Background: Derived from an adaptive bacterial immune system, the clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated 9 (Cas9) system has shown great potential in high-throughput functional genomic screening, especially for protein-coding genes. However, it is still challenging to apply the similar strategy to study non-coding genomic elements such as long non-coding RNAs (lncRNAs) or clusters of microRNAs, because short insertions or deletions may not be sufficient to generate loss-of-function phenotypes. Methods: Here, we presented a systematic strategy for designing a CRISPR-based paired-sgRNA library for highthroughput screening in non-coding regions. Due to the abundance of lncRNAs and their diverse regulatory roles in vivo, we repurposed microarray datasets to select 600 highly expressed lncRNAs in non-small-cell lung cancer and designed two schemes for lncRNA deletion with~20 paired-sgRNAs for each lncRNA. Through Golden-Gate assembly, we generated a pooled CRISPR-based library with a total of 12,878 sgRNA pairs. Results: Over 80% of paired-sgRNAs were recovered from final pooled library with a relatively even distribution. Cleavage efficiency of sgRNA pairs was validated through experiments of transient transfection and viral infection. Moreover, randomly selected paired-sgRNAs showed that efficient deletion of genomic DNA could be achieved with a deletion size within the range of 500 to 3000 bp. Conclusions: In summary, we have demonstrated a strategy to design and construct a pooled paired-sgRNA library to generate genomic deletion in the lncRNA regions, validated their deletion efficiency and explored the relationship of deletion efficiency with respect to deletion size. This method would be also suitable for investigation of other uncharacterized non-coding genomic regions in mammalian cells in an efficient and cost-effective manner.Author summary: The CRISPR/Cas9 system has shown great potential in functional genomic screening, especially for protein-coding genes. However, it is challenging to apply the similar strategy to study non-coding genomic elements, because short insertions or deletions may not be sufficient to generate loss-of-function phenotypes. In this paper, we proposed a strategy to design and construct a CRISPR-based paired-sgRNA library for chromosomal deletions of lncRNA loci in mammalian cells and confirm the cleavage efficiency through experiments. This approach demonstrates a simple and scalable tool for genome-wide functional study of non-coding elements in mammalian cells. # Present address: Zhejiang

The efficacy of the first-line treatment for hypopharyngeal carcinoma (HPC), a predominantly male cancer, at advanced stage is only about 50% without reliable molecular indicators for its prognosis. In this study, HPC biopsy samples collected before and after the first-line treatment are classified into different groups according to treatment responses. We analyze the changes of HPC tumor microenvironment (TME) at the single-cell level in response to the treatment and identify three gene modules associated with advanced HPC prognosis. We estimate cell constitutions based on bulk RNA-seq of our HPC samples and build a binary classifier model based on non-malignant cell subtype abundance in TME, which can be used to accurately identify treatment-resistant advanced HPC patients in time and enlarge the possibility to preserve their laryngeal function. In summary, we provide a useful approach to identify gene modules and a classifier model as reliable indicators to predict treatment responses in HPC.

No abstract

Objective: Effective adjuvant therapeutic strategies are urgently needed to overcome MAPK inhibitor (MAPKi) resistance, which is one of the most common forms of resistance that has emerged in many types of cancers. Here, we aimed to systematically identify the genetic interactions underlying MAPKi resistance, and to further investigate the mechanisms that produce the genetic interactions that generate synergistic MAPKi resistance. Methods: We conducted a comprehensive pair-wise sgRNA-based high-throughput screening assay to identify synergistic interactions that sensitized cancer cells to MAPKi, and validated 3 genetic combinations through competitive growth, cell viability, and spheroid formation assays. We next conducted Kaplan-Meier survival analysis based on The Cancer Genome Atlas database and conducted immunohistochemistry to determine the clinical relevance of these synergistic combinations. We also investigated the MAPKi resistance mechanisms of these validated synergistic combinations by using co-immunoprecipitation, Western blot, qRT-PCR, and immunofluorescence assays. Results: We constructed a systematic interaction network of MAPKi resistance and identified 3 novel synergistic combinations that effectively targeted MAPKi resistance (ITGB3 + IGF1R, ITGB3 + JNK, and HDGF + LGR5). We next analyzed their clinical relevance and the mechanisms by which they sensitized cancer cells to MAPKi exposure. Specifically, we discovered a novel protein complex, HDGF-LGR5, that adaptively responded to MAPKi to enhance cancer cell stemness, which was up-or downregulated by the inhibitors of ITGB3 + JNK or ITGB3 + IGF1R. Conclusions: Pair-wise sgRNA library screening provided systematic insights into elucidating MAPKi resistance in cancer cells. ITGB3-+ IGF1R-targeting drugs (cilengitide + linsitinib) could be used as an effective therapy for suppressing the adaptive formation of the HDGF-LGR5 protein complex, which enhanced cancer stemness during MAPKi stress.