Life History Traits and Niche Instability Impact Accuracy and Temporal Transferability for Historically Calibrated Distribution Models of North American Birds

A primary assumption of environmental niche models (ENMs) is that models are both accurate and transferable across geography or time; however, recent work has shown that models may be accurate but not highly transferable. While some of this is due to modeling technique, individual species ecologies may also underlie this phenomenon. Life history traits certainly influence the accuracy of predictive ENMs, but their impact on model transferability is less understood. This study investigated how life history traits influence the predictive accuracy and transferability of ENMs using historically calibrated models for birds. In this study I used historical occurrence and climate data (1950-1990s) to build models for a sample of birds, and then projected them forward to the ‘future’ (1960-1990s). The models were then validated against models generated from occurrence data at that ‘future’ time. Internal and external validation metrics, as well as metrics assessing transferability, and Generalized Linear Models were used to identify life history traits that were significant predictors of accuracy and transferability. This study found that the predictive ability of ENMs differs with regard to life history characteristics such as range, migration, and habitat, and that the rarity versus commonness of a species affects the predicted stability and overlap and hence the transferability of projected models. Projected ENMs with both high accuracy and transferability scores, still sometimes suffered from over- or under- predicted species ranges. Life history traits certainly influenced the accuracy of predictive ENMs for birds, but while aspects of geographic range impact model transferability, the mechanisms underlying this are less understood.     

肾癌预后分子生物标记物研究进展

肾癌发病率约占全身恶性肿瘤的3%。肾癌组织学行为多变,预后有不确定性。外科手术可以治疗局限性肾癌,但有将近20%的局限性肾癌患者原发肿瘤切除后出现转移,而且肾癌对化疗及放疗均不敏感。基于此临床上开展了许多辅助试验的研究,并建立了许多模型来研究肾癌术后的预后,而模型的精准度一般都需要依据肾癌的生物标记物监测。有很多分子生物标记物已经证实和肾癌预后相关,如VHL、P53、Ki-67等,本文综述了肾癌预后的分子生物标记物的最新进展。     

Prediction using partly conditional time-varying coefficients regression models

Data collected longitudinally in time provide the opportunity to develop predictive models of future observations given current data for an individual. Such models may be of particular value in defining individuals at high risk and thereby in suggesting subgroups for targeting of prevention intervention research efforts. In this paper, we propose a method for estimating predictive functions. The method uses an extension of the marginal regression analysis methods of Liang and Zeger (1986, Biometrika 73, 13-22) and is implemented using simple estimating equations. A key feature of the models is that regression coefficients are modelled as smooth functions of the times both at and for prediction. Data from a study of obesity in childhood and early adulthood is used to demonstrate the methodology. Criteria for defining individuals to be at high risk can be defined on the basis of estimated predictive functions. We suggest methods for evaluating the diagnostic accuracy (sensitivity and specificity) of such rules using cross-validation. The method holds promise as a robust and technically easy way of evaluating information about future prognosis that may be gleaned from a patient's current and past clinical status.     

Proteins related to lipoprotein profile were identified using a pharmaco-proteomic approach as markers for growth response to growth hormone (GH) treatment in short prepubertal children

Background  

The broad range in growth observed in response to growth hormone (GH) treatment is mainly caused by individual variations in both GH secretion and GH sensitivity. Individual GH responsiveness can be estimated using evidence-based models that predict the response to GH treatment; however, these models can be improved. High-throughput proteomics techniques can be used to identify proteins that may potentially be used as variables in such models in order to improve their predictive ability. Previously we have reported that proteomic analyses can identify biomarkers that discriminate between short prepubertal children with idiopathic short stature (ISS) who show good or poor growth in response to GH treatment. In this study we used a pharmaco-proteomic approach to identify novel factors that correlate with the growth response to GH treatment in prepubertal children who are short due to GH deficiency or ISS. The study included 128 short prepubertal children receiving GH treatment, of whom 39 were GH-deficient and 89 had ISS. Serum protein expression profiles at study start and after 1 year of GH treatment were analyzed using SELDI-TOF. Cross-validated regression and random permutation analyses were performed to identify significant correlations between protein expression patterns and the 2-year growth response to GH treatment.     

