Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift towards modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g. Coot) and fit can be further improved by refinement using standard pipelines (e.g. Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

Refinement of multiconformer ensemble models from multi-temperature X-ray diffraction data

Conformational ensembles underlie all protein functions. Thus, acquiring atomic-level ensemble models that accurately represent conformational heterogeneity is vital to deepen our understanding of how proteins work. Modeling ensemble information from X-ray diffraction data has been challenging, as traditional cryo-crystallography restricts conformational variability while minimizing radiation damage. Recent advances have enabled the collection of high quality diffraction data at ambient temperatures, revealing innate conformational heterogeneity and temperature-driven changes. Here, we used diffraction datasets for Proteinase K collected at temperatures ranging from 313 to 363 K to provide a tutorial for the refinement of multiconformer ensemble models. Integrating automated sampling and refinement tools with manual adjustments, we obtained multiconformer models that describe alternative backbone and sidechain conformations, their relative occupancies, and interconnections between conformers. Our models revealed extensive and diverse conformational changes across temperature, including increased bound peptide ligand occupancies, different Ca2+ binding site configurations and altered rotameric distributions. These insights emphasize the value and need for multiconformer model refinement to extract ensemble information from diffraction data and to understand ensemble-function relationships.

Refinement of Multiconformer Ensemble Models from Multi-temperature X-ray Diffraction Data

Conformational ensembles underlie all protein functions. Thus, acquiring atomic-level ensemble models that accurately represent conformational heterogeneity is vital to deepen our understanding of how proteins work. Modeling ensemble information from X-ray diffraction data has been challenging, as traditional cryo-crystallography restricts conformational variability while minimizing radiation damage. Recent advances have enabled the collection of high quality diffraction data at ambient temperatures, revealing innate conformational heterogeneity and temperature-driven changes. Here, we used diffraction datasets for Proteinase K collected at temperatures ranging from 313 to 363K to provide a tutorial for the refinement of multiconformer ensemble models. Integrating automated sampling and refinement tools with manual adjustments, we obtained multiconformer models that describe alternative backbone and sidechain conformations, their relative occupancies, and interconnections between conformers. Our models revealed extensive and diverse conformational changes across temperature, including increased bound peptide ligand occupancies, different Ca2+ binding site configurations and altered rotameric distributions. These insights emphasize the value and need for multiconformer model refinement to extract ensemble information from diffraction data and to understand ensemble-function relationships.

Ligand binding remodels protein side-chain conformational heterogeneity

While protein conformational heterogeneity plays an important role in many aspects of biological function, including ligand binding, its impact has been difficult to quantify. Macromolecular X-ray diffraction is commonly interpreted with a static structure, but it can provide information on both the anharmonic and harmonic contributions to conformational heterogeneity. Here, through multiconformer modeling of time- and space-averaged electron density, we measure conformational heterogeneity of 743 stringently matched pairs of crystallographic datasets that reflect unbound/apo and ligand-bound/holo states. When comparing the conformational heterogeneity of side chains, we observe that when binding site residues become more rigid upon ligand binding, distant residues tend to become more flexible, especially in non-solvent-exposed regions. Among ligand properties, we observe increased protein flexibility as the number of hydrogen bonds decreases and relative hydrophobicity increases. Across a series of 13 inhibitor-bound structures of CDK2, we find that conformational heterogeneity is correlated with inhibitor features and identify how conformational changes propagate differences in conformational heterogeneity away from the binding site. Collectively, our findings agree with models emerging from nuclear magnetic resonance studies suggesting that residual side-chain entropy can modulate affinity and point to the need to integrate both static conformational changes and conformational heterogeneity in models of ligand binding.

