Page 273 - Read Online

Page 2 of 10 Finzel et al. J Cancer Metastasis Treat 2018;4:21 I http://dx.doi.org/10.20517/2394-4722.2018.10

INTRODUCTION
In recent years, the field of cancer therapy has evolved from a “one-size-fits-all” approach towards precision
medicine, where therapeutic options are tailored specifically to each patient. This patient-tailored strategy
is based on the molecular characterization of the tumor through biomarker analysis using tumor biopsy
samples . It is becoming clear that genetically different tumor subtypes need to be treated with distinct
[1]
targeted approaches , for example the monoclonal antibody trastuzumab in HER2-positive breast cancers
[2]
or vemurafenib in BRAF (V600E)-positive melanoma. Nonetheless, the use of targeted therapies is limited by
either the presence of primary resistance or the development of acquired treatment resistance , and tumor
[2]
heterogeneity has been clearly associated with such resistance . The advent of deep sequencing studies has
[3]
demonstrated that human cancers display both temporal (different genetic events taking place during the
disease course) and spatial intratumor heterogeneity, harbouring subclones with both shared and unique
genomic aberrations that respond differently to targeted therapy. Spatial discrepancy can be explained by
[4]
clonal heterogeneity within the primary tumor and by the presence of metastasis. Driven by the Darwinian
model, during the metastatic process a selection of the “most efficient” clones occurs, due to external forces
such as the treatment given to the patient or the tumor environment, for example the presence of hypoxia .
[5]
It has been reported that tumors with high levels of clonal heterogeneity may show poor prognosis .
[6]
As mentioned above, heterogeneity in cancer contributes to primary and acquired resistance , and that is
[3]
why approaches providing a global vision of the genomic landscape of the tumor are important for selecting
the most appropriate targeted therapy for each patient. Although solid biopsies are the standard way of
[7]
tumor characterization and will continue to play a central role in cancer management , they show some
limitations. One of them is that they may not capture tumor heterogeneity, as the aberrations found in a
single solid biopsy can be different depending on the area where the sampling was performed, and this could
lead to a biased characterization of the tumor that would influence therapy decision . Fortunately, this
[4]
limitation can be partially overcome by the use of liquid biopsies, such as the free circulating tumor DNA
(ctDNA) in blood. ctDNA belongs to the pool of the total cell-free DNA (cfDNA) molecules; in individuals
without cancer, the concentration of cfDNA is low, but tumor patients generally have significantly higher
levels of cfDNA because of the high turnover of cancer cells. The ctDNA contains DNA mutations of both
primary and metastatic lesions , since it is released from multiple tumor regions. Therefore, one potential
[7]
advantage of ctDNA over tissue biopsies is the detection of molecular heterogeneity ; as such, ctDNA can
[8]
[9]
harbour mutations that are undetected in the corresponding solid biopsy .
In this study we set out to determine the different genetic information revealed by solid and liquid biopsies,
and examine the clinical utility of adding ctDNA profiling to the information obtained through tissue
biopsies. To this end, we analysed data from 351 patients who had been previously characterized through
sequencing of tissue and ctDNA samples.

METHODS
Patient population
This work is a retrospective study evaluating 351 patients with stage IV solid tumors whose tissue and
blood samples were tested from May 2016 to November 2017 using the OncoSTRAT&GO™ profiling solution
(OncoDNA SA, Gosselies, Belgium), and who had failed at least one line of therapy before undergoing
molecular profiling. In all cases the oncologist suggested this solution to the patient, who gave informed
consent for the tumor analysis data to be published. For objectivity, all samples were included in our analysis
without prior selection for age, cancer type, treatment, profiling results or follow-up data.

Samples
Matched tissue and blood samples from different tumor types were included in the analysis. The cancer
types studied comprise breast (19.9%), colorectal (11.7%), lung cancer (11.4%), sarcoma (7.7%), ovarian (6.3%),