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Inactivation of DNA repair triggers neoantigen generation and impairs tumour growth

Abstract

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.

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Figure 1: Effect of MLH1 inactivation in colorectal and pancreatic mouse cancer cells.
Figure 2: Effect of MMR inactivation on treatment with immuno-modulatory antibodies.
Figure 3: Measurements of neoantigen load and TCR profiles.

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References

  1. Lord, C. J. & Ashworth, A. The DNA damage response and cancer therapy. Nature 481, 287–294 (2012)

    Article  CAS  PubMed  ADS  Google Scholar 

  2. Vilar, E. & Gruber, S. B. Microsatellite instability in colorectal cancer—the stable evidence. Nat. Rev. Clin. Oncol. 7, 153–162 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Orthwein, A. et al. A mechanism for the suppression of homologous recombination in G1 cells. Nature 528, 422–426 (2015)

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  4. Rayner, E. et al. A panoply of errors: polymerase proofreading domain mutations in cancer. Nat. Rev. Cancer 16, 71–81 (2016)

    Article  CAS  PubMed  Google Scholar 

  5. Ponti, G., Castellsagué, E., Ruini, C., Percesepe, A. & Tomasi, A. Mismatch repair genes founder mutations and cancer susceptibility in Lynch syndrome. Clin. Genet. 87, 507–516 (2015)

    Article  CAS  PubMed  Google Scholar 

  6. Jiricny, J. Postreplicative mismatch repair. Cold Spring Harb. Perspect. Biol. 5, a012633 (2013)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Woerner, S. M. et al. Detection of coding microsatellite frameshift mutations in DNA mismatch repair-deficient mouse intestinal tumors. Mol. Carcinog. 54, 1376–1386 (2015)

    Article  CAS  PubMed  Google Scholar 

  8. Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015)

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  9. Gubin, M. M. et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 (2014)

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  10. Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017)

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  11. Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015)

    Article  CAS  ADS  PubMed  Google Scholar 

  12. Gros, A. et al. Prospective identification of neoantigen-specific lymphocytes in the peripheral blood of melanoma patients. Nat. Med. 22, 433–438 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Sharma, P. & Allison, J. P. The future of immune checkpoint therapy. Science 348, 56–61 (2015)

    Article  CAS  PubMed  ADS  Google Scholar 

  14. McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016)

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  15. Juneja, V. R. et al. PD-L1 on tumor cells is sufficient for immune evasion in immunogenic tumors and inhibits CD8 T cell cytotoxicity. J. Exp. Med. 214, 895–904 (2017)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Chew, W. L. et al. A multifunctional AAV–CRISPR–Cas9 and its host response. Nat. Methods 13, 868–874 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bailey, J. M . et al. p53 mutations cooperate with oncogenic Kras to promote adenocarcinoma from pancreatic ductal cells. Oncogene 35, 4282–4288 (2016)

    Article  CAS  PubMed  Google Scholar 

  18. Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Castle, J. C. et al. Immunomic, genomic and transcriptomic characterization of CT26 colorectal carcinoma. BMC Genomics 15, 190 (2014)

    Article  PubMed  PubMed Central  Google Scholar 

  20. Bardelli, A. et al. Carcinogen-specific induction of genetic instability. Proc. Natl Acad. Sci. USA 98, 5770–5775 (2001)

    Article  CAS  PubMed  ADS  PubMed Central  Google Scholar 

  21. Fink, D., Aebi, S. & Howell, S. B. The role of DNA mismatch repair in drug resistance. Clin. Cancer Res. 4, 1–6 (1998)

    CAS  PubMed  Google Scholar 

  22. Cahill, D. P. et al. Loss of the mismatch repair protein MSH6 in human glioblastomas is associated with tumor progression during temozolomide treatment. Clin. Cancer Res. 13, 2038–2045 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Yip, S. et al. MSH6 mutations arise in glioblastomas during temozolomide therapy and mediate temozolomide resistance. Clin. Cancer Res. 15, 4622–4629 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Medico, E. et al. The molecular landscape of colorectal cancer cell lines unveils clinically actionable kinase targets. Nat. Commun. 6, 7002 (2015)

