comparison of circadian characteristics for cytotoxic lymphocyte subsets in non-small cell lung...

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Clinical and Experimental Medicine ISSN 1591-8890Volume 12Number 3 Clin Exp Med (2012) 12:181-194DOI 10.1007/s10238-011-0153-6

Comparison of circadian characteristics forcytotoxic lymphocyte subsets in non-smallcell lung cancer patients versus controls

Gianluigi Mazzoccoli, Robert B. Sothern,Paola Parrella, Lucia A. Muscarella, VitoMichele Fazio, Francesco Giuliani, et al.

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ORIGINAL ARTICLE

Comparison of circadian characteristics for cytotoxic lymphocytesubsets in non-small cell lung cancer patients versus controls

Gianluigi Mazzoccoli • Robert B. Sothern • Paola Parrella • Lucia A. Muscarella •

Vito Michele Fazio • Francesco Giuliani • Victoria Polyakova • Igor M. Kvetnoy

Received: 15 March 2011 / Accepted: 11 July 2011 / Published online: 11 September 2011

� Springer-Verlag 2011

Abstract Lymphocyte subsets are major cellular com-

ponents of the adaptive immune response and in most cases

show 24-h (circadian) variations in health. In order to

determine overall levels and circadian characteristics of

cytotoxic natural killer (NK) and T and B lymphocyte

subsets, blood samples were collected every 4 h for 24 h

from eleven male controls (C) without neoplastic disease

and nine men with untreated non-small cell lung cancer

(NSCLC) and analyzed for 3 hormones (melatonin,

cortisol, and interleukin 2 [IL2]) and for 11 lymphocyte

subpopulations classified by cell surface clusters of dif-

ferentiation (CD) and antigen receptors. Circadian rhyth-

micity for each variable was evaluated by ANOVA and

24 h cosine fitting and groups compared. Rhythms in

melatonin and cortisol (peaks near 01:30 and 08:00 h)

indicated identical synchronization to the light–dark sche-

dule and probable persistent entrainment of rhythms for

both groups in metabolism or proliferation of healthy tis-

sues normally tightly coupled to the sleep–wake cycle.

Twenty-four hours means were significantly higher in

NSCLC for CD16, CD25, cortisol, and IL2 and lower for

CD8, CD8bright, and cdTCR. A significant circadian

rhythm was found in C with daytime peaks for CD8,

CD8dim, CD16, Vd2TCR, and cortisol and nighttime

peaks for CD3, CD4, CD20, and melatonin, and in

NSCLC, with daytime peaks for CD16, cdTCR, Vd2TCR

and cortisol, and nighttime peaks for CD4, CD25, and

melatonin. Thus, NSCLC was associated with significant

increases or decreases in proportions for several lympho-

cyte subsets that may reflect disease development, but peak

times were nevertheless similar between C and NSCLC for

each variable, suggesting that timed circadian administra-

tion (chronotherapy) of immunotherapy and other cancer

treatments may improve efficacy due to persistent circadian

entrainment of healthy tissues.

Keywords Lymphocyte subpopulations � NSCLC �Circadian rhythmicity � Cortisol � Melatonin � IL2

Introduction

The host immune defense influences the appearance and

the development of malignant neoplastic diseases acting

G. Mazzoccoli (&)

Department of Internal Medicine and Chronobiology Unit,

Scientific Institute and Regional General Hospital

‘‘Casa Sollievo della Sofferenza’’,

San Giovanni Rotondo, Foggia, Italy

e-mail: [email protected]

R. B. Sothern

The Rhythmometry Laboratory, College of Biological Sciences,

University of Minnesota, St. Paul, MN, USA

P. Parrella � L. A. Muscarella � V. M. Fazio

Research Laboratory of Oncology, Scientific Institute and

Regional General Hospital ‘‘Casa Sollievo della Sofferenza’’,


F. Giuliani

Computing Unit, Scientific Institute and Regional General

Hospital ‘‘Casa Sollievo della Sofferenza’’,


V. Polyakova � I. M. Kvetnoy

Department of Cell Biology and Pathology,

Institute of Bioregulation and Gerontology and Russian

Academy for Medical Sciences, St. Petersburg, Russia

V. Polyakova � I. M. Kvetnoy

Department of Pathology, Ott Institute of Obstetrics and

Gynecology, Russian Academy for Medical Sciences,

St. Petersburg, Russia

123

Clin Exp Med (2012) 12:181–194

DOI 10.1007/s10238-011-0153-6

Author's personal copy

upon the promotion or the control of malignant cells. In this

regard, immune-competent cells such as lymphocytes and

macrophages play a key role in the immuno-surveillance, are

an essential component of the biological host reaction, and are

the basic elements in the immunotherapy of cancer [1, 2]. The

activation and development of an adaptive immune response

is initiated by the engagement of a T-cell antigen receptor

(TCR) by an antigenic peptide major histocompatibility

complex (MHC), and the outcome of this engagement is

determined by both positive and negative signals determining

co-stimulation and co-inhibition [3–5].

Tumor-infiltrating lymphocytes (TIL), which are white

blood cells that have left the bloodstream and migrated into

a tumor, are populations of antigen-specific MHC-restric-

ted T cells, usually CD8? cytotoxic T cells, whose

response may be stimulated by a T helper 1 cytokine

milieu. This includes interferon gamma (IFN-c), interleu-

kin (IL) 2, IL12, and cdTCR? cells that are homoge-

neously composed of non-MHC-restricted cytolytic cells.

TIL are also influenced by natural killer (NK) cells (which

are large granular lymphocytes that express neither a/b or

c/d T-cell receptor nor CD3 on their surface, can lyse a

number of different tumor cells, and may be stimulated by

IFN-c, IL2, IL12, and/or IL18) and the lymphokine-acti-

vated killer cells (LAK), which are a mixed population of

peripheral blood lymphocytes that develop non-MHC-

restricted lytic activity for malignant cells after culture in

vitro with high concentrations of IL2 [6–10]. Additional

tumor-infiltrating leukocytes include monocytes recruited

from the circulation that differentiate into stable resident

tumor-associated macrophages (TAM) within the tumor

microenvironment [11]. TAM are key regulators between

inflammation and cancer and have pro-tumoral functions

that become a key link in facilitating cancer progression

and metastatic invasion [12]. TAM, which can be identified

as CD16? macrophages by their morphology, are more

stable in malignant tissues than in the circulation [13].

