Accelerating Discovery of Functional Mutant Alleles in Cancer

Abstract

Most mutations in cancer are rare, which complicates the identification of therapeutically significant mutations and thus limits the clinical impact of genomic profiling in patients with cancer. Here, we analyzed 24,592 cancers including 10,336 prospectively sequenced patients with advanced disease to identify mutant residues arising more frequently than expected in the absence of selection. We identified 1,165 statistically significant hotspot mutations of which 80% arose in 1 in 1,000 or fewer patients. Of 55 recurrent in-frame indels, we validated that novel AKT1 duplications induced pathway hyperactivation and conferred AKT inhibitor sensitivity. Cancer genes exhibit different rates of hotspot discovery with increasing sample size, with few approaching saturation. Consequently, 26% of all hotspots in therapeutically actionable oncogenes were novel. Upon matching a subset of affected patients directly to molecularly targeted therapy, we observed radiographic and clinical responses. Population-scale mutant allele discovery illustrates how the identification of driver mutations in cancer is far from complete.Significance: Our systematic computational, experimental, and clinical analysis of hotspot mutations in approximately 25,000 human cancers demonstrates that the long right tail of biologically and therapeutically significant mutant alleles is still incompletely characterized. Sharing prospective genomic data will accelerate hotspot identification, thereby expanding the reach of precision oncology in patients with cancer. Cancer Discov; 8(2); 174-83. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 127.

Figure 1. The long tail of mutational hotspots in cancer

a) The frequency distribution of genes containing one or more single-codon hotspots (top, dark blue) and in-frame indel hotspots (light blue). At bottom, the count of single-codon and in-frame indel hotspots in the same genes. b) Shown is the statistical significance of mutational hotspots inferred from the analysis of the full cohort (pan-cancer, y-axis) and the most significant individual cancer type (x-axis). A subset of hotspots are annotated (circled in black) and include mutations significant in both analyses (upper right), those significant only when combinating all cancer types and data (leftmost) and those significant only within a given cancer type (bottom). c) The proportion of hotspots that were significant only in individual organ types, only in the pan-cancer analysis, or both.

Figure 2. Oncogenic indel hotspots

a) The distribution of recurrent indel hotspot types discovered here. b) Duplications were significantly more common than either deletions or insertions in oncogenes (asterisk, p-value < 0.01). c) The paralogous indels are shown defining the AKT1 and AKT2 duplication hotspot. The affected cancer types are similar to those that harbor known activating L52, and Q79 hotspot mutations and include estrogen receptor (ER)-positive HER2-negative breast cancers that lack other PI3K pathway alterations. d) MCF10A cells stably expressing the indicated AKT1 mutations are shown and expression and/or phosphorylation levels were assayed by Western blot indicating the AKT1 P68_C77dup induces elevated levels of phosphorylated Akt and S6 comparable to or exceeding that of known activating E17K or Q79K hotspots. e) Cell survival upon AKT blockade with AZD5363 in AKT1-mutant cells indicated that P68-C77dup-mutant cells were most sensitive to AKT inhibition, more so than the canonical E17K hotspot. f) Schematic of MAP2K1 from amino acids 60 to 140 indicates the position of single-codon hotspots (green arrows) is distal from the position of the indel hotspot (blue lines are individual indels in affected tumors). Arcing red lines reflect the distance in angstroms between the indels and single-codon hotspots in the protein structure. g) The rate of co-mutation with other MAPK effectors varied by MAP2K1 hotspot, with P124 mutations always associated with upstream pathway activation and predominantly in melanomas, while others (E203, G128, F53, C121, and K57) were only partially co-mutated, and the MAP2K1 indel hotspot never arose in tumors with another MAPK driver mutation. h) All but one MAP2K1 P124-mutant tumors possessed another known driver of MAPK signaling, of which most were BRAF V600E (59% of total) and these and others were mostly cutaneous melanomas. Conversely, the MAP2K1 I99_I107 indel hotspot never arose in an otherwise MAPK-altered tumor in a diversity of cancer types.

Figure 3. Saturation analysis and the discovery of actionability of mutational hotspots

a) Downsampling and clustering analysis revealed four distinct classes of genes with different rates of hotspot acquisition (light and dark grey and light and dark blue) from the number of sequenced samples necessary to identify a given fraction of all hotspots in affected genes. Shown in gray are all genes. In red and purple are genes that are either saturating in their hotspot discovery (green) or were rapidly increasing and now fatiguing (purple). In red and blue are those genes in either their still linear and accelerating phases of hotspot discovery. b) An estimate of the number of additional specimens to be sequenced to identify an additional hotspot in each gene in each of the four aforementioned classes (clinically actionable genes are identified). c) Of hotspot mutations identified in one of 18 clinically actionable cancer genes (see panel b for genes), the fraction of hotspots used to guide the use of standard-of-care or investigational therapies at present (see Methods) versus those that were identified here but are clinically uncharacterized. d) Initial response of a triple-negative breast cancer patient to neratinib treatment whose tumor harbored a novel ERBB2 V697 hotspot mutation. e) A complete response observed in a patient with gallbladder cancer harboring a novel BRAF L485W hotspot mutation to the ERK inhibitor BVD-523. f) A model by which advanced treatment-refractory patients can be directed to molecularly driven therapies based on computational weight-of-evidence alone as an efficient means for determining mutant allele function and expand biomarkers of drug response.