Closing the global cancer divide-performance of breast cancer care services in a middle income developing country

Gerard Cc Lim
Cancer Study Group

Emran N Aina
Cancer Study Group

Soon K Cheah
Cancer Study Group

Fuad Ismail
Cancer Study Group

Gwo F Ho
Cancer Study Group

Lye M Tho
Cancer Study Group

Cheng H Yip
Cancer Study Group

Nur A Taib
Cancer Study Group

Kwang J Chong
Cancer Study Group

Jayendran Dharmaratnam
Cancer Study Group

Matin M Abdullah
Cancer Study Group

Ahmad K Mohamed
Cancer Study Group

Kean F Ho
Cancer Study Group

Kananathan Ratnavelu
Cancer Study Group

Chiao M Lim
Cancer Study Group

Kin W Leong
Cancer Study Group

Ibrahim A Wahid
Cancer Study Group

Teck O Lim
Cancer Study Group

Hpmrs Breast
Cancer Study Group

Closing the global cancer divide-performance of breast cancer care services in a middle income developing country
R E S E A R C H A R T I C L E Open Access
Background: Cancer is the leading cause of deaths in the world. A widening disparity in cancer burden has emerged between high income and low-middle income countries. Closing this cancer divide is an ethical imperative but there is a dearth of data on cancer services from developing countries. Methods: This was a multi-center, retrospective observational cohort study which enrolled women with breast cancer (BC) attending 8 participating cancer centers in Malaysia in 2011. All patients were followed up for 12 months from diagnosis to determine their access to therapies. We assess care performance using measures developed by Quality Oncology Practice Initiative, American Society of Clinical Oncology/National Comprehensive Cancer Network, American College of Surgeons' National Accreditation Program for Breast Centers as well as our local guideline. Results: Seven hundred and fifty seven patients were included in the study; they represent about 20% of incident BC in Malaysia. Performance results were mixed. Late presentation was 40%. Access to diagnostic and breast surgery services were timely; the interval from presentation to tissue diagnosis was short (median = 9 days), and all who needed surgery could receive it with only a short wait (median = 11 days). Performance of radiation, chemo and hormonal therapy services showed that about 75 to 80% of patients could access these treatments timely, and those who could not were because they sought alternative treatment or they refused treatment. Access to Trastuzumab was limited to only 19% of eligible patients. Conclusions: These performance results are probably acceptable for a middle income country though far below the 95% or higher adherence rates routinely reported by centres in developed countries. High cost trastuzumab was inaccessible to this population without public funding support.

Background

Cancer is the leading cause of deaths and disability in the world, and a widening disparity in cancer burden has emerged between high income and low-middle income countries (LMIC) [1,2]. Once thought to be exclusively a burden for developed countries, developing countries today bear an increasing proportion of the burden; In 1970, 15% of newly reported cancers were in developing countries, compared with 56% in 2008 [3]. By 2030, the proportion is expected to be 70% [3][4][5]. This is due to population growth, ageing and changing lifestyles (smoking, diet etc.). Developing countries also bear an increasing share of the burden in cancer deaths; two-thirds of the 7 · 6 million deaths every year from cancer worldwide occur in LMIC [4,5]. This is due to improving survival in developed countries in the past 3 decades as a result of earlier detection and new and more effective treatments [6], but little of these advances are accessible to most people in LMIC.

Closing this cancer divide between rich and poor countries is not just an ethical imperative. There is sound economic justification too for reducing avoidable cancer deaths which are costly in economic productivity terms. The Global Task Force on Expanded Access to Cancer Care and Control in Developing Countries has recently presented a compelling case for comprehensive action on expanded access to cancer care and control [1,2]. In responding to its call for action, we had reviewed the cancer care services in Malaysia, a middle income country, and it was immediately obvious there was hardly any data at all to inform national cancer care planning. Similarly there is little granularity to the performance of cancer services in other developing countries too.

Evaluating the performance of cancer services however is challenging. Unlike for HIV, with which cancer care is often compared [7], where access could simply be measured by the number of HIV patients treated with antiretroviral drugs, there is no simple method for measuring access to cancer care. Cancer care is far more complex and multi-faceted. Early diagnosis is important, which requires an active screening service and tissue diagnosis; tumor characteristics are heterogeneous and there are multiple treatment modalities which have to be individually tailored guided by specific tumor characteristics while taking into consideration patient's preference and local availability of treatments. Cancer is increasingly a chronic disease and its care could stretch over years, which further complicates its measurement.

Fortunately, there have been recent advances in the development of evidence based rigorous and scientifically sound performance metrics [8][9][10][11][12]. These measures basically recommend a specific treatment modality for a sub-group of cancer patients defined by specific tumor characteristics, and further specify a time interval from diagnosis when treatment should be initiated. These measures have been adopted by national bodies tasked with healthcare quality oversight such as the National Quality Forum in US [13]. This has helped standardize the collection of cancer care data and enable the evaluation of the extent to which cancer care in a country adhere with current evidence as described by the performance measures. It has also helped identify factors contributing to sub-optimal care, so that appropriate strategies and interventions could be implemented to improve the delivery of services.

We adopted these performance metrics to measure performance of cancer care services in Malaysia, a middle income developing country in South East Asia with a population of 28.9 million and GDP per capita of RM 30,420 (~USD9,000) in 2011 [14]. In 2012, Malaysia's age standardized cancer incidence was 143.6 per 100,000 population, cancer mortality 85.8 per 100,000 population, giving a Mortality: Incidence ratio of 60% [15]. Historically, the Malaysian health care system, like other former British colonies such as Hong Kong and Sri Lanka in the region, has retained a tax-funded public health service, much like the British National Health Service, alongside a private sector in a mixed health economy. However, as the economy matures, the private health sector, largely financed by private insurance, employer provided benefits or out-of-pocket payment has become increasingly sizeable. In 2006, annual health spending was 4.3% of GDP, of which public and private finance accounted for 45% and 55% respectively [16]. Finance and provision for cancer care in Malaysia follow the same public-private split; the public sector where patients receive publicly funded therapy and the private sector where patients pay out of pocket or their health insurance or employers fund treatment.

Methods

We conducted a multi-center, retrospective observational cohort study to measure the performance of breast cancer care services in Malaysia. A central ethics committee, the Ministry of Health's Medical and Research Ethics Committee, has approved the study and granted waiver from the requirement to obtain individual informed consent from patients. The waiver is justified by this being an observational study based entirely on data abstracted from medical record, and that such data are already routinely collected for healthcare quality assurance purpose.

Study population

The study population consisted of women with breast cancer diagnosed in year 2011. The 9 largest cancer centres (centers with 2 or more mega-voltage machines) from both the public and private sector in Peninsular Malaysia were invited to participate of which 8 agreed to. Each centre was required to enroll all patients diagnosed and treated in the year 2011. Only Malaysian patients with primary breast carcinoma are included. Cases are identified through hospital register as well as operative surgery, chemotherapy and radiotherapy records. Case ascertainment for 3 of the centers was independently verified to be complete (100%).

Study assessment and definitions

At enrollment, data were abstracted from patients' medical and histo-pathology (HPE) reports by trained data collectors. Demographic data abstracted include age, sex race and nationality; tumor characteristics include histologic type, grade, location, extent, and size; lymph node and distant organ metastases. Staging of disease was based on the American Joint Committee on Cancer (AJCC 7 th Edition) criteria. AJCC stage I or II disease were considered early breast cancer (EBC), stage III locally advanced BC (LABC) and stage IV metastatic BC (MBC). After enrollment, all patients were followed up for 12 months to collect data on their subsequent exposure to cancer-directed therapies, which were abstracted from medical, operative surgery, chemotherapy and radiotherapy records.

For the purpose of measuring breast cancer care performance, we adopted the performance measures (Table 1) developed by Quality Oncology Practice Initiative (QOPI) [8,9], American Society of Clinical Oncology/National Comprehensive Cancer Network (ASCO-NCCN) [10,11], American College of Surgeons' National Accreditation Program for Breast Centers (NAPBC) [12] as well as our local clinical practice guideline [17].

Independent data audit

A copy of the HPE report was retrieved for all patients enrolled from all sites to verify tumor diagnosis and characteristics. In addition, patients' demographic and treatment data from 3 sites were also subjected to independent data verification against source documents on site. The accuracy of the collected data with respect to demographics, surgery, radiotherapy, chemotherapy, hormonal therapy and trastuzumab treatment were all >95%.

Statistical methods

Continuous variables are described by summary statistics such as mean, median, and standard deviation and categorical (nominal/ordinal) variables by the frequencies of each category. The precision of the estimates is described by 95% confidence interval (CI).

Results

The 8 participating centres enrolled a total of 889 patients in 2011. One hundred and thirty two patients were excluded because of incomplete data (121 patients for date of diagnosis, 11 for tumor staging). Thus the final sample size was 757 subjects, which represent about 20% of all incident breast cancers in Malaysia in 2011. Table 2 shows the patients' demographic and tumor characteristics. The mean age of the women was only 53 years; about 40% was aged <50 years. 61% of patients were diagnosed with Early Breast Cancer (Stage 1 or 2, EBC), another 27% with Locally Advanced Cancer and 11% with late stage metastatic cancer. 65% were ER+, 57% PR+, 28% HER2+ and 12% triple negative.

