Understanding large scale sequencing datasets through changes to protein folding.

The expansion of high-quality, low-cost sequencing has created an enormous opportunity to understand how genetic variants alter cellular behaviour in disease. The high diversity of mutations observed has however drawn a spotlight onto the need for predictive modelling of mutational effects on phenotype from variants of uncertain significance. This is particularly important in the clinic due to the potential value in guiding clinical diagnosis and patient treatment. Recent computational modelling has highlighted the importance of mutation induced protein misfolding as a common mechanism for loss of protein or domain function, aided by developments in methods that make large computational screens tractable. Here we review recent applications of this approach to different genes, and how they have enabled and supported subsequent studies. We further discuss developments in the approach and the role for the approach in light of increasingly high throughput experimental approaches.

Interpreting the effect of mutations to protein binding sites from large-scale genomic screens.

Predicting the functionality of missense mutations is extremely difficult. Large-scale genomic screens are commonly performed to identify mutational correlates or drivers of disease and treatment resistance, but interpretation of how these mutations impact protein function is limited. One such consequence of mutations to a protein is to impact its ability to bind and interact with partners or small molecules such as ATP, thereby modulating its function. Multiple methods exist for predicting the impact of a single mutation on protein-protein binding energy, but it is difficult in the context of a genomic screen to understand if these mutations with large impacts on binding are more common than statistically expected. We present a methodology for taking mutational data from large-scale genomic screens and generating functional and statistical insights into their role in the binding of proteins both with each other and their small molecule ligands. This allows a quantitative and statistical analysis to determine whether mutations impacting protein binding or ligand interactions are occurring more or less frequently than expected by chance. We achieve this by calculating the potential impact of any possible mutation and comparing an expected distribution to the observed mutations. This method is applied to examples demonstrating its ability to interpret mutations involved in protein-protein binding, protein-DNA interactions, and the evolution of therapeutic resistance.

Large-scale computational modelling of the M1 and M2 synovial macrophages in rheumatoid arthritis.

Macrophages play an essential role in rheumatoid arthritis. Depending on their phenotype (M1 or M2), they can play a role in the initiation or resolution of inflammation. The M1/M2 ratio in rheumatoid arthritis is higher than in healthy controls. Despite this, no treatment targeting specifically macrophages is currently used in clinics. Thus, devising strategies to selectively deplete proinflammatory macrophages and promote anti-inflammatory macrophages could be a promising therapeutic approach. State-of-the-art molecular interaction maps of M1 and M2 macrophages in rheumatoid arthritis are available and represent a dense source of knowledge; however, these maps remain limited by their static nature. Discrete dynamic modelling can be employed to study the emergent behaviours of these systems. Nevertheless, handling such large-scale models is challenging. Due to their massive size, it is computationally demanding to identify biologically relevant states in a cell- and disease-specific context. In this work, we developed an efficient computational framework that converts molecular interaction maps into Boolean models using the CaSQ tool. Next, we used a newly developed version of the BMA tool deployed to a high-performance computing cluster to identify the models' steady states. The identified attractors are then validated using gene expression data sets and prior knowledge. We successfully applied our framework to generate and calibrate the M1 and M2 macrophage Boolean models for rheumatoid arthritis. Using KO simulations, we identified NFkB, JAK1/JAK2, and ERK1/Notch1 as potential targets that could selectively suppress proinflammatory macrophages and GSK3B as a promising target that could promote anti-inflammatory macrophages in rheumatoid arthritis.

SMAD4 and KCNQ3 alterations are associated with lymph node metastases in oesophageal adenocarcinoma.

