Cyclin-dependent kinase-specific activity predicts the prognosis of stage I and stage II non-small cell lung cancer

Hiroshi Kubo
Takashi Suzuki
Tomoko Matsushima
Hideki Ishihara
Kazuya Uchino
Satoshi Suzuki
Sachiyo Tada
Masahiro Yoshimura
Takashi Kondo
Cyclin-dependent kinase-specific activity predicts the prognosis of stage I and stage II non-small cell lung cancer
R E S E A R C H A R T I C L E Open Access
Background: Lung cancer is one of the leading causes of cancer death worldwide. Even with complete resection, the prognosis of early-stage non-small cell lung cancer is poor due to local and distant recurrence, and it remains unclear which biomarkers are clinically useful for predicting recurrence or for determining the efficacy of chemotherapy. Recently, several lines of evidence have indicated that the enzymatic activity of cyclin-dependent kinases could be a clinically relevant prognostic marker for some cancers. We investigated whether the specific activity of cyclin-dependent kinases 1 and 2 could predict recurrence or death in early non-small cell lung cancer patients.Methods: Patients with newly diagnosed, pathologically confirmed non-small cell lung cancer were entered into this blinded cohort study. The activity of cyclin-dependent kinases was determined in 171 samples by the C2P® assay, and the results were subjected to statistical analysis with recurrence or death as a clinical outcome.Results:The Cox proportional hazards model revealed that the activity of cyclin-dependent kinase 1, but not 2, was a predictor of recurrence, independent of sex, age, and stage. By contrast, cyclin-dependent kinase 2 activity was a predictor of death, independent of sex and stage.Conclusion: This study suggested the possible clinical use of cyclin-dependent kinase 1 as a predictor of recurrence and cyclin-dependent kinase 2 as a predictor of overall survival in early-stage non-small cell lung cancer. Thus, a combination of activity of cyclin-dependent kinases 1 and 2 is useful in decision-making regarding treatment strategies for non-small cell lung cancer after surgery.

Background

Lung cancer is one of the leading causes of cancer death worldwide. Despite recent advances in cancer treatment, the prognosis of lung cancer is not sufficient compared with that of other solid organ tumors [1]. Even after complete surgical resection, the 5-year survival of earlystage non-small cell lung cancer (NSCLC) patients is only approximately 65% [2][3][4]. This poor prognosis is due to the high recurrence after resection [5,6], which supports the presence of occult metastases. The survival benefit of adjuvant platinum-based chemotherapy has been established in stage II-III NSCLC [7][8][9]; however, there is no data supporting the use of adjuvant treatments for stage IA NSCLC, and the use of adjuvant chemotherapy for stage IB NSCLC is controversial [10]. Recently developed molecular biomarkers predict only non-squamous NSCLC [11,12], and no biomarkers for squamous cell carcinoma (SCC) have reached the validation stage. Therefore, in the setting of lung cancer, the identification of biomarkers for predicting the outcome after surgery and selecting patients who could benefit from adjuvant chemotherapy is crucial.

A breakdown in the cell cycle machinery induces the uncontrolled proliferation of tumors. This process is initiated by a variety of molecules in a cascade that activates the cyclin-dependent kinases (CDKs), which play a role in the progression of the cell cycle. On the molecular level, the activity of CDKs is regulated by subunits known as cyclins, and by phosphorylation and dephosphorylation of key residues, for example, Thr14, Tyr15, and Thr160 in CDK2 [13]. A series of pathological investigations of the molecules that stimulate CDKs have clearly demonstrated their clinical significance for cancer diagnosis and treatment. For example, clinical evidence has indicated that the overexpression of cyclin E and cyclin B, which bind to and activate CDK2 and CDK1, respectively, correlates with tumorigenesis, prognosis, and sensitivity to chemotherapy in a variety of malignancies, as does the inactivation of CDK inhibitors, such as p21WAF1 and p27Kip1 [14][15][16][17][18][19][20]. The pairing between the CDK and cyclin isotypes is specific. However, the amount of cyclin protein did not correspond perfectly with CDK activity in our investigation (data not shown). Similar results regarding the association between cyclin E and the activity of its associated kinase were reported by another group [21]. Therefore, we hypothesized that the direct measurement of CDK activity might produce relevant clinical indications for cancer diagnosis and treatment. Previously, we reported that the CDK-based risk score (C2P® assay, Sysmex, Japan) predicted the risk of distant recurrence in early breast cancer patients [22]. The C2P® assay is determined using a combination of the specific activity of CDK1 (CDK1SA) and CDK2 (CDK2SA). The feasibility of this assay was confirmed in a cohort study in Caucasian breast cancer patients [23] and in colon cancer patients [24]. CDKSAs were also significantly associated with a pathologically complete response (pCR) after weekly administration of paclitaxel followed by 5-fluorouracil, epirubicin, and cyclophosphamide in breast cancer [25].

In lung cancer, many cell cycle-related molecules have been reported to be correlated with prognosis [26][27][28][29][30][31]. Here, we investigated whether the activity of CDK1 and CDK2 could predict the recurrence of NSCLC or the death of stage I and II NSCLC patients.

Methods

Study design

This blinded cohort study was approved by the local ethics committees of Tohoku University and Hyogo Cancer Centre. All patients provided written informed consent. A total of 213 patients who were newly diagnosed with pathologically confirmed NSCLC at the two centers were enrolled in this study. The eligibility criteria were as follows: SCC, adenocarcinoma, and stage I-II disease. All patients underwent complete resection, and none received adjuvant or neoadjuvant chemotherapy. CDK1SA and CDK2SA were determined in 171 samples using a C2P® assay (Sysmex, Kobe, Japan), and the results were subjected to statistical analysis to evaluate recurrence or death as a clinical outcome. Tumor tissue was dissected immediately after resection, snap-frozen and stored at −80°C at each facility. Then, the samples were sent to the Sysmex Corporation (Kobe, Japan) and subjected to the C2P® assay. Tissues with extreme blood contamination were excluded from this study, because the expression level of CDKs is underestimated in the presence of more than 1600 ng/μL of hemoglobin. The histologic types were centrally reviewed at Tohoku University.

