Evolutionary dynamics of neoantigens in growing tumors

Modeling tumors as species-rich ecological communities

Many advanced cancers resist therapeutic intervention. This process is fundamentally related to intra-tumor heterogeneity: multiple cell populations, each with different mutational and phenotypic signatures, coexist within a tumor and its metastatic nodes. Like species in an ecosystem, many cancer cell populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity nor are able to predict its consequences. Here we propose that the Generalized Lotka-Volterra model (GLV), a standard tool to describe complex, species-rich ecological communities, provides a suitable framework to describe the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties, such as outgrowth and multistability, provide a new understanding of the disease. Additionally, we discuss potential extensions of the model and their application to three active areas of cancer research, namely phenotypic plasticity, the cancer-immune interplay and the resistance of metastatic tumors to treatment. Our work outlines a set of questions and a tentative road map for further research in cancer ecology.

Are we getting closer to a successful neoantigen cancer vaccine?

Although significant advances in immunotherapy have revolutionized the treatment of many cancer types over the past decade, the field of vaccine therapy, an important component of cancer immunotherapy, despite decades-long intense efforts, is still transmitting signals of promises and awaiting strong data on efficacy to proceed with regulatory approval. The field of cancer vaccines faces standard challenges, such as tumor-induced immunosuppression, immune response in inhibitory tumor microenvironment (TME), intratumor heterogeneity (ITH), permanently evolving cancer mutational landscape leading to neoantigens, and less known obstacles: neoantigen gain/loss upon immunotherapy, the timing and speed of appearance of neoantigens and responding T cell clonotypes and possible involvement of immune interference/heterologous immunity, in the complex interplay between evolving tumor epitopes and the immune system. In this review, we discuss some key issues related to challenges hampering the development of cancer vaccines, along with the current approaches focusing on neoantigens. We summarize currently well-known ideas/rationales, thus revealing the need for alternative vaccine approaches. Such a discussion should stimulate vaccine researchers to apply out-of-box, unconventional thinking in search of new avenues to deal with critical, often yet unaddressed challenges on the road to a new generation of therapeutics and vaccines.

Epigenome and early selection determine the tumour-immune evolutionary trajectory of colorectal cancer

Immune system control is a major hurdle that cancer evolution must circumvent. The relative timing and evolutionary dynamics of subclones that have escaped immune control remain incompletely characterized, and how immune-mediated selection shapes the epigenome has received little attention. Here, we infer the genome- and epigenome-driven evolutionary dynamics of tumour-immune coevolution within primary colorectal cancers (CRCs). We utilise our existing CRC multi-region multi-omic dataset that we supplement with high-resolution spatially-resolved neoantigen sequencing data and highly multiplexed imaging of the tumour microenvironment (TME). Analysis of somatic chromatin accessibility alterations (SCAAs) reveals frequent somatic loss of accessibility at antigen presenting genes, and that SCAAs contribute to silencing of neoantigens. We observe that strong immune escape and exclusion occur at the outset of CRC formation, and that within tumours, including at the microscopic level of individual tumour glands, additional immune escape alterations have negligible consequences for the immunophenotype of cancer cells. Further minor immuno-editing occurs during local invasion and is associated with TME reorganisation, but that evolutionary bottleneck is relatively weak. Collectively, we show that immune evasion in CRC follows a "Big Bang" evolutionary pattern, whereby genetic, epigenetic and TME-driven immune evasion acquired by the time of transformation defines subsequent cancer-immune evolution.

Pan-cancer analysis of 60S Ribosomal Protein L7-Like 1 (RPL7L1) and validation in liver hepatocellular carcinoma

BACKGROUND AND AIMS

There is an association between cancer and increased ribosome biogenesis. At present, the RPL7L1 (60S Ribosomal Protein L7-Like 1) were less reported by literature search. Study reports that RPL7L1 is associated with mouse embryonic and skeletal muscle. The study of RPL7L1 on tumors has not been reported.

METHODS

Our team downloaded the pan-cancer dataset that is uniformly normalized from the UCSC database (N=19131). Our study examined the relationship between RPL7L1 expression level and clinical prognosis with methylation, anti-tumour immunity, functional states, MSI, TMB, DNSss, LOH and chemotherapeutic responses in 43 cancer types and subtypes.

RESULTS AND CONCLUSIONS

RPL7L1 was overexpressed in nine tumor types. Gene mutation, tumor microenvironment and methylation modification of RPL7L1 plays a key role in patient prognosis. And the high expression of RPL7L1 was associated with TMB, MSI, LOH especially LIHC and HNSC. We experimentally verified that genes can promote the proliferation and migration of tumor cells. Our study suggested that RPL7L1 biomarker can be used for treating cancer, detecting it, and predicting its prognosis.

Integrated Multi-omics Analyses Identify CDCA5 as a Novel Biomarker Associated with Alternative Splicing, Tumor Microenvironment, and Cell Proliferation in Colon Cancer Via Pan-cancer Analysis

Background: CDCA5 has been reported as a gene involved in the cell cycle, however current research provides little details. Our goal was to figure out its functions and probable mechanisms in pan-cancer. Methods: Pan-cancer bulk sequencing data and web-based analysis tools were applied to analyze CDCA5's correlations with the gene expression, clinical prognosis, genetic alterations, promoter methylation, alternative splicing, immune checkpoints, tumor microenvironment and enrichment. Real‑time PCR, cell clone formation assay, CCK-8 assay, cell proliferation assay, migration assay, invasion assay and apoptosis assay were used to evaluate the effect of CDCA5 silencing on colon cancer cell lines. Results: CDCA5 is highly expressed in most tumors, which has been linked to a poor prognosis. Immune checkpoints analysis revealed that CDCA5 was associated with the immune gene CD276 in various tumors. Single-cell analysis showed that CDCA5 correlated with proliferating T cell infiltration in COAD. Enrichment analysis demonstrated that CDCA5 may modify cell cycle genes to influence p53 signaling. The examination of DLD1 cells revealed that CDCA5 increased the proliferation and blocked cell apoptosis. Conclusion: This study contributes to the knowledge of the role of CDCA5 in carcinogenesis, highlighting the prognostic potential and carcinogenic involvement of CDCA5 in pan-cancer.

