The mutation rate and cancer

Systemic inflammation in response to radiation drives the genesis of an immunosuppressed tumor microenvironment

The composition of the tumor immune microenvironment has become a major determinant of response to therapy, particularly immunotherapy. Clinically, a tumor microenvironment lacking lymphocytes, so-called "cold" tumors, are considered poor candidates for immune checkpoint inhibition. In this review, we describe the diversity of the tumor immune microenvironment in breast cancer and how radiation exposure alters carcinogenesis. We review the development and use of a radiation-genetic mammary chimera model to clarify the mechanism by which radiation acts. Using the chimera model, we demonstrate that systemic inflammation elicited by a low dose of radiation is key to the construction of an immunosuppressive tumor microenvironment, resulting in aggressive, rapidly growing tumors lacking lymphocytes. Our experimental studies inform the non-mutagenic mechanisms by which radiation affects cancer and provide insight into the genesis of cold tumors.

Heterogeneity and evolution of DNA mutation rates in microsatellite stable colorectal cancer

Historically, DNA sequence mutability has been considered relatively uniform and low in tumors with chromosomal instability (CIN), based on the assumption that high mutability would be detrimental in karyotypically aberrant contexts. Recent in silico analyses have challenged this view, suggesting some heterogeneity in mutation rates across CIN tumors; however, these predictions lack experimental validation. It also remains unclear how the intertumor variability of mutation rates compares to intratumor diversification and evolves along disease progression, whether mutation rates are functionally relevant in CIN cancers, and which mutational processes shape mutational accrual during CIN tumor onset and evolution. To address these gaps, we performed mutation accumulation experiments using clonal populations of patient-derived tumoroids from seven CIN, microsatellite-stable colorectal cancers (CRCs), and one microsatellite-unstable CRC. Each tumor exhibited a distinctive mutation rate footprint that was conserved among different clones from the same ancestor. In contrast, mutation rates diverged markedly across different tumors, with variations in magnitude within microsatellite-stable tumors as prominent as those distinguishing them from microsatellite-unstable tumors. New mutations reflected mutational processes associated with defective DNA replication and repair, which were not detected in normal tissues. Last, both mutation accumulation assays and high-depth whole-exome sequencing of subclonal variants showed higher mutation rates in metastatic lesions compared with matched primary tumors, suggesting positive selection for cells with increasing mutability during cancer dissemination. By providing an empirical assessment of mutation rates in human cancer, our data delineate heterogeneity, heritability, and progression-associated evolvability of DNA mutational instability as hallmarks of microsatellite-stable CRC.

SLC12A9 is an immunological and prognostic biomarker for glioma

BACKGROUND

Glioma is one of the most common malignant brain tumors. It has a high rate of progression and a poor prognosis, and effective biomarkers still need to be identified. The solute carrier family 12 (SLC12) family has been reported to be involved in various physiological and pathological processes, but their functional roles in glioma remain unclear.

METHODS

Using public datasets, we studied the mutation and expression level of SLC12 family genes in glioma and identified the significantly differentially expressed member solute carrier family 12 member 9 (SLC12A9). We further predicted the prognostic role of SLC12A9 in glioma by using the Kaplan-Meier method and Cox regression analysis. Then, we performed biological functional enrichment analysis. We focused on the relationships between SLC12A9 expression and immune infiltration in glioma. Meanwhile, we conducted in vitro experiments to evaluate the effect of SLC12A9 expression on glioma cells.

RESULTS

Among the members of the SLC12 family, SLC12A9 had the highest mutation rate in glioma, with gene amplification as the major mutation type, and its expression was significantly upregulated in glioma. Higher SLC12A9 expression was significantly associated with older age, higher grade, wild-type isocitrate dehydrogenase (IDH), and a worse prognosis. The functional enrichment analysis indicated that SLC12A9 is mainly related to ion channel annotation. Gene set enrichment analysis (GSEA) revealed that SLC12A9 was mainly related to the DNA replication pathway. Furthermore, we found that SLC12A9 correlated with tumor-infiltrating immune cells and immune checkpoints. Thus, SLC12A9 may be involved in regulating the immune response of glioma. Finally, our in vitro experiments revealed that silencing SLC12A9 dramatically inhibited glioma cell growth and migration.

CONCLUSIONS

We showed that SLC12A9 may be a new predictive biomarker for glioma diagnosis, prognosis, and immunotherapy response, offering helpful guidelines to advance glioma treatment.

Machine learning optimized DriverDetect software for high precision prediction of deleterious mutations in human cancers

The detection of cancer-driving mutations is important for understanding cancer pathology and therapeutics development. Prediction tools have been created to streamline the computation process. However, most tools available have heterogeneous sensitivity or specificity. We built a machine learning-derived algorithm, DriverDetect that combines the outputs of seven pre-existing tools to improve the prediction of candidate driver cancer mutations. The algorithm was trained with cancer gene-specific mutation datasets of cancer patients to identify cancer drivers. DriverDetect performed better than the individual tools or their combinations in the validation test. It has the potential to incorporate future novel prediction algorithms and can be retrained with new datasets, offering an expanded application to pan-cancer analysis for cross-cancer study. (115 words).

CarcSeq detection of lorcaserin-induced clonal expansion of Pik3ca H1047R mutants in rat mammary tissue

Lorcaserin is a 5-hydroxytryptamine 2C (serotonin) receptor agonist and a nongenotoxic rat carcinogen, which induced mammary tumors in male and female rats in a 2-yr bioassay. Female Sprague Dawley rats were treated by gavage daily with 0, 30, or 100 mg/kg lorcaserin, replicating bioassay dosing but for shorter duration, 12 or 24 wk. To characterize exposure and eliminate possible confounding by a potentially genotoxic degradation product, lorcaserin and N-nitroso-lorcaserin were quantified in dosing solutions, terminal plasma, mammary, and liver samples using ultra-high-performance liquid chromatography-electrospray tandem mass spectrometry. N-nitroso-lorcaserin was not detected, supporting lorcaserin classification as nongenotoxic carcinogen. Mammary DNA samples (n = 6/dose/timepoint) were used to synthesize PCR products from gene segments encompassing hotspot cancer driver mutations, namely regions of Apc, Braf, Egfr, Hras, Kras, Nfe2l2, Pik3ca, Setbp1, Stk11, and Tp53. Mutant fractions (MFs) in the amplicons were quantified by CarcSeq, an error-corrected next-generation sequencing approach. Considering all recovered mutants, no significant differences between lorcaserin dose groups were observed. However, significant dose-responsive increases in Pik3ca H1047R mutation were observed at both timepoints (ANOVA, P < 0.05), with greater numbers of mutants and mutants with greater MFs observed at 24 wk as compared with 12 wk. These observations suggest lorcaserin promotes outgrowth of spontaneously occurring Pik3ca H1047R mutant clones leading to mammary carcinogenesis. Importantly, this work reports approaches to analyze clonal expansion and demonstrates CarcSeq detection of the carcinogenic impact (selective Pik3ca H0147R mutant expansion) of a nongenotoxic carcinogen using a treatment duration as short as 3 months.

Germline mutation rate predicts cancer mortality across 37 vertebrate species

Background and objectives

Cancer develops across nearly every species. However, cancer occurs at unexpected and widely different rates throughout the animal kingdom. The reason for this variation in cancer susceptibility remains an area of intense investigation. Cancer evolves in part through the accumulation of mutations, and therefore, we hypothesized that germline mutation rates would be associated with cancer prevalence and mortality across species.

