Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients.

Background: Cancer driver genes are usually ranked by mutation frequency, which does not necessarily reflect their driver strength. We hypothesize that driver strength is higher for genes preferentially mutated in patients with few driver mutations overall, because these few mutations should be strong enough to initiate cancer. Methods: We propose formulas for the Driver Strength Index (DSI) and the Normalized Driver Strength Index (NDSI), the latter independent of gene mutation frequency. We validate them using TCGA PanCanAtlas datasets, established driver prediction algorithms and custom computational pipelines integrating SNA, CNA and aneuploidy driver contributions at the patient-level resolution. Results: DSI and especially NDSI provide substantially different gene rankings compared to the frequency approach. E.g., NDSI prioritized members of specific protein families, including G proteins GNAQ, GNA11 and GNAS, isocitrate dehydrogenases IDH1 and IDH2, and fibroblast growth factor receptors FGFR2 and FGFR3. KEGG analysis shows that top NDSI-ranked genes comprise EGFR/FGFR2/GNAQ/GNA11-NRAS/HRAS/KRAS-BRAF pathway, AKT1-MTOR pathway, and TCEB1-VHL-HIF1A pathway. Conclusion: Our indices are able to select for driver gene attributes not selected by frequency sorting, potentially for driver strength. Genes and pathways prioritized are likely the strongest contributors to cancer initiation and progression and should become future therapeutic targets.

Cancer types with high numbers of driver events are largely preventable.

There is a long-standing debate on whether cancer is predominantly driven by extrinsic risk factors such as smoking, or by intrinsic processes such as errors in DNA replication. We have previously shown that the number of rate-limiting driver events per tumor can be estimated from the age distribution of cancer incidence using the gamma/Erlang probability distribution. Here, we show that this number strongly correlates with the proportion of cancer cases attributable to modifiable risk factors for all cancer types except the ones inducible by infection or ultraviolet radiation. The correlation was confirmed for three countries, three corresponding incidence databases and risk estimation studies, as well as for both sexes: USA, males (r = 0.80, P = 0.002), females (r = 0.81, P = 0.0003); England, males (r = 0.90, P < 0.0001), females (r = 0.67, P = 0.002); Australia, males (r = 0.90, P = 0.0004), females (r = 0.68, P = 0.01). Hence, this study suggests that the more driver events a cancer type requires, the more of its cases are due to preventable anthropogenic risk factors.

Comprehensive patient-level classification and quantification of driver events in TCGA PanCanAtlas cohorts.

There is a growing need to develop novel therapeutics for targeted treatment of cancer. The prerequisite to success is the knowledge about which types of molecular alterations are predominantly driving tumorigenesis. To shed light onto this subject, we have utilized the largest database of human cancer mutations-TCGA PanCanAtlas, multiple established algorithms for cancer driver prediction (2020plus, CHASMplus, CompositeDriver, dNdScv, DriverNet, HotMAPS, OncodriveCLUSTL, OncodriveFML) and developed four novel computational pipelines: SNADRIF (Single Nucleotide Alteration DRIver Finder), GECNAV (Gene Expression-based Copy Number Alteration Validator), ANDRIF (ANeuploidy DRIver Finder) and PALDRIC (PAtient-Level DRIver Classifier). A unified workflow integrating all these pipelines, algorithms and datasets at cohort and patient levels was created. We have found that there are on average 12 driver events per tumour, of which 0.6 are single nucleotide alterations (SNAs) in oncogenes, 1.5 are amplifications of oncogenes, 1.2 are SNAs in tumour suppressors, 2.1 are deletions of tumour suppressors, 1.5 are driver chromosome losses, 1 is a driver chromosome gain, 2 are driver chromosome arm losses, and 1.5 are driver chromosome arm gains. The average number of driver events per tumour increases with age (from 7 to 15) and cancer stage (from 10 to 15) and varies strongly between cancer types (from 1 to 24). Patients with 1 and 7 driver events per tumour are the most frequent, and there are very few patients with more than 40 events. In tumours having only one driver event, this event is most often an SNA in an oncogene. However, with increasing number of driver events per tumour, the contribution of SNAs decreases, whereas the contribution of copy-number alterations and aneuploidy events increases.

The Erlang distribution approximates the age distribution of incidence of childhood and young adulthood cancers.

BACKGROUND: It is widely believed that cancers develop upon acquiring a particular number of (epi) mutations in driver genes, but the law governing the kinetics of this process is not known. We have previously shown that the age distribution of incidence for the 20 most prevalent cancers of old age is best approximated by the Erlang probability distribution. The Erlang distribution describes the probability of several successive random events occurring by the given time according to the Poisson process, which allows an estimate for the number of critical driver events. METHODS: Here we employ a computational grid search method to find global parameter optima for five probability distributions on the CDC WONDER dataset of the age distribution of childhood and young adulthood cancer incidence. RESULTS: We show that the Erlang distribution is the only classical probability distribution we found that can adequately model the age distribution of incidence for all studied childhood and young adulthood cancers, in addition to cancers of old age. CONCLUSIONS: This suggests that the Poisson process governs driver accumulation at any age and that the Erlang distribution can be used to determine the number of driver events for any cancer type. The Poisson process implies the fundamentally random timing of driver events and their constant average rate. As waiting times for the occurrence of the required number of driver events are counted in decades, and most cells do not live this long, it suggests that driver mutations accumulate silently in the longest-living dividing cells in the body-the stem cells.

