Abstract P5-03-16: Changes in preferences for ovarian cancer prevention strategies during the COVID-19 pandemic: Results of a discrete choice experiment

Background: The COVID-19 pandemic influenced patient health care decisions, but there is little information about the pandemic’s impact on decisions about cancer risk reduction. This includes women at elevated risk of breast or ovarian cancer considering risk-reducing salpingo-oophorectomy (RRSO), risk-reducing salpingectomy (RRS), or other preventive measures. During the pandemic patients needed to balance their concerns about cancer risk reduction with their risks associated with elective health procedures, a risk which changed as vaccines became available. Methods: To address the impact of the COVID-19 pandemic on cancer prevention decision making, we recruited N=396 pre-menopausal women with a personal history of breast cancer or familial history suggestive of increased breast and/or ovarian cancer risk between 4/2019 and 3/2022. We conducted a discrete choice experiment in which patients were asked to choose between two scenarios that specified type of surgery (RRSO, RRS vs. non-surgical surveillance), age of menopause (natural versus immediate), quality of menopausal symptoms (mild, moderate, severe), and risk of ovarian cancer, heart disease, or osteoporosis. Risk of ovarian cancer for the scenarios provided varied in discrete intervals from 0% to 40%. We examined temporal trends during the pandemic using interactions with time coinciding approximately with the beginning of pandemic, peak vaccination period, and the Omicron wave. Results: We identified significant temporal interactions on a woman’s prevention decisions. In 2019, women at higher risk of ovarian cancer were more likely to choose prevention scenarios that favored lower ovarian cancer risk (odds ratio [OR] = 0.48; 95% CI = 0.37, 0.69 per 10% increase in ovarian cancer risk difference). This association decreased through the pre-vaccine period of 2020 by OR=2.61/month (95% CI = 1.21, 5.65). By June 2020, the effect of a 10% increase in ovarian cancer risk on intervention choice had attenuated substantially (OR=0.84, 95% CI 0.67, 1.00). By January 2022, the effect strengthened (OR= 0.69, 95% CI .49, .88), but had not reached pre-pandemic levels. Before 3/2020, natural age of menopause (versus immediate) had a strong impact on the choice of a scenario (OR=3.56, 95% CI 1.65-7.65). At the beginning of the pandemic, the effect was reduced by 0.47/month (95% CI 0.22-0.99). The rate of attenuation slowed over time, such that the effect of having a natural age of menopause on choice was OR= 1.56 (95% CI 0.65, 2.46) by January 2022. Tests for temporal interactions were statistically significant for both ovarian cancer risk and age of menopause. Conclusions: Our results suggest that over the course of the pandemic, women seemed more accepting of higher risks of ovarian cancer and immediate (post treatment) menopause when considering preventive options. There was an inverse U shape curve of the effect of ovarian cancer risk on choices over time (Figure A), but the strength of the relationship had not reached pre-pandemic levels by January 2022. This may reflect patient tolerance for side effects as the pandemic evolved. These results suggest that factors such as ovarian cancer risk and delay of menopause influenced personal prevention choices, but that these choices were influenced by events related to events that hallmarked the COVID-19 pandemic.

Citation Format: Brian Egleston, Mary Daly, Kaitlyn Lew, Lisa Bealin, Alexander Husband, Jill Stopfer, Pawel Przybysz, Olga Tchuvatkina, Yu-Ning Wong, Judy Garber, Timothy Rebbeck. Changes in preferences for ovarian cancer prevention strategies during the COVID-19 pandemic: Results of a discrete choice experiment. [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-03-16.