Methodology

We collected previously published data on germline mutation rate and cancer mortality data for 37 vertebrate species.

Results

Germline mutation rate was positively correlated with cancer mortality (P-value = 0.0008; R2 = 0.13). Controlling for species' average parental age, maximum longevity, adult body mass or domestication did not improve the model fit (the change (Δ) in Akaike Information Criterion (AIC) was less than 2). However, this model fit was better than a model controlling for species trophic level (ΔAIC > 2).

Conclusions and implications

The increased death rate from cancer in animals with increased germline mutation rates may suggest underlying hereditary cancer predisposition syndromes similar to those diagnosed in human patients. Species with higher germline mutation rates may benefit from close monitoring for tumors due to increased genetic risk for cancer development. Early diagnoses of cancer in these species may increase their chances of overall survival, especially for threatened and endangered species.

The complex effects of modern oncogenic environments on the fitness, evolution and conservation of wildlife species

Growing evidence indicates that human activities are causing cancer rates to rise in both human and wildlife populations. This is due to the inability of ancestral anti-cancer defences to cope with modern environmental risks. The evolutionary mismatch between modern oncogenic risks and evolved cancer defences has far-reaching effects on various biological aspects at different timeframes, demanding a comprehensive study of the biology and evolutionary ecology of the affected species. Firstly, the increased activation of anti-cancer defences leads to excessive energy expenditure, affecting other biological functions and potentially causing health issues like autoimmune diseases. Secondly, tumorigenesis itself can impact important fitness-related parameters such as competitiveness, predator evasion, resistance to parasites, and dispersal capacity. Thirdly, rising cancer risks can influence the species' life-history traits, often favoring early reproduction to offset fitness costs associated with cancer. However, this strategy has its limits, and it may not ensure the sustainability of the species if cancer risks continue to rise. Lastly, some species may evolve additional anti-cancer defences, with uncertain consequences for their biology and future evolutionary path. In summary, we argue that the effects of increased exposure to cancer-causing substances on wildlife are complex, ranging from immediate responses to long-term evolutionary changes. Understanding these processes, especially in the context of conservation biology, is urgently needed.

Evolutionary graph theory beyond single mutation dynamics: on how network-structured populations cross fitness landscapes

Spatially resolved datasets are revolutionizing knowledge in molecular biology, yet are under-utilized for questions in evolutionary biology. To gain insight from these large-scale datasets of spatial organization, we need mathematical representations and modeling techniques that can both capture their complexity, but also allow for mathematical tractability. Evolutionary graph theory utilizes the mathematical representation of networks as a proxy for heterogeneous population structure and has started to reshape our understanding of how spatial structure can direct evolutionary dynamics. However, previous results are derived for the case of a single new mutation appearing in the population and the role of network structure in shaping fitness landscape crossing is still poorly understood. Here we study how network-structured populations cross fitness landscapes and show that even a simple extension to a two-mutational landscape can exhibit complex evolutionary dynamics that cannot be predicted using previous single-mutation results. We show how our results can be intuitively understood through the lens of how the two main evolutionary properties of a network, the amplification and acceleration factors, change the expected fate of the intermediate mutant in the population and further discuss how to link these models to spatially resolved datasets of cellular organization.

A review of Bruton's tyrosine kinase inhibitors in multiple sclerosis

Bruton's tyrosine kinase (BTK) inhibitors are an emerging class of therapeutics in multiple sclerosis (MS). BTK is expressed in B-cells and myeloid cells, key progenitors of which include dendritic cells, microglia and macrophages, integral effectors of MS pathogenesis, along with mast cells, establishing the relevance of BTK inhibitors to diverse autoimmune conditions. First-generation BTK inhibitors are currently utilized in the treatment of B-cell malignancies and show efficacy in B-cell modulation. B-cell depleting therapies have shown success as disease-modifying treatments (DMTs) in MS, highlighting the potential of BTK inhibitors for this indication; however, first-generation BTK inhibitors exhibit a challenging safety profile that is unsuitable for chronic use, as required for MS DMTs. A second generation of highly selective BTK inhibitors has shown efficacy in modulating MS-relevant mechanisms of pathogenesis in preclinical as well as clinical studies. Six of these BTK inhibitors are undergoing clinical development for MS, three of which are also under investigation for chronic spontaneous urticaria (CSU), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). Phase II trials of selected BTK inhibitors for MS showed reductions in new gadolinium-enhancing lesions on magnetic resonance imaging scans; however, the safety profile is yet to be ascertained in chronic use. Understanding of the safety profile is developing by combining safety insights from the ongoing phase II and III trials of second-generation BTK inhibitors for MS, CSU, RA and SLE. This narrative review investigates the potential of BTK inhibitors as an MS DMT, the improved selectivity of second-generation inhibitors, comparative safety insights established thus far through clinical development programmes and proposed implications in female reproductive health and in long-term administration.

One CD4+TCR and One CD8+TCR Targeting Autochthonous Neoantigens Are Essential and Sufficient for Tumor Eradication

PURPOSE

To achieve eradication of solid tumors, we examined how many neoantigens need to be targeted with how many T-cell receptors (TCR) by which type of T cells.

EXPERIMENTAL DESIGN

Unmanipulated, naturally expressed (autochthonous) neoantigens were targeted with adoptively transferred TCR-engineered autologous T cells (TCR-therapy). TCR-therapy used CD8+ T-cell subsets engineered with TCRs isolated from CD8+ T cells (CD8+TCR-therapy), CD4+ T-cell subsets engineered with TCRs isolated from CD4+ T cells (CD4+TCR-therapy), or combinations of both. The targeted tumors were established for at least 3 weeks and derived from primary autochthonous cancer cell cultures, resembling natural solid tumors and their heterogeneity as found in humans.

RESULTS

Relapse was common with CD8+TCR-therapy even when targeting multiple different autochthonous neoantigens on heterogeneous solid tumors. CD8+TCR-therapy was only effective against homogenous tumors artificially derived from a cancer cell clone. In contrast, a combination of CD8+TCR-therapy with CD4+TCR-therapy, each targeting one neoantigen, eradicated large and established solid tumors of natural heterogeneity. CD4+TCR-therapy targeted a mutant neoantigen on tumor stroma while direct cancer cell recognition by CD8+TCR-therapy was essential for cure. In vitro data were consistent with elimination of cancer cells requiring a four-cell cluster composed of TCR-engineered CD4+ and CD8+ T cells together with antigen-presenting cells and cancer cells.

CONCLUSIONS

Two cancer-specific TCRs can be essential and sufficient to eradicate heterogeneous solid tumors expressing unmanipulated, autochthonous targets. We demonstrate that simplifications to adoptive TCR-therapy are possible without compromising efficacy.

Ergodicity Breaking and Self-Destruction of Cancer Cells by Induced Genome Chaos

During the progression of some cancer cells, the degree of genome instability may increase, leading to genome chaos in populations of malignant cells. While normally chaos is associated with ergodicity, i.e., the state when the time averages of relevant parameters are equal to their phase space averages, the situation with cancer propagation is more complex. Chromothripsis, a catastrophic massive genomic rearrangement, is observed in many types of cancer, leading to increased mutation rates. We present an entropic model of genome chaos and ergodicity and experimental evidence that increasing the degree of chaos beyond the non-ergodic threshold may lead to the self-destruction of some tumor cells. We study time and population averages of chromothripsis frequency in cloned rhabdomyosarcomas from rat stem cells. Clones with frequency above 10% result in cell apoptosis, possibly due to mutations in the BCL2 gene. Potentially, this can be used for suppressing cancer cells by shifting them into a non-ergodic proliferation regime.

Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis

Cancer occurs as a consequence of multiple somatic mutations that lead to uncontrolled cell growth. Mutual exclusivity and co-occurrence of mutations imply-but do not prove-that mutations exert synergistic or antagonistic epistatic effects on oncogenesis. Knowledge of these interactions, and the consequent trajectories of mutation and selection that lead to cancer has been a longstanding goal within the cancer research community. Recent research has revealed mutation rates and scaled selection coefficients for specific recurrent variants across many cancer types. However, there are no current methods to quantify the strength of selection incorporating pairwise and higher-order epistatic effects on selection within the trajectory of likely cancer genotoypes. Therefore, we have developed a continuous-time Markov chain model that enables the estimation of mutation origination and fixation (flux), dependent on somatic cancer genotype. Coupling this continuous-time Markov chain model with a deconvolution approach provides estimates of underlying mutation rates and selection across the trajectory of oncogenesis. We demonstrate computation of fluxes and selection coefficients in a somatic evolutionary model for the four most frequently variant driver genes (TP53, LRP1B, KRAS and STK11) from 565 cases of lung adenocarcinoma. Our analysis reveals multiple antagonistic epistatic effects that reduce the possible routes of oncogenesis, and inform cancer research regarding viable trajectories of somatic evolution whose progression could be forestalled by precision medicine. Synergistic epistatic effects are also identified, most notably in the somatic genotype TP53 LRP1B for mutations in the KRAS gene, and in somatic genotypes containing KRAS or TP53 mutations for mutations in the STK11 gene. Large positive fluxes of KRAS variants were driven by large selection coefficients, whereas the flux toward LRP1B mutations was substantially aided by a large mutation rate for this gene. The approach enables inference of the most likely routes of site-specific variant evolution and estimation of the strength of selection operating on each step along the route, a key component of what we need to know to develop and implement personalized cancer therapies.

Clonal Extinction Drives Tumorigenesis

Before a tumor is diagnosed and surgically removed, it has been growing for many months or even years [...].

Cancer-inspired genomics mapper model for the generation of synthetic DNA sequences with desired genomics signatures

Genome data are crucial in modern medicine, offering significant potential for diagnosis and treatment. Thanks to technological advancements, many millions of healthy and diseased genomes have already been sequenced; however, obtaining the most suitable data for a specific study, and specifically for validation studies, remains challenging with respect to scale and access. Therefore, in silico genomics sequence generators have been proposed as a possible solution. However, the current generators produce inferior data using mostly shallow (stochastic) connections, detected with limited computational complexity in the training data. This means they do not take the appropriate biological relations and constraints, that originally caused the observed connections, into consideration. To address this issue, we propose cancer-inspired genomics mapper model (CGMM), that combines genetic algorithm (GA) and deep learning (DL) methods to tackle this challenge. CGMM mimics processes that generate genetic variations and mutations to transform readily available control genomes into genomes with the desired phenotypes. We demonstrate that CGMM can generate synthetic genomes of selected phenotypes such as ancestry and cancer that are indistinguishable from real genomes of such phenotypes, based on unsupervised clustering. Our results show that CGMM outperforms four current state-of-the-art genomics generators on two different tasks, suggesting that CGMM will be suitable for a wide range of purposes in genomic medicine, especially for much-needed validation studies.

Sanitation enzymes: Exquisite surveillance of the noncanonical nucleotide pool to safeguard the genetic blueprint

Reactive oxygen species (ROS) are common products of normal cellular metabolism, but their elevated levels can result in nucleotide modifications. These modified or noncanonical nucleotides often integrate into nascent DNA during replication, causing lesions that trigger DNA repair mechanisms such as the mismatch repair machinery and base excision repair. Four superfamilies of sanitization enzymes can effectively hydrolyze noncanonical nucleotides from the precursor pool and eliminate their unintended incorporation into DNA. Notably, we focus on the representative MTH1 NUDIX hydrolase, whose enzymatic activity is ostensibly nonessential under normal physiological conditions. Yet, the sanitization attributes of MTH1 are more prevalent when ROS levels are abnormally high in cancer cells, rendering MTH1 an interesting target for developing anticancer treatments. We discuss multiple MTH1 inhibitory strategies that have emerged in recent years, and the potential of NUDIX hydrolases as plausible targets for the development of anticancer therapeutics.

Armored modified vaccinia Ankara in cancer immunotherapy

Cancer immunotherapy relies on unleashing the patient´s immune system against tumor cells. Cancer vaccines aim to stimulate both the innate and adaptive arms of immunity to achieve durable clinical responses. Some roadblocks for a successful cancer vaccine in the clinic include the tumor antigen of choice, the adjuvants employed to strengthen antitumor-specific immune responses, and the risks associated with enhancing immune-related adverse effects in patients. Modified vaccinia Ankara (MVA) belongs to the family of poxviruses and is a versatile vaccine platform that combines several attributes crucial for cancer therapy. First, MVA is an excellent inducer of innate immune responses leading to type I interferon secretion and induction of T helper cell type 1 (Th1) immune responses. Second, it elicits robust and durable humoral and cellular immunity against vector-encoded heterologous antigens. Third, MVA has enormous genomic flexibility, which allows for the expression of multiple antigenic and costimulatory entities. And fourth, its replication deficit in human cells ensures a excellent safety profile. In this review, we summarize the current understanding of how MVA induces innate and adaptive immune responses. Furthermore, we will give an overview of the tumor-associated antigens and immunomodulatory molecules that have been used to armor MVA and describe their clinical use. Finally, the route of MVA immunization and its impact on therapeutic efficacy depending on the immunomodulatory molecules expressed will be discussed.

Oxidative Stress Linking Obesity and Cancer: Is Obesity a 'Radical Trigger' to Cancer?

Obesity is on the rise worldwide, and consequently, obesity-related non-communicable diseases are as well. Nutritional overload induces metabolic adaptations in an attempt to restore the disturbed balance, and the byproducts of the mechanisms at hand include an increased generation of reactive species. Obesity-related oxidative stress causes damage to vulnerable systems and ultimately contributes to neoplastic transformation. Dysfunctional obese adipose tissue releases cytokines and induces changes in the cell microenvironment, promoting cell survival and progression of the transformed cancer cells. Other than the increased risk of cancer development, obese cancer patients experience higher mortality rates and reduced therapy efficiency as well. The fact that obesity is considered the second leading preventable cause of cancer prioritizes the research on the mechanisms connecting obesity to cancerogenesis and finding the solutions to break the link. Oxidative stress is integral at different stages of cancer development and advancement in obese patients. Hypocaloric, balanced nutrition, and structured physical activity are some tools for relieving this burden. However, the sensitivity of simultaneously treating cancer and obesity poses a challenge. Further research on the obesity-cancer liaison would offer new perspectives on prevention programs and treatment development.

