Emergence of ERBB2 Mutation as a Biomarker and an Actionable Target in Solid Cancers

Abstract

The oncogenic role ERBB2 amplification is well established in breast and gastric cancers. This has led to the development of a well-known portfolio of monoclonal antibodies and kinase inhibitors targeting the ERBB2 kinase. More recently, activating mutations in the ERBB2 gene have been increasingly reported in multiple solid cancers and were shown to play an oncogenic role similar to that of ERBB2 amplification. Thus, ERBB2 mutations define a distinct molecular subtype of solid tumors and serve as actionable targets. However, efforts to target ERBB2 mutation has met with limited clinical success, possibly because of their low frequency, inadequate understanding of the biological activity of these mutations, and difficulty in separating the drivers from the passenger mutations. Given the current impetus to deliver molecularly targeted treatments for cancer, there is an important need to understand the therapeutic potential of ERBB2 mutations. Here we review the distribution of ERBB2 mutations in different tumor types, their potential as a novel biomarker that defines new subsets in many cancers, and current data on preclinical and clinical efforts to target these mutations. IMPLICATIONS FOR PRACTICE: A current trend in oncology is to identify novel genomic drivers of solid tumors and developing precision treatments that target them. ERBB2 amplification is an established therapeutic target in breast and gastric cancers, but efforts to translate this finding to other solid tumors with ERBB2 amplification have not been effective. Recently the focus has turned to targeting activating ERBB2 mutations. The year 2018 marked an important milestone in establishing ERBB2 mutation as an important actionable target in multiple cancer types. There have been several recent preclinical and clinical studies evaluating ERBB2 mutation as a therapeutic target with varying success. With increasing access to next-generation sequencing technologies in the clinic, oncologists are frequently identifying activating ERBB2 mutations in patients with cancer. There is a significant need both from the clinician and bench scientist perspectives to understand the current state of affairs for ERBB2 mutations.

Keywords: ERBB2 mutation; Gastrointestinal cancer; HER2; Non‐small cell lung cancer; Tyrosine kinase.

Figure 1.

Frequency of ERBB2 mutations from select studies. (A): Oncoplot of mutations plus copy number alterations of key genes involved in the ERBB2 pathway (left) and mutations only oncoplot of the same gene list (right) in tumor samples across cancer types for which both DNA and RNA sequencing was available in cBioPortal. Frequency of ERBB2 mutations and other key ERBB2 pathway mutations are schematically illustrated. (B): ERBB2 mutation frequency in pooled data of different tumor types from 14 next‐generation sequencing studies is shown. (C): Frequency of ERBB2 mutations whose expression was confirmed from RNAseq is schematically represented.

Figure 2.

Schematic representation of the frequency of mutation type among ERBB2‐mutated cancers. (A): Bubble size represents the frequency of each mutation (shown on the x‐axis along with the exon) within each cancer type (y‐axis). The approximate percentage of each ERBB2 mutation type among the total ERBB2‐mutated samples within a cancer type is shown inside the bubble. Smaller bubble sizes indicate less than 5% frequency. The bubbles were color‐coded according to their drug sensitivity based on the MANO study [36]. Green, sensitive to five inhibitors lapatinib, sapitinib, afatinib, neratinib, and osimertinib; red, resistant to all the five inhibitors; blue, lapatinib resistance; gray, sapitinib resistance; black, data not available. The classification of ERBB2 mutations based on the location of ERBB2 receptor and the type of mutation is shown below (blue). (B): The preclinical activity (drug sensitivity) of different mutations toward ERBB2 inhibitors is shown. The table is color‐coded according to the drug sensitivity based on the MANO study [36]: green, sensitive; yellow, intermediate resistance; red, highly resistant. Abbreviations: ECD, extracellular domain; ins, insertion; JMD, juxtamembrane domain; KD, kinase domain; TMD, transmembrane domain.

Figure 3.

ERBB2 inhibitors. Schematic representation of selective ERBB2 as well as pan‐ERBB inhibitors along with the IC50 value for ERBB2 kinase and maximum achievable serum concentration. Abbreviations: C_max, maximum achievable serum concentration; IC50, inhibitory activity.

Figure 4.

ERBB2 signaling depicting actionable targets. Molecular mechanisms underlying resistance toward ERBB2 kinase inhibitors are schematically shown. Homo‐ and heterodimers involving ERBB2 are shown along with ERBB2 blockers (antibodies and inhibitors in green). Other actionable drug targets whose activity can be blocked to overcome drug resistance along with the examples of their available inhibitors are also shown. The PI3K‐AKT pathway, which is prominent in resistance toward ERBB2 blockers, is highlighted in red.