The loss of neoantigens is an important reason for immune escape in multiple myeloma patients with high intratumor heterogeneity

OBJECTIVES

Intratumor heterogeneity (ITH) is an important factor for clinical outcomes in patients with multiple myeloma (MM). High ITH has been proven to be a key reason for tumor immune escape and treatment resistance. Neoantigens are thought to be associated with ITH, but the specific correlation and functional basis for this remains unclear.

METHODS

We study this question through the whole-exome sequencing (WES) data from 43 high ITH newly diagnosed MM patients in our center. Mutant allele tumor heterogeneity (MATH) was conducted to quantify ITH. The cutoff value for high intratumor heterogeneity was determined by comparing MATH of different kinds of tumors. NeoPredPipe was performed to predict neoantigens and binding affinity.

RESULTS

Compared to other tumors, MM has a relatively low tumor mutation burden but a high ITH. Patients with high MATH had significantly shorter progression-free survival times than those with low MATH (p = 0.001). In high ITH samples, there is a decrease in strong-binding neoantigens (p = 0.019). The loss of strong-binding neoantigens is a key factor for insensitivity to therapy (p = 0.015). Loss of heterozygosity in HLA was not observed. In addition, patients with fewer neoantigens loss had higher rates of disease remission (p = 0.047). CD8 + T cells (p = 0.012) and NK cells (p = 0.011) decreased significantly in patients with high neoantigens loss rate. A prediction model based on neoantigens was built to evaluate the strength of immune escape.

CONCLUSION

The loss of strong-binding neoantigens explains why tumors with high ITH have a higher degree of immune escape and may be feasible for deciding the clinical treatment of MM.

Steering and controlling evolution - from bioengineering to fighting pathogens

Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.

Subclonal accumulation of immune escape mechanisms in microsatellite instability-high colorectal cancers

BACKGROUND

Intratumor heterogeneity (ITH) in microsatellite instability-high (MSI-H) colorectal cancer (CRC) has been poorly studied. We aimed to clarify how the ITH of MSI-H CRCs is generated in cancer evolution and how immune selective pressure affects ITH.

METHODS

We reanalyzed public whole-exome sequencing data on 246 MSI-H CRCs. In addition, we performed a multi-region analysis from 6 MSI-H CRCs. To verify the process of subclonal immune escape accumulation, a novel computational model of cancer evolution under immune pressure was developed.

RESULTS

Our analysis presented the enrichment of functional genomic alterations in antigen-presentation machinery (APM). Associative analysis of neoantigens indicated the generation of immune escape mechanisms via HLA alterations. Multiregion analysis revealed the clonal acquisition of driver mutations and subclonal accumulation of APM defects in MSI-H CRCs. Examination of variant allele frequencies demonstrated that subclonal mutations tend to be subjected to selective sweep. Computational simulations of tumour progression with the interaction of immune cells successfully verified the subclonal accumulation of immune escape mutations and suggested the efficacy of early initiation of an immune checkpoint inhibitor (ICI) -based treatment.

CONCLUSIONS

Our results demonstrate the heterogeneous acquisition of immune escape mechanisms in MSI-H CRCs by Darwinian selection, providing novel insights into ICI-based treatment strategies.

Sequential mutations in exponentially growing populations

Stochastic models of sequential mutation acquisition are widely used to quantify cancer and bacterial evolution. Across manifold scenarios, recurrent research questions are: how many cells are there with n alterations, and how long will it take for these cells to appear. For exponentially growing populations, these questions have been tackled only in special cases so far. Here, within a multitype branching process framework, we consider a general mutational path where mutations may be advantageous, neutral or deleterious. In the biologically relevant limiting regimes of large times and small mutation rates, we derive probability distributions for the number, and arrival time, of cells with n mutations. Surprisingly, the two quantities respectively follow Mittag-Leffler and logistic distributions regardless of n or the mutations' selective effects. Our results provide a rapid method to assess how altering the fundamental division, death, and mutation rates impacts the arrival time, and number, of mutant cells. We highlight consequences for mutation rate inference in fluctuation assays.

A novel integrated approach to predicting cancer immunotherapy efficacy

Immunotherapies have revolutionized cancer treatment modalities; however, predicting clinical response accurately and reliably remains challenging. Neoantigen load is considered as a fundamental genetic determinant of therapeutic response. However, only a few predicted neoantigens are highly immunogenic, with little focus on intratumor heterogeneity (ITH) in the neoantigen landscape and its link with different features in the tumor microenvironment. To address this issue, we comprehensively characterized neoantigens arising from nonsynonymous mutations and gene fusions in lung cancer and melanoma. We developed a composite NEO2IS to characterize interplays between cancer and CD8+ T-cell populations. NEO2IS improved prediction accuracy of patient responses to immune-checkpoint blockades (ICBs). We found that TCR repertoire diversity was consistent with the neoantigen heterogeneity under evolutionary selections. Our defined neoantigen ITH score (NEOITHS) reflected infiltration degree of CD8+ T lymphocytes with different differentiation states and manifested the impact of negative selection pressure on CD8+ T-cell lineage heterogeneity or tumor ecosystem plasticity. We classified tumors into distinct immune subtypes and examined how neoantigen-T cells interactions affected disease progression and treatment response. Overall, our integrated framework helps profile neoantigen patterns that elicit T-cell immunoreactivity, enhance the understanding of evolving tumor-immune interplays and improve prediction of ICBs efficacy.

Genetic immune escape landscape in primary and metastatic cancer

Studies have characterized the immune escape landscape across primary tumors. However, whether late-stage metastatic tumors present differences in genetic immune escape (GIE) prevalence and dynamics remains unclear. We performed a pan-cancer characterization of GIE prevalence across six immune escape pathways in 6,319 uniformly processed tumor samples. To address the complexity of the HLA-I locus in the germline and in tumors, we developed LILAC, an open-source integrative framework. One in four tumors harbors GIE alterations, with high mechanistic and frequency variability across cancer types. GIE prevalence is generally consistent between primary and metastatic tumors. We reveal that GIE alterations are selected for in tumor evolution and focal loss of heterozygosity of HLA-I tends to eliminate the HLA allele, presenting the largest neoepitope repertoire. Finally, high mutational burden tumors showed a tendency toward focal loss of heterozygosity of HLA-I as the immune evasion mechanism, whereas, in hypermutated tumors, other immune evasion strategies prevail.