Metastasis in oesophageal adenocarcinoma (OAC) is an important predictor of survival. Radiological staging is used to stage metastases in patients, and guide treatment selection, but is limited by the accuracy of the approach. Improvements in staging will lead to improved clinical decision making and patient outcomes. Sequencing studies on primary tumours and pre-cancerous tissue have revealed the mutational landscape of OAC, and increasingly cheap and widespread sequencing approaches offer the potential to improve staging assessment. In this work we present an analysis of lymph node metastases found by radiological and pathological sampling, identifying new roles of the genes SMAD4 and KCNQ3 in metastasis. Through transcriptomic analysis we find that both genes are associated with canonical Wnt pathway activity, but KCNQ3 is uniquely associated with changes in planar cell polaritiy associated with non-canonical Wnt signalling. We go on to validate our observations in KCNQ3 in cell line and xenograph systems, showing that overexpression of KCNQ3 reduces wound closure and the number of metastases observed. Our results suggest both genes as novel biomarkers of metastatic risk and offer new potential routes to drug targeting.

KCNQ potassium channels modulate Wnt activity in gastro-oesophageal adenocarcinomas.

Voltage-sensitive potassium channels play an important role in controlling membrane potential and ionic homeostasis in the gut and have been implicated in gastrointestinal (GI) cancers. Through large-scale analysis of 897 patients with gastro-oesophageal adenocarcinomas (GOAs) coupled with in vitro models, we find KCNQ family genes are mutated in ∼30% of patients, and play therapeutically targetable roles in GOA cancer growth. KCNQ1 and KCNQ3 mediate the WNT pathway and MYC to increase proliferation through resultant effects on cadherin junctions. This also highlights novel roles of KCNQ3 in non-excitable tissues. We also discover that activity of KCNQ3 sensitises cancer cells to existing potassium channel inhibitors and that inhibition of KCNQ activity reduces proliferation of GOA cancer cells. These findings reveal a novel and exploitable role of potassium channels in the advancement of human cancer, and highlight that supplemental treatments for GOAs may exist through KCNQ inhibitors.

Somatic mutations in facial skin from countries of contrasting skin cancer risk.

The incidence of keratinocyte cancer (basal cell and squamous cell carcinomas of the skin) is 17-fold lower in Singapore than the UK1-3, despite Singapore receiving 2-3 times more ultraviolet (UV) radiation4,5. Aging skin contains somatic mutant clones from which such cancers develop6,7. We hypothesized that differences in keratinocyte cancer incidence may be reflected in the normal skin mutational landscape. Here we show that, compared to Singapore, aging facial skin from populations in the UK has a fourfold greater mutational burden, a predominant UV mutational signature, increased copy number aberrations and increased mutant TP53 selection. These features are shared by keratinocyte cancers from high-incidence and low-incidence populations8-13. In Singaporean skin, most mutations result from cell-intrinsic processes; mutant NOTCH1 and NOTCH2 are more strongly selected than in the UK. Aging skin in a high-incidence country has multiple features convergent with cancer that are not found in a low-risk country. These differences may reflect germline variation in UV-protective genes.

Mutations observed in somatic evolution reveal underlying gene mechanisms.

Highly sensitive DNA sequencing techniques have allowed the discovery of large numbers of somatic mutations in normal tissues. Some mutations confer a competitive advantage over wild-type cells, generating expanding clones that spread through the tissue. Competition between mutant clones leads to selection. This process can be considered a large scale, in vivo screen for mutations increasing cell fitness. It follows that somatic missense mutations may offer new insights into the relationship between protein structure, function and cell fitness. We present a flexible statistical method for exploring the selection of structural features in data sets of somatic mutants. We show how this approach can evidence selection of specific structural features in key drivers in aged tissues. Finally, we show how drivers may be classified as fitness-enhancing and fitness-suppressing through different patterns of mutation enrichment. This method offers a route to understanding the mechanism of protein function through in vivo mutant selection.

Notch1 mutations drive clonal expansion in normal esophageal epithelium but impair tumor growth.

