Relative quantification based on logistic models for individual polymerase chain reactions

Estimating Real-Time qPCR Amplification Efficiency from Single-Reaction Data

Methods for estimating the qPCR amplification efficiency E from data for single reactions are tested on six multireplicate datasets, with emphasis on their performance as a function of the range of cycles n₁–n₂ included in the analysis. The two-parameter exponential growth (EG) model that has been relied upon almost exclusively does not allow for the decline of E(n) with increasing cycle number n through the growth region and accordingly gives low-biased estimates. Further, the standard procedure of “baselining”—separately estimating and subtracting a baseline before analysis—leads to reduced precision. The three-parameter logistic model (LRE) does allow for such decline and includes a parameter E₀ that represents E through the baseline region. Several four-parameter extensions of this model that accommodate some asymmetry in the growth profiles but still retain the significance of E₀ are tested against the LRE and EG models. The recursion method of Carr and Moore also describes a declining E(n) but tacitly assumes E₀ = 2 in the baseline region. Two modifications that permit varying E₀ are tested, as well as a recursion method that directly fits E(n) to a sigmoidal function. All but the last of these can give E₀ estimates that agree fairly well with calibration-based estimates but perform best when the calculations are extended to only about one cycle below the first-derivative maximum (FDM). The LRE model performs as well as any of the four-parameter forms and is easier to use. Its proper implementation requires fitting to it plus a suitable baseline function, which typically requires four–six adjustable parameters in a nonlinear least-squares fit.

Critique of the pairwise method for estimating qPCR amplification efficiency: beware of correlated data!

Background

A recently proposed method for estimating qPCR amplification efficiency E analyzes fluorescence intensity ratios from pairs of points deemed to lie in the exponential growth region on the amplification curves for all reactions in a dilution series. This method suffers from a serious problem: The resulting ratios are highly correlated, as they involve multiple use of the raw data, for example, yielding ~ 250 E estimates from ~ 25 intensity readings. The resulting statistics for such estimates are falsely optimistic in their assessment of the estimation precision.

Results

Monte Carlo simulations confirm that the correlated pairs method yields precision estimates that are better than actual by a factor of two or more. This result is further supported by estimating E by both pairwise and Cq calibration methods for the 16 replicate datasets from the critiqued work, and then comparing the ensemble statistics for these methods.

Conclusion

Contrary to assertions in the proposing work, the pairwise method does not yield E estimates a factor of 2 more precise than estimates from Cq calibration fitting (the standard curve method). On the other hand, the statistically correct direct fit of the data to the model behind the pairwise method can yield E estimates of comparable precision. Ways in which the approach might be improved are discussed briefly.

Identification and validation of potential reference gene for effective dsRNA knockdown analysis in Chilo partellus

Chilo partellus is an invasive polyphagous pest that has not been effectively managed with chemical pesticides. To select potential dsRNAs for use in an alternate control strategy, it is crucial to identify and evaluate stable reference genes for knockdown expression studies. This study evaluates the expression stability of seven candidate reference genes in C. partellus larvae fed on crude bacterially-expressed dsRNAs and purified dsRNAs at different time intervals, as well as the developmental stages and sexes. The expression stabilities of the reference genes were evaluated with different software programmes, such as BestKeeper, NormFinder, deltaCt, geNorm, and RefFinder. The overall results rank ELF as the most stably expressed reference gene when larvae were fed with crude bacteria-induced dsRNAs and purified dsRNA. However, Tubulin and HSP70 were more stable under different developmental stages and sexes. The expression levels of larvae that were fed crude bacteria-induced dsRNAs of Chitinase and Acetylcholinesterase were normalized with the four most stable reference genes (ELF, HSP70, V-ATPase and Tubulin) and the least stable reference gene (18S and HSP70) based on the geNorm algorithm. The least stable reference gene showed inconsistent knockdown expression, thereby confirming that the validation of a suitable reference gene is crucial to improve assay accuracy for dsRNA-targeted gene selection in C. partellus.

