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Missense driver mutations in cancer are concentrated in a few hotspots1. Various mechanisms have been proposed to explain this skew, including biased mutational processes2, phenotypic differences3–6 and immunoediting of neoantigens7,8; however, to our knowledge, no existing model weighs the relative contribution of these features to tumour evolution. We propose a unified theoretical ‘free fitness’ framework that parsimoniously integrates multimodal genomic, epigenetic, transcriptomic and proteomic data into a biophysical model of the rate-limiting processes underlying the fitness advantage conferred on cancer cells by driver gene mutations. Focusing on TP53, the most mutated gene in cancer1, we present an inference of mutant p53 concentration and demonstrate that TP53 hotspot mutations optimally solve an evolutionary trade-off between oncogenic potential and neoantigen immunogenicity. Our model anticipates patient survival in The Cancer Genome Atlas and patients with lung cancer treated with immunotherapy as well as the age of tumour onset in germline carriers of TP53 variants. The predicted differential immunogenicity between hotspot mutations was validated experimentally in patients with cancer and in a unique large dataset of healthy individuals. Our data indicate that immune selective pressure on TP53 mutations has a smaller role in non-cancerous lesions than in tumours, suggesting that targeted immunotherapy may offer an early prophylactic opportunity for the former. Determining the relative contribution of immunogenicity and oncogenic function to the selective advantage of hotspot mutations thus has important implications for both precision immunotherapies and our understanding of tumour evolution.

The p53 protein is structurally and functionally divided into five domains. The proline-rich domain is localized at amino acids 55–100. 319 missense mutations were identified solely in the proline domain from human cancers. Six hotspot mutations were identified at amino acids 72, 73, 82, 84, 89, and 98. Codon 72 contains a polymorphism that changes from proline (and African descent) to arginine (with Caucasian descent) with increasing latitudes northward and is under natural selection for pigmentation and protection from UV light exposure. Cancers associated with mutations in the proline domain were considerably enriched for melanomas and skin cancers compared to mutations in other p53 domains. These hotspot mutations are enriched at UV mutational signatures disrupting amino acid signals for binding SH-3-containing proteins important for p53 function. Among the protein–protein interaction sites identified by hotspot mutations were MDM-2, a negative regulator of p53, XAF-1, promoting p53 mediated apoptosis, and PIN-1, a proline isomerase essential for structural folding of this domain.

Purpose: A regulated p53-dependent stress response is crucial in suppressing tumor formation and mediating the response to commonly used cancer therapeutics. However, little is known about the human, inherited genetics of this important signaling pathway.Experimental Design: Studies of human genetic variants in the p53 tumor suppressor gene and MDM2 oncogene have shown that single nucleotide polymorphisms (SNP) can affect p53 signaling, confer cancer risk, and alter outcome, and also suggest that the pathway is under evolutionary selective pressure. Here, we attempt to accelerate the identification of functional p53 pathway SNPs by incorporating these characteristics into an analysis of 142 genes that are known to affect p53 signaling.Results: We report that a genomic scan for recent natural selection denotes that of the 142 genes studied, the PPP2R5E gene that encodes a regulatory subunit of the tumor suppressing protein phosphatase 2A resides in a naturally selected genomic region. We go on to show that a selected SNP in PPP2R5E (ϵ-SNP2) associates with significant allelic differences in the onset (up to 19.2 years; P = 0.0002) and risk (odds ratio, up to 8.1; P = 0.0009) of soft tissue sarcoma development, as well as overall survival (relative risk, up to 3.04; P = 0.026).Conclusions: The PPP2R5E gene is identified as harboring genetic variants that can affect human cancer and are possibly under evolutionary selection pressure. (Clin Cancer Res 2009;15(19):6301–8)

Supplementary Data from Recent Natural Selection Identifies a Genetic Variant in a Regulatory Subunit of Protein Phosphatase 2A that Associates with Altered Cancer Risk and Survival

