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Molecular Oncology, Markers, Clinical Correlates |
1 Institute of Clinical Chemistry, University of Munich, Munich, Germany; and 2 Department of Oncology and 3 Institute of Clinical Chemistry, Asklepios Hospital Gauting, Munich, Germany
ABSTRACT
Purpose: We investigated the potential of circulating, nucleosomal DNA for the early prediction of the efficacy of chemotherapy in patients with advanced lung cancer.
Experimental Design: In serum of 212 patients with newly diagnosed non–small cell lung cancer (stages III and IV) undergoing chemotherapy, nucleosomes (ELISA, Roche) were measured at days 1, 3, 5, and 8 of the first cycle and before each new therapeutic cycle. Additionally, carcinoembryonic antigen and cytokeratin 19 fragments (CYFRA 21–1; Elecsys, Roche) were determined before each cycle. The therapeutic success was classified by computed tomography before start of the third cycle according to the World Health Organization criteria.
Results: In univariate analysis, responders (patients with remission) showed significantly (P < 0.05) lower values for the area under the curve of days 1 to 8 (AUC 1–8) of nucleosomes, the pretherapeutic baseline values of cycle 2 (BV2) and cycle 3 (BV3) of nucleosomes, and higher decreases of the baseline values from cycle 1 to 2 (BV1–2) and from cycle 1 to 3 (BV1–3) compared with nonresponders (patients with stable or progressive disease). Additionally, CYFRA 21–1 (BV1, BV2, BV3, BV1–2, BV1–3) and carcinoembryonic antigen (BV1–2) discriminated significantly between both groups.
In multivariate analysis including all parameters available until end of the first therapeutic cycle, nucleosomes (AUC 1–8), CYFRA 21–1 (BV1), stage, and age were independent predictors of therapy response with nucleosomes (AUC 1–8) having the strongest impact.
Conclusion: Circulating nucleosomes in combination with oncological biomarkers are valuable for the early estimation of the efficacy of chemotherapy in patients with lung cancer.
INTRODUCTION
Lung cancer is worldwide one of the most frequent tumors in men and has an increasing prevalence in women (1 , 2) . Often it is detected accidentally and in advanced stages. In these situations, the therapeutic options are limited to chemo- and radiotherapy, which are associated with insufficient or only temporary success in many cases (3) . However, there are many efforts to improve the therapeutic modalities by the introduction of new antibiotic drugs and combinations of chemo- and radiotherapeutic strategies.
The efficacy of the therapy applied is mostly controlled by imaging techniques and can be estimated after various cycles of chemotherapy or at the end of the therapy by macroscopic alterations of the tumor volume or diameter (4) . However, it would be useful to know earlier whether a tumor responds to the treatment, to adapt the procedure individually and to change the regimen in time. One potential approach is an intense monitoring of the initial treatment phase by biochemical parameters that correlate with the number of the killed tumor cells or with the aggressiveness of the tumor.
Typical products of cell death are nucleosomes, complexes that are formed from a core particle of several histone components and DNA on the outside (5 , 6) . Linker DNA connects the nucleosomes to a chain-like structure. During cell death, endonucleases bind preferentially to these easy accessible linking sites between the nucleosomes and cut the chromatin into multiple mono- and oligonucleosomes (7 , 8) . In cases of enhanced cell death, as during chemotherapy, they are also released into the circulation and can be detected in elevated amounts in serum or plasma (9 , 10) .
Previous investigations into the spontaneous concentrations of nucleosomes in serum showed low levels in healthy individuals. In contrast, high amounts were found in patients with various malignant tumors but also in patients with benign pathologic conditions like severe inflammations. Among various tumor types, lung cancer was associated with the highest values of circulating nucleosomes (11 , 12) .