Patients' demographic and tumor characteristics

Cancer care performance Table 3 summarizes the performance results of Malaysian cancer diagnostic services. For patients first presenting at a treatment centre, it took a median of 9 days to arrive at a diagnosis of cancer. All patients (100%) had a pathology report confirming malignancy. One hundred and seventy

Diagnostic services

Pathology report confirming malignancy QOPI [8,9] Biomarker information QOPI [8,9] Treatment services  [8,9] three patients however had no information on one or more tumor biomarkers (5% ER, 5% PR and 22% HER2). Table 4 summarizes the performance results of Malaysian cancer treatment services. Breast cancer surgery was highly accessible; 671 (89%) patients had surgery with a median time from diagnosis to surgery of only 11 days. Only 25% of patients underwent breast conserving surgery.

Performance for the 3 treatment modalities, radiation, chemotherapy and hormonal therapy, were comparable. Four hundred and seventy three (62%) patients had chemotherapy with a median time from diagnosis to treatment of 51 days. Most had an alkylating agents (95%), anthracycline antibiotics (86%) or anti-metabolites (76%); only 41% of patients had a taxane. 75% of patients eligible for chemotherapy had care that adhere with the performance measure and received therapies within the prescribed time.

Four hundred and sixty nine (62%) patients had radiotherapy with a median time from diagnosis to treatment of 194 days. Half of them had whole breast external irradiation while 38% had tumour bed (boost) irradiation.    (28) No. (%) missing information on HER2 169 (22) No. (%) HER2 -379 (50) No. (%) Triple positive (ER+, PR + HER+) 95 (13) No. (%) Triple negative (ER-, PR-HER-) 94 (12) No. (%) missing information ER, PR and/or HER

173(23)

*Results on Staging and Histologic findings (tumor size, node, histology grade) may not be consistent with one another because data on the former were abstracted from patients' medical records or treatment plan while latter were abstracted from histo-pathology report submitted by participating sites. 77% of eligible patients had radiotherapy after breast conserving surgery within the prescribed time, while 81% who had radiotherapy after mastectomy had care that adhered with the performance measure. Four hundred and thirty (57%) patients had hormonal therapy with a median time from diagnosis to treatment of 171 days. Most had tamoxifen (85%), only 13% of patients had an aromatase inhibitor. And 76% of eligible patients had care that adhered with the performance measure. For a subset of these patients (N = 89) whose care did not adhere with the performance measures, further investigations showed the common reasons for non-adherence were the patients having sought care in another centre (33%), sought alternative or traditional treatment (16%) or they had refused treatment (50%). For patients with HER2 positive cancer, access to targeted therapy (trastuzumab) was very limited; only 19% of eligible patients could be treated.

Discussion and conclusion

Breast cancer is a common disease across the world but outcomes vary significantly between high and low income countries. Most women diagnosed with breast cancer in high-income countries can reasonably expect to be cured and enjoy a long life expectancy. Such progress has been made possible by screening programmes that enable early detection and by the use of multiple modality treatments. However, in low and middle income countries, underresourced and under-performing health services continue to fail to deliver adequate screening and treatments leading to poor outcomes for patients with breast cancer.

In the year 2011, we measure the performance of breast cancer care to inform our advocacy for better cancer services in Malaysia. The study sample is large (20% of incident cases in 2011) but it is not likely to be representative of the population it aims to describe. The cancer care performance results presented here are likely to be better than they really were (optimistic bias). Firstly, patients were enrolled from 8 of the 9 leading cancer centers in Peninsular Malaysia where cancer specialist manpower and physical infrastructure are concentrated. Clearly large number of BC patients in Malaysia received care in less well-resourced settings and they are not included in this study. Secondly, only patients with complete data can be included in the performance measure analysis. For example, for the measure "Patients under age 70 with Stage I to III ER/PR negative Breast cancer who received Chemotherapy within 4 months of diagnosis", to be included in this analysis require a patient to have complete data (non-missing) on date of diagnosis and date treatment was started, details on staging (T1c or Stage II or III), ER and PR, age (18-69 years) and treatment course (only the first is counted). However as shown in Table 2, critical data to inform clinical decision making were frequently missing (30% for tumor size, 22% for HER2 etc.) in the real world practice in developing countries. In so far that patients with more complete information are likely to receive better care, the results are optimistically biased. Thus, the cancer care performance results presented here represent the upper bound of what is achievable in a middle income country. The results however are unlikely to be affected by selection bias due to selective enrolment as we had 100% case ascertainment within centres. Similarly selective reporting is also unlikely as an independent audit has verified the accuracy of the data. The estimate of non-adherence with performance measure does not take into account individual physician practice style or patient preference; the performance measures were designed to ignore such considerations [10]. The performance results were mixed. Late presentation was 40% which is the same as more than 10 years ago when this was first reported [18,19], indicating little progress at all in cancer screening services in the past 10 years, notwithstanding stage shift due to changes in AJCC definition over the years. On the positive side, we found timely access to diagnostic and breast surgery services. The interval from presentation to tissue diagnosis was short, and all who needed surgery could receive it with only a short wait. Performance of radiation, chemotherapy and hormonal therapy services were probably acceptable. About 75 to 80% of patients could access these treatments in a timely fashion, and those who could not were because they sought alternative treatment or treatment elsewhere, or they simply refused treatment. These performance results are probably creditable for a middle income country though obviously they are far below the 95% or higher adherence rates routinely reported by many centres of excellence in developed countries [20][21][22][23]. Access to trastuzumab was the only problematic area in Malaysian cancer care. This was entirely due to the high cost and inadequate public funding for the treatment.

The results presented here merely describe the mean performance of the cancer care provided by 8 leading centers in Malaysia, 2 of these centres are publicly owned. We did not address the likely variation in cancer care performance between centres or between public and privately owned centres for several reasons. The study protocol explicitly prohibits comparative performance analysis between centres. This was necessary to attract centers to voluntarily contribute data to this study mostly at their own expense. Besides the sensitivity of comparative performance analysis, defining whether a patient is private or public is not straightforward in a highly fragmented cancer care system, such as the one in Malaysia. The boundary between centers are ill-defined and porous. One of the public centre in our sample also treat fees paying patients within the same centre alongside public patients. Another public hospital in the sample routinely outsource radiotherapy services to private centres. Oncologists in public hospitals commonly (probably almost all of them) practice privately and many of their private patients see them in their public practice too and vice versa. And finally in the course of their cancer care, all patients frequently move between centres whether within the public or private sector or between the 2 sectors.

A high performing health service is crucial to translating medical advances into improved health for the population. To our knowledge, this is the first time cancer service in a developing country has been subjected to measurement using standardized performance metrics. Clearly there is room for improvement. The results are useful too as a baseline against which future improvement will be measured. The results also highlight the importance of routine performance measurement in healthcare, which is under-developed in many developing countries despite their high cancer burden [24]. Investment in health without monitoring the return on the investment and without holding the recipients of health funding and providers of healthcare accountable would be unconscionable [25].

Many strategies and solutions have been proposed to improve cancer services in developing countries [1,26,27]. First, the fragmented cancer services typically found in many middle-income countries including Malaysia need to be reformed; we should explore novel models of care delivery [28,29]. Second, the innovative financing, pricing and procurement strategies which had successfully aided the fight against communicable diseases such as HIV/ AIDS, could similarly be employed in cancer care to improve access to high cost medicines. The pharmaceutical industry has been responsive to the needs of developing countries by offering "access schemes" [7] or second brand product [30] which substantially reduce prices and render the treatment more accessible. Ultimately, leadership and advocacy for cancer care needs to be strengthened. Cancer service in Malaysia as described in this study has much to learn yet from other high cost services such as dialysis. Able leadership and the will to radically reform the financing and delivery of dialysis service informed by rigorous health policy research were crucial to achieving universal access to dialysis in Malaysia [31]. We need to do the same for cancer care.