Supplementary Table 5 from Chemosensitivity Profiles Identify Polymorphisms in the p53 Network Genes 14-3-3τ and CD44 That Affect Sarcoma Incidence and Survival

Supplementary Figure A6-2 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

Supplementary Figure A6-1 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

Supplementary Table 1 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

Supplementary Table 6 from Chemosensitivity Profiles Identify Polymorphisms in the p53 Network Genes 14-3-3τ and CD44 That Affect Sarcoma Incidence and Survival

Gene expression analysis has identified biologically relevant subclasses of breast cancer. However, most classification schemes do not robustly cluster all HER2+ breast cancers, in part due to limitations and bias of clustering techniques used. In this article, we propose an alternative approach that first separates the HER2+ tumors using a gene amplification signal for Her2/neu amplicon genes and then applies consensus ensemble clustering separately to the HER2+ and HER2− clusters to look for further substructure. We applied this procedure to a microarray data set of 286 early-stage breast cancers treated only with surgery and radiation and identified two basal and four luminal subtypes in the HER2− tumors, as well as two novel and robust HER2+ subtypes. HER2+ subtypes had median distant metastasis-free survival of 99 months [95% confidence interval (95% CI), 83–118 months] and 33 months (95% CI, 11–54 months), respectively, and recurrence rates of 11% and 58%, respectively. The low recurrence subtype had a strong relative overexpression of lymphocyte-associated genes and was also associated with a prominent lymphocytic infiltration on histologic analysis. These data suggest that early-stage HER2+ cancers associated with lymphocytic infiltration are a biologically distinct subtype with an improved natural history. [Cancer Res 2007;67(22):10669–76]

Supplementary Table 3a from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

Supplementary Appendix 1 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

The p53 regulatory network responds to cellular stresses by initiating processes such as cell cycle arrest and apoptosis. These responses inhibit cellular transformation and mediate the response to many forms of cancer therapies. Functional variants in the genes comprising this network could help identify individuals at greater risk for cancer and patients with poorer responses to therapies, but few such variants have been identified as yet. We use the NCI60 human tumor cell line anticancer drug screen in a scan of single nucleotide polymorphisms (SNP) in 142 p53 stress response genes and identify 7 SNPs that exhibit allelic differences in cellular responses to a large panel of cytotoxic chemotherapeutic agents. The greatest differences are observed for SNPs in 14-3-3τ (YWHAQ; rs6734469, P = 5.6 × 10−47) and CD44 (rs187115, P = 8.1 × 10−24). In soft-tissue sarcoma patients, we find that the alleles of these SNPs that associate with weaker growth responses to chemotherapeutics associate with poorer overall survival (up to 2.89 relative risk, P = 0.011) and an earlier age of diagnosis (up to 10.7 years earlier, P = 0.002). Our findings define genetic markers in 14-3-3τ and CD44 that might improve the treatment and prognosis of soft-tissue sarcomas. Cancer Res; 70(1); 172–80

Supplementary Table 2 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

PDF file - 14.9MB, Loss of P53 expedited the kinetics of MEF cell reprogramming

PDF file - 65K

Supplementary Figure 1 from Chemosensitivity Profiles Identify Polymorphisms in the p53 Network Genes 14-3-3τ and CD44 That Affect Sarcoma Incidence and Survival

Supplementary Table 4 from Chemosensitivity Profiles Identify Polymorphisms in the p53 Network Genes 14-3-3τ and CD44 That Affect Sarcoma Incidence and Survival

PDF file - 88K, Different from P53, P63 and P73 were not directly involved in iPSC generation.

Supplementary Tables 1-3 from Chemosensitivity Profiles Identify Polymorphisms in the p53 Network Genes 14-3-3τ and CD44 That Affect Sarcoma Incidence and Survival

PDF file - 415K, Normal stem cells can be cultured under temperatures ranged from 32C to 39C and normal iPSC can be formed from MEF cells under these temperatures.