Focusing on the kinetics of nucleosomes in the serum of patients with malignant tumors during chemotherapy, a typical course could be observed. In the first days of the therapy, the levels of the nucleosomes increased rapidly, followed by a decrease in the treatment-free period and similar peaks in the following cycles. The baseline values, which were determined before each new cycle, increased typically in many patients with macroscopic progression and decreased in patients with remission of disease (12 , 13) . This approach is comparable with the use of other biochemical markers like tumor-associated antigens (14) .
Because of the short half-time of nucleosomes in circulation (15) and their correlation with the extent of cell death (16) , the course of nucleosomes during the first cycles of chemotherapy could already contain valuable information about the response of the tumor to the treatment applied. Therefore we concentrated in a further step on the first two cycles of chemotherapy, which was applied on patients with advanced lung cancer, to enlighten the potential use of nucleosomes for the early prediction of the therapeutic efficacy and to compare its power with well established biochemical parameters.
PATIENTS AND METHODS
Study Population.
Two hundred and twelve patients with inoperable non–small-cell lung cancer (NSCLC; stages III and IV) under the care of the Department of Oncology of the Asklepios Clinics Gauting were included in our study. All patients were investigated initially by whole body computed tomography, bone scan, and bronchoscopy. Histology was available in all cases and confirmed 60 patients having squamous cell carcinoma, 84 patients having adenocarcinoma, and 68 patients having NSCLC without any further specification.
All patients received first-line chemotherapy regimens containing alternatively carboplatin area under the curve (AUC) 5 (day 1), 8 mg/m2 mitomycin c (day 1) and 6 mg/m2 vinblastin (day 1) or 8 mg/m2 mitomycin c (day 1) and 25 mg/m2 vinorelbin (days 1 and 8) or 1,250 mg/m2 gemcitabine (days 1 and 8) and 80 mg/m2 cisplatin (day 1), each followed by the next cycle at day 22.
Generally, the study was done prospectively. All patients with newly diagnosed lung cancer who received first-line chemotherapy were included without any selection criteria.
Classification of Response to Therapy.
Before start of the third cycle of chemotherapy, staging investigations were done consisting of clinical examination, whole body computed tomography, and laboratory examinations. The response to therapy was classified according to the World Health Organization classifications defining "remission" as reduction of the tumor volume 
50%, "progression" as increase of the tumor volume 25% or appearance of new tumor manifestations, and "no change" as reduction of the tumor volume <50% or increase <25%. In our study population, 83 patients reached remission (39.2%), 69 suffered from progression (32.5%), and 60 had no change of disease (28.3%)
Because the approach of the first-line therapies was a clear reduction of the tumor volume, the patients with progression and no change were joined to the group of nonresponders to therapy (129 patients, 60.8%). Patients with remission were classified as responders to therapy (Table 1)
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The samples were centrifuged at 3000 x g for 15 minutes and treated with 10 mmol/L EDTA (pH 8) immediately after centrifugation. Subsequently, they were stored at –70°C and analyzed in batches containing all samples of a single patient. The details of the preanalytic handling are described in Holdenrieder et al. (17) .
Nucleosomes were determined by the Cell Death Detection-ELISAplus of Roche Diagnostics (Mannheim, Germany). This assay was modified for its use in serum matrix as specified in Holdenrieder et al. (17) . Nucleosomes were quantified in arbitrary units (AU).
Additionally, the baseline values of tumor-associated antigens were determined before each therapeutic cycle (BV1, BV2, and BV3) by routine methods at the day of sample collection without any storage procedures: carcinoembryonic antigen (CEA) and cytokeratin 19 fragments (CYFRA 21–1; Elecsys 2010, Roche Diagnostics).
Statistical Analysis.
The following values of nucleosomes were considered for the statistical analysis:
Although the baseline values expressed the spontaneous concentration of nucleosomes in serum, AUC 1–8 reflected the changes that were induced by therapy. Additionally, the baseline values (BV1, BV2, and BV3) and their kinetics (BV1–2 and BV1–3) of the tumor-associated antigens CEA and CYFRA 21–1 were included in the statistical analysis.