Molecular features of exceptional response to neoadjuvant anti-androgen therapy in high-risk localized prostate cancer

High-risk localized prostate cancer (HRLPC) is associated with a substantial risk of recurrence and disease mortality. Recent clinical trials have shown that intensifying anti-androgen therapies administered before prostatectomy can induce pathologic complete responses or minimal residual disease, called exceptional response, although the molecular determinants of these clinical outcomes are largely unknown. Here, we perform whole-exome and transcriptome sequencing on pre-treatment multi-regional tumor biopsies from exceptional responders (ERs) and non-responders (NRs, pathologic T3 or lymph node-positive disease) to intensive neoadjuvant anti-androgen therapies. Clonal SPOP mutation and SPOPL copy-number loss are exclusively observed in ERs, while clonal TP53 mutation and PTEN copy-number loss are exclusively observed in NRs. Transcriptional programs involving androgen signaling and TGF-β signaling are enriched in ERs and NRs, respectively. These findings may guide prospective validation studies of these molecular features in large HRLPC clinical cohorts treated with neoadjuvant anti-androgens to improve patient stratification.

Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge

This paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density.

qFit 3: Protein and ligand multiconformer modeling for X-ray crystallographic and single-particle cryo-EM density maps

New X-ray crystallography and cryo-electron microscopy (cryo-EM) approaches yield vast amounts of structural data from dynamic proteins and their complexes. Modeling the full conformational ensemble can provide important biological insights, but identifying and modeling an internally consistent set of alternate conformations remains a formidable challenge. qFit efficiently automates this process by generating a parsimonious multiconformer model. We refactored qFit from a distributed application into software that runs efficiently on a small server, desktop, or laptop. We describe the new qFit 3 software and provide some examples. qFit 3 is open-source under the MIT license, and is available at https://github.com/ExcitedStates/qfit-3.0.

Genomic Predictors of Good Outcome, Recurrence, or Progression in High-Grade T1 Non-Muscle-Invasive Bladder Cancer

High-grade T1 (HGT1) bladder cancer is the highest risk subtype of non-muscle-invasive bladder cancer with unpredictable outcome and poorly understood risk factors. Here, we examined the association of somatic mutation profiles with nonrecurrent disease (GO, good outcome), recurrence (R), or progression (PD) in a cohort of HGT1 patients. Exome sequencing was performed on 62 HGT1 and 15 matched normal tissue samples. Both tumor only (TO) and paired analyses were performed, focusing on 95 genes known to be mutated in bladder cancer. Somatic mutations, copy-number alterations, mutation load, and mutation signatures were studied. Thirty-three GO, 10 R, 18 PD, and 1 unknown outcome patients were analyzed. Tumor mutational burden (TMB) was similar to muscle-invasive disease and was highest in GO, intermediate in PD, and lowest in R patients (P = 0.017). DNA damage response gene mutations were associated with higher TMB (P < 0.0001) and GO (P = 0.003). ERCC2 and BRCA2 mutations were associated with GO. TP53, ATM, ARID1A, AHR, and SMARCB1 mutations were more frequent in PD. Focal copy-number gain in CCNE1 and CDKN2A deletion was enriched in PD or R (P = 0.047; P = 0.06). APOBEC (46%) and COSMIC5 (34%) signatures were most frequent. APOBEC-A and ERCC2 mutant tumors (COSMIC5) were associated with GO (P = 0.047; P = 0.0002). pT1b microstaging was associated with a genomic cluster (P = 0.05) with focal amplifications of E2F3/SOX4, PVRL4, CCNE1, and TP53 mutations. Findings were validated using external public datasets. These findings require confirmation but suggest that management of HGT1 bladder cancer may be improved via molecular characterization to predict outcome. SIGNIFICANCE: Detailed genetic analyses of HGT1 bladder tumors identify features that correlate with outcome, e.g., high mutational burden, ERCC2 mutations, and high APOBEC-A/ERCC2 mutation signatures were associated with good outcome.

Robust Sequence Determinants of α-Synuclein Toxicity in Yeast Implicate Membrane Binding