Patients

A total of 213 patients with primary NSCLC who had undergone surgery between July 2000 and September 2009 were recruited for this study. Twenty-four cases were excluded due to extreme blood contamination of the samples. C2P® assay measurements were performed on 189 frozen samples; in 18 cases, the CDK expression levels were below the detection threshold, and these samples were excluded from the analysis. Finally, 171 cases were subjected to statistical analyses, including 53 SCCs and 118 adenocarcinomas. The median follow-up period was 43.9 months (70-2820 days).

Measurement of CDK1SA and CDK2SA

The C2P® assay [15,22] was used to measure the specific activity of CDKs. In brief, lysates of freshly frozen samples were applied to the wells of a 96-well PVDF filter plate (Millipore, Billerica, MD, USA). The expression of CDK protein was detected quantitatively by sequential reactions with primary anti-CDK antibodies, biotinylated anti-rabbit antibodies and fluorescein-labeled streptavidin. To measure the kinase activity, CDK molecules were immunoprecipitated from the lysate using protein beads. CDK SA activity (maU/eU) was calculated as CDK activity units (maU/μL lysate), which were divided by their corresponding CDK expression units (eU/μL lysate). maU (CDK activity unit) and eU (CDK expression unit) were defined as the enzyme activity and expression equivalent to 1 ng of recombinant kinase, respectively. When the expression level was lower than the detection limit of the assay, the case was excluded from the analysis. The detection limits for the expression level of CDK1 and CDK2 are 0.1 and 0.003 eU/μL lysate, respectively.

Statistical analysis

Recurrence-free survival (RFS) was calculated from the date of surgery to the date of first local or distant recurrence; patients who were alive without recurrence at the time of data collection and those who died without any evidence of the disease on the date of death were censored. The overall survival (OS) was calculated from the date of surgery to the date of death; patients who were alive were censored.

The data were analyzed using MedCalc version 12.3 (MedCalc Software, Ostend, Belgium), and survival between the groups was compared using the Kaplan-Meier method and an unstratified Cox proportional hazards model or the log-rank test. Correlation tables were analyzed using the chi-square test. Receiver operating characteristic (ROC) curves and the corresponding area under the curve (AUC) for the compared models were computed to simulate predictive accuracy.

Possible prognostic variables that were analyzed in this study included age (≥70 vs <70 years), sex (male or female), tumor size (>3 vs ≤3 cm), nodal status (negative or positive), pathological stage (≥IB vs IA), histological type (SCC or adenocarcinoma), CDK1SA (≥12.6 vs <12.6) and CDK2SA (≥222 vs <222). A value of p <0.05 was considered significant.

Results

We obtained fresh-frozen samples from 213 cases from two centers: Tohoku University Hospital and Hyogo Cancer Centre. Twenty-four cases were excluded due to extreme blood contamination. The C2P® assay was performed on 189 frozen samples (see Additional file 1); in 18 cases, the CDK expression levels were below the detection threshold, and these cases were excluded from the analysis (assay success rate =90%). Finally, 171 samples were subjected to statistical analysis ( Figure 1). The patients who were analyzed included 106 (62%) males and 65 (38%) females, with a median age of 70 years (38-86) ( Table 1). The median tumor size was 3.0 cm (0.9-10.0). A total of 150 cases (88%) were node-negative, and 21 cases (12%) were positive. The histologic type was centrally confirmed as SCC in 53 cases (31%) and adenocarcinoma in 118 cases (69%). The overall recurrence rate and overall survival rate at final follow-up were 22% (local, 8%; distant, 14%) and 78% (133/ 171), respectively. Thirty-five out of 37 recurrent cases received platinum-based chemotherapy. The distribution of CDK1SA and CDK2SA did not vary significantly between the two independent cohorts based on the chi-square test (p =0.2102 and p =0.3557, respectively; Table 2). To examine the prognostic significance of the CDK1SA and CDK2SA results, ROC analysis was performed with overall recurrence as a clinical outcome.

The area under the ROC curves (AUC) of CDK1SA (p =0.0498) and CDK2SA (p =0.4206) were 0.607 and 0.545, respectively, which indicated that CDK1SA, but not CDK2SA, was likely to be predictive of recurrence. The analysis revealed that the Youden Indexes for CDK1SA and CDK2SA were maximized at 12.6 maU/eU and 222 maU/eU, respectively. Therefore, these values were tentatively set as the cut-off points in this study. Coincidentally, the optimal cut-off value of lung cancer approximated that of our colon study (11 maU/eU) [24]. The correlation analyses between CDKSA and the clinicopathologic parameters revealed that none of the parameters was associated with CDK1SA, while CDK2SA was significantly correlated with stage (p =0.0267) and histology (p <0.0001) ( Table 2).

With a cut-off value of 12.6 maU/eU, the cases were classified as low CDK1SA (54%, 92 cases) or high CDK1SA (46%, 79 cases). In the Kaplan-Meier analysis, patients with low CDK1SA tumors showed significantly higher RFS than those with high CDK1SA tumors based on a log-rank test (Figure 2A, HR 2.26, 95% CI 1.18-4.32; p =0.0147); however, no prognostic value was observed ( Figure 2B, p =0.0921).

CDK1SA and conventional clinicopathologic parameters, including sex (male vs female), age (<70 vs ≥70), tumor size (≤3 cm vs >3 cm), pathological lymph node status (positive vs negative), clinical stage (IA vs IB-IIB), and histology (SCC vs adenocarcinoma) were analyzed using a Cox proportional hazards model with recurrence or death as a clinical outcome (Tables 3 and 4      Subanalysis by histology revealed that the predictive value of CDK1SA for recurrence was stronger in adenocarcinoma than in SCC ( Figure 4A and B, adenocarcinoma HR 2.26, p =0.0439; SCC HR 2.09, p =0.2182). On the contrary, in the SCC cases, the Cox regression analysis for recurrence revealed that only CDK2SA was statistically significant ( Figure 4C and D, Table 6, HR 3.86, 95% CI 1.05-14.2; p =0.0428).

