Tumors accumulate thousands of mutations, and sequencing them has given rise to methods for finding cancer drivers via mutational recurrence. However, these methods require large cohorts and underperform for low recurrence. Recently, ultra-deep sequencing has enabled accurate measurement of VAFs (variant-allele frequencies) for mutations, allowing the determination of evolutionary trajectories. Here, based solely on the VAF spectrum for an individual sample, we report on a method that identifies drivers and quantifies tumor growth. Drivers introduce perturbations into the spectrum, and our method uses the frequency of hitchhiking mutations preceding a driver to measure this. As validation, we use simulation models and 993 tumors from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium with previously identified drivers. Then we apply our method to an ultra-deep sequenced acute myeloid leukemia (AML) tumor and identify known cancer genes and additional driver candidates. In summary, our framework presents opportunities for personalized driver diagnosis using sequencing data from a single individual.

Essential functions of mitogen-activated protein kinases (MAPKs) depend on their capacity to selectively phosphorylate a limited repertoire of substrates. MAPKs harbor a conserved groove located outside of the catalytic cleft that binds to short linear sequence motifs found in substrates and regulators. However, the weak and transient nature of these “docking” interactions poses a challenge to defining MAPK interactomes and associated sequence motifs. Here, we describe a yeast-based genetic screening pipeline to evaluate large collections of MAPK docking sequences in parallel. Using this platform, we analyzed a combinatorial library based on the docking sequences from the MAPK kinases MKK6 and MKK7, defining features critical for binding to the stress-activated MAPKs JNK1 and p38α. Our screen of a library consisting of ~12,000 sequences from the human proteome revealed multiple MAPK-selective interactors, including many that did not conform to previously defined docking motifs. Analysis of p38α/JNK1 exchange mutants identified specific docking groove residues that mediate selective binding. Last, we verified that docking sequences identified in the screen functioned in substrate recruitment in vitro and in cultured cells. Together, these studies establish an approach to characterize MAPK docking sequences and provide a resource for future investigation of signaling downstream of p38 and JNK.

The Pan-cancer Analysis of Whole Genomes (PCAWG) project provides an unprecedented opportunity to comprehensively characterize a vast set of uniformly annotated coding and non-coding mutations present in thousands of cancer genomes. Classical models of cancer progression posit that only a small number of these mutations strongly drive tumor progression and that the remaining ones (termed “putative passengers”) are inconsequential for tumorigenesis. In this study, we leveraged the comprehensive variant data from PCAWG to ascertain the molecular functional impact of each variant. The impact distribution of PCAWG mutations shows that, in addition to high- and low-impact mutations, there is a group of medium-impact putative passengers predicted to influence gene activity. Moreover, the predicted impact relates to the underlying mutational signature: different signatures confer divergent impact, differentially affecting distinct regulatory subsystems and gene categories. We also find that impact varies based on subclonal architecture (i.e., early vs. late mutations) and can be related to patient survival. Finally, we note that insufficient power due to limited cohort sizes precludes identification of weak drivers using standard recurrence-based approaches. To address this, we adapted an additive effects model derived from complex trait studies to show that aggregating the impact of putative passenger variants (i.e. including yet undetected weak drivers) provides significant predictability for cancer phenotypes beyond the PCAWG identified driver mutations (12.5% additive variance). Furthermore, this framework allowed us to estimate the frequency of potential weak driver mutations in the subset of PCAWG samples lacking well-characterized driver alterations.