. (%) PR+ 434(57) No. (%) missing information on PR 40(5)

Table 1
1Performance measures for evaluating breast cancer care services in Malaysia# Performance measure
Source

Table 2
2Patient and tumor characteristics at diagnosisPatient
characteristics

Statistics
Results

Number of
patients

Number
757(100%)

Age, years
Mean (SD)
53(11)

Median (IQR)
53(46, 61)

(Min, Max)
(23, 87)

Age distribution
No. (%) age < 40
88(12)

No. (%) age 40 to 49
199(26)

No. (%) age 50 to 59
262(35)

No. (%) age > =60
208(27)

Table 2
2Patient and tumor characteristics at diagnosis (Continued)No. (%) ER+/PR+
516(68)

No. (%) missing information on ER & PR
36(5)

No. (%) HER2 ISH + or IHC + if ISH
missing or unknown

209

Table 3
3Performance of cancer diagnostic services for a breast cancer cohort in Malaysia, in year 2011#
Performance of cancer diagnostic services
N = 757

1.
Median (IQR) duration from first presentation
at site to diagnosis, days

9(4, 9)

2.
Number (%) of patients with Pathology report
confirming malignancy

757(100)

3.
Number (%) of patients with information on ER
720(95)

4.
Number (%) of patients with information on PR
717(95)

5.
Number (%) of patients with information on HER2
588(78)

6.
Number (%) of patients without information on
ER or PR or HER2

173(23)

Table 4
4Performance of cancer treatment services for a breast cancer cohort in Malaysia in year 2011#
Performance measures for cancer treatment
services

Number of patients eligible
for inclusion for the
performance measure
(Denominator)

Percent of patients
whose care adhere
with performance
measure

95% CI of percent of
patients whose care adhere
with performance measure

1.
Patients under age 70 with Stage I to III Breast
cancer who received Surgery within 2 months of
diagnosis

610
82%
(79, 85)

2.
Patients under age 70 with Stage I (T1c) to III ER/PR
negative Breast cancer who received
Chemotherapy within 4 months of diagnosis

171
75%
(68, 81)

3.
Patients under age 70 with Stage I to III Breast
cancer who received Radiation therapy after breast
conserving surgery within 1 year of diagnosis

159
77%
(69, 83)

4.
Patients under age 70 Node + Breast cancer who
received Radiation therapy after mastectomy within
1 year of diagnosis

89
81%
(71, 88)

5.
Patients under age 70 with Stage I (T1c) to III ER or
PR positive Breast cancer who received Tamoxifen
or AI within 1 year of diagnosis

428
76%
(72, 80)

6.
Patients under age 70 with Stage I (T1c) to III HER2
positive Breast cancer who received Trastuzumab
within 1 year of diagnosis

172
19%
(14, 26)

http://www.biomedcentral.com/1471-2407/14/212
AcknowledgementWe wish to thank all those whose names are not mentioned here who render their excellent service especially during the data collection and data cleaning. The full list of contributors for this study, which constitute the HPMRS Breast Cancer Study Group, can be obtained at web appendix; http:// www.hrms.com.my/hpmrs/page.jsp?content=pHome.Competing interestsThe authors declare no competing interests, whether of the financial or non-financial kind.Authors' contributions GCCL, ENA, GFH, CHY, NAT, KJC, JD, MMA, AKM, KFH, KR, CML, KWL, IAW and TOL jointly conceived the study idea, wrote the report and provided subject matter expertise. TOL in addition, designed the study, manage the project and data and conducted the data analysis. All authors read and approved the final manuscript.
Global Task Force on Expanded Access to Cancer Care and Control in Developing Countries: Closing the cancer divide: a blueprint to expand access to low and middle income countries. Boston, MA: Harvard Global Equity InitiativeGlobal Task Force on Expanded Access to Cancer Care and Control in Developing Countries: Closing the cancer divide: a blueprint to expand access to low and middle income countries. Boston, MA: Harvard Global Equity Initiative; 2011. http://ghsm.hms.harvard.edu/programs/ncd/ (accessed 3 Feb 2013).

Expansion of cancer care and control in countries of low and middle income: a call to action. P Farmer, J Frenk, F M Knaul, L N Shulman, G Alleyne, L Armstrong, R Atun, D Blayney, L Chen, R Feachem, M Gospodarowicz, J Gralow, S Gupta, A Langer, J Lob-Levyt, C Neal, A Mbewu, D Mired, P Piot, K S Reddy, J D Sachs, M Sarhan, J R Seffrin, Lancet. 376Farmer P, Frenk J, Knaul FM, Shulman LN, Alleyne G, Armstrong L, Atun R, Blayney D, Chen L, Feachem R, Gospodarowicz M, Gralow J, Gupta S, Langer A, Lob-Levyt J, Neal C, Mbewu A, Mired D, Piot P, Reddy KS, Sachs JD, Sarhan M, Seffrin JR: Expansion of cancer care and control in countries of low and middle income: a call to action. Lancet 2010, 376:1186-1193.

Lyon: International Agency for Research on Cancer. P Boyle, B Levin, World Cancer ReportBoyle P, Levin B: World Cancer Report 2008. Lyon: International Agency for Research on Cancer; 2008.

Breakaway: The Global Burden of Cancer-Challenges and Opportunities. A Report from the Economist Intelligence Unit. N Beaulieu, D Bloom, R Bloom, R Stein, Beaulieu N, Bloom D, Bloom R, Stein R: Breakaway: The Global Burden of Cancer-Challenges and Opportunities. A Report from the Economist Intelligence Unit. 2009. https://assets-livestrong-org.s3.amazonaws.com/media/ site_proxy/data/c49ced3068f7205319cb1edf653dd91e0baee3ba.pdf (accessed July 27, 2010).

Lyon: International Agency for Research on Cancer. J Ferlay, H R Shin, F Bray, D Forman, C Mathers, D M Parkin, GLOBOCAN 2008: Cancer Incidence and Mortality Worldwide. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM: GLOBOCAN 2008: Cancer Incidence and Mortality Worldwide. Lyon: International Agency for Research on Cancer; 2010.

A Jemal, M J Thun, L A Ries, H L Howe, H K Weir, M M Center, E Ward, X C Wu, C Eheman, R Anderson, U A Ajani, B Kohler, B K Edwards, A Jemal, M J Thun, L A Ries, H L Howe, H K Weir, M M Center, E Ward, X C Wu, C Eheman, R Anderson, U A Ajani, B Kohler, B K Edwards, Annual report to the nation on the status of cancer. 100featuring trends in lung cancerJemal A, Thun MJ, Ries LA, Howe HL, Weir HK, Center MM, Ward E, Wu XC, Eheman C, Anderson R, Ajani UA, Kohler B, Edwards BK, Jemal A, Thun MJ, Ries LA, Howe HL, Weir HK, Center MM, Ward E, Wu XC, Eheman C, Anderson R, Ajani UA, Kohler B, Edwards BK: Annual report to the nation on the status of cancer, 1975-2005, featuring trends in lung cancer, tobacco use, and tobacco control. J Natl Cancer Inst 2008, 100:1672-1694.

Scaling up cancer diagnosis and treatment in developing countries: what can we learn from the HIV/AIDS epidemic?. C T International, Ann Oncol. 21International CT: Scaling up cancer diagnosis and treatment in developing countries: what can we learn from the HIV/AIDS epidemic? Ann Oncol 2010, 21:680-682.

Advancing performance measurement in oncology: Quality Oncology Practice Initiative (QOPI) participation and quality outcomes. F X Campion, L R Larson, P J Kadlubek, C C Earle, Mn N, J Oncol Pract. 7Campion FX, Larson LR, Kadlubek PJ, Earle CC, MN N: Advancing performance measurement in oncology: Quality Oncology Practice Initiative (QOPI) participation and quality outcomes. J Oncol Pract 2011, 7:31S-35S.

. Qopi Quality, Measures, QOPI Quality Measures. Available at: http://qopi.asco.org/Documents/ QOPISpring2012MeasuresSummary_000.pdf.

of Clinical Oncology/National Comprehensive Cancer Network Quality Measures. C E Desch, K K Mcniff, E C Schneider, D Schrag, J Mcclure, E Lepisto, M S Donaldson, K L Kahn, J C Weeks, C Y Ko, A K Stewart, S B Edge, J Clin Oncol. 26American SocietyDesch CE, McNiff KK, Schneider EC, Schrag D, McClure J, Lepisto E, Donaldson MS, Kahn KL, Weeks JC, Ko CY, Stewart AK, Edge SB: American Society of Clinical Oncology/National Comprehensive Cancer Network Quality Measures. J Clin Oncol 2008, 26:3631-3637.

Quality Measures for Breast and Colorectal cancer care. Asco-Nccn, ASCO-NCCN Quality Measures for Breast and Colorectal cancer care. Available at: http://www.asco.org/ASCOv2/Practice+%26+Guidelines/Quality+Care/ Quality+Measurement+%26+Improvement/ASCO-NCCN+Quality+Measures.

. National Accreditation Program for Breast Centers. Breast Center Standards Manual. Available atNational Accreditation Program for Breast Centers (NAPBC): 2012 Breast Center Standards Manual. Available at: http://napbc-breast.org/.

Available at: www. National Quality ForumNational Quality Forum. Available at: www.qualityforum.org/projects/ ongoing/cancer/index.asp.

. Globocan, . GLOBOCAN 2012. Available at: http://globocan.iarc.fr/.

Malaysia's National Health Account Project: Health Expenditure Report 1997-2006. Kuala Lumpur: Ministry of Health Malaysia. Malaysia's National Health Account Project: Health Expenditure Report 1997-2006. Kuala Lumpur: Ministry of Health Malaysia; 2008.

Clinical Practice Guidelines: Management of Breast Cancer. Amm Moh, Malaysia, Kuala Lumpur: Ministry of Health MalaysiaMOH and AMM Malaysia: Clinical Practice Guidelines: Management of Breast Cancer. 2nd edition. Kuala Lumpur: Ministry of Health Malaysia; 2010.

Reducing by half the percentage of late presentation of breast and cervix cancer over 4 years: a pilot study of clinical downstaging in Sarawak. B Devi, T S Tang, M Corbex, Ann Oncol. 18Devi B, Tang TS, Corbex M: Reducing by half the percentage of late presentation of breast and cervix cancer over 4 years: a pilot study of clinical downstaging in Sarawak, Malaysia. Ann Oncol 2007, 18:1172-1176.

