Single-point mutations in kinase proteins can affect their stability and fitness, and computational analysis of these effects can provide insights into the relationships among protein sequence, structure, and function for this enzyme family. To assess the impact of mutations on protein stability, we used a sequence-based Potts Hamiltonian model trained on a kinase family multiple-sequence alignment (MSA) to calculate the statistical energy (fitness) effects of mutations and compared these against relative folding free energies (ΔΔGs) calculated from all-atom molecular dynamics free energy perturbation (FEP) simulations in explicit solvent. The fitness effects of mutations in the Potts model (ΔEs) showed good agreement with experimental thermostability data (Pearson r = 0.68), similar to the correlation we observed with ΔΔGs predicted from structure-based relative FEP simulations. Recognizing the possible advantages of using Potts models to rapidly estimate protein stability effects of kinase mutations seen in cancer genomics data, we used the Potts statistical energy model to estimate the stability effects of 65 conservative and nonconservative mutations across three distinct kinases (Wee1, Abl1, and Cdc7) with somatic mutations reported in the Genomic Data Commons (GDC) database. The ΔEs of these mutations calculated from the Potts model are consistent with the corresponding ΔΔGs from FEP simulations (Pearson ratio of 0.72). The agreement between these methods suggests that the Potts model may be used as a sequence-based tool for high-throughput screening of mutational effects as part of a computational pipeline for predicting the stability effects of mutations. We also demonstrate how the scalability of the fitness-based Potts model calculations permits analyses that are not easily accessed using FEP simulations. To this end, we employed site-saturation mutagenesis in the Potts model in order to investigate the relative stability effects of mutations seen in different cancer evolutionary scenarios. We used this approach to analyze the effects of drug pressure in Abl kinase by contrasting the relative fitness penalties of somatic mutations seen in miscellaneous cancer types with those calculated for mutations associated with cancer drug resistance. We observed that, in contrast to somatic mutations of Abl seen in various tumors that appear to have evolved neutrally, cancer mutations that evolved under drug pressure in Abl-targeted therapies tend to preserve enzyme stability.

Potts Hamiltonian Models and Molecular Dynamics Free Energy Simulations for Predicting the Impact of Mutations on Protein Kinase Stability.

Cancer evolution is explained by the accumulation of driver mutations and subsequent positive selection by acquired growth advantages, like Darwin's evolution theory. However, whether the negative selection of cells that have lost malignant properties contributes to cancer progression has not yet been fully investigated. Using intestinal metastatic tumor‐derived organoids carrying Apc, Kras, Tgfbr2, and Trp53 quadruple mutations, we demonstrate here that approximately 30% of subclones of the organoids show loss of metastatic ability to the liver while keeping the driver mutations and oncogenic pathways. Notably, highly metastatic subclones also showed a gradual loss of metastatic ability during further passages. Such non‐metastatic subclones revealed significantly decreased survival and proliferation ability in Matrigel and collagen gel culture conditions, which may cause elimination from the tumor tissues in vivo. RNA sequencing indicated that stemness‐related genes, including Lgr5 and Myb, were significantly downregulated in non‐metastatic subclones as well as subclones that lost metastatic ability during additional passages. Furthermore, a CGH analysis showed that non‐metastatic subclones were derived from a minor population of parental organoid cells. These results indicate that metastatic ability is continuously lost with decreased stem cell property in certain subpopulations of malignant tumors, and such subpopulations are eliminated by negative selection. Therefore, it is possible that cancer evolution is regulated not only by positive selection but also by negative selection. The mechanism underlying the loss of metastatic ability will be important for the future development of therapeutic strategies against metastasis.

Frequent loss of metastatic ability in subclones of Apc, Kras, Tgfbr2, and Trp53 mutant intestinal tumor organoids

T cell development occurs in the thymus, where uncommitted progenitors are directed into a range of sublineages with distinct functions. The goal is to generate a TCR repertoire diverse enough to recognize potential pathogens while remaining tolerant of self. Decades of intensive research have characterized the transcriptional programs controlling critical differentiation checkpoints at the population level. However, greater precision regarding how and when these programs orchestrate differentiation at the single-cell level is required. Single-cell RNA sequencing approaches are now being brought to bear on this question, to track the identity of cells and analyze their gene expression programs at a resolution not previously possible. In this review, we discuss recent advances in the application of these technologies that have the potential to yield unprecedented insight to T cell development.

Single-Cell RNA Sequencing Approaches for Tracing T Cell Development.

Polo-like kinase 1 (PLK1) is an essential cell cycle mitotic kinase component that plays an important role in cell cycle progression and has been reported to be involved in various cancers, including neuroblastoma (NB). PLK1 also regulates G2/M transition, chromosomal segregation, spindle assembly maturation, and mitotic exit. NB is an early embryonic-stage heterogeneous solid tumor and accounts for 15% of all pediatric cancer-related deaths. Therefore, we aimed to develop a targeting strategy for PLK1 by repurposing HMN-214 in NB. HMN-214 is a prodrug of HMN-176 and is known to selectively interfere with PLK1 function. In the present study, we performed the transcriptomic analysis of a large cohort of primary NB patient samples and revealed that PLK1 expression is inversely correlated with the overall survival of NB patients. Additionally, we found that PLK1 strongly correlates with NB disease and stage progression. HMN-214 significantly inhibited NB proliferation and colony formation in both MYCN-amplified and -nonamplified cell lines in a dose-dependent manner. Furthermore, HMN-214 induces apoptosis and significantly obstructs the cell cycle at the G2/M phase in NB cells by inhibiting multiple cell-cycle-related genes, such as PLK1, WEE1, CDK1, CDK2, Cyclin B1, CHK1, and CHK2. HMN-214 significantly inhibits cell cycle regulator CDK1 and the phosphorylation and activation of PLK1 in NB. In the NB 3D spheroid tumor model, HMN-214 significantly and in a dose-dependent manner inhibits spheroid tumor mass and growth. Overall, our study highlights that targeting PLK1 using HMN-214 is a novel therapeutic approach for NB.

