Tumor Cell Density Research Articles

A Matched-Pair Analysis of Nuclear Morphologic Features Between Core Needle Biopsy and Surgical Specimen in Thyroid Tumors Using a Deep Learning Model.

Core needle biopsy (CNB) is a method that can be used as an alternative to fine-needle aspiration for the sampling of thyroid nodules to provide a preoperative diagnosis. Nuclear atypia is of paramount importance in diagnosing thyroid tumors. We aimed to identify the differences in nuclear morphologic features between CNB and surgical specimens. Quantitative image analysis was performed on whole slide images (WSI) of paired CNB and surgical specimens from 50 follicular cell-derived tumors including adenoma, non-invasive follicular thyroid neoplasm with papillary-like nuclear features, and carcinoma. The nuclear features of tumor cells were extracted from WSI using a pre-trained deep-learning model. In matched-pair analysis, the ratios (surgical specimens-to-CNB) of the mean nuclear diameters and the areas of the tumor cells were 1:0.75-0.85 and 1:0.54-0.73 according to the tumor types, respectively. The ratio of the nuclear optical density of tumor cells was 1.68-2.44 times higher in CNB specimens than it was in their corresponding surgical specimens. The significant nuclear size reduction and optical density increase in CNB were most prominent in papillary carcinoma cells. Normal follicular cells showed nuclear size reduction in CNB but no change in nuclear optical density between CNB and surgical specimens. Nuclear vacuolar artifacts mimicking nuclear pseudoinclusions were frequently seen in CNB regardless of the tumor types. In conclusion, tumor cells in CNB showed marked nuclear shrinkage, darker nuclear staining, and nuclear vacuolar changes. Awareness of the morphological differences according to the sampling types can facilitate correct diagnosis in thyroid histopathology.

MR imaging profile and histopathological characteristics of tumour vasculature, cell density and proliferation rate define two distinct growth patterns of human brain metastases from lung cancer

PurposeNon-invasive prediction of the tumour of origin giving rise to brain metastases (BMs) using MRI measurements obtained in radiological routine and elucidating the biological basis by matched histopathological analysis.MethodsPreoperative MRI and histological parameters of 95 BM patients (female, 50; mean age 59.6 ± 11.5 years) suffering from different primary tumours were retrospectively analysed. MR features were assessed by region of interest (ROI) measurements of signal intensities on unenhanced T1-, T2-, diffusion-weighted imaging and apparent diffusion coefficient (ADC) normalised to an internal reference ROI. Furthermore, we assessed BM size and oedema as well as cell density, proliferation rate, microvessel density and vessel area as histopathological parameters.ResultsApplying recursive partitioning conditional inference trees, only histopathological parameters could stratify the primary tumour entities. We identified two distinct BM growth patterns depending on their proliferative status: Ki67high BMs were larger (p = 0.02), showed less peritumoural oedema (p = 0.02) and showed a trend towards higher cell density (p = 0.05). Furthermore, Ki67high BMs were associated with higher DWI signals (p = 0.03) and reduced ADC values (p = 0.004). Vessel density was strongly reduced in Ki67high BM (p < 0.001). These features differentiated between lung cancer BM entities (p ≤ 0.03 for all features) with SCLCs representing predominantly the Ki67high group, while NSCLCs rather matching with Ki67low features.ConclusionInterpretable and easy to obtain MRI features may not be sufficient to predict directly the primary tumour entity of BM but seem to have the potential to aid differentiating high- and low-proliferative BMs, such as SCLC and NSCLC.

A simple integrated microfluidic platform for the research of hydrogels containing gradients in cell density induced breast cancer electrochemotherapy

Cell density is important for tumour metastasis, treatment and prognosis. Characterizing changes in cell density for electrochemotherapy (ECT) can reveal sub-populations in pathological states, and adjust treatment program. In this work, a simple and convenient microfluidic platform was developed to study the effect cell density on ECT by integrating the improved cell gradient generator, cell culture chamber and indium tin oxide interdigital electrodes. Agarose, as extracellular matrix (ECM), was used to 3D cell culture to imitate in vivo microenvironment. The precision and reproducibility of cell density gradient with agarose solution were achieved because the hydrophobic modification of microchannels surface resulted in reducing cell adhesion and residue. ECT cytotoxicity assay with difference in cell densities was studied. The results showed that tumour cell density is one of the most factors for ECT treatment and ECT cytotoxicity has a certain of cell density-depended. But only electroporation on low cell density level, ECM would be one of the most key factors for ECT cytotoxicity, which would provide a new idea for chip-based cell assay in clinical diagnosis and drug screening in ordinary laboratories.

Image analysis reveals differences in tumor multinucleations in Black and White patients with human papillomavirus-associated oropharyngeal squamous cell carcinoma.

