DEK regulates B-cell proliferative capacity and is associated with aggressive disease in low-grade B-cell lymphomas.

This study sheds light on the pivotal role of the oncoprotein DEK in B-cell lymphoma. We reveal DEK expression correlates with increased tumor proliferation and inferior overall survival in cases diagnosed with low-grade B-cell lymphoma (LGBCL). We also found significant correlation between DEK expression and copy number alterations in LGBCL tumors, highlighting a novel mechanism of LGBCL pathogenesis that warrants additional exploration. To interrogate the mechanistic role of DEK in B-cell lymphoma, we generated a DEK knockout cell line model, which demonstrated DEK depletion caused reduced proliferation and altered expression of key cell cycle and apoptosis-related proteins, including Bcl-2, Bcl-xL, and p53. Notably, DEK depleted cells showed increased sensitivity to apoptosis-inducing agents, including venetoclax and staurosporine, which underscores the therapeutic potential of targeting DEK in B-cell lymphomas. Overall, our study contributes to a better understanding of DEK's role as an oncoprotein in B-cell lymphomas, highlighting its potential as both a promising therapeutic target and a novel biomarker for aggressive LGBCL. Further research elucidating the molecular mechanisms underlying DEK-mediated tumorigenesis could pave the way for improved treatment strategies and better clinical outcomes for patients with B-cell lymphoma.

Characteristics and outcomes of incidentally diagnosed diffuse large B-cell lymphoma and implications for cancer screening.

Differences in characteristics and outcomes between incidental and symptomatic presentations of Large B-Cell Lymphoma.

Medical history and lifestyle factors have limited impact on time-to-first-treatment in patients with chronic lymphocytic leukemia.

Background: Chronic lymphocytic leukemia (CLL) is a heterogeneous disease. Whereas some patients have an indolent disease, others experience an aggressive course and early death. Our aim was to investigate if modifiable and non-modifiable medical history and lifestyle factors prior to diagnosis had an impact on the natural course of the disease. Method: In 1154 CLL patients, we assessed if the weight, physical activity, smoking, and alcohol consumption or non-modifiable characteristics including family history of lymphoid malignancy and medical history were associated with time-to-first-treatment (TTFT) and adjusted all results for the CLL-International Prognostic Index (CLL-IPI). Results: TTFT was shorter for patients with high/very high-risk CLL-IPI than those with low/intermediate risk CLL-IPI. In the adjusted analysis we did not find additional impact on TTFT besides CLL-IPI from any environmental characteristics assessed. Conclusions: We found limited impact of environmental factors on the natural course of CLL (measured as the TTFT in treatment naïve patients) providing valuable knowledge, and potential relief, to share with patients at the time of diagnosis.

Transformation in Marginal Zone Lymphoma: Results from a Prospective Cohort and a Meta-Analysis of the Literature.

Marginal zone lymphoma (MZL) includes extranodal (EMZL), splenic (SMZL), and nodal (NMZL) subtypes. Histological transformation (HT) to large B-cell lymphomas is well documented but with a large variability in published cumulative incidence rates. We report results from the Molecular Epidemiology Resource (MER) cohort for the cumulative incidence of HT (with death as competing risk) and associated risk factors and outcomes. We also conduct a meta-analysis of available studies on the cumulative incidence of HT. From 2002-2015, 529 patients with MZL were enrolled in the MER (69% EMZL, 16% SMZL, 15% NMZL). Ten-year overall survival (OS) from diagnosis was 66%. HT occurred in 21 patients, with 5-year and 10-year cumulative incidence of HT of 2.7% (95% confidence interval [CI] 0.02-0.05) and 3.6% (95%CI 0.02-0.06), respectively. HT was associated with an increased risk of death (subdistribution hazard ratio (HR)=3.95; 95%CI 2.06-7.55). Predictors of HT were ≥2 extranodal sites and MALT-IPI score ≥2. OS was 79% at 5 and 55% at 10 years after HT. Age at HT≥70 years was the only predictor of OS after HT (HR=3.57; 95%CI 1.34-9.48). In meta-analysis of 12 studies (6,161 patients), the 5- and 10-year cumulative incidence of HT across all subtypes were 5% (95%CI 0.05-0.06) and 8% (95%CI 0.07-0.09), respectively. Rates were lower in EMZL (3% and 5%) than in SMZL (7% and 13%) and NMZL (9% and 13%). While HT is relatively uncommon in the first decade after MZL diagnosis, it is associated with an inferior outcome and needs new approaches to prevention and management.