Spectrum of breast cancer in Malaysian women: overview. A N Hisham, C H Yip, World J Surg. 27Hisham AN, Yip CH: Spectrum of breast cancer in Malaysian women: overview. World J Surg 2003, 27:921-993.

Outcome Report Year. Cleveland clinic: Outcome Report Year 2011. Available at: http://my. clevelandclinic.org/Documents/outcomes/2011/outcomes-cancer-2011.pdf.

CVMC) Cancer Program Reporrt of 2011 program Activities. Central Vermont Medical Center. Central Vermont Medical Center (CVMC) Cancer Program Reporrt of 2011 program Activities. Available at: http://www.cvmc.org/sites/default/files/PDFs/ 2012-Cancer-Program-Report.pdf.

. Maine Medical Center Cancer Institute: Breast Cancer Quality Report on Clinical Outcomes. Maine Medical Center Cancer Institute: Breast Cancer Quality Report on Clinical Outcomes. 2011. Available at: http://www.mmc.org/workfiles/ mmc_oncology/BreastCareQualityGuide_FNL.pdf.

. Shaw Regional Cancer Center, Annual Report. Available. Shaw Regional Cancer Center: 2012 Annual Report. Available at: http://www. shawcancercenter.com/media/219365/shaw_annual_report_2012-small.pdf.

Monitoring and surveillance of chronic non-communicable diseases: progress and capacity in high-burden countries. M A Alwan, D R Maclean, L M Riley, E T Espaignet, C D Mathers, G A Stevens, D Bettcher, Lancet. 376Alwan MA, MacLean DR, Riley LM, d'Espaignet ET, Mathers CD, Stevens GA, Bettcher D: Monitoring and surveillance of chronic non-communicable diseases: progress and capacity in high-burden countries. Lancet 2010, 376:1861-1868.

Beyond disease burden: towards solution-oriented population health. I Roberts, R Jackson, Lancet. 381Roberts I, Jackson R: Beyond disease burden: towards solution-oriented population health. Lancet 2013, 381:2219-2221.

Global cancer burden and sustainable health development. M Kulendran, D R Leff, K Kerr, P P Tekkis, T Athanasiou, A Darzi, Lancet. 381Kulendran M, Leff DR, Kerr K, Tekkis PP, Athanasiou T, Darzi A: Global cancer burden and sustainable health development. Lancet 2013, 381:427-429.

Optimisation of breast cancer management in low-resource and middle-resource countries: executive summary of the Breast Health Global Initiative consensus. B O Anderson, E Cazap, El Saghir, N S Yip, C H Khaled, H M Iv, C Otero, A Adebamowo, R A Badwe, J B Harford, Lancet Oncol. 12Anderson BO, Cazap E, El Saghir NS, Yip CH, Khaled HM IV, Otero C, Adebamowo A, Badwe RA, Harford JB: Optimisation of breast cancer management in low-resource and middle-resource countries: executive summary of the Breast Health Global Initiative consensus, 2010. Lancet Oncol 2011, 12:387-398.

Care redesign -a path forward for providers. T H Lee, N Engl J Med. 3675Lee TH: Care redesign -a path forward for providers. N Engl J Med 2012, 367:5.

What is value in health care. M Porter, N Engl J Med. 363Porter M: What is value in health care. N Engl J Med 2010, 363:2477-2481.

Insight: chasing cheaper cancer drugs. Reuters London Sun. B Hirschler, Hirschler B: Insight: chasing cheaper cancer drugs. Reuters London Sun Apr 2012. Available at: http://www.reuters.com/article/2012/04/01/us-cancer- medicines-idUSBRE83005B20120401.

How public and private reforms dramatically improved access to dialysis therapy in Malaysia. T O Lim, A Goh, Y N Lim, M Zaki, A B Suleiman, 10.1186/1471-2407-14-212Health Aff. 29Lim TO, Goh A, Lim YN, Zaki M, Suleiman AB: How public and private reforms dramatically improved access to dialysis therapy in Malaysia. Health Aff 2010, 29:2214-2222. doi:10.1186/1471-2407-14-212

Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution. Lim, BMC Cancer. 14212Closing the global cancer divideperformance of breast cancer care services in a middle income developing country. Submit your manuscript at www.biomedcentral.com/submitCite this article as: Lim et al.: Closing the global cancer divide- performance of breast cancer care services in a middle income developing country. BMC Cancer 2014 14:212. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit


Comparison of STR profiling from low template DNA extracts with and without the consensus profiling method

Kelly S Grisedale
Angela Van Daal
Comparison of STR profiling from low template DNA extracts with and without the consensus profiling method
R E S E A R C H Open Access
Background: The consensus profiling method was introduced to overcome the exaggerated stochastic effects associated with low copy number DNA typing. However, little empirical evidence has been provided which shows that a consensus profile, derived from dividing a sample into separate aliquots and including only alleles seen at least twice, gives the most informative profile, compared to a profile obtained by amplifying the entire low template DNA extract in one reaction. Therefore, this study aimed to investigate the quality of consensus profiles compared to profiles obtained using the whole low template extract for amplification. Methods: A total of 100 pg and 25 pg DNA samples were amplified with the PowerPlex W ESI 16 Kits using 30 or 34 PCR cycles. A total of 100 pg and 25 pg DNA samples were then divided into three aliquots for a 34-cycle PCR and a consensus profile derived that included alleles that appeared in at least two of the replicates. Profiles from the non-split samples were compared to the consensus profiles focusing on peak heights, allele drop out, locus drop out and allele drop in. Results: Performing DNA profiling on non-split extracts produced profiles with a higher percentage of correct loci compared to the consensus profiling technique. Consensus profiling did eliminate any spurious alleles from the final profile. However, there was a notable increase in allele and locus drop out when a LTDNA sample was divided prior to amplification.Conclusions: The loss of information that occurs when a sample is split for amplification indicates that consensus profiling may not be producing the most informative DNA profile for samples where the template amount is limited.

Background

Polymerase chain reaction (PCR)-based short tandem repeat (STR) analysis is considered the method of choice for forensic DNA profiling. The prominence of the technology is due to the sensitivity of detection from exponential amplification of target molecules by the PCR and the highly polymorphic nature of STRs [1]. This general method allows for small amounts of DNA, between 200 pg and 2.5 ng, to be analyzed with commercial DNA profiling kits [2][3][4][5][6].

In the late 1990s, the Low Copy Number (LCN) technique was introduced to increase the sensitivity of the PCR so that substantially less DNA could be profiled [7]. With this particular process the number of PCR cycles was increased from 28 to 34 resulting in increased results from single cell DNA analysis [7]. The term LCN is often used interchangeably with Low Template DNA (LTDNA). However, in this paper, LCN will refer specifically to the technique of increasing the number of PCR cycles, whereas LTDNA will refer generically to the analysis of samples with 100 pg or less starting template. Amounts less than 100 pg are considered likely to produce results below the stochastic threshold for standard interpretation [8].

The LCN technique can increase the number of alleles observed in a LTDNA profile. However, interpretation difficulties can arise from the exaggerated stochastic effects associated with low levels of starting template. Such effects are well documented and include heterozygote peak imbalance, allele and locus drop out, increased stutter height and allele drop in [9][10][11][12][13].

To accommodate the inherent stochastic effects of LCN DNA typing, a method of replicate analysis has been adopted (referred to as the 'Biological Model'). In this model, a sample is divided into separate aliquots, generally two or three, and a consensus profile is derived from the replicates that only includes alleles that appear in two or more of the replicates [9]. This biological method is particularly useful for the elimination of nonrepeating spurious alleles that appear in a profile as a result of allele drop in [9][10][11][12][13]. Other methods of replicate analysis, such as dividing the sample into four aliquots and including alleles seen in two of the replicates [11], generating a composite profile that includes all alleles seen in the replicate profiles [14] or pooling the sample aliquots post-PCR for a single capillary electrophoresis (CE) injection, [10] have been suggested as possible alternatives to the original biological model.

Critics of the biological model suggest that splitting an already low level sample into multiple aliquots would increase the stochastic effects seen in LTDNA profiles because fewer template molecules are subject to the PCR process in each reaction. As a result, differences are more likely to be seen in replicates of the "same" sample [15]. Additionally, by creating a consensus profile, valuable information from the replicates can be lost, with one study reporting the loss of approximately one third of the alleles obtained [12]. Therefore, critics of the Biological Model advocate efforts should be made to concentrate LTDNA samples rather than diluting and splitting for replicate analysis [15].

Advocates of the Biological Model maintain that a loss of reproducibility is the normal result of LTDNA profiling and, therefore, it is not the existence of variability, but rather the magnitude and potential consequences of any variability that needs to be assessed and reported [16,17]. It has been stated that replicate analyses are preferable to concentrating a sample as this would not usually increase the overall concentration of DNA above the 100 pg stochastic threshold, with stochastic effects still expected to occur in non-replicated samples [17]. However, little empirical evidence has been provided which shows that splitting a LTDNA extract and creating a consensus profile produces a more accurate STR profile than a concentrated LTDNA sample or vice versa.