INTESTINAL ORGANOIDS FOR STUDYING THE EFFECTS OF LOW-DOSE/LOW-DOSE-RATE RADIATION

Radiation response differs depending on the dose and dose rate in intestinal stem cells; however, the underlying mechanisms are not clear. To understand the effects of low-dose and low-dose-rate radiation, the authors established an organoid system that mimics the in vivo environment and sporadic low-dose-rate irradiation conditions in vitro. Organoid-forming potential and the number of stem cells in the organoids derived from 1 Gy-irradiated cells were lower than those from non-irradiated cells; however, the difference was not significant, although 1 Gy-irradiated stem cells exhibited significant growth disadvantage in the mixed-organoid with non-irradiated and irradiated stem cells. Furthermore, the authors irradiated a cell with X-ray microbeams and performed time-lapse observations and found that irradiated cells did not remain in the organoid. These results suggest that radiation-induced stem cell competition can occur in intestinal organoids and contribute to a low risk of cancers at low-dose-rate exposures.

Diverse mutational landscapes in human lymphocytes

The lymphocyte genome is prone to many threats, including programmed mutation during differentiation1, antigen-driven proliferation and residency in diverse microenvironments. Here, after developing protocols for expansion of single-cell lymphocyte cultures, we sequenced whole genomes from 717 normal naive and memory B and T cells and haematopoietic stem cells. All lymphocyte subsets carried more point mutations and structural variants than haematopoietic stem cells, with higher burdens in memory cells than in naive cells, and with T cells accumulating mutations at a higher rate throughout life. Off-target effects of immunological diversification accounted for approximately half of the additional differentiation-associated mutations in lymphocytes. Memory B cells acquired, on average, 18 off-target mutations genome-wide for every on-target IGHV mutation during the germinal centre reaction. Structural variation was 16-fold higher in lymphocytes than in stem cells, with around 15% of deletions being attributable to off-target recombinase-activating gene activity. DNA damage from ultraviolet light exposure and other sporadic mutational processes generated hundreds to thousands of mutations in some memory cells. The mutation burden and signatures of normal B cells were broadly similar to those seen in many B-cell cancers, suggesting that malignant transformation of lymphocytes arises from the same mutational processes that are active across normal ontogeny. The mutational landscape of normal lymphocytes chronicles the off-target effects of programmed genome engineering during immunological diversification and the consequences of differentiation, proliferation and residency in diverse microenvironments.

Mathematical modeling for mutator phenotype and clonal selection advantage in the risk analysis of lung cancer

Cancer is one of the leading diseases for human mortality. Although substantial research works have been conducted to investigate the initiation and progression of cancer disease, it is still an active debate regarding the function of mutations conferring a clone advantage and the importance of mutator phenotypes caused by the mutation of stability genes. To address this issue further, we develop a mathematical model based on the incidence data of non-small cell lung cancer and small cell lung cancer from the Surveillance Epidemiology and End Results registry in the USA. The key biological parameters have been analyzed to investigate the potential effective measures for inhibiting the risk of lung cancer. Although the first event is the gene mutation that leads to clonal expansion of cells for lung cancer, the simulation results show that the clonal advantage of cancer cells alone is insufficient to cause tumorigenesis. Our analysis suggests that mutations in genes that keep genetic stability are critical in the development of lung cancer. This implies that mutator phenotype is an important indicator for the diagnosis of lung cancer, which can enable early detection and treatment to reduce the risk of lung cancer effectively. Furthermore, the parameter analysis indicates that it would be highly effective to control the risk of lung cancer by inhibiting the transformation rate from the normal cells to mutated cells and the clonal expansion of cells with fewer gene mutations.

Emerging Concepts of Vascular Cell Clonal Expansion in Atherosclerosis

Clonal expansion is a process that can drive pathogenesis in human diseases, with atherosclerosis being a prominent example. Despite advances in understanding the etiology of atherosclerosis, clonality studies of vascular cells remain in an early stage. Recently, several paradigm-shifting preclinical studies have identified clonal expansion of progenitor cells in the vasculature in response to atherosclerosis. This review provides an overview of cell clonality in atherosclerotic progression, focusing particularly on smooth muscle cells and macrophages. We discuss key findings from the latest research that give insight into the mechanisms by which clonal expansion of vascular cells contributes to disease pathology. The further probing of these mechanisms will provide innovative directions for future progress in the understanding and therapy of atherosclerosis and its associated cardiovascular diseases.

Data Perturbation and Recovery of Time Series Gene Expression Data

Cells, in order to regulate their activities, process transcripts by controlling which genes to transcribe and by what amount. The transcription level of genes often change over time. Rate of change of gene transcription varies between genes. It can even change for the same gene across different members of a population. Thus, for a given gene, it is important to study the transcription level not only at a single time point, but across multiple time points to capture changes in patterns of gene expression which underlies several phenotypic or external factors. In such a dataset perturbation can happen due to which it may have missing transcription values for different samples at different time points. In this paper, we define three data perturbation models that are significant with respect to random deletion. We also define a recovery method that recovers data loss in the perturbed dataset such that the error is minimized. Our experimental results show that the recovery method compensates for the loss made by perturbation models. We show by means of two measures, namely, normalized distance and Pearson's correlation coefficient that the distance between the original and perturbed dataset is more than the distance between original and recovered dataset.

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.

Potential Therapeutic and Prognostic Values of LSM Family Genes in Breast Cancer

In recent decades, breast cancer (BRCA) has become one of the most common diseases worldwide. Understanding crucial genes and their signaling pathways remain an enormous challenge in evaluating the prognosis and possible therapeutics. The "Like-Smith" (LSM) family is known as protein-coding genes, and its member play pivotal roles in the progression of several malignancies, although their roles in BRCA are less clear. To discover biological processes associated with LSM family genes in BRCA development, high-throughput techniques were applied to clarify expression levels of LSMs in The Cancer Genome Atlas (TCGA)-BRCA dataset, which was integrated with the cBioPortal database. Furthermore, we investigated prognostic values of LSM family genes in BCRA patients using the Kaplan-Meier database. Among genes of this family, LSM4 expression levels were highly associated with poor prognostic outcomes with a hazard ratio of 1.35 (95% confidence interval 1.21-1.51, p for trend = 3.4 × 10-7). MetaCore and GlueGo analyses were also conducted to examine transcript expression signatures of LSM family members and their coexpressed genes, together with their associated signaling pathways, such as "Cell cycle role of APC in cell cycle regulation" and "Immune response IL-15 signaling via MAPK and PI3K cascade" in BRCA. Results showed that LSM family members, specifically LSM4, were significantly correlated with oncogenesis in BRCA patients. In summary, our results suggested that LSM4 could be a prospective prognosticator of BRCA.

Stem Cells and Cancer Stem Cells: The Jekyll and Hyde Scenario and Their Implications in Stem Cell Therapy

"Jekyll and Hyde" refers to persons with an unpredictably dual personality, who are battling between good and evil within themselves In this regard, even cells consist of good and evil counterparts. Normal stem cells (NSCs) and cancer stem cells (CSCs) are two types of cells that share some similar characteristics but have distinct functions that play a major role in physiological and pathophysiological development. In reality, NSCs such as the adult and embryonic stem cells, are the good cells and the ultimate treatment used in cell therapy. CSCs are the corrupted cells that are a subpopulation of cancer cells within the cancer microenvironment that grow into a massive tumour or malignancy that needs to be treated. Hence, understanding the connection between NSCs and CSCs is important not just in cancer development but also in their therapeutic implication, which is the focus of this review.

