Age influences on the molecular presentation of tumours

Cancer is often called a disease of aging. There are numerous ways in which cancer epidemiology and behaviour change with the age of the patient. The molecular bases for these relationships remain largely underexplored. To characterise them, we analyse age-associations in the nuclear and mitochondrial somatic mutational landscape of 20,033 tumours across 35 tumour-types. Age influences both the number of mutations in a tumour (0.077 mutations per megabase per year) and their evolutionary timing. Specific mutational signatures are associated with age, reflecting differences in exogenous and endogenous oncogenic processes such as a greater influence of tobacco use in the tumours of younger patients, but higher activity of DNA damage repair signatures in those of older patients. We find that known cancer driver genes such as CDKN2A and CREBBP are mutated in age-associated frequencies, and these alter the transcriptome and predict for clinical outcomes. These effects are most striking in brain cancers where alterations like SUFU loss and ATRX mutation are age-dependent prognostic biomarkers. Using three cancer datasets, we show that age shapes the somatic mutational landscape of cancer, with clinical implications.

Insights on the mutational landscape of the SARS-CoV-2 Omicron variant

The SARS-COV2 Omicron variant has sparked global concern due to the possibility of enhanced transmissibility and escape from vaccines and therapeutics. In this study, we describe the mutational landscape of the Omicron variant using amino acid interaction (AAI) networks. AAI network analysis is particularly well suited for interrogating the impact of constellations of mutations as occur on Omicron that may function in an epistatic manner. Our analyses suggest that as compared to previous variants of concern, the Omicron variant has increased antibody escape breadth due to mutations in class 3 and 4 antibody epitopes as well as increased escape depth due to accumulated mutations in class 1 antibody epitopes. We note certain RBD mutations that might further enhance Omicron escape, and in particular advise careful surveillance of two subclades bearing R346S/K mutations. Further, AAI network analysis suggests that the function of certain therapeutic monoclonal antibodies may be disrupted by Omicron mutations as a result of the cumulative indirect perturbations to the epitope surface properties, despite point-mutation analyses suggesting these antibodies are tolerant of the set of Omicron mutations in isolation. Finally, for several Omicron mutations that do not appear to contribute meaningfully to antibody escape, we find evidence for a plausible role in enhanced transmissibility via disruption of RBD-down conformational stability at the RBD-RBD interface.

Incorporating the mutational landscape of SARS-COV-2 variants and case-dependent vaccination rates into epidemic models

Coronavirus Disease (COVID-19), which began as a small outbreak in Wuhan, China in December 2019, became a global pandemic within months due to its high transmissibility. In the absence of pharmaceutical treatment, various non-pharmaceutical interventions (NPIs) to contain the spread of COVID-19 brought the entire world to a halt. After almost a year of seemingly returning to normalcy with the world's quickest vaccine development, the advent of more infectious and vaccine-resistant coronavirus variants is bringing the situation back to where it was a year ago. In the light of this new situation, we conducted a study to portray the possible scenarios based on the three key factors: impact of interventions (pharmaceutical and NPIs), vaccination rate, and vaccine efficacy. In our study, we assessed two of the most crucial factors, transmissibility and vaccination rate, in order to reduce the spreading of COVID in a simple but effective manner. In order to incorporate the time-varying mutational landscape of COVID-19 variants, we estimated weighted transmissibility composed of the proportion of existing strains that naturally vary over time. Additionally, we consider time-varying vaccination rates based on the number of daily new cases. Our method for calculating the vaccination rate from past active cases is an effective approach in forecasting probable future scenarios as it actively tracks people's attitudes toward immunization as active cases change. Our simulations show that if a large number of individuals cannot be vaccinated in a short period of time, adopting NPIs is the best approach to manage disease transmission with the emergence of new vaccine breakthrough variants and more infectious variants.

The Mutational Landscape of PTK7 in Congenital Scoliosis and Adolescent Idiopathic Scoliosis

Depletion of ptk7 is associated with both congenital scoliosis (CS) and adolescent idiopathic scoliosis (AIS) in zebrafish models. However, only one human variant of PTK7 has been reported previously in a patient with AIS. In this study, we systemically investigated the variant landscape of PTK7 in 583 patients with CS and 302 patients with AIS from the Deciphering Disorders Involving Scoliosis and COmorbidities (DISCO) study. We identified a total of four rare variants in CS and four variants in AIS, including one protein truncating variant (c.464_465delAC) in a patient with CS. We then explored the effects of these variants on protein expression and sub-cellular location. We confirmed that the c.464_465delAC variant causes loss-of-function (LoF) of PTK7. In addition, the c.353C>T and c.2290G>A variants identified in two patients with AIS led to reduced protein expression of PTK7 as compared to that of the wild type. In conclusion, LoF and hypomorphic variants are associated with CS and AIS, respectively.

Mutational Landscape in Myeloproliferative Neoplasms: Implications on Prognosis and Clinical Management

Mutations are a critical piece in understanding how myeloproliferative neoplasms (MPNs) occur, specifically the pathobiology of JAK/STAT activation. Mutations play such an important role, in fact, that they are a key part of the diagnostic classification for these diseases. Furthermore, the mutational landscape of MPNs affects both the prognosis and the biology of disease progression. Current research in the field is focused on understanding how and why these mutations occur, as well as how to attack them to address disease at the time of progression or even before disease progression has occurred.