Tumour ReviewComprehensive intra-individual genomic and transcriptional heterogeneity: Evidence-based Colorectal Cancer Precision Medicine
Introduction
Genome and transcriptome sequencing and editing technologies, complemented by machine learning, are revolutionizing biomedical research shaping a shift from inexact science to Precision Medicine [1], [2], [3], [4], [5], [6], [7], [8]. Especially for cancer, progress has been so impressive that a transition from research on spatiotemporal tumor heterogeneity [9], [10], [11] to early phase clinical trials is now beginning [4], [12], [13], [14], [15], [16], [17]. Precise characterization of dynamic intra-individual genomic and transcriptomic landscapes could enhance prediction of drug response in individual patients and optimize combinatorial therapy towards clinical Cancer Precision Medicine [7], [8], [18], [19].
Colorectal cancer (CRC) has gathered immense research and clinical interest, with the standardization of treatment on the basis of clinico-pathologic and genetic characteristics (KRAS/NRAS/BRAF and MSI status), as well as tumor staging, reflecting an initial step towards personalized medicine [20]. Multi-modal treatment of resectable tumors, including liver metastases, has showcased rapid advancements while progress in non-resectable disease remains slow [20], [21]. Indeed, targeted therapy, which represents a major hope for clinical oncology, has not met expectations in the adjuvant or neo-adjuvant settings [20], while anti-EGFR (for KRAS/NRAS wild-type disease) and anti-VEGF agents have only moderate efficacy in the advanced or metastatic settings [22]. These advances and limitations mirror the standards of modern oncology, including the static tumor homogeneity and single biopsy-guided diagnostic approach, as well as the linear transcription dogma-based drug development [20], [23], [24].
A most rational perspective to overcome current unmet needs through understanding genome- and transcriptome-wide molecular mechanisms underlying drug resistance and relapse in each individual patient is provided by accumulating evidence on genetic, genomic and transcriptional heterogeneity both at bulk [10] and single-cell [9], [25] resolution. A recent explosion in static tumor next-generation sequencing (NGS) analysis has led to the identification of new cancer-driver genes, mutations and oncotargets [26]. However, considering dynamic clonal evolution [27], spatiotemporal detection of genomic clones with both multi-regional (MR) tumor NGS and matched circulating cell-free DNA (cfDNA) or circulating tumor DNA (ctDNA) NGS to identify bulk intratumor (ITH) [28] and circulating [29] heterogeneity respectively enable the delineation and improvement of therapeutic failure and relapse [30]. Moreover, beyond bulk sequencing, breakthrough technological systems, such as single-cell transcriptomics, CRISPR-Cas9 and their combination have returned exciting data on cell-by-cell variability-dependent drug sensitivity [9], [31], [32], [33]. This pre-clinical effort is strongly backed by cancer models, particularly patient-derived xenografts and organoids [34]. Nevertheless, the design of optimized therapy comprises a framework combining both standard linear transcription-based drug development [23], [33] and breakthrough discovery of next-generation drugs disrupting aberrant linear and non-linear regulatory networks [8].
The structure of this review is delineated in Fig. 1. We discuss valid published data on basket trials, bulk and single-cell NGS coupled with cf/ctDNA-NGS, DNA and RNA editing, as well as early-phase clinical trials, aiming for a transition from research to clinical Colorectal Cancer Precision Medicine. This shift sets the stage for overcoming three major clinical challenges: non-invasive screening for early diagnosis, individualized prediction of therapeutic response and discovery of multiple novel drug targets.
Section snippets
Clinical standards: state-of-the-art
The two pillars of Modern Oncology over the past decade have been the static tumor homogeneity concept and the single-biopsy approach for diagnosis, staging and treatment [20], as well as the single-gene transcription dogma [23], governing drug development. Regarding CRC, multi-modal treatment of resectable disease, not only for non-metastatic tumors, but for liver and/or lung metastasis as well in contrast to most other cancer types, has marked substantial progress [20]. Complete tumor
Data analysis on genomic and transcriptomic landscapes
Over the past decade DNA and RNA sequencing and editing technologies have transformed life sciences. Shortly after its market launch in 2005, NGS was introduced in the modENCODE [54] and ENCODE [55] projects, aiming to comprehensively characterize structural and functional, coding and non-coding genome elements. Since early integration of NGS into small studies, ease-of-application, continuously dropping cost and validity [56], coupled with recent recommendations on large sample sizes required
Future outlook
Exploiting breakthrough technologies and appropriate methodologies, emerging research focuses on overcoming three major challenges: late diagnosis, lack of robust predictive biomarkers and limited efficacy of available targeted agents. Innovative approaches exploring genome- and transcriptome-wide aberrations in time and space encourage the realization of long-term research dreams, including non-invasive screening, individualized drug sensitivity prediction and development of a drug bank to
Conclusions
Genome and transcriptome sequencing and editing technologies, coupled with interaction mapping and machine learning systems, as well as the static and dynamic exploration of genetic, genomic and transcriptional heterogeneity within early and underway clinical trials highlight a shift from research to clinical evidence-based Cancer Precision Medicine. Future prospective CRC studies implementing a triple comparison between primary and metastatic tumor ITH with serial liquid biopsies could provide
Acknowledgements
The authors reports no external assistance.
Declaration of Competing Interest
The authors have no competing interests to report.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.
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