Understanding biological differences between different racial groups of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) patients, who have differences in terms of incidence, survival, and tumor morphology, can facilitate accurate prognostic biomarkers, which can help develop personalized treatment strategies. This study evaluated whether there were morphologic differences between HPV-associated tumors from Black and White patients in terms of multinucleation index (MuNI), an image analysis-derived metric that measures density of multinucleated tumor cells within epithelial regions on hematoxylin-eosin images and previously has been prognostic in HPV-associated OPSCC patients. In this study, the authors specifically evaluated whether the same MuNI cutoff that was prognostic of overall survival (OS) and disease-free survival in their previous study, TTR , is valid for Black and White patients, separately. We also evaluated population-specific cutoffs, TB for Blacks and TW for Whites, for risk stratification. MuNI was statistically significantly different between Black (mean, 3.88e-4; median, 3.67e-04) and White patients (mean, 3.36e-04; median, 2.99e-04), with p=.0078. Using TTR , MuNI was prognostic of OS in the entire population with hazard ratio (HR) of 1.71 (p=.002; 95% confidence interval [CI], 1.21-2.43) and in White patients with HR of 1.72 (p=.005; 95% CI, 1.18-2.51). Population-specific cutoff, TW , yielded improved HR of 1.77 (p=.003; 95% CI, 1.21-2.58) for White patients, whereas TB did not improve risk-stratification in Black patients with HR of 0.6 (p=.3; HR, 0.6; 95% CI, 0.2-1.80). Histological difference between White and Black patient tumors in terms of multinucleated tumor cells suggests the need for considering population-specific prognostic biomarkers for personalized risk stratification strategies for HPV-associated OPSCC patients.

Computed tomography and magnetic resonance imaging findings in dogs with vaginal leiomyoma and leiomyosarcoma.

In humans, magnetic resonance imaging (MRI) is preferred over computed tomography (CT) for the assessment of pelvic lesions. Although CT findings of several pelvic tumours have been reported in veterinary medicine, MRI findings are limited. The purpose of this study was to retrospectively compare the CT and MRI findings in dogs with vaginal leiomyoma and leiomyosarcoma. This retrospective study of five dogs compared the CT and MRI findings of intrapelvic lesions, including vaginal leiomyoma (n=4) and leiomyosarcoma (n= 1). No invasion of the surrounding tissue was detected on histopathological examination. In this retrospective study, the following parameters of CT and MRI were recorded for each dog: the border between the lesion and the adjacent pelvic organs, including the prostate, rectum or urethra; signal intensity (SI) of the lesion; enhancement pattern; presence of haemorrhage; necrosis or cystic areas and lymphadenopathy. Because SI on MRI is affected by cell density, tumour cell density was analysed using a microscope slide. In vaginal leiomyoma, the border between the lesion and the surrounding pelvic organ tends to be clearer on MRI than on CT. In vaginal leiomyosarcoma, the border was comparable between MRI and CT. Each lesion showed heterogeneous enhancement on CT and MRI scans. In each lesion, the assessment of haemorrhage, necrosis, cystic areas and lymphadenopathy was comparable between MRI and CT. The SI of the lesion on T2WI of the vaginal leiomyoma and leiomyosarcoma were hyperintense in four cases (4/4; 100%) and mixed intense in one case (1/1; 100%), respectively. The cell density of leiomyosarcoma is higher than that of leiomyomas. The SI on T2WI may be useful for differentiating leiomyoma from leiomyosarcoma. MRI may be useful to differentiate vaginal leiomyomas from leiomyosarcomas and evaluate margins.

Water transport regulates nucleus volume, cell density, Young’s modulus, and E-cadherin expression in tumor spheroids

Cell volume is maintained by the balance of water and solutes across the cell membrane and plays an important role in mechanics and biochemical signaling in cells. Here, we assess the relationship between cell volume, mechanical properties, and E-cadherin expression in three-dimensional cultures for ovarian cancer. To determine the effect of water transport in multi-cellular tumors, ovarian cancer spheroids were subjected to hypotonic and hypertonic shock using water and sucrose mixtures, respectively. Increased osmolality resulted in decreased nucleus volume, increased Young’s modulus, and increased tumor cell density in ovarian cancer spheroids. Next, we looked at the reversibility of mechanics and morphology after 5 min of osmotic shock and found that spheroids had a robust ability to return to their original state. Finally, we quantified the size of E-cadherin clusters at cell-cell junctions and observed a significant increase in aggregate size following 30 min of hypertonic and hypotonic osmotic shocks. Yet, these effects were not apparent after 5 min of osmotic shock, illustrating a temporal difference between E-cadherin regulation and the immediate mechanical and morphology changes. Still, the osmotically induced E-cadherin aggregates which formed at the 30-minute timepoint was reversible when spheroids were replenished with isotonic medium. Altogether, this work demonstrated an important role of osmolality in transforming mechanical, morphology, and molecular states.

Mathematical Modeling and Analysis of CD200-CD200R in Cancer Treatment.