Moving towards the detection of frailty with biomarkers: A population health study

Aging adults experience increased health vulnerability and compromised abilities to cope with stressors, which are the clinical manifestations of frailty. Frailty is complex, and efforts to identify biomarkers to detect frailty and pre-frailty in the clinical setting are rarely reproduced across cohorts. We developed a predictive model incorporating biological and clinical frailty measures to identify robust biomarkers across data sets. Data were from two large cohorts of older adults: “Invecchiare in Chianti (Aging in Chianti, InCHIANTI Study”) (n = 1453) from two small towns in Tuscany, Italy, and replicated in the Atherosclerosis Risk in Communities Study (ARIC) (n = 6508) from four U.S. communities. A complex systems approach to biomarker selection with a tree-boosting machine learning (ML) technique for supervised learning analysis was used to examine biomarker population differences across both datasets. Our approach compared predictors with robust, pre-frail, and frail participants and examined the ability to detect frailty status by race. Unique biomarker features identified in the InCHIANTI study allowed us to predict frailty with a model accuracy of 0.72 (95% confidence interval (CI) 0.66–0.80). Replication models in ARIC maintained a model accuracy of 0.64 (95% CI 0.66–0.72). Frail and pre-frail Black participant models maintained a lower model accuracy. The predictive panel of biomarkers identified in this study may improve the ability to detect frailty as a complex aging syndrome in the clinical setting. We propose several concrete next steps to keep research moving toward detecting frailty with biomarker-based detection methods.     

Biomarker models as surrogates for the disposition index in the Insulin Resistance Atherosclerosis Study

Aims Insulin sensitivity and acute insulin response measure key components of Type?2 diabetes aetiology that contribute independently to risk in the Insulin Resistance Atherosclerosis Study. As insulin sensitivity and acute insulin response are not routinely measured in a clinical setting, we evaluated three fasting biomarker models, homeostasis model assessment of insulin sensitivity (HOMA-%S), β-cell function (HOMA-%B) and a Diabetes Risk Score, as potential surrogates for risk associated with insulin sensitivity, acute insulin response and the interaction of these two measures, the disposition index. Methods Models were calculated from baseline plasma biomarker concentrations for 664 participants who underwent a frequently sampled intravenous glucose tolerance test. To assess relationships among biomarker models and test measures, we calculated improvement in risk estimation gained by combining each fasting measure with each frequently sampled intravenous glucose tolerance test measure using logistic regression. Results The strongest correlates of acute insulin response, insulin sensitivity and disposition index were HOMA-%B (r(s) (2) =?0.23), HOMA-%S (r(s) (2) =?0.48) and Diabetes Risk Score (r(s) (2) =?0.34), respectively. Individual areas under the curves for prediction of diabetes were 0.549 (HOMA-%B), 0.694 (HOMA-%S), 0.700 (insulin sensitivity), 0.714 (acute insulin response), 0.756 (Diabetes Risk Score) and 0.817 (disposition index). Models combining acute insulin response with Diabetes Risk Score (area under the curve 0.798) or HOMA-%S (area under the curve 0.805) nearly equalled disposition index, outperforming other individual measures (P?相似文献   

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug’s known mechanism of action. Also, the models predict each drug’s potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets.     

Strategies to enhance immunomodulatory properties and reduce heterogeneity in mesenchymal stromal cells during ex vivo expansion

Therapies using mesenchymal stromal cells (MSCs) to treat immune and inflammatory conditions are now at an exciting stage of development, with many MSC-based products progressing to phase II and III clinical trials. However, a major bottleneck in the clinical translation of allogeneic MSC therapies is the variable immunomodulatory properties of MSC products due to differences in their tissue source, donor heterogeneity and processes involved in manufacturing and banking. This variable functionality of MSC products likely contributes to the substantial inconsistency observed in the clinical outcomes of phase III trials of MSC therapies; several trials have failed to reach the primary efficacy endpoint. In this review, we discuss various strategies to consistently maintain or enhance the immunomodulatory potency of MSCs during ex vivo expansion, which will enable the manufacture of allogeneic MSC banks that have high potency and low variability. Biophysical and biochemical priming strategies, the use of culture additives such as heparan sulfates, and genetic modification can substantially enhance the immunomodulatory properties of MSCs during in vitro expansion. Furthermore, robust donor screening, the use of biomarkers to select for potent MSC subpopulations, and rigorous quality testing to improve the release criteria for MSC banks have the potential to reduce batch-to-batch heterogeneity and enhance the clinical efficacy of the final MSC product. Machine learning approaches to develop predictive models of individual patient response can enable personalized therapies and potentially establish correlations between in vitro potency measurements and clinical outcomes in human trials.     