Supplementary Figure 1 from The Regulation of AMPK β1, TSC2, and PTEN Expression by p53: Stress, Cell and Tissue Specificity, and the Role of These Gene Products in Modulating the IGF-1-AKT-mTOR Pathways

The p53 protein is a transcription factor that prevents tumors from developing. In spontaneous and inherited cancers there are many different missense mutations in the DNA binding domain of the TP53 gene that contributes to tumor formation. These mutations produce a wide distribution in the transcriptional capabilities of the mutant p53 proteins with over four logs differences in the efficiencies of forming cancers in many diverse tissue types. These inherited and spontaneous TP53 mutations produce proteins that interact with both genetic and epigenetic cellular modifiers of p53 function and their inherited polymorphisms to produce a large number of diverse phenotypes in individual patients. This manuscript reviews these variables and discusses how the combinations of TP53 genetic alterations interact with genetic polymorphisms, epigenetic alterations, and environmental factors to begin predicting and modifying patient outcomes and provide a better understanding for new therapeutic opportunities.

It is only recently that drugs targeting K-RAS and Tp53 missense mutations have been developed, and along with the allele specific nature of some of these drugs comes the possibility of combining them with the immunologic therapies for cancers. It has taken about 40 years since their discoveries to understand the pathways they command, how they function, and how they interact with the environment of the cells they control. This communication focuses on the transfer of some of the hard won information about the p53 protein, its mutations, structures, and activities learned in the basic science laboratory and translated to the clinic.

The Mdm2 oncoprotein and its association with p53 were discovered 30 years ago, and a cornucopia of activities and regulatory pathways have been associated with it. In this review, we will raise questions about Mdm2 and its cousin Mdm4 that we consider worth pursuing in future research, reaching from molecular structures and intracellular activities all the way to development, evolution, and cancer therapy. We anticipate that such research will not only close a few gaps in our knowledge but could add new dimensions to our current view. This compilation of questions contributes to the preparation for the 10th Mdm2 Workshop in Tokyo.

Significance A major problem in data science is representation of data so that the variables driving key functions can be uncovered and explored. Correlation analysis is widely used to simplify networks of feature variables by reducing redundancies, but makes limited use of the network topology, relying on comparison of direct neighbor variables. The proposed method incorporates relational or functional profiles of neighboring variables along multiple common neighbors, which are fitted with Gaussian mixture models and compared using a data metric based on a version of optimal mass transport tailored to Gaussian mixtures. Hierarchical interactive visualization of the result leads to effective unbiased hypothesis generation. In a cancer gene expression study, this method uncovered an unanticipated immunosuppressive mechanism resembling maternal–fetal immune tolerance., We present a technique to construct a simplification of a feature network which can be used for interactive data exploration, biological hypothesis generation, and the detection of communities or modules of cofunctional features. These are modules of features that are not necessarily correlated, but nevertheless exhibit common function in their network context as measured by similarity of relationships with neighboring features. In the case of genetic networks, traditional pathway analyses tend to assume that, ideally, all genes in a module exhibit very similar function, independent of relationships with other genes. The proposed technique explicitly relaxes this assumption by employing the comparison of relational profiles. For example, two genes which always activate a third gene are grouped together even if they never do so concurrently. They have common, but not identical, function. The comparison is driven by an average of a certain computationally efficient comparison metric between Gaussian mixture models. The method has its basis in the local connection structure of the network and the collection of joint distributions of the data associated with nodal neighborhoods. It is benchmarked on networks with known community structures. As the main application, we analyzed the gene regulatory network in lung adenocarcinoma, finding a cofunctional module of genes including the pregnancy-specific glycoproteins (PSGs). About 20% of patients with lung, breast, uterus, and colon cancer in The Cancer Genome Atlas (TCGA) have an elevated PSG+ signature, with associated poor group prognosis. In conjunction with previous results relating PSGs to tolerance in the immune system, these findings implicate the PSGs in a potential immune tolerance mechanism of cancers.