In the distribution of 35 recurrent cases on a scatter diagram with logarithmic scales according to CDK1SA and CDK2SA, we observed that the distribution of the non-survivors slightly shifted to the higher CDKSAs area (data not shown). This observation let us to perform Kaplan-Meier analyses, and it was found that the prognostic power of CDK2SA, but not of CDK1SA ( Figure 5A), was significant in 35 patients who were treated with chemotherapy after recurrence ( Figure 5B, HR for death 4.30, 95% CI 1.56-11.8; p <0.0001).

Discussion

In this study, we demonstrated that CDK1SA and CDK2SA could identify individuals that were at high risk for recurrence and death among early-stage NSCLC patients after surgical resection. Even with complete resection, the prognosis of early-stage NSCLC is not good due to local and distant recurrence [6], and it remains unclear which biomarkers are clinically useful for predicting recurrence, although some single molecules and gene signatures of non-squamous cell carcinomas are being validated in a large number of cohorts [11,32]. Previously, cyclin expression and prognosis were reported to be correlated in lung cancer patients [33]; however, this is the first study revealing that CDK activity is a promising predictor for early-stage NSCLCs. Furthermore, CDK1SA and CDK2SA are the first biomarkers that can be used to predict the prognosis of both adenocarcinoma and SCC. Because CDKs are the targets of new anti-cancer drugs and the development of many CDK inhibitors is underway [34], this study will provide a basis for future personalized medicine using CDK inhibitors in NSCLC patients.

CDK1SA and CDK2SA have different implications in lung cancer. CDK1SA predicted recurrence after surgery, whereas CDK2SA predicted the overall survival of stage I and II NSCLC patients ( Figure 2). Sub-analysis of 134 patients with stage IA and IB disease showed that CDK1SA was an independent predictor only for recurrence, but the power of prediction was much better than conventional criteria such as tumor size and stage ( Figure 3 and Table 5). The clinical relevance of  CDK1SA as a marker for recurrence prediction is in agreement with the conclusion of the colon study [24]. In this colon study, CDK1SA was significantly elevated in microsatellite-stable tumors. Because most of colorectal cancers with stable microsatellites demonstrate chromosomal instability [35], CDK1SA may have value as a marker of genomic instability. Genomic instability has been reported to predict clinical outcomes in multiple cancer types, including lung cancer [36][37][38][39]; therefore, prediction of recurrence demonstrated in this study may reflect the genomic instability of the tumors. According to sub-analysis by histology, the predictive value of CDK1SA for recurrence was greater in adenocarcinoma than in SCC ( Figure 4A and B). Since adenocarcinoma is the most common histologic type of colon cancer, there is a similarity in the role of CDK1SA in colon and lung adenocarcinoma. Interestingly, Cox regression analysis for recurrence revealed that CDK2SA expression was statistically significant in SCC but not in adenocarcinoma ( Figure 4C, D and Table 6). This result suggests that CDK1 and CDK2 have different roles in adenocarcinoma and SCC. In contrast to CDK1SA, CDK2SA predicted overall survival after surgery. This finding may be related to the   chemo-sensitivity of the patients. Kaplan-Meier analysis indicated that the prognostic power of CDK2SA was significant in patients treated primarily with platinumbased chemotherapy after recurrence ( Figure 5), suggesting that CDK2SA can predict platinum sensitivity/ resistance. A similar trend was observed in our ovary study: tumors with high CDK2SA were more platinumresistant in patients who underwent incomplete resection and subsequent platinum-based chemotherapy (unpublished data). The ability of cyclin E-associated kinase activity to predict the response to platinum-based chemotherapy in ovarian cancer patients was also reported by another group [21]. In addition, inhibition of CDK2, but not CDK1, induced growth arrest in lung cancer cell lines through anaphase catastrophe [40]. Taken together, these data indicate that CDK2 would be a good target for lung cancer treatment, and the measurement of CDK2SA could be useful for identifying patients who would receive the full benefit of CDK2 inhibitors. A limitation of this study was that the number of cases in the sub-analysis for the outcome after platinum-based chemotherapy was low; however, the significant difference was quite clear ( Figure 5). Prospective studies should be performed to clarify the predictive capacity of CDK2SA in platinum sensitivity/ resistance in early-stage NSCLC patients after surgical resection.

In summary, this study suggested the possible clinical use of CDK1SA for recurrence prediction and CDK2SA for the prognosis of stage I and II NSCLC. Moreover, CDK2SA might be a predictor of platinum-based chemotherapy sensitivity/resistance. To the best of our knowledge, this is the first report that suggests a relationship between chemosensitivity and CDK activity in lung cancer. Thus, a combination of CDK1SA and CDK2SA might be helpful in decision-making regarding NSCLC treatment strategies.

Conclusions

CDK1SA is a predictor of recurrence and CDK2SA is a predictor of overall survival in early-stage NSCLC after surgery.

Additional file

Additional file 1: Distribution of lung tumors according to CDK1SA and CDK2SA. Adenocarcinoma cases and SCC cases are plotted on a scatter diagram with logarithmic scales according to CDK1SA and CDK2SA. Black square; the specific activity was defined as 0.5 when the activity of CDK is lower than the detection limit of the assay. The detection limits for the activity of CDK1 and CDK2 are 10 and 2 maU/μL lysate, respectively.

