Genetic variation meets replication origins.

Genome replication programs are highly orchestrated. In this issue, Koren and colleagues leverage whole-genome sequencing data to discover that DNA replication timing patterns differ between individuals and are associated with genetic variants. These so-called rtQTLs represent a new form of quantitative trait loci that may influence gene dosage and mutational frequency and provide mechanistic insights into the regulation of DNA replication.

Identifying Optimal Loci for the Molecular Diagnosis of Microsatellite Instability.

BACKGROUND: Microsatellite instability (MSI) predicts oncological response to checkpoint blockade immunotherapies. Although microsatellite mutation is pathognomonic for the condition, loci have unequal diagnostic value for predicting MSI within and across cancer types. METHODS: To better inform molecular diagnosis of MSI, we examined 9438 tumor-normal exome pairs and 901 whole genome sequence pairs from 32 different cancer types and cataloged genome-wide microsatellite instability events. Using a statistical framework, we identified microsatellite mutations that were predictive of MSI within and across cancer types. The diagnostic accuracy of different subsets of maximally informative markers was estimated computationally using a dedicated validation set. RESULTS: Twenty-five cancer types exhibited hypermutated states consistent with MSI. Recurrently mutated microsatellites associated with MSI were identifiable in 15 cancer types, but were largely specific to individual cancer types. Cancer-specific microsatellite panels of 1 to 7 loci were needed to attain ≥95% diagnostic sensitivity and specificity for 11 cancer types, and in 8 of the cancer types, 100% sensitivity and specificity were achieved. Breast cancer required 800 loci to achieve comparable performance. We were unable to identify recurrent microsatellite mutations supporting reliable MSI diagnosis in ovarian tumors. Features associated with informative microsatellites were cataloged. CONCLUSIONS: Most microsatellites informative for MSI are specific to particular cancer types, requiring the use of tissue-specific loci for optimal diagnosis. Limited numbers of markers are needed to provide accurate MSI diagnosis in most tumor types, but it is challenging to diagnose breast and ovarian cancers using predefined microsatellite locus panels.

Saturation editing of genomic regions by multiplex homology-directed repair.

Saturation mutagenesis--coupled to an appropriate biological assay--represents a fundamental means of achieving a high-resolution understanding of regulatory and protein-coding nucleic acid sequences of interest. However, mutagenized sequences introduced in trans on episomes or via random or "safe-harbour" integration fail to capture the native context of the endogenous chromosomal locus. This shortcoming markedly limits the interpretability of the resulting measurements of mutational impact. Here, we couple CRISPR/Cas9 RNA-guided cleavage with multiplex homology-directed repair using a complex library of donor templates to demonstrate saturation editing of genomic regions. In exon 18 of BRCA1, we replace a six-base-pair (bp) genomic region with all possible hexamers, or the full exon with all possible single nucleotide variants (SNVs), and measure strong effects on transcript abundance attributable to nonsense-mediated decay and exonic splicing elements. We similarly perform saturation genome editing of a well-conserved coding region of an essential gene, DBR1, and measure relative effects on growth that correlate with functional impact. Measurement of the functional consequences of large numbers of mutations with saturation genome editing will potentially facilitate high-resolution functional dissection of both cis-regulatory elements and trans-acting factors, as well as the interpretation of variants of uncertain significance observed in clinical sequencing.

Accurate Pan-Cancer Molecular Diagnosis of Microsatellite Instability by Single-Molecule Molecular Inversion Probe Capture and High-Throughput Sequencing.

