Liquid biopsies and cancer omics

Abstract

The development of the sequencing technologies allowed the generation of huge amounts of molecular data from a single cancer specimen, allowing the clinical oncology to enter the era of the precision medicine. This massive amount of data is highlighting new details on cancer pathogenesis but still relies on tissue biopsies, which are unable to capture the dynamic nature of cancer through its evolution. This assumption led to the exploration of non-tissue sources of tumoral material opening the field of liquid biopsies. Blood, together with body fluids such as urines, or stool, from cancer patients, are analyzed applying the techniques used for the generation of omics data. With blood, this approach would allow to take into account tumor heterogeneity (since the circulating components such as CTCs, ctDNA, or ECVs derive from each cancer clone) in a time dependent manner, resulting in a somehow "real-time" understanding of cancer evolution. Liquid biopsies are beginning nowdays to be applied in many cancer contexts and are at the basis of many clinical trials in oncology.

Fig. 1. The multiomics approach on liquid biopsies.

The information collected from a single blood specimen can reflect the evolution of a single cancer from many biological points of view. Circulating tumor cells (CTCs) highly reflect the complexity of the pathology, especially regarding tumor heterogeneity. Full genomic (whole genome sequencing) and transcriptomic analysis can be applied to CTCs and can be used as well for the growth of cancer organoids. Circulating proteins can also reflect the tumor secretome and can be analyzed through spectrometric approaches such as proteomics and phosho-proteomics. Moreover, circulating tumor DNA can be purified and used for whole or targeted sequencing. Picture created with Biorender.com.

Fig. 2. Integrated multiomics approaches in the natural history of breast cancer.

Liquid biopsies can capture the complex tumoral genomic and proteomic landscape all along tumor evolution as they can be periodically be repeated due to their relative low harmfulness. This might be of crucial importance and will allow developing early diagnostic tools to detect localized breast cancers, as well as to develop decisional algorithms for the selection of the best therapy at the right moment. All the information acquired will also lead to the identification of precise predictive biomarkers for monitoring the phases of the disease, as well as for the prediction of cancer recurrence. All together, these data will generate e fast and reactive precision medicine approach for the treatment of breast cancer. Picture created with Biorender.com.