Competing interests

This study was supported by the Sysmex Corporation (Kobe, Japan). The sponsor was involved in the study design as well as the data collection, analysis, and interpretation. ST and TM of Sysmex Corporation had access to the full raw data. HI was previously affilaited with the Sysmex Corporation, was authorized to access to the primary raw data, and performed the initial analysis of this study. However, HI is now affilaited with the other company, Nittobo Medical Co. Ltd., which has no relation with the Sysmex Corporation; and has no access to the full raw data. Otherwise, the authors declare that they have no competing interests.

Authors' contributions HK: Conception and design, collection and assembly of data, data analysis and interpretation, manuscript writing, final approval of manuscript; TS: Collection and assembly of data, pathological analysis and interpretation, final approval of manuscript; TM and ST: Collection and assembly of data, data analysis and interpretation, manuscript writing, final approval of manuscript; HI: Collection and assembly of data, data analysis and interpretation, final approval of manuscript; KU, SS, MY, and TK: Provision of study material and patients, obtain informed content from the patients, final approval of manuscript. All authors read and approved the final manuscript.

Figure 1
1Flow chart of patient enrolment and reasons for exclusion.

Figure 2
2Analysis of recurrence and survival by risk category. (A) Recurrence-free survival and (B) overall survival according to CDK1SA-based risk with a cut-off value of 12.6 maU/eU. (C) Recurrence-free survival and (D) overall survival according to CDK2SA-based risk with a cut-off value of 222 maU/eU.

Figure 3
3Analysis of recurrence by risk category in stage I NSCLC. (A) Recurrence-free survival according to CDK1SA-based risk with a cut-off value of 12.6 maU/eU. (B) Recurrence-free survival according to CDK2SA-based risk with a cut-off value of 222 maU/eU.

Figure 4
4Analysis of recurrence by risk category in adenocarcinoma (A) (C) and SCC (B) (D). (A)(B) Recurrence-free survival according to CDK1SA-based risk with a cut-off value of 12.6 maU/eU. (C)(D) Recurrence-free survival according to CDK2SA-based risk with a cut-off value of 222 maU/eU.

Figure 5
5Analysis of survival by risk category in recurrent cases. The zero timepoint indicates the diagnosis of recurrence. (A) Survival according to CDK1SA-based risk with a cut-off value of 12.6 maU/eU. (B) Survival according to CDK2SA-based risk with a cut-off value of 222 maU/eU.

). Univariate analysis for recurrence revealed that sex (HR 2.53, 95% CI 1.16-5.52; p =0.0200), age (HR 2.80, 95% CI 1.36-5.77; p =0.0054), tumor size (HR 1.99, 95% CI 1.04-3.81; p =0.0380), pathological lymph node status(HR 3.20, 95% CI 1.51-6.77; p =0.0025), stage (HR 2.54,
95% CI 1.30-4.99; p =0.0070) and CDK1SA (HR 2.26,
95% CI 1.16-4.43; p =0.0177) were statistically signifi-
cant (p <0.05). Age, stage and CDK1SA remained signifi-
cant by multivariate analysis for recurrence (Table 3, age

Table 1
1Clinical and pathological characteristics of patientsTotal collective
Tohoku University
Hyogo Cancer Centre

Table 2
2Association between CDK-based risk groups and clinicopathological parameters eU, the cases were classified as low CDK2SA (77%, 131 cases) or high CDK2SA (23%, 40 cases). Patients with low CDK2SA tumors showed significantly higher OS than those with high CDK2SA tumors based on a log-rank test (Figure 2D, HR 2.49, 95% CI 1.19-5.21; p =0.0033). According to the univariate analysis for death, CDK2SA (HR 2.56, 95% CI 1.34-4.87; p =0.0045) was statistically significant along with sex (HR 5.51, 95% CI 1.96-15.5; p =0.0013), tumor size (HR 2.36, 95% CI 1.22-4.55, p =0.0111), pathological lymph node status (HR 2.82, 95% CI 1.34-5.96; p =0.0069) and stage (HR 2.75, 95% CI 1.39-5.44; p =0.0039). By multivariate analysis, sex, stage, and CDK2SA remained significant (sex HR 4.14, p =0.0081; stage HR 2.09, p =0.0421; CDK2SA HR 1.97, p =0.0500).CDK1SA (cut-off = 12.6)
CDK2SA (cut-off = 222)

Low
High
Significance (Chi-square)
Low
High
Significance (Chi-square)

Sex
Male
57
49
76
30

Female
35
30
p =0.8817
55
10
p =0.0800

Age
< 70 years
43
40
66
17

≥ 70 years
49
39
p =0.7230
65
23
p =0.4888

Tumor size
≤ 3c m
5 5
4 0
7 6
1 9

> 3 cm
37
39
p =0.2955
55
21
p =0.3223

pN
−
85
65
118
32

+
7
1 4
p =0.0759
13
8
p =0.1544

Stage
IA
53
36
73
16

IB
25
20
37
8

IIA
12
19
18
13

IIB
2
4
p =0.1652
3
3
p =0.0267

Histology
Adenocarcinoma
64
54
101
17

SCC
28
25
p =0.9961
30
23
p <0.0001

Facility
Tohoku Univ
53
37
72
18

Hyogo CC
39
42
p =0.2102
59
22
p =0.3557

Table 3
3Cox proportional hazards models for recurrenceUnivariate analysis
Multivariate analysis
Multivariate analysis

HR (95% CI)
p value
HR (95% CI)
p value
HR (95% CI)
p value

Sex
Male
2.53 (1.16-5.52)
0.0200
1.99 (0.90-4.42)
0.0913
2.00 (0.91-4.40)
0.0876

Age
≥ 70 years
2.80 (1.36-5.77)
0.0054
3.06 (1.48-6.34)
0.0028
2.79 (1.35-5.75)
0.0056

Tumor size
> 3 cm
1.99 (1.04-3.81)
0.0380

pN
+
3.20 (1.51-6.77)
0.0025

Stage
≥ IB
2.54 (1.30-4.99)
0.0070
2.16 (1.08-4.31)
0.0306
2.23 (1.13-4.43)
0.0222

