Molecular alterations in genes involved in DNA mismatch repair (MMR) promote cancer initiation and foster tumour progression1. Cancers deficient in MMR frequently show favourable prognosis and indolent progression2. The functional basis of the clinical outcome of patients with tumours that are deficient in MMR is not clear. Here we genetically inactivate MutL homologue 1 (MLH1) in colorectal, breast and pancreatic mouse cancer cells. The growth of MMR-deficient cells was comparable to their proficient counterparts in vitro and on transplantation in immunocompromised mice. By contrast, MMR-deficient cancer cells grew poorly when transplanted in syngeneic mice. The inactivation of MMR increased the mutational burden and led to dynamic mutational profiles, which resulted in the persistent renewal of neoantigens in vitro and in vivo, whereas MMR-proficient cells exhibited stable mutational load and neoantigen profiles over time. Immune surveillance improved when cancer cells, in which MLH1 had been inactivated, accumulated neoantigens for several generations. When restricted to a clonal population, the dynamic generation of neoantigens driven by MMR further increased immune surveillance. Inactivation of MMR, driven by acquired resistance to the clinical agent temozolomide, increased mutational load, promoted continuous renewal of neoantigens in human colorectal cancers and triggered immune surveillance in mouse models. These results suggest that targeting DNA repair processes can increase the burden of neoantigens in tumour cells; this has the potential to be exploited in therapeutic approaches.
European Nucleotide Archive
We thank B. Van Emburgh, A. Sogari, C. Falcomatà, V. Amodio and C. Cancelliere for assistance with in vitro experiments; F. Sassi and S. Giove for help with the immunohistochemistry; M. Buscarino for suggestions on MSI analysis; D. Cantarella for performing RNA-seq; G. Corti and L. Novara for bioinformatic analysis and comments; D. Sangiolo and M. Rescigno for discussion; D. Hananhan and K. Shchors for providing the PDAC cell model; and C. Torrance and J. Roix for scientific support, suggestions and critically reading the manuscript. This study was supported by European Community’s Seventh Framework Programme under grant agreement no. 602901 MErCuRIC (A.Bard. and G.G.); no. 635342-2 MoTriColor (A.Bard. and S.S.); IMI contract no. 115749 CANCER-ID (A.Bard.); AIRC 2010 Special Program Molecular Clinical Oncology 5 per mille, Project no. 9970 (A.Bard. and S.S.); AIRC IG no. 16788 (A.Bard.); AIRC IG no. 17707 (F.D.N.); Fondazione Piemontese per la Ricerca sul Cancro-ONLUS 5 per mille 2011 Ministero della Salute (A.Bard. and F.D.N.); AIRC-IG Grant no.15645 and Swiss National Science Foundation (SNSF), Sinergia Grant (CRSII3 160742/1) (E.G.); AIRC and the EU under a Marie Curie COFUND (G.G.); AIRC 3-year fellowship (G.S.); grant Terapia Molecolare Tumori by Fondazione Oncologia Niguarda Onlus (A.S.-B. and S.S.); and Merck grant for Oncology Innovation -GOI- 2016 (A.Bard.).
Extended data figures
As described in details in the M&M section, output calculations from exome data and RNAseq expression analyses were combined to generate predicted neoantigens. The lists (in excel format) reported here are based on the output from NetMHC4.0. The excel file incudes: gene, accession number, chromosome coordinates, nucleotide change, amino-acidic change, allele frequency, predicted antigenic peptide and number of different epitopes generated. The amount of generated epitopes (N. family) in each datasheet of the excel file indicates the total number of neoantigens (private, shared and common) present in the indicated time-point. For constitutively and transiently Cas9-expressing CT26 cells, time points correspond to those listed in Figure 3a. For RCM1, SW620 and SCKO1 the time points are described in Fig.4e. Whenever multiple peptides, obtained from the same variation, were identified, the best ranking ones are specified.