CD200 is a cell membrane protein that binds to its receptor, CD200 receptor (CD200R). The CD200 positive tumor cells inhibit the cellular functions of M1 and M2 macrophages and dendritic cells (DCs) through the CD200-CD200R complex, resulting in downregulation of Interleukin-10 and Interleukin-12 productions and affecting the activation of cytotoxic T lymphocytes. In this work, we provide two ordinary differential equation models, one complete model and one simplified model, to investigate how the binding affinities of CD200R and the populations of M1 and M2 macrophages affect the functions of the CD200-CD200R complex in tumor growth. Our simulations demonstrate that (i) the impact of the CD200-CD200R complex on tumor promotion or inhibition highly depends on the binding affinity of the CD200R on M2 macrophages and DCs to the CD200 on tumor cells, and (ii) a stronger binding affinity of the CD200R on M1 macrophages or DCs to the CD200 on tumor cells induces a higher tumor cell density in the CD200 positive tumor. Thus, the CD200 blockade would be an efficient treatment method in this case. Moreover, the simplified model shows that the binding affinity of CD200R on macrophages is the major factor to determine the treatment efficacy of CD200 blockade when the binding affinities of CD200R on M1 and M2 macrophages are significantly different to each other. On the other hand, both the binding affinity of CD200R and the population of macrophages are the major factors to determine the treatment efficacy of CD200 blockade when the binding affinities of CD200R on M1 and M2 macrophages are close to each other. We also analyze the simplified model to investigate the dynamics of the positive and trivial equilibria of the CD200 positive tumor case and the CD200 deficient tumor case. The bifurcation diagrams show that when M1 macrophages dominate the population, the tumor cell density of the CD200 positive tumor is higher than the one of CD200 deficient tumor. Moreover, the dynamics of tumor cell density change from tumor elimination to tumor persistence to oscillation, as the maximal proliferation rate of tumor cells increases.

Chaihu Guizhi Decoction plus or minus formula combined with capecitabine inhibits IL-6/STAT3 signaling to suppress triple-negative breast cancer xenografts in nude mice

To investigate the effect of Chaihu Guizhi Decoction (CHGZD) combined with capecitabine on growth and apoptosis of subcutaneous triple-negative breast cancer xenografts in nude mice and explore the possible mechanism. Nude mouse models bearing subcutaneous triple-negative breast cancer xenografts were randomized into 6 groups (n=10) for treatment with distilled water (model group), low (10.62 g/kg), medium (21.23 g/kg) and high (42.46 g/kg) doses of CHGZD, capecitabine (0.2 mg/kg), or the combination of CHGZD (42.46 g/kg) and capecitabine (0.2 mg/k) once daily for 21 consecutive days. The general condition of mice was observed, and after 21-day treatments, the tumors were dissected for measurement of tumor volume and weight and histopathological examination with HE staining. Serum IL-6 levels of the mice were determined with enzyme-linked immunosorbent assay (ELISA), and the expression levels of IL-6, STAT3, p-STAT3, Bax, Bcl-2 and cyclin D1 in the tumor tissues were detected using real-time PCR and Western blotting. Compared with those in the model group, the tumor-bearing mice receiving treatments with CHGZD showed significantly increased food intake with good general condition, sensitive responses, increased body weight, and lower tumor mass (P < 0.01). Compared with capecitabine treatment alone, treatment with CHGZD alone at the medium and high doses and the combined treatment all resulted in significantly higher tumor inhibition rates (P < 0.01), induced obvious tumor tissue degeneration and reduced the tumor cell density. Treatments with CHGZD, both alone and in combination with capecitabine, significantly decreased serum IL-6 level, lowered the mRNA expression levels of IL-6 and STAT3, the protein expressions of IL-6, STAT3 and P-STAT3 (P < 0.05), and the mRNA and protein expressions of Bcl-2 and cyclin D1 (P < 0.05), and increased the mRNA and protein expressions of Bax in the tumor tissues (P < 0.05). CHGZD combined with capecitabine can significantly inhibit tumor growth in nude mice bearing triple-negative breast cancer xenografts, the mechanism of which may involve the inhibition of IL-6/STAT3 signaling pathway and regulation of Bax, Bcl-2 and cyclin D1 expressions to suppress tumor cell proliferation and differentiation and induce cell apoptosis.