    Article  CAS  PubMed  ADS  Google Scholar 

  25. Sartore-Bianchi, A. et al. Digital PCR assessment of MGMT promoter methylation coupled with reduced protein expression optimises prediction of response to alkylating agents in metastatic colorectal cancer patients. Eur. J. Cancer 71, 43–50 (2017)

    Article  CAS  PubMed  Google Scholar 

  26. Lee, S. Y. Temozolomide resistance in glioblastoma multiforme. Genes Dis. 3, 198–210 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cahill, D. P., Codd, P. J., Batchelor, T. T., Curry, W. T. & Louis, D. N. MSH6 inactivation and emergent temozolomide resistance in human glioblastomas. Clin. Neurosurg. 55, 165–171 (2008)

    PubMed  Google Scholar 

  28. Calegari, M. A. et al. A phase 2 study of temozolomide in pretreated metastatic colorectal cancer with MGMT promoter methylation. Br. J. Cancer 116, 1279–1286 (2017)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bykov, V. J. et al. Restoration of the tumor suppressor function to mutant p53 by a low-molecular-weight compound. Nat. Med. 8, 282–288 (2002)

    Article  CAS  PubMed  Google Scholar 

  30. Maletzki, C., Schmidt, F., Dirks, W. G., Schmitt, M. & Linnebacher, M. Frameshift-derived neoantigens constitute immunotherapeutic targets for patients with microsatellite-instable haematological malignancies: frameshift peptides for treating MSI+ blood cancers. Eur. J. Cancer 49, 2587–2595 (2013)

    Article  CAS  PubMed  Google Scholar 

  31. Gilles, M. E. et al. Nucleolin targeting impairs the progression of pancreatic cancer and promotes the normalization of tumor vasculature. Cancer Res. 76, 7181–7193 (2016)

    Article  CAS  PubMed  Google Scholar 

  32. Corbett, T. H., Griswold, D. P. Jr, Roberts, B. J., Peckham, J. C. & Schabel, F. M. Jr. Tumor induction relationships in development of transplantable cancers of the colon in mice for chemotherapy assays, with a note on carcinogen structure. Cancer Res. 35, 2434–2439 (1975)

    CAS  PubMed  Google Scholar 

  33. Nanni, P., de Giovanni, C., Lollini, P. L., Nicoletti, G. & Prodi, G. TS/A: a new metastasizing cell line from a BALB/c spontaneous mammary adenocarcinoma. Clin. Exp. Metastasis 1, 373–380 (1983)

    Article  CAS  PubMed  Google Scholar 

  34. Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Maione, F. et al. Semaphorin 3A overcomes cancer hypoxia and metastatic dissemination induced by antiangiogenic treatment in mice. J. Clin. Invest. 122, 1832–1848 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Llosa, N. J. et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints. Cancer Discov. 5, 43–51 (2015)

    Article  CAS  PubMed  Google Scholar 

  37. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009)

    PubMed  PubMed Central  Google Scholar 

  39. Siravegna, G. et al. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat. Med. 21, 795–801 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Andreatta, M. & Nielsen, M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 32, 511–517 (2016)

    Article  CAS  PubMed  Google Scholar 

  41. Wang, K. et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 38, e178 (2010)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Gaide, O. et al. Common clonal origin of central and resident memory T cells following skin immunization. Nat. Med. 21, 647–653 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Chen, L., Liu, P., Evans, T. C. & Ettwiller, L. M. DNA damage is a pervasive cause of sequencing errors, directly confounding variant identification. Science 355, 752–756 (2017)

    Article  CAS  PubMed  ADS  Google Scholar 

Download references

Acknowledgements

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.).

