| Literature DB >> 35402285 |
Ioannis D Kyrochristos1,2, Georgios K Glantzounis3, Anna Goussia4, Alexia Eliades5, Achilleas Achilleos5, Kyriakos Tsangaras5, Irene Hadjidemetriou5, Marilena Elpidorou5, Marios Ioannides5, George Koumbaris5, Michail Mitsis3,6, Philippos C Patsalis5,7, Dimitrios Roukos1,3,8.
Abstract
Introduction: The mechanisms underlying high drug resistance and relapse rates after multi-modal treatment in patients with colorectal cancer (CRC) and liver metastasis (LM) remain poorly understood. Objective: We evaluate the potential translational implications of intra-patient heterogeneity (IPH) comprising primary and matched metastatic intratumor heterogeneity (ITH) coupled with circulating tumor DNA (ctDNA) variability.Entities:
Keywords: actionable mutations; circulating variability; comprehensive intra-patient heterogeneity; intratumor heterogeneity; next-generation sequencing; precision cancer medicine
Year: 2022 PMID: 35402285 PMCID: PMC8986149 DOI: 10.3389/fonc.2022.855463
Source DB: PubMed Journal: Front Oncol ISSN: 2234-943X Impact factor: 6.244
Figure 1Description of patient samples. Twenty-eight patients diagnosed with colorectal cancer and liver metastasis were enrolled in the study. After initial histopathologic quality control for adequacy of tumor tissue 18 patients were subjected to further analysis. For 16 patients both FFPE tissue as well as blood samples were collected at different time points from diagnosis, before and after surgery and during treatment and monitoring. For two patients FFPE samples were available from different sites of the primary and metastatic site. A total of 122 samples passed QC requirements (92.4%) for NGS analysis.
Custom 77-gene panel for targeted next-generation sequencing analysis.
| Single Nucleotide Variants (SNVs) / Insertions andDeletions (Indels) (67 genes) | Copy-Number Alterations (17 genes) | Translocations (10 genes) |
|---|---|---|
| AKT1, ALK, APC, AR, ARAF, ATM, ATRX, BARD1, BRAF, BRCA1,BRCA2, BRIP1, CDH1, CDKN2A, CHEK2, CIC, CTNNB1,DDR2, DICER1, EGFR, ERBB2, ERBB3, ERBB4, ESR1, FBXW7, FOXA1, FOXL2, FUBP1, GATA3,GNA11, GNAQ, GNAS, H3F3A, IDH1, IDH2, JAK2, KEAP1, KIT, KRAS, MAP2K1, MAP3K1, MET, MLH1, MRE11A, MSH2, MSH6, MTOR, NBN, NF1, NRAS, NTRK1, PALB2, PIK3CA, PIK3CB, PMS2, POLE, PTEN, RAD51C, RAD51D, RAF1, RET, RUNX1, SMAD4, SPOP, STK11, TERT, TP53 | AR, CDKN2A, EGFR, ERBB2, ESR1, FGFR1, FGFR2, | ALK, BRAF, FGFR3, NF1, NTRK1, NTRK2, NTRK3, RET, ROS1, TMPRSS2 |
*Includes MSI assessment.
Figure 2Bioinformatics analysis pipeline.
Figure 3Summary of the distribution of genetic alterations among all tissue and liquid biopsy samples from our cohort of 18 patients with metastatic colorectal adenocarcinoma. (A) Oncoplot shows the patients in a horizontal orientation and the gene and corresponding driver mutations in the vertical orientation. (B) Frequency of mutations observed per gene: The frequency of mutations is estimated based on the cumulative list of variants per patient. The number of variants identified in all patients is shown. (C) Distribution of different types of genetic alterations identified in the study. (D) Overall detection of potentially actionable mutations and their clonal status in our study: 71% of all patients with detectable actionable mutations harbored subclonal putative oncotargets, defined as being present in some but not all tumor samples.
Figure 4Assessment of tumor heterogeneity (A) Total number of variants per patient in the cohort. (B) Molecular heterogeneity between primary (PM) and metastatic (MT) lesions. (C) Clinical actionability of genetic findings. Based on the variants identified, patients were allocated in different clinical benefit groups: EGFR monoclonal antibody (mAb), BRAF inhibitor, drugs approved in other indications, and drugs under clinical and pre-clinical investigation.
| ITH of the primary tumor | The proportion of patients harbouring variable levels of ITH of the primary | 8/15 patients with multi-regional samples from the primary tumor, 53% |
| ITH of the liver metastasis | The proportion of patients harbouring variable levels of ITH of the liver metastatic lesion | 9/16 patients with multi-regional samples from liver metastases, 56% |
| Genetic heterogeneity between primary and matched metastatic | The proportion of patients harbouring mutations detected either in the primary or the matched metastatic tumor but not in both | 9/17 patients with matched primary and metastatic tumor samples, 53% |
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| The proportion of patients harboring de novo mutations in liver metastases not found in the primary tumor, including potentially actionable variants indicating dynamic clonal evolution | 6/17 patients with matched primary and metastatic tumor samples, 35% |
| Detection of cfDNA mutations | Proportion of patients in which tumor mutations were detected in cfDNA pre- operatively | 8/16 patients, 50% |
| Potentially actionable mutations | 9/18 (50%) and 17/18 (94%) patients could benefit from repurposing of already approved drugs and agents under clinical or pre-clinical development respectively | 12/17 (71%) patients with detectable mutations harbored potentially actionable alterations not ubiquitously shared by all tumor samples, indicating the need for spatiotemporal sampling to increase therapeutic accuracy |