NOTCH1 mutant clones occupy the majority of normal human esophagus by middle age but are comparatively rare in esophageal cancers, suggesting NOTCH1 mutations drive clonal expansion but impede carcinogenesis. Here we test this hypothesis. Sequencing NOTCH1 mutant clones in aging human esophagus reveals frequent biallelic mutations that block NOTCH1 signaling. In mouse esophagus, heterozygous Notch1 mutation confers a competitive advantage over wild-type cells, an effect enhanced by loss of the second allele. Widespread Notch1 loss alters transcription but has minimal effects on the epithelial structure and cell dynamics. In a carcinogenesis model, Notch1 mutations were less prevalent in tumors than normal epithelium. Deletion of Notch1 reduced tumor growth, an effect recapitulated by anti-NOTCH1 antibody treatment. Notch1 null tumors showed reduced proliferation. We conclude that Notch1 mutations in normal epithelium are beneficial as wild-type Notch1 favors tumor expansion. NOTCH1 blockade may have therapeutic potential in preventing esophageal squamous cancer.

Heterogeneity of the cancer cell line metabolic landscape.

The unravelling of the complexity of cellular metabolism is in its infancy. Cancer-associated genetic alterations may result in changes to cellular metabolism that aid in understanding phenotypic changes, reveal detectable metabolic signatures, or elucidate vulnerabilities to particular drugs. To understand cancer-associated metabolic transformation, we performed untargeted metabolite analysis of 173 different cancer cell lines from 11 different tissues under constant conditions for 1,099 different species using mass spectrometry (MS). We correlate known cancer-associated mutations and gene expression programs with metabolic signatures, generating novel associations of known metabolic pathways with known cancer drivers. We show that metabolic activity correlates with drug sensitivity and use metabolic activity to predict drug response and synergy. Finally, we study the metabolic heterogeneity of cancer mutations across tissues, and find that genes exhibit a range of context specific, and more general metabolic control.

HOXA9 has the hallmarks of a biological switch with implications in blood cancers.

Blood malignancies arise from the dysregulation of haematopoiesis. The type of blood cell and the specific order of oncogenic events initiating abnormal growth ultimately determine the cancer subtype and subsequent clinical outcome. HOXA9 plays an important role in acute myeloid leukaemia (AML) prognosis by promoting blood cell expansion and altering differentiation; however, the function of HOXA9 in other blood malignancies is still unclear. Here, we highlight the biological switch and prognosis marker properties of HOXA9 in AML and chronic myeloproliferative neoplasms (MPN). First, we establish the ability of HOXA9 to stratify AML patients with distinct cellular and clinical outcomes. Then, through the use of a computational network model of MPN, we show that the self-activation of HOXA9 and its relationship to JAK2 and TET2 can explain the branching progression of JAK2/TET2 mutant MPN patients towards divergent clinical characteristics. Finally, we predict a connection between the RUNX1 and MYB genes and a suppressive role for the NOTCH pathway in MPN diseases.

p53 mutation in normal esophagus promotes multiple stages of carcinogenesis but is constrained by clonal competition.

Aging normal human oesophagus accumulates TP53 mutant clones. These are the origin of most oesophageal squamous carcinomas, in which biallelic TP53 disruption is almost universal. However, how p53 mutant clones expand and contribute to cancer development is unclear. Here we show that inducing the p53R245W mutant in single oesophageal progenitor cells in transgenic mice confers a proliferative advantage and clonal expansion but does not disrupt normal epithelial structure. Loss of the remaining p53 allele in mutant cells results in genomically unstable p53R245W/null epithelium with giant polyaneuploid cells and copy number altered clones. In carcinogenesis, p53 mutation does not initiate tumour formation, but tumours developing from areas with p53 mutation and LOH are larger and show extensive chromosomal instability compared to lesions arising in wild type epithelium. We conclude that p53 has distinct functions at different stages of carcinogenesis and that LOH within p53 mutant clones in normal epithelium is a critical step in malignant transformation.

The NALCN channel regulates metastasis and nonmalignant cell dissemination.

We identify the sodium leak channel non-selective protein (NALCN) as a key regulator of cancer metastasis and nonmalignant cell dissemination. Among 10,022 human cancers, NALCN loss-of-function mutations were enriched in gastric and colorectal cancers. Deletion of Nalcn from gastric, intestinal or pancreatic adenocarcinomas in mice did not alter tumor incidence, but markedly increased the number of circulating tumor cells (CTCs) and metastases. Treatment of these mice with gadolinium-a NALCN channel blocker-similarly increased CTCs and metastases. Deletion of Nalcn from mice that lacked oncogenic mutations and never developed cancer caused shedding of epithelial cells into the blood at levels equivalent to those seen in tumor-bearing animals. These cells trafficked to distant organs to form normal structures including lung epithelium, and kidney glomeruli and tubules. Thus, NALCN regulates cell shedding from solid tissues independent of cancer, divorcing this process from tumorigenesis and unmasking a potential new target for antimetastatic therapies.

