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Christopher A Cassa

Publications and source records attributed to Christopher A Cassa.

7 recordsLinked to original sources
PubMed2026

LDLR Variant Classification Through Activity-Normalized Prime Editing Screening.

BACKGROUND: Inherited variants in the LDL (low-density lipoprotein) receptor (LDLR) gene are the most common cause of familial hypercholesterolemia, significantly increasing coronary artery disease risk. Early identification of pathogenic LDLR variants enables prompt lipid-lowering therapy and cascade testing of at-risk relatives; however, most LDLR variants observed in the population have uncertain or absent clinical classifications, leaving many patients without actionable information. METHODS: We developed the first activity-normalized prime editing screening pipeline to measure the impact of 5184 LDLR coding variants on LDL-cholesterol (LDL-C) uptake. Each prime editing guide RNA is paired with a genotypic outcome reporter to correct for variable editing efficiency, overcoming a key limitation of previous pooled genome editing screens. A statistical framework further improves variant effect estimates by jointly analyzing all missense variants at each amino acid position. RESULTS: We show that prime editing of the reporter construct correlates with endogenous variant installation frequency, validating the activity normalization approach. The resulting scores capture a continuous spectrum of functional effects, robustly separate pathogenic versus benign ClinVar variants, and show concordance with LDL-C levels in UK Biobank participants. We calibrate functional evidence strengths to the ACMG/AMP variant interpretation framework, enabling integration into a clinical variant classification workflow. By combining functional, computational, population, and contextual evidence, 322 of 434 LDLR variants currently classified as variants of uncertain significance, conflicting, or absent from ClinVar appear to meet evidence thresholds for reclassification and can be prioritized for expert review, substantially expanding the pool of actionable variant classifications. The screen also reveals a cluster of gain-of-function variants in LDLR class A repeat 5, at least some of which enhance LDL-C uptake through increased apolipoprotein B interaction, with implications for therapeutic genome editing. Last, prime editing uniquely detects splice-altering coding variants missed by cDNA-based screens and pathogenicity predictors, revealing an advantage of endogenous variant installation. CONCLUSIONS: Altogether, activity-normalized prime editing provides a scalable framework for LDLR variant classification that substantially expands the proportion of variants with evidence for genetic diagnosis and reveals novel biology with therapeutic relevance.

CRISPR screening↗
PubMed2025

Extracting and calibrating evidence of variant pathogenicity from population biobank data.

Genomic medicine requires a robust evidence base of variant phenotypic impacts, which remains incomplete even in extensively studied genes with monogenic disease associations. Here, we evaluated the broad potential of using population cohort data to identify evidence that can be used in variant assessment. Across 41 genes related to 18 clinically actionable monogenic phenotypes, we calculated variant-level odds ratios of disease enrichment using data from 469,803 UK Biobank participants. We found significant differences in odds ratio values between ClinVar-labeled pathogenic and benign variants in 11 phenotypes, spanning both common and rare disorders. To facilitate clinical translation, we calibrated the strength of evidence provided by variant-level odds ratios to align with American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) interpretation guidelines (PS4 criterion) and found that odds ratios may reach "moderate," "strong," or "very strong" evidence, varying by phenotype and gene. Overall, we found that 2.6% (N = 12,350) of participants harbor a rare variant of uncertain significance (VUS) with at least moderate evidence of pathogenicity-an indication of potentially unrecognized disease risk. Finally, by incorporating computational and functional data alongside population-based odds ratios, we identified variants that met the criteria for clinical reclassification. Notably, using this approach, we identified that 12.4% of rare VUSs in LDLR seen in participants meet diagnostic criteria to be classified as likely pathogenic, demonstrating its potential to scale the reclassification of VUSs.

Humans↗
PubMed2006

An unsupervised classification method for inferring original case locations from low-resolution disease maps.

