Novel Genomic Differences in Cell-Free Circulating DNA Profiles of Young- Versus Older-Onset Colorectal Cancer

Abstract

We present the first analysis examining molecular alterations detected utilizing a clinically available cell-free circulating tumor DNA (cfDNA) assay in a cohort of patients with advanced colorectal cancer (aCRC) diagnosed <50 versus ≥50 years of age. Patient characteristics and mutation frequencies were compared using cfDNA tests from 5873 patients. Patients <50 had more sequence alterations in APC, CTNNB1, and SMAD4, as well as a higher frequency of focal BRAF and ERBB2(HER2) amplifications. Our study adds further evidence suggesting that young- versus older-onset CRC may have distinct molecular underpinnings, with prognostic and therapeutic implications.

Background

Colorectal cancer (CRC) occurrence has declined over the past several decades; however, in younger individuals, there has been a notable increase in incidence.^1–3 Younger patients are more likely to present with advanced disease, potentially due to lack of screening, delays in diagnosis, or more rapidly progressing disease.^4,5 While data are conflicting, there is some evidence that younger patients may have worse prognosis and outcomes.^4,5

A minority of patients with young-onset CRC have a hereditary syndrome, and while modern lifestyle and dietary trends have been identified as potential risk factors, an established cause for this increase has yet to be determined.^6–8 Previous studies using tissue-based approaches have compared the frequency of somatic alterations in patients with young- versus older-onset CRC and found significant, yet inconsistent differences between cohorts, suggesting that young- versus older-onset CRC may have distinct molecular and clinical features.^6,8–10 We aimed to describe differences in cell-free circulating tumor DNA (cfDNA) in CRC patients by age to further elucidate molecular differences between young- versus older-onset advanced CRC (aCRC).

Methods

We retrospectively analyzed a deidentified database containing results from consecutive patients treated within the United States with aCRC (stage IIIB or higher) who underwent clinical testing with a validated, next-generation sequencing (NGS) cfDNA assay (Guardant360^®, Guardant Health) between October 2015 and March 2019.^11,12 Patient age, sex, and cancer type were extracted from test request forms (TRF). If a patient had longitudinal testing, only their first chronological test was included. Since date of diagnosis was not consistently provided, we categorized patients by their age at the time of testing. Using the subset of samples with the age of diagnosis included, we assessed how many patients were incorrectly categorized as being in the ≥50 cohort when they were actually diagnosed at age <50. Using this same subset, we analyzed how many samples were drawn ≤6 months versus >6 months after the patient's diagnosis.

During the study period, the assay, which has been previously described, evolved from a 70- to a 73-gene panel (Supplementary Table S1).¹² Microsatellite instability high (MSI-H) status was available for a subset of cases, upon validation of its inclusion in the assay.¹³ During the study period, the assay's bioinformatics pipeline implemented an aneuploidy distinction feature which identifies focal gene amplifications as those with a statistically higher copy number relative to other genes across the chromosome arm (see Supplementary Table S1 for the list of genes in which this differentiation can be made).

Clinical characteristics and mutation frequencies were compared between the <50 and ≥50 age groups, after excluding variants of uncertain significance and synonymous variants. Each gene was assessed by alteration type, including sequence alterations (single-nucleotide variants [SNV]/indels/splice site variants), amplifications, and fusions, with frequencies determined on a per-patient basis (patients with multiple sequence alterations in the same gene were counted once). Only patients with focal amplification determination data available were included in the denominator for calculation of amplification frequency. Sequence alterations were combined for the pathway analysis and pathway-level frequency was calculated using the total number of functional sequence alterations in that pathway divided by the overall number of sequence alterations identified (see Supplementary Table S2 for the genes included in each pathway). The maximum variant allele frequency (maxVAF) represents the tumor-derived alteration with the highest level of cfDNA in the sample (calculated based on the number of tumor-derived DNA molecules at each location divided by the total number of unique cfDNA molecules at the given nucleotide position). For select factors, subanalyses comparing patients aged <40 versus ≥40, patients aged <35 versus ≥70, men aged <50 versus women <50, and women aged <50 (premenopausal) versus women ≥50 were completed and these results are detailed in the Supplementary Data. Demographic and clinicopathological characteristics were compared between cohorts. Fisher's exact test was used to compare categorical variables and Wilcoxon rank sum tests were used for continuous variables.

