Tumor Cell Density Research Articles

Novel KMT2B gene mutation in MUC4 positive low-grade fibromyxoid sarcoma

BackgroundLow-grade Fibromyxoid Sarcoma(LGFM)is a rare fibrosarcoma, which mainly occurs in young people and is mostly seen in the trunk and limbs. The tumor is usually FUS-CREB3L2 fusion caused by t(7;16)(q32-34;p11)chromosome translocation, and rarely FUS-CREB3L1 and EWSR1-CREB3L1 fusion. MUC4 diffuse strong positive can be used as a specific index of LGFM. LGFM is similar to Sclerosing Epithelioid Fibrosarcoma(SEF) and may have the same origin.Case presentationWe report a case of LGFM in the chest wall. A female who is 59 years old. In 2016, CT showed dense nodule shadow and focal thickening of the left pleura, the patient underwent surgery, Pathological report that low to moderate malignant fibrosarcoma(fibromyxoid type). The CT re-examination in 2021 showed that the tumors on the left chest wall were significantly larger than before. Pathological examination showed the disease is composed of alternating collagen like and mucinous areas. Under high-power microscope, the tumor cells are consistent in shape, spindle or short spindle, and the tumor cells are arranged in bundles. In local areas, the density of tumor cells is significantly increased, mixed with collagen fibers, and small focal SEF appear. The result of immunohistochemistry showed that SMA, Desmin, CD34, STAT6, S100, SOX10, HMB45 and Melan A were negative, EMA was weakly positive, MUC4 was diffuse and strongly positive, and Ki67 index was low (3%).ConclusionSequencing results showed that MET, EGFR, KMT2B and RET gene were mutated in LGFM, and KMT2B gene had cancer promoting effect, but there was no literature report in LGFM, which may be of certain significance for the diagnosis and treatment of LGFM.

Oxyepiberberine from the Active Site of Coptidis Rhizoma Inhibits Lung Adenocarcinoma by Regulating SRC-related Pathways in vivo

Background Huanglian (HL), also known as Coptis chinensis Franch., was initially documented in “Sheng Nong’s Herbal Classic” as a high-quality product used to cure ailments such as fever, indigestion, bloating, nausea, dysentery, diarrhea, abdominal pain, and tuberculosis. Nowadays, it is extensively employed in clinical settings to alleviate pulmonary diseases, like tuberculosis, lobular lung disease, and pulmonary abscess, and is worth exploring further. Nevertheless, there is limited research on the precise advancements of anti-lung adenocarcinoma (LUAD). Objectives Using biological experiments and network pharmacology, the objective of this research was to investigate the most effective parts and chemical constituents of C. chinensis against LUAD and ascertain the probable targets and pathways. Materials and Methods First, an MTT experiment verified the impact of each part on A549 and BEAS-2B cells. High performance liquid chromatography (HPLC) was then used to identify the chemical components of the effective substance in C. chinensis against LUAD. Then, network pharmacology was used to screen potential pathways and binding target proteins. Finally, a subcutaneous tumor model was established in mice using LLC cells to observe the effects of the effective components of C. chinensis against LUAD in tumor-bearing mice. Results The MTT assay demonstrated the efficacy of oxyepiberberine (OPB) as an anti-LUAD compound with minimal toxicity and side effects. According to the results of network pharmacology and molecular docking, SRC appears to be the primary target of C. chinensis active substance against LUAD. In vivo experiments revealed that the OPB group exhibited lowered toxicity and side effects. Additionally, the density of tumor cells decreased and the nuclei were destroyed in OPB groups of tumor tissues. Finally, OPB could regulate apoptosis, migration, and protein expression significantly related to the SRC pathway in tumor cells. Conclusion Our research reveals the anti-LUAD properties of C. chinensis through a combination of experimentation and network analysis. These findings establish a theoretical foundation for C. chinensis as a clinical antitumor agent. Furthermore, our work provided an essential groundwork for further investigations into the medicinal properties of C. chinensis.

