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Tumor Mutation Burder as a predictive biomarker

Tumor Mutation Burder as a predictive biomarker. Dr Francesco Pantano, MD PhD Medical Oncology Department Campus Bio Medico of Rome. Relevance of TMB in immunotherapy-treated patients. TMB is a surrogate of neo-antigen load

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Tumor Mutation Burder as a predictive biomarker

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  1. TumorMutationBurderas a predictivebiomarker Dr Francesco Pantano, MD PhD MedicalOncologyDepartment Campus Bio Medico of Rome

  2. Relevance of TMB in immunotherapy-treatedpatients • TMB is a surrogate of neo-antigenload • While not all mutations result in immunogenic neo-antigens… • TMB represents a quantifiable measure of the number of mutations in atumour • Patients with tumours that have high neo-antigen load are more likely to achieve clinical benefit from immune checkpointinhibitors Stenzinger A et al., Genes Chromosomes Cancer2019

  3. Evolution of TMB as a biomarker Chan et al., Annals of Oncology 2018

  4. Evidence for correlation of response rate to TMB acrosstumortypes Correlation coefficient = 0.74, meaning that 55% of the differences in the ORR across cancer types may be explained by variations ofTMB Yarchoan et al., NEJM 2017

  5. NSCLC isassociated to a high TMB Schumacherand Schreiber, Science 2015

  6. Data from clinical trials and cohorts Impact of TMB on response, duration of response, PFS and OS with ICIs

  7. TMB ispredictive of clinical benefit with anti-PD(L)-1 Rizvi et al., Science 2015

  8. TMB is correlate with response rate Impact of TMB seems more pronouncedwith anti-CTLA-4 + anti-PD(L)-1combinations CheckMate 026, tTMB, WES Mystic, bTMB, GuardantOMNI Peters, AACR 2017; Peters, AACR 2019

  9. Prospectivevalidation of bTMB impact on response rate B-FIRST Kim et al., ESMO 2018

  10. TMB butnot PD-L1 expressionispredictive of responseduration and of long term benefit from anti-PD(L)-1 therapy TMB assessed withMSK-IMPACT Rizvi H et al., ASCO2018

  11. Prospectivevalidation of TMB predictive impact on PFS benefit from Ipilimumab-Nivolumab vs. chemotherapy Hellmann M. et al., NEJM 2018

  12. PFS in patients with high TMB (≥ 10 MUT/MB) by tumor PD-L1 expression: CHECKMATE 227 Hellmann M. et al., NEJM 2018

  13. Prospective TMB ispredictive of os benefit acrosstumourtypes Samstein, Nat. Gen 2019

  14. Prospective TMB isnotconsistentlyassociated with OS benefit from anti-PD(L)-1 ± anti-CTLA-4 therapy OAK phase III trial: atezolizumabvs. docetaxel, allcomers PFS OS Gandara, Nat. Med 2018

  15. Prospectivepreliminaryoverallsurvival with nivolumab + ipilimumab vs chemotherapy in patients with high TMB(≥10 MUT/MB) Nivo +ipi 100 (n =139) MedianOS,b mo 23.0 80 HR 0.7 1-y OS=67% 95%CI 0.56,1 60 1-y OS=58% Nivolumab +ipilimumab 40 Chemothe 20 0 0 3 6 9 12 15 18 21 24 27 30 risk Months pi 139 120 112 98 90 71 44 16 5 0 0 o 160 148 129 104 90 75 45 23 9 1 0 OS(%)a rapy No.at Nivo+ i Chem • Database lock:March 15, 2018; minimum follow-up: 14.2 months; 53% of patientswerecensored • In the chemotherapyarm, 31.3% receivedsubsequentimmunotherapy(38.3% amongthosewith diseaseprogressionc) Hellmann, NEJM2018