The many naturally occurring daily and other body

rhythms that have evolved as adjustments to environmental

changes due to the solar day and year enable organisms to

predict when environmental challenges are most likely to

occur and, as such, have a regulating influence on the

‘‘normal’’ functioning of the body’s many processes.

Human physiology is characterized by the rhythmic

recurrence of biological processes [14, 15], and circadian

(about 24 h) rhythms represent the most common biolog-

ical rhythm [16]. The 24-h rhythmic variations in physi-

ology and behavior are controlled by a highly complex

internal cellular system involving a master circadian clock

in the suprachiasmatic nuclei (SCN) of the hypothalamus,

extra-SCN cerebral clocks, and peripheral oscillators, all

comprised of a set of proteins that generate self-sustaining

transcriptional–translational feedback loops with a period

of about 24 h [17–19]. At the cellular level, the circadian

rhythms in the expression of the so-called clock genes that

include Period (Per1, Per2), Bmal1, Clock (or its paralog

Npas2), Rev-Erba, and cryptochrome (Cry1, Cry2), among

others, regulate a circadian clock that persists under a

variety of circumstances [20–23].

Immune functions show temporal changes related to

circadian variations of circulating antibodies, total lym-

phocytes, and cell-mediated immune responses, thereby

indicating a circadian organization of cellular immune

function. Peripheral blood levels of T cytotoxic lympho-

cytes, natural killer cells, and cdTCR-bearing cells are at

their lowest levels at night and rise to a maximum around

midday, thereby indicating a diurnal pattern of the cyto-

toxic arm of immune function, whereas CD4? T helper

lymphocytes have higher nocturnal levels [24–26]. The

circadian variations of lymphocyte subsets have been

related to circadian changes in the process of cell produc-

tion, release, and destruction, and to the action of hormones

(e.g., cortisol), monoamines (e.g., epinephrine and melato-

nin), and cytokines and chemokines that influence cell redis-

tribution to the bone marrow, mobilization, and migration to

lymphoid and non-lymphoid organs and peripheral tissues

[27–30]. Immune cells have specific receptors for these fac-

tors, and their circadian rhythmicity characterizes immune

system function that allows the occurrence of timed windows

of interaction among key lymphocyte subsets, immunomod-

ulating hormones, and cytokines/chemokines. Circadian

functional changes in immune cell function depend on the

circadian variation in gene expression related to transcrip-

tional and posttranslational feedback loops generated by a set

of interplaying clock proteins and time-keeping clock genes

mentioned above [31–33].

Among the strategies implementable in cancer therapy,

some deal with the enhancement of immunological response,

especially cell-mediated immune response, obtained by using

biological response modifiers and adoptive immunotherapy.

Immunological alterations and, in particular, abnormalities in

the proportions of various lymphocyte subpopulations have

been found in a number of malignant tumors [34, 35]. The

purpose of our study was to evaluate 24-h profiles of eleven

lymphocyte surface molecules and subsets in circulating

leukocytes and 3 hormones (melatonin, cortisol, and IL2) in

controls without neoplastic disease and patients with untreated

non-small cell lung cancer (NSCLC) and test for differences

between these groups in overall levels and circadian rhythm

characteristics.

Methods

Subjects gave written informed consent, and the study was

approved by the local Scientific and Ethical Committee.

182 Clin Exp Med (2012) 12:181–194

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Peripheral blood samples were collected at intervals of 4 h

for 24 h from eleven male control subjects free from

neoplastic disease, consisting of seven patients suffering

from irritable bowel syndrome and four patients suffering

from back pain and osteoarthrosis (i.e., chronic non-

inflammatory bone disease), aged 35–53 years (mean ±

SD = 43.6 ± 5.9 years), and 9 male patients suffering

from non-small cell lung cancer (NSCLC), aged

43–63 years (mean ± SD = 51.0 ± 7.2 years). Inclusion

criteria for controls were as follows: age \80 years, body

mass index (BMI) \30 and [20, normal physical activity

level, no psychiatric disorder, no alcohol intake, no chronic

conditions, and normal blood pressure levels. Absence of

any major disease was assessed by medical history and

physical examination, basal screening with blood and urine

tests, ECG, chest X-ray, and upper and lower abdominal

ultrasound scans.

Inclusion criteria for patients suffering from lung cancer

were as follows: age \80 years, BMI \30 and [20, nor-

mal physical activity level, no psychiatric disorder, alcohol

intake or chronic conditions, normal blood pressure levels,

no treatment (surgery, chemotherapy, and radiotherapy),

performance status [80% by Karnofsky scale or \2 by

ECOG score, and tumor cell type (non-small cell lung

cancer) (NSCLC). The extent of the tumor was evaluated

by clinical examination, bronchoscopy, computed tomog-

raphy (CT) of the brain, chest, upper abdomen, and ultra-

sonography of the liver. From small biopsies and cytology,

the carcinoma was more centrally located in 3 cases

(squamous cell carcinoma) and more peripherally located

in the tissue lining in 6 cases (adenocarcinoma). All

patients were combined into a single NSCLC group, since

both diagnoses indicated that a cancerous process was

present. The pathological diagnosis was based on light

microscopy according to the WHO classification, and the

extent of cancer in each patient’s body was staged

according to the TNM (tumor–nodes–metastasis) classifi-

cation of the International Union Against Cancer staging

system after reviewing the clinical, radiologic, and patho-

logic data. The numbers of pT1, pT2, and pT3-4 cases

(representing primary tumor size) were 2, 4, and 3,

respectively, and all 9 cases showed metastasis to regional

lymph nodes. When this information was compiled to

determine a staging category used to assess prognosis

(representing the extent of cancer progression or spread at

diagnosis), the numbers of stage II (nearby lymph node

involved), III (local involvement outside lung), and IV

(distant metastasis) cases were 5, 2, and 2, respectively (see

Table 1).

