Immune Evasion
Summary
Immune evasion is the process by which cancer cells avoid recognition and elimination by the adaptive immune system. It is an evolutionary process operating at the somatic level: immune surveillance exerts negative selection against clones bearing immunogenic neo-antigens, and clones that acquire mechanisms to evade this surveillance — through HLA loss, neoantigen depletion, or immunosuppressive signaling — expand under positive selection. CIN plays a dual role: it generates neoantigens that increase immune visibility, but it also provides the genetic substrate for immune escape through CNA-mediated HLA LOH and neoantigen-coding segment deletion (Turajlic et al., 2019; McGranahan & Swanton, 2017).
Mechanisms
Genetic Immune Evasion
HLA loss of heterozygosity. Subclonal loss of one HLA allele removes the tumor cell’s ability to present neoantigens on that MHC-I molecule. In NSCLC, pervasive evidence of positive selection was found for HLA LOH events — they occur more frequently than expected by chance, consistent with strong selective pressure favoring immune escape (McGranahan et al., 2017, cited in Turajlic et al., 2019). Because CIN generates HLA loss as a byproduct of chromosomal missegregation, CIN-driven tumors have a higher rate of HLA LOH than CIN-quiet tumors (chromosomal-instability).
Neoantigen depletion. Loss of chromosomal segments containing neoantigen-coding mutations eliminates the antigens that would otherwise mark the clone for immune clearance. A single CNA event can delete multiple neoantigens simultaneously, making CNA-based depletion more efficient than SNV-based antigen loss (which requires independent mutations at each epitope) (neo-antigen).
Beta-2-microglobulin (B2M) loss. B2M is an obligate component of MHC-I. Biallelic loss of B2M eliminates all MHC-I surface expression, providing complete immune invisibility at the cost of triggering NK-cell-mediated clearance (missing-self recognition). B2M loss is recurrent in tumors with high mutational burden — the same hypermutation that generates neoantigens also generates B2M-inactivating mutations.
Non-Genetic Immune Evasion
PD-L1 upregulation. Tumor cells expressing PD-L1 engage PD-1 on tumor-infiltrating T cells, inducing T-cell exhaustion. PD-L1 expression can be constitutive (driven by oncogenic signaling, e.g., MYC amplification, PTEN loss) or adaptive (upregulated in response to IFN-γ secreted by activated T cells — a negative feedback loop).
Immunosuppressive microenvironment. Tumors recruit regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and tumor-associated macrophages (TAMs) that secrete immunosuppressive cytokines (IL-10, TGF-β) and inhibit effector T-cell function. This is a form of niche construction — the tumor remodels its microenvironment to create an immune-privileged site.
Antigen presentation machinery defects. Beyond HLA and B2M loss, defects in the antigen processing and presentation pathway (TAP1/TAP2 transporters, tapasin, ERAP1/2 peptidases) reduce neoantigen presentation without genetic loss of the antigens themselves.
Immune Evasion as an Evolutionary Process
Immune evasion is best understood through the lens of cancer immunoediting — a three-phase evolutionary process (Schreiber et al., 2011, as cited in Turajlic et al., 2019):
Elimination → Equilibrium → Escape
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Elimination. Immunogenic clones bearing neoantigens are recognized and destroyed by CD8+ T cells. This is negative selection — the immune system prunes the most visible clones from the population.
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Equilibrium. Surviving clones (bearing fewer or less immunogenic neoantigens) persist but are held in check by residual immune pressure. Net tumor growth is near zero. This phase can last years — it is the immunological correlate of clonal stasis.
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Escape. Clones acquiring immune-evasion mechanisms (HLA LOH, PD-L1 upregulation, B2M loss) are released from immune control and expand. Escape is a clonal-sweep driven by immune selection — the escape clone outcompetes non-escape clones not through intrinsic growth advantage but through relief from immune predation.