qPCR data analysis: Better results through iconoclasm

The standard approach for quantitative estimation of genetic materials with qPCR is calibration with known concentrations for the target substance, in which estimates of the quantification cycle (C_q ) are fitted to a straight-line function of log(N ₀), where N ₀ is the initial number of target molecules. The location of C_q for the unknown on this line then yields its N ₀. The most widely used definition for C_q is an absolute threshold that falls in the early growth cycles. This usage is flawed as commonly implemented: threshold set very close to the baseline level, which is estimated separately, from designated "baseline cycles." The absolute threshold is especially poor for dealing with the scale variability often observed for growth profiles. Scale-independent markers, like the first derivative maximum (FDM) and a relative threshold (C_r ) avoid this problem. We describe improved methods for estimating these and other C_q markers and their standard errors, from a nonlinear algorithm that fits growth profiles to a 4-parameter log-logistic function plus a baseline function. Further, by examining six multidilution, multireplicate qPCR data sets, we find that nonlinear expressions are often preferred statistically for the dependence of C_q on log(N ₀). This means that the amplification efficiency E depends on N ₀, in violation of another tenet of qPCR analysis. Neglect of calibration nonlinearity leads to biased estimates of the unknown. By logic, E estimates from calibration fitting pertain to the earliest baseline cycles, not the early growth cycles used to estimate E from growth profiles for single reactions. This raises concern about the use of the latter in lengthy extrapolations to estimate N ₀. Finally, we observe that replicate ensemble standard deviations greatly exceed predictions, implying that much better results can be achieved from qPCR through better experimental procedures, which likely include reducing pipette volume uncertainty.

Modeling qRT-PCR dynamics with application to cancer biomarker quantification

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is widely used for molecular diagnostics and evaluating prognosis in cancer. The utility of mRNA expression biomarkers relies heavily on the accuracy and precision of quantification, which is still challenging for low abundance transcripts. The critical step for quantification is accurate estimation of efficiency needed for computing a relative qRT-PCR expression. We propose a new approach to estimating qRT-PCR efficiency based on modeling dynamics of polymerase chain reaction amplification. In contrast, only models for fluorescence intensity as a function of polymerase chain reaction cycle have been used so far for quantification. The dynamics of qRT-PCR efficiency is modeled using an ordinary differential equation model, and the fitted ordinary differential equation model is used to obtain effective polymerase chain reaction efficiency estimates needed for efficiency-adjusted quantification. The proposed new qRT-PCR efficiency estimates were used to quantify GUCY2C (Guanylate Cyclase 2C) mRNA expression in the blood of colorectal cancer patients. Time to recurrence and GUCY2C expression ratios were analyzed in a joint model for survival and longitudinal outcomes. The joint model with GUCY2C quantified using the proposed polymerase chain reaction efficiency estimates provided clinically meaningful results for association between time to recurrence and longitudinal trends in GUCY2C expression.

The paracrine hormone for the GUCY2C tumor suppressor, guanylin, is universally lost in colorectal cancer

BACKGROUND

Although colorectal cancer is a disease characterized by sequential accumulation of mutations in epithelial cells, mechanisms leading to genomic vulnerability contributing to tumor initiation remain undefined. GUCY2C has emerged as an intestine-specific tumor suppressor controlling epithelial homeostasis through circuits canonically disrupted in cancer. Surprisingly, the GUCY2C tumor suppressor is universally overexpressed by human colorectal cancer cells. This apparent paradox likely reflects silencing of GUCY2C through loss of its paracrine hormone guanylin. Here, we quantified expression of guanylin mRNA and protein in tumors and normal epithelia from patients with colorectal cancer.

METHODS

Guanylin mRNA was quantified in tumors and normal adjacent epithelia from 281 patients by the reverse transcriptase-polymerase chain reaction. Separately, the guanylin protein was quantified by immunohistochemistry in 54 colorectal tumors and 30 specimens of normal intestinal epithelium.

RESULTS

Guanylin mRNA in colorectum varied more than a 100-fold across the population. Guanylin mRNA was reduced 100- to 1,000-fold in >85% of tumors compared with matched normal adjacent mucosa (P < 0.001). Loss of guanylin mRNA was greatest in tumors from patients <50 years old (P < 0.005) and with the highest expression in normal adjacent mucosa (Spearman correlation coefficient = 0.61; P < 0.001). In a separate validation cohort, guanylin protein was detected in all 30 normal colorectal mucosa specimens, but in none of 54 colorectal tumors.

CONCLUSIONS

Colorectal cancer may initiate as a disease of paracrine hormone insufficiency through loss of guanylin expression, silencing the GUCY2C tumor suppressor and disrupting homeostatic mechanisms regulating colorectal epithelia cells.

IMPACT

Intestinal tumorigenesis may be prevented by oral GUCY2C hormone replacement therapy.

