Plasma Cell-Free DNA and Circulating Tumor Cells as Prognostic Biomarkers in Small Cell Lung Cancer Patients

Patricia Mondelo-Macía; Jorge García-González; Alicia Abalo; Manuel Mosquera-Presedo; Santiago Aguín; María Mateos; Rafael López-López; Luis León-Mateos; Laura Muinelo-Romay; Roberto Díaz-Peña

Disclosures

Transl Lung Cancer Res. 2022;11(10):1995-2009.

Abstract and Introduction

Abstract

Background: Lack of biomarkers for treatment selection and monitoring in small cell lung cancer (SCLC) patients with the limited therapeutic options, result in poor outcomes. Therefore, new prognostic biomarkers are needed to improve their management. The prognostic value of cell-free DNA (cfDNA) and circulating tumor cells (CTCs) have been less explored in SCLC.

Methods: We quantified cfDNA in 46 SCLC patients at different times during first-line of chemotherapy or chemo-immunotherapy. Moreover, CTCs were analyzed in 21 patients before therapy onset using CellSearch® system. The possible association between both biomarkers and patients' outcomes was investigated in order to develop a prognostic model.

Results: High cfDNA levels before therapy were associated with shorter progression-free survival (PFS) and overall survival (OS). Furthermore, cfDNA levels at 3 weeks and at progression disease were also associated with patients' outcomes. Multivariate analyses confirmed the independence of cfDNA levels as a prognostic biomarker. Finally, the three-risk category prognostic model developed included Eastern Cooperative Oncology Group Performance Status (ECOG PS), gender and baseline cfDNA levels was associated with a higher risk of progression and death.

Conclusions: We confirmed the prognostic utility of cfDNA quantitative analysis in SCLC patients before and during therapy. Our novel risk prognostic model in clinical practice will allow to identify patients who could benefit with actual therapies.

Introduction

Small cell lung cancer (SCLC), which accounts for 15% of all lung cancer cases, is characterized by its aggressiveness, its strong association with tobacco and the poor outcome. About 70% of patients present extensive disease SCLC (ED-SCLC) where only 2% survive 5 years after diagnosis.^[1–3] For many years, chemotherapy was the unique option to treat this tumor type. However, the scenario has changed in the last years.^[3] New therapies, such as immunotherapy, have been recently incorporated into the management of SCLC patients and, although some survival improvements have been reported in the patients with ED-SCLC,^[4–8] the majority of them do not benefit from this new treatment.^[9] The genomic profile of SCLC is characterized by extensive chromosomal rearrangements and a high mutational burden, including in nearly all, inactivation of the tumor suppressor genes TP53 and RB1.^[10] However, nowadays the selection of treatment in SCLC patients is not dependent on the characteristics of the tumor,^[11] and the criteria to stratify patients is not clear, since no predictive biomarkers have been validated for the clinical practice.^[12] In this context, the use of liquid biopsies as a tool to guide treatment and/or for monitoring the patients' response represent a valuable alternative.^[13,14]

Circulating tumor DNA (ctDNA), derived from tissue tumor cells, has demonstrated its clinical utility and represents a promising tool for guiding precision medicine in several cancer types.^[15,16] In SCLC, different studies have investigated the importance and the clinical value of analyzing ctDNA levels. However, driver mutations known in SCLC are limited to RB1 and TP53 genes.^[17] In contrast, total cell-free DNA (cfDNA) consists of a heterogeneous and complex DNA fraction released in body fluids by any cell type through cell death mechanisms.^[18,19] The short half-life of cfDNA enables real-time monitoring for response or relapse, being an easy-to-implement biomarker to monitor cancer evolution and response to therapy.^[20] In fact, in lung cancer and other solid tumors, cfDNA analysis has been explored as a prognostic marker and surrogate for monitoring treatment response.^[21,22] High cfDNA level or positive ctDNA detection is clearly correlated tumor burden. Besides pre-treatment levels of ctDNA have value to predict long-term survival in locally advanced non-small cell lung cancer (NSCLC).^[23] Early cfDNA/ctDNA changes can be detected at first follow-up to predict radiographic response being their decreasing levels also been associated with improved survival rates as our group previously described in NSCLC.^[24–26]

