Cancer Res Treat.  2024 Oct;56(4):1171-1182. 10.4143/crt.2024.016.

Longitudinal Comparative Analysis of Circulating Tumor DNA and Matched Tumor Tissue DNA in Patients with Metastatic Colorectal Cancer Receiving Palliative First-Line Systemic Anti-Cancer Therapy

• Lee Sb
• Kim JW
• Kim HG
• Hwang SH
• Kim KJ
• Lee JH
• Seo J
• Kang M
• Jung EH
• Suh KJ
• Kim SH
• Kim JW
• Kim YJ
• Kim JH
• Kwon NJ
• Lee KW

• KMID: 2560251
• DOI: http://doi.org/10.4143/crt.2024.016

Abstract

Purpose
This study aimed to compare tumor tissue DNA (ttDNA) and circulating tumor DNA (ctDNA) to explore the clinical applicability of ctDNA and to better understand clonal evolution in patients with metastatic colorectal cancer undergoing palliative first-line systemic therapy.
Materials and Methods
We performed targeted sequencing analysis of 88 cancer-associated genes using germline DNA, ctDNA at baseline (baseline-ctDNA), and ctDNA at progressive disease (PD-ctDNA). The results were compared with ttDNA data.
Results
Among 208 consecutively enrolled patients, we selected 84 (41 males; median age, 59 years; range, 35 to 90 years) with all four sample types available. A total of 202 driver mutations were found in 34 genes. ttDNA exhibited the highest mutation frequency (n=232), followed by baseline-ctDNA (n=155) and PD-ctDNA (n=117). Sequencing ctDNA alongside ttDNA revealed additional mutations in 40 patients (47.6%). PD-ctDNA detected 13 novel mutations in 10 patients (11.9%) compared to ttDNA and baseline-ctDNA. Notably, seven mutations in five patients (6.0%) were missense or nonsense mutations in APC, TP53, SMAD4, and CDH1 genes. In baseline-ctDNA, higher maximal variant allele frequency (VAF) values (p=0.010) and higher VAF values of APC (p=0.012), TP53 (p=0.012), and KRAS (p=0.005) mutations were significantly associated with worse overall survival.
Conclusion
While ttDNA remains more sensitive than ctDNA, our ctDNA platform demonstrated validity and potential value when ttDNA was unavailable. Post-treatment analysis of PD-ctDNA unveiled new pathogenic mutations, signifying cancer’s clonal evolution. Additionally, baseline-ctDNA’s VAF values were prognostic after treatment.

Keyword

Circulating tumor DNA; Colorectal neoplasms; Anti-cancer therapy; Clonal evolution

Figure

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  Fig. 1. Overview of sample collection scheme and data analysis pipeline. (A) For each patient, we collected four types of DNA samples: tumor tissue DNA from tumor tissue, germline DNA (gDNA) from blood, circulating tumor DNA at baseline (baseline-ctDNA), and circulating tumor at progressive disease (PD-ctDNA). (B) Tumor tissue samples consisted of electronic medical record (EMR) data (n=67) and raw sequencing data (n=17). (C) To discriminate somatic mutations from germline variants as well as potential sequencing artifacts, we created a panel of normals from blood BAM files. This panel was provided to the Mutect2 command when performing somatic short variant discovery. (D) We devised a text parsing algorithm to automatically extract variant information from EMR data. (E) Variants in variant call format (VCF) were functionally annotated using Ensembl Variant Effect Predictor and then filtered so that our downstream analyses only included significant driver mutations. Filtered variants were combined with parsed EMR data to a single mutation annotation format (MAF) file using the pymaf submodule from the fuc package.

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  Fig. 2. Oncoplot of 84 colorectal cancer patients for the ten most mutated genes. The vertically stacked bar plot on the top shows tumor mutational burden (TMB) for each patient with different colors representing different DNA sample types. The horizontal clustered bar plot on the right denotes mutation prevalence for each sample type. ACMP, Axen Cancer Master Panel; AIM, additionally identified mutation; MSI, microsatellite instability; MSI-L, microsatellite instability-low; MSS, microsatellite stable; PD, progressive disease; SNUBH_V1, SNUBH Pan-Cancer Panel version 1; SNUBH_V2, SNUBH Pan-Cancer Panel version 2; TS500, TruSight Oncology 500; VAF, variant allele frequency.

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  Fig. 3. OS according to variant allele frequency (VAF) values. (A) Among patients with any mutations in circulating tumor DNA at baseline (baseline-ctDNA) (n=62), those with higher maximal VAF values of more than 0.059 showed significantly worse overall survival (p=0.010). (B) Among patients harboring APC mutations (n=37), the overall survival was significantly worse in those with high APC VAF values of more than 0.133 (p=0.012). (C) For TP53 mutations (n=29), the overall survival was significantly shorter in patients with high TP53 VAF values of greater than 0.154 (p=0.012). (D) For KRAS mutations (n=27), the overall survival was significantly inferior in patients with high KRAS VAF values of more than 0.081 (p=0.005).

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  Fig. 4. Representative examples of cancer evolution dynamics. The highest variant allele frequency (VAF) in all the samples was assigned 100%. And then, the VAF of the other mutations was normalized compared to the highest one. Six representative cases are shown: (A) CRC105 patient, (B) CRC035 patient, (C) CRC080 patient, (D) CRC019 patient, (E) CRC036 patient, and (F) CRC065 patient. CRC, colorectal cancer.

Reference

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