Comparing real-time quantitative polymerase chain reaction analysis methods for precision, linearity, and accuracy of estimating amplification efficiency

New methods are used to compare seven qPCR analysis methods for their performance in estimating the quantification cycle (Cq) and amplification efficiency (E) for a large test data set (94 samples for each of 4 dilutions) from a recent study. Precision and linearity are assessed using chi-square (χ(2)), which is the minimized quantity in least-squares (LS) fitting, equivalent to the variance in unweighted LS, and commonly used to define statistical efficiency. All methods yield Cqs that vary strongly in precision with the starting concentration N0, requiring weighted LS for proper calibration fitting of Cq vs log(N0). Then χ(2) for cubic calibration fits compares the inherent precision of the Cqs, while increases in χ(2) for quadratic and linear fits show the significance of nonlinearity. Nonlinearity is further manifested in unphysical estimates of E from the same Cq data, results which also challenge a tenet of all qPCR analysis methods - that E is constant throughout the baseline region. Constant-threshold (Ct) methods underperform the other methods when the data vary considerably in scale, as these data do.

Accurate and precise DNA quantification in the presence of different amplification efficiencies using an improved Cy0 method

Quantitative real-time PCR represents a highly sensitive and powerful technology for the quantification of DNA. Although real-time PCR is well accepted as the gold standard in nucleic acid quantification, there is a largely unexplored area of experimental conditions that limit the application of the Ct method. As an alternative, our research team has recently proposed the Cy0 method, which can compensate for small amplification variations among the samples being compared. However, when there is a marked decrease in amplification efficiency, the Cy0 is impaired, hence determining reaction efficiency is essential to achieve a reliable quantification. The proposed improvement in Cy0 is based on the use of the kinetic parameters calculated in the curve inflection point to compensate for efficiency variations. Three experimental models were used: inhibition of primer extension, non-optimal primer annealing and a very small biological sample. In all these models, the improved Cy0 method increased quantification accuracy up to about 500% without affecting precision. Furthermore, the stability of this procedure was enhanced integrating it with the SOD method. In short, the improved Cy0 method represents a simple yet powerful approach for reliable DNA quantification even in the presence of marked efficiency variations.

Auxin promotes susceptibility to Pseudomonas syringae via a mechanism independent of suppression of salicylic acid-mediated defenses

Auxin is a key plant growth regulator that also impacts plant-pathogen interactions. Several lines of evidence suggest that the bacterial plant pathogen Pseudomonas syringae manipulates auxin physiology in Arabidopsis thaliana to promote pathogenesis. Pseudomonas syringae strategies to alter host auxin biology include synthesis of the auxin indole-3-acetic acid (IAA) and production of virulence factors that alter auxin responses in host cells. The application of exogenous auxin enhances disease caused by P. syringae strain DC3000. This is hypothesized to result from antagonism between auxin and salicylic acid (SA), a major regulator of plant defenses, but this hypothesis has not been tested in the context of infected plants. We further investigated the role of auxin during pathogenesis by examining the interaction of auxin and SA in the context of infection in plants with elevated endogenous levels of auxin. We demonstrated that elevated IAA biosynthesis in transgenic plants overexpressing the YUCCA 1 (YUC1) auxin biosynthesis gene led to enhanced susceptibility to DC3000. Elevated IAA levels did not interfere significantly with host defenses, as effector-triggered immunity was active in YUC1-overexpressing plants, and we observed only minor effects on SA levels and SA-mediated responses. Furthermore, a plant line carrying both the YUC1-overexpression transgene and the salicylic acid induction deficient 2 (sid2) mutation, which impairs SA synthesis, exhibited additive effects of enhanced susceptibility from both elevated auxin levels and impaired SA-mediated defenses. Thus, in IAA overproducing plants, the promotion of pathogen growth occurs independently of suppression of SA-mediated defenses.

