Predicting drug susceptibility of non–small cell lung cancers based on genetic lesions
Martin L. Sos, et al.
Martin L. Sos, et al.
View: Text | PDF
Technical Advance Oncology

Predicting drug susceptibility of non–small cell lung cancers based on genetic lesions

Abstract

Somatic genetic alterations in cancers have been linked with response to targeted therapeutics by creation of specific dependency on activated oncogenic signaling pathways. However, no tools currently exist to systematically connect such genetic lesions to therapeutic vulnerability. We have therefore developed a genomics approach to identify lesions associated with therapeutically relevant oncogene dependency. Using integrated genomic profiling, we have demonstrated that the genomes of a large panel of human non–small cell lung cancer (NSCLC) cell lines are highly representative of those of primary NSCLC tumors. Using cell-based compound screening coupled with diverse computational approaches to integrate orthogonal genomic and biochemical data sets, we identified molecular and genomic predictors of therapeutic response to clinically relevant compounds. Using this approach, we showed that v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations confer enhanced Hsp90 dependency and validated this finding in mice with KRAS-driven lung adenocarcinoma, as these mice exhibited dramatic tumor regression when treated with an Hsp90 inhibitor. In addition, we found that cells with copy number enhancement of v-abl Abelson murine leukemia viral oncogene homolog 2 (ABL2) and ephrin receptor kinase and v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (avian) (SRC) kinase family genes were exquisitely sensitive to treatment with the SRC/ABL inhibitor dasatinib, both in vitro and when it xenografted into mice. Thus, genomically annotated cell-line collections may help translate cancer genomics information into clinical practice by defining critical pathway dependencies amenable to therapeutic inhibition.

Authors

Martin L. Sos, Kathrin Michel, Thomas Zander, Jonathan Weiss, Peter Frommolt, Martin Peifer, Danan Li, Roland Ullrich, Mirjam Koker, Florian Fischer, Takeshi Shimamura, Daniel Rauh, Craig Mermel, Stefanie Fischer, Isabel Stückrath, Stefanie Heynck, Rameen Beroukhim, William Lin, Wendy Winckler, Kinjal Shah, Thomas LaFramboise, Whei F. Moriarty, Megan Hanna, Laura Tolosi, Jörg Rahnenführer, Roel Verhaak, Derek Chiang, Gad Getz, Martin Hellmich, Jürgen Wolf, Luc Girard, Michael Peyton, Barbara A. Weir, Tzu-Hsiu Chen, Heidi Greulich, Jordi Barretina, Geoffrey I. Shapiro, Levi A. Garraway, Adi F. Gazdar, John D. Minna, Matthew Meyerson, Kwok-Kin Wong, Roman K. Thomas

×

Figure 6

Identification of functionally relevant targets for dasatinib activity.

Options: View larger image (or click on image) Download as PowerPoint
Identification of functionally relevant targets for dasatinib activity. ...
(A) Left panel shows that cell lines with copy number gain involving at least 1 gene encoding dasatinib target are labeled with asterisks and black columns. The probability of these cells being dasatinib sensitive was calculated by Fisher’s exact test. In right panel, dasatinib GI50 values are shown as box plots (representing the 25th to 75th percentile; whisker representing the 95th percentile; dots representing outliers) for cell lines with (TESP+ 1 gene) and without (TESP– 1 gene) copy number gain of dasatinib target genes (Wilcoxon test). (B) H322M cells harboring amplified SRC were either left untreated or transduced with an empty vector control (H322Mcont) or with shRNA targeting SRC (H322MSRCkd). After puromycin selection, levels of SRC in H322M cells transduced with the indicated vectors were analyzed by immunoblotting (top). The H322MSRCkd lanes were run on the same gel but were noncontiguous, as indicated by the white line. Viability was quantified by cell counting. Error bars represent SD between different experiments. (C) H322M cells were transduced with vectors encoding either active SRC or active SRC with a gatekeeper mutation SRC (T341M). Stable cells were treated with dasatinib for 96 hours. Viability is shown as percentage of untreated controls. Error bars indicate SD of 3 independent experiments. (D) Dasatinib-sensitive (TESP+; H322M) or -resistant cells (TESP–; A549) were grown s.c. in nude mice. After 14 days of treatment (vehicle, dasatinib), tumor volumes were measured as diameters. SD of tumor volume in the cohort of treated and untreated mice was calculated and is depicted as error bars.