Viable tumor cell density after neoadjuvant chemotherapy assessed using deep learning model reflects the prognosis of osteosarcoma

Prognosis after neoadjuvant chemotherapy (NAC) for osteosarcoma is generally predicted using manual necrosis-rate assessments; however, necrosis rates obtained in these assessments are not reproducible and do not adequately reflect individual cell responses. We aimed to investigate whether viable tumor cell density assessed using a deep-learning model (DLM) reflects the prognosis of osteosarcoma. Seventy-one patients were included in this study. Initially, the DLM was trained to detect viable tumor cells, following which it calculated their density. Patients were stratified into high and low-viable tumor cell density groups based on DLM measurements, and survival analysis was performed to evaluate disease-specific survival and metastasis-free survival (DSS and MFS). The high viable tumor cell density group exhibited worse DSS (p = 0.023) and MFS (p = 0.033). DLM-evaluated viable density showed correct stratification of prognosis groups. Therefore, this evaluation method may enable precise stratification of the prognosis in osteosarcoma patients treated with NAC.

Apparent diffusion coefficient in intrahepatic cholangiocarcinoma diffusion-weighted magnetic resonance imaging noninvasively predicts Ki-67 expression.

Tumor Ki-67 expression reflects prognosis and cancer grade, and biopsy-based preoperative assessment of Ki-67 expression is key to treatment. Apparent diffusion coefficient (ADC) values obtained with this imaging may noninvasively predict Ki-67 by reflecting tumor cell density and limited water molecule movement from irregular alignment. This study aimed to investigate the ability of ADC values to predict Ki-67 expression in intrahepatic cholangiocarcinoma (ICC). We retrospectively analyzed 39 cases of ICC confirmed by surgical pathology. All patients had undergone magnetic resonance imaging, and ADC values (mean, minimum, and maximum) were calculated. Ki-67 expression was assessed by immunohistochemistry, and patients were divided into groups of high (n=18) and low (n=21) Ki-67 expression. To assess the diagnostic performance of the ADC values for Ki-67 expression, we used the receiver operating characteristic curve and compared the areas under the curve (AUC). The mean and minimum ADC values were significantly lower in the group with high Ki-67 expression. For predicting high Ki-67 expression, the AUC values were 0.701 for mean ADC, 0.818 for minimum ADC, and 0.571 for maximum ADC. The diagnostic sensitivity and specificity of the minimum ADC values were 88.9% and 76.2%, respectively. In addition, with ADC values combined, the AUC increased to 0.831. Apparent diffusion coefficient is a useful predictor of Ki-67 expression level in ICC. Apparent diffusion coefficient values, especially minimum ADC values, can noninvasively predict ICC associated with high Ki-67 expression.

MR imaging findings of stage I intravenous leiomyomatosis: a retrospective single-center study in 19 cases.

The aim was to explore the magnetic resonance imaging (MRI) features of stage-I intravenous leiomyomatosis (IVL). From January 2019 to January 2023, clinical, pathological, and MRI data were collected from 19 cases confirmed by surgical pathology. Two radiologists retrospectively measured the tumor sizes, T1WIs, T2WIs, and ADC values and evaluated contrast-enhanced T1WIs, DWIs, complications and parauterine infiltrations. The number of tumor cells and the total nuclear area were measured. The percentage of tumor cell area out of the total area was used as the tumor cell density. Nineteen patients with stage-I IVL aged 33 to 66years (mean age: 46 ± 7.6years) were included in this study. All 19 cases were located in the myometrium or parametrium, with a mean diameter of 11.2 ± 4.8cm. Among these cases, 14 (73.6%) were associated with leiomyoma, and six (31.6%) involved the broad ligament. Isointensity was observed in the T1WIs of 12 cases (63.2%), while slight hypointensity was seen in five patients (26.3%). Isointensity was observed in the on T2WIs of four cases (21.1%), and iso- or slight hyperintensity was observed in 15 cases (78.9%). A significant difference was detected between the normalized T2WIs of IVL and myometrium (p < 0.001). A Pearson correlation test showed demonstrated a negative correlation between the ADC and tumor cell density values (r = -0.946, p < 0.001). Tortuous vessels were present in 17 cases (89.5%) within or next to the lesions, and multiple winding cord-like filling defects were seen in 11 cases (57.9%) within the tortuous vessels on the T2WIs. Identifying the characteristic MRI features of stage-I IVL helped improve the diagnostic accuracy achieves for this rare tumor. Stage-I IVL often presents as a large mass accompanied by leiomyoma, and it easily invades the broad ligament. TIWI signals exhibited isointensity, and T2WI signals contained iso- or slight hyperintensity. Tortuous vessels were present within or next to the lesions, and multiple winding cord-like filling defects were observed within the tortuous vessels on the T2WIs.

Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.

Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field of brain cancer is glioblastoma, the most common and fatal brain cancer. At the histologic level, glioblastoma is characterized by abundant phenotypic variability that is poorly linked with patient prognosis. At the transcriptional level, 3 molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome. We address genotype-phenotype correlations by applying an Xception convolutional neural network to a discovery set of 276 digital hematozylin and eosin (H&E) slides with molecular subtype annotation and an independent The Cancer Genome Atlas-based validation cohort of 178 cases. Using this approach, we achieve high accuracy in H&E-based mapping of molecular subtypes (area under the curve for classical, mesenchymal, and proneural = 0.84, 0.81, and 0.71, respectively; P < 0.001) and regions associated with worse outcome (univariable survival model P < 0.001, multivariable P = 0.01). The latter were characterized by higher tumor cell density (P < 0.001), phenotypic variability of tumor cells (P < 0.001), and decreased T-cell infiltration (P = 0.017). We modify a well-known convolutional neural network architecture for glioblastoma digital slides to accurately map the spatial distribution of transcriptional subtypes and regions predictive of worse outcome, thereby showcasing the relevance of artificial intelligence-enabled image mining in brain cancer.

Toward image-based personalization of glioblastoma therapy: A clinical and biological validation study of a novel, deep learning-driven tumor growth model.

The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation. One hundred and twenty-four patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model. To further evaluate clinical potential, we performed the same growth modeling on preoperative magnetic resonance imaging data from 30 patients of our institution and compared model-derived tumor volume and recurrence coverage with standard radiotherapy plans. The parameter ratio Dw/ρ(P < .05 in TCGA) as well as the simulated tumor volume (P < .05 in TCGA/UCSF) were significantly inversely correlated with overall survival. Interestingly, we found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter. Depending on the cutoff value for tumor cell density, we observed a significant improvement in recurrence coverage without significantly increased radiation volume utilizing model-derived target volumes instead of standard radiation plans. Identifying a significant correlation between computed growth parameters and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve the accuracy of radiation planning in the near future.

Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol.

Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma.

Abstract C016: Evaluating clinical biomarkers of FHD-286, a potent and selective inhibitor of BRG1/BRM, in metastatic uveal melanoma

Abstract The BRG/Brahma-associated factors (BAF) family of chromatin remodeling complexes (also referred to as the mSWI/SNF complex) regulates chromatin accessibility and gene expression through its ATP-dependent remodeling activity. FHD-286, a first-in-class compound that potently and selectively inhibits the ATPase components of the BAF complex, BRG1 and BRM, was evaluated in a Phase I dose escalation in subjects with metastatic uveal melanoma (mUM) (FHD-286-001). The pharmacodynamics of FHD-286 were assessed to demonstrate proof-of-mechanism and understand the downstream molecular and clinical impact of BRG1/BRM inhibition. In addition, circulating tumor DNA (ctDNA) was evaluated as an early predictor of overall survival benefit. Seventy-three subjects were on a daily dosing regimen ranging from 2.5 to 10 mg or an intermittent regimen of 1-week on/1-week-off ranging from 10 to 22.5 mg. Tumor biopsies were collected at screening and either Cycle 3, or end of treatment. Biomarker changes in the tumor were observed by histological assessment, by IHC/IF biomarker assays, and RNA sequencing. Observations were suggestive of a differentiation effect by FHD-286 on the tumor cells, including a decrease in stemness genes and an increase in mature melanocytic markers. Evidence of necrosis and a trend of decreasing tumor cell density were also seen. In addition, ctDNA was measured in serial plasma samples using a targeted NGS panel. A reduction in ctDNA was shown in approximately 50% of subjects, which correlated with an increased overall survival benefit. Lastly, serial blood samples collected in Paxgene RNA tubes were analyzed by RNA sequencing. A robust dose-dependent gene signature was identified as a peripheral readout of target engagement at steady-state in both dosing regimens and across dose cohorts. In summary, we observed changes in the tumor cells at the biopsy site and ctDNA that suggest FHD-286 has a biological impact on uveal melanoma that may lead to clinical benefit. Given the limitations and challenges to obtain tumor biopsies from every subject and at a frequent rate, the identified PD gene signature in the blood is a valuable tool to measure target engagement in this study, and other potential solid tumor indications. Citation Format: Jessica Wan, Kim Horrigan, GiNell Elliott, Liv Johannessen, Mike Collins, Alexander J. Lazar, Dillon Corrigan, Caitlin Patriquin, Sam Agresta, Jessica Piel, Martin Hentemann. Evaluating clinical biomarkers of FHD-286, a potent and selective inhibitor of BRG1/BRM, in metastatic uveal melanoma [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C016.