Chaos and multistability behaviors in 4D dissipative cancer growth/decay model with unstable line of equilibria

The objective of this paper is to study the chaos and multistability behaviors in a 4D dissipative chaotic cancer growth/decay model. The 4D chaotic cancer growth/decay model has chaotic 2-torus and 2-torus quasi-periodic unique behaviors which reflect the fact that the tumor cell density has 2-torus quasi-periodic bifurcation between two values. As the tumor cell production rate is increasing, the bifurcation is growing more rapidly as chaotic 2-torus evolution and the tumor cell density becomes unstable. The 4D cancer growth/decay model has an unstable line of equilibria with saddle-focus behavior. The chaos and multistability behaviors are explored with different qualitative and quantitative dynamic tools like Lyapunov exponents, Lyapunov dimension, bifurcation diagram and Poincaré map. Tumor cell escalation/de-escalation, glucose level, number of tumor cells are considered to analyses chaos and multistability behaviors. The existence of multistability behavior in the 4D cancer model reveals that the different phenotypes are adopted by tumor cells, some of them become metastatic, adopt different behaviors and turn into a genomic event. The multistability behavior in the 4D chaotic cancer growth/decay model may be of capital importance in the dynamic evolution of the tumor since complication may occurs even after the required therapy. Simulations are done in MATLAB environment and are presented for effective verification of numerical approach. MATLAB simulated results correspond successful achievement of the objective.

Abstract 5064: Patient-specific forecasting of prostate cancer growth during active surveillance using an imaging-informed mechanistic model

Abstract Active surveillance (AS) is a suitable management option for many newly-diagnosed prostate cancer (PCa) cases, which usually exhibit low to intermediate clinical risk. In AS, patients are closely monitored via multiparametric magnetic resonance imaging (mpMRI), prostate-specific antigen tests, and biopsies until these reveal an increase in PCa risk that warrants treatment. Thus, AS can contribute to reduce the current overtreatment of PCa and avoid treatment side-effects that may adversely impact patients’ lives without necessarily improving their longevity. However, current AS protocols rely on assessing PCa according to population-based studies, which complicates the design of personalized monitoring plans and the early detection of tumor progression. To address these issues, we propose to predict PCa growth using patient-specific forecasts based on an mpMRI-informed mechanistic model. Here, we present a preliminary study in a cohort of seven PCa patients who enrolled in AS and had three mpMRI scans over a period of 1.4 to 4.9 years. Our mechanistic model describes PCa growth in terms of tumor cell density dynamics, consisting of mobility and proliferation modeled via a diffusion process and a logistic law, respectively. We segment each patient’s prostate geometry on T2-weighted images and estimate tumor cell density from standard apparent diffusion coefficient (ADC) maps. To facilitate PCa modeling, we co-register each patient’s mpMRI datasets with a biomechanical elastic method that aligns the prostate segmentations across the imaging datasets. The model is initialized with the data from the first mpMRI scan. Model calibration relies on a nonlinear least-squares algorithm that minimizes the mismatch between ADC-measured and model-predicted tumor cell density at the second mpMRI date. For validation, we initialize the patient-specific calibrated model with the second mpMRI dataset and forecast PCa growth at the third mpMRI date, which we compare to the corresponding ADC-estimated tumor cell density. Model calibration resulted in a concordance correlation coefficient (CCC) &amp;gt; 0.99 for both tumor volume and global tumor cell count across the cohort. For each patient, the spatial fit of tumor cell density at the second scan date rendered a median Dice score and CCC of 0.79 and 0.52, respectively. Model validation resulted in CCCs of 0.88 and 0.81 for tumor volume and global tumor cell count across the cohort. Comparing the patient-specific ADC-estimated and model forecasts of tumor cell density at the third scan date, we obtained a median Dice score and CCC of 0.65 and 0.46, respectively. Thus, while improvement and further testing in larger cohorts are still needed, these results do suggest that our computational forecasting approach is a promising technology to predict the spatiotemporal growth of PCa during AS on a patient-specific basis. Citation Format: Guillermo Lorenzo, Jon S. Heiselman, Michael A. Liss, Michael I. Miga, Hector Gomez, Thomas E. Yankeelov, Thomas J. Hughes, Alessandro Reali. Patient-specific forecasting of prostate cancer growth during active surveillance using an imaging-informed mechanistic model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5064.

Abstract 1008: Cell density-related variability in chemotherapeutic resistance patterns in human cancer cells