Balancing DNA repair to prevent ageing and cancer

DNA damage is a constant stressor to the cell. Persistent damage to the DNA over time results in an increased risk of mutation and an accumulation of mutations with age. Loss of efficient DNA damage repair can lead to accelerated ageing phenotypes or an increased cancer risk, and the trade-off between cancer susceptibility and longevity is often driven by the cell's response to DNA damage. High levels of mutations in DNA repair mutants often leads to excessive cell death and stem cell exhaustion which may promote premature ageing. Stem cells themselves have distinct characteristics that enable them to retain low mutation rates. However, when mutations do arise, stem cell clonal expansion can also contribute to age-related tissue dysfunction as well as heightened cancer risk. In this review, we will highlight increasing DNA damage and mutation accumulation as hallmarks common to both ageing and cancer. We will propose that anti-ageing interventions might be cancer preventative and discuss the mechanisms through which they may act.

Optimal solution of the fractional order breast cancer competition model

In this article, a fractional order breast cancer competition model (F-BCCM) under the Caputo fractional derivative is analyzed. A new set of basis functions, namely the generalized shifted Legendre polynomials, is proposed to deal with the solutions of F-BCCM. The F-BCCM describes the dynamics involving a variety of cancer factors, such as the stem, tumor and healthy cells, as well as the effects of excess estrogen and the body's natural immune response on the cell populations. After combining the operational matrices with the Lagrange multipliers technique we obtain an optimization method for solving the F-BCCM whose convergence is investigated. Several examples show that a few number of basis functions lead to the satisfactory results. In fact, numerical experiments not only confirm the accuracy but also the practicability and computational efficiency of the devised technique.

Naming CHO cells for bio-manufacturing: Genome plasticity and variant phenotypes of cell populations in bioreactors question the relevance of old names

Chinese Hamster Ovary [CHO] cells are the workhorse for production of modern biopharmaceuticals. They are however immortalized cells with a high propensity for genetic change. Judging from published culture records, CHO cell populations have undergone hundreds of population doublings since their origin in the late 1950s. Different cell populations were established and named from 1 to 3 decades after their generation, such as CHO-Pro-, CHO-K1, CHO-DG44, CHO-S, CHO-DUK, CHO-DXB-11 to indicate origin and certain phenotypic features. These names are commonly used in scientific publications still today. This article discusses the relevance of such names. We argue that they provide a false sense of identity. To substantiate this, we provide the long (and poorly recorded) history of CHO cells as well as their highly complex genetics. Finally, we suggest an alternative naming system for CHO cells which provides more relevant information. While the implementation of a new naming convention will require substantial discussions among members of the relevant community, it should improve interpretation and comparability between laboratories. This, in turn will help scientific communities and industrial users to attain and further the full potential of CHO cells.

An evolutionary approach to clonally complex hematologic disorders

Emerging clonal complexity has brought into question the way in which we perceive and, in turn, treat disorders of the hematopoietic system. Former models of cell-intrinsic clonal dominance driven by acquisition of driver genes in a stereotypic sequence are often insufficient in explaining observations such as clonal hematopoiesis, and new paradigms are in order. Here, we review the evidence both within the hematologic malignancy field and also borrow from perspectives rooted in evolutionary biology to reframe pathogenesis of hematologic disorders as dynamic processes involving complex interplays of genetic and non-genetic subclones and the tissue microenvironment in which they reside.

Are We Benign? What Can Wnt Signaling Pathway and Epithelial to Mesenchymal Transition Tell Us about Intracranial Meningioma Progression

Epithelial to mesenchymal transition (EMT), which is characterized by the reduced expression of E-cadherin and increased expression of N-cadherin, plays an important role in the tumor invasion and metastasis. Classical Wnt signaling pathway has a tight link with EMT and it has been shown that nuclear translocation of β-catenin can induce EMT. This research has showed that genes that are involved in cadherin switch, CDH1 and CDH2, play a role in meningioma progression. Increased N-cadherin expression in relation to E-cadherin was recorded. In meningioma, transcription factors SNAIL, SLUG, and TWIST1 demonstrated strong expression in relation to E- and N-cadherin. The expression of SNAIL and SLUG was significantly associated with higher grades (p = 0.001), indicating their role in meningioma progression. Higher grades also recorded an increased expression of total β-catenin followed by an increased expression of its active form (p = 0.000). This research brings the results of genetic and protein analyzes of important molecules that are involved in Wnt and EMT signaling pathways and reveals their role in intracranial meningioma. The results of this study offer guidelines and new markers of progression for future research and reveal new molecular targets of therapeutic interventions.

Evolving insights: how DNA repair pathways impact cancer evolution

Viewing cancer as a large, evolving population of heterogeneous cells is a common perspective. Because genomic instability is one of the fundamental features of cancer, this intrinsic tendency of genomic variation leads to striking intratumor heterogeneity and functions during the process of cancer formation, development, metastasis, and relapse. With the increased mutation rate and abundant diversity of the gene pool, this heterogeneity leads to cancer evolution, which is the major obstacle in the clinical treatment of cancer. Cells rely on the integrity of DNA repair machineries to maintain genomic stability, but these machineries often do not function properly in cancer cells. The deficiency of DNA repair could contribute to the generation of cancer genomic instability, and ultimately promote cancer evolution. With the rapid advance of new technologies, such as single-cell sequencing in recent years, we have the opportunity to better understand the specific processes and mechanisms of cancer evolution, and its relationship with DNA repair. Here, we review recent findings on how DNA repair affects cancer evolution, and discuss how these mechanisms provide the basis for critical clinical challenges and therapeutic applications.

Inference of mutability landscapes of tumors from single cell sequencing data

One of the hallmarks of cancer is the extremely high mutability and genetic instability of tumor cells. Inherent heterogeneity of intra-tumor populations manifests itself in high variability of clone instability rates. Analogously to fitness landscapes, the instability rates of clonal populations form their mutability landscapes. Here, we present MULAN (MUtability LANdscape inference), a maximum-likelihood computational framework for inference of mutation rates of individual cancer subclones using single-cell sequencing data. It utilizes the partial information about the orders of mutation events provided by cancer mutation trees and extends it by inferring full evolutionary history and mutability landscape of a tumor. Evaluation of mutation rates on the level of subclones rather than individual genes allows to capture the effects of genomic interactions and epistasis. We estimate the accuracy of our approach and demonstrate that it can be used to study the evolution of genetic instability and infer tumor evolutionary history from experimental data. MULAN is available at https://github.com/compbel/MULAN.

The Resistance Mechanisms of Lung Cancer Immunotherapy

Immunotherapy has revolutionized lung cancer treatment in the past decade. By reactivating the host’s immune system, immunotherapy significantly prolongs survival in some advanced lung cancer patients. However, resistance to immunotherapy is frequent, which manifests as a lack of initial response or clinical benefit to therapy (primary resistance) or tumor progression after the initial period of response (acquired resistance). Overcoming immunotherapy resistance is challenging owing to the complex and dynamic interplay among malignant cells and the defense system. This review aims to discuss the mechanisms that drive immunotherapy resistance and the innovative strategies implemented to overcome it in lung cancer.