Real-world impact of differences in the WHO and ICC classifications of non-Hodgkin lymphoma: a LEO Cohort Study analysis.

Recent introduction of two different lymphoma classifications has raised concerns about consistency in diagnosis, management, and clinical trial enrollment. Data from a large cohort reflecting real-world clinical practice suggest that differences between the classifications will impact <1% of non-Hodgkin lymphomas.

Risk of Incident Melanoma Among Individuals With Low-Count Monoclonal B-Cell Lymphocytosis.

PURPOSE: Chronic lymphocytic leukemia (CLL)-phenotype monoclonal B-cell lymphocytosis (MBL) is a premalignant condition that is roughly 500-fold more common than CLL. It is unknown whether the two-fold increased risk of developing melanoma associated with CLL extends to individuals with MBL. METHODS: Using the Mayo Clinic Biobank, we identified participants who were 40 years or older with no previous hematological malignancies, who resided in the 27 counties around Mayo Clinic, and who had available biospecimens for screening. Eight-color flow cytometry was used to screen for MBL. Individuals with MBL were classified as low-count MBL (LC-MBL) or high-count MBL on the basis of clonal B-cell percent. Incident melanomas were identified using International Classification of Diseases codes and confirmed via medical records review. Cox regression models were used to estimate hazard ratios (HRs) and 95% CI. RESULTS: Of the 7,334 participants screened, 1,151 were identified with a CD5-positive MBL, of whom 1,098 had LC-MBL. After a median follow-up of 3.2 years (range, 0-13.5), 131 participants developed melanoma, of whom 36 individuals were positive for MBL. The estimated 5-year cumulative incidence of melanoma was 3.4% and 2.0% among those with and without MBL, respectively. After adjusting for age, sex, and history of previous melanoma, individuals with MBL exhibited a 1.86-fold (95% CI, 1.25 to 2.78) risk of melanoma. This elevated risk persisted when analysis was restricted to those without a history of melanoma (HR, 2.05 [95% CI, 1.30 to 3.23]). Individuals with LC-MBL had a 1.92-fold (95% CI, 1.29 to 2.87) increased risk of developing melanoma overall and a 2.74-fold increased risk (95% CI, 1.50 to 5.03) of melanoma in situ compared with those without MBL. CONCLUSION: LC-MBL is associated with an approximately two-fold increased risk of melanoma overall and a 2.74-fold increased risk of melanoma in situ.

Transcriptomic classification of diffuse large B-cell lymphoma identifies a high-risk activated B-cell-like subpopulation with targetable MYC dysregulation.

Immunochemotherapy has been the mainstay of treatment for newly diagnosed diffuse large B-cell lymphoma (ndDLBCL) yet is inadequate for many patients. In this work, we perform unsupervised clustering on transcriptomic features from a large cohort of ndDLBCL patients and identify seven clusters, one called A7 with poor prognosis, and develop a classifier to identify these clusters in independent ndDLBCL cohorts. This high-risk cluster is enriched for activated B-cell cell-of-origin, low immune infiltration, high MYC expression, and copy number aberrations. We compare and contrast our methodology with recent DLBCL classifiers to contextualize our clusters and show improved prognostic utility. Finally, using pre-clinical models, we demonstrate a mechanistic rationale for IKZF1/3 degraders such as lenalidomide to overcome the low immune infiltration phenotype of A7 by inducing T-cell trafficking into tumors and upregulating MHC I and II on tumor cells, and demonstrate that TCF4 is an important regulator of MYC-related biology in A7.

Evaluating the impact of laboratory-based eligibility criteria by race/ethnicity in first-line clinical trials of DLBCL.