The two groups were matched with regards to gender,

BMI, and season of sampling, and all NSCLC patients

were subjected to the same social routine, with identical

mealtimes and sleep–wakefulness schedules during the

week preceding the sampling day (lights on at 07:00 h and

lights off at 23:00 h). Sleep was allowed only between

23:00 (lights off) and 07:00 h (lights on). During the

daytime between 07:15 and 20:15 h, all subjects stayed in

the clinic and standardized meals were provided at appro-

priate times for breakfast (07:30 h), lunch (12:30 h), and

dinner (18:30 h). During the overnight sampling period, a

dim blue light (\100 lux) was used.

After a 36-h pre-screening span for each subject, blood

samples (6/subject) were collected intravenously every 4 h

at 10:00, 14:00, 18:00, 22:00, 02:00, and 06:00 h, centri-

fuged immediately after collection, and frozen at -20�C

for later determinations. In each blood sample, phenotypes

characteristic of eleven lymphocyte subpopulations (CD3,

CD4, CD8, CD8bright, CD8dim, CD16, CD20, CD25,

HLA-DR, cdTCR, and Vd2TCR) chosen according to the

presence of specific clusters of differentiation (CD) and

antigen receptors at the cell surface (see Table 2 for

descriptions of cells expressing each specific CD molecule)

were analyzed in peripheral blood anticoagulated with

sodium ethylenediamine tetraacetic acid (EDTA).

Analyses of lymphocyte subpopulations were performed

on unfixed cell preparations with a 5-parameter flow

cytometer (FACSCalibur, Becton–Dickinson FACS Sys-

tems, Sunnyvale, CA, USA) and a panel of monoclonal

antibodies (mAbs) to lymphocyte surface antigens (CD3

FITC/CD8 PE/CD45 PerCP/CD4 APC, CD16 PE, CD19

PE, CD25 PE Cy7, HLA-DR FITC, and PAN cdTCR:

Becton–Dickinson Biosciences, San Jose, CA, USA). We

used fluorochrome-labeled mAbs conjugated with phyco-

erythrin (PE), PE Cy7, peridinin chlorophyll protein

(PerCP), allophycocyanin (APC), and fluorescein isothio-

cyanate (FITC), and 10 ll mAbs was added to 100 ml

EDTA blood in Trucount tubes (BD Biosciences, San Jose,

Table 1 Patients with non-small cell lung cancer: age and disease

status

Patient Age

(years)

Tumor type TNMa

classification

Overallb

cancer stage

1 45 Adenocarcinoma pT2 II

2 46 Adenocarcinoma pTl II

3 57 Adenocarcinoma pT3-4 II

4 58 Squamous pT2 II

5 63 Squamous pTl II

6 49 Adenocarcinoma pT2 III

7 54 Adenocarcinoma pT2 III

8 43 Adenocarcinoma pT3-4 IV

9 44 Squamous pT3-4 IV

a TNM tumor, node, metastasis descriptors of lung cancer; pathologic

tumor (pT) stage listedb Used to assess prognosis and represents extent of cancer progres-

sion or spread

Clin Exp Med (2012) 12:181–194 183

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CA, USA). After a 15-min incubation at room temperature,

the erythrocytes were disintegrated, and after centrifuga-

tion, the supernatants were washed with PBS. Non-lym-

phocytic cells contaminating the preparations were

excluded from analysis using scatter gates set on the 90�light scatter profile. At least 10,000 cells were acquired on

the FACScan. Absolute counts of T-cell subsets were cal-

culated based on the proportion of the respective T-cell

subpopulation and on absolute counts obtained by the

procedure. The number of fluorescent cells was expressed

as a percentage of the total lymphocytes.

Analysis of blood samples with the flow cytometer must

be performed with the same instrument setting and at the

same time, so different blood samples were stored for

variable lengths of time until examination of the entire set.

Since fresh blood samples age gradually over time, there

can be some changes between fresh blood and stored blood,

which may result in a decreased reliability in the cyto-

metric counting of leukocytes in the stored blood [36]. In

addition, erythrocyte lysing procedures cause substantial

cell loss with respect to individual values for every single

subclass and patient, and the absolute counts determined by

a ‘‘lyse no-wash’’ flow cytometry procedure may also be

influenced by the cell loss determined by the lysis process

[37]. For these reasons, we elected to evaluate rhythmic

changes in the percentages of cells rather than in the

absolute counts based on total lymphocytes.

Melatonin and cortisol, to confirm normal synchroni-

zation of the subjects to the local light–dark schedule [38],

and IL2 were also determined in serum. All samples were

analyzed in duplicate in a single assay; the intra-assay and

interassay coefficients of variation were, respectively,

below 4% and 7% for melatonin, 3% and 5% for cortisol,

and 5 and 7% for IL2. Melatonin was measured by

radioimmunoassay (Melatonin RIA kit, Buehlmann Labo-

ratories AG, Schonenbuch, Switzerland), cortisol by

Immuno-assay in ElectroChemiluminescence (Cortisol

ECL, Cobas Roche, Burgess Hill, West Sussex, England),

and IL2 by immuno-enzymatic assay (IL2 EIA, Techno-

genetics, Milan, Italy).

Statistical analysis

After normalizing data for each variable for each subject to

percent of individual mean to reduce intersubject vari-

ability in levels, overall data for each group (C, NSCLC

overall and by stages II, III, and IV) were analyzed by

variable, using both original units and normalized values,

for a 24 h time-effect across the 6 timepoints by one-way

analysis of variance (ANOVA). Analysis of each time

series for circadian rhythm characteristics was accom-

plished by the single cosinor procedure [39] by approxi-

mation of each time series of data by the least-squares

linear regression fit of a single component (24 h) cosine

using the Chronolab statistical package [40]. A P value for

the rejection of the zero-amplitude assumption was deter-

mined by an F test of the variance accounted for by the fit

of the cosine versus the variance accounted for by a

straight-line approximation of the arithmetic mean.

Rhythm detection was considered statistically significant if

P B 0.05.