Age-related diseases as vicious cycles.

The mortality rates of age-related diseases (ARDs) increase exponentially with age. Processes described by the exponential growth function typically involve a branching chain reaction or, more generally, a positive feedback loop. Here I propose that each ARD is mediated by one or several positive feedback loops (vicious cycles). I then identify critical vicious cycles in five major ARDs: atherosclerosis, hypertension, diabetes, Alzheimer's and Parkinson's. I also propose that the progression of ARDs can be halted by selectively interrupting the vicious cycles and suggest the most promising targets.

The number of key carcinogenic events can be predicted from cancer incidence.

The widely accepted multiple-hit hypothesis of carcinogenesis states that cancers arise after several successive events. However, no consensus has been reached on the quantity and nature of these events, although "driver" mutations or epimutations are considered the most probable candidates. By using the largest publicly available cancer incidence statistics (20 million cases), I show that incidence of 20 most prevalent cancer types in relation to patients' age closely follows the Erlang probability distribution (R2 = 0.9734-0.9999). The Erlang distribution describes the probability y of k independent random events occurring by the time x, but not earlier or later, with events happening on average every b time intervals. This fits well with the multiple-hit hypothesis and potentially allows to predict the number k of key carcinogenic events and the average time interval b between them, for each cancer type. Moreover, the amplitude parameter A likely predicts the maximal populational susceptibility to a given type of cancer. These parameters are estimated for 20 most common cancer types and provide numerical reference points for experimental research on cancer development.

In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state.

Populations in developed nations throughout the world are rapidly aging, and the search for geroprotectors, or anti-aging interventions, has never been more important. Yet while hundreds of geroprotectors have extended lifespan in animal models, none have yet been approved for widespread use in humans. GeroScope is a computational tool that can aid prediction of novel geroprotectors from existing human gene expression data. GeroScope maps expression differences between samples from young and old subjects to aging-related signaling pathways, then profiles pathway activation strength (PAS) for each condition. Known substances are then screened and ranked for those most likely to target differential pathways and mimic the young signalome. Here we used GeroScope and shortlisted ten substances, all of which have lifespan-extending effects in animal models, and tested 6 of them for geroprotective effects in senescent human fibroblast cultures. PD-98059, a highly selective MEK1 inhibitor, showed both life-prolonging and rejuvenating effects. Natural compounds like N-acetyl-L-cysteine, Myricetin and Epigallocatechin gallate also improved several senescence-associated properties and were further investigated with pathway analysis. This work not only highlights several potential geroprotectors for further study, but also serves as a proof-of-concept for GeroScope, Oncofinder and other PAS-based methods in streamlining drug prediction, repurposing and personalized medicine.

T cells and reactive oxygen species.

Reactive oxygen species (ROS) have been long considered simply as harmful by-products of metabolism, which damage cellular proteins, lipids, and nucleic acids. ROS are also known as a weapon of phagocytes, employed against pathogens invading the host. However, during the last decade, an understanding has emerged that ROS also have important roles as signaling messengers in a multitude of pathways, in all cells, tissues, and organs. T lymphocytes are the key players of the adaptive immune response, which both coordinate other immune cells and destroy malignant and virus-infected cells. ROS have been extensively implicated in T-cell hyporesponsiveness, apoptosis, and activation. It has also become evident that the source, the kinetics, and the localization of ROS production all influence cell responses. Thus, the characterization of the precise mechanisms by which ROS are involved in the regulation of T-cell functions is important for our understanding of the immune response and for the development of new therapeutic treatments against immune-mediated diseases. This review summarizes the 30-year-long history of research on ROS in T lymphocytes, with the emphasis on the physiological roles of ROS.

TCR-triggered extracellular superoxide production is not required for T-cell activation.

BACKGROUND: In the last decade, reactive oxygen species (ROS) production has been shown to occur upon T-cell receptor (TCR) stimulation and to affect TCR-mediated signalling. However, the exact reactive species that are produced, how ROS are generated and their requirement for T-cell activation, proliferation or cytokine production remain unclear, especially in the case of primary human T cells. Moreover, several groups have questioned that ROS are produced upon TCR stimulation. RESULTS: To shed some light onto this issue, we specifically measured superoxide production upon TCR ligation in primary human and mouse T lymphocytes. We showed that superoxide is indeed produced and released into the extracellular space. Antioxidants, such as superoxide dismutase and ascorbate, abolished superoxide production, but surprisingly did not affect activation, proliferation and cytokine secretion in TCR-stimulated primary human T cells. It has been suggested that T cells produce ROS via the NADPH oxidase 2 (NOX2). Therefore, we investigated whether T-cell activation is affected in NOX2-deficient mice (gp91phox-/-). We found that T cells from these mice completely lack inducible superoxide production but display normal upregulation of activation markers and proliferation. CONCLUSIONS: Collectively, our data indicate that primary T cells produce extracellular superoxide upon TCR triggering, potentially via NOX2 at the plasma membrane. However, superoxide is not required for T-cell activation, proliferation and cytokine production.