Heritable gene expression variability and stochasticity govern clonal heterogeneity in circadian period

A ubiquitous feature of the circadian clock across life forms is its organization as a network of cellular oscillators, with individual cellular oscillators within the network often exhibiting considerable heterogeneity in their intrinsic periods. The interaction of coupling and heterogeneity in circadian clock networks is hypothesized to influence clock’s entrainability, but our knowledge of mechanisms governing period heterogeneity within circadian clock networks remains largely elusive. In this study, we aimed to explore the principles that underlie intercellular period variation in circadian clock networks (clonal period heterogeneity). To this end, we employed a laboratory selection approach and derived a panel of 25 clonal cell populations exhibiting circadian periods ranging from 22 to 28 h. We report that a single parent clone can produce progeny clones with a wide distribution of circadian periods, and this heterogeneity, in addition to being stochastically driven, has a heritable component. By quantifying the expression of 20 circadian clock and clock-associated genes across our clone panel, we found that inheritance of expression patterns in at least three clock genes might govern clonal period heterogeneity in circadian clock networks. Furthermore, we provide evidence suggesting that heritable epigenetic variation in gene expression regulation might underlie period heterogeneity.

How do genetically identical cells exhibit a different circadian phenotype? This study reveals that a single parent clone can produce progeny with a wide distribution of circadian periods and that this heterogeneity, in addition to being stochastically driven, has a heritable component, likely via heritable epigenetic variation in gene expression regulation.

Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data

Single-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide array of methods for CNA detection has been either developed specifically for or adapted to single-cell DNA sequencing data. Understanding the strengths and limitations that are unique to each of these methods is very important for obtaining accurate copy number profiles from single-cell DNA sequencing data. We benchmarked three widely used methods–Ginkgo, HMMcopy, and CopyNumber–on simulated as well as real datasets. To facilitate this, we developed a novel simulator of single-cell genome evolution in the presence of CNAs. Furthermore, to assess performance on empirical data where the ground truth is unknown, we introduce a phylogeny-based measure for identifying potentially erroneous inferences. While single-cell DNA sequencing is very promising for elucidating and understanding CNAs, our findings show that even the best existing method does not exceed 80% accuracy. New methods that significantly improve upon the accuracy of these three methods are needed. Furthermore, with the large datasets being generated, the methods must be computationally efficient.

Copy number aberrations, or CNAs, refer to evolutionary events that act on cancer genomes by deleting segments of the genomes or introducing new copies of existing segments. These events have been implicated in various types of cancer; consequently, their accurate detection could shed light on the initiation and progression of tumor, as well as on the development of potential targeted therapeutics. Single-cell DNA sequencing technologies are now producing the type of data that would allow such detection at the resolution of individual cells. However, to achieve this detection task, methods have to implement several steps of “data wrangling” and dealing with technical artifacts. In this work, we benchmarked three widely used methods for CNA detection from single-cell DNA data, namely Ginkgo, HMMcopy, and CopyNumber. To accomplish this study, we developed a novel simulator and devised a phylogeny-based measure of potentially erroneous CNA calls. We find that none of these methods has high accuracy, and all of them can be computationally very demanding. These findings call for the development of more accurate and more efficient methods for CNA detection from single-cell DNA data.

Ailanthone: A novel potential drug for treating human cancer

Cancer is the second leading cause of death after cardiovascular disease. In 2015, >8.7 million people died worldwide due to cancer, and by 2030 this figure is expected to increase to ~13.1 million. Tumor chemotherapy drugs have specific toxicity and side effects, and patients can also develop secondary drug resistance. To prevent and treat cancer, scientists have developed novel drugs with improved antitumor effects and decreased toxicity. Ailanthone (AIL) is a quassinoid extract from the traditional Chinese medicine plant Ailanthus altissima, which is known to have anti-inflammatory and antimalarial effects. An increasing number of studies have focused on AIL due to its antitumor activity. AIL can inhibit cell proliferation and induce apoptosis by up- or downregulating cancer-associated molecules, which ultimately leads to cancer cell death. Antitumor effects of AIL have been observed in melanoma, acute myeloid leukemia, bladder, lung, breast, gastric and prostate cancer and vestibular neurilemmoma. To the best of our knowledge, the present study is the first review to describe the antitumor mechanisms of AIL.

Intratumor Heterogeneity: The Rosetta Stone of Therapy Resistance

Advances in our understanding of molecular mechanisms of tumorigenesis have translated into knowledge-based therapies directed against specific oncogenic signaling targets. These therapies often induce dramatic responses in susceptible tumors. Unfortunately, most advanced cancers, including those with robust initial responses, eventually acquire resistance to targeted therapies and relapse. Even though immune-based therapies are more likely to achieve complete cures, acquired resistance remains an obstacle to their success as well. Acquired resistance is the direct consequence of pre-existing intratumor heterogeneity and ongoing diversification during therapy, which enables some tumor cells to survive treatment and facilitates the development of new therapy-resistant phenotypes. In this review, we discuss the sources of intratumor heterogeneity and approaches to capture and account for it during clinical decision making. Finally, we outline potential strategies to improve therapeutic outcomes by directly targeting intratumor heterogeneity.

Molecular pathology of Lynch syndrome

Lynch syndrome (LS) is characterised by predisposition to colorectal, endometrial, and other cancers and is caused by inherited pathogenic variants affecting the DNA mismatch repair (MMR) genes MLH1, MSH2, MSH6, and PMS2. It is probably the most common predisposition to cancer, having an estimated prevalence of between 1/100 and 1/180. Resources such as the International Society for Gastrointestinal Hereditary Cancer's MMR gene variant database, the Prospective Lynch Syndrome Database (PLSD), and the Colon Cancer Family Register (CCFR), as well as pathological and immunological studies, are enabling advances in the understanding of LS. These include defined criteria by which to interpret gene variants, the function of MMR in the normal control of apoptosis, definition of the risks of the various cancers, and the mechanisms and pathways by which the colorectal and endometrial tumours develop, including the critical role of the immune system. Colorectal cancers in LS can develop along three pathways, including flat intramucosal lesions, which depend on the underlying affected MMR gene. This gives insights into the limitations of colonoscopic surveillance and highlights the need for other forms of anti-cancer prophylaxis in LS. Finally, it shows that the processes of autoimmunisation and immunoediting fundamentally constrain the development of tumours in LS and explain the efficacy of immune checkpoint blockade therapy in MMR-deficient tumours. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Lack of Association Between Radiographic Tumor Burden and Efficacy of Immune Checkpoint Inhibitors in Advanced Lung Cancer

BACKGROUND

Historically, tumor burden has been considered an impediment to efficacy of immunotherapeutic agents, including vaccines, stem cell transplant, cytokine therapy, and intravesical bacillus Calmette-Guérin. This effect has been attributed to hypoxic zones in the tumor core contributing to poor T-cell infiltration, formation of immunosuppressive stromal cells, and development of therapy-resistant cell populations. However, the association between tumor burden and efficacy of immune checkpoint inhibitors is unknown. We sought to determine the association between radiographic tumor burden parameters and efficacy of immune checkpoint inhibitors in advanced lung cancer.

MATERIALS AND METHODS

We performed a retrospective analysis of patients with advanced lung cancer treated with immune checkpoint inhibitors. Demographic, disease, and treatment data were collected. Serial tumor dimensions were recorded according to RECIST version 1.1. Associations between radiographic tumor burden (baseline sum of longest diameters, longest single diameter) and clinical outcomes (radiographic response, progression-free survival, and overall survival) were determined using log-rank tests, Cox proportional-hazard regression, and logistic regression.

RESULTS

Among 105 patients, the median baseline sum of longest diameters (BSLD) was 6.4 cm; median longest single diameter was 3.6 cm. BSLD was not associated with best radiographic, progression-free survival, or overall survival. In univariate and multivariate analyses, no significant associations were observed for the other radiographic parameters and outcomes when considered as categorical or continuous variables.

