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CRAC (Clinical Relevance of Alterations in Cancer): a Knowledge Base for the Selection of Molecularly Matched Therapy for Solid Tumors

CRAC (Clinical Relevance of Alterations in Cancer): a Knowledge Base for the Selection of Molecularly Matched Therapy for Solid Tumors

Lebedeva A.A., Kavun A.I., Veselovsky E.M., Mileyko V.A., Ivanov M.V.
Key words: database; precision oncology; NGS; clinical interpretation; molecularly matched therapy.
2022, volume 14, issue 6, page 15.

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Multigene testing using NGS (next-generation sequencing) provides a large amount of information and can detect multiple molecular alterations. Subsequent clinical interpretation is a time-consuming process necessary to select a treatment strategy. Existing databases often contain inconsistent information and are not regularly updated. The use of ESCAT levels of evidence requires a deep understanding of the nature of alterations and does not answer the question of which therapy option to select when multiple biomarkers with the same level of evidence are detected. To address these issues, we created the Clinical Relevance of Alterations in Cancer (CRAC) database on the relevance of detected alterations in specific genes, which are often analyzed as part of NGS panels. The team of oncologists and biologists assigned a CRAC score from 1 to 10 to each biomarker (a type of genomic alteration characteristic of specific genes) for 15 malignancies; an average score was entered into the database. CRAC scores are a numerical reflection of the following factors: therapy availability and the prospects of drug treatment with experimental drugs for patients with a particular type of tumor. A total of 134 genes and 15 of the most common tumor types have been selected for CRAC. The biomarker–nosology associations with CRAC scores in the range of 1–3 are the most frequent (n=2719 out of 3495; 77.8%), the least frequent ones (n=52 out of 3495; 1.5%) are with the highest CRAC scores 9 and 10. To estimate the practical effectiveness of the CRAC database, 208 reports on comprehensive molecular profiling were retrospectively analyzed; the applicability of CRAC was compared with the ESCAT level of evidence system. The highest CRAC scores corresponded to the ESCAT maximum levels of evidence: the range of scores 8–10 corresponded to evidence levels I and II. No biomarker within the same level of evidence was represented by the same CRAC score; the largest range of CRAC scores was observed for biomarkers of levels evidence IIIA and IV — from 2 to 10 and from 1 to 9, respectively. The use of CRAC scores allowed to identify additional 95 alterations with CRAC scores of 1–5 in the studied patients.

The developed database is available at: https://crac.oncoatlas.ru/.

  1. Mateo J., Steuten L., Aftimos P., André F., Davies M., Garralda E., Geissler J., Husereau D., Martinez-Lopez I., Normanno N., Reis-Filho J.S., Stefani S., Thomas D.M., Westphalen C.B., Voest E. Delivering precision oncology to patients with cancer. Nat Med 2022; 28(4): 658–665, https://doi.org/10.1038/s41591-022-01717-2.
  2. Pishvaian M.J., Blais E.M., Brody J.R., Lyons E., DeArbeloa P., Hendifar A., Mikhail S., Chung V., Sahai V., Sohal D.P.S., Bellakbira S., Thach D., Rahib L., Madhavan S., Matrisian L.M., Petricoin E.F. 3rd. Overall survival in patients with pancreatic cancer receiving matched therapies following molecular profiling: a retrospective analysis of the Know Your Tumor registry trial. Lancet Oncol 2020; 21(4): 508–518, https://doi.org/10.1016/s1470-2045(20)30074-7.
  3. Coquerelle S., Darlington M., Michel M., Durand M., Borget I., Baffert S., Marino P., Perrier L., Durand-Zaleski I.; NGSEco Group. Impact of next generation sequencing on clinical practice in oncology in France: better genetic profiles for patients improve access to experimental treatments. Value Health 2020; 23(7): 898–906, https://doi.org/10.1016/j.jval.2020.03.005.
  4. Sicklick J.K., Kato S., Okamura R., Schwaederle M., Hahn M.E., Williams C.B., De P., Krie A., Piccioni D.E., Miller V.A., Ross J.S., Benson A., Webster J., Stephens P.J., Lee J.J., Fanta P.T., Lippman S.M., Leyland-Jones B., Kurzrock R. Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study. Nat Med 2019; 25(5): 744–750, https://doi.org/10.1038/s41591-019-0407-5.
  5. Fountzilas E., Said R., Tsimberidou A.M. Expanded access to investigational drugs: balancing patient safety with potential therapeutic benefits. Expert Opin Investig Drugs 2018; 27(2): 155–162, https://doi.org/10.1080/13543784.2018.1430137.