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Authors and Affiliations

Authors

Contributions

G.G. and A.Bard. conceived the study. G.G., A.M., F.Ma, R.F. and M.M. performed animal experiments. A.Bart., B.M., G.S. and M.M. performed DNA analyses and experiments. G.R. and G.C. conducted bioinformatics data analyses. M.R. performed cell-based drug screens. L.B. designed and performed the TMZ experiments on human cells. S.L. performed gene-editing experiments. G.G., G.L., A.M., E.G. and N.A.-A. conducted studies on mouse cells. F.Mo, F.D.N., A.O., F.d.B., F.P., S.S. and A.S.-B. designed and performed clinical studies, obtained tissue samples and performed patient data analysis. S.M. and M.D. were involved in data analysis. G.G. and A.Bard. wrote the manuscript. A.Bard. and F.D.N. supervised the study. All authors read and approved the final submitted manuscript

Corresponding author

Correspondence to Alberto Bardelli.

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Competing interests

A.Bard. and G.G. are inventors on patent applications related to this research. The transfer of certain materials to third parties is subject to terms contained within license and intellectual property agreements held between PhoreMost Limited, the University of Turin, A.Bard. and G.G. A.Bard. is a shareholder of PhoreMost Limited. A.Bard. and G.G. are co-founders and shareholders of NeoPhore Limited; A.Bard is a member of the NeoPhore scientific advisory board. A.Bard. receives research funding from Merck related to this project. The other authors declare no competing financial interests.

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Extended data figures and tables

Extended Data Figure 1 Microsatellite profiles of mouse cancer cell lines and Mlh1 alterations in genome-edited CT26 cells.

a, The microsatellite profile of the indicated cell lines was compared to the germline DNA of the corresponding mouse strain, as described in the Methods. CT26 and TS/A were compared to BALB/c. MC38 was compared to C57BL/6, and PDAC was compared to FVB/N. b, Exome sequencing data were used to identify molecular alterations of the Mlh1 sequence in the indicated cell models. Alignment of sequence reads showed deletions of the Mlh1 gene after gene editing using sgRNA2 and sgRNA3 guides. The clone generated by editing with guide 2 (KO1) carried a deletion of 8 base pairs that induced multiple frameshifts. Guide 3 (KO2) produced a deletion that resulted in a frameshift of nine codons. Two further clones obtained with guide 3 (KO3 and KO4) also had two insertions, and KO3 carried two additional deletions; the effects of these changes are frameshifts. The upper sequence corresponds to the mouse reference assembly mm10. c, Western blot analysis of the indicated CT26 and PDAC cell lines. For western blot source data, see Supplementary Fig. 1; we show a representative experiment of the routine western blots performed to validate gene editing.

Extended Data Figure 2 Effect of MLH1 inactivation in MC38 and TS/A cell lines.

a, Expression of the MLH1 protein in MC38 control and the indicated Mlh1-knockout clones. The indicated MC38 cell models were injected subcutaneously (105 cells) into NOD–SCID mice (n = 5, left) and into syngeneic C57BL/6 mice (n = 5, right). Mean ± s.e.m., independent samples. Statistical analysis: two-tailed Student’s t-test. b, Mlh1 in TS/A cells was edited with the indicated guides. One hundred and twenty days after the establishment of the knockout, cells were injected subcutaneously in NOD–SCID mice (5 × 105 cells per mouse, n = 7 mice) and the growth was monitored until the mice were euthanized (left). These TS/A cells were injected subcutaneously (5 × 105 cells per mouse; middle, n = 5 mice) and orthotopically (5 × 105 cells per mouse; right, n = 7 mice) into syngeneic BALB/c mice. For western blot source data, see Supplementary Fig. 1. The growth was monitored until the mice were euthanized. Mean ± s.e.m., samples were independent. Statistical analysis: two-tailed Student’s t-test. Western blots in a and b are representative of at least two experiments.

Source data

Extended Data Figure 3 Effect of MLH1 inactivation on microsatellite instability in mouse tumour cell lines.

The MSI status was evaluated by comparing mononucleotide repeats of CT26 (upper left), MC38 (lower left), PDAC (upper right) and TS/A (lower right). The mononucleotide regions Bat64, L24372-A27 and U12235-A24 were used to evaluate microsatellite instability.

Extended Data Figure 4 Growth of CT26, MC38, TS/A and PDAC cell lines and Cas9 expression in CT26 cells.