Abstract Assessment of chemosensitivity and resistance varies with cancer cell of origin, drug and culture conditions. However, it is also not uncommon to find multiple different IC50 values for the same chemotherapeutic treatments acting on the same tumor cell lines. The aim of this research was to investigate this discrepancy in phenotypes and elucidate relevant variables that influence them. We assessed chemosensivity as a function of tumor cell density by plating TOV-21G ovarian, HT29 colorectal, and U2OS osteosarcoma cell lines in 96-well plates at various seeding densities per well and treated them with various concentrations of cisplatin, 5-fluorouracil (5-FU), and etoposide, respectively. CellTiter Glo assays were performed at 72 hours of treatment to determine IC50. Cisplatin IC50 for TOV-21G cells plated at 50,000, 20,000, 10,000, 5,000, 2,000, 1,000, and 500 cells/well were 12.00 μM, 5.47 μM, 3.16 μM, 2.45 μM, 2.54 μM, 2.83 μM, and 2.66 μM, respectively. Etoposide IC50 for U2OS cells plated at the same seeding densities were 6.50 μM, 5.19 μM, 3.23 μM, 2.98 μM, 2.49 μM, 2.13 μM, and 1.88 μM, respectively. IC50 values for HT29 cells treated with 5-FU at 2,000, 1,000, and 500 cells/well were 35.89 μM, 19.74 μM, and 13.42 μM, respectively. TOV-21G, U2OS, and HT29 cells all showed differences in IC50 values depending on cell plating density. TOV-21G and U2OS cells both showed a gradual decrease and plateau in IC50 as the cancer cell density decreased from 50,000 cells/well to 500 cells/well. HT29 cells displayed a sharper IC50 decrease as the cell density decreased from 2,000 cells/well to 500 cells/well. Our experiments demonstrate that IC50 values are not static at 72 hours and that tumor cell density is an important variable in influencing observed tumor resistance to traditionally effective drugs. As the density of cancer cells increases, the cells develop more resistance to the associated chemotherapy and require higher quantities of drug to achieve the same level of growth inhibition. We are currently exploring underlying molecular mechanisms that may influence these changes in IC50 including cell survival signaling proteins, cell-cell contact or growth factors that may influence cell density-related chemoresistance. Citation Format: Thomas Brown, Ujwal Punyamurtula, Jill Strandberg, Wafik S. El-Deiry. Cell density-related variability in chemotherapeutic resistance patterns in human cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1008.

Abstract 504: Membrane-associated heat shock protein mHsp70 is involved in cancer cell invasion and tumor recurrence in malignant brain tumors

Abstract Heat shock proteins (HSPs) constitute a large family of highly conserved proteins acting as molecular chaperones that play a key role in intracellular proteostasis, regulation of apoptosis, and protection from various stress factors (including hypoxia, thermal stress, oxidative stress). Apart from their intracellular localization, members of different HSPs families such as Hsp70 have been found to be localized on the plasma membrane of malignantly transformed cells, including multiforme glioblastoma (GBM). However, the role of the membrane-bound mHsp70 has not yet been elucidated in the pathophysiology of GBM. We have assessed the mHsp70 expression in biopsies obtained from neuro-oncological patients employing inverted confocal microscopy (Leica Microsystems) and CellVoyager CQ1 Benchtop High-Content Analysis System (Yokogawa). Live-cell imaging of the patient-derived biopsy revealed the increased cellular density of mHsp70-positive tumor cells particularly in the tissue along the tumor-brain margin (biopsy was derived 3 mm from contrast-enhancing margin and also within hyperintense FLAIR). Time-lapse analysis of the tumor sample further demonstrated the migration of mHsp70-positive cells into the surrounding culture medium. Subsequent high-powered multiplex immunofluorescence analysis (Akoya Biosystems Inc.) confirmed markedly increased single-cell infiltration of mHsp70(+) tumor cells (that were also co-immunofluorescently stained for SOX2, Nestin, and Oct4) in the normal brain tissues. In conclusion, expression of mHsp70 on the plasma membrane of tumor cells corresponds to the highly invasive potential of the GBM cells and recurrence of these tumors in neuro-oncological patients. This research was funded by the Ministry of Science and Higher Education of the Russian Federation (Agreement № 075-15-2020-901). Citation Format: Maxim Shevtsov, Ruslana Tagaeva, Natalia Yudintceva, Danila Bobkov, Darya Goncharova, Alexander Kim, Evgeniy Fedorov, Anastasia Nechaeva, Anastasia Mihrina, Anna Patruhina, Vyacheslav Fedorov, Konstantin Samochernykh. Membrane-associated heat shock protein mHsp70 is involved in cancer cell invasion and tumor recurrence in malignant brain tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 504.

Abstract 2738: Image-driven biophysical model to predict pathologic response to neoadjuvant therapy in breast cancer

