Many of the birth defects associated with trisomy exhibit both variable expressivity and incomplete penetrance. This variability suggests that it is allelic variation and not simply the presence of an additional chromosome that leads to the development of certain trisomy-associated birth defects. With the proper tools, one may use trisomic populations to identify genes involved in the development of specific birth defects. A trisomic population may be advantageous over a normal population if the defect is over-represented in the trisomic population. Alternatively, one can view the trisomic populations as a "model system" to offer insight into aspects of both normal and abnormal embryonic development. Standard disomic linkage disequilibrium mapping approaches need to be adjusted to account for the presence of the additional genetic material in the trisomic individuals. We present an approach for linkage disequilibrium mapping of variable phenotypes in a trisomic population that adequately accounts for the additional alleles and the pattern of non-independent inheritance. We establish the laboratory methods and statistical tools necessary to conduct an association study in a trisomic population. As an example, we have applied these tools to a pilot study of Down syndrome-associated congenital heart defects.

A trisomic transmission disequilibrium test.

Certain congenital disorders that are rare in the general population are quite common in individuals with trisomic conditions. For example, complete atrioventricular septal defect occurs in about 20% of individuals with Down syndrome, an approximately 500-fold increase in risk as compared to individuals without Down syndrome. Genetic variation on the chromosome involved in the trisomy may affect susceptibility to these trisomy-specific disorders. That is, increased dosage of a variant may be directly involved in increasing the risk of a disorder, or it may be indirectly involved by causing up- or downregulation of other genes. As in standard disomic gene-mapping, one can search for genes using linkage or association methods. Within association methods, one can consider case-control methods or family-based control methods such as the transmission disequilibrium test (TDT). Most gene-mapping methods need to be substantially redesigned for use with trisomic data. In this paper, we present a "trisomic TDT", a statistical method of testing for nonrandom transmission of alleles from parents to trisomic children. We demonstrate the method on a dataset of parent-child trios in which the child has Down syndrome.