Zehua Wang

Specific and sensitive biomarker detection is essential to early cancer diagnosis. In this study, we demonstrate an ultrasensitive electrochemical biosensor with the ability to detect multiple pancreatic carcinoma (PC)-related microRNA biomarkers. By employing DNA tetrahedral nanostructure capture probes to enhance the detection sensitivity as well as a disposable 16-channel screen-printed gold electrode (SPGE) detection platform to enhance the detection efficiency, we were able to simultaneously detect four PC-related miRNAs: miRNA21, miRNA155, miRNA196a, and miRNA210. The detection sensitivity reached to as low as 10 fM. We then profiled the serum levels of the four miRNAs for PC patients and healthy individuals with our multiplexing electrochemical biosensor. Through the combined analyses of the four miRNAs, our results showed that PC patients could be discriminated from healthy controls with fairly high sensitivity. This multiplexing PCR-free miRNA detection sensor shows promising applications in early diagnosis of PC disease.

Emerging evidence has shown long non-coding RNAs (lncRNAs) play important roles in cancer drug response. Here we report a lncRNA pharmacogenomic landscape by integrating multi-dimensional genomic data of 1005 cancer cell lines and drug response data of 265 anti-cancer compounds. Using Elastic Net (EN) regression, our analysis identifies 27,341 lncRNA-drug predictive pairs. We validate the robustness of the lncRNA EN-models using two independent cancer pharmacogenomic datasets. By applying lncRNA EN-models of 49 FDA approved drugs to the 5605 tumor samples from 21 cancer types, we show that cancer cell line based lncRNA EN-models can predict therapeutic outcome in cancer patients. Further lncRNA-pathway co-expression analysis suggests lncRNAs may regulate drug response through drug-metabolism or drug-target pathways. Finally, we experimentally validate that EPIC1, the top predictive lncRNA for the Bromodomain and Extra-Terminal motif (BET) inhibitors, strongly promotes iBET762 and JQ-1 resistance through activating MYC transcriptional activity.

Boson sampling is a well-defined task that is strongly believed to be intractable for classical computers, but can be efficiently solved by a specific quantum simulator. However, an outstanding problem for large-scale experimental boson sampling is the scalability. Here we report an experiment on boson sampling with photon loss, and demonstrate that boson sampling with a few photons lost can increase the sampling rate. Our experiment uses a quantum-dot-micropillar single-photon source demultiplexed into up to seven input ports of a 16×16 mode ultralow-loss photonic circuit, and we detect three-, four- and fivefold coincidence counts. We implement and validate lossy boson sampling with one and two photons lost, and obtain sampling rates of 187, 13.6, and 0.78 kHz for five-, six-, and seven-photon boson sampling with two photons lost, which is 9.4, 13.9, and 18.0 times faster than the standard boson sampling, respectively. Our experiment shows an approach to significantly enhance the sampling rate of multiphoton boson sampling.