CONCLUSION

Although tumor burden has been considered a mediator of efficacy of earlier immunotherapies, in advanced lung cancer it does not appear to affect outcomes from immune checkpoint inhibitors.

IMPLICATIONS FOR PRACTICE

Historically, tumor burden has been considered an impediment to the efficacy of various immunotherapies, including vaccines, cytokines, allogeneic stem cell transplant, and intravesical bacillus Calmette-Guérin. However, in the present study, no association was found between tumor burden and efficacy (response rate, progression-free survival, overall survival) of immune checkpoint inhibitors in advanced lung cancer. These findings suggest that immune checkpoint inhibitors may provide benefit across a range of disease burden, including bulky tumors considered resistant to other categories of immunotherapy.

The Acidic Tumor Microenvironment as a Driver of Cancer

Acidic metabolic waste products accumulate in the tumor microenvironment because of high metabolic activity and insufficient perfusion. In tumors, the acidity of the interstitial space and the relatively well-maintained intracellular pH influence cancer and stromal cell function, their mutual interplay, and their interactions with the extracellular matrix. Tumor pH is spatially and temporally heterogeneous, and the fitness advantage of cancer cells adapted to extracellular acidity is likely particularly evident when they encounter less acidic tumor regions, for instance, during invasion. Through complex effects on genetic stability, epigenetics, cellular metabolism, proliferation, and survival, the compartmentalized pH microenvironment favors cancer development. Cellular selection exacerbates the malignant phenotype, which is further enhanced by acid-induced cell motility, extracellular matrix degradation, attenuated immune responses, and modified cellular and intercellular signaling. In this review, we discuss how the acidity of the tumor microenvironment influences each stage in cancer development, from dysplasia to full-blown metastatic disease.

Towards Multidrug Adaptive Therapy

A new ecologically inspired paradigm in cancer treatment known as "adaptive therapy" capitalizes on competitive interactions between drug-sensitive and drug-resistant subclones. The goal of adaptive therapy is to maintain a controllable stable tumor burden by allowing a significant population of treatment-sensitive cells to survive. These, in turn, suppress proliferation of the less-fit resistant populations. However, there remain several open challenges in designing adaptive therapies, particularly in extending these therapeutic concepts to multiple treatments. We present a cancer treatment case study (metastatic castrate-resistant prostate cancer) as a point of departure to illustrate three novel concepts to aid the design of multidrug adaptive therapies. First, frequency-dependent "cycles" of tumor evolution can trap tumor evolution in a periodic, controllable loop. Second, the availability and selection of treatments may limit the evolutionary "absorbing region" reachable by the tumor. Third, the velocity of evolution significantly influences the optimal timing of drug sequences. These three conceptual advances provide a path forward for multidrug adaptive therapy. SIGNIFICANCE: Driving tumor evolution into periodic, repeatable treatment cycles provides a path forward for multidrug adaptive therapy.

A combined computational and experimental strategy identifies mutations conferring resistance to drugs targeting the BCR-ABL fusion protein

Drug resistance is of increasing concern, especially during the treatments of infectious diseases and cancer. To accelerate the drug discovery process in combating issues of drug resistance, here we developed a computational and experimental strategy to predict drug resistance mutations. Using BCR-ABL as a case study, we successfully recaptured the clinically observed mutations that confer resistance imatinib, nilotinib, dasatinib, bosutinib, and ponatinib. We then experimentally tested the predicted mutants in vitro. We found that although all mutants showed weakened binding strength as expected, the binding constants alone were not a good indicator of drug resistance. Instead, the half-maximal inhibitory concentration (IC50) was shown to be a good indicator of the incidence of the predicted mutations, together with change in catalytic efficacy. Our suggested strategy for predicting drug-resistance mutations includes the computational prediction and in vitro selection of mutants with increased IC50 values beyond the drug safety window.

Change in mutation frequency at a TP53 hotspot during culture of ENU-mutagenised human lymphoblastoid cells

Mutations in oncogenes or tumour suppressor genes cause increases in cell growth capacity. In some cases, fully malignant cancer cells develop after additional mutations occur in initially mutated cells. In such instances, the risk of cancer would increase in response to growth of these initially mutated cells. To ascertain whether such a situation might occur in cultured cells, three independent cultures of human lymphoblastoid GM00130 cells were treated with N-ethyl-N-nitrosourea to induce mutations, and the cells were maintained for 12 weeks. Mutant frequencies and spectra of the cells at the MspI and HaeIII restriction sites located at codons 247-250 of the TP53 gene were examined. Mutant frequencies at both sites in the gene exhibited a declining trend during cell culture and reached background levels after 12 weeks; this was also supported by mutation spectra findings. These results indicate that the mutations detected under our assay conditions are disadvantageous to cell growth.

Genetic instability as a driver for immune surveillance

*: BackgroundGenetic instability is known to relate with carcinogenesis by providing tumors with a mechanism for fast adaptation. However, mounting evidence also indicates causal relation between genetic instability and improved cancer prognosis resulting from efficient immune response. Highly unstable tumors seem to accumulate mutational burdens that result in dynamical landscapes of neoantigen production, eventually inducing acute immune recognition. How are tumor instability and enhanced immune response related? An important step towards future developments involving combined therapies would benefit from unraveling this connection. *: MethodsIn this paper we present a minimal mathematical model to describe the ecological interactions that couple tumor adaptation and immune recognition while making use of available experimental estimates of relevant parameters. The possible evolutionary trade-offs associated to both cancer replication and T cell response are analysed, and the roles of mutational load and immune activation in governing prognosis are studied. *: ResultsModeling and available data indicate that cancer-clearance states become attainable when both mutational load and immune migration are enhanced. Furthermore, the model predicts the presence of well-defined transitions towards tumor control and eradication after increases in genetic instability numerically consistent with recent experiments of tumor control after Mismatch Repair knockout in mice. *: ConclusionsThese two main results indicate a potential role of genetic instability as a driver of transitions towards immune control of tumors, as well as the effectiveness of increasing mutational loads prior to adoptive cell therapies. This mathematical framework is therefore a quantitative step towards predicting the outcomes of combined therapies where genetic instability might play a key role.

Association of Uveal Melanoma Metastatic Rate With Stochastic Mutation Rate and Type of Mutation

Importance

It is necessary to understand the mechanisms of metastasis of uveal melanoma to advise patients and develop treatments for this tumor.

Objective

To examine the stochastic properties of primary uveal melanoma including the mutation rate as a function of tumor size and metastatic rate relative to the type of mutation.

Design, Setting, and Participants

We computed the mutation rate in different sized uveal melanomas using previously published large data sets. Tumor volume was estimated using the spherical cap method. We also calculated the metastatic rate using an updated data set of patients with uveal melanoma with known mutations in BAP1, SF3B1, and EIF1AX provided by the Rotterdam Ocular Melanoma Study Group. Data were analyzed from 2 studies, one taking place from August 25, 1970, to August 27, 2008, and the other taking place between 1993 and 2013. Data were analyzed between 2016 and 2017.

Main Outcomes and Measures

Mutation rates and metastic rates.

Results

Based on the 5-year metastatic rates, mutation rates ranged from 1.09 × 10-8 to 7.86 × 10-7 per cell division, using our calculation algorithm. A higher mutation rate was found for tumors with smaller thicknesses. EIF1AX mutations were not exclusive of other mutations because 2 cases with EIF1AX mutations and metastasis also had BAP1 mutations. None of the tumors with only an EIF1AX mutation metastasized. After plotting the yearly metastatic rate vs time after treatment, we observed a small peak at 1 year and a large peak at 3.5 years after treatment for BAP1 mutations, with peaks between 2 and 3 years and at 7 years for SF3B1 mutations.