ABSTRACT: Underrepresentation of racial and ethnic subgroups in cancer clinical trials remains a persistent challenge. Restrictive clinical trial eligibility criteria have been shown to exacerbate this problem. We previously identified that up to 24% of patients treated with standard immunochemotherapy would have been excluded from recent first-line trials in diffuse large B-cell lymphoma (DLBCL) based on 5 laboratory-based criteria. These ineligible patients had worse clinical outcomes and increased deaths related to lymphoma progression, suggesting the potential exclusion of patients who could have benefited most from the novel therapies being evaluated. Using data from the prospectively enrolled Lymphoma Epidemiology Outcomes cohort study, with demographics broadly similar to the US patients diagnosed with lymphoma, we evaluated the impact of laboratory eligibility criteria from recent first-line DLBCL trials across various racial and ethnic backgrounds. There were significant differences in the baseline laboratory values by race/ethnicity with Black/African American (AA) patients having the lowest mean hemoglobin and highest creatinine clearance. Based on recent clinical trial eligibility criteria, AA and Hispanic patients had higher rates of laboratory-based ineligibility than non-Hispanic White patients. The largest gap in the clinical outcomes between eligible and noneligible patients was noted within AA patients with an overall survival hazard ratio based on POLARIX clinical trial criteria of 4.09 (95% confidence interval, 1.83-9.14). A thoughtful approach to the utility of each criterion and cutoffs for eligibility needs to be evaluated in the context of its differential impact across various racial/ethnic groups.

Utility of Targeted Sequencing Compared to FISH for Detection of Chronic Lymphocytic Leukemia Copy Number Alterations.

Chronic lymphocytic leukemia (CLL) is characterized by multiple copy number alterations (CNAs) and somatic mutations that are central to disease prognosis, risk stratification, and mechanisms of therapy resistance. Fluorescence in situ hybridization (FISH) panels are widely used in clinical applications as the gold standard for screening prognostic chromosomal abnormalities in CLL. DNA sequencing is an alternative approach to identifying CNAs but is not an established method for clinical CNA screening. We sequenced DNA from 509 individuals with CLL or monoclonal B-cell lymphocytosis (MBL), the precursor to CLL, using a targeted sequencing panel of 59 recurrently mutated genes in CLL and additional amplicons across regions affected by clinically relevant CNAs [i.e., del(17p), del(11q), del(13q), and trisomy 12]. We used the PatternCNV algorithm to call CNA and compared the concordance of calling clinically relevant CNAs by targeted sequencing to that of FISH. We found a high accuracy of calling CNAs via sequencing compared to FISH. With FISH as the gold standard, the specificity of targeted sequencing was >95%, sensitivity was >86%, positive predictive value was >90%, and negative predictive value was >84% across the clinically relevant CNAs. Using targeted sequencing, we were also able to identify other common CLL-associated CNAs, including del(6q), del(14q), and gain 8q, as well as complex karyotype, defined as the presence of 3 or more chromosomal abnormalities, in 26 patients. In a single and cost-effective assay that can be performed on stored DNA samples, targeted sequencing can simultaneously detect CNAs, somatic mutations, and complex karyotypes, which are all important prognostic features in CLL.

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.

OBJECTIVES: Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. MATERIALS AND METHODS: A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. RESULTS: Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). DISCUSSION: These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. CONCLUSIONS: Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.

Assessing and attenuating the impact of selection bias on spatial cluster detection studies.

