Quantification of neoantigen-mediated immunoediting in cancer evolution

The dynamic role of ferroptosis in cancer immunoediting: Implications for immunotherapy

Currently, cancer immunotherapy strategies are primarily formulated based on the patient's present condition, representing a "static" treatment approach. However, cancer progression is inherently "dynamic," as the immune environment is not fixed but undergoes continuous changes. This dynamism is characterized by the ongoing interactions between tumor cells and immune cells, which ultimately lead to alterations in the tumor immune microenvironment. This process can be effectively elucidated by the concept of cancer immunoediting, which divides tumor development into three phases: "elimination," "equilibrium," and "escape." Consequently, adjusting immunotherapy regimens based on these distinct phases may enhance patient survival and improve prognosis. Targeting ferroptosis is an emerging area in cancer immunotherapy, and our findings reveal that the antioxidant systems associated with ferroptosis possess dual roles, functioning differently across the three phases of cancer immunoediting. Therefore, this review delve into the dual role of the ferroptosis antioxidant system in tumor development and progression. It also propose immunotherapy strategies targeting ferroptosis at different stages, ultimately aiming to illuminate the significant implications of targeting ferroptosis at various phases for cancer immunotherapy.

Comprehensive assessment of computational methods for cancer immunoediting

Cancer immunoediting reflects the role of the immune system in eliminating tumor cells and shaping tumor immunogenicity, which leaves marks in the genome. In this study, we systematically evaluate four methods for quantifying immunoediting. In colorectal cancer samples from The Cancer Genome Atlas, we found that these methods identified 78.41%, 46.17%, 36.61%, and 4.92% of immunoedited samples, respectively, covering 92.90% of all colorectal cancer samples. Comparison of 10 patient-derived xenografts (PDXs) with their original tumors showed that different methods identified reduced immune selection in PDXs ranging from 44.44% to 60.0%. The proportion of such PDX-tumor pairs increases to 77.78% when considering the union of results from multiple methods, indicating the complementarity of these methods. We find that observed-to-expected ratios highly rely on neoantigen selection criteria and reference datasets. In contrast, HLA-binding mutation ratio, immune dN/dS, and enrichment score of cancer cell fraction were less affected by these factors. Our findings suggest integration of multiple methods may benefit future immunoediting analyses.

Frequency-dependent selection of neoantigens fosters tumor immune escape and predicts immunotherapy response

Cancer is an evolutionary process shaped by selective pressure from the microenvironments. However, recent studies reveal that certain tumors undergo neutral evolution where there is no detectable fitness difference amongst the cells following malignant transformation. Here, through computational modeling, we demonstrate that negative frequency-dependent selection (or NFDS), where the immune response against cancer cells depends on the clonality of neoantigens, can lead to an immunogenic landscape that is highly similar to neutral evolution. Crucially, NFDS promotes high antigenic heterogeneity and early immune evasion in hypermutable tumors, leading to poor responses to immune checkpoint blockade (ICB) therapy. Our model also reveals that NFDS is characterized by a negative association between average clonality and total burden of neoantigens. Indeed, this unique feature of NFDS is common in the whole-exome sequencing (WES) datasets (357 tumor samples from 275 patients) from four melanoma cohorts with ICB therapy and a non-small cell lung cancer (NSCLC) WES dataset (327 tumor samples from 100 patients). Altogether, our study provides quantitative evidence supporting the theory of NFDS in cancer, explaining the high prevalence of neutral-looking tumors. These findings also highlight the critical role of frequency-dependent selection in devising more efficient and predictive immunotherapies.

