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. 2017 Mar;7(3):264-276.
doi: 10.1158/2159-8290.CD-16-0828. Epub 2016 Dec 28.

Evolution of Neoantigen Landscape During Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

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Free PMC article

Evolution of Neoantigen Landscape During Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

Valsamo Anagnostou et al. Cancer Discov. .
Free PMC article

Abstract

Immune checkpoint inhibitors have shown significant therapeutic responses against tumors containing increased mutation-associated neoantigen load. We have examined the evolving landscape of tumor neoantigens during the emergence of acquired resistance in patients with non-small cell lung cancer after initial response to immune checkpoint blockade with anti-PD-1 or anti-PD-1/anti-CTLA-4 antibodies. Analyses of matched pretreatment and resistant tumors identified genomic changes resulting in loss of 7 to 18 putative mutation-associated neoantigens in resistant clones. Peptides generated from the eliminated neoantigens elicited clonal T-cell expansion in autologous T-cell cultures, suggesting that they generated functional immune responses. Neoantigen loss occurred through elimination of tumor subclones or through deletion of chromosomal regions containing truncal alterations, and was associated with changes in T-cell receptor clonality. These analyses provide insight into the dynamics of mutational landscapes during immune checkpoint blockade and have implications for the development of immune therapies that target tumor neoantigens.Significance: Acquired resistance to immune checkpoint therapy is being recognized more commonly. This work demonstrates for the first time that acquired resistance to immune checkpoint blockade can arise in association with the evolving landscape of mutations, some of which encode tumor neoantigens recognizable by T cells. These observations imply that widening the breadth of neoantigen reactivity may mitigate the development of acquired resistance. Cancer Discov; 7(3); 264-76. ©2017 AACR.See related commentary by Yang, p. 250This article is highlighted in the In This Issue feature, p. 235.

Conflict of interest statement

Disclosure of Potential Conflicts of Interest

The terms of these arrangements are managed by the Johns Hopkins University in accordance with its conflict of interest policies.

Figures

Figure 1
Figure 1. Overview of next-generation sequencing, neoantigen prediction and functional T cell analyses
Whole exome sequencing was performed on the pre-treatment and post-progression tumor and matched normal samples. Exome data were applied in a neoantigen prediction pipeline that evaluates antigen processing, MHC binding and gene expression to generate neoantigens specific to the patient’s HLA haplotype. Truncal neoantigens were identified by correcting for tumor purity and ploidy and the TCR repertoire was evaluated at baseline, at the time of response and upon emergence of resistance. Putative eliminated neoantigens at the time of resistance were used to generate peptides and stimulate autologous T cells, followed by TCR next-generation sequencing.
Figure 2
Figure 2. Mutation cellularity analyses for eliminated mutations in pre-treatment and post-progression tumor samples
Mutation cellularities at baseline (T1) and upon progression (T2) were estimated with the SCHISM pipeline; a cellularity of 0 was observed for 18, 10, 7, and 6 sequence alterations in resistant T2 tumors for CGLU116, CGLU117, CGLU127 and CGLU161, respectively (panel A). These somatic mutations were lost either by loss of heterozygosity or subclonal elimination at the time of emergence of therapeutic resistance to immune checkpoint blockade. Somatic mutations in SLC26A7, PGAP1, HELB and ANKRD12 that are associated with functionally validated neoantigens were detected in the pre-treatment tumors but not in the resistant tumor or matched normal DNA; MAF denotes the mutant allele frequency (panel B).
Figure 3
Figure 3. Emergence of resistance to immune checkpoint blockade is associated with elimination of mutation associated neoantigens by loss of heterozygosity and a more diverse T-cell repertoire independent of PD-L1 expression
Panel A shows computed tomographic (CT) images of patient CGLU117 at baseline, at the time of therapeutic response and at time of acquired resistance. Pre-treatment CT image of the abdomen, demonstrates a right adrenal mass (T1, circled), radiologic tumor regression is noted after 2 months of treatment, followed by disease relapse at 4 months from treatment initiation with a markedly increased right adrenal metastasis (T2, circled). 3rd follow up CT demonstrates further disease progression in the adrenal lesion. Tumor burden kinetics for target lesions by RECIST criteria are shown in panel B. Peripheral T cell expansion of a subset of intratumoral clones was noted to peak at the time of response and decrease to baseline levels at the time of resistance (panel C). Productive TCR frequency denotes the frequency of a specific rearrangement that can produce a functional protein receptor among all productive rearrangements. Panels D and E show B allele frequency graphs for chromosome 17, a value of 0.5 indicates a heterozygous genotype whereas allelic imbalance is observed as a deviation from 0.5. The region that undergoes LOH in the resistant tumor (panel E, orange box) contains 3 mutation associated neoantigens that are thus eliminated. No differences in CD8+ T cell density (panel F, G) or PD-L1 expression (panel H, I) were observed between baseline and resistant tumors.
Figure 4
Figure 4. Neoantigen-specific TCR expansion in stimulated T cell cultures
Peptides generated from the eliminated mutation associated neoantigen candidates were synthesized and used to pulse autologous peripheral T cells for patient CGLU116. T cells were stimulated with respective mutant and wild type peptides and cultured for 10 days, followed by next generation TCR sequencing of expanded T cell cultures. Reactive TCR clonotypes were matched to clones found in infiltrating tumor lymphocytes. Neoantigen-specific TCR reactivity was observed for the mutant peptides associated with mutant HELB987P>S (SASPLSVV; panel A), SCL26A7117R>Q (ISANAVEQIV; panel B) and PGAP1903Y>F (AFGSAHLFR and VIAFGSAHLFR; panel C) compared to their wild type counterparts. An oligoclonal TCR expansion was observed for both mutant (STPSASPLSV) and wild type (STPSASPLPVV) peptides associated with a single base substitution in HELB (panel D). Adjusted p values are given for pairwise comparisons between productive frequencies in peptide stimulated versus unstimulated T cells. Solid bars represent mutant and bars with diagonal pattern denote wild type peptides.

Comment in

  • Debugging the Black Box.
    Yang JC. Yang JC. Cancer Discov. 2017 Mar;7(3):250-251. doi: 10.1158/2159-8290.CD-17-0070. Cancer Discov. 2017. PMID: 28264866 Free PMC article.

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