Optimal cancer evasion in a dynamic immune microenvironment generates diverse post-escape tumor antigenicity profiles

The failure of cancer treatments, including immunotherapy, continues to be a major obstacle in preventing durable remission. This failure often results from tumor evolution, both genotypic and phenotypic, away from sensitive cell states. Here, we propose a mathematical framework for studying the dynamics of adaptive immune evasion that tracks the number of tumor-associated antigens available for immune targeting. We solve for the unique optimal cancer evasion strategy using stochastic dynamic programming and demonstrate that this policy results in increased cancer evasion rates compared to a passive, fixed strategy. Our foundational model relates the likelihood and temporal dynamics of cancer evasion to features of the immune microenvironment, where tumor immunogenicity reflects a balance between cancer adaptation and host recognition. In contrast with a passive strategy, optimally adaptive evaders navigating varying selective environments result in substantially heterogeneous post-escape tumor antigenicity, giving rise to immunogenically hot and cold tumors.

Early immune pressure initiated by tissue-resident memory T cells sculpts tumor evolution in non-small cell lung cancer

Tissue-resident memory T (T_RM) cells provide immune defense against local infection and can inhibit cancer progression. However, it is unclear to what extent chronic inflammation impacts T_RM activation and whether T_RM cells existing in tissues before tumor onset influence cancer evolution in humans. We performed deep profiling of healthy lungs and lung cancers in never-smokers (NSs) and ever-smokers (ESs), finding evidence of enhanced immunosurveillance by cells with a T_RM-like phenotype in ES lungs. In preclinical models, tumor-specific or bystander T_RM-like cells present prior to tumor onset boosted immune cell recruitment, causing tumor immune evasion through loss of MHC class I protein expression and resistance to immune checkpoint inhibitors. In humans, only tumors arising in ES patients underwent clonal immune evasion, unrelated to tobacco-associated mutagenic signatures or oncogenic drivers. These data demonstrate that enhanced T_RM-like activity prior to tumor development shapes the evolution of tumor immunogenicity and can impact immunotherapy outcomes.

A multispecies framework for modeling adaptive immunity and immunotherapy in cancer

Predator-prey theory is commonly used to describe tumor growth in the presence of selective pressure from the adaptive immune system. These interactions are mediated by the tumor immunopeptidome (what the tumor "shows" the body) and the T-cell receptor (TCR) repertoire (how well the body "sees" cancer cells). The tumor immunopeptidome comprises neoantigens which can be gained and lost throughout tumorigenesis and treatment. Heterogeneity in the immunopeptidome is predictive of poor response to immunotherapy in some tumor types, suggesting that the TCR repertoire is unable to support a fully polyclonal response against every neoantigen. Importantly, while tumor and T-cell populations are known to compete with each other for intratumoral resources, whether between-lineage competition among peripheral T cells influences the TCR repertoire is unknown and difficult to interrogate experimentally. Computational models may offer a way to investigate these phenomena and deepen our understanding of the tumor-immune axis. Here, we construct a predator-prey-like model and calibrate it to preclinical and clinical data to describe tumor growth and immunopeptidome diversification. Simultaneously, we model the expansion of antigen-specific T-cell lineages and their consumption of both lineage-specific antigenic resources and lineage-agnostic, shared resources. This predator-prey-like framework accurately described clinically observed immunopeptidomes; recapitulated response-associated effects of immunotherapy, including immunoediting; and allowed exploration of treatment of tumors with varying growth and mutation rates.

Agent-based methods facilitate integrative science in cancer

In this opinion, we highlight agent-based modeling as a key tool for exploration of cell-cell and cell-environment interactions that drive cancer progression, therapeutic resistance, and metastasis. These biological phenomena are particularly suited to be captured at the cell-scale resolution possible only within agent-based or individual-based mathematical models. These modeling approaches complement experimental work (in vitro and in vivo systems) through parameterization and data extrapolation but also feed forward to drive new experiments that test model-generated predictions.

Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors

In cancer, evolutionary forces select for clones that evade the immune system. Here we analyzed >10,000 primary tumors and 356 immune-checkpoint-treated metastases using immune dN/dS, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome, to measure immune selection in cohorts and individuals. We classified tumors as immune edited when antigenic mutations were removed by negative selection and immune escaped when antigenicity was covered up by aberrant immune modulation. Only in immune-edited tumors was immune predation linked to CD8 T cell infiltration. Immune-escaped metastases experienced the best response to immunotherapy, whereas immune-edited patients did not benefit, suggesting a preexisting resistance mechanism. Similarly, in a longitudinal cohort, nivolumab treatment removes neoantigens exclusively in the immunopeptidome of nonimmune-edited patients, the group with the best overall survival response. Our work uses dN/dS to differentiate between immune-edited and immune-escaped tumors, measuring potential antigenicity and ultimately helping predict response to treatment.

Tumor-Derived Extracellular Vesicles in Cancer Immunoediting and Their Potential as Oncoimmunotherapeutics

The tumor microenvironment (TME) within and around a tumor is a complex interacting mixture of tumor cells with various stromal cells, including endothelial cells, fibroblasts, and immune cells. In the early steps of tumor formation, the local microenvironment tends to oppose carcinogenesis, while with cancer progression, the microenvironment skews into a protumoral TME and the tumor influences stromal cells to provide tumor-supporting functions. The creation and development of cancer are dependent on escape from immune recognition predominantly by influencing stromal cells, particularly immune cells, to suppress antitumor immunity. This overall process is generally called immunoediting and has been categorized into three phases; elimination, equilibrium, and escape. Interaction of tumor cells with stromal cells in the TME is mediated generally by cell-to-cell contact, cytokines, growth factors, and extracellular vesicles (EVs). The least well studied are EVs (especially exosomes), which are nanoparticle-sized bilayer membrane vesicles released by many cell types that participate in cell/cell communication. EVs carry various proteins, nucleic acids, lipids, and small molecules that influence cells that ingest the EVs. Tumor-derived extracellular vesicles (TEVs) play a significant role in every stage of immunoediting, and their cargoes change from immune-activating in the early stages of immunoediting into immunosuppressing in the escape phase. In addition, their cargos change with different treatments or stress conditions and can be influenced to be more immune stimulatory against cancer. This review focuses on the emerging understanding of how TEVs affect the differentiation and effector functions of stromal cells and their role in immunoediting, from the early stages of immunoediting to immune escape. Consideration of how TEVs can be therapeutically utilized includes different treatments that can modify TEV to support cancer immunotherapy.