BACKGROUND: Microsatellite instability (MSI) is an emerging actionable phenotype in oncology that informs tumor response to immune checkpoint pathway immunotherapy. However, there remains a need for MSI diagnostics that are low cost, highly accurate, and generalizable across cancer types. We developed a method for targeted high-throughput sequencing of numerous microsatellite loci with pan-cancer informativity for MSI using single-molecule molecular inversion probes (smMIPs). METHODS: We designed a smMIP panel targeting 111 loci highly informative for MSI across cancers. We developed an analytical framework taking advantage of smMIP-mediated error correction to specifically and sensitively detect instability events without the need for typing matched normal material. RESULTS: Using synthetic DNA mixtures, smMIPs were sensitive to at least 1% MSI-positive cells and were highly consistent across replicates. The fraction of identified unstable microsatellites discriminated tumors exhibiting MSI from those lacking MSI with high accuracy across colorectal (100% diagnostic sensitivity and specificity), prostate (100% diagnostic sensitivity and specificity), and endometrial cancers (95.8% diagnostic sensitivity and 100% specificity). MSI-PCR, the current standard-of-care molecular diagnostic for MSI, proved equally robust for colorectal tumors but evidenced multiple false-negative results in prostate (81.8% diagnostic sensitivity and 100% specificity) and endometrial (75.0% diagnostic sensitivity and 100% specificity) tumors. CONCLUSIONS: smMIP capture provides an accurate, diagnostically sensitive, and economical means to diagnose MSI across cancer types without reliance on patient-matched normal material. The assay is readily scalable to large numbers of clinical samples, enables automated and quantitative analysis of microsatellite instability, and is readily standardized across clinical laboratories.

Identification of Novel Protein Expression Changes Following Cisplatin Treatment and Application to Combination Therapy.

Determining the effect of chemotherapeutic treatment on changes in protein expression can provide important targets for overcoming resistance. Due to challenges in simultaneously measuring large numbers of proteins, a paucity of data exists on global changes. To overcome these challenges, we utilized microwestern arrays that allowed us to measure the abundance and modification state of hundreds of cell signaling and transcription factor proteins in cells following drug exposure. HapMap lymphoblastoid cell lines (LCLs) were exposed to cisplatin, a chemotherapeutic agent commonly used to treat testicular, head and neck, non-small cell lung, and gynecological cancers. We evaluated the expression of 259 proteins following 2, 6, and 12 h of cisplatin treatment in two LCLs with discordant sensitivity to cisplatin. Of these 259 proteins, 66 displayed significantly different protein expression changes (p < 0.05). Fifteen of these proteins were evaluated in a second pair of LCLs with discordant sensitivities to cisplatin; six demonstrated significant differences in expression. We then evaluated a subset of 63 proteins in a second set of LCLs with discordant sensitivity, and 40% of those that were significant in the first pair were also significant in the second part with concordant directionality (p < 0.05). We functionally validated one of the top proteins identified, PDK1, and demonstrated a synergistic relationship between cisplatin and a PDK1 inhibitor in multiple lung cancer lines. This study highlights the potential for identifying novel targets through an understanding of cellular changes in protein expression and modification following drug treatments.

Identification and validation of genetic variants that influence transcription factor and cell signaling protein levels.

Many genetic variants associated with human disease have been found to be associated with alterations in mRNA expression. Although it is commonly assumed that mRNA expression changes will lead to consequent changes in protein levels, methodological challenges have limited our ability to test the degree to which this assumption holds true. Here, we further developed the micro-western array approach and globally examined relationships between human genetic variation and cellular protein levels. We collected more than 250,000 protein level measurements comprising 441 transcription factor and signaling protein isoforms across 68 Yoruba (YRI) HapMap lymphoblastoid cell lines (LCLs) and identified 12 cis and 160 trans protein level QTLs (pQTLs) at a false discovery rate (FDR) of 20%. Whereas up to two thirds of cis mRNA expression QTLs (eQTLs) were also pQTLs, many pQTLs were not associated with mRNA expression. Notably, we replicated and functionally validated a trans pQTL relationship between the KARS lysyl-tRNA synthetase locus and levels of the DIDO1 protein. This study demonstrates proof of concept in applying an antibody-based microarray approach to iteratively measure the levels of human proteins and relate these levels to human genome variation and other genomic data sets. Our results suggest that protein-based mechanisms might functionally buffer genetic alterations that influence mRNA expression levels and that pQTLs might contribute phenotypic diversity to a human population independently of influences on mRNA expression.