This research was approved by the Quorum Institutional Review Board (IRB) for the generation of deidentified data sets for research purposes.

Results

During the study period, 5873 unique patients with aCRC underwent cfDNA NGS. The median age at the time of cfDNA blood draw was 60 years (range: 18–96). The majority of patients were male (56.1%), and there were significantly more men in the ≥50 cohort compared to the <50 cohort (57.0% vs. 52.7%; p = 0.0064; Table 1).

Table 1.

Patient Demographic and Sample Information

Characteristic	Patients <50 (%)	Patients ≥50 (%)	p	Patients <40 (%)	Patients ≥40 (%)	p	Overall
All patients tested (n = 5873)
Age of diagnosis	1296 (22.1%)	4577 (77.9%)	—	415 (7.1%)	5458 (92.9%)	—	5873
Gender
Male	683 (52.7%)	2609 (57.0%)	0.0064	220 (53.0%)	3072 (56.3%)	0.199	3292 (56.1%)
Female	613 (47.3%)	1968 (43.0%)	0.0064	195 (47.0%)	2386 (43.7%)	0.199	2581 (43.9%)
Samples with ≥1 cfDNA alteration (n = 5174)
Median maxVAF	6.97	4.22	<0.0001	7.90	4.50	0.0399	4.70
Median number of alterations	5	5	0.5202	5	5	0.4252	5
Patients with ≥1 cfDNA alteration detected assessed for MSI-H status (n = 2611)
MSI-H detected	26 (5.0%)	77 (3.7%)	0.1646	11 (7.1%)	92 (3.7%)	0.0505	103 (3.5%)

Bold values indicate p < 0.05.

Patient demographic and sample information for N = 5873 total unique tests from advanced colorectal cancer patients submitted for clinical testing via a cell-free circulating tumor DNA assay Between October 2015 and March 2019. Fisher's exact test was used to compare the frequency of male and female patients and the frequency of MSI-H samples. Wilcoxon rank sum tests were used to compare the median maxVAF and median number of alterations per sample.

cfDNA, cell-free circulating tumor DNA; maxVAF, maximum variant allele frequency; MSI-H, microsatellite instability high.

At least one cfDNA alteration was identified in 88.1% (n = 5174) of patients. While the median maxVAF was significantly higher in patients <50 (6.97 vs. 4.22; p < 0.0001), there was no significant difference in the median number of alterations per sample between cohorts (5 vs. 5; p = 0.5202). Microsatellite testing results were available for 2611 patients (51% of patients with ≥1 cfDNA alteration detected), and the frequency of MSI-H was higher in the <50 cohort, but not statistically significant (5.0% vs. 3.7%; p = 0.1646). BRAF V600E alterations were significantly more frequent in MSI-H patients who were ≥50 (36.4% vs. 7.7%; p = 0.0055; Supplementary Table S3).

Next, we examined the frequency of sequence alterations by gene, focusing on the 24 most frequently mutated genes in this cohort (Supplementary Fig. S1). Sequence alterations in TP53, APC, KRAS, and PIK3CA were the most frequent in both cohorts, consistent with previous molecular studies of CRC.^10,14 There were significantly more alterations in ATM and TERT (promoter mutations only) in patients ≥50, and significantly more alterations in APC, SMAD4, ARID1A, PTEN, and CTNNB1 in patients <50 (Fig. 1A and Supplementary Fig. S2A, B). There was no significant difference in the frequency of gene fusions for any of the six genes analyzed on the cfDNA assay (Fig. 1B and Supplementary Fig. S2C, D). In total, 12.5% (2/16) of the BRCA1 alterations and 11.4% (4/35) of the BRCA2 alterations detected in patients age <50 were suspected germline, compared with 9.2% (6/65) of the BRCA1 alterations and 2.9% (3/104) of the BRCA2 alterations in patients age ≥50 (Supplementary Table S4). While numbers were limited for subanalyses of men versus women aged <50 (n = 549 and n = 495, respectively) and women aged <50 versus ≥50 (n = 495 and n = 1750, respectively), there were significant differences in the per-patient frequency of sequence alterations in notable genes, including, BRAF, KRAS, and APC (Supplementary Fig. S3A, B).

FIG. 1.