Abstract Introduction: In previous work, we developed an image-driven, patient-specific biophysical model of breast tumor response to neoadjuvant therapy (NAT). In this work, we use this model to characterize response during the course of NAT to predict pathological complete response (pCR). pCR is an important binary pathological outcome metric as patients who achieve pCR experience improved survival outcomes. We hypothesize that with optimized imaging time points within the I-SPY 2 trial study design, we will be able to more accurately characterize dynamic response to therapy and predict eventual pCR. Methods: Our retrospective study included 84 patients (26 pCR, 58 non-pCR) from the publicly-available ACRIN-6698 study.4-7 This substudy to the ongoing I-SPY 2 trial evaluates the effectiveness of diffusion weighted magnetic resonance imaging (DW-MRI) for assessing breast cancer response to NAT. We use serial DW-MRI data to compute the apparent diffusion coefficient (ADC), which is used to approximate tumor cell density distribution between baseline and midtreatment (12 weeks). Cellularity data is fit to our model of tumor growth/treatment response1-3 to obtain biophysical estimates of response, including the tumor cell proliferation rate (k). Estimated spatial proliferation maps of the tumor throughout NAT between imaging time points are used to create a histogram of tumor cell proliferation for each patient and histogram metrics are extracted as features to predict pCR. Receiver operating characteristic (ROC) curves were generated and the area under the curve (AUC), sensitivity (sens), and specificity (spec) were calculated to evaluate prediction performance. Results: Parameterizations of tumor biophysical properties were calculated between baseline and midtreatment. In univariate analysis with model-based proliferative area we achieved a ROC AUC of 0.72 (85% sens, 55% spec). Conventional metrics of percent change in tumor area and mean ADC achieved ROC AUC of 0.52 (50% sens and 69% spec; 23% sens and 97% spec). We constructed multivariate logistic regression models to combine parameters to build signatures for predicting pCR. We achieved AUC of 0.88 (85% sens and 81% spec) and 0.97 (100% sens and 88% spec) for our model metrics alone, and in combination with size and ADC metrics, respectively. Conclusions: Use of image-driven mathematical modeling with the ACRIN-6698 subset of the I-SPY 2 trial enables accurate prediction of tumor response during NAT based on baseline and mid-treatment imaging data. This initial study provides promise for improved prediction accuracy with biophysical modeling. These results suggest that a biophysical modeling method has the potential to aid in response characterization. In future work, we will apply our model to the remainder of the ACRIN-6698 subset to more rigorously assess prediction of pCR and investigate incorporation of tumor perfusion and diffusion data. Citation Format: Haley J. Bowers, Jared A. Weis, Alexandra Thomas, Emily Douglas, Katherine Ansley. Image-driven biophysical model to predict pathologic response to neoadjuvant therapy in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2738.

EPEN-13. Clinically relevant molecular hallmarks of PFA ependymomas display intratumoral heterogeneity and correlate with tumor morphology

Abstract PFA ependymomas are aggressive posterior fossa tumors that mainly effect children and have a poor prognosis despite intensive therapy. Histopathology reveals glial neoplasms that usually show perivascular pseudorosettes throughout the tumor. Tumor cell density may vary within a tumor and sometimes shows well demarcated nodules of extremely densely packed tumor cells within surrounding loose glial tissue harboring diffusely distributed tumor cells. To determine potential molecular differences in such areas and to understand the clinical meaning, we analysed 70 clinically well-annotated PFA samples and 9 corresponding relapse samples. Areas of low and high cell density were digitally identified with a cut-off of 8,500 cells/mm². Global DNA methylation and copy-number profiling was performed in 52/79 cases for the entire tumor area of the sample and in 15/79 cases for the cell-dense and less cell-dense regions, separately. Fluorescence-in-situ-hybridization was performed for 20 cases, and scRNA-Seq data were analysed for 12 cases. The proportion of cell-dense areas proved to be highly variable on a continuum ranging from 0 % to 100 % of the tumor area. It was significantly higher in relapses in comparison to the respective primary tumors (p=0.036). Also, cell-dense areas displayed a significantly higher proportion of proliferating tumor cells (p&amp;lt;0.01). Global DNA methylation was similar, but not identical in different tumor areas. In 6/15 cases, the PFA subtype changed as determined by the current version of the brain tumor classfier (v12, www.molecularneuropathology.org). Chromosomal changes at 1q and 6q were only detectable in cell-dense areas in 5/12 and in 3/12 cases, respectively. Finally, scRNA-Seq data confirmed a widespread heterogeneity regarding 1q and 6q changes in PFA ependymoma cells. These data suggest that PFA ependymoma habor a previously unrecognized heterogeneity that has to be accounted for when selecting material for the analysis of clinically relevant DNA methylation profiles or copy number changes.

Defining the area of mitoses counting in invasive breast cancer using whole slide image

AbstractAlthough counting mitoses is part of breast cancer grading, concordance studies showed low agreement. Refining the criteria for mitotic counting can improve concordance, particularly when using whole slide images (WSIs). This study aims to refine the methodology for optimal mitoses counting on WSI. Digital images of 595 hematoxylin and eosin stained sections were evaluated. Several morphological criteria were investigated and applied to define mitotic hotspots. Reproducibility, representativeness, time, and association with outcome were the criteria used to evaluate the best area size for mitoses counting. Three approaches for scoring mitoses on WSIs (single and multiple annotated rectangles and multiple digital high-power (×40) screen fields (HPSFs)) were evaluated. The relative increase in tumor cell density was the most significant and easiest parameter for identifying hotspots. Counting mitoses in 3 mm2 area was the most representative regarding saturation and concordance levels. Counting in area <2 mm2 resulted in a significant reduction in mitotic count (P = 0.02), whereas counting in area ≥4 mm2 was time-consuming and did not add a significant rise in overall mitotic count (P = 0.08). Using multiple HPSF, following calibration, provided the most reliable, timesaving, and practical method for mitoses counting on WSI. This study provides evidence-based methodology for defining the area and methodology of visual mitoses counting using WSI. Visual mitoses scoring on WSI can be performed reliably by adjusting the number of monitor screens.