Protein quantitative trait loci identify novel candidates modulating cellular response to chemotherapy.

Annotating and interpreting the results of genome-wide association studies (GWAS) remains challenging. Assigning function to genetic variants as expression quantitative trait loci is an expanding and useful approach, but focuses exclusively on mRNA rather than protein levels. Many variants remain without annotation. To address this problem, we measured the steady state abundance of 441 human signaling and transcription factor proteins from 68 Yoruba HapMap lymphoblastoid cell lines to identify novel relationships between inter-individual protein levels, genetic variants, and sensitivity to chemotherapeutic agents. Proteins were measured using micro-western and reverse phase protein arrays from three independent cell line thaws to permit mixed effect modeling of protein biological replicates. We observed enrichment of protein quantitative trait loci (pQTLs) for cellular sensitivity to two commonly used chemotherapeutics: cisplatin and paclitaxel. We functionally validated the target protein of a genome-wide significant trans-pQTL for its relevance in paclitaxel-induced apoptosis. GWAS overlap results of drug-induced apoptosis and cytotoxicity for paclitaxel and cisplatin revealed unique SNPs associated with the pharmacologic traits (at p<0.001). Interestingly, GWAS SNPs from various regions of the genome implicated the same target protein (p<0.0001) that correlated with drug induced cytotoxicity or apoptosis (p ≤ 0.05). Two genes were functionally validated for association with drug response using siRNA: SMC1A with cisplatin response and ZNF569 with paclitaxel response. This work allows pharmacogenomic discovery to progress from the transcriptome to the proteome and offers potential for identification of new therapeutic targets. This approach, linking targeted proteomic data to variation in pharmacologic response, can be generalized to other studies evaluating genotype-phenotype relationships and provide insight into chemotherapeutic mechanisms.

Genetic Variants Contributing to Colistin Cytotoxicity: Identification of TGIF1 and HOXD10 Using a Population Genomics Approach.

Colistin sulfate (polymixin E) is an antibiotic prescribed with increasing frequency for severe Gram-negative bacterial infections. As nephrotoxicity is a common side effect, the discovery of pharmacogenomic markers associated with toxicity would benefit the utility of this drug. Our objective was to identify genetic markers of colistin cytotoxicity that were also associated with expression of key proteins using an unbiased, whole genome approach and further evaluate the functional significance in renal cell lines. To this end, we employed International HapMap lymphoblastoid cell lines (LCLs) of Yoruban ancestry with known genetic information to perform a genome-wide association study (GWAS) with cellular sensitivity to colistin. Further association studies revealed that single nucleotide polymorphisms (SNPs) associated with gene expression and protein expression were significantly enriched in SNPs associated with cytotoxicity (p ≤ 0.001 for gene and p = 0.015 for protein expression). The most highly associated SNP, chr18:3417240 (p = 6.49 × 10-8), was nominally a cis-expression quantitative trait locus (eQTL) of the gene TGIF1 (transforming growth factor ß (TGFß)-induced factor-1; p = 0.021) and was associated with expression of the protein HOXD10 (homeobox protein D10; p = 7.17 × 10-5). To demonstrate functional relevance in a murine colistin nephrotoxicity model, HOXD10 immunohistochemistry revealed upregulated protein expression independent of mRNA expression in response to colistin administration. Knockdown of TGIF1 resulted in decreased protein expression of HOXD10 and increased resistance to colistin cytotoxicity. Furthermore, knockdown of HOXD10 in renal cells also resulted in increased resistance to colistin cytotoxicity, supporting the physiological relevance of the initial genomic associations.

Enhanced prediction of Src homology 2 (SH2) domain binding potentials using a fluorescence polarization-derived c-Met, c-Kit, ErbB, and androgen receptor interactome.

