Carotenoid-Enriched Fractions From Spondias mombin Demonstrate HER2 ATP Kinase Domain Inhibition: Computational and In Vivo Animal Model of Breast Carcinoma Studies

Human epidermal growth factor 2 (HER2) is overexpressed in about 20% of breast cancer and is associated with a poor prognosis. We report in this study that carotenoid-enriched fractions from Spondias mombin demonstrate HER2 ATP kinase domain inhibition. HER2 breast carcinoma was modeled in female Wistar rats and authenticated via immunohistochemical studies. Inhibition of HER2 ATP kinase domain by the carotenoid-enriched fractions was investigated by molecular docking, atomistic simulation, and the expression of HER2 mRNA in HER2-positive breast carcinoma model in female Wistar rats. The therapeutic efficacy of the treatments (carotenoid-rich fractions) was determined by biochemical, tumor volume, and histopathological analysis. Immunohistochemical analysis revealed 7,12-dimethylbenz[a]anthracene (DMBA)-induced HER2-positive breast carcinoma. Phytoconstituents of the carotenoid-enriched fractions astaxanthin, 7,7′,8,8′-tetrahydro-β,β-carotene, beta-carotene-15,15′-epoxide, and lapatinib (standard drug) demonstrate inhibition of HER2 with docking scores of −3.0, −8.5, −11.5, and −10.6 kcal/mol, respectively; and during atomistic simulation, the compounds ruptured the canonical active-state K753/E770 salt-bridge interaction. The treatment similarly downregulated HER2 mRNA expression significantly at p < 0.05. It also upregulated the expression of p53 and p27 mRNAs significantly at p < 0.05 and reduced creatinine and urea concentrations in the serum at p < 0.05. The tumor volume was also significantly reduced when compared with that of the untreated group. Carotenoid-enriched fractions from S. mombin demonstrate anti-HER2 positive breast carcinoma potentials via HER2 ATP kinase domain inhibition.

Comprehensive analysis of HER2 status through genomic, transcription, and translation among 5,305 patients with diverse malignancies.

3632 Background: HER2 alterations is a predictive biomarker for anti-HER2 regimen. Success in breast and gastric cancers with anti-HER2 therapies translated to explore their efficacy in other tumors. Currently, measurement of HER2 can be done by assessing the expression of protein [e.g. immunohistochemistry (IHC)] or gene amplification of copy number variation (CNV) [e.g. fluorescence in situ hybridization or next-generation sequencing (NGS)]. But little is known about the transcription level (mRNA) of HER2. Herein, we investigated HER2 mRNA expression and its association with gene and protein expressions among diverse cancer types. Methods: Between 2015-2019, HER2 status was evaluated using IHC, qRT-PCR and NGS by Paradigm Diagnostics (CLIA-certified laboratory). All tumors in the database were included for analysis. Correlations between all 3 tests were done. An illustrative patient who was treated with anti-HER2 therapy base on the mRNA testing is presented. Results: HER2 testing was performed on 5305 patients (pts) with diverse cancers including NSCLC (n=1175), breast (n=1040) and colon (n=566); 4.1% (161/3926) had amplification through NGS, 33.3% (615/1848) had mRNA overexpression and 9.3% (236/2533) had overexpression by IHC. Of 723 pts who had all three tests performed, we found 7.5% (54/723) of pts with all three HER2 markers being positive (CNV [+]/mRNA [+]/ IHC [+]). Meanwhile, variety of amplification/ expression patterns were seen (see Table). CNV positivity translated to protein expression in 95% of cases. While only 4% of pts were IHC positive when CNV and mRNA were negative. 20% (144/723) of pts had mRNA overexpression alone among diverse cancer types. Representative case of 70yo female with metastatic cholangiocarcinoma harboring mRNA overexpression (but negative for CNV, IHC unclear due to sample insufficiency) who had near complete response to anti-HER2 therapy with progression-free survival of 24+ months is presented. Conclusions: HER2 status can be discordant with different assays but NGS positivity has excellent correlation with mRNA and protein expression. Of importance, HER2 mRNA can be overexpressed in 20% of pts even when gene amplification and protein expression are negative. Further studies are warranted to determine the clinical utility of mRNA as a biomarker for HER2 and potential use for anti-HER2 targeted therapies. [Table: see text]

Pathological complete response in HER2 positive breast cancer treated with trastuzumab and chemotherapy: Predictive factors report.

e12133 Background: Achieving a pathological complete response (pCR) in HER2 positive disease is a surrogate of survival. Several factors were studied in order to predict pCR, but only negative estrogen receptor is correlated to pCR. Methods: 168 patients (March 2002-December 2016) with neoadjuvant trastuzumab-based treatment were retrospectively analyzed in order to find if common clinicopathological factors, HER2 expression and immune infiltrates could correlate with pCR. Results: Median age was 55 years (29-86), median tumor size of 39mm and 53% of tumors had initial nodal involvement. 45% of patients were estrogen and 71% progesterone receptor negative and median ki67 expression was of 43%. Otherwise HER2 expression was analyzed by immunohistochemistry, FISH amplification and mRNA expression; we also determined the presence of immune infiltrates stromal and intratumoral L45 lymphocytes, PD1 and PD-L1. A 38% pCR rate was found in whole population. In the univariate model we found and association with pCR and: estrogen receptor negative (p = 0.003; OR 2.5), high HER2 expression by mRNA in tissue > 3.9 (p = 0.012; OR 8.4), stromal L45 expression > 35% (p = 0.001; OR 5.3), stromal PD1 expression > 10 (p = 0.004; OR 4.5). The most powerful factor associated with pCR is HER2 mRNA expression > 3.9, with 82% of pCR rate in those tumors; followed of L45 stromal expression > 35%, with 76% of pCR rate. Conclusions: Factors related to pCR are mostly estrogen receptor negative and high HER2 expression by mRNA and high L45 stromal infiltrates. With those variables, we could design a predictive model in order to perform the best neoadjuvant treatment to increase the pCR rate.