Histology
SCC
1.23 (0.62-2.44)
0.5567

CDK1SA
≥ 12.6
2.26 (1.16-4.43)
0.0177
2.25 (1.14-4.42)
0.0195

CDK2SA
≥ 222
1.82 (0.93-3.56)
0.0837
1.53 (0.79-3.01)
0.2165

Table 4
4Cox proportional hazards models for deathUnivariate analysis
Multivariate analysis
Multivariate analysis

HR (95% CI)
p value
HR (95% CI)
p value
HR (95% CI)
p value

Sex
Male
5.51 (1.96-15.46)
0.0013
4.29 (1.50-12.30)
0.0070
4.14 (1.45-11.78)
0.0081

Age
≥ 70 years
1.24 (0.66-2.35)
0.5088

Tumor size
> 3 cm
2.36 (1.22-4.55)
0.0111

pN
+
2.82 (1.34-5.96)
0.0069

Stage
≥ IB
2.75 (1.39-5.44)
0.0039
1.79 (0.87-3.71)
0.1174
2.09 (1.03-4.24)
0.0421

Histology
SCC
2.49 (1.31-4.72)
0.0053
1.53 (0.78-2.97)
0.2156
1.23 (0.62-2.47)
0.5555

CDK1SA
≥ 12.6
1.74 (0.91-3.32)
0.0962
1.53 (0.79-2.97)
0.2122

CDK2SA
≥ 222
2.56 (1.34-4.87)
0.0045
1.97 (1.00-3.87)
0.0500

Table 5
5Cox proportional hazards models for recurrence (Stage I)Univariate analysis

HR (95% CI)
p value

Sex
Male
2.17 (0.85-5.53)
0.1050

Age
≥ 70 years
1.79 (0.75-4.24)
0.1908

Tumor size
> 3 cm
1.41 (0.59-3.35)
0.4413

Stage
≥ IB
1.67 (0.71-3.90)
0.2390

Histology
SCC
1.10 (0.41-2.96)
0.8568

CDK1SA
≥ 12.6
2.57 (1.08-6.09)
0.0335

CDK2SA
≥ 222
1.19 (0.44-3.21)
0.7381

Table 6
6Cox proportional hazards models for recurrence
(SCC)

Univariate analysis

HR (95% CI)
p value

Sex
Male
NA

Age
≥70 years
1.48 (0.47-4.65)
0.5014

Tumor size
>3 cm
1.77 (0.48-6.52)
0.3905

pN
+
1.94 (0.59-6.44)
0.2793

Stage
≥IB
2.42 (0.54-10.99)
0.2530

CDK1SA
≥12.6
2.09 (0.63-6.91)
0.2286

CDK2SA
≥222
3.86 (1.05-14.17)
0.0428

NA, not analyzed due to bias (89% of SCC patients were male).

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AcknowledgementsThis work was supported by Sysmex Corporation, Kobe, Japan.Submit your next manuscript to BioMed Central and take full advantage of:
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Cyclin-dependent kinase-specific activity predicts the prognosis of stage I and stage II non-small cell lung cancer. Kubo, BMC Cancer. 14755Cite this article asCite this article as: Kubo et al.: Cyclin-dependent kinase-specific activity predicts the prognosis of stage I and stage II non-small cell lung cancer. BMC Cancer 2014 14:755.


Shifting the burden or expanding access to care? Assessing malaria trends following scale-up of community health worker malaria case management and reactive case detection
2017

David A Larsen
Anna Winters
Sanford Cheelo
Busiku Hamainza
Mulakwa Kamuliwo
John M Miller
Daniel J Bridges
Shifting the burden or expanding access to care? Assessing malaria trends following scale-up of community health worker malaria case management and reactive case detection

Malar J
16441201710.1186/s12936-017-2088-1RESEARCHSurveillanceEliminationCommunity case managementReactive case detection
Background: Malaria is a significant burden to health systems and is responsible for a large proportion of outpatient cases at health facilities in endemic regions. The scale-up of community management of malaria and reactive case detection likely affect both malaria cases and outpatient attendance at health facilities. Using health management information data from 2012 to 2013 this article examines health trends before and after the training of volunteer community health workers to test and treat malaria cases in Southern Province, Zambia.Results: An estimated 50% increase in monthly reported malaria infections was found when community health workers were involved with malaria testing and treating in the community (incidence rate ratio 1.52, p < 0.001). Furthermore, an estimated 6% decrease in outpatient attendance at the health facility was found when community health workers were involved with malaria testing and treating in the community.Conclusions:These results suggest a large public health benefit to both community case management of malaria and reactive case detection. First, the capacity of the malaria surveillance system to identify malaria infections was increased by nearly one-third. Second, the outpatient attendance at health facilities was modestly decreased. Expanding the capacity of the malaria surveillance programme through systems such as community case management and reactive case detection is an important step toward malaria elimination.

Background

Malaria accounts for an estimated 16% of child mortalities in sub-Saharan Africa [1]. Inequities in access to treatment can lead to increased morbidity and mortality rates in populations distant from health centres due to further delays in treatment [2,3]. In Zambia, treatment-seeking behaviour is perhaps the largest barrier to ensuring prompt access to anti-malarials [4]. Expanding malaria treatment services through community case management (CCM) of malaria has shown to be an effective method of improving individual access to malaria treatment [5], but information regarding the impact of CCM on the health system remains understudied.

Long wait times and staff shortages are associated with poor health systems throughout sub-Saharan Africa [6][7][8], a problem to which malaria contributes directly with its estimated 165 million incident cases in 2013. There are simply too many malaria cases for the number of health workers on the continent [9]. The HIV/AIDS pandemic, outmigration of trained health staff, and a lack of funding to health systems contribute to the problem of an inadequate health system, which in turn leads to challenges in reducing mortality caused by preventable and treatable infectious diseases [10]. This shortage of health workers is felt in Zambia [11], and most prominently in rural areas [12].

