Rabbit Anti-Human KRAS (C-terminal) Polyclonal antibody

Circulating tumor DNA (ctDNA) is a promising biomarker for tumor genotyping and therapy monitoring. Herein, we developed a digital PCR chip with embedded microwell and bidirectional partition network for highly sensitive ctDNA analysis. The embedded microwell contributes to increasing microreaction density (up to 7000 microwells/cm(2)) and reducing evaporation during amplification. The bidirectional partition network can achieve fast and random distribution of targets, ensuring the precise quantification of nucleic acid. We used plasmids, artificial samples and 32 clinical blood samples from non-small cell lung cancer patients to test the performance of this platform. The results demonstrated that our chip has not only comparable quantification performance to commercial counterpart but also the ability to detect EGFR mutations with as low as 0.01% mutation rate and 20 alter molecules in 27 ng genomic DNA. The identification of EGFR mutations in plasma using developed chip exhibited 85.71% sensitivity and 94.44% specificity for L858R mutation and 100% sensitivity and 86.96% specificity for T790 M mutation. Moreover, the monitoring of mutant allele in plasma was accomplished in this work. In conclusion, the developed chip has a potential in lung tumor genotyping and therapy monitoring for precision medicine, even other tumors.

Simple Summary For pathologists, how to precisely diagnose cancer from microscopy slides of tumor tissue samples so that each patient may receive the optimal treatment for his specific type of disease is a major task. Recent research based on digital pathology image analysis enables new approaches to assess tumor-host interaction at a microscopic level. The current study applies a novel spatial analysis method which computes Immunogradient indicators to estimate the migration of immune cells towards the tumor across the tumor/stroma interface. These indicators, computed for two types of immune cells (CD8 and CD20), proved to be independent prognostic factors in this study of 87 patients with colorectal cancer. The indicators were combined with infiltrative tumor growth pattern, assessed by a pathologist, into a new immuno-interface score which enabled prediction of the patient survival independent of other clinical, pathology and molecular characteristics of the tumor. The study demonstrates the value of computational pathology to advance the precision of clinical decision-making. Tumor-associated immune cells have been shown to predict patient outcome in colorectal (CRC) and other cancers. Spatial digital image analysis-based cell quantification increases the informative power delivered by tumor microenvironment features and leads to new prognostic scoring systems. In this study we evaluated the intratumoral density of immunohistochemically stained CD8, CD20 and CD68 cells in 87 cases of CRC (48 were microsatellite stable, MSS, and 39 had microsatellite instability, MSI) in both the intratumoral tumor tissue and within the tumor-stroma interface zone (IZ) which was extracted by a previously developed unbiased hexagonal grid analytics method. Indicators of immune-cell gradients across the extracted IZ were computed and explored along with absolute cell densities, clinicopathological and molecular data, including gene mutation (BRAF, KRAS, PIK3CA) and MSI status. Multiple regression modeling identified (p < 0.0001) three independent prognostic factors: CD8+ and CD20+ Immunogradient indicators, that reflect cell migration towards the tumor, were associated with improved patient survival, while the infiltrative tumor growth pattern was linked to worse patient outcome. These features were combined into CD8-CD20 Immunogradient and immuno-interface scores which outperformed both tumor-node-metastasis (TNM) staging and molecular characteristics, and importantly, revealed high prognostic value both in MSS and MSI CRCs.