The hepatitis delta virus (HDV) exhibits high genetic and evolutionary variability and is classified into eight genotypes (HDV-1 to -8). HDV-1 is the most widespread genotype worldwide and includes several subtypes. It predominates mainly in Europe, the Middle East, North America, and Northern Africa, and is associated with both severe and mild forms of liver disease. In this study, we performed phylogenetic and phylodynamic analyses of HDV strains circulating in Regione Lazio, Italy, to understand when these strains were introduced into the Lazio region and to define their genetic variability in Italy. Fifty HDV RNA positive patient samples were amplified using a nested RT-PCR approach targeting the HDV R0 region and sequenced. A phylogenetic tree of patient-derived sequences and reference sequences representing HDV-1 to -8 was constructed using the GTRGAMMA model in RAxML v8. The results indicated that HDV-1 was the predominant genotype with HDV-1d being the most frequently inferred subtype. HDV-1 sequences clustering with subtypes 1b and 1e were also identified. A phylodynamic analysis of HDV-1 sequences employing a Bayesian birth-death model inferred a clock rate of 3.04 × 10⁻⁴ substitutions per site per million years, with a 95% Highest Posterior Density (HPD) interval of 3.45 × 10⁻⁵ to 5.72 × 10⁻⁴. A Bayesian birth-death analysis with tree calibration based on a sample dating approach indicated multiple original sources of infection (from the late 1950s to late 1980s). Overall, these results suggest that HDV sequences from the native Italian and non-Italian patients analyzed in this study represent multiple lineages introduced across a wide period. A common ancestral origin should be excluded.

Background: Hepatitis delta virus (HDV) infection represents the most severe form of viral hepatitis and is a significant global health challenge. Bulevirtide (BLV) is a novel therapeutic treatment that has resulted in variable response rates in HBV/HDV-coinfected patients. We evaluated clinical, virological, and polymorphic factors for the purpose of predicting BLV treatment success. Methods: Thirty HBV/HDV-coinfected patients received BLV monotherapy (2 mg/day) for 24 to 48 weeks. Baseline (BL) serum samples were collected to assess clinical parameters and virological markers (HDV RNA, HBV DNA, HBsAg, HBcrAg, anti-HBc IgG) at treatment weeks 24 (TW24) and 48 (TW48). Additionally, full-genome HDV sequencing and a phylogenetic analysis were performed. Finally, analyses of the HDAg protein sequence and HDV RNA secondary structure were conducted to evaluate potential associations with treatment response. Results: A significant reduction in HDV RNA levels was observed at TW48, with a virological response (HDV RNA undetectable or ≥2 Log decline from BL) achieved by 58% of patients. Median BL levels of anti-HBc IgG were significantly different between virological responders (39.3 COI; interquartile range [IQR] 31.6–47.1) and virological non-responders (244.7 COI; IQR 127.0–299.4) (p = 0.0001). HDV genotype 1e was predominant across the cohort, and no specific HDAg polymorphisms predicted the response. However, secondary structure analysis of HDV RNA revealed that a specific pattern of internal loops in the region 63–100 nucleotides downstream of the editing site may influence treatment response by impacting editing efficacy. Conclusions: This study revealed key factors influencing BLV efficacy in HBV/HDV coinfection. Lower baseline anti-HBc IgG levels strongly correlated with a positive virological response, suggesting that the liver’s inflammatory state affects treatment success. Additionally, the analysis of HDV RNA secondary structure in patients receiving BLV treatment revealed a higher editing efficiency in virological responders, highlighting areas for further research.

Approximately 71 million people worldwide are infected with the hepatitis C virus (HCV). Injectable drug use represents the most common route of transmission in Europe and other developed countries. We studied the molecular characteristics of the HCV infection among mono-infected people who used drugs (PWUD) in Italy. Among 208 PWUD with anti-HCV antibodies, 101 (48.6%) were HCV RNA-positive, the majority (47%) were infected with the HCV genotype (Gt)1a, followed by Gt3a (34.9%), Gt4 (9.1%), Gt1b (4.5%), and Gt2 (4.5%). Bayesian phylogenetic analyses of clustered HCV NS5B sequences from 66 HCV-positive PWUDs with available plasma samples indicated age and neighborhood proximity as the most common characteristics between closely related HCV strains. Population dynamics, as measured by a coalescent Bayesian skyline analysis, revealed an increase in HCV Gt1a infections from the mid-1980s to mid-1990s. While HCV Gt3a infections were first detected in the 1980s, patient numbers with this genotype subtype remained relatively constant. For both Gt1a and Gt3a, Birth–Death Bayesian Skyline analyses produced higher reproduction numbers post 2014. For earlier time intervals, slow growths were observed for both Gt1a and Gt3a with reproduction numbers (Re) of approximately 1. The evolutionary rates for Gt1a and Gt3a were estimated as 2.23 × 10⁻⁴ and 3.85 × 10⁻⁴, respectively.