Empirical best linear unbiased prediction in cultivar trials using factor-analytic variance-covariance structures

 Results of multi-environment trials to evaluate new plant cultivars may be displayed in a two-way table of genotypes by environments. Different estimators are available to fill the cells of such tables. It has been shown previously that the predictive accuracy of the simple genotype by environment mean is often lower than that of other estimators, e.g. least-squares estimators based on multiplicative models, such as the additive main effects multiplicative interaction (AMMI) model, or empirical best-linear unbiased predictors (BLUPs) based on a two-way analysis-of-variance (ANOVA) model. This paper proposes a method to obtain BLUPs based on models with multiplicative terms. It is shown by cross-validation using five real data sets (oilseed rape, Brassica napus L.) that the predictive accuracy of BLUPs based on models with multiplicative terms may be better than that of least-squares estimators based on the same models and also better than BLUPs based on ANOVA models. Received: 18 October 1997 / Accepted: 31 March 1998     

利用胃癌MMP相关生物标志物预测MMP生物调节剂的活性

目的:根据疗效和安全性,MMP生物调节剂的临床试验结果成为急需探讨的问题.因为,目前为止MMP生物调节剂的临床试验是按照传统化疗方案进行,不是选择适合人群为基础.因此可预测性生物标志物的选择将对-抗MMP治疗是非常重要的.方法:在6个胃癌细胞株中MMP-2、MMP-9、MT1-MMP以及TIMP-2水平和用TIMP-2治疗之后在MT1-MMP以及MMP-2变化可做为生物标志物而被测定.做为MMP生物调节剂,Suramin和Fumagillin的药物敏感性是通过计算IC50值来测定.利用回归方程,用这些生物标志物推断对药物敏感性.最后其预测精度由ex vivo实验证实.结果:Suramin做为pro-MMP-2的特异性抑制剂.在对pro-MMP-2基础活性高的细胞群,其IC50值＜30ug/ml.相反,Fumagillin对胶原蛋白酶活性具有广谱的抑制作用.(IC50＜10-5M).通过回归分析,解释对Suramin敏感性的回归方程是40.7(pro-MMP-2),-10.0(MT1-MMP),-40.7,对Fumagillin 敏感性的回归方程是-11.5,(pro-MMP-2)-10.6(TIMP-2),+11.5.在exvivo,Suramin和Fumagillin的回归方程预测精度均为60%.结论:用生物标志物预测MMP抑制剂的敏感性已成为可能,利用生物标志物,不仅快速、安全筛选MMP抑制剂,而且通过个体化可改善治疗效果.     

乳腺癌免疫治疗：生物标志物、药物及疫苗

虽然近年来肿瘤的治疗取得较大进展,乳腺癌依旧是威胁女性健康的主要杀手。近年来,乳腺癌相关的免疫治疗取得较大进展,肿瘤浸润淋巴细胞(TILs)、程序性死亡受体 1(PD 1)及其配体PD L1、肿瘤突变负荷等肿瘤标志物对乳腺癌免疫治疗具有预测作用,并与乳腺癌的预后相关。免疫检查点抑制剂,例如PD-1/PD-L1及细胞毒性T淋巴细胞抗原4(CTLA 4)抑制剂在乳腺癌中取得极大进展,各期临床试验结果显示不同的效用。肿瘤疫苗的使用为乳腺癌免疫治疗的另一途径,虽然部分疫苗在临床试验中取得较好成效,但绝大多数仍需深入研究,乳腺癌免疫治疗之途仅为开端,依旧需要大量研究。本文简要介绍了乳腺癌免疫治疗相关的生物标志物、免疫检查点抑制剂以及肿瘤疫苗的研究进展。     

Confidence regions for treatment effects in subgroups in biomarker stratified designs