Malaria and other illnesses such as pneumonia and diarrheal disease can be safely addressed by community health workers (CHW) with 1-2 week trainings to administer simple diagnostics and medicines [13]. In Zambia, CCM of malaria through CHWs has been implemented in various settings following a policy shift to allow CHWs to conduct malaria rapid diagnostic tests and treat those testing positive with artemether-lumefantrine [14,15]. Expanding on this policy in Southern, Central and Western Provinces of Zambia, a reactive case detection (RCD) system has been implemented wherein CHWs perform case investigations for incident malaria cases suspected of being locally acquired [16]. RCD is based on the premise that incident malaria may be representative of local malaria transmission, likely in the immediate vicinity of the incident case's household [17]. While CCM refers to CHW efforts to test and treat symptomatic community members who seek care for their illness, RCD is an additional service whereby CHWs follow-up incident cases to their household, and then test and treat around the incident case. CCM with RCD is increasingly viewed as a key element of the Zambian Government's national elimination efforts especially in areas in the final hurdle of identifying residual foci and stomping out local remaining infections (Malaria elimination strategy-Zambia 2016). This article assesses the impact of CCM and RCD on the malaria burden at the health system level.

Methods

Study site

This study took place in 7 districts in Southern Province, Zambia (Fig. 1). Malaria transmission in Southern Province is presumed to be low-malaria indicator surveys (MIS) conducted in 2008, 2010, 2012 and 2015 found parasite prevalence in children to be < 10% each year and large declines in malaria-related costs have been observed at two major hospitals [18]. The primary malaria vectors in Southern Province are Anopheles arabiensis and Anopheles funestus. Indoor residual spraying in the province is conducted annually although the proportion houses sprayed across the province is typically less than 30%, with many of the IRS resources targeted Fig. 1 Districts in Zambia included in the analysis toward higher transmission districts bordering Lake Kariba (not included in this analysis). High coverage of insecticide-treated mosquito nets is observed (63.6% of households reporting at least one LLIN) and nets have been the mainstay of malaria prevention in the mostly rural areas where malaria is most prevalent [19].

CCM and RCD system

Starting in 2011, the Government of the Republic of Zambia (GRZ) and partners implemented a CCM and RCD system in Southern Province, Zambia. This consisted of passive malaria CCM testing and treatment at the CHW health post (typically their household) and then in response to the identification of a positive case, RCD activities within 140 m of the incident case household [16]. Incident cases identified at health facilities also received RCD follow-up. By the end of 2014, a total of 1481 CHWs had been trained throughout Southern, Central and Western Provinces. CHWs conducted RCD as volunteers, although they did receive cell phone talk time and bicycles in remuneration for submitting monthly reports to the Health management information system (HMIS) via a DHIS2 (District Health Information Software 2) mobile-to-web system. Testing by CHWs is done with malaria rapid diagnostic tests, usually Standard Diagnostics SD Bioline ® , and treatment for those testing positive follows the national malaria treatment guidelines, the majority of whom receive artemetherlumefantrine (AL), the first-line drug for uncomplicated malaria.

Data

Monthly health facility data were retrieved from the National Malaria Elimination Centre (NMEC) surveillance database (which runs DHIS2) from January 2010 through December 2013. Reported malaria data for Zambia currently divides its databases into the standard health facility data (HMIS) and the expanded community-based data. Specifically, the following data elements were retrieved from the standard health facility database: total outpatient attendance, number of clinical malaria cases (those not confirmed by diagnostic), number of malaria diagnostic tests performed, number of confirmed malaria cases. The following data elements were retrieved from the community database: number of individuals seeking treatment from the CHW, number of suspected malaria cases tested for malaria, number of confirmed malaria cases, number of people tested for a malaria infection during case investigations, and number of people testing positive for a malaria infection during case investigations.

Monthly enhanced vegetation index and temperature were retrieved from the MODerate resolution Imaging Spectrometer (MODIS) satellite mission. Mean monthly values of EVI and temperature for areas within a 5 km buffer of each health centre were extracted and included in the study. Values were extracted using the raster package [20,21] in R version 3.1.0 [22]. Monthly EVI and temperature were tested for time lags at 1 and 2 months, and a lag of 1 month was selected.

Analyses

Due to known challenges with routinely reported malaria data [23], reporting completeness was measured by the proportion of facilities with non-missing values of malaria cases and non-zero values of outpatient attendance. Two separate analyses were conducted, one for the entire time period 2010-2013 and one for the time period 2012-2013 when data completeness was above 60%. Two separate groups of outcomes were considered, namely confirmed malaria infections (outcome A) and total outpatient attendance (outcome B). The outcome of confirmed malaria infections (outcome A) addresses the question of whether the CCM and RCD systems have increased access to diagnosis and treatment for malaria cases. A random effects negative binomial regression was applied to monthly confirmed malaria cases with the total outpatient attendance included as the offset to account for variations in treatment-seeking behaviour. A sinusoidal function of time was used to account for the seasonality of malaria, and additionally tested to determine if malaria infections increased in time with a categorical year covariate. EVI and temperature (lagged 1 month) were categorized as above or below the median and included as environmental factors. The type of facility (hospital, rural health centre, urban health centre and health post) was also included. These analyses were conducted considering two specific outcomes:

A.1. The number of confirmed malaria cases identified at the health facility. A.2. The number of confirmed malaria cases identified at the health facility and by CHWs, i.e., including both CCM and RCD.

The above outcomes allow us to determine whether community activities have affected malaria incidence at the health facility (A.1), and/or whether there has been any change in treatment access due to the communityled efforts (A.2).

The outcome of total outpatient attendance (outcome B) addresses the question of whether the CCM and RCD systems have decreased the outpatient attendance at the health centre. A random effects linear regression was conducted of a log-transformed monthly total outpatient attendance at the health centre with the previously stated covariates included. This analysis was conducted with two specific outcomes:

B.1. The total outpatient attendance at health facilities only. B.2. The total outpatient attendance at health facilities combined with patients seen by CHWs in the community.

