Developing methods for mapping genetic heterogeneity in tumors : from bulk to single-cell resolution

Tumors are complex ecosystems composed of billions of cells that constantly evolve over time. Intra-tumor heterogeneity (ITH) represents the diversity of this complex environment quantifiable both at the genetic and phenotypic level. Recently, next-generation sequencing (NGS) costs have dropped and NGS has become the standard tool for studying ITH. Moreover, advances in single-cell sequencing technologies allow studying tumor composition, evolution, and alterations at unprecedented scale and resolution.

Besides the extreme importance of NGS technologies in cancer biology, their versatility has also played a pivotal role in the SARS-CoV-2 pandemic that started at the end of 2019. Indeed, thanks to NGS technologies, thousands of SARS-CoV-2 genomes have been sequenced worldwide enabling phylogenetic reconstruction of the spread and evolution of the virus. Furthermore, NGS techniques are currently used for genomic surveillance of the virus, which includes tracing the emergence of new viral variants that can potentially reduce the susceptibility to the newly developed vaccines.

In paper I, we developed CUTseq, a reduced representation genome sequencing method that can be used to tag and amplify low-input genomic deoxyribonucleic acid (gDNA) samples to generate multiplexed NGS libraries. The method enables reproducible calling of copy number alterations (CNAs) gDNA extracted from cell lines and formalin-fixed paraffinembedded (FFPE) tissue specimens. We used CUTseq to assess genetic ITH by profiling multiple regions of individual FFPE sections of primary and metastatic breast cancer lesions. Finally, we developed a rapid and cost-effective version of CUTseq that allows highthroughput preparation of NGS libraries using a contactless liquid-dispensing robot.

In paper II, we developed single-cell CUTseq (scCUTseq), an NGS method that builds on the design of CUTseq from paper I and includes a whole-genome amplification (WGA) step upfront to amplify gDNA from single cells. We leveraged on the nanodispensing device used in paper I to reduce the volume of reactions to nanoliters, which lowers reagent costs and scales up the number of cells that can be processed in parallel. The scalability of scCUTseq was demonstrated on cell lines, showing no difference between live and fixed samples. To assess the sensitivity of the method, we detected a 7 megabase (Mb) deletion induced by CRISPR/Cas9 occurring at low frequency (4.5%) in human TK6 lymphoblastoid cells. Finally, we applied the method to build a spatially resolved single-cell ITH atlas of CNAs in two prostate cancers.

In paper III, we developed COVseq, a versatile and cost-effective NGS method that can be used to prepare multiplexed libraries from dozens of SARS-CoV-2 ribonucleic acid (RNA) samples in parallel using the approaches described in paper I and II. The method was first benchmarked with a commercial kit showing high correlation of breadth of coverage and single-nucleotide variants (SNVs) detection. To demonstrate the reproducibility of the method, COVseq was applied to 274 diagnostic samples enabling the detection of the UK variant of concern B.1.1.7. Finally, a comparative cost analysis showed that COVseq can reduce the preparation and sequencing costs to ~$20 per sample.

List of scientific papers

I. Zhang Z., Garnerone S., Simonetti M., Harbers L., Nicoś M., Mirzazadeh R., Venesio T., Sapino A., Hartman J., Marchiò C., Bienko M., Crosetto N. (2019). CUTseq is a versatile method for preparing multiplexed DNA sequencing libraries from low-input samples. Nat Commun. 10 (1), 4732.
https://doi.org/10.1038/s41467-019-12570-2

II. Manuscript - Zhang N.*, Harbers L.*, Simonetti M.*, Longo G., Berrino E., Su P., Schultz N., Tarish F., Wang W., Onorato S., Roukos V., Marchiò C., Helleday T., Bienko M., Crosetto N. (2021). High clonal diversity and spatial genetic admixture in early prostate cancer and surrounding normal tissue. *These authors contributed equally. [Manuscript]

III. Simonetti M.*, Zhang N.*, Harbers L.*, Milia M.G., Brossa S., Nguyen H. T. T., Cerutti F., Berrino E., Sapino A., Bienko M., Sottile A., Ghisetti V., Crosetto N. (2021). COVseq is a cost-effective workflow for mass-scale SARS-CoV-2 genomic surveillance. Nat Commun. 12 (1), 3903. *These authors contributed equally.
https://doi.org/10.1038/s41467-021-24078-9