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The addition of bevacizumab to chemotherapy has prolonged overall and progression-free survival rates for metastatic colorectal cancer (mCRC). However, KRAS-mutant (KRAS-mut) CRC, lacking an ideal targeted agent, represents an inferior-response subgroup of patients. In the present study, we investigated a combination approach of bevacizumab + olaparib in KRAS-mut CRC in a preclinical setting. The combined therapy effectively prevented tumor growth in a KRAS-mut cancer cell-derived xenograft model, although this effect was not observed in vitro. Under bevacizumab treatment, we detected intratumor hypoxia and impaired homologous recombination repair (HRR), accompanied by vascular regression. We explored the underlying mechanism of this combined therapy by mimicking a hypoxic condition in vitro using cobalt chloride (CoCl2). The results showed that hypoxia impairs HRR and therefore sensitized KRAS-mut CRC cell lines HCT-116, SW620, and Lovo to olaparib. Furthermore, under this hypoxic condition, olaparib could arrest the cell cycle in the G2/M phase, increase DNA damage and dramatically induce cell apoptosis in KRAS-mut CRC cells. Taken together, these results indicated that the combination of bevacizumab + olaparib could be a potential therapeutic approach in a KRAS-mut CRC cohort.

Due to inherent irregularities in recyclable materials, sorting valuable metals (e.g., aluminum and copper) via mechanical means is a difficult task resisting full automation. A particularly hard challenge in the domain is the separation of scrap metal pieces with physically attached impurities, which is further complicated by variations in different batches of recyclable materials. In this work, leveraging the latest development in machine learning and robot learning, we develop an image-based sorting system for tackling this challenging task. In addition to delivering a highly accurate deep learning model for reliably distinguishing pure scrap pieces from pieces containing impurities with over 95% precision/recall, we further automate the process of sample preparation, data acquisition/labeling/analysis, and machine learning model training.

Genomic profiling has revolutionized treatment options for patients with oncogene-driven cancers, such as epidermal growth factor receptor (EGFR) mutant carcinoma. Rearranged during transfection (RET) rearrangement, as one of the main activated oncogenes, has been well studied and found to be involved in the malignant behavior of carcinogenesis, resulting in acquired resistance to EGFR tyrosine kinase inhibitors and inducing an intrinsic resistance to immunotherapy. Thus, targeted therapies have been investigated against RET arrangement cancers, including several multi-kinase inhibitors and selective RET inhibitors. However, modest efficacy, a relatively high rate of toxicity, and poor effectiveness against brain metastasis are common limitations of multi-targeted novel molecular inhibitors. A promising prospect was shown recently in selective RET inhibitors in several ongoing clinical trials. In this review, we reviewed the concurrent dilemmas of targeted therapies against RET arrangement cancer from preclinical and clinical studies and proposed several clinical considerations for clinical practice prospectively.

Breast cancer is a highly heterogeneous disease at the molecular level and >90% of mortalities are due to metastasis and its associated complications. The present study determined the impact of molecular subtypes on metastatic behavior and overall survival (OS) of patients with metastatic breast cancer. The influence of molecular subtypes on the sites and number of metastases in 166 patients with metastatic breast cancer from a single center were assessed; and the influence of molecular subtypes on the sites and number of metastases and OS in 15,322 metastatic cases among 329,770 patients with primary breast cancer from the Surveillance, Epidemiology and End Results database were assessed. Analysis of both datasets revealed that different molecular subtypes exhibited differences in the prevalence of different metastatic sites and number of metastases. A larger proportion of bone metastasis was observed in the hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)+ subtype than in other subtypes, more lung metastasis was observed in the HR-/HER2+ subtype and more liver metastasis occurred in the HR+/HER2+ and HR-/HER2+ subtypes. Single-site metastasis was more common for the HR+/HER2− subtype than in other subtypes, while 2–3 sites of metastases were more common for the HR+/HER2+ subtype and ≥4 sites of metastases were more frequent in the HR-/HER2+ and HR-/HER2- subtypes. The mean OS of patients with primary breast cancer in the HR+/HER2− subtype group was the longest (78.5 months), while the HR-/HER2- group had the shortest mean OS (69.1 months). The mean OS of the metastatic HR+/HER2+ group was the longest (46.0 months), while the mean OS of the metastatic HR-/HER2- group was the shortest (18.5 months). In conclusion, the results of the present study suggested that different molecular subtypes of breast cancer have different metastatic behavior, as well as mean OS.

Motivation We introduce PRINCESS, a privacy-preserving international collaboration framework for analyzing rare disease genetic data that are distributed across different continents. PRINCESS leverages Software Guard Extensions (SGX) and hardware for trustworthy computation. Unlike a traditional international collaboration model, where individual-level patient DNA are physically centralized at a single site, PRINCESS performs a secure and distributed computation over encrypted data, fulfilling institutional policies and regulations for protected health information. Results To demonstrate PRINCESS’ performance and feasibility, we conducted a family-based allelic association study for Kawasaki Disease, with data hosted in three different continents. The experimental results show that PRINCESS provides secure and accurate analyses much faster than alternative solutions, such as homomorphic encryption and garbled circuits (over 40 000× faster). Availability and Implementation https://github.com/achenfengb/PRINCESS_opensource Supplementary information Supplementary data are available at Bioinformatics online.