The evolutionarily conserved p53 protein and its cellular pathways mediate tumour suppression through an informed, regulated and integrated set of responses to environmental perturbations resulting in either cellular death or the maintenance of cellular homeostasis. The p53 and MDM2 proteins form a central hub in this pathway that receives stressful inputs via MDM2 and respond via p53 by informing and altering a great many other pathways and functions in the cell. The MDM2-p53 hub is one of the hubs most highly connected to other signalling pathways in the cell, and this may be why TP53 is the most commonly mutated gene in human cancers. Initial or truncal TP53 gene mutations (the first mutations in a stem cell) are selected for early in cancer development inectodermal and mesodermal-derived tissue-specific stem and progenitor cells and then, following additional mutations, produce tumours from those tissue types. In endodermal-derived tissue-specific stem or progenitor cells, TP53 mutations are functionally selected as late mutations transitioning the mutated cell into a malignant tumour. The order in which oncogenes or tumour suppressor genes are functionally selected for in a stem cell impacts the timing and development of a tumour.

PurposeNetwork analysis methods can potentially quantify cancer disturbances in gene networks without introducing fitted parameters or variable selection. A new network curvature-based method is introduced to provide an integrated measure of variability within cancer gene networks. The method is applied to high grade serous ovarian cancers (HGSOCs) to predict response to immune checkpoint inhibitors (ICIs), and to rank key genes associated with prognosis.MethodsCopy number alterations (CNAs) from targeted and whole exome sequencing data were extracted for HGSOC patients (n= 45) treated with ICIs. CNAs at a gene level were represented on a protein-protein interaction network to define patient-specific networks with a fixed topology. A version of Ollivier-Ricci curvature was used to identify genes that play a potentially key role in response to immunotherapy and further to stratify patients at high risk of mortality. Overall survival (OS) was defined as the time from the start of ICI treatment to either death or last follow-up. Kaplan-Meier analysis with log-rank test was performed to assess OS between the high and low curvature classified groups.ResultsThe network curvature analysis stratified patients at high risk of mortality with p=0.00047 in Kaplan-Meier analysis. Genes with high curvature were in accordance with CNAs relevant to ovarian cancer.ConclusionNetwork curvature using CNAs has the potential to be a novel predictor for OS in HGSOC patients treated with immunotherapy.

Multiple endocrine neoplasia type 1 (MEN1) is a genetic syndrome in which patients develop neuroendocrine tumors (NETs), including pancreatic neuroendocrine tumors (PanNETs). The prolonged latency of tumor development in MEN1 patients suggests a likelihood that other mutations cooperate with Men1 to induce PanNETs. We propose that Pten loss combined with Men1 loss accelerates tumorigenesis. To test this, we developed two genetically engineered mouse models (GEMMs)—MPR (Men1flox/flox Ptenflox/flox RIP-Cre) and MPM (Men1flox/flox Ptenflox/flox MIP-Cre) using the Cre-LoxP system with insulin-specific biallelic inactivation of Men1 and Pten. Cre in the MPR mouse model was driven by the transgenic rat insulin 2 promoter while in the MPM mouse model was driven by the knock-in mouse insulin 1 promoter. Both mouse models developed well-differentiated (WD) G1/G2 PanNETs at a much shorter latency than Men1 or Pten single deletion alone and exhibited histopathology of human MEN1-like tumor. The MPR model, additionally, developed pituitary neuroendocrine tumors (PitNETs) in the same mouse at a much shorter latency than Men1 or Pten single deletion alone as well. Our data also demonstrate that Pten plays a role in NE tumorigenesis in pancreas and pituitary. Treatment with the mTOR inhibitor rapamycin delayed the growth of PanNETs in both MPR and MPM mice, as well as the growth of PitNETs, resulting in prolonged survival in MPR mice. Our MPR and MPM mouse models are the first to underscore the cooperative roles of Men1 and Pten in cancer, particularly neuroendocrine cancer. The early onset of WD PanNETs mimicking the human counterpart in MPR and MPM mice at 7 weeks provides an effective platform for evaluating therapeutic opportunities for NETs through targeting the MENIN-mediated and PI3K/AKT/mTOR signaling pathways.