This study aims to investigate whether concentrating a sample for LTDNA analysis will result in an increased quality STR profile compared with the current practice of splitting extracts into separate aliquots and constructing a consensus profile from the split sample profiles. Samples with known profiles will be used for all experiments. Profiles from low template samples will be compared to high template reference profiles to assess profile quality. Profile quality will be measured in terms of the presence of allele drop out, locus drop out and allele drop in, as well as an analysis of the peak heights and peak height ratios in profiles obtained using the different analysis methods. For each sample, a loading cocktail of 10 μl Hi-Di TM Formamide (Life Technologies) and 1 μl of CC5 Internal Lane Standard 500 (Promega Corp) was mixed with 1 μl of amplified product and denatured for three minutes at 95°C. After cooling, samples were injected on the 3130 using a 3 kv, 5-second injection as is the recommended PowerPlex W ESI 16 protocol. Data were analyzed using Genemapper ID W software version 3.2.1 (Life Technologies) and PowerPlex W ESI 16 panel and bin files. A detection threshold of 50 RFU was used for analysis of all sample profiles as per Tucker et al. [18].

Methods

Profile interpretation

Electropherograms for all LTDNA samples were compared with 500 pg control profiles (the recommended template amount for PowerPlex W ESI 16 Kits), noting peak heights, allele drop out, locus drop out and allele drop in. Peak height ratios were calculated by dividing the height of the smaller peak in a heterozygote pair by the height of the larger peak. A peak height ratio of zero was recorded if one allele in the pair failed to amplify. Peak height ratio averages were calculated in two ways. The first calculation used only the heterozygote loci that showed both alleles. The second calculation used all loci in the first calculation, as well as known heterozygote loci that had a peak height ratio of 0% due to allele drop out. While a single peak, and in effect a 0% peak height ratio, would not normally be evaluated when analyzing an unknown profile, these profiles were obtained from known sources. If the peak height ratios are to be used as a measure of how well both alleles at a locus amplify during the PCR then the 0% peak height ratios are an important indicator of the efficiency of the entire reaction. If both alleles at a heterozygous locus failed to amplify, the locus was not used in calculating the peak height ratio average and median.

Locus specific stutter filters provided by the Power-Plex W ESI 16 manufacturer are as follows: 4% (THO1), 8% (D16S539), 9% (D18S1179), 10% (D2S441), 11% (FGA), 12% (D3S1358 and D10S1248), 14% (D19S433), 15% (D1S1656, vWA and D21S11), 17% (D18S51), 18% (D2S1338), 19% (D12S391) and 25% (D22S1045). Since the profiles were from known single source origins a general stutter threshold of 15% was also applied to samples that were subjected to a standard cycle PCR. Stutter has been shown to increase when measures, such as increasing the number of PCR cycles, are taken to improve the detection of low template samples [9]. To compensate for the increased stutter seen in LCN profiles, a stutter threshold of 20% was applied to samples that underwent the increased cycle PCR, based on the method of Caragine et al., who observed 97% of stutter was filtered out using a 20% filter for low template samples amplified with an increased cycle PCR and increased injection conditions [10]. If the peak height of an allele in the −4 stutter position exceeded the relevant threshold it was designated as an allele and categorized as allele drop in. No stutter threshold was set for +4 stutter, and consequently any alleles that were present in the +4 stutter positions were designated as alleles and deemed to be allele drop in.

For each of the replicate samples in the second series of reactions, consensus profiles were constructed based on the method outlined by Caragine et al. [10], such that an allele had to be seen in at least two replicates to be included as a true allele in the composite profile.

Results

The first series of reactions, which amplified 100 pg or 25 pg in a single STR amplification, resulted in 15 profiles at standard cycles and 15 profiles at increased cycles for each starting template amount. Each set of 15 profiles comprised 240 total loci. Of the total loci, 183 (approximately 76%) were heterozygous. The second series of reactions, in which 15 100 pg or 25 pg samples were divided into 3 aliquots for an increased cycle amplification, produced 45 profiles at increased cycles, and as such 15 consensus profiles, for each template amount. Each set of 45 profiles consisted of 720 total loci, with 549 (approximately 76%) of these being heterozygous. Each set of 15 consensus profiles comprised 240 total loci, 183 of which were heterozygous.

Allele drop out

The amplification of 100 pg of starting template using the standard cycling protocol resulted in profiles with little observable allele drop out. Only six (3%) of the heterozygote loci showed allele drop out, with each drop out event occurring in different profiles (Table 1). When the number of cycles was increased to 34, allele drop out was eliminated. However, when the 100 pg samples were split for amplification, the resulting consensus profiles showed an increase in allele drop out. Of the 15 consensus profiles, 16 examples of allele drop out were seen, representing 9% of the total heterozygote loci. The number of drop out alleles per consensus profile ranged from0 to 4, with an average of 1.73 drop out events per profile.

Allele drop out significantly increased as the amount of starting template was reduced. Using the standard cycle protocol, amplification of 25 pg of starting template resulted in 80 occurrences of allele drop out over 15 profiles, representing 44% of the total heterozygous loci ( Table 1). The number of drop out alleles per profile ranged between 3 and 8, with an average of 5.4 allele drop out events per profile. When the samples were subjected to an increased cycle PCR, the percentage of allele drop out was reduced to 33%, with 61 examples of allele drop out over 15 profiles. The number of drop out alleles in each profile obtained using the increased cycling method ranged between 2 and 7, with an average of 4.07 per profile. However, when 25 pg of starting template was split for amplification the resulting consensus profiles showed an increase in allele drop out, with 92 cases over the 15 consensus profiles, which corresponds to 50% of the total heterozygous loci. The number of drop out alleles per profile ranged between 2 and 10, with an average of 7.67 drop out events in each profile.

Locus drop out

For the purpose of this study, locus drop out was defined as the single allele from a homozygous locus, or both alleles from a heterozygous locus, missing from the profile. In the latter case, both missing alleles were not each counted individually as allele drop out. Locus drop out was not seen in any of the profiles obtained from 100 pg starting template, regardless of whether the sample was amplified using the standard or increased cycle PCR (Table 2). When the 100 pg samples were divided into three 33 pg aliquots and used to construct a consensus profile, the individual profiles did show some locus drop out, with 12 instances seen across the 720 loci. The consensus profiles derived from the aliquots were complete and correct since locus drop out did not occur at the same locus more than once in any set of three replicate profiles.

Locus drop out was much more evident in the 25 pg samples ( Table 2). Under standard cycling conditions, 51 examples of locus drop out were recorded over the 240 total loci (21%). Between 0 and 7 loci dropped out per sample, with an average locus drop out of 3.4 per sample. This drop out was reduced when the number of PCR cycles was increased, with only 13 (5%) of the total loci dropping out. Under the increased cycle amplification condition, the number of locus drop out events per sample ranged between none and two, with an average of less than one drop out locus per profile. However, when the samples were split and a consensus profile was derived, locus drop out increased, with 79 instances seen in the 15 consensus profiles, representing 33% of the total loci. The number of drop out loci per sample ranged from 3 to 10, with an average of 5.27 loci dropping out in each consensus profile.

Allele drop in

Allele drop in was minimal under standard cycling conditions, with only two additional alleles seen across all 100 pg sample profiles, one of which was seen in the −4 stutter position and the other seen in the +4 stutter position (Table 3). Drop in increased when the samples were amplified with the increased cycle PCR, with 32 additional alleles seen in the resulting 15 profiles. The number of drop in alleles per sample ranged between 0 and 4, with an average of 2.13 additional alleles seen in each sample. Allele drop in also occurred in the profiles of the split samples, with a total of 32 additional alleles seen in the 45 split sample profiles. However, the consensus method requirement for an allele to be seen twice effectively counteracted this drop in, so that no additional alleles were seen in the 15 consensus profiles.

A similar pattern was observed in the 25 pg sample profiles. No additional alleles were seen in the standard

PCR Cycle Amplification

PCR Cycle Amplification

pg Starting Template

PCR Cycle Amplification

PCR Cycle Amplification

Consensus Profiles (n = 240) 79 33% a n is the total number of loci.

cycle PCR profiles, but 5 drop in alleles were noted in the 15 increased cycle PCR profiles. When the samples were divided into 3 aliquots for amplification, 14 additional alleles were seen in the 45 split sample profiles. However, again, the consensus method eliminated this drop in, so that no additional alleles were seen in the consensus profiles.

Of the 87 additional alleles observed across all profiles, 26 were seen in the '-4' stutter position. Alleles in this position were only counted as drop in if their peak height exceeded the nominated stutter ratio filters (locus specific stutter filters followed by a manual examination using a 15% filter for samples amplified with the standard number of cycles and 20% for samples amplified with an increased cycle PCR). Indeed, 140 additional peaks were actually seen in −4 stutter positions; however, 114 were removed from the final profiles by the stutter filters. Twenty-four additional alleles were observed in '+4' stutter positions. While 23 of the 24 additional alleles in +4 positions had peak heights less than 20% of the true allele, a filter was not set for +4 stutter; therefore, additional alleles in this position were counted as drop in. The remaining 37 additional alleles were placed throughout the profiles.