Rhythm characteristics determined from the best-fitting

cosine model include the following: the ‘‘MESOR’’ (the

middle of the cosine representing an adjusted overall 24 h

average, which equals the arithmetic mean if sampling is

equidistant and there are no missing data or timepoints);

the ‘‘amplitude’’ (A, half the distance from the peak and

trough of the best-fitting curve); and the ‘‘phase’’ of the

Table 2 Lymphocyte cell surface molecules and cluster of differentiation (CD) subtypes

Label Description

CD3? The signaling component of the TCR (T-cell receptor) complex, found on T cells (= total T cells)

CD4? A co-receptor for MHC Class II, found on T helper/inducer cell subsets

CD8? A co-receptor for MHC Class I, found on T suppressor/cytotoxic cell subsets

CD8?dim Subset of memory CD8? T cells (= cytotoxic T cells)

CD8?bright An activated phenotype of CD8? T cells (= T suppressor cells)

CD16? A low-affinity Fc (fragment crystallizable) c R(region)III receptor for IgG, found on NK cells, macrophages, and neutrophils

CD20? A type III transmembrane protein found on all mature B cells (= total B cells)

CD25? Alpha chain of the IL2 receptor; a type I transmembrane protein found on activated T and B cells, some thymocytes, myeloid

precursors, and oligo-dendrocytes that associate with CD122 to form a heterodimer that can act as a high-affinity receptor for

IL2: typically 1/3 of CD20 cells express CD25

cdTCR T-cell receptor, epitope of d chain of TCR found on cdTCR-expressing cells responsible for recognizing antigens bound to major

histocompatibility complex (MHC) molecules; 2–5% of T cells have TCRs consisting of gamma and delta chains

Vd2TCR Variable (V) domain region 2 of the TCR d fragment Vd: a subset of cdTCR cell that responds to non-peptidic antigens

HLA-DR? Human leukocyte antigen D chain receptor, a transmembrane human MHC II family member expressed primarily on B cells on

which it presents antigenic peptides for recognition by the T-cell receptor on CD4? T cells

184 Clin Exp Med (2012) 12:181–194

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cosine model (Ø, referred to local midnight), with the peak

of a single component cosine called the ‘‘acrophase’’ (aØ,

acro = peak).

When two series (e.g., C vs. NSCLC) are paired in an

analysis, the cosine-fitting program automatically per-

formed a parameter t test between rhythm characteristics

(MESOR, amplitude, acrophase) for each variable and a

t test to compare individual timepoint means between the

groups (i.e., C vs. NSCLC at 02:00, 06:00 h, etc.).

A linear regression was also performed to look for

trends in each variable in the NSCLC patients when

grouped according to the two staging categories mentioned

earlier: TNM, representing primary tumor size (i.e., pT1,

pT2, and pT3-4), and overall cancer staging, representing

overall cancer progression (i.e., stages II, III, and IV).

Results

Linear regression results for trends across cancer stages are

presented in Table 3, while results of ANOVA and cosinor

analyses for time-effects for each variable and group are

presented in Table 4 for hormones and Table 5 for lym-

phocyte subpopulations. For each variable, a composite

Table 3 Linear regression on

variables in NSCLC versus

disease status

Serum samples (n = 54) were

obtained every 4 h for 24 h

from 9 male patients with non-

small cell lung cancer

(NSCLC). See L subtype

descriptions in Table 2. Two

staging systems are compared

for trends

TNM tumor, node, metastasis

descriptors of lung cancer:

pathologic tumor (pT) stage

listed

Overall cancer stage represents

extent of cancer progression or

spread used most often to assess

prognosis

Statistically significant values

are given in italics (P \ 0.05)

Variable Units TNM classification Overall cancer stage

R Slope P value R Slope P value

Lymphocyte (L) subpopulations

CD3? %L 0.58 -5.50 \0.001 0.11 -0.95 0.423

CD4? %L 0.16 -2.80 0.165 0.25 3.30 0.067

CD8? %L 0.40 -3.30 0.003 0.05 0.36 0.730

CD8?bright %L 0.36 -1.80 0.008 0.04 0.20 0.747

CD8?dim %L 0.27 -1.30 0.048 0.08 0.36 0.552

CD16? %L 0.28 -2.90 0.041 0.50 -4.70 \0.001

CD20? %L 0.03 -0.18 0.818 0.09 -0.45 0.516

CD25? %L 0.60 -1.80 \0.001 0.27 0.75 0.044

HLA-DR? %L 0.06 0.44 0.662 0.09 -0.45 0.516

cdTCR %L 0.14 -0.40 0.304 0.20 0.51 0.140

Vc2TCR %L 0.04 -0.08 0.796 0.44 0.75 0.004

Hormones

Melatonin pg/ml 0.02 -0.68 0.890 0.02 -0.51 0.908

Cortisol lg/dl 0.25 -4.10 0.070 0.53 7.80 \0.001

IL2 IU/ml 0.37 0.14 0.006 0.33 0.11 0.014

Table 4 Statistical evaluation of 24-h variations for serum hormones in controls versus non-small cell lung cancer

Variable Units Group N subj (Original units)

MESOR ± SE

Analyses for time-effect (using data normalized to % of mean)**

ANOVA 24 h cosine and parameters:

P P Amp (%) ± SE aØ ± SE (95% limits)

Melatonin pg/ml C 11 37.60 ± 2.69 \0.001 \0.001 63.9 ± 7.4 01:35 ± 00:27 h (00:40, 02:28 h)

NSCLC 9 33.19 ± 2.82 \0.001 \0.001 67.7 ± 10.2 01:36 ± 00:34 h (00:24, 02:44 h)

Cortisol lg/dl C 11 11.95 ± 0.81 \0.001 \0.001 50.1 ± 6.9 07:48 ± 00:31 h (06:44, 08:52 h)

NSCLC 9 18.20* ± 1.63 \0.001 \0.001 27.0* ± 6.9 08:22 ± 00:59 h (06:20, 10:24 h)

IL2 lU/ml C 11 0.44 ± 0.02 0.961 0.906 2.5 ± 5.7 18:18 ± 08:36 h –

NSCLC 9 0.54* ± 0.04 0.593 0.376 11.9* ± 8.4 16:14 ± 02:42 h –

At each of 6 times (02:00, 06:00, 10:00, 14:00, 18:00, and 22:00 h), blood samples were obtained from 11 male controls (C), aged 35–53 years, without

neoplastic disease, and 9 men, aged 43–63 years, with untreated non-small cell lung cancer (NSCLC). IL2 interleukin-2

