Distinct genomic and immunologic tumor evolution in germline TP53-driven breast cancers.

Pathogenic germline TP53 alterations cause Li-Fraumeni Syndrome (LFS), and breast cancer is the most common cancer in LFS females. We performed first of its kind multimodal analysis of LFS breast cancer (LFS-BC) compared to sporadic premenopausal BC. Nearly all LFS-BC underwent biallelic loss of TP53 with no recurrent oncogenic variants except ERBB2 (HER2) amplification. Compared to sporadic BC, in situ and invasive LFS-BC exhibited a high burden of short amplified aneuploid segments (SAAS). Pro-apoptotic p53 target genes BAX and TP53I3 failed to be up-regulated in LFS-BC as was seen in sporadic BC compared to normal breast tissue. LFS-BC had lower CD8+ T-cell infiltration compared to sporadic BC yet higher levels of proliferating cytotoxic T-cells. Within LFS-BC, progression from in situ to invasive BC was marked by an increase in chromosomal instability with a decrease in proliferating cytotoxic T-cells. Our study uncovers critical events in mutant p53-driven tumorigenesis in breast tissue.

Genome-first approach of the prevalence and cancer phenotypes of pathogenic or likely pathogenic germline TP53 variants.

Pathogenic or likely pathogenic (P/LP) germline TP53 variants are the primary cause of Li-Fraumeni syndrome (LFS), a hereditary cancer predisposition disorder characterized by early-onset cancers. The population prevalence of P/LP germline TP53 variants is estimated to be approximately one in every 3,500 to 20,000 individuals. However, these estimates are likely impacted by ascertainment biases and lack of clinical and genetic data to account for potential confounding factors, such as clonal hematopoiesis. Genome-first approaches of cohorts linked to phenotype data can further refine these estimates by identifying individuals with variants of interest and then assessing their phenotypes. This study evaluated P/LP germline (variant allele fraction ≥30%) TP53 variants in three cohorts: UK Biobank (UKB, n = 200,590), Geisinger (n = 170,503), and Penn Medicine Biobank (PMBB, n = 43,731). A total of 109 individuals were identified with P/LP germline TP53 variants across the three databases. The TP53 p.R181H variant was the most frequently identified (9 of 109 individuals, 8%). A total of 110 cancers, including 47 hematologic cancers (47 of 110, 43%), were reported in 71 individuals. The prevalence of P/LP germline TP53 variants was conservatively estimated as 1:10,439 in UKB, 1:3,790 in Geisinger, and 1:2,983 in PMBB. These estimates were calculated after excluding related individuals and accounting for the potential impact of clonal hematopoiesis by excluding heterozygotes who ever developed a hematologic cancer. These varying estimates likely reflect intrinsic selection biases of each database, such as healthcare or population-based contexts. Prospective studies of diverse, young cohorts are required to better understand the population prevalence of germline TP53 variants and their associated cancer penetrance.

Exploring the genetic and molecular basis of differences in multiple myeloma of individuals of African and European descent.

Multiple Myeloma is a typical example of a neoplasm that shows significant differences in incidence, age of onset, type, and frequency of genetic alterations between patients of African and European ancestry. This perspective explores the hypothesis that both genetic polymorphisms and spontaneous somatic mutations in the TP53 tumor suppressor gene are determinants of these differences. In the US, the rates of occurrence of MM are at least twice as high in African Americans (AA) as in Caucasian Americans (CA). Strikingly, somatic TP53 mutations occur in large excess (at least 4-6-fold) in CA versus AA. On the other hand, TP53 contains polymorphisms specifying amino-acid differences that are under natural selection by the latitude of a population and have evolved during the migrations of humans over several hundred thousand years. The p53 protein plays important roles in DNA strand break repair and, therefore, in the surveillance of aberrant DNA recombination, leading to the B-cell translocations that are causal in the pathogenesis of MM. We posit that polymorphisms in one region of the TP53 gene (introns 2 and 3, and the proline-rich domain) specify a concentration of the p53 protein with a higher capacity to repress translocations in CA than AA patients. This, in turn, results in a higher risk of acquiring inactivating, somatic mutations in a different region of the TP53 gene (DNA binding domain) in CA than in AA patients. Such a mechanism, by which the polymorphic status of a gene influencing its own "spontaneous" mutation frequency, may provide a genetic basis to address ethnicity-related differences in the incidence and phenotypes of many different forms of cancer.

Accurate modeling of peptide-MHC structures with AlphaFold.

Major histocompatibility complex (MHC) proteins present peptides on the cell surface for T cell surveillance. Reliable in silico prediction of which peptides would be presented and which T cell receptors would recognize them is an important problem in structural immunology. Here, we introduce an AlphaFold-based pipeline for predicting the three-dimensional structures of peptide-MHC complexes for class I and class II MHC molecules. Our method demonstrates high accuracy, outperforming existing tools in class I modeling accuracy and class II peptide register prediction. We validate its performance and utility with new experimental data on a recently described cancer neoantigen/wild-type peptide pair and explore applications toward improving peptide-MHC binding prediction.

Proceedings of the inaugural Dark Genome Symposium: November 2022.