IDH-wild-type glioblastoma cell density and infiltration distribution influence on supramarginal resection and its impact on overall survival: a mathematical model.

Recent studies have proposed resection of the T2 FLAIR hyperintensity beyond the T1 contrast enhancement (supramarginal resection [SMR]) for IDH-wild-type glioblastoma (GBM) to further improve patients' overall survival (OS). GBMs have significant variability in tumor cell density, distribution, and infiltration. Advanced mathematical models based on patient-specific radiographic features have provided new insights into GBM growth kinetics on two important parameters of tumor aggressiveness: proliferation rate (ρ) and diffusion rate (D). The aim of this study was to investigate OS of patients with IDH-wild-type GBM who underwent SMR based on a mathematical model of cell distribution and infiltration profile (tumor invasiveness profile). Volumetric measurements were obtained from the selected regions of interest from pre- and postoperative MRI studies of included patients. The tumor invasiveness profile (proliferation/diffusion [ρ/D] ratio) was calculated using the following formula: ρ/D ratio = (4π/3)2/3 × (6.106/[VT21/1 - VT11/1])2, where VT2 and VT1 are the preoperative FLAIR and contrast-enhancing volumes, respectively. Patients were split into subgroups based on their tumor invasiveness profiles. In this analysis, tumors were classified as nodular, moderately diffuse, or highly diffuse. A total of 101 patients were included. Tumors were classified as nodular (n = 34), moderately diffuse (n = 34), and highly diffuse (n = 33). On multivariate analysis, increasing SMR had a significant positive correlation with OS for moderately and highly diffuse tumors (HR 0.99, 95% CI 0.98-0.99; p = 0.02; and HR 0.98, 95% CI 0.96-0.99; p = 0.04, respectively). On threshold analysis, OS benefit was seen with SMR from 10% to 29%, 10% to 59%, and 30% to 90%, for nodular, moderately diffuse, and highly diffuse, respectively. The impact of SMR on OS for patients with IDH-wild-type GBM is influenced by the degree of tumor invasiveness. The authors' results show that increasing SMR is associated with increased OS in patients with moderate and highly diffuse IDH-wild-type GBMs. When grouping SMR into 10% intervals, this benefit was seen for all tumor subgroups, although for nodular tumors, the maximum beneficial SMR percentage was considerably lower than in moderate and highly diffuse tumors.

Single-cell immune mapping of adenoid cystic carcinoma (ACC) reveals potential therapeutic targets for the aggressive solid subtype.

6090 Background: ACC is a common salivary gland malignancy for which there is no FDA approved therapies. Despite a quiet genome, ACC has significant tumoral heterogeneity, which may facilitate the metastatic relapse. Studies focusing on the deep profile of ACC tumor microenvironment (TME) are lacking. Here we explored the TME of ACC using imaging mass cytometry (IMC) and assessed TME attributes and its association with histology and clinical outcomes. Methods: Two tissue microarrays from 62 ACC patients (pts) were built and stained with 37 metal-tagged markers. IMC was performed with the Fluidigm Helios CyTOF instrument utilizing the Hyperion Imaging System Laser ablation. Tissue and cell segmentation and multiplex imaging analysis were performed with Visiopharm software using pre-trained artificial intelligence algorithms. Comparison of cell types and markers densities among histology sub-groups were analyzed with Wilcoxon signed-rank test and its association with overall survival (OS) with log-rank and Cox Proportional Hazards. Results: Of 62 pts, 37% (23/62) had solid histology, 19% (12/62) were metastatic at diagnosis, and 55% (30/54) had disease recurrence during follow-up. With a median follow-up of 7.9 y, the median OS was 9.3y (CI 95%, 7.8-NR). Pts with solid subtype had poorer OS than non-solid histology (5.2 vs 14.6 y, p = 0.004). The IMC final dataset comprised 507,524 single-cells. No significant differences were found in cell subpopulations density between solid and non-solid histology. The most represented cell population in the stroma were macrophages, followed by CD8+ T cells, and fibroblasts. A higher percentage of M2-macrophage was associated with poor survival (p = 0.001), whereas, a higher percentage of M1-macrophage (M1:M2 ratio) was associated with better prognosis. No other immune cell type, fibroblasts or immune cell functional markers (TIGIT, TIM3, granzyme B, and PD-L1) correlated with survival. Regarding tumor markers, a higher expression of BCL-2, B7-H4, and Ki67 in the tumor cells were associated with worst survival; and remained statistically associated after adjustment for histology and staging (all p &lt; 0.001). Solid histology had a significantly higher density of tumor cells expressing B7-H4, BCL-2, and Ki67 compared to cribriform and tubular histology. A higher expression of myoepithelial marker (p63+) were associated with a better survival when compared with tumors with low p63 expression. Conclusions: ACC’s TME is composed mainly of macrophages. Despite having no significant differences in cellular composition, a higher density of tumor cells expressing BCL-2, B7-H4 and Ki67 were found in the solid histology and these markers were independent predictors of poor prognosis. The overexpression of BCL-2 and B7-H4 in the solid histology provides a scientific rationale for BCL-2 and/or B7-H4 targeting for the most aggressive ACC.

