Intra-Tumoral Heterogeneity of HER2, FGFR2, cMET and ATM in Gastric Cancer: Optimizing Personalized Healthcare through Innovative Pathological and Statistical Analysis.

Current drug development efforts on gastric cancer are directed against several molecular targets driving the growth of this neoplasm. Intra-tumoral biomarker heterogeneity however, commonly observed in gastric cancer, could lead to biased selection of patients. MET, ATM, FGFR2, and HER2 were profiled on gastric cancer biopsy samples. An innovative pathological assessment was performed through scoring of individual biopsies against whole biopsies from a single patient to enable heterogeneity evaluation. Following this, false negative risks for each biomarker were estimated in silico. 166 gastric cancer cases with multiple biopsies from single patients were collected from Shanghai Renji Hospital. Following pre-set criteria, 56 ~ 78% cases showed low, 15 ~ 35% showed medium and 0 ~ 11% showed high heterogeneity within the biomarkers profiled. If 3 biopsies were collected from a single patient, the false negative risk for detection of the biomarkers was close to 5% (exception for FGFR2: 12.2%). When 6 biopsies were collected, the false negative risk approached 0%. Our study demonstrates the benefit of multiple biopsy sampling when considering personalized healthcare biomarker strategy, and provides an example to address the challenge of intra-tumoral biomarker heterogeneity using alternative pathological assessment and statistical methods

Risk assessment against different biopsy numbers in each biomarker.

<p>The risks of false negative detection along with different biopsy numbers in MET IHC <b>(A)</b>, MET FISH <b>(B)</b>, ATM IHC <b>(C)</b>, FGFR2 FISH <b>(D)</b>, and HER2 <b>(E)</b>. <b>Note:</b> *Estimated by resampling, 95% CI: 7.5%–20.94%.</p

Comparison of biomarker positive/negative rates using either surgical or biopsy samples.

<p>The positive/negative rate for each biomarker is comparable between the biopsy samples in this study and surgical samples profiled in our previous studies, including ATM [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143207#pone.0143207.ref029" target="_blank">29</a>] and other biomarkers [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143207#pone.0143207.ref026" target="_blank">26</a>]. A slight increase in positive rates for the biomarkers (or decrease in negative rate for ATM) was observed, which could possibly be explained by the detection of positive samples which were previously missed when analyzing surgical samples due to intra-tumoral heterogeneity. Both biopsy and surgical samples were collected from the same local hospital [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143207#pone.0143207.ref026" target="_blank">26</a>], except for ATM [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143207#pone.0143207.ref029" target="_blank">29</a>].</p

An illustration of heterogeneity distribution of each biomarker.

<p>Heterogeneity distribution of MET protein expression <b>(A)</b>, <i>MET</i> average gene copy number <b>(B)</b>, ATM protein expression <b>(C)</b>, <i>FGFR2</i> amplification <b>(D)</b>, and HER2 positivity <b>(E)</b>. AMP: amplification. AVG: average copy number.</p

Pathological assessment of biomarker status on each individual biopsy.

<p>This is an example case which shows the individual score given to each biopsy for each biomarker. In addition, the figure also shows the heterogeneity of biomarker status between different biopsies in the same case.</p

Overview of biopsy number in clinical samples.

<p><b>(A) Distribution of total and positive biopsy number.</b> In this Chinese GC cohort, total biopsy numbers range from 1 to 9, with a median of 4. Positive biopsy (biopsy with tumor) is slightly lower than total biopsy number. <b>(B) Distribution of positive biopsy number.</b> Majority of biopsy numbers fall into 3~4 (47%) and 5~6 (25%).</p

Patient-Derived Gastric Carcinoma Xenograft Mouse Models Faithfully Represent Human Tumor Molecular Diversity.

Patient-derived cancer xenografts (PDCX) generally represent more reliable models of human disease in which to evaluate a potential drugs preclinical efficacy. However to date, only a few patient-derived gastric cancer xenograft (PDGCX) models have been reported. In this study, we aimed to establish additional PDGCX models and to evaluate whether these models accurately reflected the histological and genetic diversities of the corresponding patient tumors. By engrafting fresh patient gastric cancer (GC) tissues into immune-compromised mice (SCID and/or nude mice), thirty two PDGCX models were established. Histological features were assessed by a qualified pathologist based on H&E staining. Genomic comparison was performed for several biomarkers including ERBB1, ERBB2, ERBB3, FGFR2, MET and PTEN. These biomarkers were profiled to assess gene copy number by fluorescent in situ hybridization (FISH) and/or protein expression by immunohistochemistry (IHC). All 32 PDGCX models retained the histological features of the corresponding human tumors. Furthermore, among the 32 models, 78% (25/32) highly expressed ERBB1 (EGFR), 22% (7/32) were ERBB2 (HER2) positive, 78% (25/32) showed ERBB3 (HER3) high expression, 66% (21/32) lost PTEN expression, 3% (1/32) harbored FGFR2 amplification, 41% (13/32) were positive for MET expression and 16% (5/32) were MET gene amplified. Between the PDGCX models and their parental tumors, a high degree of similarity was observed for FGFR2 and MET gene amplification, and also for ERBB2 status (agreement rate = 94~100%; kappa value = 0.81~1). Protein expression of PTEN and MET also showed moderate agreement (agreement rate = 78%; kappa value = 0.46~0.56), while ERBB1 and ERBB3 expression showed slight agreement (agreement rate = 59~75%; kappa value = 0.18~0.19). ERBB2 positivity, FGFR2 or MET gene amplification was all maintained until passage 12 in mice. The stability of the molecular profiles observed across subsequent passages within the individual models provides confidence in the utility and translational significance of these models for in vivo testing of personalized therapies