To evaluate all variables in a similar way, cutoffs were determined according to a defined procedure and tested for their power to discriminate between responders and nonresponders, as follows:
Univariate analysis was done by 
2 test. All parameters that showed substantial discriminating power in univariate analysis were included in a multivariate logistical regression analysis to evaluate the independent predictive factors for the response to therapy. In this model, interaction between mode of therapy, histology, and markers were also tested. A P value of P < 0.05 was considered statistically significant. All statistical analyses were done with SAS software (version 8.1, SAS Institute Inc. Cary, NC).
RESULTS
When the median of all patients investigated was used as cutoff for the discrimination between responders and nonresponders, the pretherapeutic levels of circulating nucleosomes (baseline value 1 = BV1) could not distinguish significantly between both groups (median = 388 AU; P = 0.169). However, the baseline values of nucleosomes before start of the second cycle (BV2; 216 AU; P = 0.0001) as well as before start of the third cycle of therapy (BV3; 184 AU; P < 0.0001) were significantly lower for patients with response than for those with no response to therapy. Consistently, patients with response showed significantly higher decreases from baseline value 1 to 2 (P = 0.046) and from baseline value 1 to 3 (P = 0.036). Interestingly the course during the first week of therapy already revealed significant differences between both groups: Patients with response to therapy exhibited only slight increases (pretherapeutic value, 344 AU; maximum value, 436 AU) followed by notable decreases until day 8 (140 AU). In patients with no response, the nucleosome values rose higher (pretherapeutic value, 442 AU; maximum value, 632 AU) and dropped less efficiently until day 8 (290 AU). When the area under the curve of all values available during the first week (AUC 1–8) was calculated, it was significantly lower for responders than for nonresponders (P = 0.005; Fig. 1
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With regard to CYFRA 21–1, responders had markedly lower values for all time points investigated compared with nonresponders. Already the pretherapeutic BV1 showed a strong tendency to discriminate between both groups (P = 0.059), and the difference was highly significant for the baseline value before start of the second cycle (BV2; P < 0.0001) and the baseline value before start of the third cycle (BV3; P < 0.0001). Furthermore, we observed in patients with response, a significantly stronger decrease of the baseline values from cycle 1 to 2 (P = 0.0006) and from cycle 1 to 3 (P < 0.0001) compared with those without response to therapy (Fig. 2)
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In multivariate analysis, all univariately significant parameters were included that were available until start of the second cycle of therapy. Among them, staging, mode of therapy, baseline value 2 of nucleosomes, and baseline value 2 of CYFRA 21–1 proved to be independent predictive factors for therapy outcome. When the time frame was restricted to the first cycle, staging, and age, AUC 1–8 of nucleosomes and baseline value 1 of CYFRA 21–1 remained as independent predictors of the therapy outcome with AUC 1–8 of nucleosomes having the strongest impact. In these models, no interactions among mode of therapy, markers, and therapy response nor among histology, markers, and therapy response were found. Table 3
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Many studies dealing with circulating nucleic acids in serum and plasma revealed their high potential for diagnosis of cancer disease (18, 19, 20) . Techniques like the PCR and mass spectroscopy contribute to a high diagnostic sensitivity (21) . Characteristic mutations, alterations of microsatellites, and epigenetic changes provide a high specificity for the detection of cancerous DNA (22, 23, 24, 25, 26) . Already at time of diagnosis, these parameters also contain valuable prognostic information for many cancers (22 , 23) . Besides the qualitative aspects of circulating DNA, their quantity was also shown to be elevated in the serum and plasma of cancer patients (11 , 12 , 27, 28, 29, 30, 31) . However, because of the overlap with some benign diseases that are critical for the differential diagnosis of cancer, the diagnostic value of DNA concentration is limited (12) . Nevertheless several studies have shown its prognostic value for various cancer types (32, 33, 34) . Because the determination of these qualitative and quantitative parameters can be done in serum and plasma that is easily obtainable, these methods could also be used in serial measurements; in patients with EBV-associated cancers, the response to radiotherapy correlated well with the kinetics of EBV-DNA quantity in plasma (35 , 36) .