Subgroup analysis has important applications in the analysis of controlled clinical trials. Sometimes the result of the overall group fails to demonstrate that the new treatment is better than the control therapy, but for a subgroup of patients, the treatment benefit may exist; or sometimes, the new treatment is better for the overall group but not for a subgroup. Hence we are interested in constructing a simultaneous confidence interval for the difference of the treatment effects in a subgroup and the overall group. Subgroups are usually formed on the basis of a predictive biomarker such as age, sex, or some genetic marker. While, for example, age can be detected precisely, it is often only possible to detect the biomarker status with a certain probability. Because patients detected with a positive or negative biomarker may not be truly biomarker positive or negative, responses in the subgroups depend on the treatment therapy as well as on the sensitivity and specificity of the assay used in detecting the biomarkers. In this work, we show how (approximate) simultaneous confidence intervals and confidence ellipsoid for the treatment effects in subgroups can be found for biomarker stratified clinical trials using a normal framework with normally distributed or binary data. We show that these intervals maintain the nominal confidence level via simulations.     

Leveraging nonlinear dynamic models to predict progression of neuroimaging biomarkers

Using biomarkers to model disease course effectively and make early prediction is a challenging but critical path to improving diagnostic accuracy and designing preventive trials for neurological disorders. Leveraging the domain knowledge that certain neuroimaging biomarkers may reflect the disease pathology, we propose a model inspired by the neural mass model from cognitive neuroscience to jointly model nonlinear dynamic trajectories of the biomarkers. Under a nonlinear mixed‐effects model framework, we introduce subject‐ and biomarker‐specific random inflection points to characterize the critical time of underlying disease progression as reflected in the biomarkers. A latent liability score is shared across biomarkers to pool information. Our model allows assessing how the underlying disease progression will affect the trajectories of the biomarkers, and, thus, is potentially useful for individual disease management or preventive therapeutics. We propose an EM algorithm for maximum likelihood estimation, where in the E step, a normal approximation is used to facilitate numerical integration. We perform extensive simulation studies and apply the method to analyze data from a large multisite natural history study of Huntington's Disease (HD). The results show that some neuroimaging biomarker inflection points are early signs of the HD onset. Finally, we develop an online tool to provide the individual prediction of the biomarker trajectories given the medical history and baseline measurements.     

PD-L1 versus tumor mutation burden: Which is the better immunotherapy biomarker in advanced non-small cell lung cancer?

PD-L1 and tumor mutation burden (TMB) are the most widely used immunotherapy biomarkers to identify populations who would attain clinical benefit, with the higher values predicting better therapeutic efficacy. This review addresses the predictive values and unresolved challenges of these two biomarkers. PD-1 and PD-L1 inhibitors have induced durable and effective responses in patients with advanced non-small cell lung cancer, confirmed by multiple clinical trials and real-world studies. Different clinical trials, involving both PD-1/PD-L1 inhibitors alone and combination regimens, adopted either PD-L1 or TMB to stratify the patients, although the predictive capabilities of these two biomarkers are different. In the first-line setting, PD-L1 of 50% or more as a cut-off value can help select candidates for pembrolizumab or atezolizumab monotherapy; however, these two biomarkers poorly predict the efficacy of immunotherapy combination regimens as first-line treatments. In the second-line setting, although patients can benefit from nivolumab regardless of PD-L1 expression, both PD-L1 and blood TMB can be used as biomarkers to find patients suitable for atezolizumab. Except for inaccurate predictiveness, there are many unresolved problems with regard to the two biomarkers, such as the lack of standard detection methods, and their susceptibilities to other dynamic changes. The predictive values of TMB and PD-L1 were low in most circumstances; however, PD-L1 expression greater than ≥ 50% can help select appropriate patients for pembrolizumab and atezolizumab, respectively, as first-line monotherapies. Higher PD-L1 or TMB was associated with greater efficacy for atezolizumab as a second-line monotherapy.     