Analyses for time-effect: ANOVA analysis of variance across timepoint means using all grouped values (6/subj) as % of individual mean, Cosine least-

squares fit of 24 h cosine to all grouped data as % of individual mean, MESOR middle of cosine = 24 h mean, Amp amplitude of cosine, aØ (acro-

phase) peak of cosine in hours:minutes from local midnight (95% limits listed if P B 0.05)

* After MESOR or Amp indicates significant difference from controls by parameter test. No group differences found for any acrophase comparison

** All Vd2TCR values = 0.0 for 2 NSCLC patients (for group n = 9, mean = 0.82 ± 0.18; p = 0.07 from C) and could not be used in time-effect analyses

Clin Exp Med (2012) 12:181–194 185

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time plot (chronogram) using original values was created to

show the timepoint means ± SE for both groups and the

superimposed best-fitting 24 h cosines. Hormones (cortisol,

melatonin, and IL2) are displayed in Fig. 1, while lymphocyte

subpopulations are displayed in Figs. 2 and 3. An acrophase

chart allows visual comparison of the similarity in 24 h timing

between the two groups for each variable and the sequencing

of peaks over the 24 h among the variables (Fig. 4).

The presence or absence of a trend in the data over

staging categories depended upon which staging system

was used (Table 3). When using the TNM classification,

decreasing trends were four CD3, CD8, CD8bright,

CD8dim, CD16, and CD25, while using the overall cancer

staging system, a decreasing trend was found for CD16 and

increasing trend for CD25 and Vc2TCR (an increasing trend

in CD4 was borderline significant (P = 0.067). An increasing

trend was also found for IL2 using either staging method. For

cortisol, a large positive trend was found when using the

overall cancer staging system, supporting our use of this cat-

egorization (based upon an increase in stress related to

advanced cancer staging) when analyzing cancer subgroups

for rhythms in the other variables (a decreasing trend with

P = 0.070 was found when using the TNM system).

Serum levels of melatonin and cortisol each showed a

prominent 24-h oscillation in both groups, with highest values

at night for melatonin and near awakening for cortisol (Fig. 1,

top and middle panels). Mean melatonin values in C did

not significantly differ from NSCLC at any timepoint or

overall (mean ± SE): MESOR: NSCLC = 33.2 ± 2.8 vs.

C = 37.6 ± 2.7 pg/ml; (P = 0.261), and mean cortisol

Table 5 Statistical evaluation of 24-h variations for 11 circulating lymphocyte subpopulations in controls versus non-small cell lung cancer

Lymphocyte

(L) subpopulation

Group N subj (Original units: %L)

MESOR ± SE

Analyses for time-effect (using data normalized to % of mean)**

ANOVA 24 h cosine and parameters:

P P Amp (%) ± SE aØ ± SE (95% limits)

CD3? C 11 80.54 ± 0.80 0.046 0.050 1.3 ± 0.5 02:58 ± 01:31 h (23:28, 06:28 h)

NSCLC 9 82.55 ± 0.97 0.260 0.106 1.4 ± 0.6 20:58 ± 01:46 h –

CD4? C 11 44.30 ± 1.09 0.006 0.003 6.8 ± 1.9 01:23 ± 01:05 h (23:04, 03:40 h)

NSCLC 9 45.85 ± 1.38 \0.001 \0.001 14.3* ± 3.2 23:12 ± 00:52 h (21:24, 01:00 h)

CD8? C 11 29.33 ± 1.25 0.002 0.002 6.9 ± 1.9 12:08 ± 01:02 h (09:56, 14:20 h)

NSCLC 9 24.32* ± 0.84 0.581 0.365 4.0 ± 2.8 11:11 ± 02:40 h –

CD8?bright C 11 20.91 ± 0.98 0.300 0.242 4.5 ± 2.6 13:47 ± 02:14 h –

NSCLC 9 15.92* ± 0.51 0.703 0.402 4.1 ± 3.0 10:34 ± 02:48 h –

CD8?dim C 11 8.81 ± 0.39 0.003 \0.001 13.9 ± 3.5 11:21 ± 00:57 h (09:20, 13:20 h)

NSCLC 9 8.57 ± 0.49 0.809 0.476 5.7 ± 4.7 13:01 ± 03:07 h –

CD16? C 11 7.52 ± 0.55 0.020 0.023 12.4 ± 4.4 12:59 ± 01:22 h (10:00, 16:00 h)

NSCLC 9 16.65* ± 1.05 0.049 0.032 10.8 ± 4.0 10:29 ± 01:24 h (07:20, 13:40 h)

CD20? C 11 11.04 ± 0.61 0.255 0.053 10.6 ± 4.3 20:56 ± 01:32 h (19:24, 23:57 h)

NSCLC 9 10.12 ± 0.57 0.285 0.622 3.3 ± 3.4 01:07 ± 03:54 h –

CD25? C 11 4.69 ± 0.37 0.130 0.126 11.2 ± 5.4 00:24 ± 01:51 h –

NSCLC 9 5.70* ± 0.30 0.008 0.028 13.7 ± 5.0 22:57 ± 01:23 h (19:52, 02:04 h)

HLA-DR? C 11 14.88 ± 0.53 0.098 0.322 5.0 ± 3.3 23:06 ± 02:31 h –

NSCLC 9 14.09 ± 0.74 0.709 0.664 3.6 ± 4.0 19:48 ± 04:l2 h –

cdTCR C 11 3.50 ± 0.28 0.062 0.201 9.6 ± 5.3 12:20 ± 02:06 h –

NSCLC 9 2.34* ± 0.28 0.004 0.002 30.6* ± 8.0 14:00 ± 01:00 h (11:52, 16:08 h)

Vd2TCR C 11 1.25 ± 0.15 0.001 \0.001 35.1 ± 8.7 11:51 ± 00:57 h (09:52, 13:52 h)

NSCLC 7** 1.05 ± 0.18 0.225 0.041 24.6 ± 9.4 17:18 ± 01:27 h (13:56, 20:40 h)

At each of 6 times (02:00, 06:00, 10:00, 14:00, 18:00, and 22:00 h), blood samples were obtained from 11 male controls (C), aged 35–53 years,

without neoplastic disease, and 9 men, aged 43–63 years, with untreated non-small cell lung cancer (NSCLC). See Table 2 for lymphocyte

subpopulation descriptions

Analyses for time-effect: ANOVA analysis of variance across timepoint means using all grouped values (6/subj) as % of individual mean,

