Biological Variables Research Articles

Ecological correlates of population genetics in Linum suffruticosum, an heterostylous polyploid and taxonomic complex endemic to the western Mediterranean Basin

Abstract Linum suffruticosum s.l. is a taxonomic complex widespread in the western Mediterranean basin. The complex is characterized by a high phenotypic and cytogenetic diversity, and by a unique three-dimensional heterostyly system that makes it an obligate outcrosser. We studied the patterns of genetic diversity and structure of populations throughout the entire distribution of L. suffruticosum s.l. with microsatellite markers. We analyzed their relationships with various biological and ecological variables, including the morph ratio and sex organ reciprocity of populations measured with a novel multi-dimensional method. Populations consistently showed an approximate 1:1 morph ratio with high sex organ reciprocity and high genetic diversity. We found high genetic differentiation of populations, showing a pattern of isolation by distance. The Rif mountains in NW Africa were the most important genetic barrier. The taxonomic treatment within the group was not related to the genetic differentiation of populations, but to their environmental differentiation. Genetic diversity was unrelated to latitude, elevation, population size, niche suitability or breeding system. However, there was a clear influence of ploidy level on the genetic diversity of populations, and a seeming center-periphery pattern in its distribution. Our results suggest that polyploidization events, high outcrossing rates, isolation by distance and important geographical barriers to gene flow have played major roles in the microevolutionary history of this species complex.

Incidence and predictors of intracranial hemorrhage after intravenous thrombolysis with tenecteplase.

Despite its increasing use, there are limited data on the risk of intracranial hemorrhage (ICH) after intravenous thrombolysis with tenecteplase in the setting of acute ischemic stroke. Our aim was to investigate the incidence and predictors of ICH after tenecteplase administration. We reviewed data from the prospective ongoing multicenter TETRIS (Tenecteplase Treatment in Ischemic Stroke) registry. Patients with available day-1 imaging were included in this study. Clinical, imaging and biological variables were collected. Follow-up imaging performed 24 h after IVT was locally reviewed by senior neuroradiologists and neurologists. The incidence of parenchymal hematoma (PH) and any ICH were investigated. Potential predictors of PH and any ICH were assessed in multivariable logistic regressions. Subgroup analyses focusing on patients intended for endovascular treatment were performed. PH and any ICH occurred in 126/1321 (incidence rate: 9.5%, 95% CI 8.1-11.2) and 521/1321 (39.4%, 95% CI 36.8-42.1) patients, respectively. Symptomatic ICH was observed in 77/1321 (5.8%; 95% CI 4.7-7.2). PH occurrence was significantly associated with poorer functional outcomes (p < 0.0001) and death (p < 0.0001) after 3 months. Older age (aOR = 1.03; 95% CI 1.01-1.05), male gender (aOR = 2.07; 95% CI 1.28-3.36), a history of hypertension (aOR = 2.08; 95% CI 1.19-3.62), a higher baseline NIHSS (aOR = 1.07; 95% CI 1.03-1.10) and higher admission blood glucose level (aOR = 1.12; 95% CI 1.05-1.19) were independently associated with PH occurrence. Similar associations were observed in the subgroup of patients intended for endovascular treatment. We quantified the incidence of ICH after IVT with tenecteplase in a real-life prospective registry and determined independent predictors of ICH. These findings allow to identify patients at high risk of ICH.

Navigating the path of reproducibility in microRNA-based biomarker research with ring trials.

The development of microRNA (miRNA)-based biomarkers has gained significant attention due to their potential diagnostic, prognostic and therapeutic applications. However, the reproducibility of miRNA biomarker research faces unique challenges, primarily due to the influence of pre-analytical and analytical factors. The absence of standardized procedures contributes to inconsistencies across studies, alongside challenges in reference gene selection, data analysis methods and miRNA profiling platforms. Inter-laboratory comparison trials, or ring trials, offer a strategic approach to address technical and biological variability in miRNA biomarker studies. These trials promote standardization, identify sources of variability and strengthen the correlation between miRNAs and clinical outcomes. Despite their underutilization in miRNA biomarker research, ring trials represent a valuable tool for enhancing reproducibility and expediting the translation of miRNA-based biomarkers into clinical applications.