Protein conformations are shaped by cellular environments, but how environmental changes alter the conformational landscapes of specific proteins in vivo remains largely uncharacterized, in part due to the challenge of probing protein structures in living cells. Here, we use deep mutational scanning to investigate how a toxic conformation of α-synuclein, a dynamic protein linked to Parkinson's disease, responds to perturbations of cellular proteostasis. In the context of a course for graduate students in the UCSF Integrative Program in Quantitative Biology, we screened a comprehensive library of α-synuclein missense mutants in yeast cells treated with a variety of small molecules that perturb cellular processes linked to α-synuclein biology and pathobiology. We found that the conformation of α-synuclein previously shown to drive yeast toxicity-an extended, membrane-bound helix-is largely unaffected by these chemical perturbations, underscoring the importance of this conformational state as a driver of cellular toxicity. On the other hand, the chemical perturbations have a significant effect on the ability of mutations to suppress α-synuclein toxicity. Moreover, we find that sequence determinants of α-synuclein toxicity are well described by a simple structural model of the membrane-bound helix. This model predicts that α-synuclein penetrates the membrane to constant depth across its length but that membrane affinity decreases toward the C terminus, which is consistent with orthogonal biophysical measurements. Finally, we discuss how parallelized chemical genetics experiments can provide a robust framework for inquiry-based graduate coursework.

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

A newly described coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease 2019 (COVID-19), has infected over 2.3 million people, led to the death of more than 160,000 individuals and caused worldwide social and economic disruption1,2. There are no antiviral drugs with proven clinical efficacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and efforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry, identifying 332 high-confidence protein-protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 receptors. Further studies of these host-factor-targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19.

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

A newly described coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease 2019 (COVID-19), has infected over 2.3 million people, led to the death of more than 160,000 individuals and caused worldwide social and economic disruption^1,2. There are no antiviral drugs with proven clinical efficacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and efforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry, identifying 332 high-confidence protein-protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 receptors. Further studies of these host-factor-targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19.

A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing

An outbreak of the novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 290,000 people since the end of 2019, killed over 12,000, and caused worldwide social and economic disruption 1,2 . There are currently no antiviral drugs with proven efficacy nor are there vaccines for its prevention. Unfortunately, the scientific community has little knowledge of the molecular details of SARS-CoV-2 infection. To illuminate this, we cloned, tagged and expressed 26 of the 29 viral proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), which identified 332 high confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 existing FDA-approved drugs, drugs in clinical trials and/or preclinical compounds, that we are currently evaluating for efficacy in live SARS-CoV-2 infection assays. The identification of host dependency factors mediating virus infection may provide key insights into effective molecular targets for developing broadly acting antiviral therapeutics against SARS-CoV-2 and other deadly coronavirus strains.

Integrative Molecular Characterization of Resistance to Neoadjuvant Chemoradiation in Rectal Cancer

PURPOSE

Molecular properties associated with complete response or acquired resistance to concurrent chemotherapy and radiotherapy (CRT) are incompletely characterized.Experimental Design: We performed integrated whole-exome/transcriptome sequencing and immune infiltrate analysis on rectal adenocarcinoma tumors prior to neoadjuvant CRT (pre-CRT) and at time of resection (post-CRT) in 17 patients [8 complete/partial responders, 9 nonresponders (NR)].

RESULTS

CRT was not associated with increased tumor mutational burden or neoantigen load and did not alter the distribution of established somatic tumor mutations in rectal cancer. Concurrent KRAS/TP53 mutations (KP) associated with NR tumors and were enriched for an epithelial-mesenchymal transition transcriptional program. Furthermore, NR was associated with reduced CD4/CD8 T-cell infiltrates and a post-CRT M2 macrophage phenotype. Absence of any local tumor recurrences, KP/NR status predicted worse progression-free survival, suggesting that local immune escape during or after CRT with specific genomic features contributes to distant progression.

CONCLUSIONS

Overall, while CRT did not impact genomic profiles, CRT impacted the tumor immune microenvironment, particularly in resistant cases.

Everolimus and pazopanib (E/P) benefit genomically selected patients with metastatic urothelial carcinoma

BACKGROUND

Metastatic urothelial carcinoma (mUC) is a genomically diverse disease with known alterations in the mTOR pathway and tyrosine kinases including FGFR. We investigated the efficacy and safety of combination treatment with everolimus and pazopanib (E/P) in genomically profiled patients with mUC.

METHODS

mUC patients enrolled on a Phase I dose escalation study and an expansion cohort treated with E/P were included. The primary end point was objective response rate (ORR); secondary end points were safety, duration of response (DOR), progression-free survival (PFS) and overall survival (OS). Patients were assessed for mutations and copy number alterations in 300 relevant cancer-associated genes using next-generation sequencing and findings were correlated with outcomes. Time-to-event data were estimated with Kaplan-Meier methods.