The Biologic Diversity of Colorectal Carcinoma (CRC) is Present in the Early Stage: Quantitative Analysis of Malignant Polyps

Abstract Introduction/Objective Risk factors in malignant polyps to predict recurrence in T1 colorectal carcinoma (CRC) include depth of invasion, margin status, lymphovascular invasion, tumor differentiation and budding. However, the effect of other factors, such as tumor volume and tumor cellularity, are not well studied. We quantitatively, using image analysis, determined the strength and interdependence of the depth of invasion, margin status, tumor volume and tumor cell density on the probability of having residual adenocarcinoma on resection and long term patient outcomes. Methods/Case Report Endoscopic mucosal resection (EMR) specimens of polyps with T1 CRC were identified by retrospective search of the pathology database over a 14-year period (01/01/2008 to 05/05/2022). Depth of invasion, distance of tumor to margin, tumor volume (defined by the invasive area comprising the tumor cells and associated others cells) and cellularity (number of adenocarcinoma cells/area) were measured using Aperio eSlide Manager Leica Biosystems Imaging, Inc. The depth of invasion was measured from the muscularis mucosa. Available outcome data was collected from subsequent segmental resection and oncology consultations and imaging from the patient charts. Results (if a Case Study enter NA) 22 patients of malignant polyps with EMR were identified; 11 patients underwent subsequent resection with residual tumor in 4 and lymph node metastasis in 1. Follow-up period was from 3 to 12 yrs, with recurrence in none. The median tumor cellularity was 2712 cells/mm2 with SD 1421 cells/mm2 and was similar in residual versus non residual tumors with no correlation with LVI. Distance of tumor (um) from deep margin was measured in 20 cases and by Receiver Operator Characteristic analysis and calculation of the area under the curve (AUC) was a good test of residual tumor (AUC = 0.88) while depth of invasion was not a good test (AUC =0.5). Tumor volume and adenocarcinoma cells/area were not correlated; however, interestingly, tumor volume and depth of invasion were linearly correlated (p&amp;lt;0.001). Conclusion The distance of tumor to the margin is a better predictor for residual cancer than depth of invasion. The linear correlation of tumor volume and depth of invasion implies the same shape of invasion which is interestingly independent of tumor cellularity. This finding further strengthens the hypothesis that cancer associated cells in the tumor microenvironment are important in driving the biological behavior of malignant polyps.

Nitrogen isotopic composition as a gauge of tumor cell anabolism-to-catabolism ratio.

Studies have suggested that cancerous tissue has a lower 15N/14N ratio than benign tissue. However, human data have been inconclusive, possibly due to constraints on experimental design. Here, we used high-sensitivity nitrogen isotope methods to assess the 15N/14N ratio of human breast, lung, and kidney cancer tissue at unprecedented spatial resolution. In lung, breast, and urothelial carcinoma, 15N/14N was negatively correlated with tumor cell density. The magnitude of 15N depletion for a given tumor cell density was consistent across different types of lung cancer, ductal in situ and invasive breast carcinoma, and urothelial carcinoma, suggesting similar elevations inthe anabolism-to-catabolism ratio. However, tumor 15N depletion was higher in a more aggressive metaplastic breast carcinoma. These findings may indicate the ability of certain cancers to more effectively channel N towards growth. Our results support 15N/14N analysis as a potential tool for screening biopsies and assessing N metabolism in tumor cells.