The growth of the indicated cell models was measured with CellTiter-Glo at the indicated time points. Arbitrary units represent the ratio between the signal at each time point and at time zero. All data are presented as mean (six technical replicates in the same experiment) ± s.d. CT26, MC38, TS/A cells were plated at 1,000 cells per well. PDAC cells were plated at 5,000 cells per well. a, The growth of CT26, MC38, TS/A and PDAC cells that stably expressed Cas9 was measured as described. b, As in a, but with CT26 and PDAC cells that transiently expressed Cas9. The MSI status was evaluated as in Extended Data Fig. 3 for CT26 and PDAC cells generated by transiently expressing Cas9. The mononucleotide regions Bat64 and U12235-A24 were used to evaluate microsatellite instability. c, The indicated CT26 clones, generated by constitutively or transiently expressing Cas9, were tested for Cas9 expression by western blot analysis. To verify the loss of Cas9, we performed one experiment. For western blot source data, see Supplementary Fig. 1.

Extended Data Figure 5 T-cell infiltration in tumours generated with CT26 cells that constitutively and transiently express Cas9.

a, Immunofluorescence of CD8+ cells in control and Mlh1-knockout clones shown in Fig. 2a. Staining was performed on Mlh1-proficient and Mlh1-deficient tumours to assess CD8 and IFNγ levels. b, Immune infiltrates (CD45+, CD4+ and CD8+) were measured by FACS analyses in the indicated tumour samples (n = 5). The percentage of CD45+ cells relative to total live cell events (one million events were acquired per sample) is shown. The percentage of CD4+ and CD8+ cells is given relative to CD45+ cells. c, Immune infiltrates (CD4+ and CD8+) were measured by FACS analyses in the indicated samples (3 mice per group). The percentage of CD8+ and CD4+ T cells is calculated relative to total live CD45+ cells. d, Immunofluorescence of DAPI and CD8+ T cells in the indicated tumour samples. In a and d, the images are representative sections from one mouse. The staining was performed on four independent mice. e, Percentage of CD45+ (relative to total live cells), CD8+ and CD4+ (relative to CD45+ live cells) is shown for all mice included in the experimental arm (5 mice per group). For b and e, we analysed the data with a two-way ANOVA. All data are presented as mean ± s.d.

Extended Data Figure 6 In vitro neoantigen evolution of CT26 cells that constitutively expressed Cas9.

Exome data of the indicated CT26 cells were analysed over time. Coding variants identified by exome sequencing were calculated as in Fig. 3a and described in the Methods. Private, shared and common neoantigens are defined as in Fig. 3a. First time point, 30 days post-Mlh1 knockout; second time point, 120 days post-Mlh1 knockout; and third time point, 210 days post-Mlh1 knockout.

Extended Data Figure 7 Clonal and sub-clonal mutational and neoantigen profiles in CT26 cells.

a, Mutational load of the sub-cloned and non-sub-cloned CT26 calculated at 133 and 246 days, respectively, post-Mlh1 knockout. The number of mutations was obtained considering SNVs and indels. Each bar shows the mutations per Mb, and the expressed private neoantigens obtained from a single exome sequencing of the represented clones. b, Allele frequency distribution of SNVs and frameshifts of the indicated clones. Each violin represents a clone of the CT26 cell line. The violins are described in Fig. 3b. c, Number of private and shared neoantigens (SNVs and indels) before injection of CT26 control (Ctrl) and Mlh1-knockout (pre-injection) in syngeneic animals, and 20 days later (post-injection). d, Distribution of the 20 most-frequent TCR rearrangements, identified in peripheral blood from four mice injected with the indicated CT26 clones. The TCR analysis was performed on blood samples obtained 13 days after injection of the tumour cells, as described in the Methods. Violin plots are described in the legend of Fig. 3b.

Extended Data Figure 8 Effects of pharmacological agents on CT26 and MC38 clones.

a, b, CT26 cells (a) and MC38 cells (b) were plated in complete medium in 24-well plates, at 1,000 cells per well. The next day drugs were added in serum-free medium. After seven days, cells were fixed and stained with crystal violet. Crystal violet was then dissolved and quantified by spectrophotometer. All data are presented as mean (four technical replicates in the same experiment) ± s.d. The figure is representative of at least two independent experiments.