Half of all human cancers contain TP53 mutations, and in many other cancers, the function of the p53 protein is compromised. The diversity of these mutations and phenotypes presents a challenge to the development of drugs that target p53 mutant cancer cells. This review describes the rationale for many different approaches in the development of p53 targeted therapies: ( a) viruses and gene therapies, ( b) increased levels and activity of wild-type p53 proteins in cancer cells, ( c) p53 protein gain-of-function inhibitors, ( d) p53 protein loss-of-function structural correctors, ( e) mutant p53 protein synthetic lethal drugs interfering with the p53 pathway, and ( f) cellular immune responses to mutant p53 protein antigens. As these types of therapies are developed, tested, and evaluated, the best of them will have a significant impact upon cancer treatments and possibly prevention.

1AbstractRadiation exposure of healthy cells can halt cell cycle temporarily or permanently. In this work, two single cell datasets that monitored the time evolution of p21 and p53, one subjected to gamma irradiation and the other to x-ray irradiation, are analyzed to uncover the dynamics of this process. New insights into the biological mechanisms were found by decomposing the p53 and p21 signals into transient and oscillatory components. Through the use of dynamic time warping on the oscillatory components of the two signals, we found that p21 signaling lags behind its lead signal, p53, by about 3.5 hours with oscillation periods of around 6 hours. Additionally, through various quantification methods, we showed how p21 levels, and to a lesser extent p53 levels, dictate whether the cells are arrested in their cell cycle and how fast these cells divide depending on their long-term trend in these signals.

Missense driver mutations in cancer are concentrated in a few hotspots

Tissue-specific stem cells are the target for selected mutations in oncogenes or tumor suppressor genes that enhance the fitness of these cells, resulting in a self-limited clonal expansion and eventual cancer development. The initial or truncal mutations in the stem cell select for subsequent mutations that enhance their fitness, producing a reproducible order of mutations, selected for in each tissue type, during cancer development. Mutations in stem cells occur randomly, but the selection for increased fitness, occurs non-randomly, conferring a functional order on the selection of mutations. Tissue-specific stem cells are “units of natural selection” for somatic stem cells throughout life. This is why inherited cancer-causing mutations, which, by definition, are initial or truncal mutations, are observed to cause cancers with limited tissue specificities, even though the mutations are present in stem cells for all tissue types. In future studies, we need to understand why the same signal transduction pathways function differently in different tissue-specific stem cells. We also need to understand the truncal mutations for each cancer type, so as to eradicate the stem cell clones for that cancer before they produce a malignant tumor. To accomplish these objectives, we need to carry out new kinds of clinical trials with drugs that target mutations in tissue-specific stem cells.

Tandem satellite repeats account for 3% of the human genome. One of them, Human Satellite II (HSATII), is highly expressed in several epithelial cancers and cancer cell lines. Here we report an acute induction of HSATII RNA in human cells infected with two herpes viruses. We show that human cytomegalovirus (HCMV) IE1 and IE2 proteins cooperate to induce HSATII RNA affecting several aspects of the HCMV replication cycle, viral titers and infected-cell processes. HSATII RNA expression in tissue from two chronic HCMV colitis patients correlates with the strength of CMV antigen staining. Thus, endogenous HSATII RNA synthesis after herpesvirus infections appears to have functionally important consequences for viral replication and may provide a novel insight into viral pathogenesis. The HSATII induction seen in both infected and cancer cells suggests possible convergence upon common HSATII-based regulatory mechanisms in these seemingly disparate diseases., The human genome includes a large amount of repetitive sequence, such as human satellite II (HSATII), but their function remains largely unknown. Here, Nogalski et al. show that herpesvirus infection induces HSATII RNA expression, which in turn affects virus replication and cell motility.