NIMG-17. TOWARDS IMAGE - BASED PERSONALIZATION OF GLIOBLASTOMA THERAPY: A COMPREHENSIVE VALIDATION STUDY OF A NOVEL, DEEP LEARNING - DRIVEN TUMOR GROWTH MODEL FOR INDIVIDUALIZED RADIOTHERAPY

Abstract BACKGROUND The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment planning. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy by estimating the tumor cell density in the whole brain. Here, we performed biological and clinical validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation. METHODS 124 patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model that is adjusted to the patient’s anatomy using deep learning. To further evaluate clinical potential, we performed the same growth modeling on preoperative MRI data from 30 patients of our institution and compared model-derived tumor volumes (that serve as alternative clinical target volumes) and recurrence coverage with standard radiotherapy plans. RESULTS The parameter ratio Dw/ρ (p &amp;lt; 0.05 in TCGA) as well as the simulated tumor volume (0.5 tumor cell density) (p &amp;lt; 0.05 in TCGA/UCSF) were significantly inversely correlated with overall survival. We found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter ρ, proving its biological plausibility. Depending on the cutoff value for tumor cell density, we observed significant improvement of recurrence coverage without significantly increased total radiation volume utilizing model-derived target volumes instead of standard radiation plans. More precisely, a cutoff value of 0.5 tumor cell density yielded the best results here. CONCLUSIONS Identifying a significant correlation between computed growth parameters, and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve accuracy of radiation planning in the near future.

CTIM-04. TOPICAL DIPHENCYPRONE IMMUNOTHERAPY FOR CUTANEOUS NEUROFIBROMAS ASSOCIATED WITH NF1, A PHASE 1 STUDY

Abstract Neurofibromatosis type 1 (NF1) is a tumor predisposition syndrome affecting ~1 in 2500 people. Affected patients develop peripheral nerve sheath tumors of the skin known as cutaneous neurofibromas (CN), with variable disease burden ranging from 0-1 tumors to over 10,000. Patients cite deformity, pain, and pruritus from CN as one of the most difficult features of the disease. To date, there are no practical or effective pharmacotherapies for these tumors. This is a single-center, phase I, open label trial to evaluate the safety, tolerability, and efficacy of diphencyprone (DPCP), a topical hapten compound that induces a delayed-type. Twelve adult patients with NF1 and &amp;gt;3 CN of maximum diameter &amp;gt;4 mm were enrolled. DPCP 0.04% ointment was administered topically once weekly to up to 20 cNFs in a localized body region for 10 weeks. Endpoints included (primary) safety and tolerability of DPCP ointment via CTCAE reporting and subjective scoring of symptoms. Efficacy was assessed using RECIST (Response Evaluation Criteria in Solid Tumors) guidelines in conjunction with whole body photography and manual tri-axial tumor measurements. One treated tumor is tracked throughout the study with optical coherence tomography (OCT) imaging. Skin biopsies of a CN and surrounding tissue are collected at baseline, on Day 17 after treatment initiation, and four weeks post-treatment for exploratory endpoints qRT-PCR, single-cell RNA sequencing, and immunohistochemistry to detect differences in immune cell infiltration, tumor cell density, and transcriptional changes throughout the treatment course. Clinical trial information: NCT05438290.

P12.12.A DISTRIBUTION AND TUMOR CELL PROXIMITY OF IMMUNE CELLS IN THE TUMOR CORE, TRANSITION ZONE AND PERIPHERY OF GLIOBLASTOMAS

Abstract BACKGROUND Glioblastoma is the most frequent and malignant brain tumor. Immune therapies have had limited effect but not much is known about the frequency and type of immune cells in the transition zone and periphery of the tumors. In these areas, tumor cells migrate into the brain parenchyma and prevent total tumor resection thereby leading to recurrence. This study aims to quantify the type and distribution of immune cells in glioblastomas. MATERIAL AND METHODS A cohort of 67 glioblastomas with strong P53 immunoreactivity in the tumor cells was established. Slides contained tissue from central tumor and/or transition zone and/or tumor periphery. A chromogenic multiplex was established, where tissue sections were stained with P53 (tumor cells), FOXP3 (FOXP3+ regulatory T cells), CD8 (CD8+ cytotoxic T cells) and IBA1 (microglia/macrophages). Cells were counted in tumor core, transition zone and periphery using Visiopharm software based on training of convolutional neural networks. Moreover, the number of tumor cells with immune cells in proximity (30 µm) was quantified, and the distance from a reference cell to another cell type within 30 µm was calculated. RESULTS The densities of CD8+, FOXP3+ and IBA1+ cells were significantly higher in the core (10, 1 and 473 cells/mm2) than in the periphery (3, 0,2 and 179 cells/mm2) (P &amp;lt; 0.001), and in the transition zone (10, 1 and 396 cells/mm2) than in the periphery (P &amp;lt; 0.001). However, the CD8+ and IBA1+ cells/tumor cell ratio increased from core (0,004 and 0,18) to transition zone (0,01 and 0,36) and to periphery (0,015 and 0,8) (P &amp;lt; 0.001). The highest density of tumor cells with CD8+ cells in proximity was in the tumor core (55 cells/mm2 in core, 22 cells/mm2 in transition zone, 3 cells/mm2 in periphery) (P &amp;lt; 0.001), whereas the highest density of tumor cells with IBA1+ cells (1412 cells/mm2 in core, 7412 cells/mm2 in transition zone, 98 cells/mm2 in periphery) and FOXP3+ cells (0 cells/mm2 in core, 3 cells/mm2 in transition zone, and 0 cells/mm2 in periphery) in proximity was in the transition zone (P &amp;lt; 0.001). CONCLUSION Although levels of immune cells are highest in the tumor core - the higher proximity and ratio of immune cells to tumor cells in the transition zone/periphery suggest immune-suppressive mechanisms to be active in these areas.

