Accurate Classification of Cardiomyopathy Diagnosis by Chromatin Accessibility

Tissue-specific chromatin accessibility identifies active genomic regions, integrates genetic information with exposure history, provides unique signatures that remain stable over longer timescales than gene transcription, and requires limited input material.¹ Chromatin accessibility profiles have enabled precise molecular subtyping of a wide variety of tumors.² For cardiomyopathies, however, tissue biopsy is not routinely performed, and pathogenesis is most often determined by clinical information alone. Nevertheless, cardiologists are commonly faced with diagnostic dilemmas, such as a patient who has cardiomyopathy with obstructive coronary artery disease involving <3 vessels. Here, knowledge of cardiomyopathy pathogenesis would likely affect prognosis³ and guide management.⁴ A more precise molecular diagnosis could also help clinicians avoid incorrect diagnoses on the basis of clinical criteria alone, as recently described for hypertrophic cardiomyopathy (HCM).⁵ Here, we propose to leverage the rich information content of chromatin accessibility to improve the accuracy of cardiomyopathy classification.

Human heart specimens were obtained from the Southwest Transplant Alliance, the UTSW Cardiac Tissue Biobank (STU 092010-193), and the HCM patient registry (STU 082017-072) after approval by the local institutional review board and proper informed consent. Healthy specimens were obtained from donor hearts rejected for cardiac transplantation, and neither the underlying diagnosis nor genetic pathogenesis was established in patients with nonischemic cardiomyopathy (NICM). Disease specimens were matched with healthy controls, although one unmatched healthy specimen was retained in our final analysis attributable to the quality control failure of individual cardiomyopathy samples. The assay for transposase-accessible chromatin with sequencing was performed on cardiomyocyte nuclei.² Sequencing reads were processed and mapped to the reference GRCh38 human genome. A Random Forest Classifier was built using uniquely accessible regions with the number of trees set to 1000. Leave-one-out cross-validation was performed with 100 iterations of leaving out 1 replicate pair for testing and using the remaining samples to build the classifier. Anonymized data have been made publicly available at dbGaP (https://www.ncbi.nlm.nih.gov/gap/).

We profiled chromatin accessibility (Figure A and B) in 21 individuals (6 healthy; 5 ischemic cardiomyopathy; 5 NICM; 5 HCM). Uniform Manifold Approximation and Projection analysis of chromatin accessibility resolved healthy and cardiomyopathy samples by diagnosis (Figure C). In a 2-way comparison of healthy versus cardiomyopathy samples, 1066 regions were differentially accessible (Figure D). Focusing on uniquely accessible genomic regions, we generated a heat map to highlight differential accessibility between healthy tissue and individual cardiomyopathy subtypes (Figure E). Analysis of coaccessible loci from each group yielded transcription factor motifs and potential downstream target genes (Figure F). On the basis of this analysis, we speculate that MEF2 (myocyte enhancer factor-2) transcription factors activate contractile gene expression in ischemic cardiomyopathy, nuclear hormone receptors influence cellular adhesion programs in NICM, and ETS (E twenty-six domain) transcription factors activate aberrant developmental processes in HCM.

Next, we sought to improve cardiomyopathy discrimination by using only the focused set of chromatin accessibility peaks from Figure E. Uniform Manifold Approximation and Projection analysis showed that the refined subset of peaks improved cardiomyopathy classification (Figure G; compare with Figure C). Consequently, we trained a Random Forest Classifier to nominate a cardiomyopathy diagnosis on the basis of the informative subset of chromatin accessibility peaks (Figure H). Leave-one-out cross-validation was performed across all samples, and the area under the receiver-operator curve demonstrated excellent algorithm performance (Figure I; red line). Given that clinical benchtop sequencers can generate 20 million reads within hours, we repeated the leave-one-out cross-validation analysis after random selection of 20 million reads from each sample (ie, downsampling) and observed little drop-off in algorithm performance (Figure I; blue line).

Last, we present 3 simulated applications using the diagnostic algorithm with 20 million randomly selected reads per sample. First, chromatin accessibility was profiled in a single replicate for a patient who had HCM with limited tissue availability (Figure J), which was consistent with internal validations (Figure H). Second, we analyzed specimens from a patient with NICM before and after left ventricular assist device implantation. This example confirmed the pathogenesis in the pre–left ventricular assist device sample and suggested that left ventricular assist device treatment alters chromatin accessibility (Figure J). Third, we highlight a case in which ischemic cardiomyopathy was diagnosed by coronary angiography before bypass surgery, yet chromatin accessibility at transplant favored an NICM classification (Figure J). Consistent with this observation, the patient developed 4-chamber enlargement without regional wall motion abnormalities between bypass surgery and heart transplant, suggesting a superimposed NICM, although definitive clinical evidence is lacking.

In conclusion, our study establishes proof-of-concept for a cardiomyopathy diagnostic algorithm using chromatin accessibility signatures at a sequencing depth achievable by benchtop instruments. Nonetheless, 2 important limitations deserve mention. First, all specimens were taken from the left ventricle to ensure matched anatomical locations, rather than the conventional right ventricular biopsy site. Second, a small number of samples were included in the study, so future external validation is necessary to confirm these findings. If replicated, however, the proposed strategy could enable rapid diagnosis (Figure K) and facilitate precision medicine approaches for patients with cardiomyopathy.