Most circulating DNA is likely to be bound to histones that conserve DNA from endonucleatic digestion in serum and plasma and to exist in form of mono- and oligonucleosomes (37, 38, 39) . These nucleosomes can be detected directly by an ELISA that is easy to handle and cost effective (17) . First investigations with this method confirmed the elevated nucleosome values in cancer patients compared with healthy persons and the correlation of the nucleosome kinetics with the therapeutic efficacy during radio- and chemotherapy (12 , 13) . As an important mechanism by which nucleosomes are released into circulation is apoptotic cell death (9 , 11 , 16) , which is induced by these therapies most effectively at the initial phase, and as the half-life of nucleosomes in circulation is very short (15) , we focused particularly on the first cycles of chemotherapy and investigated whether nucleosomes might be an early prediction of the response to therapy. If so, this would open the appealing possibility to modify and tune the therapeutic regimen at an early time point or even to change it completely.
In univariate analysis, patients with response to therapy tended to have lower pretherapeutic nucleosome levels than nonresponders. Because these values were not influenced by treatment, they would have rather prognostic than predictive impact. More related to therapy were the precyclic values before cycle 2 and 3 and the kinetics from cycle 1 to 2 as well as from cycle 1 to 3. Because response to therapy was clearly correlated to low absolute values and strong decreases, the balance of continuous release and elimination of nucleosomes in circulation was shown to be modulated effectively by successful treatment. In contrast, patients with no response to therapy continued to have higher rates of release and/or lower rates of elimination of circulating nucleosomes.
The direct therapeutic effect would be reflected most evidently by the values during the first week of cycle 1 of chemotherapy. Because antitumor therapy generally aims at the effective reduction of tumor cells, response to therapy was expected to be linked to an initial high release of nucleosomes. However, patients responsive to therapy showed lower maximum values during the first week as well as lower levels at day 8 compared with nonresponders. When all values during the first week were integrated in the AUC 1–8, responders exhibited highly significant lower values than nonresponders. This observation was surprising but could be attributed to various reasons. Patients with no response to therapy seem to have more aggressive tumor disease related to (a) a high spontaneous turnover of cell death and proliferation, (b) a high number of undifferentiated cells prone to be killed by antitumor therapy, and (c) dysfunctional DNA-repair mechanisms unable to save cells after substantial damages by therapy. Because patients with metastasized disease showed a poorer response, (d) the access to blood circulation of nucleosomes released by dying cells might also contribute to the more pronounced peak in nonresponders. Finally, (e) the elimination of nucleosomes from circulation by renal, hepatic, or intraplasmatic processes seems to be impaired in patients with insufficient response, which is also illustrated by the significantly higher value 1 week after start of the therapy.
There are several studies that confirm the rapid increase of circulating nucleosomes, DNA or EBV-DNA followed by a decrease in the first week during chemo- and radiotherapy and also after surgery. Lo et al. (36) could show that EBV-DNA values in patients with nasopharyngeal cancer rose immediately after start of radiotherapy (median time to the peak 3.0 days) and declined afterward with a median half-life of 3.8 days. Kuroi et al. (40) found a specific peak of circulating nucleosomes 48 to 72 hours after start of chemotherapy in patients with breast cancer. Trejo-Becerril et al. (41) confirmed these results in patients with cervical cancer undergoing chemotherapy. After surgery of patients with nasopharyngeal cancer, the initial peak of EBV-DNA followed by a rapid decrease could also be observed. The median half-life of the decay was calculated as 139 minutes (42) . Failure of complete and rapid elimination of EBV-DNA was correlated to disease recurrence later on.