Extended Data Figure 9 TMZ-dependent effects on mouse cell lines.

a, Tumour-forming ability of CT26 (left) and MC38 (right) cells, treated or not treated with TMZ. Cells were injected subcutaneously (5 × 105 cell per mouse, n = 5 mice, left; n = 4 mice, right) into syngeneic mice. Mean ± s.e.m. (independent samples, one representative experiment of two performed). The statistical analysis applied was a two-tailed Student’s t-test. b, Exome data of the indicated cell models were compared to one another to assess mutational load (SNVs and indels) and predicted neoantigens. Mutations per Mb (left) and predicted private neoantigens (right) are listed. c, CT26 and MC38 cells were treated for four months with 100 μM of TMZ until resistant populations emerged; at this point, DNA was extracted and cells were analysed for microsatellite status. The corresponding cell lines before TMZ treatment were used as a comparison. The mononucleotide regions Bat64, AA003063-A23 and U12235-A24 were used to evaluate microsatellite instability. d, MLH1 protein expression in CT26 and MC38 cells before and after TMZ exposure (one representative experiment of two performed). e, Exome sequencing data were used to identify molecular alterations of the Mlh1 sequence. Alignment of sequence reads showed a deletion of 5 and 19 base pairs, respectively, that generated a frameshift and a premature termination codon (p.D64fs*39 c.187delGACAA, p.I59fs*78 c.174delAATTCAGATCCAAGACAAT).

Source data

Extended Data Figure 10 TMZ-dependent effects on human cancer cells.

a, Forty-seven CRC cell lines were tested with TMZ in long-term colony forming assays, from which half-maximal inhibitory concentration (IC50) values were obtained. MGMT promoter methylation status (from microarray probe number cg12434587), gene expression (normalized Z-score) and MSI status of each cell line are also annotated. **, IC50 obtained through dissolution of clonogenic assay, crystal violet staining and assessment of the absorbance. The dotted line corresponds to 12.5 μM, which is the plasmatic concentration reported in patients. b, The indicated CRC cell lines (before and after they developed resistance to TMZ) were tested for MGMT level by western blot. The MGMT expression is representative of at least two experiments. For western blot source data, see Supplementary Fig. 1. c, Sensitivity to TMZ treatment of six cell lines, before and after the acquisition of drug resistance. d, Mutations per Mb in parental and TMZ-resistant cells at the indicated time points (left). Predicted private neoantigens found only in cells collected at 20 days (right). The table lists the variations of MMR genes found only in TMZ-resistant cells. e, Clinical characteristics of patients with metastatic CRC that had acquired resistance to TMZ treatment are indicated. All patients had histologically confirmed metastatic CRC, with MGMT promoter methylation assessed by methylation-specific-PCR and mismatch repair-proficient status assessed by both immunohistochemistry and multiplex PCR (all analyses were carried out as per standard practice using archival tumour samples obtained prior to any treatment). CAPTEM, capecitabine plus TMZ (clinical trial number NCT02414009); TEMIRI, TMZ plus irinotecan; SD: stable disease; PR: partial response. f, MGMT immunohistochemical expression and mutational load (mutations per Mb) in tissue biopsies of patients with metastatic CRC, before and after TMZ-based therapy. The table lists alterations in MMR genes that were present only in tissue biopsies obtained at progression post-TMZ treatment. n.a., data not available. Patient identifiers (Pt.), genes and amino acid changes, effects of variations and allele frequencies are listed. Fisher’s exact test was performed to calculate the significance (P < 0.05).

Supplementary information

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Supplementary Figure 1

This file contains uncropped western blot scans (size marker and protein identified are indicated). (PDF 1135 kb)

Supplementary Table 1 - List of predicted neoantigens in murine and human cell lines.

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. (XLS 3854 kb)

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Germano, G., Lamba, S., Rospo, G. et al. Inactivation of DNA repair triggers neoantigen generation and impairs tumour growth. Nature 552, 116–120 (2017). https://doi.org/10.1038/nature24673

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