Many human diseases are associated with aberrant regulation of phosphoprotein signaling networks. Src homology 2 (SH2) domains represent the major class of protein domains in metazoans that interact with proteins phosphorylated on the amino acid residue tyrosine. Although current SH2 domain prediction algorithms perform well at predicting the sequences of phosphorylated peptides that are likely to result in the highest possible interaction affinity in the context of random peptide library screens, these algorithms do poorly at predicting the interaction potential of SH2 domains with physiologically derived protein sequences. We employed a high throughput interaction assay system to empirically determine the affinity between 93 human SH2 domains and phosphopeptides abstracted from several receptor tyrosine kinases and signaling proteins. The resulting interaction experiments revealed over 1000 novel peptide-protein interactions and provided a glimpse into the common and specific interaction potentials of c-Met, c-Kit, GAB1, and the human androgen receptor. We used these data to build a permutation-based logistic regression classifier that performed considerably better than existing algorithms for predicting the interaction potential of several SH2 domains.

Investigating models of protein function and allostery with a widespread mutational analysis of a light-activated protein.

To investigate the relationship between a protein's sequence and its biophysical properties, we studied the effects of more than 100 mutations in Avena sativa light-oxygen-voltage domain 2, a model protein of the Per-Arnt-Sim family. The A. sativa light-oxygen-voltage domain 2 undergoes a photocycle with a conformational change involving the unfolding of the terminal helices. Whereas selection studies typically search for winners in a large population and fail to characterize many sites, we characterized the biophysical consequences of mutations throughout the protein using NMR, circular dichroism, and ultraviolet/visible spectroscopy. Despite our intention to introduce highly disruptive substitutions, most had modest or no effect on function, and many could even be considered to be more photoactive. Substitutions at evolutionarily conserved sites can have minimal effect, whereas those at nonconserved positions can have large effects, contrary to the view that the effects of mutations, especially at conserved positions, are predictable. Using predictive models, we found that the effects of mutations on biophysical function and allostery reflect a complex mixture of multiple characteristics including location, character, electrostatics, and chemistry.

Targeted protein-omic methods are bridging the gap between proteomic and hypothesis-driven protein analysis approaches.

While proteomic methods have illuminated many areas of biological protein space, many fundamental questions remain with regard to systems-level relationships between mRNAs, proteins and cell behaviors. While mass spectrometric methods offer a panoramic picture of the relative expression and modification of large numbers of proteins, they are neither optimal for the analysis of predefined targets across large numbers of samples nor for assessing differences in proteins between individual cells or cell compartments. Conversely, traditional antibody-based methods are effective at sensitively analyzing small numbers of proteins across small numbers of conditions, and can be used to analyze relative differences in protein abundance and modification between cells and cell compartments. However, traditional antibody-based approaches are not optimal for analyzing large numbers of protein abundances and modifications across many samples. In this article, we will review recent advances in methodologies and philosophies behind several microarray-based, intermediate-level, 'protein-omic' methods, including a focus on reverse-phase lysate arrays and micro-western arrays, which have been helpful for bridging gaps between large- and small-scale protein analysis approaches and have provided insight into the roles that protein systems play in several biological processes.

Antitumor Potency of an Anti-CD19 Chimeric Antigen Receptor T-Cell Therapy, Lisocabtagene Maraleucel in Combination With Ibrutinib or Acalabrutinib.

Chimeric antigen receptor (CAR) T-cell therapy is a promising treatment for patients with CD19 B-cell malignancies. Combination strategies that improve CAR T-cell potency, limit tumor environment-mediated immune dysfunction, and directly reduce tumor burden may increase the potential for durable clinical benefit of CAR T-cell therapy. Lisocabtagene maraleucel (liso-cel) is a product therapy candidate being tested in patients with relapsed/refractory non-Hodgkin lymphoma or chronic lymphocytic leukemia. This study assessed the in vitro and in vivo functionality of CAR T cells transduced to express the anti-CD19 CAR of liso-cel in combination with ibrutinib or acalabrutinib. In prolonged stimulation assays, the presence of ibrutinib or acalabrutinib improved the CAR T-cell effector function. RNA-Seq analysis and surface marker profiling of these CAR T cells treated with ibrutinib but not acalabrutinib revealed gene expression changes consistent with skewing toward a memory-like, type 1 T-helper, Bruton tyrosine kinase phenotype. Ibrutinib or acalabrutinib improved CD19 tumor clearance and prolonged survival of tumor-bearing mice when used in combination with CAR T cells. A combination of the defined cell product therapy candidate, liso-cel, with ibrutinib or acalabrutinib is an attractive approach that may potentiate the promising clinical responses already achieved in CD19 B-cell malignancies with each of these single agents.