Comparison of frequency of alterations between advanced colorectal cancer patients aged <50 and ≥50. (A) Frequency of sequence alterations (single-nucleotide variants, insertions/deletions, splice site variants) by gene, calculated on a per-patient basis. Denominator represents the number of patients in the corresponding age category tested with a version of the cfDNA assay including the given gene with at least one cfDNA alteration detected. (B) Frequency of actionable fusions calculated on a per-patient basis. (C) Frequency of focal amplifications, calculated on a per-patient basis. Denominator represents the number of patients in the corresponding age group with at least one cfDNA alteration detected from the time the aneuploidy distinction feature was implemented (a total of n = 4888 patients). Color images are available online.

Amplification data from 1771 patients (34%) underwent analysis using the aneuploidy distinction feature. Focal amplifications in BRAF, CCND1, and ERBB2(HER2) were significantly more frequent in patients <50 (Fig. 1C and Supplementary Fig. S2E, F).

Overall, the frequency of functional sequence alterations by pathway was largely the same between cohorts, however, there was a significantly higher frequency of alterations in the SWItch/Sucrose Non-Fermentable (SWI/SNF) pathway in patients aged <50 and a numerically higher number of alterations in the RAS/RAF/MAPK pathway in patients aged ≥50 (Fig. 2).

FIG. 2.

Frequency of sequence alterations by pathway. Numerator represents total number of functional alterations in the genes included in that pathway and denominator represents total number of sequence alterations identified in the corresponding age cohort (variants of uncertain significance and synonymous variants excluded from the numerator). Color images are available online.

In total, 41.4% of samples included the patient's age of diagnosis. Using these samples, we found that 6.3% of patients switched from the ≥50 to the <50 cohort when incorporating age of diagnosis (Supplementary Fig. S4). We also found that the majority of samples (74.4%) were drawn >6 months after the patient's diagnosis.

Discussion

There has been an increase in the incidence of young-onset CRC, with more individuals diagnosed with late-stage disease.^15–17 Delays in diagnosis do not fully explain this difference, suggesting that there may be undiscovered clinical and/or molecular distinctions between young- and older-onset CRC, and thus examination of somatic genetics may provide insights into the underlying biology of these cancers.^15,16 Previous research examining molecular differences between young- and older-onset CRC patients using tissue testing included variable age cutoffs, various stages, and have not consistently differentiated between mutation types (a summary of findings from these studies can be found in Supplementary Table S5). To our knowledge, this is the first study to comprehensively assess potential differences in genetic alterations identified via cfDNA in aCRC patients aged <50 versus ≥50 and details significant molecular differences between young- and older-onset aCRC patients, most notably in the RAS/RAF/MAPK pathway.

Activating alterations in KRAS, NRAS, and BRAF are predictive biomarkers of resistance to anti-epidermal growth factor receptor (EGFR) antibody therapy in both the primary and acquired resistance settings.¹⁸ Current National Comprehensive Cancer Network (NCCN) guidelines recommend molecular testing for RAS genes and BRAF in all aCRC patients, and also include several recommended systemic therapy regimens involving BRAF ± MEK inhibitors for patients with BRAF V600E.¹⁹ We found that KRAS alterations were significantly more frequent in patients ≥40, which is consistent with several previous studies, and sequence alterations in the RAS/RAF/MAPK pathway were numerically more frequent in patients ≥50.^6,10 Interestingly, we found that focal BRAF amplification was significantly more frequent in patients aged <50.

Emerging data suggest that ERBB2(HER2) amplification may be a negative predictor of response to anti-EGFR therapy, and there are several ongoing clinical trials of ERBB2(HER2)-targeted therapy in ERBB2-amplified, RAS/RAF wild-type aCRC.^20–22 Thus, the identification of more frequent ERBB2(HER2) amplifications in young-onset aCRC patients may have important implications in terms of therapy selection for these patients as well as patient selection for clinical trials.

In patients with microsatellite stable tumors, Lieu et al. found APC alterations to be more common in patients >50, while Willauer et al. found fewer APC alteration in patients aged 18 to 29 compared with other young-onset patients. In our analysis, we found the lowest frequency of APC alterations in patients <30 (47.3%) and the highest frequency in patients 40–49 (62.8%) and 50–59 (61.9%), suggesting that APC may play less of a role in very young-onset aCRC. Notably, CTNNB1 was significantly more common in patients <50, consistent with both Willauer et al. and Lieu et al.^6,10

SMAD4, a member of the TGF-β pathway, has recently been associated with worse outcomes and, potentially, a predisposition to chemoresistance, particularly to 5-fluorouracil (5-FU)-based therapy.²³ While further research is needed to validate this work, our finding that SMAD4 alterations were significantly more frequent in patients <50 has potential future implications for therapy selection in these patients.