Spatial cluster analyses are commonly used in epidemiologic studies of case-control data to detect whether certain areas in a study region have an excess of disease risk. Case-control studies are susceptible to potential biases including selection bias, which can result from non-participation of eligible subjects in the study. However, there has been no systematic evaluation of the effects of non-participation on the findings of spatial cluster analyses. In this paper, we perform a simulation study assessing the effect of non-participation on spatial cluster analysis using the local spatial scan statistic under a variety of scenarios that vary the location and rates of study non-participation and the presence and intensity of a zone of elevated risk for disease for simulated case-control studies. We find that geographic areas of lower participation among controls than cases can greatly inflate false-positive rates for identification of artificial spatial clusters. Additionally, we find that even modest non-participation outside of a true zone of elevated risk can decrease spatial power to identify the true zone. We propose a spatial algorithm to correct for potentially spatially structured non-participation that compares the spatial distributions of the observed sample and underlying population. We demonstrate its ability to markedly decrease false positive rates in the absence of elevated risk and resist decreasing spatial sensitivity to detect true zones of elevated risk. We apply our method to a case-control study of non-Hodgkin lymphoma. Our findings suggest that greater attention should be paid to the potential effects of non-participation in spatial cluster studies.

Multiomic analysis identifies a high-risk signature that predicts early clinical failure in DLBCL.

Recent genetic and molecular classification of DLBCL has advanced our knowledge of disease biology, yet were not designed to predict early events and guide anticipatory selection of novel therapies. To address this unmet need, we used an integrative multiomic approach to identify a signature at diagnosis that will identify DLBCL at high risk of early clinical failure. Tumor biopsies from 444 newly diagnosed DLBCL were analyzed by WES and RNAseq. A combination of weighted gene correlation network analysis and differential gene expression analysis was used to identify a signature associated with high risk of early clinical failure independent of IPI and COO. Further analysis revealed the signature was associated with metabolic reprogramming and identified cases with a depleted immune microenvironment. Finally, WES data was integrated into the signature and we found that inclusion of ARID1A mutations resulted in identification of 45% of cases with an early clinical failure which was validated in external DLBCL cohorts. This novel and integrative approach is the first to identify a signature at diagnosis, in a real-world cohort of DLBCL, that identifies patients at high risk for early clinical failure and may have significant implications for design of therapeutic options.

Synoptic Reporting by Summarizing Cancer Pathology Reports using Large Language Models.

Background: Synoptic reporting, the documenting of clinical information in a structured manner, is known to improve patient care by reducing errors, increasing readability, interoperability, and report completeness. Despite its advantages, manually synthesizing synoptic reports from narrative reports is expensive and error prone when the number of structured fields are many. While the recent revolutionary developments in Large Language Models (LLMs) have significantly advanced natural language processing, their potential for innovations in medicine is yet to be fully evaluated. Objectives: In this study, we explore the strengths and challenges of utilizing the state-of-the-art language models in the automatic synthesis of synoptic reports. Materials and Methods: We use a corpus of 7,774 cancer related, narrative pathology reports, which have annotated reference synoptic reports from Mayo Clinic EHR. Using these annotations as a reference, we reconfigure the state-of-the-art large language models, such as LLAMA-2, to generate the synoptic reports. Our annotated reference synoptic reports contain 22 unique data elements. To evaluate the accuracy of the reports generated by the LLMs, we use several metrics including the BERT F1 Score and verify our results by manual validation. Results: We show that using fine-tuned LLAMA-2 models, we can obtain BERT Score F1 of 0.86 or higher across all data elements and BERT F1 scores of 0.94 or higher on over 50% (11 of 22) of the questions. The BERT F1 scores translate to average accuracies of 76% and as high as 81% for short clinical reports. Conclusions: We demonstrate successful automatic synoptic report generation by fine-tuning large language models.

Defining primary refractory large B-cell lymphoma.

ABSTRACT: Patients with large B-cell lymphoma (LBCL) that fail to achieve a complete response (CR) or who relapse early after anthracycline-containing immunochemotherapy (IC) have a poor prognosis and are commonly considered to have "primary refractory disease." However, different definitions of primary refractory disease are used in the literature and clinical practice. In this study, we examined variation in the time to relapse used to define refractory status and association with survival outcomes in patients with primary refractory LBCL in a single-center prospective cohort with validation in an independent multicenter cohort. Patients with newly diagnosed LBCL were enrolled in the Molecular Epidemiological Resource cohort (MER; N = 949) or the Lymphoma Epidemiology of Outcomes cohort (LEO; N = 2755) from September 2002 to May 2021. Primary refractory LBCL was defined as no response (stable disease [SD]) or progressive disease (PD) during, or by the end of, frontline (1L) IC (primary PD; PPD); partial response at end of treatment (EOT PR); or relapse within 3 to 12 months after achieving CR at EOT to 1L IC (early relapse). In the MER cohort, patients with PPD had inferior overall survival (OS; 2-year OS rate: 15% MER, 31% LEO) when compared with other subgroups considered in defining primary refractory disease, EOT PR (2-year OS rate: 38% MER, 50% LEO) and early relapse (2-year OS rate: 44% MER, 58% LEO). Among patients receiving 1L IC with curative intent, we identified that patients with PPD are the key subgroup with poor outcomes. We propose a definition of primary refractory LBCL as SD or PD during, or by the end of, 1L treatment.