Evaluation of the Effects of Anti-PD-1 Therapy on Triple-Negative Breast Cancer in Mice by Diffusion Kurtosis Imaging and Dynamic Contrast-Enhanced Imaging.

The monitoring of immunotherapies is still based on changes in the tumor size in imaging, with a long evaluation period and low sensitivity. To investigate the effectiveness of diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing the therapeutic efficacy of anti-programmed death-1 (PD-1) therapy in a mouse triple negative breast cancer (TNBC) model. Prospective. A total of 54 BALB/c mouse subcutaneous 4T1 transplantation models of TNBC. A 3.0-T; turbo spin echo (TSE) T2-weighted imaging, DKI with seven b values (0, 500, 1000, 1500, 2000, 2500, and 3000 sec/mm2 ) and T1-twist DCE acquisition series. DKI and DCE-MRI parameters were evaluated by two radiologists independently. Regions of interest (ROIs) were drawn manually on the maximum cross-sectional area of the lesion; care was taken to avoid necrotic areas. The tumor cell density, the CD45 and CD31 levels were analyzed by two pathologists. The two-tailed unpaired t-test, Mann-Whitney U test, Fisher's exact test and Pearson correlation coefficient were performed. A P< 0.05 was considered statistically significant. The apparent diffusion coefficient (ADC), mean diffusivity (MD), Ktrans and Kep values were significantly different between the two groups at each time point after treatment. There were significant differences in the mean kurtosis (MK) and Ve values between the two groups at 5 and 10 days after treatment but no significant differences at 15 days (P=0.317 and 0.183, respectively). The ADC and MD values were significantly correlated with tumor cell density (ADC, r=-0.833; MD, r=0.890) and the CD45 level (ADC, r=0.720; MD, r=0.718). The Ktrans and Kep values were significantly correlated with the CD31 level (Ktrans , r=0.820; Kep , r=0.683). DKI and DCE-MRI could reflect the changes in tumor microstructure and tumor tissue vasculature after anti-PD-1 therapy, respectively. 1 TECHNICAL EFFICACY: Stage 4.

Geometry-Aware Neural Solver for Fast Bayesian Calibration of Brain Tumor Models.

Modeling of brain tumor dynamics has the potential to advance therapeutic planning. Current modeling approaches resort to numerical solvers that simulate the tumor progression according to a given differential equation. Using highly-efficient numerical solvers, a single forward simulation takes up to a few minutes of compute. At the same time, clinical applications of tumor modeling often imply solving an inverse problem, requiring up to tens of thousands of forward model evaluations when used for a Bayesian model personalization via sampling. This results in a total inference time prohibitively expensive for clinical translation. While recent data-driven approaches become capable of emulating physics simulation, they tend to fail in generalizing over the variability of the boundary conditions imposed by the patient-specific anatomy. In this paper, we propose a learnable surrogate for simulating tumor growth which maps the biophysical model parameters directly to simulation outputs, i.e. the local tumor cell densities, whilst respecting patient geometry. We test the neural solver in a Bayesian model personalization task for a cohort of glioma patients. Bayesian inference using the proposed surrogate yields estimates analogous to those obtained by solving the forward model with a regular numerical solver. The near real-time computation cost renders the proposed method suitable for clinical settings. The code is available at https://github.com/IvanEz/tumor-surrogate.

Simulating the behaviour of glioblastoma multiforme based on patient MRI during treatments.

Glioblastoma multiforme is a brain cancer that still shows poor prognosis for patients despite the active research for new treatments. In this work, the goal is to model and simulate the evolution of tumour associated angiogenesis and the therapeutic response to glioblastoma multiforme. Multiple phenomena are modelled in order to fit different biological pathways, such as the cellular cycle, apoptosis, hypoxia or angiogenesis. This leads to a nonlinear system with 4 equations and 4 unknowns: the density of tumour cells, the [Formula: see text] concentration, the density of endothelial cells and the vascular endothelial growth factor concentration. This system is solved numerically on a mesh fitting the geometry of the brain and the tumour of a patient based on a 2D slice of MRI. We show that our numerical scheme is positive, and we give the energy estimates on the discrete solution to ensure its existence. The numerical scheme uses nonlinear control volume finite elements in space and is implicit in time. Numerical simulations have been done using the different standard treatments: surgery, chemotherapy and radiotherapy, in order to conform to the behaviour of a tumour in response to treatments according to empirical clinical knowledge. We find that our theoretical model exhibits realistic behaviours.