BACKGROUND: Widespread availability of geographic information systems software has facilitated the use of disease mapping in academia, government and private sector. Maps that display the address of affected patients are often exchanged in public forums, and published in peer-reviewed journal articles. As previously reported, a search of figure legends in five major medical journals found 19 articles from 1994-2004 that identify over 19,000 patient addresses. In this report, a method is presented to evaluate whether patient privacy is being breached in the publication of low-resolution disease maps. RESULTS: To demonstrate the effect, a hypothetical low-resolution map of geocoded patient addresses was created and the accuracy with which patient addresses can be resolved is described. Through georeferencing and unsupervised classification of the original image, the method precisely re-identified 26% (144/550) of the patient addresses from a presentation quality map and 79% (432/550) from a publication quality map. For the presentation quality map, 99.8% of the addresses were within 70 meters (approximately one city block length) of the predicted patient location, 51.6% of addresses were identified within five buildings, 70.7% within ten buildings and 93% within twenty buildings. For the publication quality map, all addresses were within 14 meters and 11 buildings of the predicted patient location. CONCLUSION: This study demonstrates that lowering the resolution of a map displaying geocoded patient addresses does not sufficiently protect patient addresses from re-identification. Guidelines to protect patient privacy, including those of medical journals, should reflect policies that ensure privacy protection when spatial data are displayed or published.

Boston↗
PubMed2005

A context-sensitive approach to anonymizing spatial surveillance data: impact on outbreak detection.

OBJECTIVE: The use of spatially based methods and algorithms in epidemiology and surveillance presents privacy challenges for researchers and public health agencies. We describe a novel method for anonymizing individuals in public health data sets by transposing their spatial locations through a process informed by the underlying population density. Further, we measure the impact of the skew on detection of spatial clustering as measured by a spatial scanning statistic. DESIGN: Cases were emergency department (ED) visits for respiratory illness. Baseline ED visit data were injected with artificially created clusters ranging in magnitude, shape, and location. The geocoded locations were then transformed using a de-identification algorithm that accounts for the local underlying population density. MEASUREMENTS: A total of 12,600 separate weeks of case data with artificially created clusters were combined with control data and the impact on detection of spatial clustering identified by a spatial scan statistic was measured. RESULTS: The anonymization algorithm produced an expected skew of cases that resulted in high values of data set k-anonymity. De-identification that moves points an average distance of 0.25 km lowers the spatial cluster detection sensitivity by less than 4% and lowers the detection specificity less than 1%. CONCLUSION: A population-density-based Gaussian spatial blurring markedly decreases the ability to identify individuals in a data set while only slightly decreasing the performance of a standardly used outbreak detection tool. These findings suggest new approaches to anonymizing data for spatial epidemiology and surveillance.

Algorithms↗
PubMed2005

A software tool for creating simulated outbreaks to benchmark surveillance systems.

BACKGROUND: Evaluating surveillance systems for the early detection of bioterrorism is particularly challenging when systems are designed to detect events for which there are few or no historical examples. One approach to benchmarking outbreak detection performance is to create semi-synthetic datasets containing authentic baseline patient data (noise) and injected artificial patient clusters, as signal. METHODS: We describe a software tool, the AEGIS Cluster Creation Tool (AEGIS-CCT), that enables users to create simulated clusters with controlled feature sets, varying the desired cluster radius, density, distance, relative location from a reference point, and temporal epidemiological growth pattern. AEGIS-CCT does not require the use of an external geographical information system program for cluster creation. The cluster creation tool is an open source program, implemented in Java and is freely available under the Lesser GNU Public License at its Sourceforge website. Cluster data are written to files or can be appended to existing files so that the resulting file will include both existing baseline and artificially added cases. Multiple cluster file creation is an automated process in which multiple cluster files are created by varying a single parameter within a user-specified range. To evaluate the output of this software tool, sets of test clusters were created and graphically rendered. RESULTS: Based on user-specified parameters describing the location, properties, and temporal pattern of simulated clusters, AEGIS-CCT created clusters accurately and uniformly. CONCLUSION: AEGIS-CCT enables the ready creation of datasets for benchmarking outbreak detection systems. It may be useful for automating the testing and validation of spatial and temporal cluster detection algorithms.

Algorithms↗
PubMed2005

Reverse geocoding: concerns about patient confidentiality in the display of geospatial health data.

Widespread availability geographic information systems (GIS) software has facilitated the use health mapping in both academia and government. Maps that display patients as points are often exchanged in public forums (journals, meetings, web). However,even these low resolution maps may reveal confidential patient location information. In this report, we describe a method to test whether privacy is being breached. We reverse geocode from maps with cases and describe the accuracy with which patient addresses can be extracted.

Confidentiality↗