Abstract P6-02-08: Identifying preferences that may motivate choice of ovarian cancer risk prevention strategies using a discrete choice experiment

Background: Women with a familial or hereditary risk for ovarian cancer are at a much greater risk of developing ovarian cancer compared with women in the general population. This high risk demands prevention strategies to reduce ovarian cancer incidence and mortality. Currently there is little information about how women with a hereditary risk for ovarian cancer make trade-offs when choosing among prevention strategies and their associated risks. In anticipation of the likelihood that when given more personalized risk estimates, patients may have different preferences based on their mutation specific cancer risk as well as demographic and clinical factors, it is critical that we have the necessary information to develop counseling models that are tailored to individual patients’ preferences for cancer risk reduction and tolerance of associated risks. Methods: We performed a discrete choice experiment to investigate how women at higher risk of ovarian cancer weigh benefits (e.g. reduced risk of ovarian) versus costs (e.g. increased risk of heart disease) in choosing a treatment strategy. N=396 pre-menopausal women with a personal history of breast cancer or familial history suggestive of increased breast and/or ovarian cancer risk were surveyed from August, 2019, to January, 2022. Participants were asked to choose between two sets of attributes that specified type of surgery (risk-reducing salpingo-oophorectomy [RRSO], risk reducing salpingectomy [RRS] vs. non-surgical surveillance), age of menopause (natural versus immediate), quality of menopausal symptoms (mild, moderate, severe), and risk of ovarian cancer, heart disease, or osteoporosis. Risks of disease varied in discrete intervals. We fit a Bradley-Terry logistic regression to estimate preferences. The binary response was the randomly generated choice set selected versus the set not selected. Results: Women were more likely to choose sets with either surveillance (odds ratio [OR]= 1.28, 95% confidence interval [CI] 0.98, 1.67) or RRSO (OR= 1.39, 95% CI 1.07, 1.81) over RSS. In weighing trade-offs in the choice sets that included type of surgery, women had a stronger independent preference for reducing the risk of ovarian cancer (OR= 0.66 of choosing set per 10% increase in risk, 95% CI 0.62, 0.71) than in reducing the risk of osteoporosis (OR= 0.82 per 10% increase, 95% CI 0.75, 0.90) or heart disease (OR = 0.82 per 10% increase, 95% CI 0.76,0.88). Women also had a strong preference for delaying the expected age of ovarian cancer (OR= 1.34 per 10-year increase in age, 95% CI 1.19, 1.51). Women had strong preferences for having a natural age of menopause (OR= 1.58 compared to immediate menopause post-treatment, 95% CI 1.27, 1.95), and better less severe symptoms (OR= 0.65 for each ordinal increase in the severity of symptoms, 95% CI 0.60, 0.70). Conclusions: Our results suggest that women may prefer either surveillance or the most extensive type of surgery (RRSO) over more limited surgery (RRS). In weighing trade-offs, reducing the risk of ovarian cancer seemed to be more important than reducing the risk of osteoporosis or heart disease. Still, having a natural age of menopause and reducing the severity of symptoms could motivate the choice of treatment. Our work will allow us to estimate thresholds of measured factors that may motivate women to choose a specific treatment strategy.

Citation Format: Mary Daly, Brian Egleston, Kaitlyn Lew, Lisa Bealin, Alexander Husband, Jill Stopfe, Pawel Przybysz, Olga Tchuvatkina, Yu-Ning Wong, Judy Garber, Timothy Rebbeck. Identifying preferences that may motivate choice of ovarian cancer risk prevention strategies using a discrete choice experiment. [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-02-08.

Sharing behavioral data through a grid infrastructure using data standards

OBJECTIVE

In an effort to standardize behavioral measures and their data representation, the present study develops a methodology for incorporating measures found in the National Cancer Institute's (NCI) grid-enabled measures (GEM) portal, a repository for behavioral and social measures, into the cancer data standards registry and repository (caDSR).

METHODS

The methodology consists of four parts for curating GEM measures into the caDSR: (1) develop unified modeling language (UML) models for behavioral measures; (2) create common data elements (CDE) for UML components; (3) bind CDE with concepts from the NCI thesaurus; and (4) register CDE in the caDSR.

RESULTS

UML models have been developed for four GEM measures, which have been registered in the caDSR as CDE. New behavioral concepts related to these measures have been created and incorporated into the NCI thesaurus. Best practices for representing measures using UML models have been utilized in the practice (eg, caDSR). One dataset based on a GEM-curated measure is available for use by other systems and users connected to the grid.