In November 2022 the first Dark Genome Symposium was held in Boston, USA. The meeting was hosted by Rome Therapeutics and Enara Bio, two biotechnology companies working on translating our growing understanding of this vast genetic landscape into therapies for human disease. The spirit and ambition of the meeting was one of shared knowledge, looking to strengthen the network of researchers engaged in the field. The meeting opened with a welcome from Rosana Kapeller and Kevin Pojasek followed by a first session of field defining talks from key academics in the space. A series of panels, bringing together academia and industry views, were then convened covering a wide range of pertinent topics. Finally, Richard Young and David Ting gave their views on the future direction and promise for patient impact inherent in the growing understanding of the Dark Genome.

Accurate modeling of peptide-MHC structures with AlphaFold.

Major histocompatibility complex (MHC) proteins present peptides on the cell surface for T-cell surveillance. Reliable in silico prediction of which peptides would be presented and which T-cell receptors would recognize them is an important problem in structural immunology. Here, we introduce an AlphaFold-based pipeline for predicting the three-dimensional structures of peptide-MHC complexes for class I and class II MHC molecules. Our method demonstrates high accuracy, outperforming existing tools in class I modeling precision and class II peptide register prediction. We explore applications of this method towards improving peptide-MHC binding prediction.

Long-term p21 and p53 dynamics regulate the frequency of mitosis events and cell cycle arrest following radiation damage.

Radiation exposure of healthy cells can halt cell cycle temporarily or permanently. In this work, we analyze the time evolution of p21 and p53 from two single cell datasets of retinal pigment epithelial cells exposed to several levels of radiation, and in particular, the effect of radiation on cell cycle arrest. Employing various quantification methods from signal processing, we show how p21 levels, and to a lesser extent p53 levels, dictate whether the cells are arrested in their cell cycle and how frequently these mitosis events are likely to occur. We observed that single cells exposed to the same dose of DNA damage exhibit heterogeneity in cellular outcomes and that the frequency of cell division is a more accurate monitor of cell damage rather than just radiation level. Finally, we show how heterogeneity in DNA damage signaling is manifested early in the response to radiation exposure level and has potential to predict long-term fate.

Fundamental immune-oncogenicity trade-offs define driver mutation fitness.

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 genotypes and phenotypes of missense mutations in the proline domain of the p53 protein.

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.

Targeting the P53 Protein for Cancer Therapies: The Translational Impact of P53 Research.

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.

Geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors.

Network analysis methods can potentially quantify cancer aberrations 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. Copy 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. The network curvature analysis stratified patients at high risk of mortality with p = 0.00047 in Kaplan-Meier analysis in HGSOC patients receiving ICI. Genes with high curvature were in accordance with CNAs relevant to ovarian cancer. Network curvature using CNAs has the potential to be a novel predictor for OS in HGSOC patients treated with immunotherapy.

Spontaneous and inherited TP53 genetic alterations.

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.

Bispecific antibodies come to the aid of cancer immunotherapy.

Three collaborative studies published by the groups of Vogelstein, Gabelli, and Zhou report the development of specially designed bispecific antibodies that may help in overcoming the limitations of current immunotherapies. The bispecific antibodies have been designed to couple cells harboring HLA-presented tumor-specific antigens from Tp53 mutant or Ras mutant with CD4 and CD8 T cells, thus facilitating immune-mediated clearance of the cancer cells.

Genetic and stochastic influences upon tumor formation and tumor types in Li-Fraumeni mouse models.

p53 is the most frequently mutated gene in human cancers. Li-Fraumeni syndrome patients inheriting heterozygous p53 mutations often have a much-increased risk to develop cancer(s) at early ages. Recent studies suggest that some individuals inherited p53 mutations do not have the early onset or high frequency of cancers. These observations suggest that other genetic, environmental, immunological, epigenetic, or stochastic factors modify the penetrance of the cancerous mutant Tp53 phenotype. To test this possibility, this study explored dominant genetic modifiers of Tp53 mutations in heterozygous mice with different genetic backgrounds. Both genetic and stochastic effects upon tumor formation were observed in these mice. The genetic background of mice carrying Tp53 mutations has a strong influence upon the tissue type of the tumor produced and the number of tumors formed in a single mouse. The onset age of a tumor is correlated with the tissue type of that tumor, although identical tumor tissue types can occur at very different ages. These observations help to explain the great diversity of cancers in different Li-Fraumeni patients over lifetimes.

Non-Random Selection of Cancer-Causing Mutations in Tissue-Specific Stem Cells Cause Cancer.

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.

Identification of relevant genetic alterations in cancer using topological data analysis.

Large-scale cancer genomic studies enable the systematic identification of mutations that lead to the genesis and progression of tumors, uncovering the underlying molecular mechanisms and potential therapies. While some such mutations are recurrently found in many tumors, many others exist solely within a few samples, precluding detection by conventional recurrence-based statistical approaches. Integrated analysis of somatic mutations and RNA expression data across 12 tumor types reveals that mutations of cancer genes are usually accompanied by substantial changes in expression. We use topological data analysis to leverage this observation and uncover 38 elusive candidate cancer-associated genes, including inactivating mutations of the metalloproteinase ADAMTS12 in lung adenocarcinoma. We show that ADAMTS12-/- mice have a five-fold increase in the susceptibility to develop lung tumors, confirming the role of ADAMTS12 as a tumor suppressor gene. Our results demonstrate that data integration through topological techniques can increase our ability to identify previously unreported cancer-related alterations.

Functional network analysis reveals an immune tolerance mechanism in cancer.

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.