We have shown recently the typical kinetic patterns of circulating nucleosomes in serum during chemo- and radiotherapy in patients with various tumor types (12 , 13) . There we showed that response to chemotherapy was correlated with decreasing precyclic baseline values whereas progression was linked with increasing or constantly high values. However, this study is to our knowledge the first one revealing the potential of the course of circulating nucleosomes during the very initial phase of chemotherapy for the prediction of therapy response.
Concerning oncological biomarkers in NSCLC, CEA and CYFRA 21–1 are used often for differential diagnosis of NSCLC (43 , 44) . Here we could show that CEA and particularly CYFRA 21–1 have great potential for monitoring therapy and predicting of therapy response. This was confirmed recently for the kinetics of CYFRA 21–1 in a smaller study including 58 patients with NSCLC (45) . Because CYFRA 21–1 is located at least partly inside epithelial tumor cells and is released during or after cell death (46) , the correlation of therapy response and decreasing CYFRA 21–1 levels could be explained by an effective reduction of the tumor cell number.
An essential point for the outcome of a study is the definition of appropriate cutoffs for the parameters investigated. Because the aim of the study was to evaluate all parameters in a comparable way, the median of all the patients was chosen as rationale for defining the cutoffs. In case of significant discrimination between responders and nonresponders by the median, the quartiles (25th and 75th quantiles) of the sample were additionally tested for whether they could enhance the grade of significance. Choosing this procedure, we took into account that some markers have a better power of discrimination at low or high levels. Although the results for each variable could have been better by optimization of the cutoff, a rationale for the definition of the cutoffs avoided the overfitting of the results to this special setting of patients and enabled the transfer to other samples.
The early prediction of therapy response by clinical and biochemical parameters would offer the possibility of having this important information before staging investigations and of modulating the treatment already at the initial phase. Among the parameters that were available until start of the second cycle of chemotherapy, stage and mode of therapy proved to be independent predictive factors in multivariate analysis as well as the baseline value 2 of nucleosomes and CYFRA 21–1. Concerning the time frame restricted to the first cycle, nucleosomes (AUC 1–8) and CYFRA 21–1 (baseline value 1) remained independent predictive parameters in addition to the clinical parameters stage and age. In the latter setting, the AUC 1–8 of nucleosomes was the only variable that was directly influenced by the therapy and that had the strongest predictive impact. These results indicate the power of circulating nucleosomes (if determined in a close meshed manner during the initial phase of chemotherapy) and CYFRA 21–1 in addition to well known clinical prognostic factors for the early prediction of the therapy response. After confirmation by multicenter trials, these results encourage the introduction of nucleosomes and CYFRA 21–1 in new therapeutic study protocols.
ACKNOWLEDGMENTS
We appreciate the valuable assistance of E. Dankelmann, B. Faderl, B. Kordes, M. A. Lercher, A. Markus, B. Poell, M. Siakavara, H. Wagner and the laboratory assistants of the Hospital Gauting in the logistic process of the study; A. Mitlewski from the University Hospital Grosshadern for the measurement of nucleosomes; and P. Bialk, H. Bodenmueller, B. Eckert, and P. Heiss from Roche Diagnostics, Germany, for providing the nucleosome assays.
FOOTNOTES
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Requests for reprints: Stefan Holdenrieder, Klinikum der Universität München, Institute für Klinische Chemie, Marchioninistrasse 15, D-81377 Munich, Germany. Phone: 0049-89-7095-3231, Fax: 0049-89-7095-6298; Email: Stefan.Holdenrieder{at}med.uni-muenchen.de
Received 4/ 2/04; revised 6/10/04; accepted 6/21/04.
REFERENCES
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and nonresponders to chemotherapy 
by nucleosomes. Bars indicate 25th percentile, median, and 75th percentile of the baseline values before therapy cycles 1 (BV1), 2 (BV2), 3 (BV3), and AUC 1–8 of the first treatment cycle.