Open problems in mathematical biology

Biology is data-rich, and it is equally rich in concepts and hypotheses. Part of trying to understand biological processes and systems is therefore to confront our ideas and hypotheses with data using statistical methods to determine the extent to which our hypotheses agree with reality. But doing so in a systematic way is becoming increasingly challenging as our hypotheses become more detailed, and our data becomes more complex. Mathematical methods are therefore gaining in importance across the life- and biomedical sciences. Mathematical models allow us to test our understanding, make testable predictions about future behaviour, and gain insights into how we can control the behaviour of biological systems. It has been argued that mathematical methods can be of great benefit to biologists to make sense of data. But mathematics and mathematicians are set to benefit equally from considering the often bewildering complexity inherent to living systems. Here we present a small selection of open problems and challenges in mathematical biology. We have chosen these open problems because they are of both biological and mathematical interest.

Phenotypic plasticity and genetic control in colorectal cancer evolution

Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity1. The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, we find that the majority of intratumour variation in gene expression is not strongly heritable but rather 'plastic'. Somatic expression quantitative trait loci analysis identified a number of putative genetic controls of expression by cis-acting coding and non-coding mutations, the majority of which were clonal within a tumour, alongside frequent structural alterations. Consistently, computational inference on the spatial patterning of tumour phylogenies finds that a considerable proportion of CRCs did not show evidence of subclonal selection, with only a subset of putative genetic drivers associated with subclone expansions. Spatial intermixing of clones is common, with some tumours growing exponentially and others only at the periphery. Together, our data suggest that most genetic intratumour variation in CRC has no major phenotypic consequence and that transcriptional plasticity is, instead, widespread within a tumour.

Game of clones: Battles in the field of carcinogenesis

Recent advances in bulk sequencing approaches as well as genomic decoding at the single-cell level have revealed surprisingly high somatic mutational burdens in normal tissues, as well as increased our understanding of the landscape of "field cancerization", that is, molecular and immune alterations in mutagen-exposed normal-appearing tissues that recapitulated those present in tumors. Charting the somatic mutational landscapes in normal tissues can have strong implications on our understanding of how tumors arise from mutagenized epithelium. Making sense of those mutations to understand the progression along the pathologic continuum of normal epithelia, preneoplasias, up to malignant tissues will help pave way for identification of ideal targets that can guide new strategies for preventing or eliminating cancers at their earliest stages of development. In this review, we will provide a brief history of field cancerization and its implications on understanding early stages of cancer pathogenesis and deviation from the pathologically "normal" state. The review will provide an overview of how mutations accumulating in normal tissues can lead to a patchwork of mutated cell clones that compete while maintaining an overall state of functional homeostasis. The review also explores the role of clonal competition in directing the fate of normal tissues and summarizes multiple mechanisms elicited in this phenomenon and which have been linked to cancer development. Finally, we highlight the importance of understanding mutations in normal tissues, as well as clonal competition dynamics (in both the epithelium and the microenvironment) and their significance in exploring new approaches to combatting cancer.

Harnessing gene fusion-derived neoantigens for 'cold' breast and prostate tumor immunotherapy

Breast and prostate cancers are generally considered immunologically 'cold' tumors due to multiple mechanisms rendering them unresponsive to immune checkpoint blockade therapies. With little success in garnering positive outcomes in modern immunotherapeutic clinical trials, it is prudent to re-examine the role of immunogenic neoantigens in these cold tumors. Gene fusions are driver mutations in hormone-driven cancers that can result in alternative mutation-specific neoantigens to promote immunotherapy sensitivity. This review focuses on 1) gene fusion formation mechanisms in neoantigen generation; 2) gene fusion neoantigens in cancer immunotherapeutic strategies and associated clinical trials; and 3) challenges and opportunities in computational and liquid biopsy technologies. This review is anticipated to initiate further research into gene fusion neoantigens of cold tumors for further experimental validation.

BCHE as a Prognostic Biomarker in Endometrial Cancer and Its Correlation with Immunity

Background

In developed countries, the most common gynecologic malignancy is endometrial carcinoma (EC), making the identification of EC biomarkers extremely essential. As a natural enzyme, butyrylcholinesterase (BCHE) is found in hepatocytes and plasma. There is a strong correlation between BCHE gene mutations and cancers and other diseases. The aim of this study was to analyze the role of BCHE in patients with EC.

Methods

A variety of analyses were conducted on The Cancer Genome Atlas (TCGA) data, including differential expression analysis, enrichment analysis, immunity, clinicopathology, and survival analysis. The Gene Expression Omnibus (GEO) database was used to validate outcomes. Using R tools, Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) analyses revealed the potential mechanisms of BCHE in EC. Sangerbox tools were used to delve into the relations between BCHE expression and tumor microenvironment, including microsatellite instability (MSI), tumor neoantigen count (TNC), and tumor mutation burden (TMB). BCHE's genetic alteration analysis was conducted by cBioPortal. In addition, the Human Protein Atlas (HPA) was used to validate the outcomes by immunohistochemistry, and an analysis of the protein-protein interaction network (PPI) was performed with the help of the STRING database.

Results

Based on our results, BCHE was a significant independent prognostic factor for patients with EC. The prognosis with EC was affected by age, stage, grade, histological type, and BCHE. GSEA showed that BCHE was closely related to pathways regulating immune response, including transforming growth factor-β (TGF-β) signaling pathways and cancer immunotherapy through PD1 blockade pathways. The immune analysis revealed that CD4+ regulatory T cells (Tregs) were negatively correlated with BCHE expression and the immune checkpoint molecules CD28, ADORA2A, BTNL2, and TNFRSF18 were all significantly related to BCHE. BCHE expression was also associated with TMB by genetic alteration analysis.

Conclusions

Identifying BCHE as a biomarker for EC might help predict its prognosis and could have important implications for immunotherapy.

Network-based machine learning approach to predict immunotherapy response in cancer patients

Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions. We curate more than 700 ICI-treated patient samples with clinical outcomes and transcriptomic data, and observe that NetBio-based predictions accurately predict ICI treatment responses in three different cancer types—melanoma, gastric cancer, and bladder cancer. Moreover, the NetBio-based prediction is superior to predictions based on other conventional ICI treatment biomarkers, such as ICI targets or tumor microenvironment-associated markers. This work presents a network-based method to effectively select immunotherapy-response-associated biomarkers that can make robust ML-based predictions for precision oncology.