Molecular staging of node negative patients with colorectal cancer

Metastatic disease is the principle cause of death from colorectal cancer. In that context, the most significant indicator of overall survival and therapeutic response to adjuvant chemotherapy is the presence of metastatic tumor cells in regional lymph nodes. Although histopathologic analysis of lymph nodes is central to all colorectal cancer staging paradigms, its prognostic and predictive value is limited. Indeed, about 30% of patients with histopathology-negative lymph nodes (pN0) die from metastatic disease, reflected by microscopic lymph node metastases that are overlooked by standard techniques. These unrecognized tumor cells are especially important when considering racial disparities in outcomes in colorectal cancer patients, where blacks with lymph node-negative disease have the largest discrepancies in outcomes, with more than 40% excess mortality compared to Caucasian patients. However, the significance of tumor cells in regional lymph nodes remains uncertain, and approximately 50% of colorectal cancer patients with nodal metastases detected by histopathology remain free of recurrent disease. Accurate identification of occult metastases in regional lymph nodes, and defining their value as prognostic markers of recurrence risk and predictive markers of response to adjuvant chemotherapy remains one challenge in the management of colorectal cancer patients. Guanylyl cyclase C (GUCY2C), a receptor which is expressed primarily in intestinal cells normally, but is universally over-expressed by colorectal cancer cells, has been validated to detect prognostically significant occult metastases using quantitative RT-PCR (RT-qPCR). Biomarker validation was achieved through a prospective, multicenter, blinded clinical trial. In that trial, occult tumor burden estimated across all regional lymph nodes by GUCY2C RT-qPCR predicted clinical outcomes, identifying node-negative patients with a low (near zero) risk, and those with >80% risk, of developing disease recurrence. Moreover, there was disproportionately higher occult tumor burden in black, compared to white, patients which contributes to racial disparities in outcomes in colorectal cancer. The diagnostic paradigm quantifying occult tumor burden using GUCY2C qRT-PCR is positioned to reduce racial disparities in colorectal cancer mortality.

Guanylyl cyclase C as a biomarker in colorectal cancer

While histologic assessment of nodes is a component of all colon cancer staging paradigms, approximately 30% of patients with histology-negative nodes (pN0) die of disseminated disease reflected by occult nodal metastases. Undetected metastases are particularly important when considering racial disparities in colon cancer, where black subjects with pN0 disease exhibit the greatest differences in outcomes, with >40% excess mortality. Recently, guanylyl cyclase C (GCC), a protein normally restricted to intestinal cells, but universally expressed by colorectal cancer cells, was validated for detecting occult metastases. Indeed, occult tumor burden across regional lymph nodes estimated by GCC quantitative reverse transcription PCR identifies pN0 patients with near zero risk, and those with >80% risk, of unfavorable outcomes. Disproportionately high occult tumor burden in black patients underlies racial disparities in stage-specific mortality. These studies position the platform encompassing quantification of occult tumor burden by GCC quantitative reverse transcription PCR for translation, as a detect-treat paradigm to reduce racial disparities in colon cancer mortality.

Analysis of Arabidopsis JAZ gene expression during Pseudomonas syringae pathogenesis

The jasmonates (JAs) comprise a family of plant hormones that regulate several developmental processes and mediate responses to various abiotic and biotic stresses, including pathogens. JA signalling is manipulated by several strains of the bacterial pathogen Pseudomonas syringae, including P. syringae strain DC3000, using the virulence factor coronatine (COR) as a mimic of jasmonyl-L-isoleucine (JA-Ile). To better understand the JA-Ile-mediated processes contributing to P. syringae disease susceptibility, it is important to investigate the regulation of JA signalling during infection. In Arabidopsis thaliana, JASMONATE ZIM-DOMAIN (JAZ) proteins are negative regulators of JA signalling. The transcription factor JASMONATE INSENSITIVE1 (JIN1/ATMYC2) has been implicated in the regulation of JAZ gene expression. To investigate the regulation of JAZ genes during P. syringae pathogenesis, we examined JAZ gene expression during infection of Arabidopsis by DC3000. We found that eight of the 12 JAZ genes are induced during infection in a COR-dependent manner. Unexpectedly, the induction of the majority of JAZ genes during infection was not dependent on JIN1, indicating that JIN1 is not the only transcription factor regulating JAZ genes. A T-DNA insertion mutant and an RNA interference line disrupted for the expression of JAZ10, one of the few JAZ genes regulated by JIN1 during infection, exhibited enhanced JA sensitivity and increased susceptibility to DC3000, with the primary effect being increased disease symptom severity. Thus, JAZ10 is a negative regulator of both JA signalling and disease symptom development.

Occult tumor burden contributes to racial disparities in stage-specific colorectal cancer outcomes

BACKGROUND

There are differences in outcomes in blacks compared with whites with lymph node-negative (pN0) colorectal cancer. Recurrence in pN0 patients suggests the presence of occult metastases undetected by conventional approaches. This study explores the association of racial differences in outcomes with occult tumor burden in regional lymph nodes.