PH-Weighted amine chemical exchange saturation transfer echo planar imaging visualizes infiltrating glioblastoma cells.

Given the invasive nature of glioblastoma, tumor cells exist beyond the contrast-enhancing (CE) region targeted during treatment. However, areas of non-enhancing (NE) tumors are difficult to visualize and delineate from edematous tissue. Amine chemical exchange saturation transfer echo planar imaging (CEST-EPI) is a pH-sensitive molecular magnetic resonance imaging technique that was evaluated in its ability to identify infiltrating NE tumors and prognosticate survival. In this prospective study, CEST-EPI was obtained in 30 patients and areas with elevated CEST contrast ("CEST+" based on the asymmetry in magnetization transfer ratio: MTRasym at 3 ppm) within NE regions were quantitated. Median MTRasym at 3 ppm and volume of CEST + NE regions were correlated with progression-free survival (PFS). In 20 samples from 14 patients, image-guided biopsies of these areas were obtained to correlate MTRasym at 3 ppm to tumor and non-tumor cell burden using immunohistochemistry. In 15 newly diagnosed and 15 recurrent glioblastoma, higher median MTRasym at 3ppm within CEST + NE regions (P = .007; P = .0326) and higher volumes of CEST + NE tumor (P = .020; P < .001) were associated with decreased PFS. CE recurrence occurred in areas of preoperative CEST + NE regions in 95.4% of patients. MTRasym at 3 ppm was correlated with presence of tumor, cell density, %Ki-67 positivity, and %CD31 positivity (P = .001; P < .001; P < .001; P = .001). pH-weighted amine CEST-EPI allows for visualization of NE tumor, likely through surrounding acidification of the tumor microenvironment. The magnitude and volume of CEST + NE tumor correlates with tumor cell density, degree of proliferating or "active" tumor, and PFS.

Tolerable treatment of glioblastoma with redox-cycling ‘mitocans': a comparative study in vivo

ABSTRACTObjectives: The present study describes a pharmacological strategy for the treatment of glioblastoma by redoxcycling ‘mitocans’ such as quinone/ascorbate combination drugs, based on their tumor-selective redox-modulating effects and tolerance to normal cells and tissues.Methods: Experiments were performed on glioblastoma mice (orthotopic model) treated with coenzyme Q0/ascorbate (Q0/A). The drug was injected intracranially in a single dose. The following parameters were analyzed in vivo using MRI orex vivo using conventional assays: tumor growth, survival, cerebral and tumor perfusion, tumor cell density, tissue redox-state, and expression of tumor-associated NADH oxidase (tNOX).Results: Q0/A markedly suppressed tumor growth and significantly increased survival of glioblastoma mice. This was accompanied by increased oxidative stress in the tumor but not in non-cancerous tissues, increased tumor blood flow, and downregulation of tNOX. The redox-modulating and anticancer effects of Q0/A were more pronounced than those of menadione/ascorbate (M/A) obtained in our previous study. No adverse drug-related side-effects were observed in glioblastoma mice treated with Q0/A.Discussion: Q0/A differentiated cancer cells and tissues, particularly glioblastoma, from normal ones by redox targeting, causing a severe oxidative stress in the tumor but not in non-cancerous tissues. Q0/A had a pronounced anticancer activity and could be considered safe for the organism within certain concentration limits. The results suggest that the rate of tumor resorption and metabolism of toxic residues must be controlled and maintained within tolerable limits to achieve longer survival, especially at intracranial drug administration.

