Variants of uncertain significance fundamentally limit the clinical utility of genetic information. The challenge they pose is epitomized by BRCA1, a tumour suppressor gene in which germline loss-of-function variants predispose women to breast and ovarian cancer. Although BRCA1 has been sequenced in millions of women, the risk associated with most newly observed variants cannot be definitively assigned. Here we use saturation genome editing to assay 96.5% of all possible single-nucleotide variants (SNVs) in 13 exons that encode functionally critical domains of BRCA1. Functional effects for nearly 4,000 SNVs are bimodally distributed and almost perfectly concordant with established assessments of pathogenicity. Over 400 non-functional missense SNVs are identified, as well as around 300 SNVs that disrupt expression. We predict that these results will be immediately useful for the clinical interpretation of BRCA1 variants, and that this approach can be extended to overcome the challenge of variants of uncertain significance in additional clinically actionable genes.

Crosstalk between different types of post-translational modifications (PTMs) on the same protein molecule adds specificity and combinatorial logic to signal processing, but has not been characterized on a large-scale basis. Here, we developed two methods to identify protein isoforms that are both phosphorylated and ubiquitylated in the yeast Saccharomyces cerevisiae, identifying 466 proteins with 2,100 phosphorylation sites co-occurring with 2,189 ubiquitylation sites. We applied these methods quantitatively to identify phosphorylation sites that regulate protein degradation via the ubiquitin-proteasome system. Our results demonstrate that distinct phosphorylation sites are often used in conjunction with ubiquitylation, and these sites are more highly conserved than the entire set of phosphorylation sites. Finally, we investigated how the phosphorylation machinery can be regulated by ubiquitylation. We found evidence for novel regulatory mechanisms of kinases and 14-3-3 scaffold proteins via proteasome-independent ubiquitylation.

Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer-associated genes.

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 reveal 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.

Proper centrosome duplication and spindle formation are crucial for prevention of chromosomal instability, and BRCA1 plays a role in this process. In this study, transient inhibition of BRCA1 function in cell lines derived from mammary tissue caused rapid amplification and fragmentation of centrosomes. Cell lines tested that were derived from nonmammary tissues did not amplify the centrosome number in this transient assay. We tested whether BRCA1 and its binding partner, BARD1, ubiquitinate centrosome proteins. Results showed that centrosome components, including ␥-tubulin, are ubiquitinated by BRCA1/BARD1 in vitro. The in vitro ubiquitination of ␥-tubulin was specific, and function of the carboxy terminus was necessary for this reaction; truncated BRCA1 did not ubiquitinate ␥-tubulin. BRCA1/BARD1 ubiquitinated lysines 48 and 344 of ␥-tubulin in vitro, and expression in cells of ␥-tubulin K48R caused a marked amplification of centrosomes. This result supports the notion that the modification of these lysines in living cells is critical in the maintenance of centrosome number. One of the key problems in understanding the biology of BRCA1 has been the identification of a specific target of BRCA1/BARD1 ubiquitination and its effect on mammary cell biology. The results of this study identify a ubiquitination target and suggest a biological impact important in the etiology of breast cancer.Cancer cells frequently have unstable numbers of chromosomes (reviewed in reference 20). One mechanism for chromosomal instability is improper centrosome duplication, since the centrosome is the organelle that organizes the spindle for separation of chromosomes during mitosis. The presence of more than two centrosomes in a cell can result in lost or fragmented chromosomes after cell division. Human tumors derived from breast and other tissues have abnormal centrosome numbers in early-stage lesions. As an example, abnormal centrosome numbers have been detected in ductal carcinoma in situ, the first stage of breast cancer (21, 33), and BRCA1 has been shown to have a role in regulating centrosome number (reviewed in reference 9).BRCA1 is a tumor suppressor that is mutated in inherited breast and ovarian cancer cases, and it is also epigenetically down-regulated in sporadic breast cancers. Strikingly, BRCA1 function is required for nearly all cell types to grow; it has many roles in the cell. These functions include the regulation of DNA damage repair, transcription, and X-chromosome inactivation (reviewed in references 37 and 41). All of these processes could be important in protecting mammary cells from uncontrolled growth, but it is not clear why loss of BRCA1 specifically results in breast and ovarian cancer.There is growing evidence that BRCA1 functions as a regulator of centrosome number. First, BRCA1 is localized to the centrosome in mitotic cells (17,23). Second, interference with BRCA1 function by various methods can cause an increased centrosome number. For example, mouse fibroblasts derived from BRCA1 exon 11 knockouts have ...