Computational Quantification of Cancer Immunoediting

The remarkable success of cancer immunotherapy has revolutionized cancer treatment, emphasizing the importance of tumor-immune interactions in cancer evolution and treatment. Cancer immunoediting describes the dual effect of tumor-immune interactions: inhibiting tumor growth by destroying tumor cells and facilitating tumor escape by shaping tumor immunogenicity. To better understand tumor-immune interactions, it is critical to develop computational methods to measure the extent of cancer immunoediting. In this review, we provide a comprehensive overview of the computational methods for quantifying cancer immunoediting. We focus on describing the basic ideas, computational processes, advantages, limitations, and influential factors. We also summarize recent advances in quantifying cancer immunoediting studies and highlight future research directions. As the methods for quantifying cancer immunoediting are continuously improved, future research will further help define the role of immunity in tumorigenesis and hopefully provide a basis for the design of new personalized cancer immunotherapy strategies.

Antigen presentation in cancer - mechanisms and clinical implications for immunotherapy

Over the past decade, the emergence of effective immunotherapies has revolutionized the clinical management of many types of cancers. However, long-term durable tumour control is only achieved in a fraction of patients who receive these therapies. Understanding the mechanisms underlying clinical response and resistance to treatment is therefore essential to expanding the level of clinical benefit obtained from immunotherapies. In this Review, we describe the molecular mechanisms of antigen processing and presentation in tumours and their clinical consequences. We examine how various aspects of the antigen-presentation machinery (APM) shape tumour immunity. In particular, we discuss genomic variants in HLA alleles and other APM components, highlighting their influence on the immunopeptidomes of both malignant cells and immune cells. Understanding the APM, how it is regulated and how it changes in tumour cells is crucial for determining which patients will respond to immunotherapy and why some patients develop resistance. We focus on recently discovered molecular and genomic alterations that drive the clinical outcomes of patients receiving immune-checkpoint inhibitors. An improved understanding of how these variables mediate tumour-immune interactions is expected to guide the more precise administration of immunotherapies and reveal potentially promising directions for the development of new immunotherapeutic approaches.

An immunogenic and oncogenic feature-based classification for chemotherapy plus PD-1 blockade in advanced esophageal squamous cell carcinoma

Although chemotherapy plus PD-1 blockade (chemo+anti-PD-1) has become the standard first-line therapy for advanced esophageal squamous cell carcinoma (ESCC), reliable biomarkers for this regimen are lacking. Here we perform whole-exome sequencing on tumor samples from 486 patients of the JUPITER-06 study and develop a copy number alteration-corrected tumor mutational burden that depicts immunogenicity more precisely and predicts chemo+anti-PD-1 efficacy. We identify several other favorable immunogenic features (e.g., HLA-I/II diversity) and risk oncogenic alterations (e.g., PIK3CA and TET2 mutation) associated with chemo+anti-PD-1 efficacy. An esophageal cancer genome-based immuno-oncology classification (EGIC) scheme incorporating these immunogenic features and oncogenic alterations is established. Chemo+anti-PD-1 achieves significant survival improvements in EGIC1 (immunogenic feature-favorable and oncogenic alteration-negative) and EGIC2 (either immunogenic feature-favorable or oncogenic alteration-negative) subgroups, but not the EGIC3 subgroup (immunogenic feature-unfavorable and oncogenic alteration-positive). Thus, EGIC may guide future individualized treatment strategies and inform mechanistic biomarker research for chemo+anti-PD-1 treatment in patients with advanced ESCC.

Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction

Neoantigens derived from somatic DNA alterations are ideal cancer-specific targets. In recent years, the combination therapy of PD-1/PD-L1 blockers and neoantigen vaccines has shown clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations result in immunogenicity among cancer cells and efficient tools to predict the immunogenicity of neoepitopes are still urgently needed. Here, we present the Seq2Neo pipeline, which provides a one-stop solution for neoepitope feature prediction using raw sequencing data. Neoantigens derived from different types of genome DNA alterations, including point mutations, insertion deletions and gene fusions, are all supported. Importantly, a convolutional neural network (CNN)-based model was trained to predict the immunogenicity of neoepitopes and this model showed an improved performance compared to the currently available tools in immunogenicity prediction using independent datasets. We anticipate that the Seq2Neo pipeline could become a useful tool in the prediction of neoantigen immunogenicity and cancer immunotherapy. Seq2Neo is open-source software under an academic free license (AFL) v3.0 and is freely available at Github.