Identifying biomarkers for response to immunotherapy in cancer remains challenging. Here, the authors develop an approach based on network biology and machine learning -NetBio- to identify molecular biomarkers of response to immunotherapy across different cancer types and cohorts.

HLA-I-restricted CD8+ T cell immunity may accelerate tumorigenesis in conjunction with VHL inactivation

CD8⁺ T cells recognize and kill tumor cells with HLA-I tumor antigens in early tumorigenesis, the efficiency of which differs according to antigen-recognition coverage, as shown in earlier tumor onset in HLA-I homozygosity. However, the universality of these associations remains unknown. Here, we assessed the tumor type and driver mutation specificity in the association between tumor onset age and HLA-I zygosity. Statistical analyses identified an unexpected negative relationship in tumors with VHL biallelic loss, wherein HLA-I heterozygosity was associated with earlier tumor onset, while all others showed either no or a positive association. Testing on an independent dataset reproduced the VHL-dependent acceleration of tumor onset in the HLA-I heterozygous group, confirming the association. Further speculation proposed VEGF-A-mediated T cell exhaustion under VHL inactivation as a potential mechanism. Our findings suggest that CD8⁺ T cell immunity in early tumor suppression can be conditional to the genetic status of tumors and may even lead to adverse consequences.

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HLA homozygosity reduces antigen coverage and is associated with earlier tumor onset

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Tumors with VHL^−/−, such as ccRCC, have the opposite association

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In VHL^−/− tumors, CD8⁺ T cell immunity may have adverse effects in imunosurveillance

Cancer evolution: special focus on the immune aspect of cancer

Cancer is an evolutionary disease. Intra-tumor heterogeneity (ITH), which describes the diversity within individual tumors, sets the foundation for evolution. The fitness of tumor cells is determined by their microenvironment, which exerts intense selection pressure that generally favors cells with survival and proliferation advantages. It has been revealed that host immunity dramatically influences the evolutionary trajectory of cancer. As technologies advance, a refined map of the immune system's involvement in cancer evolution has gradually come to our knowledge. Here we specifically view cancer through the lens of evolutionary immunological biology. We will cover the neoplastic evolution under immunosurveillance, including how the host immunity shapes the tumor evolutionary trajectory and how progressive tumors modulate the host immunity to survive. A comprehensive understanding of the interplay between cancer evolution and cancer immunity provides clues to combating cancer strategically.

Quantification of neoantigen-mediated immunoediting in cancer evolution

Immunoediting includes three temporally distinct stages, termed elimination, equilibrium, and escape, and has been proposed to explain the interactions between cancer cells and the immune system during the evolution of cancer. However, the status of immunoediting in cancer remains unclear, and the existence of neoantigen depletion in untreated cancer has been debated. Here we developed a distribution pattern-based method for quantifying neoantigen-mediated negative selection in cancer evolution. The method can provide a robust and reliable quantification for immunoediting signal in individual cancer patients. Moreover, this method demonstrated the prevalence of immunoediting in immunotherapy-naive cancer genome. The elimination and escape stages of immunoediting can be quantified separately, where tumor types with strong immunoediting-elimination exhibit a weak immunoediting-escape signal, and vice versa. The quantified immunoediting-elimination signal was predictive of clinical response to cancer immunotherapy. Collectively, immunoediting quantification provides an evolutionary perspective for evaluating the antigenicity of neoantigens and reveals a potential biomarker for precision immunotherapy in cancer.

Comprehensive profiling of 1015 patients' exomes reveals genomic-clinical associations in colorectal cancer

The genetic basis of colorectal cancer (CRC) and its clinical associations remain poorly understood due to limited samples or targeted genes in current studies. Here, we perform ultradeep whole-exome sequencing on 1015 patients with CRC as part of the ChangKang Project. We identify 46 high-confident significantly mutated genes, 8 of which mutate in 14.9% of patients: LYST, DAPK1, CR2, KIF16B, NPIPB15, SYTL2, ZNF91, and KIAA0586. With an unsupervised clustering algorithm, we propose a subtyping strategy that classisfies CRC patients into four genomic subtypes with distinct clinical characteristics, including hypermutated, chromosome instability with high risk, chromosome instability with low risk, and genome stability. Analysis of immunogenicity uncover the association of immunogenicity reduction with genomic subtypes and poor prognosis in CRC. Moreover, we find that mitochondrial DNA copy number is an independent factor for predicting the survival outcome of CRCs. Overall, our results provide CRC-related molecular features for clinical practice and a valuable resource for translational research.

Immunosuppressive niche engineering at the onset of human colorectal cancer

The evolutionary dynamics of tumor initiation remain undetermined, and the interplay between neoplastic cells and the immune system is hypothesized to be critical in transformation. Colorectal cancer (CRC) presents a unique opportunity to study the transition to malignancy as pre-cancers (adenomas) and early-stage cancers are frequently resected. Here, we examine tumor-immune eco-evolutionary dynamics from pre-cancer to carcinoma using a computational model, ecological analysis of digital pathology data, and neoantigen prediction in 62 patient samples. Modeling predicted recruitment of immunosuppressive cells would be the most common driver of transformation. As predicted, ecological analysis reveals that progressed adenomas co-localized with immunosuppressive cells and cytokines, while benign adenomas co-localized with a mixed immune response. Carcinomas converge to a common immune "cold" ecology, relaxing selection against immunogenicity and high neoantigen burdens, with little evidence for PD-L1 overexpression driving tumor initiation. These findings suggest re-engineering the immunosuppressive niche may prove an effective immunotherapy in CRC.

Typical tumor immune microenvironment status determine prognosis in lung adenocarcinoma

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Immune cells infiltration level in lung adenocarcinoma immune microenvironment were quantified and compared.

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Three distinct tumor immune microenvironment subtypes were consistent with cancer immunity cycle in cancer dynamic development.

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Immune infiltration status of three subtypes were correlated with significant mutated genes, copy number variation and cancer stemness

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Prognostic biomarker lung adenocarcinoma immune microenvironment score model was constructed to assess immune infiltration status, evaluate immunotherapy response, and predict patient prognosis.