Peak heights and peak height ratios

For the 100 pg samples amplified with 30 PCR cycles, the height of homozygous peaks ranged from 185 to 847 Relative Fluorescence Units (RFU), with an average peak height of 520 RFU. The height of heterozygous alleles ranged between 55 and 725 RFU, with an average of 261 RFU ( Table 4). The peak height ratio range for heterozygote loci was 16% to 99% with a peak height ratio average of 69%. When taking into account the heterozygote loci that had a peak height ratio of 0% due to allele drop out, the average was reduced to 67% ( Table 5). The peak heights increased when the number of PCR cycles was increased to 34. For homozygous alleles, the peak heights ranged between 622 and 7,609 RFU with an average height of 4,129 RFU. The peak height range for alleles at heterozygous loci was 139 to 5,805 RFU with an average height of 1,925 RFU (Table 4). However, increasing the number of PCR cycles resulted in a slightly reduced peak height ratio average of 65%, with a peak height ratio range of 6% to 100% (Table 5). Allele drop out was not seen in any of the 100 pg increased cycle profiles; therefore, only one calculation was performed.

The 100 pg samples that were split for amplification and were subject to 34 PCR cycles displayed peak heights higher than those subjected to the 30-cycle amplification, presumably because the increased number of cycles compensates for the decreased template amount.

The heights of homozygous peaks ranged from 166 to 6,131 RFU, with an average height of 1,610 RFU. The heterozygous loci showed a peak height range of 56 to 4,123 RFU, with an average peak height of 763 RFU ( Table 4). The peak height ratio range for heterozygous loci showing both alleles was 8% to 99%, with an average of 57%. Inclusion of the 114 heterozygote loci that had a 0% peak height ratio, the average was reduced to 45% ( Table 5).

Amplification of 25 pg starting template resulted in a peak height reduction compared with the 100 pg samples. Under standard PCR cycling conditions, the heights of homozygous peaks ranged between 51 and 337 RFU, with an average of 149 RFU. For heterozygous loci, the peak height range was 50 to 360 RFU, with an average height of 190 RFU ( Table 4). The peak height ratios range for the heterozygous loci was 35% to 99% with an average 68%. When all heterozygous loci that showed at least one allele were included in the calculation, the average peak height ratio was reduced to 29% ( Table 5).

As with the 100 pg samples, by increasing the number of PCR cycles the average peak height for the 25 pg samples also increased. For homozygous alleles, the average peak height was 1,259 RFU, with a range of 212 to 3,475 RFU. The height of heterozygous alleles ranged between 58 and 3,500 RFU, with an average height of 708 RFU (Table 4). Considering only the heterozygous loci that showed both alleles, the peak height ratio range was 10% to 99%, resulting in a peak height ratio average of 56%, a reduction compared to the average of the standard cycle samples. However, when the heterozygous loci that had a peak height ratio of 0% were included in the average calculation, the peak height ratio average was higher compared to the standard cycle profiles at 36%, due to the reduction in allele drop out (Table 5).

For the 25 pg samples split for amplification, the heights of the alleles were increased compared with the standard cycle amplification samples due to the increased number of PCR cycles utilized. For homozygous alleles, the peak heights ranged from 59 to 2,144 RFU; however, the average height was only 541 RFU. The range for heterozygous allele heights was similar at 53 to 2,077 RFU, with an average height of 386 RFU ( Table 4). The peak height ratio range of heterozygous loci showing both alleles was 16% to 100%, with an average of 64%. However, when the 250 heterozygous loci that had a peak height ratio of 0% were included in the calculation, the average was reduced to 19% (Table 5).

Discussion

This study supports previous studies that showed increasing the number of PCR cycles will increase the sensitivity of detection for STR profiling of LTDNA samples. However, while 100 pg has been noted as the upper limit for what may be considered a low template sample [7,8], this research shows that when using the current generation multiplex kits, there may be less benefit to increasing the number of PCR cycles when this amount of DNA template is available for amplification. When 100 pg of template was amplified using the PowerPlex W ESI 16 kit with an increased cycle PCR additional alleles were seen in the profiles. This is not surprising due to the increased sensitivity of the PowerPlex W ESI 16 kit, which has been shown to produce full profiles down to 62.5 pg using its standard cycling protocol [18]. Furthermore, the standard protocol for this kit already utilizes 30 PCR cycles, as opposed to other commercially produced multiplex kits that use 28 or 29 cycles as the standard cycle number.

For the 100 pg samples amplified using the standard cycling protocol, there were only 6 instances of allele drop out in the resulting 15 profiles, no locus drop out and only 2 spurious alleles seen overall. Of the two additional alleles, one was in the −4 position and one was in the +4 position to true alleles. When measured against their respective true alleles, the allele in the −4 position had a peak height ratio of 26%, while the allele in the +4 position had a peak height ratio of 36%. The position of the alleles could indicate that they are increased stutter rather than true drop in alleles. However, given the height of the additional peaks, particularly the +4 allele, it may be that they are true drop in. Furthermore, the additional alleles occurred at heterozygous loci, and in both cases the two correct alleles were also present. The presence of the 'drop in' alleles would normally indicate a potential mixture sample, with the additional alleles not part of the major profile. However, the other 14 loci showed no additional minor alleles, which would indicate the likely drop in or artifact nature of these allele peaks. In comparison, the 100 pg samples amplified with 34 cycles did not display any drop out. However, 32 additional peaks were seen across the 15 profiles, the majority of which were not in the stutter positions. Of the additional alleles, 10 were seen at homozygous loci, which could mean that the loci could be falsely interpreted as heterozygote loci. However, given that the peak height ratios of nine of the additional alleles was less than 3% of the true allele, this is unlikely. One drop in allele had a peak height ratio of 10% compared to the true allele, but this additional allele was seen in the +4 stutter position, and would also be interpreted with caution. Overall, these results suggest that performing one standard cycle PCR is preferable to performing one increased cycle reaction when 100 pg of template are available for amplification because of the large reduction in allele drop in.

When the template amount is reduced, this research demonstrates that there is significant benefit to increasing the number of PCR cycles for STR typing in terms of the increased amount of information seen in the resulting profiles. With a starting template amount of 25 pg, increasing the number of PCR cycles resulted in a 22% increase in the number of correct loci seen overall compared to the standard cycle profiles. Both allele and locus drop out were markedly reduced and the peak heights and peak height ratios both improved. However, as with the 100 pg samples, increasing the number of PCR cycles for the amplification of 25 pg did result in more drop in alleles seen in the profiles. Compared to the standard cycle profiles, which did not show any additional alleles, the increased cycle profiles displayed six loci with additional alleles. Of these additional alleles, four were seen at heterozygous loci, with three of these loci also containing both true alleles. The fourth locus did display allele drop out in conjunction with the allele drop in, so it is possible this locus would be interpreted incorrectly. The peak height ratio of the drop in allele compared to the remaining allele was only 9%, indicating that this would be interpreted with caution. However, if the drop in allele was considered not part of the major profile, the locus could then be falsely interpreted as homozygous. Of the two additional alleles that occurred at homozygous loci, one occurred in the −4 stutter position but was not removed by the stutter filter as it had a peak height ratio of 29%. The other was in the +4 stutter position, with a peak height ratio of 7%. If an increased cycle procedure were implemented for LTDNA amounts as low as 25 pg, the profile interpretation would need to accommodate the chance of an allele resulting from allele drop in.

For both starting template amounts, the data show that splitting the sample into three aliquots and constructing a consensus profile did not result in the most informative profile compared with a profile where the DNA extract was amplified in one reaction. While the consensus profile approach did eliminate allele drop in, all other measures of profile quality were improved when the sample was not split. The original purpose of the Biological Model approach was to eliminate spurious alleles from the final consensus profile and give confidence that the final profile contains only the alleles of the actual contributor. The former was demonstrated by our results, with no additional alleles in the consensus profiles. This is important, as additional alleles in the profile can result in an incorrect interpretation, where either a homozygous locus is interpreted as a heterozygous locus or, if the drop in occurs in conjunction with a drop out, the wrong genotype may be assigned for that locus. This could have serious ramifications for casework, as errors in the profile could then lead to false inclusion or exclusion of suspects, or false matches if the profile is subjected to a database search.