This evolutionary framing explains why immune evasion and therapy-resistance are deeply intertwined: both are adaptive responses to selective pressure (immune-mediated killing vs. drug-mediated killing), both can arise from pre-existing subclonal variation or de novo mutation, and both can involve the same genetic mechanisms (CNA, SNV, epigenetic plasticity).
Immune Evasion and ITH
Immune evasion shapes ITH and is shaped by it:
- High ITH → more neoantigens → stronger immune selection. Tumors with high mutational and neoantigen burden experience stronger immune pressure, which in turn selects for immune-evasion mechanisms. This is why immune evasion is particularly prevalent in hypermutated tumors (mismatch repair-deficient, POLE-mutant) and CIN-high tumors.
- Immune evasion reduces ITH. When an escape clone sweeps to fixation, it eliminates the diversity that preceded it — a selective sweep driven by immune predation rather than intrinsic fitness. The post-escape tumor is more monoclonal (lower ITH) but harder to treat with immunotherapy.
- Immunotherapy inverts the ITH-outcome relationship. High ITH predicts better response to immune checkpoint inhibitors (more neoantigens → more targets → better T-cell recognition) but worse response to conventional therapy (more substrate for resistance evolution). This is why the compression-entrenchment ITH-outcome hypothesis excludes immunotherapy-treated patients (intratumor-heterogeneity §5.1).
Clinical Significance
Immune checkpoint inhibitors (ICIs). Anti-CTLA4, anti-PD1, and anti-PD-L1 antibodies release the brakes on T-cell activation, enabling the immune system to eliminate tumor clones that would otherwise evade through PD-L1-mediated T-cell exhaustion. ICI response is predicted by: high mutational/neoantigen burden, intact MHC-I presentation, pre-existing T-cell infiltration, and absence of HLA LOH or B2M loss.
Resistance to ICIs. Acquired resistance arises through the same mechanisms that enable primary immune evasion: HLA LOH, B2M mutation, neoantigen loss (through CNA deletion or immunoediting), and alternative immune checkpoint upregulation (LAG-3, TIM-3). Resistance can be polyclonal — multiple escape clones with independent evasion mechanisms expanding in parallel (Turajlic et al., 2019).
CIN as a therapeutic vulnerability. The dual role of CIN in immune evasion (generating neoantigens AND enabling escape) suggests a therapeutic window: CIN-high tumors may be more visible to the immune system (higher neoantigen burden) but simultaneously more capable of escape (higher HLA LOH rate). Combining CIN-targeting agents with ICIs could increase neoantigen generation while blocking the escape route — a strategy that is conceptually compelling but not yet clinically validated.
Limitations
The wiki’s immune evasion coverage is skeletal. This page focuses on the genetic mechanisms of immune evasion — HLA LOH and neoantigen depletion — because these are the mechanisms discussed in the wiki’s current source corpus (primarily Turajlic et al., 2019 and McGranahan & Swanton, 2017). The broader immunoediting framework, the role of the tumor microenvironment, cytokine signaling, metabolic immune suppression, and the detailed mechanisms of ICI response and resistance are not yet developed. The page [[immune-evasion]] exists as a structural placeholder to anchor these topics as the source corpus expands.
Causal direction is hard to establish. In observational genomic data, HLA LOH and neoantigen depletion are observed in tumors — but whether they cause immune escape or are passengers of CIN is difficult to distinguish. The evidence for positive selection on HLA LOH events (McGranahan et al., 2017) comes from recurrence rates exceeding the background CNA rate, a statistical argument that is convincing but correlational.
Immune evasion is context-dependent. The same mechanism (HLA LOH) may be lethal in one immune context (tumor with high neoantigen burden, active immune infiltrate) and irrelevant in another (immune-cold tumor with no T-cell infiltration). Genomic biomarkers of immune evasion must be interpreted in the context of the tumor’s immune microenvironment, which is rarely available in large genomic cohorts.