Cosine least-squares fit of 24 h cosine to all grouped data as % of individual mean, MESOR middle of cosine = 24 h mean, Amp amplitude of

cosine, aØ (acrophase) peak of cosine in hours:minutes from local midnight (95% limits listed if P B 0.05)

* After MESOR or Amp indicates significant difference from controls by parameter test. No group differences found for any aØ comparison

** All Vd2TCR values = 0.0 for 2 NSCLC patients (for group n = 9, mean = 0.82 ± 0.18; p = 0.07 from C) and could not be used in time-

effect analyses

186 Clin Exp Med (2012) 12:181–194

123


values in NSCLC did not significantly differ from C at any

timepoint, while the overall 24 h mean for cortisol was

significantly higher by 52% in NSCLC (MESOR:

NSCLC = 18.2 ± 1.6 vs. C = 12.0 ± 0.8 lg/dl; P =

0.001). Using either original or normalized data, ANOVA

detected a significant time-effect, and cosinor analysis

detected a significant 24-h rhythm for both hormones in each

group, with virtually identical acrophases for each hormone in

each group—near 01:35 h for melatonin and near 08:00 h for

cortisol (Table 4). The timing of these ‘‘marker’’ rhythms thus

confirmed group synchronization by the subjects to their 24-h

environmental light–dark (sleep–wake) schedule, with the

anticipated peak for melatonin during darkness and for cor-

tisol in the morning near awakening.

IL2 values in cancer patients did not significantly differ

from controls at any timepoint (Fig. 1, bottom panel), but

did show a significant 23% increase in overall 24 h mean

(MESOR: NSCLC = 0.54 ± 0.04 vs. C = 0.44 ± 0.02

IU/ml, P = 0.030). No significant time-effects by ANOVA

or 24-h rhythm by cosine analysis were detected in IL2 in

either group (Table 4), although IL2 values tended to be

higher during midday in both groups (Fig. 1).

Very few differences in timepoint means were found

between C and NSCLC for any CD subset, except NSCLC

NSCLC \ C for CD8bright at 18:00 h and cdTCR at 02:00 h,

and NSCLC [ C for CD16 at all timepoints (Figs. 2, 3).

However, the overall 24 h mean (i.e., MESOR) for the cancer

group vs. controls was significantly lower for CD8,

CD8bright, and cdTCR and significantly higher for CD16 and

CD25 (Table 5) and for cortisol and IL2 (described above),

implying the need for multiple samples collected over 24 h to

establish accurate group differences in levels.

In controls, a significant circadian rhythm could be

detected for seven of the eleven lymphocyte CD subpop-

ulations, with midday peaks (acrophases) for CD8dim

(11:21 h), Vd2TCR (11:51 h), CD8 (12:08 h), and CD16

(12:59 h), and peaks during the late evening or night for

CD20? (21:43 h), CD4 (01:23 h), and CD3 (02:58 h)

(Table 5; Figs. 2, 3). Acrophases for four lymphocyte

subsets, cdTCR (12:20 h), CD8? bright (13:47 h), HLA-

DR? (23:06 h), and CD25? (00:24 h), where a 24-h

rhythm could not be definitively determined (P [ 0.05),

nevertheless provide information on the location of their

highest values and possible physiological significance.

In NSCLC patients, a significant circadian rhythm could

be detected for five of the eleven lymphocyte CD sub-

populations, with diurnal peaks for CD16 (10:29 h),

cdTCR (14:00 h), and Vd2TCR (17:18 h), and nocturnal

peaks for CD25 (22:57 h) and CD4 (23:12 h), all with

timing similar to those found for healthy controls (Table 5;

Figs. 2, 3). When the NSCLC group was comprised of only

stage II and III patients (n = 7), rhythm detection was also

significant for CD3 (P = 0.036), with a peak at night

(20:32 ± 01:25 h) (Table 4). Even though rhythm detec-

tion was not significant for some CD subpopulations

in NSCLC, their 24-h patterns and acrophases were

Fig. 1 Chronograms (x–y plots) showing 24-h profiles for melatonin,

cortisol, and IL2 with best-fitting 24 h cosine in 11 male controls

(open circle) and 9 male non-small cell lung cancer patients (filledcircle). Values of y expressed as mean ± SE. Units: pg/ml for

melatonin, lg/dl for cortisol, IU/ml for IL2. *P \ 0.05 from t test

performed between timepoint mean values or overall; Dark bar sleep/

rest 23:00–07:00 h. Cosine 24 h least-squares model fit to all data.

Arrows indicate location of cosine peak (acrophase). See Table 4 for

rhythm detection significance and parameters

Clin Exp Med (2012) 12:181–194 187

123


nevertheless similar to those observed in healthy controls

(Table 5; Fig. 4).

Discussion

An effective T-cell-mediated immune surveillance is

capable of monitoring the genetic integrity of mammalian

cells, and T lymphocytes are a fundamental component of

the specific immune responses that produce tumor rejection

beginning in the earliest phases of neoplastic disease. The

function and activity of T cells (i.e., activation, prolifera-

tion, acquisition of memory, and cytolytic function vs.

induction of anergy and programmed cell death) are ulti-

mately governed by the balance between positive and

negative signaling within T cells, conferred through

Fig. 2 Chronograms (x–y plots)

showing 24-h profiles for CD3,

CD4, CD8, CD8bright,

CD8dim, and CD16 lymphocyte

subpopulations with best-fitting

24 h cosine in 11 male controls

(open circle) and 9 male

patients (filled circle) with non-

small cell lung cancer

(NSCLC). Values of

y expressed as mean ± SE

(units % cell types of total

lymphocyte population).

*P \ 0.05 from t test performed

between timepoint mean values

or overall; Dark bar sleep/rest

23:00–07:00 h. Cosine 24 h

least-squares model fit to all

data. Arrows indicate location

of cosine peak (acrophase). See

Table 5 for rhythm detection

significance and parameters

188 Clin Exp Med (2012) 12:181–194

123


interactions between various T-cell co-regulatory receptors

and ligands. This balance is maintained through direct

mechanisms, such as the inhibition of tumor growth by

antitumor cytotoxic T-cell activity, and by cytokine-med-

iated lysis of tumor cells, or by indirect mechanisms, such

as the promotion of tumor growth by regulatory T cells that

suppress antitumor T-cell responses and by humoral

immune responses that increase chronic inflammation in

the tumor microenvironment [41, 42].