The consensus profile results showed that a large amount of information was lost when the starting template was divided for amplification. This was especially evident in the 25 pg samples. Compared to the profiles obtained with the full 25 pg using a 34-cycle reaction, the consensus profiles showed a notable increase in allele and locus drop out. When the entire sample was amplified with 34 PCR cycles, 67% of the loci were complete and correct, 25% were heterozygous loci that showed only one allele, 5% showed complete locus drop out and 3% showed allele drop in. When the 25 pg was split for amplification, the resulting consensus profile showed only 29% of the loci as complete and correct, 38% were heterozygous loci that showed only one allele and 33% displayed complete locus drop out. This loss of information that occurs when the sample is divided for amplification is not surprising, since the starting template amount in each of the split samples is barely more than the DNA that is available from a single cell, and that is only if the starting 25 pg is divided equally into thirds. In reality, any of the three aliquots could contain less than a single copy of the genome. Furthermore, if the majority of the 25 pg template happens to end up in one aliquot, then the consensus profiling method may effectively eliminate much information that would be gained from that aliquot's profile because it does not appear in one of the other aliquot profiles. While repeatability is an important measure of reliability, the fact that so much information is lost in the attempt to repeat the results would suggest that the consensus profiling method may not be giving the most informative profile for samples with such a low level of starting template. It could then be argued that the gain of having confidence that no allele drop in has occurred is not sufficient compensation for the loss of profile information. This is particularly so given the increased occurrence of stutter in the split profiles, since a stutter and drop in are in fact both incorrect alleles. The results from the 25 pg split samples show 10 additional alleles in the −4 stutter position, 1 in the +4 stutter position and 4 in other positions. As a consequence, many of the incorrect alleles being eliminated from the consensus profile are likely increased stutter, which is not actually seen in such high amounts in the non-split profiles (six additional alleles overall, two of which are in the −4 stutter position, one in the +4 stutter position, and three in random positions). Based on the allele drop in results, the use of a higher stutter filter for the −4 position and implementation of a +4 stutter filter would significantly reduce the incidence of apparent allele drop in. Therefore, a profile interpretation method that accommodates the increased stutter may be warranted.

It should be noted that, since allele and locus drop out and allele drop in still occurred when a low template DNA sample was amplified in a single reaction, a robust statistical analysis model that takes the stochastic effects into consideration must be applied to the data. A statistical analysis taking these stochastic effects into consideration should, of course, also be applied to the consensus profiles. It is noteworthy that this study was confined to single source samples. Interpretation of mixture profiles generated from LTDNA samples deriving from more than one individual would be more complex. Sample degradation or the presence of PCR inhibitorsissues commonly seen with low template sampleswould further complicate profile interpretation. However, this study has shown that a consensus profile from a split single source sample contains considerably less information than a single profile from a non-split sample. It would, therefore, be preferable to build a statistical model that can be applied to the single LTDNA profile since this should provide the most information.

Conclusions

Overall, this study has demonstrated that performing standard cycling STR typing on non-split DNA extracts will result in profiles with a higher percentage of total loci compared with the consensus profiling technique. Increasing the number of PCR cycles improves the sensitivity of the reaction compared with a standard cycle PCR. However, samples containing template amounts on the upper limits of what would be considered low template DNA may not actually benefit from the increased amplification because of the additional alleles, either drop in or stutter, that can appear in the profile. The repeat nature of the consensus profiling method does eliminate the problem of allele drop in seen with an increased number of PCR cycles, which has important implications for casework. However, consensus profiling also results in the least informative profiles due to increased allele or locus drop out. It also results in more 'incorrect' alleles in the individual profiles used to obtain the consensus profile as a result of increased stutter. Simply performing a single standard cycle PCR on the entire sample produced the most complete profiles when 100 pg of starting template are available for amplification. When only 25 pg of template are available, it would be beneficial to amplify the entire extract with an increased cycle PCR in terms of acquiring a profile with the most information possible. While this must be balanced against the possibility of drop in, it is important to realize that increased stutter alleles that are likely to appear in the split sample profiles are also incorrect alleles and are more likely to be reproducible than 'random' allele drop ins.

Performing a single STR reaction from the whole low template sample does eliminate any chance of repeating the profile. In this sense, consensus profiling may be preferred because the results are seen as repeatable. However, the impression of repeatability gained by the consensus profiling method must be balanced against the notable loss of information that occurs when a LTDNA sample is divided for amplification. While consensus profiling does have its benefits, the method may not be producing the most informative STR profiles for samples where the template amount is limited.

DNA was extracted from five whole blood samples using the BioRobot EZ1 W Workstation with the EZ1 W DNA Blood Kits (QIAGEN, Hilden, Germany) according to the manufacturer's instructions. The resulting extracted DNA was quantitated in triplicate using SensiMix TM High Resolution Melt Kits (Bioline, London, UK) according to the manufacturer's instructions, on a Rotor-Gene TM 6000 (QIAGEN) real time rotary analyzer. Extracts were diluted to low template levels of 100 pg/μl and 25 pg/μl.Short tandem repeat analysisSTR analysis was performed using the PowerPlex W ESI 16 Kits (Promega Corp, Madison, WI, USA). The manufacturer's protocol recommends 30 PCR cycles. Therefore, the samples subjected to the "Standard Cycle PCR" were amplified for 30 cycles. Samples that were analyzed using the "Increased Cycle PCR" were amplified for 34 cycles. Amplification was performed in 25 μl reaction volumes using a GeneAmp W PCR System 9700 (Life Technologies, Carlsbad, CA, USA).Two series of reactions were carried out. For the first series, 100 pg or 25 pg of DNA templates were placed into one STR amplification reaction. Samples were amplified with 30 or 34 PCR cycles. For each template amount and cycling condition, the five donor samples were amplified in triplicate, to generate a total of 15 profiles per template amount and cycling protocol. pg profiles, resulting in 15 consensus profiles for both template amounts. Reference profiles for each of the five donors were obtained using the standard cycling protocol using 500 pg DNA template as recommended by the PowerPlex W ESI 16 manufacturer (Promega Corp.). Electropherograms for all samples were obtained using the 3130 Genetic Analyzer (Life Technologies).Sample preparation

For the
second series of reactions, 15 (5 extracts amplified in
triplicate) 100 pg and 25 pg samples were divided into 3
aliquots, so that 3 reactions containing approximately
33.3 pg or approximately 8.3 pg of template DNA re-
spectively were performed for each 100 pg or 25 pg sam-
ple. Each 33.3 pg and 8.3 pg aliquot was amplified with
34 PCR cycles, giving a total of 45 of each 33.3 pg and
8.3

Table 1
1Allele drop out (ADO)Number
of ADO

% Heterozygote
Loci with ADO

100 pg Starting Template

30 PCR Cycle Amplification

Non-split Samples (n a = 183)
6
3%

34 PCR Cycle Amplification

Non-split Samples (n = 183)
0
0%

Split Samples (n = 549)
114
21%

Consensus Profiles (n = 183)
16
9%

25 pg Starting Template

30 PCR Cycle Amplification

Non-split Samples (n = 183)
80
44%

34 PCR Cycle Amplification

Non-split Samples (n = 183)
61
33%

Split Samples (n = 549)
250
46%

Consensus Profiles (n = 183)
92
50%

a n is the total number of heterozygote loci.

Table 2
2Locus drop out (LDO)Number of LDO
% Loci with LDO

100 pg Starting Template

Table 3
3Allele drop in (ADI)Allele Drop In Placement
Number of ADI
% Loci with ADI

Minus 4
Plus 4
Random

100 pg Starting Template

30 PCR Cycle Amplification

Non-split Samples (n a = 240)
1
1
0
2
0.3%

34 PCR Cycle Amplification

Non-split Samples (n = 240)
2
10
20
32
13%

Split Samples (n = 720)
11
11
10
32
4%

Consensus Profiles (n = 240)
0
0
0
0
0%

25 pg Starting Template

30 PCR Cycle Amplification

Non-split Samples (n = 240)
0
0
0
0
0%

34 PCR Cycle Amplification

Non-split Samples (n = 240)
2
1
3
6
3%

Split Samples (n = 720)
10
1
4
15
2%

Consensus Profiles (n = 240)
0
0
0
0
0%

Total (n = 2,400)
26 (30%)
24 (27%)
37 (43%)
87
4%

a n is the total number of loci.

Table 4
4Peak heightsHomozygous Peaks (RFU)
Heterozygous Peaks (RFU)

n
Range
Mean
Std Dev.
n
Range
Mean
Std Dev.

100 pg Starting Template

30 PCR Cycle Amplification

Non-split Samples
57
185 to 847
520
170
360
55 to 725
261
116

34 PCR Cycle Amplification

Non-split Samples
57
662 to 7,609
4,129
1,767
366
139 to 5,805
1,925
1,233

Split Samples
168
166 to 6,131
1,610
1,090
949
51 to 4,975
763
638

25 pg Starting Template

30 PCR Cycle Amplification

Non-split Samples
49
51 to 337
149
73
200
50 to 360
110
56

34 PCR Cycle Amplification

Non-split Samples
53
212 to 3,475
1,259
805
287
58 to 3,500
708
602

Split Samples
122
59 to 2,144
541
369
458
53 to 2,074
386
260

Table 5
5Peak height ratios (PHR) a n is the number of heterozygote loci with at least one allele present. b n is the number of heterozygote loci with both alleles present.ADO (0%) Included In the PHR Calculation
ADO (0%) Not Included In the PHR Calculation

n a
Mean
PHR

Std.
Deviation

Median
PHR

n b
Mean
PHR

Std.
Deviation

Median
PHR

100 pg Starting
Template

30 PCR Cycle
Amplification

Non-split Samples
183
67%
23%
70%
177
69%
20%
71%

34 PCR Cycle
Amplification

Non-split Samples
183
65%
22%
69%
183
65%
22%
69%

Split Samples
540
45%
32%
47%
426
57%
24%
55%

25 pg Starting
Template

30 PCR Cycle
Amplification

Non-split Samples
140
29%
36%
0%
60
68%
18%
65%

34 PCR Cycle
Amplification

Non-split Samples
174
36%
32%
38%
113
56%
22%
52%

Split Samples
353
19%
32%
0%
103
64%
24%
60%

http://www.investigativegenetics.com/content/3/1/14
AcknowledgementsThis project was financially supported by Bond University. KG is funded by an Australian Postgraduate Award. The authors would like to thank the participants in the study who donated blood and the three anonymous reviewers for their helpful comments and suggestions for the improvements of this manuscript.Competing interestsThe authors declare that they have no competing interests.Authors' contributions KG carried out the molecular studies, data analysis and drafted the manuscript. AvD conceived of the study, participated in its design and coordination, and helped to draft the manuscript. Both authors read and approved the final manuscript.
Short tandem repeat typing technologies used in human identity testing. J M Butler, Biotechniques. 43Sii-SvSupplButler JM: Short tandem repeat typing technologies used in human identity testing. Biotechniques 2007, 43(Suppl):Sii-Sv.