Coagulome and tumor microenvironment: impact of oncogenes, cellular heterogeneity and extracellular vesicles

Cancer-associated thrombosis (CAT) results from the hemostatic system being dysregulated by the progression of cancer. Despite common clinical manifestations, the mechanisms of CAT may vary greatly because cancers develop along distinct biological trajectories that are imposed by the interaction between the tumor cell genome, the epigenome, the surrounding microenvironment, and the tissue of origin. The coagulome, or repertoire of coagulation effectors, expressed by stromal, inflammatory, and cancer cells at the tumor-vascular interface and systemically, reflects this biological variability. Complex landscapes of coagulant and non-coagulant cellular populations are revealed by single-cell RNA sequencing analyses conducted on unperturbed human cancer tissues. Additionally, through mediators of cell-cell interactions, soluble coagulants, and extracellular vesicles containing tissue factor, podoplanin, and other effectors, coagulomes are projected into the pericellular milieu and systemic circulation. As this complexity is currently outside of the clinical paradigm, one could argue that better CAT management could result from a more individualized analysis of coagulomes in cancer patients.

A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses

Taxonomy lays the foundations for the study of biodiversity and its conservation. Procrustean geometric morphometrics (GMM) is a most common technique for the taxonomic assessment of phenotypic population differences. To measure biological variation and detect evolutionarily significant units, GMM is often used on its own, although it is much more powerful with an integrative approach, in combination with molecular, ecological and behavioural data, as well as with meristic morphological traits. GMM is particularly effective in taxonomic research, when applied to 2D images, which are fast and low cost to obtain. Yet, taxonomists who may want to explore the usefulness of GMM are rarely experts in multivariate statistical analyses of size and shape differences. In these twin papers, I aim to provide a detailed step-by-step guideline to taxonomic analysis employing Procrustean GMM in user-friendly software (with tips for R users). In the first part (A) of the study, I will focus on preliminary analyses (mainly, measurement error, outliers and statistical power), which are fundamental for accuracy, but often neglected. I will also use this first paper, and its appendix (Appendix A), to informally introduce, and discuss, general topics in GMM and statistics, that are relevant to taxonomic applications. In the second part (B) of the work, I will move on to the main taxonomic analyses. Thus, I will show how to compare size and shape among groups, but I will also explore allometry and briefly examine differences in variance, as a potential clue to population bottlenecks in peripheral isolates. A large sample of North American marmot mandibles provides the example data (available online, for readers to replicate the study and practice with analyses). However, as this sample is larger than in previous studies and mostly unpublished, it also offers a chance to further explore the patterns of interspecific morphological variation in a group, that has been prominent in mammalian sociobiology, and whose evolutionary divergence is complex and only partially understood.

Validating reference-based algorithms to determine cell-type heterogeneity in ovarian cancer DNA methylation studies

Information about cell composition in tissue samples is crucial for biomarker discovery and prognosis. Specifically, cancer tissue samples present challenges in deconvolution studies due to mutations and genetic rearrangements. Here, we optimized a robust, DNA methylation-based protocol, to be used for deconvolution of ovarian cancer samples. We compared several state-of-the-art methods (HEpiDISH, MethylCIBERSORT and ARIC) and validated the proposed protocol in an in-silico mixture and in an external dataset containing samples from ovarian cancer patients and controls. The deconvolution protocol we eventually implemented is based on MethylCIBERSORT. Comparing deconvolution methods, we paid close attention to the role of a reference panel. We postulate that a possibly high number of samples (in our case: 247) should be used when building a reference panel to ensure robustness and to compensate for biological and technical variation between samples. Subsequently, we tested the performance of the validated protocol in our own study cohort, consisting of 72 patients with malignant and benign ovarian disease as well as in five external cohorts. In conclusion, we refined and validated a reference-based algorithm to determine cell type composition of ovarian cancer tissue samples to be used in cancer biology studies in larger cohorts.

Dynamic mirroring: unveiling the role of digital twins, artificial intelligence and synthetic data for personalized medicine in laboratory medicine.