Interpreting variants of uncertain significance (VUS) is a central challenge in medical genetics. One approach is to experimentally measure the functional consequences of VUS, but to date this approach has been post hoc and low throughput. Here we use massively parallel assays to measure the effects of nearly 2000 missense substitutions in the RING domain of BRCA1 on its E3 ubiquitin ligase activity and its binding to the BARD1 RING domain. From the resulting scores, we generate a model to predict the capacities of full-length BRCA1 variants to support homology-directed DNA repair, the essential role of BRCA1 in tumor suppression, and show that it outperforms widely used biological-effect prediction algorithms. We envision that massively parallel functional assays may facilitate the prospective interpretation of variants observed in clinical sequencing.KEYWORDS deep mutational scanning; BRCA1; variants of uncertain significance; human genetic variation; protein function I N an era of increasingly widespread genetic testing, DNA sequencing identifies many missense substitutions with unknown effects on protein function and disease risk. In the absence of genetic evidence, experimental measurement is the most reliable way to determine the functional impact of a variant of uncertain significance (VUS). However, initiating an experiment for each new variant observed in a gene is often impractical. When experiments are done, they are nearly always performed in a retrospective manner (Bouwman et al. 2013), such that the resulting data are not useful for the patient in whom the VUS was observed.By prospectively measuring, in a high-throughput fashion, the consequences of all possible missense mutations on a gene's function, we can generate a look-up table for interpreting newly observed VUS. Although functional analysis at this scale is made possible by deep mutational scanning (Fowler and Fields 2014), a central challenge is that any single assay may not recapitulate all the activities of a given protein in human disease. To address this challenge, we hypothesized that integrating the results of assays of multiple biochemical functions would strengthen estimates of the effects of mutations on disease risk (strategy outlined in Figure 1A). As a proof-ofconcept, we initiated massively parallel functional analysis of BRCA1, a protein for which there are multiple biochemical functions as well as known pathogenic and benign missense substitutions to benchmark results.BRCA1 has been subject to intense study since its implication in hereditary, early onset breast and ovarian cancer (Miki et al. 1994). All missense substitutions in BRCA1 that are known to be pathogenic occur in either the amino-terminal RING domain or the carboxy-terminal BRCT repeat (http:// brca.iarc.fr/LOVD/home.php?select_db=BRCA1). Although the RING domain represents only 5% of the BRCA1 protein, 58% of the pathogenic missense substitutions occur within this domain. Sixty-two missense substitutions in the RING domain have been observed in patient...

The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 has spread globally, with >52,000 cases in California as of May 4, 2020. Here we investigate the genomic epidemiology of SARS-CoV-2 in Northern California from late January to mid-March 2020, using samples from 36 patients spanning 9 counties and the Grand Princess cruise ship. Phylogenetic analyses revealed the cryptic introduction of at least 7 different SARS-CoV-2 lineages into California, including epidemic WA1 strains associated with Washington State, with lack of a predominant lineage and limited transmission between communities. Lineages associated with outbreak clusters in 2 counties were defined by a single base substitution in the viral genome. These findings support contact tracing, social distancing, and travel restrictions to contain SARS-CoV-2 spread in California and other states.

Classical genetic approaches for interpreting variants, such as case-control or co-segregation studies, require finding many individuals with each variant. Because the overwhelming majority of variants are present in only a few living humans, this strategy has clear limits. Fully realizing the clinical potential of genetics requires that we accurately infer pathogenicity even for rare or private variation. Many computational approaches to predicting variant effects have been developed, but they can identify only a small fraction of pathogenic variants with the high confidence that is required in the clinic. Experimentally measuring a variant's functional consequences can provide clearer guidance, but individual assays performed only after the discovery of the variant are both time and resource intensive. Here, we discuss how multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes. The resulting large-scale functional data can be combined with machine learning and clinical knowledge for the development of ''lookup tables'' of accurate pathogenicity predictions. A coordinated effort to produce, analyze, and disseminate large-scale functional data generated by multiplex assays could be essential to addressing the variant-interpretation crisis.