Clone-based full-length RNA-seq for early diagnosis of cancer

Intra-tumor genetic heterogeneity imposes a great challenge on cancer therapy.

Resistance to molecularly targeted therapies and chemotherapy can arise from selective growth of pre-existing sub-clones that carries drug-resistance mutations, altered metabolic and/or epigenomic states, providing a survival advantages. Early detection of these subclonal states can thus significantly aid cancer therapy.

However, attempts to profile various types of primary cancer cells using single-cell techniques are relatively poor. One of the major limitations is the significant dropout rate (i.e., loss of alleles) observed in single-cell RNA-seq.

It severely affects our ability to leverage single cell RNA-seq to accurately profile somatic mutation, to reveal cancer driver mutation and even extract low/mid-level expressed genes and splicing.

For that reason, most of the efforts to expose mutations that are critical for cancer growth and can subsequently lead to more effective treatment are based on the sequencing of bulk populations.

However, due to the noise introduced by PCR, sequencing and alignment processes, bulk sequencing is limited to identify mutations with a frequency higher than 5%. Here we propose to develop a novel 3D clone-based full-length RNA-seq profiling technology.

A preliminary version of this technology for digital profiling of mRNA, already allowed us to significantly improve sensitivity comparing to gold-standard single-cell RNA-seq methods.

Using this preliminary version on clones of lung adenocarcinoma, we revealed novel cancer stem like subpopulation that could not be detected using regular single cell RNA-seq maps. Altogether, improving the ability to detect rare mutations (