For example, some research has emphasized the

importance of chronic inflammation in the microenviron-

ment of tumors as a key player in tumorigenesis, with

components of the chemokine system affecting multiple

pathways of tumor progression, including leukocytes,

Fig. 3 Chronograms (x–y plots)

showing 24-h profiles for CD20-,

CD25-, cdTCR-, Vd2TCR-, and

HLA-DR-expressing cells, with

best-fitting 24 h cosine in 11

male controls (open circle) and

9 male patients (filled circle)

with non-small cell lung cancer

(NSCLC). Values of

y expressed as mean ± SE

(units % cell types of total

lymphocyte population).

*P \ 0.05 from t test performed

between timepoint mean values

or overall; Dark bar sleep/rest

23:00–07:00 h. Cosine 24 h

least-squares model fit to all

data. Arrows indicate location

of cosine peak (acrophase). See

Table 5 for rhythm detection

significance and parameters

Clin Exp Med (2012) 12:181–194 189

123


cytokines, complement components, and various tran-

scription factors that promote tumor cell proliferation and

survival of malignant cells, invasion, and metastasis, and

subversion of adaptive immunity and response to hormones

and chemotherapeutic agents [43–45]. Cancer can also alter

immunity through direct invasion and replacement of

normal lymphoid tissue, through the production of humoral

factors that interfere with immune function, or causing

cachexia and malnutrition, which increases the severity of

the immunodeficiency. Neoplastic tissue can down-regu-

late antitumor T-cell-mediated immunity through the

tumor-associated B7-H1 molecule by interacting with the

T-cell ligand PD-1, thereby resulting in tumor-reactive

T-cell apoptosis or impairments in cytokine production and

cytotoxicity of activated antitumor T cells [46, 47].

Another mechanism may involve a severe alteration of

the physiological time structure of the human organism.

Body homeostasis is maintained by numerous rhythmic

biological functions presenting different phases, and this

phenomenon is particularly evident when the rhythms that

characterize the working of the multiple elements inter-

connected in the immune system are considered. A com-

plete loss of rhythmicity or a change of phase and/or

amplitude may alter the physiological array of rhythms

with the onset of chrono-disruption or internal desynchro-

nization [48].

In our control subjects, we found significant circadian

rhythms in the following subsets: CD20 (total B cells)-

bearing cells, with a peak in the evening, CD3 (total T

cells)- and CD4 (T helper/inducer subset)-bearing cells,

with peaks in the middle of the night, and CD8 (T sup-

pressor/cytotoxic subset)-, CD8dim (T cytotoxic lympho-

cytes)-, CD16 (natural killer)-, and Vd2TCR (a subset of

cdTCR)-expressing cells, with peaks near midday, in

Fig. 4 Acrophase (aØ) chart indicating timing of peaks for serum

levels of 3 hormones and 11 lymphocyte subpopulations in peripheral

blood sampled every 4 h for 24 h from 11 male controls (open circle)

without neoplastic disease and 9 male patients (filled circle) with non-

small cell lung cancer (NSCLC). aØ peak(s) of fitted 24 h using all

data (see Tables 3, 4); 95% confidence limits added to aØ if P B 0.05

from nonzero-amplitude test. Dark and light bars along the x-axis

indicate the duration of the sleep and wake spans, respectively

190 Clin Exp Med (2012) 12:181–194

123


antiphase to CD3- and CD4-expressing cells. A significant

rhythm for the controls as a whole was not found for

CD8bright, CD25, HLA-DR, or cdTCR, although highest

values tended to occur at the same times as highest values

in NSCLC. In the NSCLC patients, we found that the

relative percentages and the circadian rhythmicity of the

lymphocyte subsets were altered. CD8-, CD8bright (T

helper subset)-, cdTCR (cytolytic cells which have been

proposed to bridge the innate and adaptive immune

responses and to lyse tumor target cells)-, and Vd2TCR-

expressing cells were significantly diminished, whereas

CD16 and CD25 (activated T cells with expression of alpha

chain of IL2 receptor) were significantly increased, and this

increase was paralleled by increased IL2 serum levels.

Circadian rhythmicity was not detected for CD3-, CD8-,

CD8bright-, CD8dim-, CD20-, and HLA-DR-bearing

lymphocytes in the NSCLC patients, possibly due to tumor

dynamics disruption, which may or may not reflect a

benefit for the tumor.

In support of our findings of overall decreases or

increases in some CD? subsets in NSCLC, a decrease in

CD8? T suppressor cells has been observed in other types

of cancer (gastric cancer, colorectal carcinoma, urological

cancer, and breast cancer [49]), while an increase in NK

(CD16?) cells in the peripheral blood is frequently

reported in cancer patients, wherein a high proportion of

NK in TIL seems to be related to a more favorable prog-

nosis, whereas the increase in CD4? CD25? T cells seems

to be associated with a poor prognosis [50]. We noted

similar trends for these CD subtypes when categorizing our

NSCLC patients by cancer stages II–IV (Table 3). CD4?

CD25? T cells contain T regulatory cells that suppress

antigen-specific T-cell immune responses and might ham-

per effective immuno-surveillance of emerging cancer cells

and impede effective immune responses to established

tumors. CD4?CD25? T regulatory cells constitute 5–10%

of peripheral blood CD4? lymphocytes and may express

CD25 (the alpha chain of the IL2 receptor), cytotoxic T

lymphocyte-associated antigen-4 (CTLA-4), a glucocorti-

coid-induced TNFa receptor (GITR), and a member of the

forkhead transcription factors (Foxp3). Higher levels of T

regulators have also been reported in the peripheral blood

of patients with several types of tumors (malignant mela-

noma, Hodgkin lymphoma, ovarian, gastric, lung, breast,

pancreatic, and colorectal cancer) and appear also in the

tumor microenvironment [51–61].