Circulating tumor cells (CTCs) are tumor cells originated from the primary or metastatic sites that are able to enter the circulation and disseminate to distant sites, and constitutes another frequent circulating biomarker investigated in cancer.^[14] As a high metastatic tumor type, SCLC is characterized by a strong release of CTCs, with detection rates of 60.2–94%,^[17] suggesting that CTCs could be employed as a disease surrogate in SCLC. The analysis of CTCs originated from the primary or metastatic sites^[27] as a prognostic biomarker has been reported in different cancer types^[28–30] including SCLC. However, the prognostic threshold in SCLC has been not well established.^[31–35] The low proportion of CTCs in the bloodstream together with the molecular heterogeneity that characterizes these cells is the principal challenge for CTC isolation and detection. For this reason, several platforms have been developed in the last years.^[14,36] Despite their different nature, the combined analysis of total cfDNA and CTCs in patients with SCLC could provide complementary information for improving SCLC patients' management.

In this study, we hypothesized that total cfDNA levels can serve as a useful biomarker for prognostic and follow-up of SCLC patients under first line of therapy. For this purpose, we analyzed the total cfDNA levels in a cohort of 46 patients with SCLC prior to the start of therapy, at 3 weeks after the first dose, and at the time of progression of the disease. The additional value of CTCs was investigated in our cohort in order to provide a more complete view of the disease dynamics. Finally, we developed a simple model to segregate patients into three categories based on risk of progression and death (taking into account the cfDNA levels, Eastern Cooperative Oncology Group Performance Status (ECOG PS) and gender of patients). To our knowledge, this study is the first one to examine the possible role of total cfDNA levels as a prognostic and follow-up biomarker in SCLC patients. We present this article in accordance with the REMARK reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-22-273/rc).

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Abstract and Introduction
Methods
Results
Discussion
References

Table 1. Association between patients' demographics/clinical characteristics and baseline cfDNA levels
Characteristics	N (%)	Baseline cfDNA, median (GE/mL)	P value
Age (years), median [range]; mean ± SD	67 [47–83]; 66.83±8.1
Below median	24 (52.17)	6,488.23	0.79
Above median	22 (47.82)	8,403.79
Gender
Male	39 (84.78)	6,130.59	0.61
Female	7 (15.22)	15,699.93
Stage
III	5 (10.87)	1,409.11	0.031
IV	41 (89.13)	7,658.99
ECOG PS
<2	31 (67.39)	4,652.13	0.025
≥2	15 (32.61)	22,459.61
Smoking
Smoker	26 (56.52)	6,488.23	0.94
Former smoker	20 (43.48)	6,155.56
Number of metastasis
≤2	22 (47.82)	4,563.83	0.05
>2	24 (52.17)	12,973.33
Liver metastases
No	23 (50.0)	3,355.91	0.0002
Yes	23 (50.0)	24,759.83
Bone metastases
No	18 (39.13)	3,265.055	0.17
Yes	28 (60.87)	3,355.91
Lymph node metastases
No	10 (21.74)	4,563.83	0.12
Yes	36 (78.26)	7,658.99
Treatment
Chemotherapy	33 (71.74)	6,130.59	0.36
Chemotherapy plus immunotherapy	13 (28.26)	10,676.99

cfDNA, cell-free DNA; GE, genomic equivalents; SD, standard deviation; ECOG PS, Eastern Cooperative Oncology Group Performance Status.

Table 2. Univariate and multivariate Cox regression analyses of cfDNA levels, CTC counts and clinical parameters
Variable	Univariate		Multivariate
Variable	P value	HR (95% CI)	P value	HR (95% CI)
PFS
Baseline log cfDNA (high vs. low cfDNA)	0.001	5.06 (1.89–13.6)	0.005	46.0 (3.16–672)
Baseline CTC count, CellSearch (≥150 vs. <150)	0.028	3.47 (1.14–10.6)
ECOG PS (≥2 vs. <2)	<0.001	3.57 (1.72–7.40)	0.04	17.9 (1.11–289)
Sex (male vs. female)	0.2	1.76 (0.74–4.22)
Age (continue)	0.5	1.01 (0.97–1.05)
Stage (IV vs. III)	0.07	3.00 (0.91–9.91)
Number of metastasis (>2 vs. ≤2)	0.1	1.70 (0.90–3.19)
Liver metastasis (yes vs. no)	0.08	1.74 (0.93–3.23)
Smoking (smoker vs. former smoker)	0.8	0.92 (0.49–1.70)
3 weeks log cfDNA (high vs. low cfDNA)*	<0.0001	3.50 (1.69–7.23)	0.004	3.49 (1.50–8.12)
OS
Baseline log cfDNA (high vs. low cfDNA)	0.003	3.32 (1.50–7.37)	0.004	32.4 (3.05–344)
Baseline CTC count, CellSearch (≥150 vs. <150)	0.07	2.71 (0.93–7.88)
ECOG PS (≥2 vs. <2)	<0.001	4.54 (2.13–9.68)
Sex (male vs. female)	0.2	1.80 (0.75–4.36)
Age (continue)	0.5	1.01 (0.97–1.06)
Stage (IV vs. III)	0.08	2.86 (0.86–9.47)
Number of metastasis (>2 vs. ≤2)	0.5	1.23 (0.65–2.33)
Liver metastasis (yes vs. no)	0.3	1.45 (0.77–2.76)
Smoking (smoker vs. former smoker)	0.5	0.82 (0.43–1.55)
3 weeks log cfDNA (high vs. low cfDNA)*	<0.001	3.67 (1.72–7.82)	0.002	4.35 (1.68–11.3)
PD log cfDNA (high vs. low cfDNA)*	<0.001	5.73 (1.93–17.0)	0.006	9.24 (1.87–45.6)

