| Literature DB >> 32010431 |
Rabih Said1,2,3, Nicolas Guibert4,5,3, Geoffrey R Oxnard4, Apostolia M Tsimberidou1.
Abstract
The spatial and temporal genomic heterogeneity of various tumor types and advances in technology have stimulated the development of circulating tumor DNA (ctDNA) genotyping. ctDNA was developed as a non-invasive, cost-effective alternative to tumor biopsy when such biopsy is associated with significant risk, when tumor tissue is insufficient or inaccessible, and/or when repeated assessment of tumor molecular abnormalities is needed to optimize treatment. The role of ctDNA is now well established in the clinical decision in certain alterations and tumors, such as the epidermal growth factor receptor (EGFR) mutation in non-small cell lung cancer and the v-Ki-ras2 kirsten rat sarcoma viral oncogene homolog (KRAS) mutation in colorectal cancer. The role of ctDNA analysis in other tumor types remains to be validated. Evolving data indicate the association of ctDNA level with tumor burden, and the usefulness of ctDNA analysis in assessing minimal residual disease, in understanding mechanisms of resistance to treatment, and in dynamically guiding therapy. ctDNA analysis is increasingly used to select therapy. Carefully designed clinical trials that use ctDNA analysis will increase the rate of patients who receive targeted therapy, will elucidate our understanding of evolution of tumor biology and will accelerate drug development and implementation of precision medicine. In this article we provide a critical overview of clinical trials and evolving data of ctDNA analysis in specific tumors and across tumor types.Entities:
Keywords: circulating tumor DNA analysis; clinical trials; genomic profiling; targeted therapy
Year: 2020 PMID: 32010431 PMCID: PMC6968778 DOI: 10.18632/oncotarget.27418
Source DB: PubMed Journal: Oncotarget ISSN: 1949-2553
Figure 1Potential use of plasma genotyping in cancer.
Early stage disease: Screening will require the use of large NGS panels, with both high sensitivity and perfect specificity. Before surgery, determination of tumor burden in plasma has the potential to help guide neo-adjuvant or adjuvant therapy and monitor response, using large panels or patient-specific assays based on the molecular profile of the tissue biopsy when available. After surgery, NGS (large gene panels or patient-specific assays) can detect MRD and guide adjuvant therapy (early detection) or detect relapse. Low tumor shed in plasma will be the main limitation to the integration of plasma genotyping in early stage disease. Advanced stage disease: At diagnosis, ctDNA can guide genotype-directed therapy (using targeted assays focusing on a predefined gene of interest (i. e. EGFR in NSCLC) or targeted NGS covering genes of interest). The variations in allelic fractions allow for monitoring of treatment response, which may be helpful for pharmacodynamics analyses in phase I studies. When acquired resistance to targeted therapies occurs, ctDNA can detect specific mechanisms of resistance (targeted assay like for EGFR T790M or targeted NGS), taking into consideration the different clones present within the primary tumor (P) and all metastatic sites (M1, M2), and guide treatment adjustments.
Selected studies by tumor type, the gene(s) used for ctDNA analysis, and outcomes
| Year, First
| Study Type | Tumor Type | Number
| Biomarker
| Tested Genes | Outcome |
|---|---|---|---|---|---|---|
|
| ||||||
| 2016, Adrian G.
| Prospective | Advanced NSCLC | 180 | ddPCR |
| Detection of KRAS and EGFR mutations, lower turnaround time compared to tissue |
| 2016, Jeffrey C.
| Cohort | Advanced NSCLC | 102 | Hybrid capture NGS | 70 cancer-related genes | Detection of potentially actionable variants in 75% of patients; concordance with tissue, 79% |
| 2018, Nicolas
| Cohort, blinded
| Advanced NSCLC | 168 specimens
| Amplicon-based NGS | 36 cancer-related genes | Detection of EGFR mutations, rare variants and fusions with high specificity. Early detection of resistance mechanisms in serial samples. |
| 2018, Charu
| Prospective | Advanced NSCLC | 323 | Hybrid capture | 73 cancer-related genes | Detection of actionable alterations in 20% of stage IV M1b patients in plasma but not tissue. Complementarity of tissue and plasma |
| 2014, Aaron M.