Validation of short tandem repeats (STRs) for forensic usage: performance testing of fluorescent multiplex STR systems and analysis of authentic and simulated forensic samples. T R Moretti, A L Baumstark, D A Defenbaugh, K M Keys, J B Smerick, B Budowle, J Forensic Sci. 46Moretti TR, Baumstark AL, Defenbaugh DA, Keys KM, Smerick JB, Budowle B: Validation of short tandem repeats (STRs) for forensic usage: performance testing of fluorescent multiplex STR systems and analysis of authentic and simulated forensic samples. J Forensic Sci 2001, 46:647-660.

Developmental validation of a single-tube amplification of the 13 CODIS STR loci, D2S1338, D19S433 and amelogenin: the AmpFlSTR Identifiler PCR Amplification Kit. P J Collins, L K Hennessy, C S Leibelt, R K Roby, D J Reeder, P A Foxall, J Forensic Sci. 49Collins PJ, Hennessy LK, Leibelt CS, Roby RK, Reeder DJ, Foxall PA: Developmental validation of a single-tube amplification of the 13 CODIS STR loci, D2S1338, D19S433 and amelogenin: the AmpFlSTR Identifiler PCR Amplification Kit. J Forensic Sci 2004, 49:1265-1277.

Validation of the AMPFlSTR SGM plus system for use in forensic casework. E A Cotton, R F Allsop, J L Guest, R R Frazier, P Koumi, I P Callow, A Seager, R L Sparkes, Forensic Sci Int. 112Cotton EA, Allsop RF, Guest JL, Frazier RR, Koumi P, Callow IP, Seager A, Sparkes RL: Validation of the AMPFlSTR SGM plus system for use in forensic casework. Forensic Sci Int 2000, 112:151-161.

Validation of a 16 locus fluorescent multiplex system. B E Krenke, A Terba, S J Anderson, E Buel, S Culhane, C J Finis, C S Tomsey, J M Zachetti, A Masibay, D R Rabbach, E A Amiott, C J Sprecher, J Forensic Sci. 47Krenke BE, Terba A, Anderson SJ, Buel E, Culhane S, Finis CJ, Tomsey CS, Zachetti JM, Masibay A, Rabbach DR, Amiott EA, Sprecher CJ: Validation of a 16 locus fluorescent multiplex system. J Forensic Sci 2002, 47:733-785.

TWGDAM validation of AmpF_STR™ PCR amplification kits for forensic DNA casework. C L Holt, M Buoncristiani, J M Wallin, T Nguyen, K D Lazaruk, P S Walsh, J Forensic Sci. 47Holt CL, Buoncristiani M, Wallin JM, Nguyen T, Lazaruk KD, Walsh PS: TWGDAM validation of AmpF_STR™ PCR amplification kits for forensic DNA casework. J Forensic Sci 2002, 47:66-96.

DNA fingerprinting from single cells. I Findlay, A Taylor, P Quirke, R Frazier, A Urquhart, Nature. 389Findlay I, Taylor A, Quirke P, Frazier R, Urquhart A: DNA fingerprinting from single cells. Nature 1997, 389:555-556.

Low copy number -Consideration and caution. B Budowle, D L Hobson, J B Smerick, Jal Smith, Twelfth International Symposium on Human Identification. Biloxi, MSPromega CorporationBudowle B, Hobson DL, Smerick JB, Smith JAL: Low copy number - Consideration and caution. In Twelfth International Symposium on Human Identification. Biloxi, MS: Promega Corporation; 2001.

An investigation of the rigor of interpretation rules for STRs derived from less than 100 pg of DNA. P Gill, J Whitaker, C Flaxman, N Brown, J Buckleton, Forensic Sci Int. 112Gill P, Whitaker J, Flaxman C, Brown N, Buckleton J: An investigation of the rigor of interpretation rules for STRs derived from less than 100 pg of DNA. Forensic Sci Int 2000, 112:17-40.

Validation of testing and interpretation protocols for low template DNA samples using AmpFlSTR Identifiler. T Caragine, R Mikulasovich, S Tamariz, E Bajda, J Sebertyen, H Baum, M Pring, Croat Med J. 50Caragine T, Mikulasovich R, Tamariz S, Bajda E, Sebertyen J, Baum H, Pring M: Validation of testing and interpretation protocols for low template DNA samples using AmpFlSTR Identifiler. Croat Med J 2009, 50:250-267.

Low template STR typing: Effect if replicate number and consensus method on genotyping reliability and DNA database search results. Ccg Benschop, C P Van Der Beek, H C Meiland, Agm Van Gorp, A A Westen, T Sijen, Forensic Sci Int Genet. 5Benschop CCG, van der Beek CP, Meiland HC, van Gorp AGM, Westen AA, Sijen T: Low template STR typing: Effect if replicate number and consensus method on genotyping reliability and DNA database search results. Forensic Sci Int Genet 2011, 5:316-328.

Direct comparison of post-28-cycle PCR purification and modified capillary electrophoresis methods with 34-cycle "low copy number" (LCN) method for analysis of trace forensic DNA samples. L Forster, J Thompson, S Kutranov, Forensic Sci Int Genet. 2Forster L, Thompson J, Kutranov S: Direct comparison of post-28-cycle PCR purification and modified capillary electrophoresis methods with 34-cycle "low copy number" (LCN) method for analysis of trace forensic DNA samples. Forensic Sci Int Genet 2008, 2:318-328.

Validation and development of interpretation guidelines for low copy number (LCN) DNA profiling in New Zealand using AmpFlSTR SGM Plus multiplex. S Petricevic, J Whitaker, J Buckleton, S Viniter, J Patel, P Simon, H Ferraby, W Hermiz, A Russell, Forensic Sci Int Genet. 4Petricevic S, Whitaker J, Buckleton J, Viniter S, Patel J, Simon P, Ferraby H, Hermiz W, Russell A: Validation and development of interpretation guidelines for low copy number (LCN) DNA profiling in New Zealand using AmpFlSTR SGM Plus multiplex. Forensic Sci Int Genet 2010, 4:305-310.

Composite profiles in DNA analysis. J Bright, P Gill, J Buckleton, Forensic Sci Int Genet. 6Bright J, Gill P, Buckleton J: Composite profiles in DNA analysis. Forensic Sci Int Genet 2012, 6:317-321.

A: Validity of low copy number typing and applications to forensic science. B Budowle, A J Eisenberg, Van Daal, Croat Med J. 50Budowle B, Eisenberg AJ, van Daal A: Validity of low copy number typing and applications to forensic science. Croat Med J 2009, 50:207-217.

Validation issues around DNA typing of low level DNA. J Buckleton, Forensic Sci Int Genet. 3Buckleton J: Validation issues around DNA typing of low level DNA. Forensic Sci Int Genet 2009, 3:253-260.

A universal strategy to interpret DNA profiles that does not require a definition of low-copy-number. P Gill, J Buckleton, Forensic Sci Int Genet. 4Gill P, Buckleton J: A universal strategy to interpret DNA profiles that does not require a definition of low-copy-number. Forensic Sci Int Genet 2010, 4:221-227.

Developmental validation of the PowerPlex W ESI 16 and PowerPlex W ESI 17 systems: STR multiplexes for the new European standard. V C Tucker, A J Hopwood, C J Sprecher, R S Mclaren, D R Rabbach, M G Ensenberger, J M Thompson, D R Storts, 10.1186/2041-2223-3-14Forensic Sci Int Genet. 5Tucker VC, Hopwood AJ, Sprecher CJ, McLaren RS, Rabbach DR, Ensenberger MG, Thompson JM, Storts DR: Developmental validation of the PowerPlex W ESI 16 and PowerPlex W ESI 17 systems: STR multiplexes for the new European standard. Forensic Sci Int Genet 2011, 5:436-448. doi:10.1186/2041-2223-3-14

Cite this article as: Grisedale and van Daal: Comparison of STR profiling from low template DNA extracts with and without the consensus profiling method. Investigative Genetics. 314Cite this article as: Grisedale and van Daal: Comparison of STR profiling from low template DNA extracts with and without the consensus profiling method. Investigative Genetics 2012 3:14.