  6. Sruamsiri R., Ross-Degnan D., Lu C.Y., Chaiyakunapruk N., Wagner A.K. Policies and programs to facilitate access to targeted cancer therapies in Thailand. PLoS One 2015; 10(3): e0119945, https://doi.org/10.1371/journal.pone.0119945.
  7. van der Velden D.L., Hoes L.R., van der Wijngaart H., van Berge Henegouwen J.M., van Werkhoven E., Roepman P., Schilsky R.L., de Leng W.W.J., Huitema A.D.R., Nuijen B., Nederlof P.M., van Herpen C.M.L., de Groot D.J.A., Devriese L.A., Hoeben A., de Jonge M.J.A., Chalabi M., Smit E.F., de Langen A.J., Mehra N., Labots M., Kapiteijn E., Sleijfer S., Cuppen E., Verheul H.M.W., Gelderblom H., Voest E.E. The Drug Rediscovery protocol facilitates the expanded use of existing anticancer drugs. Nature 2019; 574(7776): 127–131, https://doi.org/10.1038/s41586-019-1600-x.
  8. Mosele F., Remon J., Mateo J., Westphalen C.B., Barlesi F., Lolkema M.P., Normanno N., Scarpa A., Robson M., Meric-Bernstam F., Wagle N., Stenzinger A., Bonastre J., Bayle A., Michiels S., Bièche I., Rouleau E., Jezdic S., Douillard J.Y., Reis-Filho J.S., Dienstmann R., André F. Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: a report from the ESMO Precision Medicine Working Group. Ann Oncol 2020; 31(11): 1491–1505, https://doi.org/10.1016/j.annonc.2020.07.014.
  9. Khotskaya Y.B., Mills G.B., Mills Shaw K.R. Next-generation sequencing and result interpretation in clinical oncology: challenges of personalized cancer therapy. Annu Rev Med 2017; 68: 113–125, https://doi.org/10.1146/annurev-med-102115-021556.
  10. Chakravarty D., Gao J., Phillips S.M., Kundra R., Zhang H., Wang J., Rudolph J.E., Yaeger R., Soumerai T., Nissan M.H., Chang M.T., Chandarlapaty S., Traina T.A., Paik P.K., Ho A.L., Hantash F.M., Grupe A., Baxi S.S., Callahan M.K., Snyder A., Chi P., Danila D., Gounder M., Harding J.J., Hellmann M.D., Iyer G., Janjigian Y., Kaley T., Levine D.A., Lowery M., Omuro A., Postow M.A., Rathkopf D., Shoushtari A.N., Shukla N., Voss M., Paraiso E., Zehir A., Berger M.F., Taylor B.S., Saltz L.B., Riely G.J., Ladanyi M., Hyman D.M., Baselga J., Sabbatini P., Solit D.B., Schultz N. OncoKB: a precision oncology knowledge base. JCO Precis Oncol 2017; 2017: PO.17.00011, https://doi.org/10.1200/po.17.00011.
  11. Mateo J., Chakravarty D., Dienstmann R., Jezdic S., Gonzalez-Perez A., Lopez-Bigas N., Ng C.K.Y., Bedard P.L., Tortora G., Douillard J.Y., Van Allen E.M., Schultz N., Swanton C., André F., Pusztai L. A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT). Ann Oncol 2018; 29(9): 1895–1902, https://doi.org/10.1093/annonc/mdy263.
  12. Griffith M., Spies N.C., Krysiak K., McMichael J.F., Coffman A.C., Danos A.M., Ainscough B.J., Ramirez C.A., Rieke D.T., Kujan L., Barnell E.K., Wagner A.H., Skidmore Z.L., Wollam A., Liu C.J., Jones M.R., Bilski R.L., Lesurf R., Feng Y.Y., Shah N.M., Bonakdar M., Trani L., Matlock M., Ramu A., Campbell K.M., Spies G.C., Graubert A.P., Gangavarapu K., Eldred J.M., Larson D.E., Walker J.R., Good B.M., Wu C., Su A.I., Dienstmann R., Margolin A.A., Tamborero D., Lopez-Bigas N., Jones S.J., Bose R., Spencer D.H., Wartman L.D., Wilson R.K., Mardis E.R., Griffith O.L. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet 2017; 49(2): 170–174, https://doi.org/10.1038/ng.3774.
  13. Huang L., Fernandes H., Zia H., Tavassoli P., Rennert H., Pisapia D., Imielinski M., Sboner A., Rubin M.A., Kluk M., Elemento O. The cancer precision medicine knowledge base for structured clinical-grade mutations and interpretations. J Am Med Inform Assoc 2017; 24(3): 513–519, https://doi.org/10.1093/jamia/ocw148.