| Cohort | Early-stage lung
| 103 | Hybrid capture | 16 cancer-related genes + 8 proteins | Detection of ctDNA in early stages (stage I sensitivity, 50%) |
| 2017, Christopher
| Cohorts | Early-stage lung
| 96 | Patient-specific
| 10–22 SNVs | MRD and subclonal evolution |
|
| ||||||
| 2016, Honglei
| Cohort | Esophageal, SCC | 8 | Illumina TruSight
| 90 cancer-related genes | Multigene panel has a role in detection and monitoring response to treatment |
| 2010, Hiroki
| Case-control | Esophageal, SCC | 96 patients,
| PCR-applied
|
| Poor prognostic value of CCND1 amplification |
| 2016, Masami
| Cohort | Esophageal, SCC | 13 | HiSeq2000 | 53 cancer-related genes | Multigene panel is associated with a greater accuracy of tumor recurrence compared to imaging methods (post-op) |
| 2015, Katsutoshi
| Case-control | Gastric | 52 patients,
| PCR-applied
|
| HER2 amplification can be used for therapeutic monitoring |
| 2017, Katsutoshi
| Case-control | Gastric | 60 patients,
| PCR-applied
|
| HER2 amplification can be used for therapeutic monitoring |
| 2015, Hideaki
| Cohort | Gastric | 25 | PCR - QX200, Bio-Rad |
| High concordance in detection of HER-2 between ddPCR and tissue IHC/FISH |
| 2016, Wen-Liang
| Cohort | Gastric | 277 | TaqMan qPCR | 68 mutations (8 genes) | High ctDNA levels are associated with peritoneal recurrence and poor prognosis |
| 2017, Jeanne
| Prospective,
| Colon | 230 (1046
| Safe-SeqS PCR | 15 cancer-related genes | ctDNA detection after stage II colon cancer resection provides direct evidence of residual disease and identifies patients at very high risk of recurrence. |
| 2018, Jeanne
| Prospective,
| Colon | 95 | Safe-SeqS PCR | 15 cancer-related genes | ctDNA detection after adjuvant chemotherapy for stage III colon cancer resection can identify patients at very high risk of recurrence. |
| 2017, Lone V.
| Longitudinal
| Colon | 45 (371
| QX200 PCR | Somatic structural variants, | Postoperative ctDNA analysis detects residual disease and identifies patients at very high risk of relapse. Longitudinal surveillance allows
|
| 2015, Oliver A.
| Cohort | Pancreato-biliary
| 26 | Illumina Hi-Seq
| 54 cancer-related genes | ctDNA sequencing is feasible, accurate, and sensitive in identifying tumor-derived mutations. |
| 2016, Kjersti
| Cohort | Pancreatic | 14 (53
| Mx3000P rtPCR |
| ctDNA can be used as a marker for monitoring treatment efficacy and disease progression. |
| 2016, Naoto
| Cohort | Pancreatic | 105 | TaqMan assay PCR |
| ctDNA can predict poor survival |
| 2017, Daniel
| Prospective,
| Pancreatic | 135 | Ion AmpliSeq NGS | 112 cancer-related genes | ctDNA is an independent prognostic marker in advanced pancreatic adenocarcinoma |
| 2018, Belinda
| Cohort | Pancreatic | 42 | SafeSeqS assays
|
| ctDNA analysis is a promising prognostic marker in early-stage pancreatic cancer and guides risk-adaptive treatment strategies. ctDNA detection
|
| 2016, Stine Dam
| Prospective
| Pancreatic | 95 patients,
| Methylation-specific
| 28 cancer-related genes | ctDNA promoter hypermethylation is a diagnostic biomarker that helps distinguish malignant from benign pancreatic disease. |
| 2018, Andreas
| Cohort | Biliary tract cancer | 24 | 1010× depth Sequencing | 15 cancer-related genes | The molecular landscape is represented in ctDNA. |
| 2015, Atsushi
| Cohort | Hepatocellular
| 46 | Illumina Hi-Seq
| ctDNA detection post-surgery reflects tumor progression and disease recurrence. | |
| 2016, Wenjun
| Cohort | Hepatocellular
| 41 | Illumina MiSeq™ | Cancer-related
| ctDNA mutation detection is associated with vascular invasion and predicts a shorter recurrence-free survival time. |
| 2016, Ao
| Cohort | Hepatocellular
| 48 | QX200 PCR | Cancer-related
| ctDNA analysis can detect intratumoral heterogeneity and may have a promising role in the therapeutic management. |
|
| ||||||
| 2017, Heather A.
| Prospective
| Triple-negative
| 26 | HiSeq 2500 Illumina | 33 cancer-related genes | High concordance between ctDNA analysis and tumor tissue analysis, allowing monitoring of the therapeutic effect. |
| 2014, Julia A.