Tumor-Derived Lactic Acid Modulates Activation and Metabolic Status of Draining Lymph Node Stroma.

Communication between tumors and the stroma of tumor-draining lymph nodes (TDLN) exists before metastasis arises, altering the structure and function of the TDLN niche. Transcriptional profiling of fibroblastic reticular cells (FRC), the dominant stromal population of lymph nodes, has revealed that FRCs in TDLNs are reprogrammed. However, the tumor-derived factors driving the changes in FRCs remain to be identified. Taking an unbiased approach, we have shown herein that lactic acid (LA), a metabolite released by cancer cells, was not only secreted by B16.F10 and 4T1 tumors in high amounts, but also that it was enriched in TDLNs. LA supported an upregulation of Podoplanin (Pdpn) and Thy1 and downregulation of IL7 in FRCs of TDLNs, making them akin to activated fibroblasts found at the primary tumor site. Furthermore, we found that tumor-derived LA altered mitochondrial function of FRCs in TDLNs. Thus, our results demonstrate a mechanism by which a tumor-derived metabolite connected with a low pH environment modulates the function of fibroblasts in TDLNs. How lymph node function is perturbed to support cancer metastases remains unclear. The authors show that tumor-derived LA drains to lymph nodes where it modulates the function of lymph node stromal cells, prior to metastatic colonization.

Simulations reveal that different responses to cell crowding determine the expansion of p53 and Notch mutant clones in squamous epithelia.

During ageing, normal epithelial tissues progressively accumulate clones carrying mutations that increase mutant cell fitness above that of wild-type cells. Such mutants spread widely through the tissues, yet despite this cellular homeostasis and functional integrity of the epithelia are maintained. Two of the genes most commonly mutated in human skin and oesophagus are p53 and Notch1, both of which are also recurrently mutated in cancers of these tissues. From observations taken in human and mouse epithelia, we find that clones carrying p53 and Notch pathway mutations have different clone dynamics which can be explained by their different responses to local cell crowding. p53 mutant clone growth in mouse epidermis approximates a logistic curve, but feedbacks responding to local crowding are required to maintain tissue homeostasis. We go on to show that the observed ability of Notch pathway mutant cells to displace the wild-type population in the mouse oesophageal epithelium reflects a local density feedback that affects both mutant and wild-type cells equally. We then show how these distinct feedbacks are consistent with the distribution of mutations observed in human datasets and are suggestive of a putative mechanism to constrain these cancer-associated mutants.

SARS-CoV-2 Variants Are Selecting for Spike Protein Mutations That Increase Protein Stability.

The emergence of variants of SARS-CoV-2 with mutations in their spike protein are a major cause for concern for the efficacy of vaccines and control of the pandemic. We show that mutations in the spike protein of SARS-CoV-2 are selecting for amino acid changes that result in a more thermodynamically stable protein than expected from background. We suggest that the computationally efficient analysis of mutational stability may aid in early screening of variants.

Methods for analysing lineage tracing datasets.

A single population of progenitor cells maintains many epithelial tissues. Transgenic mouse cell tracking has frequently been used to study the growth dynamics of competing clones in these tissues. A mathematical model (the 'single-progenitor model') has been argued to reproduce the observed progenitor dynamics accurately. This requires three parameters to describe the growth dynamics observed in transgenic mouse cell tracking-a division rate, a stratification rate and the probability of dividing symmetrically. Deriving these parameters is a time intensive and complex process. We compare the alternative strategies for analysing this source of experimental data, identifying an approximate Bayesian computation-based approach as the best in terms of efficiency and appropriate error estimation. We support our findings by explicitly modelling biological variation and consider the impact of different sampling regimes. All tested solutions are made available to allow new datasets to be analysed following our workflows. Based on our findings, we make recommendations for future experimental design.