In recent years, the integration of technological advancements and digitalization into healthcare has brought about a remarkable transformation in care delivery and patient management. Among these advancements, the concept of digital twins (DTs) has recently gained attention as a tool with substantial transformative potential in different clinical contexts. DTs are virtual representations of a physical entity (e.g., a patient or an organ) or systems (e.g., hospital wards, including laboratories), continuously updated with real-time data to mirror its real-world counterpart. DTs can be utilized to monitor and customize health care by simulating an individual's health status based on information from wearables, medical devices, diagnostic tests, and electronic health records. In addition, DTs can be used to define personalized treatment plans. In this study, we focused on some possible applications of DTs in laboratory medicine when used with AI and synthetic data obtained by generative AI. The first point discussed how biological variation (BV) application could be tailored to individuals, considering population-derived BV data on laboratory parameters and circadian or ultradian variations. Another application could be enhancing the interpretation of tumor markers in advanced cancer therapy and treatments. Furthermore, DTs applications might derive personalized reference intervals, also considering BV data or they can be used to improve test results interpretation. DT's widespread adoption in healthcare is not imminent, but it is not far off. This technology will likely offer innovative and definitive solutions for dynamically evaluating treatments and more precise diagnoses for personalized medicine.

The milk study protocol: A longitudinal, prospective cohort study of the relationship between human milk metabolic hormone concentration, maternal body composition, and early growth and satiety development in Samoan infants aged 1-4 months.

Current research suggests that energy transfer through human milk influences infant nutritional development and initiates metabolic programming, influencing eating patterns into adulthood. To date, this research has predominantly been conducted among women in high income settings and/or among undernourished women. We will investigate the relationship between maternal body composition, metabolic hormones in human milk, and infant satiety to explore mechanisms of developmental satiety programming and implications for early infant growth and body composition in Samoans; a population at high risk and prevalence for overweight and obesity. Our aims are (1) to examine how maternal body composition influences metabolic hormone transfer from mother to infant through human milk, and (2) to examine the influences of maternal metabolic hormone transfer and infant feeding patterns on early infant growth and satiety. We will examine temporal changes in hormone transfers to infants through human milk in a prospective longitudinal cohort of n = 80 Samoan mother-infant dyads. Data will be collected at three time points (1, 3, & 4 months postpartum). At each study visit we will collect human milk and fingerpick blood samples from breastfeeding mother-infant dyads to measure the hormones leptin, ghrelin, and adiponectin. Additionally, we will obtain body composition measurements from the dyad, observe breastfeeding behavior, conduct semi-structured interviews, and use questionnaires to document infant hunger and feeding cues and satiety responsiveness. Descriptive statistics, univariate and multivariate analyses will be conducted to address each aim. This research is designed to advance our understanding of variation in the developmental programming of satiety and implications for early infant growth and body composition. The use of a prospective longitudinal cohort alongside data collection that utilizes a mixed methods approach will allow us to capture a more accurate representation on both biological and cultural variables at play in a population at high risk of overweight and obesity.

Diurnal and day-to-day biological variation of salivary cortisol and cortisone.

There is a growing interest in the relevance of salivary cortisol and cortisone concentrations in stress-related research. To correctly attribute the magnitude of salivary cortisol and cortisone variation as an effect of a stressful event, a coherent understanding of the day-to-day intra-individual and inter-individual variability across the diurnal cycle of the two steroids is required. However, such information is currently lacking. This study aimed to overcome these existing limitations by performing an investigation of the biological variation (BV) of salivary cortisol and cortisone within one day and between five days using an LC-MS/MS method. Saliva samples were collected from 20 healthy volunteers immediately after waking up, at 8:00, 12:00, 15:00, 19:00 and 23:00 on each day over five days. All samples were analyzed in duplicate in one run. Nested ANOVA was used to calculate the sums of squares for analytical and biological components of variation. The within-subject BV of salivary cortisol and cortisone (CVI) ranged from a minimum of 29.3 and 19.0 % to a maximum of 56.5 and 49.1 %, respectively, while the between-subject biological variation (CVG) ranged from 29.7 and 29.0 % to 51.6 and 43.6 %. The reference change values (RCVs) ranged from 96 to 245 % for cortisol and from 55 to 194 % for cortisone. A medium index of individuality was observed for both compounds at all time points. This study provides updated BV estimates and RCVs for different times of day that can be used to assess the magnitude of change in biomarkers in future stress-related research.

