Precision medicine approaches to lung adenocarcinoma with concomitant MET and HER2 amplification

Abstract Background Patient-derived xenograft (PDX) models are important tools in precision medicine and for the development of targeted therapies to treat cancer patients. This study aimed to evaluate our precision medicine strategy that integrates genomic profiling and preclinical drug-screening platforms, in order to personalize cancer treatments using PDX models. Methods We performed array-comparative genomic hybridization, microarray, and targeted next-generation sequencing analyses, in order to determine the oncogenic driver mutations. PDX cells were obtained from PDXs and subsequently screened in vitro with 17 targeted agents. Results PDX tumors recapitulated the histopathologic and genetic features of the patient tumors. Among the samples from lung cancer patients that were molecularly-profiled, copy number analysis identified unique focal MET amplification in one sample, 033 T, without RTK/RAS/RAF oncogene mutations. Although HER2 amplification in 033 T was not detected in the cancer panel, the selection of HER2-amplified clones was found in PDXs and PDX cells. Additionally, MET and HER2 overexpression were found in patient tumors, PDXs, and PDX cells. Crizotinib or EGFR tyrosine kinase inhibitor treatments significantly inhibited cell growth and impaired tumor sphere formation in 033 T PDX cells. Conclusions We established PDX cell models using surgical samples from lung cancer patients, and investigated their preclinical and clinical implications for personalized targeted therapy. Additionally, we suggest that MET and EGFR inhibitor-based therapy can be used to treat MET and HER2-overexpressing lung cancers, without receptor tyrosine kinase /RAS/RAF pathway alterations

Development of a SnS Film Process for Energy Device Applications

Tin monosulfide (SnS) is a promising p-type semiconductor material for energy devices. To realize the device application of SnS, studies on process improvement and film characteristics of SnS is needed. Thus, we developed a new film process using atomic layer deposition (ALD) to produce SnS films with high quality and various film characteristics. First, a process for obtaining a thick SnS film was studied. An amorphous SnS2 (a-SnS2) film with a high growth rate was deposited by ALD, and a thick SnS film was obtained using phase transition of a-SnS2 film by vacuum annealing. Subsequently, we investigated the effect of seed layer on formation of SnS film to verify the applicability of SnS to various devices. Separately deposited crystalline SnS and SnS2 thin films were used as seed layer. The SnS film with a SnS seed showed small grain size and high film density from the low surface energy of the SnS seed. In the case of the SnS film using a SnS2 seed, volume expansion occurred by vertically grown SnS grains due to a lattice mismatch with the SnS2 seed. The obtained SnS film using the SnS2 seed exhibited a large reactive site suitable for ion exchange

Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer

Abstract Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693–0.805) in comparison to the pathologists’ scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01–1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment

Additional file 1: Figure S1. of Precision medicine approaches to lung adenocarcinoma with concomitant MET and HER2 amplification

Array comparative genomic hybridization (aCGH) of patient tumors and corresponding patient-derived xenograft. The recurrence of copy number alteration is plotted on the y-axis, and each probe is aligned along the x-axis in chromosome order. Note the similarity between the genomic profiles of the patient tumors and PDX counterparts. (TIFF 208 kb

Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as Complementary Biomarker for Immune Checkpoint Inhibition in Non-Small-Cell Lung Cancer

PURPOSE Biomarkers on the basis of tumor-infiltrating lymphocytes (TIL) are potentially valuable in predicting the effectiveness of immune checkpoint inhibitors (ICI). However, clinical application remains challenging because of methodologic limitations and laborious process involved in spatial analysis of TIL distribution in whole-slide images (WSI). METHODS We have developed an artificial intelligence (AI)-powered WSI analyzer of TIL in the tumor microenvironment that can define three immune phenotypes (IPs): inflamed, immune-excluded, and immune-desert. These IPs were correlated with tumor response to ICI and survival in two independent cohorts of patients with advanced non-small-cell lung cancer (NSCLC). RESULTS Inflamed IP correlated with enrichment in local immune cytolytic activity, higher response rate, and prolonged progression-free survival compared with patients with immune-excluded or immune-desert phenotypes. At the WSI level, there was significant positive correlation between tumor proportion score (TPS) as determined by the AI model and control TPS analyzed by pathologists (P &lt; .001). Overall, 44.0% of tumors were inflamed, 37.1% were immune-excluded, and 18.9% were immune-desert. Incidence of inflamed IP in patients with programmed death ligand-1 TPS at &lt; 1%, 1%-49%, and &gt;= 50% was 31.7%, 42.5%, and 56.8%, respectively. Median progression-free survival and overall survival were, respectively, 4.1 months and 24.8 months with inflamed IP, 2.2 months and 14.0 months with immune-excluded IP, and 2.4 months and 10.6 months with immune-desert IP. CONCLUSION The AI-powered spatial analysis of TIL correlated with tumor response and progression-free survival of ICI in advanced NSCLC. This is potentially a supplementary biomarker to TPS as determined by a pathologist.Y