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

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

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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

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Figure 5

KRAS mutations predict response to inhibition of Hsp90 in vitro and in vivo.

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KRAS mutations predict response to inhibition of Hsp90 in vitro and in ...
(A) The sensitive and resistant cell lines were sorted according to their GI50 values and annotated for the presence of KRAS mutations (asterisks and black columns). Bar height represents the respective GI50 values. The association of KRAS mutations and 17-AAG sensitivity (GI50 < 0.07 μM = sensitive; GI50 > 0.83 μM = resistant; according to the lower and upper 25th percentiles) was calculated by Fisher’s exact test for the lung cancer data set (upper panel) and for the NCI60 data set (lower panel). (B) Upper panel shows that whole-cell lysates of the indicated KRAS WT and KRAS mutated cell lines treated with different concentrations of 17-AAG were analyzed for levels of c-RAF, KRAS, cyclin D1, and AKT by immunoblotting. Lower panel shows that extracts of the indicated cells treated with either control (C) or 0.5 μM (H322 and Calu-6) or 1 μM (H2122) of 17-AAG were subjected to coimmunoprecipitation with antibodies to either KRAS (top) or Hsp90 (bottom); immunoconjugates were analyzed for levels of Hsp90 (top) or KRAS (bottom) by immunoblotting. Noncontiguous bands run on the same gel are separated by a black line (H2122). WB, Western blot. (C) Displayed are coronal MRI scans of lox-stop-loxKRASG12D mice before and after 7 days of treatment with either 17-DMAG or vehicle. The areas of lung tumors were manually segmented and measured on each magnetic resonance slice, and total tumor volume reduction was calculated for all mice treated with 17-DMAG (n = 4) and placebo (n = 3). SD of tumor volume in the cohort of treated and untreated mice was calculated and is depicted as error bars.

Copyright © 2026 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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