Native characterization and QC profiling of human amniotic mesenchymal stromal cell vesicular fractions for secretome-based therapy

Human amniotic mesenchymal stromal cells (hAMSCs) have unique immunomodulatory properties making them attractive candidates for regenerative applications in inflammatory diseases. Most of their beneficial properties are mediated through their secretome. The bioactive factors concurring to its therapeutic activity are still unknown. Evidence suggests synergy between the two main components of the secretome, soluble factors and vesicular fractions, pivotal in shifting inflammation and promoting self-healing. Biological variability and the absence of quality control (QC) protocols hinder secretome-based therapy translation to clinical applications. Moreover, vesicular secretome contains a multitude of particles with varying size, cargos and functions whose complexity hinders full characterization and comprehension.This study achieved a significant advancement in secretome characterization by utilizing native, FFF-based separation and characterizing extracellular vesicles derived from hAMSCs. This was accomplished by obtaining dimensionally homogeneous fractions then characterized based on their protein content, potentially enabling the identification of subpopulations with diverse functionalities.This method proved to be successful as an independent technique for secretome profiling, with the potential to contribute to the standardization of a qualitative method. Additionally, it served as a preparative separation tool, streamlining populations before ELISA and LC-MS characterization. This approach facilitated the categorization of distinctive and recurring proteins, along with the identification of clusters associated with vesicle activity and functions. However, the presence of proteins unique to each fraction obtained through the FFF separation tool presents a challenge for further analysis of the protein content within these cargoes.

Comparing the Structure of Fish Assemblage among Natural and Artificial Shallow Rocky Habitats

Artificial coastal structures, such as seawalls, breakwaters, and groins, can exert various impacts on the fish communities in the nearby regions. This study focuses on assessing the ecological effects of coastal infrastructure on marine environments, by comparing, at different seasons, the habitat complexity and heterogeneity, as well as their effects on fish assemblages, between the artificial habitat created with the intention of constructing a marina (Puerto Amor) and the natural habitats surrounding the Cabo de la Huerta area in Alicante (Spain). Employing an asymmetric design and examining two temporal and spatial scales, we utilized visual censuses in snorkeling to gauge the abundance and size of fish species, alongside various parameters related to habitat complexity and heterogeneity. The overarching hypothesis is that fish populations associated with artificial habitats will differ in terms of abundance, biomass, species richness, and diversity compared to fish populations associated with natural habitats, due to changes in complexity and heterogeneity. The findings indicate a shift in fish assemblages; for example, the family Labridae showed differences between the two habitat types for several species. These changes were due to the influences of the Posidonia oceanica meadow and algae like Jania rubens; being influenced by biological variables such as Ellisolandia elongata, Oculina patagonica, and Sarcotragus spinosulus; as well as physical variables such as stones, gravel, and blocks. While there is evidence of alteration in fish assemblages due to changes in habitat structure, there is also an increase in richness (9 species/m2) and total abundance and biomass (1000 ind./m2 and 1700 g/m2, respectively) in the artificial habitat. Multivariate analyses reveal that the fish community in Puerto Amor is less homogeneous than the one in the natural habitat. However, these analyses also indicate an overlap between the communities of both habitats, suggesting substantial similarity despite the noted differences. Consequently, although the habitat alteration has impacted fish populations, it has not diminished abundance, biomass, or species richness. In conclusion, the artificial rocky habitat resulting from the construction attempt at Puerto Amor harbor has fish populations with ecological significance and its removal could lead to undesirable impacts in the area, as the fish assemblages have become well established.

BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful method for profiling complex biological samples. However, batch effects typically arise from differences in sample processing protocols, experimental conditions, and data acquisition techniques, significantly impacting the interpretability of results. Correcting batch effects is crucial for the reproducibility of omics research, but current methods are not optimal for the removal of batch effects without compressing the genuine biological variation under study. We propose a suite of Batch Effect Removal Neural Networks (BERNN) to remove batch effects in large LC-MS experiments, with the goal of maximizing sample classification performance between conditions. More importantly, these models must efficiently generalize in batches not seen during training. A comparison of batch effect correction methods across five diverse datasets demonstrated that BERNN models consistently showed the strongest sample classification performance. However, the model producing the greatest classification improvements did not always perform best in terms of batch effect removal. Finally, we show that the overcorrection of batch effects resulted in the loss of some essential biological variability. These findings highlight the importance of balancing batch effect removal while preserving valuable biological diversity in large-scale LC-MS experiments.