New Candidate Biomarkers in the Female Genital Tract to Evaluate Microbicide Toxicity

Vaginal microbicides hold great promise for the prevention of viral diseases like HIV, but the failure of several microbicide candidates in clinical trials has raised important questions regarding the parameters to be evaluated to determine in vivo efficacy in humans. Clinical trials of the candidate microbicides nonoxynol-9 (N9) and cellulose sulfate revealed an increase in HIV infection, vaginal inflammation, and recruitment of HIV susceptible lymphocytes, highlighting the need to identify biomarkers that can accurately predict microbicide toxicity early in preclinical development and in human trials. We used quantitative proteomics and RT-PCR approaches in mice and rabbits to identify protein changes in vaginal fluid and tissue in response to treatment with N9 or benzalkonium chloride (BZK). We compared changes generated with N9 and BZK treatment to the changes generated in response to tenofovir gel, a candidate microbicide that holds promise as a safe and effective microbicide. Both compounds down regulated mucin 5 subtype B, and peptidoglycan recognition protein 1 in vaginal tissue; however, mucosal brush samples also showed upregulation of plasma proteins fibrinogen, plasminogen, apolipoprotein A-1, and apolipoprotein C-1, which may be a response to the erosive nature of N9 and BZK. Additional proteins down-regulated in vaginal tissue by N9 or BZK treatment include CD166 antigen, olfactomedin-4, and anterior gradient protein 2 homolog. We also observed increases in the expression of C-C chemokines CCL3, CCL5, and CCL7 in response to treatment. There was concordance in expression level changes for several of these proteins using both the mouse and rabbit models. Using a human vaginal epithelial cell line, the expression of mucin 5 subtype B and olfactomedin-4 were down-regulated in response to N9, suggesting these markers could apply to humans. These data identifies new proteins that after further validation could become part of a panel of biomarkers to effectively evaluate microbicide toxicity.     

Molecular chemoprevention by selenium: a genomic approach

Basic research and clinical chemoprevention trials support the protective role of selenium in cancer prevention but the mechanisms based on the molecular level remain to be fully defined. This mini-review focuses only on the elucidation of the molecular mechanisms of cancer prevention by selenium using the genomics approach; target organs discussed here are breast, prostate, colon and lung. The results described here support the utility of microarray technology in delineating the molecular mechanisms of cancer prevention by selenium. These results are based on studies employing human and rodent cell lines and tissues from animal models ranging from normal to frank cancer. The dose and the form of selenium are determining factors in cancer chemoprevention. The results of the microarray analysis reviewed here indicate that selenium, independent of its form and the target organ examined, alters several genes in a manner that can account for cancer prevention. Selenium can up regulate genes related to phase II detoxification enzymes, certain selenium-binding proteins and select apoptotic genes, while down regulating those related to phase I activating enzymes and cell proliferation. Independent of tissue type, selenium arrests cells in G1 phase of cell cycle, inhibits CYCLIN A, CYCLIN D1, CDC25A, CDK4, PCNA and E2F gene expressions while induces the expressions of P19, P21, P53, GST, SOD, NQO1, GADD153 and certain CASPASES. In addition to those described above, genes such as OPN, which is mainly involved in metastasis and recently reported to be down regulated by selenium, should be considered as potential molecular marker in clinical chemoprevention trials. Collectively, literature data indicate that some of these genes that were altered by selenium are also involved in the development of human cancers described in this review. It appears that androgen receptor status may influence the effect of selenium on gene expression profile in prostate cancer; whether estrogen receptor may influence the effect of selenium on gene expression in breast cancer requires further studies. Knowledge from gene array data in combination with proteomics approaches, using homogenous population of cell types with the aid of laser capture microdissection, may provide an individualized dimension of information on cancer risk and potential targets for its prevention. The molecular (genetic) biomarkers presented in this review will provide the foundation for future studies of the chemopreventive properties of structurally varied selenium compounds.     

The use of biomarkers in surveillance, medical screening, and intervention

Building on mechanistic information, much of molecular epidemiologic research has focused on validating biomarkers, that is, assessing their ability to accurately indicate exposure, effect, disease, or susceptibility. To be of use in surveillance, medical screening, or interventions, biomarkers must already be validated so that they can be used as outcomes or indicators that can serve a particular function. In surveillance, biomarkers can be used as indicators of hazard, exposure, disease, and population risk. However, to obtain rates for these measures, the population at risk will need to be assessed. In medical screening, biomarkers can serve as early indicators of disease in asymptomatic people. This allows for the identification of those who should receive diagnostic confirmation and early treatment. In intervention (which includes risk assessment and communication, risk management, and various prevention efforts), biomarkers can be used to assess the effectiveness of a prevention or control strategy as well as help determine whether the appropriate individuals are assigned to the correct intervention category. Biomarkers can be used to provide group and individual risk assessments that can be the basis for marshalling resources. Critical for using biomarkers in surveillance, medical screening, and intervention is the justification that the biomarkers can provide information not otherwise accessible by a less expensive and easier-to-obtain source of information, such as medical records, surveys, or vital statistics. The ability to use validated biomarkers in surveillance, medical screening, and intervention will depend on the extent to which a strategy for evidence-based procedures for biomarker knowledge transfer can be developed and implemented. This will require the interaction of researchers and decision-makers to collaborate on public health and medical issues.     

