Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison

BACKGROUND

Single-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used.

RESULTS

We show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required.

CONCLUSION

Our comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.

CABA-V7: a prospective biomarker selected trial of cabazitaxel treatment in AR-V7 positive prostate cancer patients

BACKGROUND

Metastatic castration-resistant prostate cancer (mCRPC) patients with positive AR-V7 expression in their circulating tumour cells (CTCs) rarely derive benefit from abiraterone and enzalutamide.

DESIGN

We performed a prospective, multicenter, single arm phase II clinical trial (CABA-V7) in mCRPC patients previously treated with docetaxel and androgen deprivation therapy.

OBJECTIVE

In this trial, we investigated whether cabazitaxel treatment resulted in clinically meaningful PSA response rates in patients with positive CTC-based AR-V7 expression and collected liquid biopsies for genomic profiling.

RESULTS

Cabazitaxel was found to be modestly effective, with only 12% of these patients obtaining a PSA response. Genomic profiling revealed that CTC-based AR-V7 expression was not associated with other known mCRPC-associated alterations. CTC-based AR-V7 status and dichotomised CTC counts were observed as independent prognostic markers at baseline.

CONCLUSIONS

AR-V7 positivity predicted poor overall survival (OS). However, cabazitaxel-treated AR-V7 positive patients and those lacking AR-V7 positivity, who received cabazitaxel as standard of care, appeared to have similar OS. Therefore, despite the low response rate, cabazitaxel may still be an effective treatment in this poor prognosis, AR-V7 positive patient population.