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

Advancements in the use of nanopharmaceuticals for cancer treatment.

Advances in nanotechnology make it possible to specifically target therapies to cancer cells and neoplasms, guide the surgical resection of tumors, and optimize the effectiveness of radiological treatments. This research article provides a concise synthesis of current knowledge in the field of galenic pharmacy focused on targeted drug delivery in oncology. This research article synthesizes current knowledge in galenic pharmacy, focusing on targeted drug delivery in oncology and reviewing recent advancements in nanopharmaceuticals for cancer treatment. The data for this review are derived from a comprehensive analysis of the most cited scientific literature (Pubmed). Recent studies, clinical trials, and technological breakthroughs related to nanopharmaceuticals have been rigorously examined. This diverse source ensures a comprehensive representation of the latest developments in the field. The results highlight the emergence of nanopharmaceuticals as a promising approach to cancer treatment. The most common in oncology remain liposomes, nanopolymers, and nanocrystals. From a galenic point of view, these three forms offer a wide range of improvements compared to conventional forms such as improvement in solubility as well as stability. The same observation is in the clinic where treatment response rates are significantly improved. The most advantageous form will depend on the specific characteristics of each patient and each type of cancer. The precise design of nanocarriers allows for targeted drug delivery, enhancing therapeutic efficacy while reducing side effects. Concrete examples of clinical applications are presented, illustrating the practical potential of these advancements. In conclusion, this review provides a holistic overview of recent developments in galenic pharmacy for targeted drug delivery in oncology. The stability of nanocarriers is a crucial challenge because it conditions the effectiveness and safety of the drugs transported. Environmental and biological variations encountered in the body can compromise this stability, jeopardizing the therapeutic effectiveness and safety of treatments. Likewise, personalized approaches are essential to address interindividual variations in treatment response, as well as patients' pharmacogenomic profiles, in order to optimize therapeutic effectiveness and minimize adverse effects.

Study of defensive behavior of a venomous snake as a new approach to understand snakebite

Snakebites affect millions of people worldwide. The majority of research and management about snakebites focus on venom and antivenom, with less attention given to snake ecology. The fundamental factor in snakebites is the snakes’ defensive biting behavior. Herein we examine the effects of environmental variables (temperature, time of day, and human stimulus) and biological variables (sex and body size) on the biting behavior of a medically significant pit viper species in Brazil, Bothrops jararaca (Viperidae), and associate it with the epidemiology of snakebites. Through experimental simulations of encounters between humans and snakes, we obtained behavioral models applicable to epidemiological situations in the State of São Paulo, Brazil. We found a significant overlap between behavioral, morphological, environmental, and epidemiological data. Variables that increase snakebites in epidemiological data also enhance the tendency of snakes to bite defensively, resulting in snakebites. We propose that snakebite incidents are influenced by environmental and morphological factors, affecting the behavior of snakes and the proportion of incidents. Thus, investigating behavior of snakes related to snakebite incidents is a valuable tool for a better understanding of the epidemiology of these events, helping the prediction and, thus, prevention of snakebites.

Interrater Agreement of Height Assessment by Rigid Proctoscopy/ Rectoscopy for Rectal Carcinoma.