| Prospective
| Breast cancer | 29 | ddPCR | PIK3CA mutations | In patients with early-stage breast cancer, mutations can be detected in tumor tissue using ddPCR, and ctDNA can be detected in blood before
|
| 2016, Diana H.
| Retrospective
| Breast cancer | 100 | Illumina Hi-Seq 2500 | TP53, PIK3CA,
| Robust concordance between tissue and blood for detection of PIK3CA mutation and ERBB2 amplification, but not for TP53 mutation and
|
| 2016, Marion
| Cohort | Breast cancer | 600 | HiSeq 2500 Illumina | 306 cancer-related genes | AKT1E17K is the most likely disease driver in selected breast cancer patients and its detection in blood is achievable in advanced-stage disease. |
| 2017, Mary Ellen
| Prospective | Breast cancer | 724 | QX200 PCR | PIK3CA | Improvement in PFS was maintained using everolimus, irrespective of PIK3CA genotypes (detected by ctDNA), and it was consistent with
|
| 2016, Sarat
| Prospective | Breast cancer | 541 | QX200 PCR | ESR1 | ESR1 mutations are prevalent in ER-positive metastatic breast cancer treated with aromatase inhibitors. Both Y537S and D538G mutations
|
| 2018, Rosaria
| Cohort | Breast cancer | 3 | QX200 PCR |
| Somatic RB1 mutations can emerge after exposure to CDK4/6 inhibitors. |
| 2016, Fei
| Prospective,
| Breast cancer | 18 | HiSeq 2500 Illumina | 368 cancer-related genes | ctDNA analysis provides information regarding resistance to treatment and guides administration of anti-HER2 targeted therapy in the metastatic setting. |
| 2017, Francesca
| Cohort | Triple-negative
| 46 | Illumina MiSeq |
| ctDNA levels decreased quickly during neoadjuvant chemotherapy (NCT) and helped identify minimal residual disease after surgery. Slow
|
| 2015, Isaac
| Prospective,
| Breast cancer | 55 | HiSeq 2500 Illumina | Cancer-related genes | In patients with early stage breast cancer, ctDNA analysis can identify patients at high risk for relapse and guide adjuvant therapy. |
| 2017, Kala
| Cohort | Breast cancer | 141 | cMethDNA assay | 10 cancer-related genes | ctDNA gene methylation is a strong predictor of survival outcomes. |
| 2017, Hiroyo
| Cohort | Breast cancer | 87 | Methylation-specific PCR | RASSF1A | Met-ctDNA is a more sensitive marker than CEA and CA15-3 and it can be used to monitor clinical tumor response to neoadjuvant chemotherapy. |
| 2016, Ming
| Cohort | Breast cancer | 749 | MethyLight | SFN, P16,
| Epigenetic markers in serum have potential for diagnosis of breast cancer. |
| 2018, Charlotte
| Prospective,
| Breast cancer | 83 | Enhanced
|
| In patients with progressive disease after first-line aromatase inhibitors, ctDNA analysis demonstrated high levels of genetic heterogeneity and
|
| 2016, Peilu
| Cohort | Breast cancer | 126 | Bio-Rad QX100 dd PCR | ESR1 |
|
|
| ||||||
| 2014, Karina Dahl
| Cohort | Ovarian cancer | 144 | QiaSymphony, multiplex qPCR | ctDNA detection | In patients treated with bevacizumab, high ctDNA levels were associated with poor PFS and OS. |
| 2016, Christine A.
| Retrospective
| Ovarian cancer | 40 | ddPCR |
| ctDNA is correlated with volume of disease at the start of treatment. |
| 2015, Elena
| Cohort | Ovarian/
| 44 | qPCR using TaqMan®, ddPCR | ctDNA detection | ctDNA is an independent predictor of survival in patients with ovarian and endometrial cancers. |
| 2017, Adriaan
| Prospective,
| Ovarian cancer | 68 | HiSeq 2500 Illumina | Chromosome instability | ctDNA analysis demonstrated that chromosomal instability can help detect ovarian cancer. |
| 2012, Maura
| Cohort | Cervical cancer | 16 | RT-qPCR | ctDNA detection | ctDNA analysis demonstrated that the HPV mutational insertion is a highly specific molecular marker and it is detected in most patients with
|
| 2017, Zhigang
| Retrospective
| Cervical cancer | 19 | ddPCR | HPV genetic components | HPV genetic insertion in ctDNA represents a promising tumor marker. |
|
| ||||||
| 2017, Matti
| Prospective,
| Prostate cancer | 319 | NimbleGenSeqCap, Illumina | 73 cancer-related genes | Biallelic gene loss detected in ctDNA can help prioritize therapy. |
| 2016, Alexander W.