METHODS

Lymph nodes (range, 2-159) from 282 prospectively enrolled pN0 colorectal cancer patients followed for a median of 24 months (range, 2-63 months) were subjected to molecular analysis. Occult tumor burden was estimated by quantifying the expression of GUCY2C, a biomarker for metastatic colorectal cancer cells. Risk categories defined using occult tumor burden was the primary outcome measure. Association of prognostic variables and risk were defined by multivariate polytomous logistic regression.

RESULTS

Occult tumor burden stratified this cohort of 259 whites and 23 blacks into categories with low (60%; recurrence rate [RR] = 2.3%; 95% confidence interval [CI], 0.1%-4.5%), intermediate (31%; RR = 33.3%; 95% CI, 23.7%-44.1%), and high (9%; RR = 68.0%; 95% CI, 46.5%-85.1%; P < .001) risk. Blacks compared with whites exhibited 4-fold greater occult metastases in individual lymph nodes (P < .001). Multivariate analysis revealed that race (P = .02), T stage (P = .02), and number of lymph nodes collected (P = .003) were independent prognostic markers of risk category. Blacks compared with whites were more likely to harbor levels of occult tumor burden, associated with the highest recurrence risk (adjusted odds ratio = 5.08; 95% CI, 1.69-21.39; P = .007).

CONCLUSIONS

Racial disparities in stage-specific outcomes in colorectal cancer are associated with differences in occult tumor burden in regional lymph nodes.

Occult tumor burden predicts disease recurrence in lymph node-negative colorectal cancer

PURPOSE

Lymph node involvement by histopathology informs colorectal cancer prognosis, whereas recurrence in 25% of node-negative patients suggests the presence of occult metastasis. GUCY2C (guanylyl cyclase C) is a marker of colorectal cancer cells that identifies occult nodal metastases associated with recurrence risk. Here, we defined the association of occult tumor burden, quantified by GUCY2C reverse transcriptase-PCR (RT-PCR), with outcomes in colorectal cancer.

EXPERIMENTAL DESIGN

Lymph nodes (range: 2-159) from 291 prospectively enrolled node-negative colorectal cancer patients were analyzed by histopathology and GUCY2C quantitative RT-PCR. Participants were followed for a median of 24 months (range: 2-63). Time to recurrence and disease-free survival served as primary and secondary outcomes, respectively. Association of outcomes with prognostic markers, including molecular tumor burden, was estimated by recursive partitioning and Cox models.

RESULTS

In this cohort, 176 (60%) patients exhibited low tumor burden (Mol(Low)), and all but four remained free of disease [recurrence rate 2.3% (95% CI, 0.1-4.5%)]. Also, 90 (31%) patients exhibited intermediate tumor burden (Mol(Int)) and 30 [33.3% (23.7-44.1)] developed recurrent disease. Furthermore, 25 (9%) patients exhibited high tumor burden (Mol(High)) and 17 [68.0% (46.5-85.1)] developed recurrent disease (P < 0.001). Occult tumor burden was an independent marker of prognosis. Mol(Int) and Mol(High) patients exhibited a graded risk of earlier time to recurrence [Mol(Int), adjusted HR 25.52 (11.08-143.18); P < 0.001; Mol(High), 65.38 (39.01-676.94); P < 0.001] and reduced disease-free survival [Mol(Int), 9.77 (6.26-87.26); P < 0.001; Mol(High), 22.97 (21.59-316.16); P < 0.001].

CONCLUSION

Molecular tumor burden in lymph nodes is independently associated with time to recurrence and disease-free survival in patients with node-negative colorectal cancer.