Biomarkers of cell death applicable to early clinical trials

The development of biomarkers of cell death to reflect tumor biology and drug-induced response has garnered interest with the development of several classes of drugs aimed at decreasing the cellular threshold for apoptosis and exploiting pre-existing oncogenic stresses. These novel anticancer drugs, directly targeted to the apoptosis regulatory machinery and aimed at abrogating survival signaling pathways, are entering early clinical trials provoking the question of how to monitor their impact on cancer patients. The parallel development of drugs with predictive biomarkers and their incorporation into early clinical trials are anticipated to support the pharmacological audit trail, to speed the development and reduce the attrition rate of novel drugs whose objective is to provoke tumor cell death. Tumor biopsies are an ideal matrix to measure apoptosis, but surrogate less invasive biomarkers such as blood samples and functional imaging are less challenging to acquire clinically. Archetypal and exploratory examples illustrating the importance of biomarkers to drug development are given. This review explores the substantive challenges associated with the validation, deployment, interpretation and utility of biomarkers of cell death and reviews recent advances in their incorporation in preclinical and early clinical trial contexts.     

Immunologic biomarkers as correlates of clinical response to cancer immunotherapy

Over the last few years, several newly developed immune-based cancer therapies have been shown to induce clinical responses in significant numbers of patients. As a result, there is a need to identify immune biomarkers capable of predicting clinical response. If there were laboratory parameters that could define patients with improved disease outcomes after immunomodulation, product development would accelerate, optimization of existing immune-based treatments would be facilitated and patient selection for specific interventions might be optimized. Although there are no validated cancer immunologic biomarkers that are predictive of clinical response currently in widespread use, there is much published literature that has informed investigators as to which markers may be the most promising. Population-based studies of endogenous tumor immune infiltrates and gene expression analyses have identified specific cell populations and phenotypes of immune cells that are most likely to mediate anti-tumor immunity. Further, clinical trials of cancer vaccines and other cancer directed immunotherapy have identified candidate immunologic biomarkers that are statistically associated with beneficial clinical outcomes after immune-based cancer therapies. Biomarkers that measure the magnitude of the Type I immune response generated with immune therapy, epitope spreading, and autoimmunity are readily detected in the peripheral blood and, in clinical trials of cancer immunotherapy, have been associated with response to treatment.     

Dynamic Sensorimotor Planning during Long-Term Sequence Learning: The Role of Variability,Response Chunking and Planning Errors

Many everyday skills are learned by binding otherwise independent actions into a unified sequence of responses across days or weeks of practice. Here we looked at how the dynamics of action planning and response binding change across such long timescales. Subjects (N = 23) were trained on a bimanual version of the serial reaction time task (32-item sequence) for two weeks (10 days total). Response times and accuracy both showed improvement with time, but appeared to be learned at different rates. Changes in response speed across training were associated with dynamic changes in response time variability, with faster learners expanding their variability during the early training days and then contracting response variability late in training. Using a novel measure of response chunking, we found that individual responses became temporally correlated across trials and asymptoted to set sizes of approximately 7 bound responses at the end of the first week of training. Finally, we used a state-space model of the response planning process to look at how predictive (i.e., response anticipation) and error-corrective (i.e., post-error slowing) processes correlated with learning rates for speed, accuracy and chunking. This analysis yielded non-monotonic association patterns between the state-space model parameters and learning rates, suggesting that different parts of the response planning process are relevant at different stages of long-term learning. These findings highlight the dynamic modulation of response speed, variability, accuracy and chunking as multiple movements become bound together into a larger set of responses during sequence learning.