While there was no difference seen in the median number of alterations detected, the median maxVAF was significantly higher in patients <50. Overall, higher maxVAF has consistently been associated with a more significant disease burden, and higher cfDNA maxVAF has been associated with worse prognosis.²⁴ However, further studies are needed to better understand which factors may impact tumor cfDNA shed and to validate the prognostic value of this marker.

A common concern with cfDNA is potential contamination by alterations stemming from clonal hematopoiesis (CH), which can be difficult to differentiate from tumor-derived variants. To evaluate the potential impact of CH, we examined the frequency of JAK2 V617F in this cohort, as this alteration is highly characteristic of myeloproliferative neoplasms and is rare in CRC, and found that a small minority (0.6%) of patients had this alteration.¹⁴ Of note, many of the genes with the highest rates of CH are not included on this cfDNA assay, and several studies have found low rates of CH in the genes currently actionable in CRC (e.g., BRAF, KRAS).²⁵

We acknowledge that this study has several limitations, predominantly related to the fact that it is a retrospective study and our knowledge of patient clinical history, including treatment regimens, is limited to what is provided by the ordering provider on the TRF. Using the 41.4% of samples that included the patient's age of diagnosis, we found that a small minority of patients (6.3%) were incorrectly classified as aged ≥50, and that the majority of samples (74.4%) were drawn >6 months after the patient's diagnosis, suggesting that these patients likely received prior therapy. While previous work has shown that the concordance between tissue and cfDNA analysis in treatment-naive aCRC patients is high when examining driver alterations, it seems likely that some of the alterations detected in this cohort (e.g., KRAS SNVs, BRAF amplifications) may represent acquired resistance alterations.²⁶ Data support the ability of cfDNA to identify the evolution of a tumor across time and treatment, and thus account for both spatial and temporal heterogeneity.^14,20,26 Thus, cfDNA testing provides a unique perspective of aCRC, including the identification of intriguing potential differences in acquired resistance mechanisms between young- and older-onset aCRC.

In summary, this study highlights the utility of cfDNA analysis in assisting in the implementation of precision oncology by identifying molecular alterations with potential therapeutic implications in aCRC, and adds further evidence to suggest that there are distinct molecular differences between young- and older-onset CRC, which are relevant to emerging and currently available targeted therapies. Thus, the one size fits all models for CRC treatment may need to be reconsidered, and future work is needed to understand how these distinct molecular characteristics may better inform treatment of these patients.

Footnotes

Acknowledgments

The authors acknowledge Richard B. Lanman, Rebecca J. Nagy, Victoria M. Raymond, and Jennifer Saam for their criticial review of analyses and feedback on the article.

Author Disclosure Statement

C.M.W., C.R.E.—stockholders and employees of Guardant Health; A.B., H.-J.L. —No disclosures.

Funding Information

No funding was received.

Supplementary Material

References

National Cancer Institute. Cancer Stat Facts: Colorectal Cancer. Accessed December 19, 2019. from: https://seer.cancer.gov/statfacts/html/colorect.html.

Siegel

, Miller

, Jemal

. Cancer statistics, 2019. CA Cancer J Clin. 2019; 69(1):7–34.

Bailey

, Hu

C-Y

, You

, et al. Increasing disparities in age-related incidence of colon and rectal cancer in the United States, 1975–2010. JAMA Surg. 2015; 150(1):17–22.

Lieu

, Renfro

, de Gramont

, et al. Association of age with survival in patients with metastatic colorectal cancer: analysis from the ARCAD clinical trials program. J Clin Oncol. 2014; 32(27):2975–82.

Mauri

, Sartore-Bianchi

, Russo

A-G

, et al. Early-onset colorectal cancer in young individuals. Mol Oncol. 2019; 13(2):109–31.

Willauer

, Liu

, Pereira

AAL

, et al. Clinical and molecular characterization of early-onset colorectal cancer. Cancer. 2019; 125(12):2002–10.