Molecular staging estimates occult tumor burden in colorectal cancer

Tumor cells in regional lymph nodes are a key prognostic marker of survival and predictive marker of response to adjuvant chemotherapy in colorectal cancer. However, clinicopathologic techniques to detect lymph node metastases remain imperfect, and approximately 30% of patients with lymph nodes negative by histology (pN0) develop recurrent disease, reflecting occult metastases that escape detection. These observations underscore an unmet clinical need for accurate approaches to identify occult nodal metastases in colorectal cancer patients. GUCY2C is a receptor whose expression normally is restricted to intestinal epithelial cells, but is universally overexpressed by colorectal cancer cells. A prospective, multicenter, blinded clinical trial established the prognostic utility of GUCY2C qRT-PCR to detect occult nodal metastases in pN0 colorectal cancer patients. Molecular staging revealed that approximately 13% of pN0 patients were free of cancer cells, while approximately 87% had GUCY2C results that suggested occult metastases. The presence of occult nodal metastases was the most powerful independent predictor of time to recurrence and disease-free survival. These observations establish the utility of molecular detection of occult nodal metastases for assessing prognostic risk in pN0 colorectal cancer patients. Advancing GUCY2C into staging paradigms in clinical laboratories will require validation in independent patient populations, definition of the relationship between the quantity of occult tumor metastases and risk, and determination of the utility of GUCY2C qRT-PCR to identify pN0 patients who might benefit from adjuvant chemotherapy.

GUCY2C reverse transcriptase PCR to stage pN0 colorectal cancer patients

The most important prognostic marker of survival and predictive marker of response to adjuvant chemotherapy in colon cancer patients is tumor cells in regional lymph nodes. Despite their importance, standard techniques to assess nodal metastases remain imperfect, as approximately 30% of patients with histology-negative lymph nodes (pN0) die of recurrent disease, reflecting occult metastases that escape detection. These observations highlight the clinical need for novel, accurate approaches to detect occult lymph node metastases in patients with colon cancer. GUCY2C is a biomarker whose expression normally is restricted to intestinal cells, but is near universally overexpressed by colorectal cancer cells. Recently, a prospective, multicenter, blinded clinical trial demonstrated for the first time that the prognostic utility of GUCY2C quantitative reverse transcriptase (qRT)-PCR to detect occult lymph node metastases in pN0 colorectal cancer patients. Molecular staging revealed that approximately 13% of pN0 patients were free of tumor cells, while approximately 87% had GUCY2C results that suggested occult metastases. The presence of occult lymph node metastases was the strongest independent predictor of time to recurrence and disease-free survival. These observations establish the utility of molecular detection of occult lymph node metastases for estimating prognostic risk in pN0 colorectal cancer patients. Advancing this molecular diagnostic into staging paradigms in clinical laboratories will require validation in independent patient populations, definition of the relationship between the quantity of occult tumor metastases and risk, and determination of the utility of GUCY2C qRT-PCR to identify pN0 patients who might benefit from adjuvant chemotherapy.

Association of GUCY2C expression in lymph nodes with time to recurrence and disease-free survival in pN0 colorectal cancer

CONTEXT

The established relationship between lymph node metastasis and prognosis in colorectal cancer suggests that recurrence in 25% of patients with lymph nodes free of tumor cells by histopathology (pN0) reflects the presence of occult metastases. Guanylyl cyclase 2C (GUCY2C) is a marker expressed by colorectal tumors that could reveal occult metastases in lymph nodes and better estimate recurrence risk.

OBJECTIVE

To examine the association of occult lymph node metastases detected by quantifying GUCY2C messenger RNA, using the reverse transcriptase-polymerase chain reaction, with recurrence and survival in patients with colorectal cancer.

DESIGN, SETTING, AND PARTICIPANTS

Prospective study of 257 patients with pN0 colorectal cancer enrolled between March 2002 and June 2007 at 9 US and Canadian centers (7 academic medical centers and 2 community hospitals) provided 2570 fresh lymph nodes measuring 5 mm or larger for histopathology and GUCY2C messenger RNA analysis. Patients were followed up for a median of 24 months (range, 2-63 months) for disease recurrence or death.

MAIN OUTCOME MEASURES

Time to recurrence (primary outcome) and disease-free survival (secondary outcome) relative to expression of GUCY2C in lymph nodes.

RESULTS

Thirty-two patients (12.5%) had lymph nodes negative for GUCY2C (pN0 [mol-]), and all but 2 remained free of disease during follow-up (recurrence rate, 6.3%; 95% confidence interval [CI], 0.8%-20.8%). Conversely, 225 patients (87.5%) had lymph nodes positive for GUCY2C (pN0 [mol+]), and 47 developed recurrent disease (20.9%; 95% CI, 15.8%-26.8%) (P = .006). Multivariate analyses revealed that GUCY2C in lymph nodes was an independent marker of prognosis. Patients who were pN0 (mol+) exhibited earlier time to recurrence (adjusted hazard ratio, 4.66; 95% CI, 1.11-19.57; P = .04) and reduced disease-free survival (adjusted hazard ratio, 3.27; 95% CI, 1.15-9.29; P = .03).

