Development of Intra-tumor Driver Mutation Prevalence as a Predictor of Efficacy of Targeted Agents
The overall objective of this study is to test the hypotheses that the intra-tumor actionable genetic alteration prevalence (IAGAP, percentage of cancer cells in a tumor carrying a genetic alteration) correlates with clinical response to targeted agents. Higher IAGAP will portend a higher response rate to targeted agents, whereas a low prevalence will predict drug resistance. To test this hypothesis, we have developed an algorithm to estimate IAGAP based on either whole exome or large panel sequencing data. We would like to evaluate the potential of IAGAP to be developed as a pan-cancer companion diagnostic for targeted agents. This is not an interventional (treatment) study. It is a retrospective analysis study designed to utilize data derived from patient participation in ORIEN Avatar at each participating site. Through site operations and enrollment practices for ORIEN Avatar, this study will require the participants permission for the following: 1) Data abstraction of clinical and other data collected per ORIEN Avatar practices at each site; and 2) Collection and distribution of germline and tumor samples for genomic sequencing and analysis; 3) Data derived from specimen molecular analysis to be shared with participating institutions as a coded, limited data-set. To achieve these objectives, the following specific aims will be addressed: 2.1. Aim 1: Acquire clinical response data and molecular data from Avatar data set and develop surrogates of objective response; 2.1.1. Aim 1A: Acquire molecular and clinical data from more than 1500 Avatar patients; 2.1.2. Aim 1B: Identify potential surrogates for clinical response; 2.2. Aim 2: Conduct a retrospective cohort study to test the accuracy of IAGAP to predict clinical outcomes in patients treated with targeted agent.
- Inclusion Criteria:
- 18 years of age or older
- Diagnosed with gynecological cancer
- Has been or will be treated with one of the following targeted agents: Abemaciclib (Verzenio) Ado-trastuzumab (Kadcyla) Afatinib (Gilotrif) Alectinib (Alecensa) Atezolizumab (Tecentriq) Avelumab (Bavencio) Axitinib (Inlyta) Bevacizumab (Avastin) Bosutinib (Bosulif) Brigatinib (Alunbrig) Cabozantinib (Cabometyx) Certinib (Zykadia) Cetuximab (Erbitux) Cobimetinib (Cotellic) Crizotinib (Xalkori) Dabrafenib (Tafinlar) Dasatinib (Sprycel) Defactinib Denosumab (Prolia, Xgeva) Durvalumab (Imfinzi) Erlotinib (Tarceva) Everolimus (Afinitor) Gefitinib (Iressa) Ibrutinib (Imbruvica) Idelalisib (Zydelig) Imatinib (Gleevec) Ipilimumab (Yervoy) Lenvatinib (Lenvima) Necitumumab (Portrazza) Nilotinib (Tasigna) Niraparib (Zejula) Nivolumab (Opdivo) Olaparib (Lynparza) Olatumab (Lartruvo) Osimeritinib (Tagrisso) Palbociclib (Ibrance) Panitumumab (Vectibix) Pazopanib (Votrient) Pembrolizumab (Keytruda) Pertuzumab (Perjeta) Ponatinib (Iclusig) Ramucirumab (Cyramza) Regorafenib (Stivarga) Ribociclib (Kisqali) Rucaparib (Rubraca) Sonidegib (Odomzo) Sorafenib (Nexavar) Sunitinib (Sutent) Temsitrolimus (Torisel) Tramedinib (Mekinist) Trastuzumab (Herceptin) Vandetanib (Caprelsa) Vemurafenib (Zelboraf) Venetoclax (Venclexta)
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