Cancer-causing BRCA2 missense mutations disrupt an intracellular protein assembly mechanism to disable genome maintenance.

Cancer-causing missense mutations in the 3418 amino acid BRCA2 breast and ovarian cancer suppressor protein frequently affect a short (∼340 residue) segment in its carboxyl-terminal domain (DBD). Here, we identify a shared molecular mechanism underlying their pathogenicity. Pathogenic BRCA2 missense mutations cluster in the DBD's helical domain (HD) and OB1-fold motifs, which engage the partner protein DSS1. Pathogenic - but not benign - DBD mutations weaken or abolish DSS1-BRCA2 assembly, provoking mutant BRCA2 oligomers that are excluded from the cell nucleus, and disable DNA repair by homologous DNA recombination (HDR). DSS1 inhibits the intracellular oligomerization of wildtype, but not mutant, forms of BRCA2. Remarkably, DSS1 expression corrects defective HDR in cells bearing pathogenic BRCA2 missense mutants with weakened, but not absent, DSS1 binding. Our findings identify a DSS1-mediated intracellular protein assembly mechanism that is disrupted by cancer-causing BRCA2 missense mutations, and suggest an approach for its therapeutic correction.

Computational Saturation Screen Reveals the Landscape of Mutations in Human Fumarate Hydratase.

Single amino acid substitutions within protein structures often manifest with clinical conditions in humans. The mutation of a single amino can significantly alter protein folding and stability, or change protein dynamics to influence function. The chemical engineering field has developed a large toolset for predicting the influence of point mutations with the aim of guiding the design of improved and more stable proteins. Here, we reverse this general protocol and adapt these tools for the prediction of damaging mutations within proteins. Mutations to fumarate hydratase (FH), an enzyme of the citric acid cycle, can lead to human diseases. The inactivation of FH by mutation causes leiomyomas and renal cell carcinoma by subsequent fumarate buildup and reduction in available malate. We present a scheme for accurately predicting the clinical effects of every possible mutation in FH by adaptation to a database of characterized damaging and benign mutations. Using energy prediction tools Rosetta and FoldX coupled with molecular dynamics simulations, we accurately predict individual mutations as well as mutational hotspots with a high disruptive capability in FH. Furthermore, through dynamic analysis, we find that hinge regions of the protein can be stabilized or destabilized by mutations, with mechanistic implications for the functional ability of the enzyme. Finally, we categorize all potential mutations in FH into functional groups, predicting which known mutations in the human population are loss of function, therefore having clinical implications, and validate our findings through metabolomics data of characterized human cell lines.

Selection of Oncogenic Mutant Clones in Normal Human Skin Varies with Body Site.

Skin cancer risk varies substantially across the body, yet how this relates to the mutations found in normal skin is unknown. Here we mapped mutant clones in skin from high- and low-risk sites. The density of mutations varied by location. The prevalence of NOTCH1 and FAT1 mutations in forearm, trunk, and leg skin was similar to that in keratinocyte cancers. Most mutations were caused by ultraviolet light, but mutational signature analysis suggested differences in DNA-repair processes between sites. Eleven mutant genes were under positive selection, with TP53 preferentially selected in the head and FAT1 in the leg. Fine-scale mapping revealed 10% of clones had copy-number alterations. Analysis of hair follicles showed mutations in the upper follicle resembled adjacent skin, but the lower follicle was sparsely mutated. Normal skin is a dense patchwork of mutant clones arising from competitive selection that varies by location. SIGNIFICANCE: Mapping mutant clones across the body reveals normal skin is a dense patchwork of mutant cells. The variation in cancer risk between sites substantially exceeds that in mutant clone density. More generally, mutant genes cannot be assigned as cancer drivers until their prevalence in normal tissue is known.See related commentary by De Dominici and DeGregori, p. 227.This article is highlighted in the In This Issue feature, p. 211.