In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 75, No. 15_Supplement ( 2015-08-01), p. 957-957
Abstract:
For the vast majority of cancer patients, existing knowledge of the function(s) of the mutant genes harbored by their tumor and the dependencies they induce is incomplete or non-existent since most cancer mutations are exceedingly rare. As a result, we now have long lists of candidate alleles but a paucity of targets whose biology is sufficiently well understood to guide therapeutics. Here we present an interim progress report on a pilot effort aiming to create a generalizable framework to systematically map the molecular consequences of cancer variants at scale (Target Accelerator). First, we created an efficient pipeline to generate cancer variants and generated an initial library of 1300 mutant cDNA clones corresponding to variants in lung cancer and diffuse large B-cell lymphoma as well as those nominated by “pan-cancer” computational analyses. Second, we established an industry-scale, next-generation pipeline to generate new cancer models (Cell Line Factory) directly from patient samples. We have leveraged this pipeline to process over 330 samples from 208 patients across 16 cancer types, with over 60% growing through at least 5 population doublings. We show that tumor genomics can be retained in such patient-derived models and that drug testing within 3 months is feasible. In addition, we use combinatorial molecular barcoding to rapidly generate a panel of pathway-primed human tumorigenesis models that are suitable for massively parallel multiplexed tumorigenesis assays in vivo (TumorPlex). We hypothesized that this integrated framework could be utilized to generate meaningful functional hypotheses in a high-throughput manner. To test this hypothesis, we introduced over 1000 cancer mutations into cell models and created gene expression signatures together with phenotypic data. In lung cancer, we show that the mutational impact of mutant alleles with known and unknown functions can be rapidly assessed by comparing signatures of wild-type and mutant alleles. We show that this generalizable approach, which does not require prior knowledge, can place variants of unknown significance into dominant gain-of-function and loss-of-function categories. As a complementary approach, we have used TumorPlex assays to test the tumorigenic potential of 550 mutant alleles nominated by Pan-Cancer computational analyses and discovered unexpected variants in the KRAS, AKT1, MAP2K1, ERBB2, PIK3CB, NFE2L2, FAM200A and POT1 genes as being potently tumorigenic. These proof-of-concept studies demonstrate initial feasibility of mapping cancer variant function at scale. Importantly, they demarcate a path by which mapping variant function and predicting vulnerabilities might soon be possible on a patient-by-patient basis, achieving the promise of precision functional genomics. Citation Format: Alice Berger, Eejung Kim, Angela Brooks, Yashaswi Shrestha, Yuen-Yi Tseng, Xiaoyun Wu, Nina Ilic, Lihua Zou, Atanas Kamburov, Xiaoping Yang, Cong Zhu, Paula Keskula, Sara Seepo, Andrew Hong, John Doench, Aravind Subramanian, Keith Ligon, Philip Kantoff, Katherine Janeway, Levi Garraway, David Root, Todd Golub, Matthew Meyerson, William Hahn, Gad Getz, Jesse Boehm. Towards precision functional genomics via next-generation functional mapping of cancer variants. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 957. doi:10.1158/1538-7445.AM2015-957
Type of Medium:
Online Resource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.AM2015-957
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2015
detail.hit.zdb_id:
2036785-5
detail.hit.zdb_id:
1432-1
detail.hit.zdb_id:
410466-3
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