RESULTS

Of the 23 patients enrolled overall, 19 had mUC. ORR was 21% (one complete response (CR), three partial responses (PR), eight with stable disease (SD). DOR, PFS and OS were 6.5, 3.6, and 9.1 months, respectively. Four patients with clinical benefit (one CR, two PR, one SD) had mutations in TSC1/TSC2 or mTOR and a 5th patient with PR had a FGFR3-TACC3 fusion.

CONCLUSIONS

Combination therapy with E/P is safe in mUC and select patients with alterations in mTOR or FGFR pathways derive significant clinical benefit.

Structural Alterations Driving Castration-Resistant Prostate Cancer Revealed by Linked-Read Genome Sequencing

Nearly all prostate cancer deaths are from metastatic castration-resistant prostate cancer (mCRPC), but there have been few whole-genome sequencing (WGS) studies of this disease state. We performed linked-read WGS on 23 mCRPC biopsy specimens and analyzed cell-free DNA sequencing data from 86 patients with mCRPC. In addition to frequent rearrangements affecting known prostate cancer genes, we observed complex rearrangements of the AR locus in most cases. Unexpectedly, these rearrangements include highly recurrent tandem duplications involving an upstream enhancer of AR in 70%-87% of cases compared with <2% of primary prostate cancers. A subset of cases displayed AR or MYC enhancer duplication in the context of a genome-wide tandem duplicator phenotype associated with CDK12 inactivation. Our findings highlight the complex genomic structure of mCRPC, nominate alterations that may inform prostate cancer treatment, and suggest that additional recurrent events in the non-coding mCRPC genome remain to be discovered.

Abstract 4350: Integrative and multiregional molecular analysis of localized, high grade prostate cancer treated with neoadjuvant androgen deprivation treatment

Neoadjuvant intense androgen deprivation treatment (ADT) in localized, high risk prostate cancer (HG PCa) has varied degrees of response. While some patients have an outstanding response with minimal residual disease [MRD; ≤0.5mm of tumor at radical prostatectomy (RP)], others have no response but delay potential curative treatment. Previous studies have demonstrated that HG PCa is heterogenous, however previous studies have not investigated whether heterogeneity itself or specific genetic molecular features correlate with response to intense neoadjuvant ADT. Herein, we provide an integrated molecular analysis of the spatial heterogeneity of HG PCa treated with neoadjuvant ADT. We examined 53 pre-treatment biopsy samples (n=35 biopsies containing tumor, n=18 normal prostate tissue) from 14 patients with matched blood normals who were treated with an intense neoadjuvant regimen of lupron plus abiraterone/enzalutamide. Patients were stratified as exceptional responders (n=8; MRD at RP) or non-responders (n=6; pT3 or lymph node positive at RP). We performed whole exome and whole transcriptome sequencing on multiple, spatially heterogeneous biopsies within each patient to examine the role of heterogeneity in response to therapy. We called somatic and germline variants, inferred mutational clonality, phylogenetic relationships, called genetic fusion, and analyzed differentially expressed genes. After QC, we observed well known prostate driver mutations (SPOP, ATM, TP53, FOXA1, PTEN, and APC) across the entire cohort (n=14 patients). Within each individual, all driver mutations were clonal across tumor cores, but the proportion of all mutations that were always clonal varied widely (0.08-0.57), median 0.3). Among known drivers, SPOP mutations were only observed in exceptional responders (4/8 v. 0/6), and TP53 and PTEN mutations were only observed in non-responders (4/6 v. 0/8). Responders and non-responders did not differ by mutational or copy number burden. Mutational heterogeneity varied greatly between samples, however this did not split between responders and non-responders. In terms of gene expression, exceptional responders showed upregulation of androgen and estrogen response pathways, while non-responders had increased expression of E2F targets and cell cycle pathways. We confirmed that a subset of localized, HG PCa are molecularly heterogeneous in terms of mutations and expression. We are presently underpowered to explain the genomic differences between excpetional responders and non-responders. However, molecular heterogeneous and homogenous HG PCa tumors can respond to intense neoadjuvant ADT therapy. We are currently analyzing a larger cohort.