  16. TMB can be predictive of OS benefit from anti-PD(L)-1 ± anti-CTLA-4 therapy PetersS et al., AACR 2019

  17. Why TMB wouldnotpredict OS benefit from ICIs in NSCLS whereasitis more consistentlypredictive of PFS benefit? • Prognosticrole? • Selection of the cut-off? • Role of cross over? • Similarthan EGFR TKI vs.chemotherapy in EGFR mutatedtumors? • High TMB tumoursmight generate more rapidlyacquiredresistance to ICIs due to genomicinstability • Role of subclonalmutations • Notconsistent with correlation of TMB with long termresponses • No data from trials assessingICIs + chemotherapycombinationsyet (ASCO 2019) • Bettercorrelation of PFS and OS benefits

  18. TMB: the nextbiomarker in addition to PD-L1? A way to refine treatment algorithm for 1st line treatment of advancedNSCLC

  19. Combining TMB and PD-L1 astwoindependentbiomarkers MSK-IMPACT CHECKMATE 012 Nivolumab +ipilimumab OAK, atezolizumab vs.docetaxel Rizvi H et al., JCO 2018; Hellmann, Cancer Cell 2018; Gandara, Nat Med2018

  20. Using TMB and PD-L1 astwoindependentbiomarkers Yarchoan et al., JCI Insights 2019; Peters S et al., AACR2017

  21. TMB as a decision-makingbiomarker: the nextstep?

  22. TMB assessment: the nextbiomarker? • The available data support the hypothesis that the number of nonsynonymous mutations (and ultimately neoantigens) significantly correlates with clinical response to immune checkpointblockade • TMB is a continuous biomarker, appearing as an acceptable surrogate of neoantigenload • Consistent correlation with response rates, duration of response, PFS benefit with anti-PD(L)-1 in NSCLCand • across tumourtypes • Correlation with OS benefit from ICIs is more variable in randomized trials vs.chemotherapy • Predictive impact on ICI benefit seems more pronounced with anti-PD(L)-1 + anti-CTLA-4combinations • Independent of PD-L1 expression: TMB will not replace PD-L1 but will supplementit • Could be used in addition to PD-L1 expression to refine 1st line treatment algorithm and selection ofpatients • Many remaining issues but advances in technology make possible to expect using soon TMB in clinicalpractice

  23. Measuring TMB • Whole genome/exome sequencing with “paired normal” • Number of variants per megabase - count and divide by coverage • Can refine the count by excluding select alterations: • Intronic/intragenic • Germline • Synonymous Chan, Ann Oncol 2019 Chalmers, Genome Med 2017

  24. Measuring TMB • “Large” targetedpanels (without “pairednormal”) • Sameassumptions ; HOWEVER: • Use predictiveapproach to removegermlinevariants • Include synonymousvariants • Excludeknown “pathogenic” variants to account for presumedbias • (use COSMIC filter) Chan, Ann Oncol 2019 Chalmers, Genome Med 2017

  25. Some gene panelsused forTMB ELCC 2019

  26. Whyusingall of a gene codingregion? KRAS mutations

  27. Ourexperience Wecreated a bioinformatic pipeline thatdetectsgenomicregionsmost informative for TMB estimation

  28. Ourexperience • Wecreateddifferent gene panels of differentlengths with ouralgorithm • Gene panelsshorterthan 1 Mb are more precise thanexisting gene panels of comparablelengths

  29. Ourexperience A 80 Kb (0.080 Mb) can be used for efficient TMB estimation and can predictclinicaloutcome of patientstreated with ICB

  30. Conclusions Shrinking the amount of DNA needed for TMB estimationmight: Reduce turn aroundtimes Lower Costs Enableblood-TMB estimation

  31. Special thanks to: Prof. Giuseppe Tonini Prof. Daniele Santini Prof. Bruno Vincenzi Dr.Paolo Manca TranslationalOncology Lab: Dr. Michele Iuliani Dr. Giulia Ribelli Dr. Sonia Simonetti

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