Conclusions and Relevance

We observed a higher mutation rate for smaller tumors, which may be explained by a greater number of cell divisions occurring during the expansion phase of smaller uveal melanomas. Regarding time to clinically detected metastases, the first 2 peaks appear to be associated with BAP1-mutated tumors and the late peak to SF3B1-mutated tumors.

Proteogenomic Analysis of Protein Sequence Alterations in Breast Cancer Cells

Cancer evolves as a result of an accumulation of mutations and chromosomal aberrations. Developments in sequencing technologies have enabled the discovery and cataloguing of millions of such mutations. The identification of protein-level alterations, typically by using reversed-phase protein arrays or mass spectrometry, has lagged, however, behind gene and transcript-level observations. In this study, we report the use of mass spectrometry for detecting the presence of mutations-missense, indels and frame shifts-in MCF7 and SKBR3 breast cancer, and non-tumorigenic MCF10A cells. The mutations were identified by expanding the database search process of raw mass spectrometry files by including an in-house built database of mutated peptides (XMAn-v1) that complemented a minimally redundant, canonical database of Homo sapiens proteins. The work resulted in the identification of nearly 300 mutated peptide sequences, of which ~50 were characterized by quality tandem mass spectra. We describe the criteria that were used to select the mutated peptide sequences, evaluate the parameters that characterized these peptides, and assess the artifacts that could have led to false peptide identifications. Further, we discuss the functional domains and biological processes that may be impacted by the observed peptide alterations, and how protein-level detection can support the efforts of identifying cancer driving mutations and genes. Mass spectrometry data are available via ProteomeXchange with identifier PXD014458.

Conversion of Stem Cells to Cancer Stem Cells: Undercurrent of Cancer Initiation

Cancer stem cells (CSCs) also known as cancer-initiating cells (CIC), are responsible for the sustained and uncontrolled growth of malignant tumors and are proposed to play significant roles in metastasis and recurrence. Several hypotheses have proposed that the events in either stem and/or differentiated cells, such as genomic instability, inflammatory microenvironment, cell fusion, and lateral gene transfer, should be considered as the possible origin of CSCs. However, until now, the exact origin of CSC has been obscure. The development of induced pluripotent stem cells (iPSCs) in 2007, by Yamanaka's group, has been met with much fervency and hailed as a breakthrough discovery by the scientific and research communities, especially in regeneration therapy. The studies on the development of CSC from iPSCs should also open a new page of cancer research, which will help in designing new therapies applicable to CSCs. Currently most reviews have focused on CSCs and CSC niches. However, the insight into the niche before the CSC niche should also be of keen interest. This review introduces the novel concept of cancer initiation introducing the conversion of iPSCs to CSCs and proposes a relationship between the inflammatory microenvironment and cancer initiation as the key concept of the cancer-inducing niche responsible for the development of CSC.

Estimating the number of genetic mutations (hits) required for carcinogenesis based on the distribution of somatic mutations

Individual instances of cancer are primarily a result of a combination of a small number of genetic mutations (hits). Knowing the number of such mutations is a prerequisite for identifying specific combinations of carcinogenic mutations and understanding the etiology of cancer. We present a mathematical model for estimating the number of hits based on the distribution of somatic mutations. The model is fundamentally different from previous approaches, which are based on cancer incidence by age. Our somatic mutation based model is likely to be more robust than age-based models since it does not require knowing or accounting for the highly variable mutation rate, which can vary by over three orders of magnitude. In fact, we find that the number of somatic mutations at diagnosis is weakly correlated with age at cancer diagnosis, most likely due to the extreme variability in mutation rates between individuals. Comparing the distribution of somatic mutations predicted by our model to the actual distribution from 6904 tumor samples we estimate the number of hits required for carcinogenesis for 17 cancer types. We find that different cancer types exhibit distinct somatic mutational profiles corresponding to different numbers of hits. Why might different cancer types require different numbers of hits for carcinogenesis? The answer may provide insight into the unique etiology of different cancer types.

Cancer is primarily a result of genetic mutations. Each individual instance of cancer is initiated by a specific combination of a small number of mutations (hits). In trying to identify these combinations of mutations, it is important to know how many hits to look for. However, there are conflicting estimates for the number of hits. We present a fundamentally different model for estimating the number of hits. We found that the number hits ranges from two-eight depending on cancer type. These findings may provide insight into the unique characteristics of different cancer types.

Mutations in DNA repair genes are associated with increased neoantigen burden and a distinct immunophenotype in lung squamous cell carcinoma

Deficiencies in DNA repair pathways, including mismatch repair (MMR), have been linked to higher tumor mutation burden and improved response to immune checkpoint inhibitors. However, the significance of MMR mutations in lung cancer has not been well characterized, and the relevance of other processes, including homologous recombination (HR) and polymerase epsilon (POLE) activity, remains unclear. Here, we analyzed a dataset of lung squamous cell carcinoma samples from The Cancer Genome Atlas. Variants in DNA repair genes were associated with increased tumor mutation and neoantigen burden, which in turn were linked with greater tumor infiltration by activated T cells. The subset of tumors with DNA repair gene variants but without T cell infiltration exhibited upregulation of TGF-β and Wnt pathway genes, and a combined score incorporating these genes and DNA repair status accurately predicted immune cell infiltration. Finally, high neoantigen burden was positively associated with genes related to cytolytic activity and immune checkpoints. These findings provide evidence that DNA repair pathway defects and immunomodulatory genes together lead to specific immunophenotypes in lung squamous cell carcinoma and could potentially serve as biomarkers for immunotherapy.

Different behaviour of DVL1, DVL2, DVL3 in astrocytoma malignancy grades and their association to TCF1 and LEF1 upregulation

Key regulators of the Wnt signalling, DVL1, DVL2 and DVL3, in astrocytomas of different malignancy grades were investigated. Markers for DVL1, DVL2 and DVL3 were used to detect microsatellite instability (MSI) and gross deletions (LOH), while immunohistochemistry and immunoreactivity score were used to determine the signal strengths of the three DVL proteins and transcription factors of the pathway, TCF1 and LEF1. Our findings demonstrated that MSI at all three DVL loci was constantly found across tumour grades with the highest number in grade II (P = 0.008). Collectively, LOHs were more frequent in high-grade tumours than in low grade ones. LOHs of DVL3 gene were significantly associated with grade IV tumours (P = 0.007). The results on protein expressions indicated that high-grade tumours expressed less DVL1 protein as compared with low grade ones. A significant negative correlation was established between DVL1 expression and malignancy grades (P < 0.001). The expression of DVL2 protein was found similar across grades, while DVL3 expression significantly increased with malignancy grades (P < 0.001). The signal strengths of expressed DVL1 and DVL3 were negatively correlated (P = 0.002). However, TCF1 and LEF1 were both significantly upregulated and increasing with astrocytoma grades (P = 0.001). A positive correlation was established between DVL3 and both TCF1 (P = 0.020) and LEF1 (P = 0.006) suggesting their joint involvement in malignant progression. Our findings suggest that DVL1 and DVL2 may be involved during early stages of the disease, while DVL3 may have a role in later phases and together with TCF1 and LEF1 promotes the activation of Wnt signalling.