Marginal zone lymphoma international prognostic index: a unifying prognostic index for marginal zone lymphomas requiring systemic treatment.

Background: Marginal zone lymphomas (MZL), comprised of three unique but related subtypes, lack a unifying prognostic score applicable to all the patients in need for systemic chemotherapy and/or immunotherapy. Methods: Patients from the prospective NF10 study (NCT02904577) with newly diagnosed MZL and receiving frontline systemic therapy at diagnosis or after observation were used to train a prognostic model. The primary endpoint was progression-free survival (PFS) from start of treatment. The model was externally validated in a pooled analysis of two independent cohorts from the University of Iowa and Mayo Clinic Molecular Epidemiology Resource and the University of Miami. Findings: We identified 501 eligible patients. After multivariable modeling, lactate dehydrogenase (LDH) above upper normal limit, hemoglobin <12 g/dL, absolute lymphocyte count <1 × 109/L, platelets <100 × 109/L, and MZL subtype (nodal or disseminated) were independently associated with inferior PFS. The proposed MZL International Prognostic index (MZL-IPI) combined these 5 factors, and we defined low (LRG, 0 factors, 27%), intermediate (IRG, 1-2 factors, 57%) and high (HRG, 3+ factors, 16%) risk groups with 5-y PFS of 85%, 66%, and 37%, respectively (c-Harrell = 0.64). Compared to the LRG, the IRG (Hazard Ratio [HR] = 2.30, 95% CI 1.39-3.80) and HRG (HR = 5.41, 95% CI 3.12-9.38) had inferior PFS. Applying the MZL-IPI to the pooled US cohort (N = 353), 94 (27%), 192 (54%), and 67 (19%) patients were classified as LRG, IRG, and HRG, respectively, and the model was validated for PFS (log-rank test p = 0.0018; c-Harrell = 0.578, 95% CI 0.54-0.62). The MZL-IPI was also prognostic for OS in both the training and the external validation sets. Interpretation: MZL-IPI is a new prognostic score for use in all patients with MZL considered for systemic treatment. Funding: The MER was supported by P50 CA97274 and U01 CA195568.

Low-frequency and rare genetic variants associated with rheumatoid arthritis risk.

Rheumatoid arthritis (RA) has an estimated heritability of nearly 50%, which is particularly high in seropositive RA. HLA alleles account for a large proportion of this heritability, in addition to many common single-nucleotide polymorphisms with smaller individual effects. Low-frequency and rare variants, such as those captured by next-generation sequencing, can also have a large role in heritability in some individuals. Rare variant discovery has informed the development of drugs such as inhibitors of PCSK9 and Janus kinases. Some 34 low-frequency and rare variants are currently associated with RA risk. One variant (19:10352442G>C in TYK2) was identified in five separate studies, and might therefore represent a promising therapeutic target. Following a set of best practices in future studies, including studying diverse populations, using large sample sizes, validating RA and serostatus, replicating findings, adjusting for other variants and performing functional assessment, could help to ensure the relevance of identified variants. Exciting opportunities are now on the horizon for genetics in RA, including larger datasets and consortia, whole-genome sequencing and direct applications of findings in the management, and especially treatment, of RA.

Follicular lymphoma B cells exhibit heterogeneous transcriptional states with associated somatic alterations and tumor microenvironments.