Citation Format: Stephanie A. Wankowicz, Michaela Bowden, Rosina Lis, Claire Margolis, Dimitri Livitz, Ignaty Leshchiner, David Liu, Meng Xiao He, Zhenwei Zhang, Gad Getz, Mary-Ellen Taplin, Eliezer Van Allen. Integrative and multiregional molecular analysis of localized, high grade prostate cancer treated with neoadjuvant androgen deprivation treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4350.

Targeted genomic landscape of metastases compared to primary tumours in clear cell metastatic renal cell carcinoma

Background

The genomic landscape of primary clear cell renal cell carcinoma (ccRCC) has been well described. However, little is known about cohort genomic alterations (GA) landscape in ccRCC metastases, or how it compares to primary tumours in aggregate. The genomic landscape of metastases may have biological, clinical, and therapeutic implications.

Methods

We collected targeted next-generation sequencing mutation calls from two independent cohorts and described the metastases GA landscape and descriptively compared it to the GA landscape in primary tumours.

Results

The cohort 1 (n = 578) consisted of 349 primary tumours and 229 metastases. Overall, the most common mutations in the metastases were VHL (66.8%), PBRM1 (41.87%), and SETD2 (24.7%). TP53 was more frequently mutated in metastases compared to primary tumours (14.85% versus 8.9%; p = 0.031). No other gene had significant difference in the cohort frequency of mutations between the metastases and primary tumours. Mutation burden was not significantly different between the metastases and primary tumours or between metastatic sites. The second cohort (n = 257) consisted of 177 primary tumours and 80 metastases. No differences in frequency of mutations or mutational burden were observed between primaries and metastases.

Conclusions

These data support the theory that ccRCC primary tumours and metastases encompass a uniform distribution of common genomic alterations tested by next-generation sequencing targeted panels. This study does not address variability between matched primary tumours and metastases or the change in genomic alterations over time and after sequential systemic therapies.

Genomic evolution and acquired resistance to preoperative chemoradiation therapy (CRT) in rectal cancer

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Background: Following pre-operative CRT for locally advanced rectal cancer, ~20% of patients achieve a pathologic complete response and 20-40% have little to no response (NR) at time of surgery. Pathologic downstaging after CRT is associated with improved outcomes but molecular predictors of response are poorly understood. The objective of this study was to characterize somatic mutations and derive genomic biomarkers distinguishing complete and excellent partial responders (R) vs NR through whole exome sequencing (WES) in pre- and post-CRT tumor samples. Methods: Rectal cancer patients treated with pre-operative CRT and having either a R or NR were identified. DNA extraction from clinical FFPE samples and WES were performed on pre/post-CRT tumor and matched germline DNA followed by genomic analysis to identify somatic mutations, insertion/deletions, and clonal/subclonal mutational evolution. RNA extraction/sequencing and GSEA was performed in parallel. Results: We identified 34 pre- and post-CRT matched tumor samples from 17 patients treated with pre-operative fluoropyrimidine-based CRT (50.4 Gy) followed by surgery within 8-10 weeks. Nine and 8 patients were classified as NR and R, respectively. All FFPE passed quality control metrics and were successfully sequenced. Mean somatic mutational burden pre-CRT was 3.78 mutations/Mb (rg, 2.25-8.43), which, unexpectedly, was not significantly increased in the post-CRT samples (4.23 mutations/Mb; rg, 1.74-8.96). NR cases had more concurrent KRAS/TP53 (KP) mutations than R cases (67% vs 13%, p < 0.05). Related to this finding, NR was associated with epithelial-to-mesenchymal transition (p < 0.001) and potentially linked to high intra-tumoral heterogeneity pre-CRT (p = 0.08). Conclusions: We have established feasibility and a pipeline for performing WES on pre- and post-CRT FFPE tumor samples. Our data suggest that evolution of CRT resistance is not through hypermutation. In conjunction with recent observations from our group (Hong, JNCI 2017; Wang, Cancer Res 2017), our findings strongly support the clinical utility of the KP genotype as a biomarker of CRT resistance and begin to offer insight into the underlying mechanisms on a genomic level.