IMMUNOGENOMICS

Immunogenomics encompasses various genomics-based inquiries that delve into the interaction between the cancer and the immune system (Fig. 7). Early work in melanoma highlighted the tumor's extensive immune interactions, paving the way for immunotherapies like checkpoint blockade therapy.

Researchers have developed computational techniques and ML algorithms to retrieve therapeutically valuable information from genomic and transcriptomic sequencing data from bulk tissue or single cells, enabling the exploration of tumors and their microenvironments [76]. These computational immunogenomic techniques can predict responses to cancer immunotherapies, such as Immune Checkpoint Inhibitors (ICIs), based on cancer-extrinsic and cancer-intrinsic factors that can be identified using sequencing, including neoantigens, TMB, and the presence of immune cells [77]. Neoantigens are novel antigens that arise from mutations and can also stimulate an immune response against tumors [78]. Genomic studies in melanoma revealed a high mutation rate, particularly due to UV damage, and identified potential biomarkers for immunotherapeutic responses. This groundwork proved pivotal in the development of revolutionary immunotherapies, such as checkpoint blockade therapy, which harnesses the immune response of the body to combat cancer [79]. Scientists have forged ahead, leveraging computational methodologies and ML algorithms to extract valuable clinical insights from extensive collections of genomic and transcriptomic sequencing data. These approaches are essential in evaluating both bulk tissue and individual cells, providing unparalleled insights into malignancies and their complex microenvironments [80]. Through computational immunogenomic assessment, researchers have been able to predict responses to cancer immunotherapies, like ICIs. These predictions are based on a comprehensive analysis of cancer-specific characteristics and extrinsic factors, identified using sequencing techniques, which include metrics such as TMB, neoantigens, and the presence or absence of diverse immune cells within the tumor environment [81].

Melanoma has emerged as a focus in genomic research due to its extensively high mutation rate, predominantly caused by UV-exposed DNA damage. These investigations have unveiled potential biomarkers that hold potential in predicting and understanding responses to immunotherapeutic interventions [82]. The recent progression in the field of immunogenomics continues to offer comprehensive insights into the intricacies of cancer-immune interactions, offering promising personalized and precise treatments based on genomic information that hold the potential to transform cancer care paradigms [83]. Clinical trials exploring immunotherapy in melanoma have demonstrated significant responses, leading to FDA approvals for anti-cytotoxic T lymphocyte antigen 4 (CTLA-4) and anti-PD-1 therapies [84]. These immunotherapies have expanded into clinical studies for other cancer types, such as bladder cancer and non-small cell lung cancer [85]. The connection between high mutation rates and immunotherapeutic responses has raised questions about whether mutational load alone predicts efficacy or if a more nuanced evaluation of neoantigens is necessary [86].

Ott et al. (2017) found that a personalized neoantigen vaccine is safe, feasible, and has the capacity to induce strong T-cell responses in clinical settings without being affected by prior or current therapies. This vaccine approach aims to address two significant challenges in cancer treatment, namely tumor heterogeneity and the selective targeting of tumors. The study observed the induction of de novo T-cell clones that can identify multiple patient-specific neoantigens, including autologous tumor cells and endogenously processed antigens. By targeting a wide range of malignant clones within each patient, the vaccine addresses tumor heterogeneity and reduces the risk of tumor escape due to antigen loss [87]. Future neoantigen vaccine trials will use improved methods for predicting antigen presentation to boost the proportion of neoantigens activating tumor-reactive T cells, as well as test their synergy with checkpoint inhibition and other immunotherapeutics [88].

However, mouse models and genomic studies have provided insights into the interaction between cancer and the immune system, identifying tumor-specific mutant antigens (neoantigens) [89]. Human studies have correlated high mutational loads with positive responses to checkpoint blockade therapies, raising questions about the predictive quality of mutational load compared to other markers like programmed death-ligand (PD-L1) and PD-1 protein expression [90]. These approaches, combined with the blockade of CTLA-4, have become pivotal in the era of immunogenomics, fundamentally altering the landscape of cancer immunotherapy. Tumor cells develop immune tolerance and evade detection by host CD4+ and CD8+ T cells within the tumor microenvironment, enabling them to escape immune monitoring [91]. While current genomic testing benefits only 10–15% of patients, improved cancer genome analyses have uncovered highly specific neoantigens from somatic mutations. These neoantigens and their corresponding T cells in the tumor environment play a crucial role in various cancer immunotherapies, such as ICIs [92]. Targeting patient-specific neoantigens holds immense promise for personalized cancer treatment. However, accurate prediction and selection of these neoantigens remain a challenge. Yet, precise identification could fast-track personalized immunotherapies, such as cancer vaccines and engineered T-cell therapy, offering tailored solutions for cancer patients [93]. This evolving field of immunogenomics leverages cancer genomics to design personalized vaccines based on tumor-specific neoantigens, offering a potential avenue for patients who have exhausted other treatment options (Table 4).