Background

Immune cells, vital components of tumor microenvironment, regulate tumor survival and progression. Lung adenocarcinoma (LUAD), the tumor with the highest mortality rate worldwide, reconstitutes tumor immune microenvironment (TIME) to avoid immune destruction. Data have shown that TIME influences LUAD prognosis and predicts immunotherapeutic efficacy. The related information about the role of TIME's characteristics in LUAD is limited.

Methods

We performed unsupervised consensus clustering via machine-learning techniques to identify TIME clusters among 1906 patients and gathered survival data. The characteristics of TIME clusters of LUAD were visualized by multi-omics analysis, pseudo-time dynamic analysis, and enrichment analysis. TIME score model was constructed by principal component analysis. Comprehensive analysis and validation were conducted to test the prognostic efficacy and immunotherapeutic response of TIME score.

Results

TIME clusters (A, B and C) were constructed and exhibited different immune infiltration states. Multi-omics analyses included significant mutated genes (SMG), copy number variation (CNV) and cancer stemness that were significantly different among the three clusters. TIME cluster A had a lower SMG, lower CNV, and lower stemness but a higher immune infiltration level compared to TIME clusters B and C. TIME score showed that patients in low TIME score group had higher overall survival rates, higher immune infiltration level and high expression of immune checkpoints. In validation cohorts, low TIME score subgroup had better drug sensitivity and favorable immunotherapeutic response.

Conclusion

We constructed a stable model of LUAD immune microenvironment characteristics that may improve the prognostic accuracy of patients, provide improved explanations of LUAD responses to immunotherapy, and provide new strategies for LUAD treatment.

T Cell Memory in Infection, Cancer, and Autoimmunity

Long-term immunological memory represents a unique performance of the adaptive immunity selected during evolution to support long-term survival of species in vertebrates, through protection against dangerous "invaders", namely, infectious agents or unwanted (e.g., tumor) cells. The balance between the development of T cell memory and various mechanisms of immunoregulation (namely, T cell effector exhaustion and regulatory T cell suppression) dictates the fate in providing protection or not in different conditions, such as (acute or chronic) infection, vaccination, cancer, and autoimmunity. Here, these different environments are taken in consideration to outline the up-to-date cellular and molecular features regulating the development or damping of immunological memory and to delineate therapeutic strategies capable to improve or control it, in order to address pathological contexts, such as infection, tumor, and autoimmunity.

Prognostic Value of Neoantigen Load in Immune Checkpoint Inhibitor Therapy for Cancer

Immune checkpoint inhibitors (ICIs) have made great progress in the field of tumors and have become a promising direction of tumor treatment. With advancements in genomics and bioinformatics technology, it is possible to individually analyze the neoantigens produced by somatic mutations of each patient. Neoantigen load (NAL), a promising biomarker for predicting the efficacy of ICIs, has been extensively studied. This article reviews the research progress on NAL as a biomarker for predicting the anti-tumor effects of ICI. First, we provide a definition of NAL, and summarize the detection methods, and their relationship with tumor mutation burden. In addition, we describe the common genomic sources of NAL. Finally, we review the predictive value of NAL as a tumor prediction marker based on various clinical studies. This review focuses on the predictive ability of NAL’s ICI efficacy against tumors. In melanoma, lung cancer, and gynecological tumors, NAL can be considered a predictor of treatment efficacy. In contrast, the use of NAL for urinary system and liver tumors requires further research. When NAL alone is insufficient to predict efficacy, its combination with other indicators can improve prediction efficiency. Evaluating the response of predictive biomarkers before the treatment initiation is essential for guiding the clinical treatment of cancer. The predictive power of NAL has great potential; however, it needs to be based on more accurate sequencing platforms and technologies.

An exploration of immunohistochemistry-based prognostic markers in patients undergoing curative resections for colon cancer

Background

The immune system recognizes and destroys cancer cells. However, cancer cells develop mechanisms to avoid detection by expressing cell surface proteins. Specific tumour cell surface proteins (e.g. HLA-G, PD-L1, CDX2) either alone or in combination with the relative presence of immune cells (CD3 and CD8 positive T-cells) in the tumour tissue may describe the cancer cells’ ability to escape eradication by the immune system. The aim was to investigate the prognostic value of immunohistochemical markers in patients with colon cancer.

Methods

We conducted a retrospective study including patients diagnosed with pT3 and pT4 colon cancers. Immunohistochemical staining with HLA-G, PD-L1, CDX2, CD3, and CD8 was performed on tissue samples with representation of the invasive margin. PD-L1 expression in tumour cells and immune cells was reported conjointly. The expression of CD3 and CD8 was reported as a merged score based on the expression of both markers in the invasive margin and the tumour centre. Subsequently, a combined marker score was established based on all of the markers. Each marker added one point to the score when unfavourable immunohistochemical features was present, and the score was categorized as low, intermediate or high depending on the number of unfavourable stains. Hazard ratios for recurrence, disease-free survival and mortality were calculated.

Results

We included 188 patients undergoing colon cancer resections in 2011–2012. The median follow-up was 41.7 months, during which 41 (21.8%) patients had recurrence and 74 (39.4%) died. In multivariable regression analysis positive HLA-G expression (HR = 3.37, 95%CI [1.64–6.93]) was associated with higher recurrence rates, while a preserved CDX2 expression (HR = 0.23, 95%CI [0.06–0.85]) was associated with a lower risk of recurrence. An intermediate or high combined marker score was associated with increased recurrence rates (HR = 20.53, 95%CI [2.68–157.32] and HR = 7.56, 95%CI [1.06–54.16], respectively). Neither high expression of PD-L1 nor high CD3-CD8 score was significantly associated with recurrence rates. Patients with a high CD3-CD8 score had a significantly longer DFS and OS.

Conclusions

In tumour cells, expression of HLA-G and loss of CDX2 expression were associated with cancer recurrence. In addition, a combination of certain tumour tissue biomarkers was associated with colorectal cancer recurrence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09169-0.

DeCiFering the elusive cancer cell fraction in tumor heterogeneity and evolution

The cancer cell fraction (CCF), or proportion of cancerous cells in a tumor containing a single-nucleotide variant (SNV), is a fundamental statistic used to quantify tumor heterogeneity and evolution. Existing CCF estimation methods from bulk DNA sequencing data assume that every cell with an SNV contains the same number of copies of the SNV. This assumption is unrealistic in tumors with copy-number aberrations that alter SNV multiplicities. Furthermore, the CCF does not account for SNV losses due to copy-number aberrations, confounding downstream phylogenetic analyses. We introduce DeCiFer, an algorithm that overcomes these limitations by clustering SNVs using a novel statistic, the descendant cell fraction (DCF). The DCF quantifies both the prevalence of an SNV at the present time and its past evolutionary history using an evolutionary model that allows mutation losses. We show that DeCiFer yields more parsimonious reconstructions of tumor evolution than previously reported for 49 prostate cancer samples.