The nervous, endocrine, and immune systems have well-

established and very close interrelationships that contribute

to the maintenance of systemic homeostasis and involve

the production and secretion of a variety of cellular

mediators known as regulatory molecules (hormones,

cytokines, chemokines, integrins, and others). Among these

factors, the main players are melatonin, cortisol, and

immune cells, such as macrophages, T lymphocytes,

eosinophilic leukocytes, and mast cells that are a rich

source of cytokines, serotonin, beta-endorphin, histamine,

and other active molecules. The hormone melatonin serves

as a chemical messenger of darkness in all species studied

to date and is an important component of the timing sys-

tems for circadian rhythms [62]. The daily external envi-

ronmental cycle of a light span followed by a dark span

synchronizes circadian rhythms by providing a signal that

is sent to the pineal gland, a neuroendocrine transducer that

is stimulated during the dark period of the external envi-

ronment and suppressed by light as perceived by the retina,

which secretes the hormone melatonin that acts as a syn-

chronizer of the internal hormonal environment to the

light–dark cycle of the external environment [63]. As a

multitasking molecule, melatonin spreads widely through-

out all body tissues and functions as a signal molecule via

receptor-independent means to modulate the physiology

and molecular biology of cells for general and local coor-

dination of intercellular relationships and to synchronize

their biological rhythms [64]. The timing of the melatonin

rhythm, with regard to phase position, indicates the status

of the internal biological clock in relation to the sleep–

wake schedule.

The human circadian system is also characterized by

cortisol secretion from the adrenals that is under the control

of the hypothalamic–pituitary–adrenal axis. Direct hypo-

thalamic neural connections link the suprachiasmatic

nuclei, the site of the circadian pacemaker, and the para-

ventricular nuclei, the site of corticotrophin-releasing hor-

mone (CRH), the principal hypothalamic factor responsible

for inducing release of ACTH from the anterior pituitary.

The episodic release of ACTH from the anterior pituitary

drives the frequency of cortisol secretory events by the

adrenal gland. Changes in the amplitude, rather than the

frequency of the secretory episodes, regulate the amount of

cortisol released in each secretory episode, and this

amplitude modulation is believed to be controlled by the

circadian pacemaker through modulation of ACTH release.

Circadian and non-circadian components underlie cortisol

diurnal patterns of variation, wherein the ultradian secre-

tory episodes and the circadian secretory amplitude result

from the kinetics of cortisol synthesis in the adrenal glands,

infusion into the peripheral circulation, and clearance from

the plasma by the liver [65–67].

When comparing NSCLC versus C, melatonin serum

levels were similar, while cortisol was significantly ele-

vated by 52% overall in NSCLC (18.2 vs. 12.0 lg/dl)

NSCLC, yet with sleep between 23:00 and 07:00 h, the

circadian rhythmicity of secretion for these two hormones

was nearly identical between the two groups as a whole,

with peaks during sleep for melatonin and just after

awakening for cortisol. However, when rhythm

Clin Exp Med (2012) 12:181–194 191

123


characteristics for cortisol were compared between NSCLC

subgroups of cancer stages II, III, and IV, it was noted that

the level for stage IV (33.97 lg/dl) was significantly ele-

vated over all other groups (C = 11.95, stage II = 15.11,

stage III = 10.15 lg/dl), and there was a loss in circadian

rhythmicity in the stage IV group. The increase in overall

cortisol levels in some of the NSCLC patients, possibly

indicating increased stress levels, and loss of rhythm may

represent a marker of altered function of the regulatory

mechanisms of neuroendocrine secretion in the presence of

advanced neoplastic disease. Increased cortisol levels may

also possibly be involved in a two-way relationship with

the changes of lymphocyte subpopulations, determining a

decrease in CD8- and cdTCR-expressing cells, that nor-

mally show the same acrophase when cortisol serum levels

reach their zenith (near awakening). Nevertheless, all

NSCLC patients showed a perfectly normal 24-h pattern in

serum melatonin (all individual peaks at 02:00 h) in rela-

tion to the controls (all peaked at 22:00 or 02:00 h).

Therefore, altered cortisol levels could indicate a periphe-

ral rhythm disturbance, while melatonin indicated the

master clock’s synchronization to the environmental sleep–

wake schedule and that all patients nevertheless maintained

circadian rhythmicity in a key body rhythm marker.

In conclusion, from around-the-clock blood sampling,

both controls and lung cancer patients were found to be

similarly synchronized to the 24-h sleep–wake schedule,

but the NSCLC patients presented anomalies in lympho-

cyte subset proportions and circadian variations that may

impair the interplay among different lymphocyte subpop-

ulations crucial for an effective immune response. These

findings are also worth considering in the context of a

therapeutic strategy that needs to take into account the

circadian patterns, in addition to complex interactions and

multiple functions that characterize the cytotoxic lympho-

cyte subpopulations. It is thus important to standardize and

report sampling time(s) of day when making comparisons

between groups and before, during and after treatments in

order to more correctly compare levels always at the same

sampling times. In addition, a chronobiologic strategy of a

correctly timed, circadian stage-dependent sampling and/or

dosing schedule versus 24-h rhythms in biochemical,

physiological, and behavioral processes will be essential in

any attempts to successfully optimize and personalize

decision making when evaluating immunomodulatory

effects determined by biological response modifiers and

adoptive immunotherapy protocols in pathological condi-

tions such as endocrine, metabolic, rheumatic, and neo-

plastic diseases, among others [68, 69]. In this regard, we

are planning to implement a protocol in which the cancer

patient stays 36 h in the ward to be studied prior to treat-

ment and during this time span undergoes 4-h blood sam-

pling and other examinations, including ultrasound

imaging, computed tomography, and biological sampling,

to define tumor histology to start primary cell cultures to

evaluate chemosensitivity and study cell kinetics. In this

way, we hope to define time-related characteristics of some

host/tumor components and, if necessary, can use the 24-h

hormone profiles [70] to adjust the scheduling of individual

anti-tumor treatment(s) with regard to ‘‘stage of rhythm’’

and not treatment time(s) as usual (i.e., ‘‘clock hour(s) of

convenience’’), in an attempt to personalize the circadian

administration of cancer treatment [69].

Acknowledgments We wish to express our gratitude and thank the

control subjects and NSCLC patients that each volunteered to par-

ticipate in this study.

Conflict of interest The authors declare that they have no conflict

of interest related to the publication of this manuscript.

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