Some guidelines for rectal carcinoma consider 12 cm, measured by rigid endoscopy to be the cutoff tumor height for optional neoadjuvant chemoradiation. Measuring differences of only a few centimeters may therefore predetermine choice of further therapy. However, rigid endoscopy may exhibit similar operator dependence as do most other clinical examination methods. Evaluation of concordance of rigid rectoscopic tumor height measurements performed by 4 experienced examiners, 2 in lithotomy and 2 in left lateral position. Assessment of tumor palpability and distance of the anal verge to the anocutaneous line were also evaluated. Prospective observational study. Academic teaching hospital, referral center for colorectal surgery. There were 50 patients, of whom were 35 males (70%). The median age was 72.5 years (53-88 years). Interrater agreement of tumor height assessment and tumor allocation beneath or beyond the 12-cm height limit. With an intraclass correlation coefficient of 0.947 (95% CI: 0.918-0.967, p < 0.001), interrater reliability of tumor height assessment was statistically rated "excellent." Despite this, in 26% of patients, there was no agreement regarding the allocation of the tumor beneath or beyond the 12-cm height limit. Furthermore there was also considerable disagreement concerning tumor palpability and the distance of the anal verge to the anocutaneous line. Patient positioning was not found to influence results. Single center study. Rigid rectal endoscopy may not be a sound pivotal basis for the consideration of optional chemoradiation in rectal carcinoma. Application of a universally valid height limit obviously ignores biological variability in body frame, gender, and acquired pelvic descent. Eligibility for neoadjuvant therapy should not rely on height measurements alone. Uniform MRI or CT imaging protocols, based on agreed terminology, including factors such as tumor height relative to pelvic frame and peritoneal reflection, may be an important diagnostic addition for such decision. See Video Abstract . DRKS00012758 (German National Study Registry), ST-D 406 (German Cancer Society).

End-user verification results of two serum separator tubes for clinical chemistry analytes according to CLSI GP34-A and CLSI GP41-A6

Tube manufacturers use different composition of gels and blood clot activator formulations in serum tube production. Our aim was to investigate the within-tube (repeatability) and between-tube variation, concordance between comparison results of BD and VacuSEL tubes. Blood samples were collected from control subjects (n = 20) and patients (n = 30) in accordance with the CLSI GP41-A6 and CLSI GP34-A guidelines. Twenty-three clinical chemistry parameters were analysed via Roche Cobas C702 Chemistry Analyzer on T0 (0 hour) and T24 (24 hour). Mean differences % were compared with Wilcoxon matched pair test. Clinical significance was evaluated based on desirable bias according to total allowable error (TEa). VacuSEL tubes demonstrated acceptable performance for the results of 20 parameters with regards to desirable bias % limits. Lactate dehydrogenase (LD) [mean difference % (%95 confidence intervals (CI) values of BD and VacuSEL tubes at T0 [6.41% (4.80–8.01%)]; sodium (Na) and total protein (TP) at T24 [-0.27% (-0.46 to −0.07%) and −1.39% (-1.87 to −0.91), respectively] were over the desirable bias limits (LD: 4.3%, Na: 0.23% and TP: 1.36%, respectively) but not exceeding total biological variation CV % [Na: 0.5 (0.0–1.0) % and TP: 2.6 (2.3–2.7) %). %95 confidence intervals (CI) of T0 LD values overlap with within-subject biological variation % (CI) limits (LD: 5.2 (4.9–5.4) %). The differences between two tubes were not medically significant and necessarily conclusive. VacuSEL serum tubes presented comparable performance with BD serum tubes.

Abstract PO1-15-01: Digital pathology models reveal case-specific characteristics of the tumor microenvironment