*, multivariate Cox regression model including sex, age, ECOG PS, stage, number of metastases, presence of liver metastasis and smoking status. The levels of cfDNA were determined as low (< cut-off) or high (≥ cut-off) based on the cut-off obtained from the ROC curve analyses. cfDNA, cell free DNA; CI, confidence interval; CTC, circulating tumor cell; ECOG PS, Eastern Cooperative Oncology Group Performance Score; PD, progression disease; HR, hazard ratio; OS, overall survival; PFS, progression-free survival; ROC, receiver operating characteristics.

Table S1. Statistics of cfDNA levels at baseline, at 3 weeks after the onset of therapy and at disease progression
	Sample size	Mean (GE/mL)	Median (GE/mL)	Standard deviation	Range (GE/mL)
Baseline, cfDNA levels	46	81174.03	6488.23	342126.8	660.7–2320370.4
Three weeks post-treatment, cfDNA levels	40	9034.88	2912.55	18500.36	709–98040
Progression disease, cfDNA levels	25	11595.13	3747.6	18398.2	705.1–80440.3

Table S2. Receiver operating characteristic curves analysis to determine the value of cfDNA levels to discriminate progression or death
Parameters	AUROC	Threshold	Sensitivity	Specificity
PFS
Log cfDNA at baseline	0.834	7.650	0.854	0.800
Log cfDNA at 3 weeks	0.754	7.879	0.629	1.000
OS
Log cfDNA at baseline	0.783	8.077	0.789	0.750
Log cfDNA at 3 weeks	0.707	7.879	0.625	0.750
Log cfDNA at progression disease	0.606	8.495	0.545	1.000

Table S3. Analysis to determine the prognostic value of CTCs to discriminate progression or death
CellSearch® CTC count	n (%)	PFS			OS
CellSearch® CTC count	n (%)	Median (days)	HR (95%CI)	P value	Median (days)	HR (95%CI)	P value
0	3 (14.29)	–	–	–	–	–	–
≥1	18 (85.71)	179	7.18 (0.92–56.3)	0.07	236	4.98 (0.65–38.1)	0.09
≥2	17 (80.95)	179	3.93 (0.88–17.6)	0.08	229	3.2 (0.72–14.2)	0.08
≥5	13 (61.91)	185	1.64 (0.61–4.41)	0.4	244	1.62 (0.58–4.52)	0.35
≥10	12 (57.14)	179	1.72 (0.66–4.5)	0.3	236	1.62 (0.6–4.41)	0.2
≥50	9 (42.86)	163	1.81 (0.7–4.67)	0.11	229	1.71 (0.63–4.68)	0.3
≥100	8 (38.1)	171	1.63 (0.62–4.26)	0.2	254	1.52 (0.55–4.25)	0.5
≥150	6 (28.57)	158	2.52 (0.81–7.86)	0.03	217	2.32 (0.76–7.15)	0.07
≥500	5 (23.81)	163	2.8 (0.81–9.7)	0.10	229	2.4 (0.73–7.9)	0.2
≥1000	3 (14.29)	154	4.6 (0.92–23)	0.04	205	2.07 (0.46–9.43)	0.2

CellSearch® CTC levels: CTC, median (range), 26 (0–4,796); mean ± SE, 579.2±284.5. The results obtaneid with the different CTCs cut-offs were calculated using the Evaluate Cutpoints tool (available in https://wnbikp.umed.lodz.pl/Evaluate-Cutpoints/). PFS, progression-free survival; OS, overall survival; HR, hazard ratio; CI, confidence interval.

