Targeting neoantigens to augment antitumour immunity

Abstract

The past decade of cancer research has been marked by a growing appreciation of the role of immunity in cancer. Mutations in the tumour genome can cause tumours to express mutant proteins that are tumour specific and not expressed on normal cells (neoantigens). These neoantigens are an attractive immune target because their selective expression on tumours may minimize immune tolerance as well as the risk of autoimmunity. In this Review we discuss the emerging evidence that neoantigens are recognized by the immune system and can be targeted to increase antitumour immunity. We also provide a framework for personalized cancer immunotherapy through the identification and selective targeting of individual tumour neoantigens, and present the potential benefits and obstacles to this approach of targeted immunotherapy.

Figure 1. Tumour antigen processing and presentation on MHC class I

To destroy established cancers, CD8⁺ T effector cells must recognize antigens displayed by major histocompatibility complex (MHC) class I molecules on tumour cells. This process begins with the ubiquitylation and proteasome degradation of endogenously synthesized proteins in the tumour cell into shorter sequences of 8–11 amino acids. These smaller peptides may undergo further cleavage by peptidases (including aminopeptidases and carboxypeptidases) in the cytosol and also in the endoplasmic reticulum (ER). Peptides enter the ER by way of the transporter associated with antigen processing (TAP) complex. In the ER, these peptides bind with variable affinities to MHC class I. Together, the peptide MHC class I complexes are delivered to the plasma membrane through the Golgi complex, where the peptide can be recognized by CD8⁺ cytotoxic T cells. Although some T cells do recognize antigens shared between both normal and tumour cells, T cell receptors (TCRs) typically bind with higher affinity to neoantigens^,, and tumours expressing higher numbers of neoantigens are more likely to induce immune-mediated tumour elimination. The processing and presentation of exogenous antigens to CD4⁺ T helper cells via MHC class II on professional antigen presenting cells (APCs) follows somewhat similar steps, but MHC class II presents longer sequences of amino acids (11–20 amino acids or longer)^,. TILs, tumour-infiltrating lymphocytes.

Figure 2. Correlation of tumour somatic mutation frequency with objective response rates to immune checkpoint blockade

The full potential of programmed cell death protein 1 (PD1)–PD1 ligand 1 (PDL1) checkpoint inhibitors remains undefined; however, from the available clinical data there is a positive correlation between tumour somatic mutation frequency and clinical benefit, measured by the objective response rate (ORR). The ORR is defined in the context of these clinical studies as the proportion of patients who achieved 30% decrease in the sum of the longest diameters of target tumours based on modified Response Evaluation Criteria in Solid Tumours (RECIST). Here, we show the somatic mutation frequency (somatic mutations per megabase) and ORR to single-agent PD1 or PDL1 inhibition across multiple solid tumours of non-viral origin for which clinical and sequencing data are available. Tumours with the highest somatic mutation rates (mismatch repair-deficient (MMR-D) colon cancer, Merkel cell polyomavirus (MCPyV)-negative Merkel cell carcinoma (MCC), melanoma and non-small-cell lung cancer (NSCLC) in current or prior tobacco smokers) have amongst the highest ORRs to PD1 PDL1 inhibition (23–44%), whereas tumours with lower mutation rates have demonstrated less frequent responses. These data are obtained from a diverse group of clinical trials conducted at different stages of disease and included are some early-stage exploratory clinical trials, which probably accounts for some of the variability that is observed. However, extrapolating from these data, we can predict the ORR to single-agent PD1–PDL1 blockade for any tumour type with the formula ORR (%) = 0.08 × ln(x)+ 9, where x is the somatic mutation frequency per Mb of DNA. The blue dashed line on the graph represents the line of best fit calculated from the formula. Many PD1–PDL1 inhibitor trials are ongoing and will further clarify the slope of this correlation. MMR-P, mismatch repair proficient; RCC, renal cell carcinoma; SCLC, small-cell lung cancer.

Figure 3. Cancers acquire immune tolerance

During tumorigenesis, cancers acquire genetic changes that result in the presentation of neoantigens. Innate immune cells and primed adaptive immune cells recognize neoantigens and work together to eliminate the newly formed cancer cells. Cancers that survive the initial elimination phase enter an equilibrium phase in which the tumour remains at a stable size. During this phase, some of the most immunogenic neoantigens are edited out as the immune system selects for less immunogenic disease clones. This process selects for clones with downregulation of antigen processing and presentation on major histocompatibility complex (MHC) class I, as well as clones that have silenced or deleted genes that provide antigens targeted by the immune system. Cancers further avoid immune recognition and destruction through the downregulation of immune activation pathways (including CD137, OX40, CD40 and CD40 ligand (CD40L)), the upregulation of immunosuppressive pathways (including programmed cell death protein 1 ligand 1 (PDL1), PDL2, lymphocyte activation gene 3 (LAG3), T cell immunoglobulin mucin receptor 3 (TIM3), indoleamine-2,3-dioxygenase 1 (IDO1), cytotoxic T lymphocyte-associated antigen 4 (CTLA4)), and changes in expression of cytokines and chemokines or their receptors. Tumours also induce the production of immunosuppressive cytokines (for example, transforming growth factor-β (TGFβ), interleukin-10 (IL-10) and vascular endothelial growth factor (VEGF)) and recruit immune cells that actively mediate tolerance (e.g. myeloid-derived suppressor cells (MDSCs), regulatory T (T_reg) cells). Activation of metabolic pathways, such as arginase, adenosine, inducible nitric oxide synthase (iNOS) and glucose consumption by tumours, also leads to immunosuppression in the tumour microenvironment. The remaining T effector (T_eff) cells in this immunosuppressive tumour microenvironment become anergic (unresponsive and cannot be activated by antigen) and exhausted. As solid tumours begin to grow, they further impair T_eff cells by inducing a stroma, a physical barrier for the immune system that consists of fibroblasts, endothelial cells and other cell types. At this point cancer begins to ‘escape’ the immune system and become clinically evident. CCR2, C-C motif chemokine receptor 2; NK, natural killer. This figure has been adapted from REF. , Macmillan Publishing Limited.

Figure 4. A framework for identifying and targeting tumour neoantigens

Whole-exome sequencing is carried out on tumour cells and matched normal tissue to identify the somatic mutations expressed in the tumour cells. Software algorithms designed to predict major histocompatibility complex (MHC)/human leukocyte antigen (HLA) binding affinity are then used to prioritize which predicted neoantigens are most attractive for immune targeting. Attributes that may be considered during the selection of a neoantigen target are listed. Next, neoantigen vaccines or cellular therapies are used to target the predicted neoantigens. Such therapies should be combined with immune checkpoint inhibitors and other immune-based therapies to overcome immunosuppressive mechanisms in the tumour microenvironment that inhibit neoantigen-specific immune responses. A2AR, adenosine A2A receptor; ACT, adoptive cell therapy; B7H3, also known as CD276; CD137, also known as 4-1BB; CD40L, CD40 ligand; CTLA4, cytotoxic T lymphocyte-associated antigen 4; GITR, glucocorticoid-induced tumour necrosis factor receptor family related protein; ICOS, inducible T cell co stimulator; IDO1, indoleamine-2,3-dioxygenase 1; LAG3, lymphocyte activation gene 3; MDSCs, myeloid derived suppressor cells; PDL1, programmed cell death 1 ligand 1; TIM3, T cell immunoglobulin mucin receptor 3.