Integrated analysis reveals prognostic value of HLA-I LOH in triple-negative breast cancer

Background

Triple-negative breast cancers (TNBCs), especially those non-immune-inflamed tumors, have a poor prognosis and limited therapies. Human leukocyte antigen (HLA)-I not only contributes to antitumor immune response and the phenotype of the tumor microenvironment, but also is a negative predictor of outcomes after immunotherapy. However, the importance of HLA functional status in TNBCs remains poorly understood.

Methods

Using the largest original multiomics datasets on TNBCs, we systematically characterized the HLA-Ⅰ status of TNBCs from the perspective of HLA-Ⅰ homogeneity and loss of heterozygosity (LOH). The prognostic significance of HLA-I status was measured. To explain the potential mechanism of prognostic value in HLA-Ⅰ status, the mutational signature, copy number alteration, neoantigen and intratumoral heterogeneity were measured. Furthermore, the correlation between HLA-Ⅰ functional status and the tumor immune microenvironment was analyzed.

Results

LOH and homogeneity in HLA-I accounted for 18% and 21% of TNBCs, respectively. HLA-I LOH instead of HLA-I homogeneity was an independent prognostic biomarker in TNBCs. In particular, for patients with non-immune-inflamed tumors, HLA-I LOH indicated a worse prognosis than HLA-I non-LOH. Furthermore, integrated genomic and transcriptomic analysis showed that HLA-I LOH was accompanied by upregulated scores of mutational signature 3 and homologous recombination deficiency scores, which implied the failure of DNA double-strand break repair. Moreover, HLA-I LOH had higher mutation and neoantigen loads and more subclones than HLA-I non-LOH. These results indicated that although HLA-I LOH tumors with failure of DNA double-strand break repair were prone to produce neoantigens, their limited capacity for antigen presentation finally contributed to poor immune selection pressure.

Conclusion

Our study illustrates the genomic landscape of HLA-I functional status and stresses the prognostic significance of HLA-I LOH in TNBCs. For “cold” tumors in TNBCs, HLA-I LOH indicated a worse prognosis than HLA-I non-LOH.

Cancer: An unknown territory; rethinking before going ahead

Cancer is a disease of altered signaling and metabolism, causing uncontrolled division and survival of transformed cells. A host of molecules, factors, and conditions have been designated as underlying causes for the inception and progression of the disease. An enormous amount of data is available, system-wide interaction networks of the genes and proteins are generated over the years and have now reached up to a level of saturation, where we need to shift our focus to the more advanced and comprehensive methods and approaches of data analysis and visualization. Even with the availability of enormous literature on this one of the most pressing pathological conditions, a successful cure of the disease seems to be obscure. New treatment plans, like immunotherapy and precision medicine, are being employed for different studies. Nevertheless, their actual benefits to the patients would be known only after the evaluation of clinical data over the next few years. Therefore, we need to look at few fundamental challenges that should be addressed in more depth before we could devise better, rigorous, and comprehensive treatment plans and may successfully reach a possible cure of the disease. This article aims at bringing attention towards some fundamental gaps in our approach towards the disease that leads to failure in devising successful therapeutics.

Genetic regressive trajectories in colorectal cancer: A new hallmark of oligo-metastatic disease?

Colorectal cancer (CRC) originates as consequence of multiple genetic alterations. Some of the involved genes have been extensively studied (APC, TP53, KRAS, SMAD4, PIK3CA, MMR genes) in highly heterogeneous and poly-metastatic cohorts. However, about 10% of metastatic CRC patients presents with an indolent oligo-metastatic disease differently from other patients with poly-metastatic and aggressive clinical course. Which are the genetic dynamics underlying the differences between oligo- and poly-metastatic CRC? The understanding of the genetic trajectories (primary→metastatic) of CRC, in patients selected to represent homogenous clinical models, is crucial to make genotype/phenotype correlations and to identify the molecular events pushing the disease towards an increasing malignant phenotype. This information is crucial to plan innovative therapeutic strategies aimed to reverse or inhibit these phenomena. In the present study, we review the genetic evolution of CRC with the intent to give a developmental perspective on the border line between oligo- and poly-metastatic diseases.

Delineating the longitudinal tumor evolution using organoid models

Cancer is an evolutionary process fueled by genetic or epigenetic alterations in the genome. Understanding the evolutionary dynamics that are operative at different stages of tumor progression might inform effective strategies in early detection, diagnosis, and treatment of cancer. However, our understanding on the dynamics of tumor evolution through time is very limited since it is usually impossible to sample patient tumors repeatedly. The recent advances in in vitro 3D organoid culture technologies have opened new avenues for the development of more realistic human cancer models that mimic many in vivo biological characteristics in human tumors. Here, we review recent progresses and challenges in cancer genomic evolution studies and advantages of using tumor organoids to study cancer evolution. We propose to establish an experimental evolution model based on continuous passages of patient-derived organoids and longitudinal sampling to study clonal dynamics and evolutionary patterns over time. Development and integration of population genetic theories and computational models into time-course genomic data in tumor organoids will help to pinpoint the key cellular mechanisms underlying cancer evolutionary dynamics, thus providing novel insights on therapeutic strategies for highly dynamic and heterogeneous tumors.