Mork

, You

, Ying

, et al. High prevalence of hereditary cancer syndromes in adolescents and young adults with colorectal cancer. J Clin Oncol. 2015; 33(31):3544–9.

Cavestro

, Mannucci

, Zuppardo

, et al. Early onset sporadic colorectal cancer: worrisome trends and oncogenic features. Dig Liver Dis. 2018; 50(6):521–32.

Chang

C-C

, Lin

P-C

, Lin

C-C

, et al. Molecular and clinicopathological differences by age at the diagnosis of colorectal cancer. Int J Mol Sci. 2017; 18(7):1441.

10.

Lieu

, Golemis

, Serebriiskii

, et al. Comprehensive genomic landscapes in early and later onset colorectal cancer. Clin Cancer Res. 2019; 25(19):5852—58.

11.

Zill

, Banks

, Fairclough

, et al. The landscape of actionable genomic alterations in cell-free circulating tumor DNA from 21,807 advanced cancer patients. Clin Cancer Res. 2018; 24(15):3528–38.

12.

Odegaard

, Vincent

, Mortimer

, et al. Validation of a plasma-based comprehensive cancer genotyping assay utilizing orthogonal tissue- and plasma-based methodologies. Clin Cancer Res. 2018; 24(15):3539–49.

13.

Willis

, Lefterova

, Artyomenko

, et al. Validation of microsatellite instability detection using a comprehensive plasma-based genotyping panel. Clin Cancer Res. 2019; 25(23):7035–45.

14.

Strickler

, Loree

, Ahronian

, et al. Genomic landscape of cell-free DNA in patients with colorectal cancer. Cancer Discov. 2017; 8(2):164–73.

15.

Fedewa

, Siegel

, Jemal

. Are temporal trends in colonoscopy among young adults concordant with colorectal cancer incidence?. J Med Screen. 2019; 26(4):179–85.

16.

Meester

RGS

, Mannalithara

, Lansdorp-Vogelaar

, Ladabaum

. Trends in incidence and stage at diagnosis of colorectal cancer in adults aged 40 through 49 years, 1975–2015. JAMA. 2019; 321(19):1933.

17.

Siegel

, Torre

, Soerjomataram

, et al. Global patterns and trends in colorectal cancer incidence in young adults. Gut. 2019; 68(12):179–85.

18.

Dienstmann

, Salazar

, Tabernero

. Overcoming resistance to anti-EGFR therapy in colorectal cancer. Am Soc Clin Oncol Educ Book. 2015; e149–56.

19.

National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines): Colon Cancer Version 3.2019. Accessed October 16, 2019. from: https://www.nccn.org/professionals/physician_gls/default.aspx#site

20.

Siravegna

, Lazzari

, Crisafulli

, et al. Radiologic and genomic evolution of individual metastases during HER2 blockade in colorectal cancer. Cancer Cell. 2018; 34(1):148–162.e7.

21.

Raghav

, Loree

, Morris

, et al. Validation of HER2 amplification as a predictive biomarker for anti–epidermal growth factor receptor antibody therapy in metastatic colorectal cancer. JCO Precis Oncol. 2019;(3):1–13.

22.

Strickler

, Zemla

, Ou

F-S

, et al. Trastuzumab and tucatinib for the treatment of HER2 amplified metastatic colorectal cancer (mCRC): initial results from the MOUNTAINEER trial. Ann Oncol. 2019; 30(suppl_5):v200: abstract 527PD.

23.

Wasserman

, Lee

, Ogino

, et al. SMAD4 loss in colorectal cancer patients correlates with recurrence, loss of immune infiltrate, and chemoresistance. Clin Cancer Res. 2019; 25(6):1948–56.

24.

Maron

, Chase

, Lomnicki

, et al. Circulating tumor DNA sequencing analysis of gastroesophageal adenocarcinoma. Clin Cancer Res. 2019; 25(23):7098–112.

25.

Jaiswal

, Fontanillas

, Flannick

, et al. Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med. 2014; 371:2488–98.

26.

Gupta

, Othman

, Chen

, et al. Guardant360 circulating tumor DNA assay is concordant with FoundationOne next-generation sequencing in detecting actionable driver mutations in anti-EGFR naive metastatic colorectal cancer. Oncologist. 2020; 25(3):235–43.

Supplementary Material

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