Ritu Pandey1,2, Nathan Johnson3, Laurence Cooke1, Benny Johnson4, Yuliang Chen1, Manjari Pandey5, Jason Chandler5, Daruka Mahadevan6. 1. Cancer Center, University of Arizona, Tucson, AZ 85724, USA. 2. Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ 85724, USA. 3. School of Medicine, Vanderbilt University, Nashville, TN 37325, USA. 4. MD Anderson Cancer Center, Houston, TX 77030, USA. 5. West Cancer Center, 7945 Wolf River Blvd, Germantown, TN 38138, USA. 6. Mays Cancer Center, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
Abstract
Molecular profiling with next generation sequencing (NGS) delivers key information on mutant gene sequences, copy number alterations, gene-fusions, and with immunohistochemistry (IHC), is a valuable tool in clinical decision making for patients entering investigational agent trials. Our objective was to elucidate mutational profiles from primary versus metastatic sites from advanced cancer patients to guide rational therapy. All phase I patients (n = 203) with advanced cancer were profiled by commercially available NGS platforms. The samples were annotated by histology, primary and metastatic site, biopsy site, gene mutations, mutation count/gene, and mutant TP53. A molecular profile of each patient was categorized into common and unique mutations, signaling pathways for each profile and TP53 mutations mapped to 3D-structure of p53 bound to DNA and pre/post therapy molecular response. Of the 171 patients analyzed, 145 had genetic alterations from primary and metastatic sites. The predominant histology was adenocarcinoma followed by squamous cell carcinoma, carcinoma of unknown primary site (CUPS), and melanoma. Of 790 unique mutations, TP53 is the most common followed by APC, KRAS, PIK3CA, ATM, PTEN, NOTCH1, BRCA2, BRAF, KMT2D, LRP1B, and CDKN2A. TP53 was found in most metastatic sites and appears to be a key driver of acquired drug resistance. We highlight examples of acquired mutational profiles pre-/post- targeted therapy in multiple tumor types with a menu of potential targeted agents. Conclusion: The mutational profiling of primary and metastatic lesions in cancer patients provides an opportunity to identify TP53 driver 'pathways' that may predict for drug sensitivity/resistance and guide rational drug combinations in clinical trials.
Molecular profiling with next generation sequencing (NGS) delivers key information on mutant gene sequences, copy number alterations, gene-fusions, and with immunohistochemistry (IHC), is a valuable tool in clinical decision making for patients entering investigational agent trials. Our objective was to elucidate mutational profiles from primary versus metastatic sites from advanced cancerpatients to guide rational therapy. All phase I patients (n = 203) with advanced cancer were profiled by commercially available NGS platforms. The samples were annotated by histology, primary and metastatic site, biopsy site, gene mutations, mutation count/gene, and mutant TP53. A molecular profile of each patient was categorized into common and unique mutations, signaling pathways for each profile and TP53 mutations mapped to 3D-structure of p53 bound to DNA and pre/post therapy molecular response. Of the 171 patients analyzed, 145 had genetic alterations from primary and metastatic sites. The predominant histology was adenocarcinoma followed by squamous cell carcinoma, carcinoma of unknown primary site (CUPS), and melanoma. Of 790 unique mutations, TP53 is the most common followed by APC, KRAS, PIK3CA, ATM, PTEN, NOTCH1, BRCA2, BRAF, KMT2D, LRP1B, and CDKN2A. TP53 was found in most metastatic sites and appears to be a key driver of acquired drug resistance. We highlight examples of acquired mutational profiles pre-/post- targeted therapy in multiple tumor types with a menu of potential targeted agents. Conclusion: The mutational profiling of primary and metastatic lesions in cancerpatients provides an opportunity to identify TP53 driver 'pathways' that may predict for drug sensitivity/resistance and guide rational drug combinations in clinical trials.
Authors: Z A Khan; S K Jonas; N Le-Marer; H Patel; R Q Wharton; A Tarragona; A Ivison; T G Allen-Mersh Journal: Clin Cancer Res Date: 2000-09 Impact factor: 12.531