  14. Tamborero D., Rubio-Perez C., Deu-Pons J., Schroeder M.P., Vivancos A., Rovira A., Tusquets I., Albanell J., Rodon J., Tabernero J., de Torres C., Dienstmann R., Gonzalez-Perez A., Lopez-Bigas N. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med 2018; 10(1): 25, https://doi.org/10.1186/s13073-018-0531-8.
  15. Gyawali B., Kesselheim A.S. The promise of ESCAT: a new system for evaluating cancer drug–target pairs. Nat Rev Clin Oncol 2018; 16(3): 147–148, https://doi.org/10.1038/s41571-018-0110-3.
  16. Pallarz S., Benary M., Lamping M., Rieke D., Starlinger J., Sers C., Wiegandt D.L., Seibert M., Ševa J., Schäfer R., Keilholz U., Leser U. Comparative analysis of public knowledge bases for precision oncology. JCO Precis Oncol 2019; 3: PO.18.00371, https://doi.org/10.1200/po.18.00371.
  17. Hayes D.F. Defining clinical utility of tumor biomarker tests: a clinician’s viewpoint. J Clin Oncol 2021; 39(3): 238–248, https://doi.org/10.1200/jco.20.01572.
  18. Burns P.B., Rohrich R.J., Chung K.C. The levels of evidence and their role in evidence-based medicine. Plast Reconstr Surg 2011; 128(1): 305–310, https://doi.org/10.1097/prs.0b013e318219c171.
  19. Li M.M., Datto M., Duncavage E.J., Kulkarni S., Lindeman N.I., Roy S., Tsimberidou A.M., Vnencak-Jones C.L., Wolff D.J., Younes A., Nikiforova M.N. Standards and guidelines for the interpretation and reporting of sequence variants in cancer: a joint consensus recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn 2017; 19(1): 4–23, https://doi.org/10.1016/j.jmoldx.2016.10.002.
  20. Meric-Bernstam F., Johnson A., Holla V., Bailey A.M., Brusco L., Chen K., Routbort M., Patel K.P., Zeng J., Kopetz S., Davies M.A., Piha-Paul S.A., Hong D.S., Eterovic A.K., Tsimberidou A.M., Broaddus R., Bernstam E.V., Shaw K.R., Mendelsohn J., Mills G.B. A decision support framework for genomically informed investigational cancer therapy. J Natl Cancer Inst 2015; 107(7): djv098, https://doi.org/10.1093/jnci/djv098.
  21. Koopman B., Groen H.J.M., Ligtenberg M.J.L., Grünberg K., Monkhorst K., de Langen A.J., Boelens M.C., Paats M.S., von der Thüsen J.H., Dinjens W.N.M., Solleveld N., van Wezel T., Gelderblom H., Hendriks L.E., Speel E.M., Theunissen T.E., Kroeze L.I., Mehra N., Piet B., van der Wekken A.J., Ter Elst A., Timens W., Willems S.M., Meijers R.W.J., de Leng W.W.J., van Lindert A.S.R., Radonic T., Hashemi S.M.S., Heideman D.A.M., Schuuring E., van Kempen L.C. Multicenter comparison of molecular tumor boards in the Netherlands: definition, composition, methods, and targeted therapy recommendations. Oncologist 2021; 26(8): e1347–e1358, https://doi.org/10.1002/onco.13580.
  22. Kato S., Kim K.H., Lim H.J., Boichard A., Nikanjam M., Weihe E., Kuo D.J., Eskander R.N., Goodman A., Galanina N., Fanta P.T., Schwab R.B., Shatsky R., Plaxe S.C., Sharabi A., Stites E., Adashek J.J., Okamura R., Lee S., Lippman S.M., Sicklick J.K., Kurzrock R. Real-world data from a molecular tumor board demonstrates improved outcomes with a precision N-of-One strategy. Nat Commun 2020; 11(1): 4965, https://doi.org/10.1038/s41467-020-18613-3.
  23. Luchini C., Lawlor R.T., Milella M., Scarpa A. Molecular tumor boards in clinical practice. Trends Cancer 2020; 6(9): 738–744, https://doi.org/10.1016/j.trecan.2020.05.008.
  24. Larson K.L., Huang B., Weiss H.L., Hull P., Westgate P.M., Miller R.W., Arnold S.M., Kolesar J.M. Clinical outcomes of molecular tumor boards: a systematic review. JCO Precis Oncol 2021; 5: 1122–1132, https://doi.org/10.1200/po.20.00495.
  25. Kandoth C., McLellan M.D., Vandin F., Ye K., Niu B., Lu C., Xie M., Zhang Q., McMichael J.F., Wyczalkowski M.A., Leiserson M.D.M., Miller C.A., Welch J.S., Walter M.J., Wendl M.C., Ley T.J., Wilson R.K., Raphael B.J., Ding L. Mutational landscape and significance across 12 major cancer types. Nature 2013; 502(7471): 333–339, https://doi.org/10.1038/nature12634.