| Cohort | Prostate cancer | 65 | Illumina MiSeq, Ion Ampliseq | 19 cancer-related genes | Genomic profiling of ctDNA is feasible in mCRPC patients and provides important insights into enzalutamide response and resistance. |
| 2015, Arun A.
| Cohort | Prostate cancer | 62 | PCR-based BEAMing | AR | AR gene aberrations in ctDNA are associated with resistance to enzalutamide and abiraterone in mCRPC. |
| 2015, Alessandro
| Cohort | Prostate cancer | 97 | Ion Torrent Sequencing | AR | Plasma AR sequencing can identify primary resistance to abiraterone. |
| 2016, Samanta
| Cohort | Prostate cancer | 59 | RT-PCR, ddPCR | AR | Detection of circulating AR copy number gain is a non-invasive biomarker for outcome of patients with CRPC treated with enzalutamide. |
| 2017, Sumanta K.
| Prospective,
| Renal cell
| 220 | HiSeq 2500 Illumina | 73 cancer-related genes | Higher rates of detection by ctDNA analysis after systemic therapy compared with baseline was noted for |
| 2018, Neeraj
| Cohort | Urothelial
| 369 | HiSeq 2500 Illumina | 73 cancer-related genes | ctDNA NGS identified similar genomic alterations with tumor tissue. The genomic landscape was similar between lower tract and upper
|
| 2017, Emil
| Cohort | Urothelial
| 831 | ddPCR | FGFR3 and PIK3CA | ctDNA levels in the urine and plasma were positively correlated and indicated that higher levels of |
|
| ||||||
| 2016, Gregory A.
| Cohort | Melanoma | 43 | ddPCR |
| ctDNA had a higher sensitivity than LDH to detect disease progression. |
| 2016, Anne C.
| Cohort | Melanoma | 38 | RT-PCR |
| ctDNA |
| 2015, Elin S.
| Cohort | Melanoma | 48 | ddPCR |
| ctDNA is a biomarker of response to kinase inhibitor therapy and it can be used to monitor resistance to treatment. |
| 2015, Maria
| Cohort | Melanoma | 22 | TaqMan assay PCR |
| Detection and accurate quantification of low- |
| 2016, Ademi
| Cohort | Melanoma | 732 | PCR-based BEAMing |
|
|
| 2016, Max
| Cohort | Melanoma | 36 | qPCR |
| Quantitative analysis of |
| 2017, Stephen Q.
| Cohort | Melanoma | 52 | Amplicon sequencing, ddPCR |
| ctDNA is a powerful complementary modality to functional imaging for real-time monitoring of tumor burden and genomic changes
|
| 2018, R.
| Cohort | Melanoma | 161 | QX200 ddPCR |
| ctDNA predicts relapse and survival in high-risk resected stage II/III melanoma and can help select patients for adjuvant therapy. |
|
| ||||||
| 2016, Manuela
| Cohort | Ewing | 20 | AccuPrime Taq DNA PCR |
| Detection of EWSR1 fusion sequence in plasma is a promising noninvasive biomarker for improved therapeutic monitoring. |
| 2016, Masanori
| Cohort | Ewing | 3 | ddPCR | EWS-ETS | Tumor specific EWS-ETS translocation breakpoints in plasma DNA is a highly personalized biomarker for relapsed disease. |
| 2018, David S.
| Cohort | Ewing,
| 166 | Illumina HiSeq 2500 | EWSR1, FUS,
| Detectable ctDNA in patients with localized disease is associated with inferior event-free survival and OS at 3 years compared to patients
|
| 2013, Jacqueline
| Prospective
| Gastrointestinal
| 38 | RT-PCR | CKIT, PDGFRA | ctDNA harboring |
| 2014, Changhoon
| Cohort | Gastrointestinal
| 30 | PCR-based BEAMing |
| Genotyping of the KIT gene in exon 17 of serum ctDNA identified mutations associated with resistance to dovitinib. |
| 2016, Noriko
| Cohort | Gastrointestinal
| 4 | Sanger sequencing,
|
| Detection of secondary C-KIT mutations in ctDNA is useful to select targeted agents and to predict antitumor effects. |
| 2018, Nicholas C.