Table S4. Progression-free survival and overall survival probabilities estimated according two risk groups
Risk groups	PFS			OS
Risk groups	HR	95% CI	P value	HR	95% CI	P value
Favorable	1	(Ref.)	–	1	(Ref.)	–
Intermediate-poor	5.37	2.32–12.4	< 0.001	6.02	2.66–13.6	< 0.001

PFS, progression-free survival; OS, overall survival; HR, hazard ratio; CI, confidence interval.

Authors and Disclosures

Patricia Mondelo-Macía^1,2, Jorge García-González^3–5, Alicia Abalo¹, Manuel Mosquera-Presedo², Santiago Aguín^3,4, María Mateos^3,4, Rafael López-López^3–5, Luis León-Mateos^2–5*, Laura Muinelo-Romay^1,5* and Roberto Díaz-Peña^1,6*

¹Liquid Biopsy Analysis Unit, Translational Medical Oncology (Oncomet), Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain; ²University of Santiago de Compostela (USC), Santiago de Compostela, Spain; ³Department of Medical Oncology, Complexo Hospitalario Universitario de Santiago de Compostela (SERGAS), Santiago de Compostela, Spain; ⁴Translational Medical Oncology (Oncomet), Health Research Institute of Santiago (IDIS), Santiago de Compostela, Spain; ⁵Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain; ⁶Laboratory of Cellular and Molecular Pathology, Institute of Biomedical Sciences, Faculty of Health Sciences, Universidad Autónoma de Chile, Talca, Chile

Correspondence to
Luis León-Mateos, MD; Laura Muinelo-Romay, PhD; Roberto Díaz-Peña, PhD. Complexo Hospitalario Universitario de Santiago de Compostela, Travesía da Choupana s/n, 15706, Santiago de Compostela, Spain. Email: luis.Angel.Leon.Mateos@sergas.es; lmuirom@gmail.com; roberto.diaz.pena@sergas.es.

Contributions
(I) Conception and design: P Mondelo-Macía, L León-Mateos, L Muinelo-Romay, R López-López, R Díaz-Peña; (II) Administrative support: R López-López, A Abalo; (III) Provision of study materials or patients: M Mosquera-Presedo, J García-González, S Aguín, M Mateos, L León-Mateos; (IV) Collection and assembly of data: J García-González, A Abalo, L León-Mateos; (V) Data analysis and interpretation: P Mondelo-Macía, R Díaz-Peña; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.
*These authors contributed equally to this work.

Conflicts of Interest
All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-22-273/coif). JGG reports consultant or advisory role fees from AstraZeneca, Boehringer-Ingelheim, Bristol-Myers Squibb, MSD, Novartis, Roche, Sanofi, and Takeda; honoraria for presentations and speaker bureaus from AstraZeneca, Bristol-Myers Squibb, Pierre-Fabre, Roche, Rovi, and Takeda; non-financial support from AstraZeneca, Bristol-Myers Squibb, Ippsen Pharma, Lilly, MSD, Roche, and Sanofi outside the submitted work. SA reports grants or contracts from Merk, BMS and MSD; consulting fees from Merk, BMS and MSD; payment or honoraria for lectures from BMS and MSD; support for attending meeting from MSD and participation on a data safety monitoring board from Merk and BMS outside the submitted work. LLM receives honoraria for lectures from Pfizer, Boehringer, Novartis, AstraZeneca, Sanofi, Bristol, MSD, Takeda; for advisory board from Sanofi, Lilly, Novartis, Boehringer, Amgen and receives support for attending meetings from MSD and AstraZeneca, outside the submitted work. RLL reports grants from Astellas, Janssen, Sanofi, Bayer, Ipsen, Roche, Novartis, Pfizer; consulting fees from Sanofi, Janssen, Astellas, Pfizer, Bayer, Roche, Ipsen, Novartis, Eisai, EUSA Pharma, BMS; payment or honoraria for lectures from Astellas, Janssen, Sanofi, Bayer, Ipsen, Pfizer, Roche, Novartis; travel, accommodations and expenses from Pharmamar, Roche, BMS and Pfizer; honoraria for participation in advisory boards from Roche, AstraZeneca, Merck, MSD, Bayer, BMS, Novartis, Janssen, Lilly, Pfizer and Leo, outside the submitted work. The other authors have no conflicts of interest to declare.