CONCLUSION

Expression of GUCY2C in histologically negative lymph nodes appears to be independently associated with time to recurrence and disease-free survival in patients with pN0 colorectal cancer.

Critical evaluation of methods used to determine amplification efficiency refutes the exponential character of real-time PCR

Background

The challenge of determining amplification efficiency has long been a predominant aspect of implementing real-time qPCR, playing a critical role in the accuracy and reliability that can be achieved. Based upon analysis of amplification profile position, standard curves are currently the gold standard for amplification efficiency determination. However, in addition to being highly resource intensive, the efficacy of this approach is limited by the necessary assumption that all samples are amplified with the same efficiency as predicted by a standard curve. These limitations have driven efforts to develop methods for determining amplification efficiency by analyzing the fluorescence readings from individual amplification reactions. The most prominent approach is based on analysis of the "log-linear region", founded upon the presumption that amplification efficiency is constant within this region. Nevertheless, a recently developed sigmoidal model has provided new insights that challenge such historically held views, dictating that amplification efficiency is not only dynamic, but is linearly coupled to amplicon DNA quantity. Called "linear regression of efficiency" or LRE, this kinetic-based approach redefines amplification efficiency as the maximal efficiency (E_max) generated at the onset of thermocycling.

Results

This study presents a critical evaluation of amplification efficiency determination, which reveals that potentially large underestimations occur when exponential mathematics is applied to the log-linear region. This discrepancy was found to stem from misinterpreting the origin of the log-linear region, which is derived not from an invariant amplification efficiency, but rather from an exponential loss in amplification rate. In contrast, LRE analysis generated E_max estimates that correlated closely to that derived from a standard curve, despite the fact that standard curve analysis is founded upon exponential mathematics. This paradoxical result implies that the quantitative efficacy of positional-based analysis relies not upon the exponential character of real-time PCR, but instead on the ability to precisely define the relative position of an amplification profile.

Conclusion

In addition to presenting insights into the sigmoidal character of the polymerase chain reaction, LRE analysis provides a viable alternative to standard curves for amplification efficiency determination, based on analysis of high-quality fluorescence readings within the central region of SYBR Green I generated amplification profiles.

A kinetic-based sigmoidal model for the polymerase chain reaction and its application to high-capacity absolute quantitative real-time PCR

Background

Based upon defining a common reference point, current real-time quantitative PCR technologies compare relative differences in amplification profile position. As such, absolute quantification requires construction of target-specific standard curves that are highly resource intensive and prone to introducing quantitative errors. Sigmoidal modeling using nonlinear regression has previously demonstrated that absolute quantification can be accomplished without standard curves; however, quantitative errors caused by distortions within the plateau phase have impeded effective implementation of this alternative approach.

Results

Recognition that amplification rate is linearly correlated to amplicon quantity led to the derivation of two sigmoid functions that allow target quantification via linear regression analysis. In addition to circumventing quantitative errors produced by plateau distortions, this approach allows the amplification efficiency within individual amplification reactions to be determined. Absolute quantification is accomplished by first converting individual fluorescence readings into target quantity expressed in fluorescence units, followed by conversion into the number of target molecules via optical calibration. Founded upon expressing reaction fluorescence in relation to amplicon DNA mass, a seminal element of this study was to implement optical calibration using lambda gDNA as a universal quantitative standard. Not only does this eliminate the need to prepare target-specific quantitative standards, it relegates establishment of quantitative scale to a single, highly defined entity. The quantitative competency of this approach was assessed by exploiting "limiting dilution assay" for absolute quantification, which provided an independent gold standard from which to verify quantitative accuracy. This yielded substantive corroborating evidence that absolute accuracies of ± 25% can be routinely achieved. Comparison with the LinReg and Miner automated qPCR data processing packages further demonstrated the superior performance of this kinetic-based methodology.

Conclusion

Called "linear regression of efficiency" or LRE, this novel kinetic approach confers the ability to conduct high-capacity absolute quantification with unprecedented quality control capabilities. The computational simplicity and recursive nature of LRE quantification also makes it amenable to software implementation, as demonstrated by a prototypic Java program that automates data analysis. This in turn introduces the prospect of conducting absolute quantification with little additional effort beyond that required for the preparation of the amplification reactions.