  26. Soussi T., Wiman K.G. TP53: an oncogene in disguise. Cell Death Differ 2015; 22(8): 1239–1249, https://doi.org/10.1038/cdd.2015.53.
  27. Hu J., Cao J., Topatana W., Juengpanich S., Li S., Zhang B., Shen J., Cai L., Cai X., Chen M. Targeting mutant p53 for cancer therapy: direct and indirect strategies. J Hematol Oncol 2021; 14(1): 157, https://doi.org/10.1186/s13045-021-01169-0.
  28. Huang J. Current developments of targeting the p53 signaling pathway for cancer treatment. Pharmacol Ther 2021; 220: 107720, https://doi.org/10.1016/j.pharmthera.2020.107720.
  29. Soria J.C., Ohe Y., Vansteenkiste J., Reungwetwattana T., Chewaskulyong B., Lee K.H., Dechaphunkul A., Imamura F., Nogami N., Kurata T., Okamoto I., Zhou C., Cho B.C., Cheng Y., Cho E.K., Voon P.J., Planchard D., Su W.C., Gray J.E., Lee S.M., Hodge R., Marotti M., Rukazenkov Y., Ramalingam S.S.; FLAURA Investigators. Osimertinib in untreated EGFR-mutated advanced non-small-cell lung cancer. N Engl J Med 2018; 378(2): 113–125, https://doi.org/10.1056/nejmoa1713137.
  30. Rosell R., Carcereny E., Gervais R., Vergnenegre A., Massuti B., Felip E., Palmero R., Garcia-Gomez R., Pallares C., Sanchez J.M., Porta R., Cobo M., Garrido P., Longo F., Moran T., Insa A., De Marinis F., Corre R., Bover I., Illiano A., Dansin E., de Castro J., Milella M., Reguart N., Altavilla G., Jimenez U., Provencio M., Moreno M.A., Terrasa J., Muñoz-Langa J., Valdivia J., Isla D., Domine M., Molinier O., Mazieres J., Baize N., Garcia-Campelo R., Robinet G., Rodriguez-Abreu D., Lopez-Vivanco G., Gebbia V., Ferrera-Delgado L., Bombaron P., Bernabe R., Bearz A., Artal A., Cortesi E., Rolfo C., Sanchez-Ronco M., Drozdowskyj A., Queralt C., de Aguirre I., Ramirez J.L., Sanchez J.J., Molina M.A., Taron M., Paz-Ares L.; Spanish Lung Cancer Group in collaboration with Groupe Français de Pneumo-Cancérologie and Associazione Italiana Oncologia Toracica. Erlotinib versus standard chemotherapy as first-line treatment for European patients with advanced EGFR mutation-positive non-small-cell lung cancer (EURTAC): a multicentre, open-label, randomised phase 3 trial. Lancet Oncol 2012; 13(3): 239–246, https://doi.org/10.1016/s1470-2045(11)70393-x.
  31. Mok T.S., Cheng Y., Zhou X., Lee K.H., Nakagawa K., Niho S., Lee M., Linke R., Rosell R., Corral J., Migliorino M.R., Pluzanski A., Sbar E.I., Wang T., White J.L., Wu Y.L. Improvement in overall survival in a randomized study that compared dacomitinib with gefitinib in patients with advanced non-small-cell lung cancer and EGFR-activating mutations. J Clin Oncol 2018; 36(22): 2244–2250, https://doi.org/10.1200/jco.2018.78.7994.
  32. Yang J.C., Wu Y.L., Schuler M., Sebastian M., Popat S., Yamamoto N., Zhou C., Hu C.P., O’Byrne K., Feng J., Lu S., Huang Y., Geater S.L., Lee K.Y., Tsai C.M., Gorbunova V., Hirsh V., Bennouna J., Orlov S., Mok T., Boyer M., Su W.C., Lee K.H., Kato T., Massey D., Shahidi M., Zazulina V., Sequist L.V. Afatinib versus cisplatin-based chemotherapy for EGFR mutation-positive lung adenocarcinoma (LUX-Lung 3 and LUX-Lung 6): analysis of overall survival data from two randomised, phase 3 trials. Lancet Oncol 2015; 16(2): 141–151, https://doi.org/10.1016/s1470-2045(14)71173-8.
Lebedeva A.A., Kavun A.I., Veselovsky E.M., Mileyko V.A., Ivanov M.V. CRAC (Clinical Relevance of Alterations in Cancer): a Knowledge Base for the Selection of Molecularly Matched Therapy for Solid Tumors. Sovremennye tehnologii v medicine 2022; 14(6): 15, https://doi.org/10.17691/stm2022.14.6.02


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