| Cohort | Soft tissue
| 11 | Ion AmpliSeq | 57 cancer-related genes | ctDNA analysis detected TP53/PIK3CA mutations concordant with the primary tumor in 2 of 4 cases. |
|
| ||||||
| 2013, Mohamad A.
| Prospective | Glioblastoma | 13 | Illumina HiSeq | EGFR (vIII deletion) | ctDNA analysis identified the EGFRvIII deletion in 3 of 13 patients, which was correlated with tumor tissue analysis and may help select
|
| 2016, Shigeki
| Retrospective | Neuroblastoma | 151 | Real-time quantitative PCR | MYCN | Serum MYCN amplification (sensitivity 86%, specificity 95% compared with tissue analysis) was associated with OS. It may help select
|
| 2012, Blandine
| Prospective | Glioma | 80 patients,
| Digital PCR,
|
| The |
|
| ||||||
| 2016, Vincent
| Cohort | Hodgkin | 94 | TaqMan assay PCR | XPO1 | The XPO1 E571K mutation in ctDNA can be used as a novel biomarker in diagnosis and detection of minimal residual disease. |
| 2016, Sarit E.
| Phase 2 trial | Diffuse large
| 40 | ddPCR | CREBBP, EP300,
| Increase in ctDNA levels at 15 days after treatment initiation was associated with resistance to treatment. |
| 2016, Florian
| Case control | Diffuse large
| 92 patients,
| CAPP-Seq | BCL2, BCL6, MYC, IGH | ctDNA levels at diagnosis were strongly correlated with clinical indices and were independently predictive of patient outcomes. |
| 2015, David M.
| Prospective
| Diffuse large
| 75 | RT-PCR | Immunoglobulin high-throughput
| ctDNA immunoglobulin high-throughput sequencing preceded radiologic evidence of recurrent disease indicating that it may be a
|
| 2015, Mark
| Retrospective
| Diffuse large
| 126 | LymphoSIGHT™ | VDJ | After first-line treatment, disease progression was evident on imaging studies a median of 3.5 months after detection on ctDNA analysis
|
|
| ||||||
| 2012, Geraldine
| Cohort from
| Colorectal,
| 105 | Sequenom
| KRAS, BRAF, PIK3CA | ctDNA analysis has potential clinical multi-purpose utility in patients with advanced cancer. |
| 2015, Filip
| Cohort from
| Colorectal,
| 157 | PCR-based
| BRAF, EGFR, KRAS, PIK3CA | Patients with > 1% of mutant ctDNA had shorter median OS compared to patients with ≤ 1%. |
| 2014, Chetan
| Cohort | Pancreatic,
| 640 | BEAMing,
| 187 cancer-related genes | ctDNA is a broadly applicable, sensitive, and specific biomarker that can be used for clinical and research purposes in patients with
|
| 2013, Muhammed
| Cohort | Breast,
| 6 | HiSeq 2500 Illumina |
| ctDNA analyses can complement invasive tumor biopsies to identify mutations associated with acquired drug resistance in advanced cancer. |
| 2015, Jean Sebastien
| Cohort | Colorectal,
| 39 | Ion AmpliSeq,
| Cancer-related genes | Targeted sequencing of ctDNA has potential clinical utility to monitor the effect of targeted therapies. |
| 2016, Maria
| Cohort | Lung,
| 171 | Illumina Hi-Seq
| 54 cancer-related genes | A large proportion of patients had detectable ctDNA aberration (s), among which the majority are targetable by an approved drug. |
| 2016, Maria
| Cohort | Brain,
| 168 | Illumina Hi-Seq
| 54 cancer-related genes | ctDNA tests provide information complementary to the tissue biopsies and may be useful in determining prognosis and treatment. |
| 2017, Yulian
| Cohort | Skin,
| 69 | Illumina Hi-Seq
| 54–70 cancer-related genes | Hyper-mutated ctDNA is correlated with response to checkpoint inhibitor-based therapy and investigation of hypermutated ctDNA
|
Abbreviations: AR: Androgen Receptor; ddPCR: droplet digital PCR; FISH: fluorescence in situ hybridization; IHC: immunohistochemistry; mCRPC = metastatic castration-resistant prostate cancer; Met: methylation; MRD: minimal residual disease; NCT: neoadjuvant chemotherapy; NGS: next-generation sequencing; NSCLC: non-small-cell lung carcinoma; OS = overall survival; PCR: Polymerase chain reaction; PFS: Progression free survival; qPCR: quantitative polymerase chain reaction; rtPCR: real-time Polymerase chain reaction; SCC: Squamous cell carcinoma; SNVs: single nucleotide variants.