Outcome B.1 gives a measure of whether the community activities have decreased the outpatient attendance at the health centre. Outcome B.2 gives a measure of how the community efforts expanded treatment-seeking behaviour or whether it is just shifted from health centre to the community. All analyses were conducted in Stata version 13.1 (Stata Corporation, College Station, Texas, USA).

Results

The increased support for malaria surveillance efforts began in 2011 in Southern Province and reporting completeness reached 60% in 2012 and remained at least at this level throughout 2013. Malaria test positivity was unavailable before 2012 and once measured was found to be < 20% throughout the study period, and < 10% in the dry season (June-December). Figure 2 shows trends of confirmed malaria cases, malaria test positivity and outpatient attendance by month 2012-2013. RCD scaled gradually in the study area beginning in 2012 to reach approximately 80% of health centres engaging in the interventions in 2013 (Fig. 2d).

Confirmed malaria infections

Months when CHWs reported any malaria testing or malaria cases, an indication of when they were out in the community conducting malaria case management was not associated with malaria incidence as measured at the health facility when not taking into account the infections found in the community (p = 0.394, Table 1). However, once the malaria cases found in the community through CCM and RCD were combined with the health facility incidence, community efforts were associated with a 52% increase in confirmed malaria infections standardized to total outpatient attendance (incidence rate ratio [IRR] = 1.52, 95% confidence interval [CI] = 1.37-1.68). CHW health posts saw more malaria infections relative to outpatient attendance than rural health centres (IRR = 1.37, 95% CI = 1.05-1.79, Table 2). Environmental factors and seasonality were associated with increased confirmed malaria infections as expected ( Table 2).

Outpatient attendance

Months when CHWs reported any malaria testing or malaria cases for CCM and RCD were associated with a 6% decrease in the total outpatient attendance at the health facility (95% CI = 1.4-10.6% decrease, Table 3). When combining the outpatient attendance at the health facility with the outpatient attendance seen during CCM and people tested during RCD, however, there was no association (Table 3). Urban health centres had more than double the outpatient attendance compared to rural health centres (119% more, 95% CI = 82-156% more, Table 4), and community health posts had 53% fewer outpatients than rural health centres (95% CI = 23-83% fewer, Table 4). Environmental factors and seasonality were associated with outpatient attendance as expected (Table 4).

Discussion

This is the first assessment of which we are aware examining how CHWs conducting CCM and RCD may be affecting the broader health system. These data suggest that CHWs performing malaria CCM and RCD not only expand access to malaria treatment but also shift a significant portion of the malaria case management case load to CHWs. Months where CHWs reported conducting CCM and RCD were associated with a 50% increase in confirmed malaria infections after accounting for temporal and seasonal trends. This increase likely reflects an improvement in the capacity of malaria surveillance rather than an actual rise in malaria transmission as test positivity rates did not appear to increase during the time period included in this analysis (Fig. 2b). Further supporting this argument is the fact that malaria incidence measured at the health centre is only a fraction of the malaria burden [24]. By equipping CHWs to conduct CCM and RCD, the surveillance 'net' is wider, more granular and more sensitive and is thus able to detect more malaria infections than a surveillance system consisting of health facilities only. Improving the quality and capacity of a surveillance system in the context of malaria elimination is especially important; maximizing the number of malaria infections detected and treated will maximize the probability of eliminating existing parasite reservoirs, identifying importation and stopping onward transmission [25]. Similarly an improved surveillance system is crucial for documenting the maintenance of zero transmission and ultimately qualifying for WHO elimination certification. CCM alone did not provide as much benefit as RCD alone, and malaria elimination programmes should examine the feasibility of conducting RCD to improve surveillance. Interestingly, the gains in malaria surveillance were made only when taking into account the malaria infections found during CCM and RCD. This strongly suggests that the CCM with RCD system that was implemented in Southern Province provides a tangible benefit in pursuing malaria elimination. Contrary to these findings other studies have found little benefit to RCD [26]. One of the main criticisms of RCD is that the RDTs currently used have difficulty detecting subclinical infections. Certainly RCD and malaria surveillance in general would benefit from improved diagnostics to identify asymptomatic infections, however these analyses suggests CCM and RCD provides a benefit with the current tools available.

Not only have community efforts improved access to malaria treatment, disaggregating malaria surveillance at the health post level improves the understanding of heterogeneity of malaria transmission within health centre catchment areas. The spatial refinement of surveillance is an important precursor to targeting malaria interventions such as indoor residual spraying, larviciding and/ or insecticide-treated mosquito net distributions to geographical areas within health centre catchments.

Months when CHWs reported being in the community doing CCM and RCD were associated with a 6% decrease in the total outpatient attendance at the health facility. There is reason to believe that the decrease in outpatient attendance is likely due to fewer individuals with fever seeking care at health facilities. With a mean monthly outpatient attendance of 823 (95% CI: 798-848), a 6% decrease translates to roughly 49 fewer outpatients received at each health centre per month. Assuming each outpatient takes 10 min of time from healthcare providers at the health centre, those 49 fewer outpatients per month leads to approximately 8 person-hours of time saved per health centre per month when CHWs are conducting malaria community case management. The decrease in outpatient attendance and person-hours saved at the health centre did not account for increased time spent by CHWs conducting RCD. Volunteer CHWs could potentially become overburdened by RCD, particularly in areas where malaria transmission is more intense. When people being tested for malaria during CCM and RCD were included in the measure of outpatient attendance, there was only an association for CCM suggesting that the lower attendance may be due to individuals seeking and receiving care in the community rather than going to the health facility.