Anti-B-cell Maturation Antigen Chimeric Antigen Receptor T cell Function against Multiple Myeloma Is Enhanced in the Presence of Lenalidomide.

Anti-B-cell maturation antigen (BCMA) chimeric antigen receptor (CAR) T cells have shown promising clinical responses in patients with relapsed/refractory multiple myeloma. Lenalidomide, an immunomodulatory drug, potentiates T cell functionality, drives antimyeloma activity, and alters the suppressive microenvironment; these properties may effectively combine with anti-BCMA CAR T cells to enhance function. Using an anti-BCMA CAR T, we demonstrated that lenalidomide enhances CAR T cell function in a concentration-dependent manner. Lenalidomide increased CAR T effector cytokine production, particularly under low CAR stimulation or in the presence of inhibitory ligand programmed cell death 1 ligand 1. Notably, lenalidomide also enhanced CAR T cytokine production, cytolytic activity, and activation profile relative to untreated CAR T cells in chronic stimulation assays. This unique potentiation of both short-term CAR T activity and long-term functionality during chronic stimulation prompted investigation of the molecular profile of lenalidomide-treated CAR T cells. Signatures from RNA sequencing and assay for transposase-accessible chromatin using sequencing indicated that pathways associated with T-helper 1 response, cytokine production, T cell activation, cell-cycle control, and cytoskeletal remodeling were altered with lenalidomide. Finally, study of lenalidomide and anti-BCMA CAR T cells in a murine, disseminated, multiple myeloma model indicated that lenalidomide increased CAR T cell counts in blood and significantly prolonged animal survival. In summary, preclinical studies demonstrated that lenalidomide potentiated CAR T activity in vivo in low-antigen or suppressive environments and delayed onset of functional exhaustion. These results support further investigation of lenalidomide and anti-BCMA CAR T cells in the clinic.

Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data.

Large datasets describing the quantitative effects of mutations on protein function are becoming increasingly available. Here, we leverage these datasets to develop Envision, which predicts the magnitude of a missense variant's molecular effect. Envision combines 21,026 variant effect measurements from nine large-scale experimental mutagenesis datasets, a hitherto untapped training resource, with a supervised, stochastic gradient boosting learning algorithm. Envision outperforms other missense variant effect predictors both on large-scale mutagenesis data and on an independent test dataset comprising 2,312 TP53 variants whose effects were measured using a low-throughput approach. This dataset was never used for hyperparameter tuning or model training and thus serves as an independent validation set. Envision prediction accuracy is also more consistent across amino acids than other predictors. Finally, we demonstrate that Envision's performance improves as more large-scale mutagenesis data are incorporated. We precompute Envision predictions for every possible single amino acid variant in human, mouse, frog, zebrafish, fruit fly, worm, and yeast proteomes (https://envision.gs.washington.edu/).

Corrigendum: Classification and characterization of microsatellite instability across 18 cancer types.

This corrects the article DOI: 10.1038/nm.4191.

Multiplex assessment of protein variant abundance by massively parallel sequencing.

Determining the pathogenicity of genetic variants is a critical challenge, and functional assessment is often the only option. Experimentally characterizing millions of possible missense variants in thousands of clinically important genes requires generalizable, scalable assays. We describe variant abundance by massively parallel sequencing (VAMP-seq), which measures the effects of thousands of missense variants of a protein on intracellular abundance simultaneously. We apply VAMP-seq to quantify the abundance of 7,801 single-amino-acid variants of PTEN and TPMT, proteins in which functional variants are clinically actionable. We identify 1,138 PTEN and 777 TPMT variants that result in low protein abundance, and may be pathogenic or alter drug metabolism, respectively. We observe selection for low-abundance PTEN variants in cancer, and show that p.Pro38Ser, which accounts for ~10% of PTEN missense variants in melanoma, functions via a dominant-negative mechanism. Finally, we demonstrate that VAMP-seq is applicable to other genes, highlighting its generalizability.