We propose that initiating truncal mutations plays a special role in tumor formation by both enhancing the survival of the initiating cancer cell and by selecting for secondary mutations that contribute to tumor progression, and that these mutations often act in a tissue-preferred fashion. Here, we explain why inherited mutations often have different tissue specificities compared with spontaneous mutations in the same gene. Initiating truncal mutations make excellent neo-antigens for immunotherapy, and understanding why one mutation selects for a second mutation in a particular tissue type could one day aid in the design of gene-targeted combination therapies.

// Robert A. Wolff 1,* , Andrea Wang-Gillam 2,* , Hector Alvarez 8,* , Herve Tiriac 3,* , Dannielle Engle 3,* , Shurong Hou 4,* , Abigail F. Groff 6,* , Anthony San Lucas 9 , Vincent Bernard 10 , Kelvin Allenson 11 , Jonathan Castillo 8 , Dong Kim 8 , Feven Mulu 8 , Jonathan Huang 8 , Bret Stephens 8 , Ignacio I. Wistuba 9 , Matthew Katz 11 , Gauri Varadhachary 1 , YoungKyu Park 3 , James Hicks 3 , Arul Chinnaiyan 5 , Louis Scampavia 4 , Timothy Spicer 4 , Chiara Gerhardinger 6 , Anirban Maitra 8 , David Tuveson 3 , John Rinn 6,13 , Gregory Lizee 12 , Cassian Yee 12 and Arnold J. Levine 7 1 Department of Gastrointestinal (GI) Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA 2 Division of Oncology, Washington University, St. Louis, MO, USA 3 Cold Spring Harbor Laboratory, New York, NY, USA 4 Department of Molecular Therapeutics, The Scripps Research Institute, Jupiter, FL, USA 5 Center for Translational Pathology, University of Michigan Medical Center, Ann Arbor, MI, USA 6 Department of Molecular and Cellular Biology, Harvard University, The Broad Institute, Cambridge, MA, USA 7 Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ, USA 8 Department of Pathology, MD Anderson Cancer Center, Houston, TX, USA 9 Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, USA 10 Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA 11 Department of Surgical Oncology, MD Anderson Cancer Center, Houston, TX, USA 12 Department of Melanoma Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA 13 Current address: University of Colorado Boulder, BioFrontiers Institute, Boulder, CO, USA * These authors have contributed equally to this paper Correspondence to: Arnold J. Levine, email: // Keywords : pancreatic cancer; genomic instability; organoids; epithelial-mesenchymal transition Received : December 20, 2017 Accepted : February 01, 2018 Epub: February 13, 2018 Published: March 13, 2018 Abstract This manuscript follows a single patient with pancreatic adenocarcinoma for a five year period, detailing the clinical record, pathology, the dynamic evolution of molecular and cellular alterations as well as the responses to treatments with chemotherapies, targeted therapies and immunotherapies. DNA and RNA samples from biopsies and blood identified a dynamic set of changes in allelic imbalances and copy number variations in response to therapies. Organoid cultures established from biopsies over time were employed for extensive drug testing to determine if this approach was feasible for treatments. When an unusual drug response was detected, an extensive RNA sequencing analysis was employed to establish novel mechanisms of action of this drug. Organoid cell cultures were employed to identify possible antigens associated with the tumor and the patient’s T-cells were expanded against one of these antigens. Similar and identical T-cell receptor sequences were observed in the initial biopsy and the expanded T-cell population. Immunotherapy treatment failed to shrink the tumor, which had undergone an epithelial to mesenchymal transition prior to therapy. A warm autopsy of the metastatic lung tumor permitted an extensive analysis of tumor heterogeneity over five years of treatment and surgery. This detailed analysis of the clinical descriptions, imaging, pathology, molecular and cellular evolution of the tumors, treatments, and responses to chemotherapy, targeted therapies, and immunotherapies, as well as attempts at the development of personalized medical treatments for a single patient should provide a valuable guide to future directions in cancer treatment.