/pdf/inhibition-of-polo-like-kinase-1-by-hmn-214-blocks-cell-21vf328e.pdf

Inhibition of Polo-like Kinase 1 by HMN-214 Blocks Cell Cycle Progression and Inhibits Neuroblastoma Growth

Abstract Cancer driver genes can undergo positive selection for various types of genetic alterations, including gain-of-function or loss-of-function mutations and copy number alterations (CNA). We investigated the landscape of different types of alterations affecting driver genes in 17,644 cancer exomes and genomes. We find that oncogenes may simultaneously exhibit signatures of positive selection and also negative selection in different gene segments, suggesting a method to identify additional tumor types where an oncogene is a driver or a vulnerability. Next, we characterize the landscape of CNA-dependent selection effects, revealing a general trend of increased positive selection on oncogene mutations not only upon CNA gains but also upon CNA deletions. Similarly, we observe a positive interaction between mutations and CNA gains in tumor suppressor genes. Thus, two-hit events involving point mutations and CNA are universally observed regardless of the type of CNA and may signal new therapeutic opportunities. An analysis with focus on the somatic CNA two-hit events can help identify additional driver genes relevant to a tumor type. By a global inference of point mutation and CNA selection signatures and interactions thereof across genes and tissues, we identify 9 evolutionary archetypes of driver genes, representing different mechanisms of (in)activation by genetic alterations. 

Copy number losses of oncogenes and gains of tumor suppressor genes generate common driver mutations

The DNA in the cells of the human body is usually copied correctly when a cell divides. However, errors (mutations) are sometimes introduced during the copying process. Although the majority of mutations have no major impact on cells, many mutations are harmful: they decrease the ability of cells to survive. There are, however, mutations that can lead to cells dividing more frequently or gaining the ability to spread, which can lead to cancer. These mutations are known as ‘driver mutations’ because they drive the growth of tumors. Since such ‘driver mutations’ provide a growth advantage to tumor cells, they are subject to positive selection, this is, their frequency in the tumor increases over time. Because of their selective advantage, driver mutations accumulate at significantly higher rates than the neutral ‘passenger mutations’ that do not play a role in tumor growth. Genes that carry driver mutations are called driver genes, while genes that carry only passenger mutations are known as passenger genes. Certain genes, however, do not fit into either category. For example, some genes that are essential for tumor growth must get rid of harmful mutations to maintain activity. Mutations of such ‘tumor essential genes’ are thus subject to ‘negative’ or ‘purifying selection’. A major goal of cancer research is to identify genes that play critical roles in tumor growth. Earlier studies have identified numerous driver genes positively selected for driver mutations, exploiting the fact that driver genes show significantly higher mutation rates than passenger genes. Identification of tumor essential genes, however, is inherently more difficult since the paucity of mutations of negatively selected genes hinders the analysis of the mutation data. The failure to provide convincing evidence for negative selection in tumors has led to suggestions that it has no role in cancer evolution. Banyai et al. used a novel approach to address the question of whether negative selection occurs in cancer. Based on characteristic differences in the patterns of mutations in cancer they distinguished clusters of passenger genes, driver genes and tumor essential genes. The group of tumor essential genes includes genes that serve to satisfy the increased demand of rapidly dividing tumor cells for nutrients’ and genes that are essential for cell migration and metastasis (the spread of cancer cells to other areas of the body). The tumor essential genes that Banyai et al. identified may prove to be valuable targets for cancer therapy, illustrating the importance of genome sequencing in cancer research. Identification of additional tumor essential genes is, however, hindered by the fact that they are likely to have low levels of mutations, which can exclude them from meaningful analyses. Progress with genomic sequencing of tumors is expected to overcome this limitation and help identify additional genes that are essential for cancer growth.

Use of signals of positive and negative selection to distinguish cancer genes and passenger genes.

Background A major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis. Most of the approaches aimed to achieve this goal focused on genes that are positively selected for mutations that drive carcinogenesis and neglected the role of negative selection. Some studies have actually concluded that negative selection has no role in cancer evolution. Results We have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human protein-coding genes. Our analyses have confirmed that the vast majority of human genes do not show detectable signals of selection, whereas tumor suppressor genes are positively selected for inactivating mutations. Oncogenes, however, were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations. Significantly, we have identified numerous human genes that show signs of strong negative selection during tumor evolution, suggesting that their functional integrity is essential for the growth and survival of tumor cells. It is worthy of note that the group of negatively selected genes includes several genes that play a central role in the Warburg effect characteristic of cancer cells as well as genes involved in the proliferation, immortalization, invasion and metastasis of tumor cells. Conclusions Our analyses suggest that the approach reported here will promote the identification of numerous novel tumor suppressor genes, oncogenes and pro-oncogenic genes that may serve as targets in cancer therapy.

/pdf/use-of-signals-of-positive-and-negative-selection-to-7597iws2fd.pdf

Use of signals of positive and negative selection to distinguish cancer genes and passenger genes

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Use of signals of positive and negative selection to distinguish cancer genes and passenger genes.

Use of signals of positive and negative selection to distinguish cancer genes and passenger genes