Mathematical modeling of multiple pathways in colorectal carcinogenesis using dynamical systems with Kronecker structure

Like many other types of cancer, colorectal cancer (CRC) develops through multiple pathways of carcinogenesis. This is also true for colorectal carcinogenesis in Lynch syndrome (LS), the most common inherited CRC syndrome. However, a comprehensive understanding of the distribution of these pathways of carcinogenesis, which allows for tailored clinical treatment and even prevention, is still lacking. We suggest a linear dynamical system modeling the evolution of different pathways of colorectal carcinogenesis based on the involved driver mutations. The model consists of different components accounting for independent and dependent mutational processes. We define the driver gene mutation graphs and combine them using the Cartesian graph product. This leads to matrix components built by the Kronecker sum and product of the adjacency matrices of the gene mutation graphs enabling a thorough mathematical analysis and medical interpretation. Using the Kronecker structure, we developed a mathematical model which we applied exemplarily to the three pathways of colorectal carcinogenesis in LS. Beside a pathogenic germline variant in one of the DNA mismatch repair (MMR) genes, driver mutations in APC, CTNNB1, KRAS and TP53 are considered. We exemplarily incorporate mutational dependencies, such as increased point mutation rates after MMR deficiency, and based on recent experimental data, biallelic somatic CTNNB1 mutations as common drivers of LS-associated CRCs. With the model and parameter choice, we obtained simulation results that are in concordance with clinical observations. These include the evolution of MMR-deficient crypts as early precursors in LS carcinogenesis and the influence of variants in MMR genes thereon. The proportions of MMR-deficient and MMR-proficient APC-inactivated crypts as first measure for the distribution among the pathways in LS-associated colorectal carcinogenesis are compatible with clinical observations. The approach provides a modular framework for modeling multiple pathways of carcinogenesis yielding promising results in concordance with clinical observations in LS CRCs.

Superlinear growth reveals the Allee effect in tumors

Integrating experimental data into ecological models plays a central role in understanding biological mechanisms that drive tumor progression where such knowledge can be used to develop new therapeutic strategies. While the current studies emphasize the role of competition among tumor cells, they fail to explain recently observed superlinear growth dynamics across human tumors. Here we study tumor growth dynamics by developing a model that incorporates evolutionary dynamics inside tumors with tumor-microenvironment interactions. Our results reveal that tumor cells' ability to manipulate the environment and induce angiogenesis drives superlinear growth-a process compatible with the Allee effect. In light of this understanding, our model suggests that, for high-risk tumors that have a higher growth rate, suppressing angiogenesis can be the appropriate therapeutic intervention.

HLA class I loss in colorectal cancer: implications for immune escape and immunotherapy

T cell-mediated immune therapies have emerged as a promising treatment modality in different malignancies including colorectal cancer (CRC). However, only a fraction of patients currently respond to treatment. Understanding the lack of responses and finding biomarkers with predictive value is of great importance. There is evidence that CRC is a heterogeneous disease and several classification systems have been proposed that are based on genomic instability, immune cell infiltration, stromal content and molecular subtypes of gene expression. Human leukocyte antigen class I (HLA-I) plays a pivotal role in presenting processed antigens to T lymphocytes, including tumour antigens. These molecules are frequently lost in different types of cancers, including CRC, resulting in tumour immune escape from cytotoxic T lymphocytes during the natural history of cancer development. The aim of this review is to (i) summarize the prevalence and molecular mechanisms behind HLA-I loss in CRC, (ii) discuss HLA-I expression/loss in the context of the newly identified CRC molecular subtypes, (iii) analyze the HLA-I phenotypes of CRC metastases disseminated via blood or the lymphatic system, (iv) discuss strategies to recover/circumvent HLA-I expression/loss and finally (v) review the role of HLA class II (HLA-II) in CRC prognosis.

Measuring evolutionary cancer dynamics from genome sequencing, one patient at a time

Cancers progress through the accumulation of somatic mutations which accrue during tumour evolution, allowing some cells to proliferate in an uncontrolled fashion. This growth process is intimately related to latent evolutionary forces moulding the genetic and epigenetic composition of tumour subpopulations. Understanding cancer requires therefore the understanding of these selective pressures. The adoption of widespread next-generation sequencing technologies opens up for the possibility of measuring molecular profiles of cancers at multiple resolutions, across one or multiple patients. In this review we discuss how cancer genome sequencing data from a single tumour can be used to understand these evolutionary forces, overviewing mathematical models and inferential methods adopted in field of Cancer Evolution.

Immune Surveillance in Clinical Regression of Preinvasive Squamous Cell Lung Cancer

Before squamous cell lung cancer develops, precancerous lesions can be found in the airways. From longitudinal monitoring, we know that only half of such lesions become cancer, whereas a third spontaneously regress. Although recent studies have described the presence of an active immune response in high-grade lesions, the mechanisms underpinning clinical regression of precancerous lesions remain unknown. Here, we show that host immune surveillance is strongly implicated in lesion regression. Using bronchoscopic biopsies from human subjects, we find that regressive carcinoma in situ lesions harbor more infiltrating immune cells than those that progress to cancer. Moreover, molecular profiling of these lesions identifies potential immune escape mechanisms specifically in those that progress to cancer: antigen presentation is impaired by genomic and epigenetic changes, CCL27-CCR10 signaling is upregulated, and the immunomodulator TNFSF9 is downregulated. Changes appear intrinsic to the carcinoma in situ lesions, as the adjacent stroma of progressive and regressive lesions are transcriptomically similar. SIGNIFICANCE: Immune evasion is a hallmark of cancer. For the first time, this study identifies mechanisms by which precancerous lesions evade immune detection during the earliest stages of carcinogenesis and forms a basis for new therapeutic strategies that treat or prevent early-stage lung cancer.See related commentary by Krysan et al., p. 1442.This article is highlighted in the In This Issue feature, p. 1426.

Hypoxia increases mutational load of breast cancer cells through frameshift mutations

Tumor hypoxia-induced downregulation of DNA repair pathways and enhanced replication stress are potential sources of genomic instability. A plethora of genetic changes such as point mutations, large deletions and duplications, microsatellite and chromosomal instability have been discovered in cells under hypoxic stress. However, the influence of hypoxia on the mutational burden of the genome is not fully understood. Here, we attempted to elucidate the DNA damage response and repair patterns under different types of hypoxic stress. In addition, we examined the pattern of mutations exclusively induced under chronic and intermittent hypoxic conditions in two breast cancer cell lines using exome sequencing. Our data indicated that hypoxic stress resulted in transcriptional downregulation of DNA repair genes which can impact the DNA repair induced during anoxic as well as reoxygenated conditions. In addition, our findings demonstrate that hypoxic conditions increased the mutational burden, characterized by an increase in frameshift insertions and deletions. The somatic mutations were random and non-recurring, as huge variations within the technical duplicates were recognized. Hypoxia also resulted in an increase in the formation of potential neoantigens in both cell lines. More importantly, these data indicate that hypoxic stress mitigates DNA damage repair pathways and causes an increase in the mutational burden of tumor cells, thereby interfering with hypoxic cancer cell immunogenicity.