Abstract Background Phenotypic characteristics and genetic content have been observed to vary within individual tumors. However, translational research and some clinical practices often rely on extrapolating single histopathology slides to be representative of whole-tumor biology, even when the extent of intra-tumor biological heterogeneity is unknown. In addition, technical factors in slide preparation and digitization can also contribute to variability. Here, we use digital pathology models to identify cell and tissue types in whole-slide images of breast cancer tumors, and quantify intra- and inter-case heterogeneity in cell and tissue features of the tumor microenvironment. Methods We developed PathExplore, a suite of convolutional neural network models trained on pathologist annotations of cell and tissue types in hematoxylin and eosin-stained slides. The models were deployed on the TCGA breast cancer dataset (n=1083 primary solid tumors). Quantitative human-interpretable features (HIFs) relating to cancer, stromal, and necrotic tissue areas, as well as the abundance and distribution of cancer cells, fibroblasts, and immune cells within these tissues, were extracted for each slide. Similarity between slides on sets of HIFs was assessed using Pearson correlation. Intra- and inter-case variability of individual HIFs was measured using the normalized percent difference (range divided by mean) for each slide pair. Case-specificity was quantified by the AUC of correlation or difference distributions for intra-case vs inter-case slide pairs. Only pairs of slides with matching metadata, including tissue source site, cancer stage, and scanner model, were considered for inter-case analysis. Results A total of 55 cases, comprising 114 slides, were identified as having more than one slide per case. Considering all 123 proportional HIFs, intra-case slide pairs showed high correlation compared to inter-case pairs (r=0.97 vs r=0.90, AUC=0.88), with 29% of slides coming from a multi-slide case most closely correlated with another slide from the same case out of the entire TCGA breast dataset. Subsets of these proportional HIFs relating to each identified cell type were also case-specific, with AUC’s ranging from 0.79 for macrophage HIFs to 0.87 for cancer cell HIFs. All proportion, density, and ratio HIFs (n=297) individually showed case specificity (AUC &amp;gt;0.5), with median intra-case differences ranging from 0.6% to 69%. Area proportion of necrosis was more variable than cancer or stroma tissue, while cancer cell count proportions were less variable than fibroblast or immune cell proportions. Cell count proportions were in general less variable and more specific than their corresponding density HIFs; for instance, the frequency of fibroblasts out of all cells in stroma had a median intra-case difference of 11% compared to 20% for the density of fibroblasts in stroma. Conclusions We quantified heterogeneity of key features of the tumor microenvironment both within and across tumors. These results reveal biological and technical variability that can inform selection and interpretation of biomarkers derived from single slides. The ability to uniquely identify slides from the same case additionally demonstrates the technical robustness of digital pathology models for yielding quantitative insights into tumor biology. Citation Format: Ylaine Gerardin, Christian Kirkup, Archit Khosla, Laura Chambre, Michael Drage, Amaro Taylor-Weiner. Digital pathology models reveal case-specific characteristics of the tumor microenvironment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-15-01.

Abstract PO5-21-04: Leveraging Digital Twins for Patient Stratification and Treatment Optimization in geriatric oncology: A Breast Cancer Multivariate Clustering Analysis

Abstract Context: Geriatric oncology necessitates an accurate evaluation of a patient's health status and cancer characteristics. Digital twins, virtual replicas of a patient and their cancer, can aid in predicting real-world behaviors and support therapeutic decision-making. Objectives: This study aims to demonstrate how digital twins can optimize treatments by simulating various scenarios based on the individual characteristics of patients and their cancers. Methods: Creation of a digital twin relies on the use of advanced manifold learning technologies. A model is built to represent the variability present in the reference cohort, capturing complex relationships and inherent data structures. A reference digital twin is generated by drawing samples from this model, thus reflecting the characteristics distribution of the reference population. Stratification (MeanShift algorithm) enables cohort structure visualization and classical statistics estimation for each stratum. Multivariate analysis was performed on a French monocentric population of 1345 patients aged over 70 years who underwent surgery for HER2-negative early breast cancer. Results: 8 cluster, created by manifold learning based on biological, demographic, and tumoral variables, exhibits unique five-year survival rates and influential factors. The clusters represent specific patient subgroups, each with its distinct set of characteristics and prognostic indicators. Table 1 provides a summary of these variables' averages within each cluster. we can thus see that the variables of clusters 3 and 6 (including only HR+ cancers) do not show statistically significant differences, unlike their 5-year survival rates (cluster 3 : 80,9% (CI95 :70-88.5) and 63.9% (CI95 :47.6-77.5) for cluster 6, p= 0,04). Provided with model results, the relative importance of these variables varies from one cluster to another, underscoring the heterogeneity among patient subgroups. Interestingly, Hormonal receptor status, nodal involvement and tumoral grade were not the predominant variables in these clusters, which challenges traditional perspectives. Table 1 : Mean value of variables per cluster (SBR grade 0=grade 1 and 2 ; 1= grade 3) Discussion: The findings underscore the vast variability of individual variable importance across clusters. Digital twins enabled the modeling of these complex interactions, providing valuable insights for clinical decision-making. Conclusion: Digital twins offer a valuable tool for personalizing care in oncogeriatrics, facilitating the identification of patient subgroups, yielding more accurate treatment outcomes, and guiding future research directions. The results affirm that incorporating digital twins into oncogeriatric care could potentially lead to the enhanced personalization of therapeutic approaches, ultimately improving patient outcomes. Table. Citation Format: Pierre Heudel, Mashal Ahmed, Felix Renard. Leveraging Digital Twins for Patient Stratification and Treatment Optimization in geriatric oncology: A Breast Cancer Multivariate Clustering Analysis [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-21-04.