CONCLUSIONS

Behavioral and population science data can be standardized by using and extending current standards. A new branch of CDE for behavioral science was developed for the caDSR. It expands the caDSR domain coverage beyond the clinical and biological areas. In addition, missing terms and concepts specific to the behavioral measures addressed in this paper were added to the NCI thesaurus. A methodology was developed and refined for curation of behavioral and population science data.

FuGEFlow: data model and markup language for flow cytometry

Background

Flow cytometry technology is widely used in both health care and research. The rapid expansion of flow cytometry applications has outpaced the development of data storage and analysis tools. Collaborative efforts being taken to eliminate this gap include building common vocabularies and ontologies, designing generic data models, and defining data exchange formats. The Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard was recently adopted by the International Society for Advancement of Cytometry. This standard guides researchers on the information that should be included in peer reviewed publications, but it is insufficient for data exchange and integration between computational systems. The Functional Genomics Experiment (FuGE) formalizes common aspects of comprehensive and high throughput experiments across different biological technologies. We have extended FuGE object model to accommodate flow cytometry data and metadata.

Methods

We used the MagicDraw modelling tool to design a UML model (Flow-OM) according to the FuGE extension guidelines and the AndroMDA toolkit to transform the model to a markup language (Flow-ML). We mapped each MIFlowCyt term to either an existing FuGE class or to a new FuGEFlow class. The development environment was validated by comparing the official FuGE XSD to the schema we generated from the FuGE object model using our configuration. After the Flow-OM model was completed, the final version of the Flow-ML was generated and validated against an example MIFlowCyt compliant experiment description.

Results

The extension of FuGE for flow cytometry has resulted in a generic FuGE-compliant data model (FuGEFlow), which accommodates and links together all information required by MIFlowCyt. The FuGEFlow model can be used to build software and databases using FuGE software toolkits to facilitate automated exchange and manipulation of potentially large flow cytometry experimental data sets. Additional project documentation, including reusable design patterns and a guide for setting up a development environment, was contributed back to the FuGE project.

Conclusion

We have shown that an extension of FuGE can be used to transform minimum information requirements in natural language to markup language in XML. Extending FuGE required significant effort, but in our experiences the benefits outweighed the costs. The FuGEFlow is expected to play a central role in describing flow cytometry experiments and ultimately facilitating data exchange including public flow cytometry repositories currently under development.

MIFlowCyt: the minimum information about a Flow Cytometry Experiment

A fundamental tenet of scientific research is that published results are open to independent validation and refutation. Minimum data standards aid data providers, users, and publishers by providing a specification of what is required to unambiguously interpret experimental findings. Here, we present the Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard, stating the minimum information required to report flow cytometry (FCM) experiments. We brought together a cross-disciplinary international collaborative group of bioinformaticians, computational statisticians, software developers, instrument manufacturers, and clinical and basic research scientists to develop the standard. The standard was subsequently vetted by the International Society for Advancement of Cytometry (ISAC) Data Standards Task Force, Standards Committee, membership, and Council. The MIFlowCyt standard includes recommendations about descriptions of the specimens and reagents included in the FCM experiment, the configuration of the instrument used to perform the assays, and the data processing approaches used to interpret the primary output data. MIFlowCyt has been adopted as a standard by ISAC, representing the FCM scientific community including scientists as well as software and hardware manufacturers. Adoptionof MIFlowCyt by the scientific and publishing communities will facilitate third-party understanding and reuse of FCM data.

Proteomics LIMS: A caBIG project, year 1

protLIMS is a web-based proteomics laboratory information management system. In February, we released version 1 of protLIMS, completed to the year one goal: The web-interface provides for recording of the biological material, protein mixture preparation, 2D-PAGE, gel image files and spot and plug mapping. Associated files may be uploaded and retrieved through the web-interface to the file system. In year two, protLIMS will be extended to accommodate mass spectrometric analyses and protein identification.