Analysis of Large-Scale Mutagenesis Data To Assess the Impact of Single Amino Acid Substitutions.

Mutagenesis is a widely used method for identifying protein positions that are important for function or ligand binding. Advances in high-throughput DNA sequencing and mutagenesis techniques have enabled measurement of the effects of nearly all possible amino acid substitutions in many proteins. The resulting large-scale mutagenesis data sets offer a unique opportunity to draw general conclusions about the effects of different amino acid substitutions. Thus, we analyzed 34,373 mutations in 14 proteins whose effects were measured using large-scale mutagenesis approaches. Methionine was the most tolerated substitution, while proline was the least tolerated. We found that several substitutions, including histidine and asparagine, best recapitulated the effects of other substitutions, even when the identity of the wild-type amino acid was considered. The effects of histidine and asparagine substitutions also correlated best with the effects of other substitutions in different structural contexts. Furthermore, highly disruptive substitutions like aspartic and glutamic acid had the most discriminatory power for detecting ligand interface positions. Our work highlights the utility of large-scale mutagenesis data, and our conclusions can help guide future single substitution mutational scans.

Corrigendum: Classification and characterization of microsatellite instability across 18 cancer types.

This corrects the article DOI: 10.1038/nm.4191.

Classification and characterization of microsatellite instability across 18 cancer types.

Microsatellite instability (MSI), the spontaneous loss or gain of nucleotides from repetitive DNA tracts, is a diagnostic phenotype for gastrointestinal, endometrial, and colorectal tumors, yet the landscape of instability events across a wider variety of cancer types remains poorly understood. To explore MSI across malignancies, we examined 5,930 cancer exomes from 18 cancer types at more than 200,000 microsatellite loci and constructed a genomic classifier for MSI. We identified MSI-positive tumors in 14 of the 18 cancer types. We also identified loci that were more likely to be unstable in particular cancer types, resulting in specific instability signatures that involved cancer-associated genes, suggesting that instability patterns reflect selective pressures and can potentially identify novel cancer drivers. We also observed a correlation between survival outcomes and the overall burden of unstable microsatellites, suggesting that MSI may be a continuous, rather than discrete, phenotype that is informative across cancer types. These analyses offer insight into conserved and cancer-specific properties of MSI and reveal opportunities for improved methods of clinical MSI diagnosis and cancer gene discovery.

Mapping 3D genome architecture through in situ DNase Hi-C.

With the advent of massively parallel sequencing, considerable work has gone into adapting chromosome conformation capture (3C) techniques to study chromosomal architecture at a genome-wide scale. We recently demonstrated that the inactive murine X chromosome adopts a bipartite structure using a novel 3C protocol, termed in situ DNase Hi-C. Like traditional Hi-C protocols, in situ DNase Hi-C requires that chromatin be chemically cross-linked, digested, end-repaired, and proximity-ligated with a biotinylated bridge adaptor. The resulting ligation products are optionally sheared, affinity-purified via streptavidin bead immobilization, and subjected to traditional next-generation library preparation for Illumina paired-end sequencing. Importantly, in situ DNase Hi-C obviates the dependence on a restriction enzyme to digest chromatin, instead relying on the endonuclease DNase I. Libraries generated by in situ DNase Hi-C have a higher effective resolution than traditional Hi-C libraries, which makes them valuable in cases in which high sequencing depth is allowed for, or when hybrid capture technologies are expected to be used. The protocol described here, which involves ∼4 d of bench work, is optimized for the study of mammalian cells, but it can be broadly applicable to any cell or tissue of interest, given experimental parameter optimization.