Abstract PO5-26-09: Digital image analysis and a novel set of cell line samples as aids in the development of a quantitative external quality assessment programme for Ki-67

Abstract Background and Aims Ki-67 is a well-established biomarker of proliferation in breast cancer (BC). However, its value in treatment decision making is hampered by a lack of analytical reproducibility. Regular participation in external quality assessment (EQA) substantially improves inter-laboratory concordance. Pre-requisites in establishing a fit-for-purpose EQA for Ki-67 are a well-validated testing substrate and a reproducible method of assessing Ki-67 scores in participants’ submitted materials. We report here on the results of work using cell line controls analysed by digital image analysis (DIA) as a first step towards providing such an EQA. Materials and Methods A formalin-fixed paraffin embedded (FFPE) cell line microarray (CLMA) was designed and produced in conjunction with Array Sciences LLC (Sausalito, USA). It was comprised of cores taken from a pure population of Sf9 caterpillar cells, which have been shown to be completely unreactive with most commercial antibodies to human Ki-67, together with cores of Sf9 cells mixed with four different human BC cell lines. These were BT-20 (85% BC cells), ZR-75-1 (75%), BT-474 (65%) and BT-483 (55%). Sections from the CLMA were mounted onto glass microscope slides together with sections from a FFPE tonsil sample and two BC samples; one BC showed high (~30%, BC-high) and the second, low proliferation (~5%, BC-low). In both BC samples proliferating cancer cells were distributed homogeneously throughout the the block. Unstained sections were distributed to laboratories participating in the Scheme’s Ki-67 BC programme; after routine IHC-staining for Ki-67 returned slides were centrally visually assessed for stain quality and subjected to DIA using an in-house application developed on Visiopharm software (Visiopharm A/S, Hoersholm, Denmark). Results Slides were returned by 37 laboratories. Analysis of Ki-67 scores obtained on the Sf9 core identified two distinct groups. The first (n = 27) were negative or showed low Ki-67 scores (mean = 1.1%, 95% CIs: 0.2-1.9%), the second (n = 10) displayed a step-change in scores (mean = 49.9%, 95% CIs: 33.2-66.7%); the means of the two groups were significantly different (P&amp;lt; 0.0001). When Ki-67 scores for each of the tissue samples were dichotomized into similar groups, the means of those groups differed significantly for the two BC samples (P&amp;lt; 0.001), but not for tonsil. Quality scores generated by visual assessment did not differ significantly between the two groups. However, when slides bearing Sf9 cores demonstrating aberrant Ki-67 scores were visually examined nuclear staining was clearly visible, and non-specific nuclear staining could also be identified in the matched BC tissue samples, but not in the tonsil sections. Correlation of Ki-67 scores between the four BC cell line cores and each of the BC tissue samples was examined using Pearson’s correlation statistics. The r statistic range was 0.57-0.71 in comparisons between BC cell line cores and the BC-high sample, and 0.53-0.70 for those with BC-low; in each case BT-483 showed the highest correlation score and BT-20 the lowest. A similar analysis was undertaken between the tonsil and the two BC tissues. The r statistic for correlation between tonsil and BC-high scores was 0.72; it was 0.64 for those between tonsil and BC-low. Conclusions By using a pure population of Sf9 cells we have developed a sensitive indicator of non-specific nuclear staining in IHC preparations stained for Ki-67 which identifies the presence of the artefact quantifiably. Cores made from Sf9/BC cell line mixtures (especially BT-483) produce Ki-67 scores which correlate with those obtained in breast cancer samples at a similar level to those achieved between tonsil and BC samples; this is true for both high and the low proliferation ranges. Cell line mixtures can be adjusted to show Ki-67 scores in the clinically relevant ranges, and they do not show the inherent biological variations seen in tissue controls such as tonsil. Citation Format: Andrew Dodson, Fitim Berisha, Dawn Wilkingson, Lila Zabaglo, Suzanne Parry. Digital image analysis and a novel set of cell line samples as aids in the development of a quantitative external quality assessment programme for Ki-67 [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-26-09.