Follicular lymphoma (FL) is an indolent non-Hodgkin lymphoma of germinal center origin, which presents with significant biologic and clinical heterogeneity. Using RNA-seq on B cells sorted from 87 FL biopsies, combined with machine-learning approaches, we identify 3 transcriptional states that divide the biological ontology of FL B cells into inflamed, proliferative, and chromatin-modifying states, with relationship to prior GC B cell phenotypes. When integrated with whole-exome sequencing and immune profiling, we find that each state was associated with a combination of mutations in chromatin modifiers, copy-number alterations to TNFAIP3, and T follicular helper cells (Tfh) cell interactions, or primarily by a microenvironment rich in activated T cells. Altogether, these data define FL B cell transcriptional states across a large cohort of patients, contribute to our understanding of FL heterogeneity at the tumor cell level, and provide a foundation for guiding therapeutic intervention.

Tumor mutational load is prognostic for progression to therapy among high-count monoclonal B-cell lymphocytosis.

ABSTRACT: High-count monoclonal B-cell lymphocytosis (HCMBL) is a precursor condition to chronic lymphocytic leukemia (CLL). We have shown that among individuals with HCMBL, the CLL-International Prognostic Index (CLL-IPI) is prognostic for time-to-first therapy (TTFT). Little is known about the prognostic impact of somatically mutated genes among individuals with HCMBL. We sequenced DNA from 371 individuals with HCMBL using a targeted sequencing panel of 59 recurrently mutated genes in CLL to identify high-impact mutations. We compared the sequencing results with that of our treatment-naïve CLL cohort (N = 855) and used Cox regression to estimate hazard ratios and 95% confidence intervals (CIs) for associations with TTFT. The frequencies of any mutated genes were lower in HCMBL (52%) than CLL (70%). At 10 years, 37% of individuals with HCMBL with any mutated gene had progressed requiring treatment compared with 10% among individuals with HCMBL with no mutations; this led to 5.4-fold shorter TTFT (95% CI, 2.6-11.0) among HCMBL with any mutated gene vs none, independent of CLL-IPI. When considering individuals with low risk of progression according to CLL-IPI, those with HCMBL with any mutations had 4.3-fold shorter TTFT (95% CI, 1.6-11.8) vs those with none. Finally, when considering both CLL-IPI and any mutated gene status, we observed individuals with HCMBL who were high risk for both prognostic factors had worse prognosis than patients with low-risk CLL (ie, 5-year progression rate of 32% vs 21%, respectively). Among HCMBL, the frequency of somatically mutated genes at diagnosis is lower than that of CLL. Accounting for both the number of mutated genes and CLL-IPI can identify individuals with HCMBL with more aggressive clinical course.

Modeling variation in mixture effects over space with a Bayesian spatially varying mixture model.

Mixture analysis is an emerging statistical tool in epidemiological research that seeks to estimate the health effects associated with mixtures of several exposures. This approach acknowledges that individuals experience many simultaneous exposures and it can estimate the relative importance of components in the mixture. Health effects due to mixtures may vary over space driven by to political, demographic, environmental, or other differences. In such cases, estimating a global mixture effect without accounting for spatial variation would induce bias in effect estimates and potentially lower statistical power. To date, no methods have been developed to estimate spatially varying chemical mixture effects. We developed a Bayesian spatially varying mixture model that estimates spatially varying mixture effects and the importance weights of components in the mixture, while adjusting for covariates. We demonstrate the efficacy of the model through a simulation study that varies the number of mixtures (one and two) and spatial pattern (global, one-dimensional, radial) and magnitude of mixture effects, showing that the model is able to accurately reproduce the spatial pattern of mixture effects across a diverse set of scenarios. Finally, we apply our model to a multi-center case-control study of non-Hodgkin lymphoma (NHL) in Detroit, Iowa, Los Angeles, and Seattle. We identify significant spatially varying positive and inverse associations with NHL for two mixtures of pesticides in Iowa and do not find strong spatial effects at the other three centers. In conclusion, the Bayesian spatially varying mixture model represents a novel method for modeling spatial variation in mixture effects.