These analyses have some limitations. Firstly, it should be noted that the use of IRR in the analysis is due to the negative binomial regression model, but the incidence also includes prevalence cases found through RCD. Second, routinely reported malaria case data often has inherent problems; reporting completeness and treatment-seeking behaviour are often noted as most problematic [23]. To mitigate the issue of reporting

Table 1 Multivariate negative binomial regression analyses showing the relationship between confirmed malaria infections and the presence of CCM and RCD interventions for 2012-2013. Results for entire time period (2010-2013) available in additional file 1

Models controlled for malaria testing rate, type of facility, environmental factors as well as time and seasonality with a sinusoidal function * p < 0.05, ** p < 0.01, *** p < 0.001

Outcome N observations (n health centers)

Incident rate ratio of any CCM alone (95% confidence interval) Incident rate ratio of any RCD alone (95% confidence interval)

Incident rate ratio of any CCM+ RCD (95% confidence interval)

Health facility incidence only 3273 (137)     infections, but in reality just have higher testing rates. Finally, the interventions were scaled gradually, limiting the ability to do a more robust analysis that might incorporate a time by intervention interaction. The associations seen here may perhaps be caused by temporal drift including varying coverage and/or effectiveness of vector control interventions such as ITNs, though measures of time were incorporated through the use of a sinusoidal function and categorical year. Despite these limitations, these findings strongly support the case for implementing community-led malaria surveillance and interventions.

Conclusions

Community implemented CCM and RCD are associated with decreased outpatient attendance at health centres and increased capacity of malaria surveillance systems, respectively. Scaling these programmes will likely lead to tangible benefits for both targeting malaria specific interventions and resources to areas with higher case loads and expanding the capacity of a currently overburdened health system. Augmenting the CHW skillset with the ability to treat diarrhea and pneumonia would likely lead to even larger gains in terms of expanding the access to care, easing the burden on health facilities, and ensuring continued use of community-based care in a malaria elimination environment. These gains need to be weighed against the additional workload placed on volunteer CHWs.

Fig. 2
2Monthly confirmed malaria cases (a), malaria test positivity (b), outpatient attendance (c) and implementation progress of community extension (d) at health centers included in the analysis

Table 2
2testing rate as a covariate in the analyses of confirmed malaria infections to account for those facilities that would appear to have higher confirmed malariaMultivariate linear regression analysis showing the relationship between outpatient attendance and the pres-
ence of CCM and RCD interventions for 2012-2013

Table 3
3Multivariate linear regression analyses showing the relationship between outpatient attendance and the presence of CCM and RCD interventions for 2012-13. Results for entire time period (2010-2013) available in additional file 1Models controlled for type of facility, environmental factors as well as time and seasonality with a sinusoidal function* p < 0.05

Outcome
N observa-
tions (n health
centres)

Percent change CCM alone
(95% confidence interval)

Percent change RCD
alone (95% confidence
interval)

Percent change CCM + RCD
(95% confidence interval)

Log-transformed outpatient attend-
ance measured at health facility

3089 (138)
− 5.99% (− 10.62 to − 1.37%)* 0.59% (− 3.31-4.48%)
− 5.99% (− 10.62 to
− 1.37%)*

Log-transformed outpatient attend-
ance at health facility including
individuals presenting and/or
tested in community

3232 (138)
− 7.72% (− 14.09 to − 1.35%)* 3.67% (− 1.84-9.18%)
2.18% (− 4.14 to 8.50%)

Table 4
4Multivariate linear regression analysis showing the relationship between outpatient attendance and the presence of CCM and RCD interventions for 2012-2013 N = 2978 observations, 138 health centresFactor
Categorization
Percent change (95% confidence interval)
p value

Community malaria treatment
Any CCM or RCD
− 5.99% (− 10.62 to 1.37%)
0.011

District
Choma
Reference
Reference

Itezhi-tezhi
− 42.74% (− 92.23 to 6.75%)
0.091

Kalomo
− 33.38% (− 64.63 to − 2.13%)
0.036

Kazungula
− 26.43% (− 64.00 to 11.13%)
0.168

Mazabuka
− 53.58% (− 87.90 to 19.26%)
0.002

Monze
− 6.66% (− 39.68 to 26.36%)
0.693

Namwala
37.24% (− 4.53 to 79.01%)
0.081

Year
2012
Reference
Reference

2013
24.29% (15.60 to 34.98%)
< 0.001

Sinetime
26.07% (15.25 to 36.89%)
< 0.001

Costime
− 8.41% (− 19.43 to 2.61%)
0.135

Type of health centre
Rural health centre
Reference
Reference

Hospital affiliated health centre
− 67.23% (− 125.6 to − 8.88%)
0.024

Hospital
− 56.22% (− 113.3 to 0.87%)
0.054

Health post
− 53.04% (− 82.72 to − 23.36%)
< 0.001

Urban health centre
119.1% (81.79 to 156.4%)
< 0.001

Enhanced vegetation index
Below median
Reference
Reference

Above median
− 5.97% (− 11.10 to − 0.85%)
0.022

Monthly maximum daytime temperature
Below median
Reference
Reference

Above median
− 0.86% (− 5.94 to 4.23%)
0.741

Monthly maximum nighttime temperature
Below median
Reference
Reference

Above median
10.06% (6.62 to 13.50%)
< 0.001

Altitude
Below median
Reference
Reference

Above median
25.79% (− 0.02 to 51.60%)
0.050

Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Authors' contributions DAL, DB and AW conceptualized the study. DAL led analyses and drafted the article. JM secured funding. All authors read and approved the final manuscript.Author detailsCompeting interestsThe authors declare that they have no competing interests.Additional fileAdditional file 1. Additional tables showing the analyses for the entire time period (2010-2013).Availability of data and materialsThe data that support the findings of this study are available from the National Malaria Elimination Centre but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the National Malaria Elimination Centre.Consent for publicationNot applicable.Ethics approval and consent to participateNot applicable. These secondary data analyses utilized aggregated, deidentified information and were thus exempt from IRB approval.• We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal
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