NEIM-08 MODELING COMPETITION BETWEEN SUBPOPULATIONS WITH VARIABLE DNA CONTENT IN RESOURCE LIMITED MICROENVIRONMENTS

Abstract Resource limitations shape the outcome of competitions between heterogeneous pre-malignant cells. One example of such heterogeneity is in the ploidy (DNA content) of pre-malignant cells. High-ploidy cells need more resources, to synthesize increasing amounts of DNA, RNA and proteins. To model how subpopulations with variable DNA-content compete in the resource limited environment of the human brain we developed a stochastic state-space model of the brain (S3MB). The model discretizes the brain into voxels, whereby the state of each voxel is defined by 8+ variables that are updated over time, including stiffness, glucose and proliferating cells of various DNA content. Well established Fokker-Planck partial differential equations govern the spatial distribution of resources and cells. We applied S3MB on sequencing and imaging data obtained from a primary GBM patient. We sequenced four surgical specimens collected during the 1st and 2nd surgeries of the GBM and used HATCHET to quantify its clonal composition and how it changes between the surgeries. HATCHET identified two aneuploid subpopulations of ploidy 1.98 and 2.29 respectively. The low-ploidy clone was dominant at the time of the first surgery and became even more dominant upon recurrence. MRI images were available before and after each surgery and registered to MNI space. T1 post and T2 flair scans acquired after the 1st surgery informed tumor cell densities per voxel. Magnetic Resonance Elastography scans and PET/CT scans informed stiffness and Glucose access per voxel. We performed a parameter search to recapitulate the GBM’s tumor cell density and ploidy composition before the 2nd surgery. Results suggest that the high-ploidy subpopulation had a higher Glucose-dependent proliferation rate, but a lower Glucose-dependent death rate, resulting in spatial differences in the distribution of the two subpopulations. Our results contribute to a better understanding of how genomics and microenvironments interact to shape cell fate decisions.

A Cellular Ground Truth to Develop MRI Signatures in Glioma Models by Correlative Light Sheet Microscopy and Atlas-Based Coregistration.

Glioblastoma is the most common malignant primary brain tumor with poor overall survival. Magnetic resonance imaging (MRI) is the main imaging modality for glioblastoma but has inherent shortcomings. The molecular and cellular basis of MR signals is incompletely understood. We established a ground truth-based image analysis platform to coregister MRI and light sheet microscopy (LSM) data to each other and to an anatomic reference atlas for quantification of 20 predefined anatomic subregions. Our pipeline also includes a segmentation and quantification approach for single myeloid cells in entire LSM datasets. This method was applied to three preclinical glioma models in male and female mice (GL261, U87MG, and S24), which exhibit different key features of the human glioma. Multiparametric MR data including T2-weighted sequences, diffusion tensor imaging, T2 and T2* relaxometry were acquired. Following tissue clearing, LSM focused on the analysis of tumor cell density, microvasculature, and innate immune cell infiltration. Correlated analysis revealed differences in quantitative MRI metrics between the tumor-bearing and the contralateral hemisphere. LSM identified tumor subregions that differed in their MRI characteristics, indicating tumor heterogeneity. Interestingly, MRI signatures, defined as unique combinations of different MRI parameters, differed greatly between the models. The direct correlation of MRI and LSM allows an in-depth characterization of preclinical glioma and can be used to decipher the structural, cellular, and, likely, molecular basis of tumoral MRI biomarkers. Our approach may be applied in other preclinical brain tumor or neurologic disease models, and the derived MRI signatures could ultimately inform image interpretation in a clinical setting.SIGNIFICANCE STATEMENT We established a histologic ground truth-based approach for MR image analyses and tested this method in three preclinical glioma models exhibiting different features of glioblastoma. Coregistration of light sheet microscopy to MRI allowed for an evaluation of quantitative MRI data in histologically distinct tumor subregions. Coregistration to a mouse brain atlas enabled a regional comparison of MRI parameters with a histologically informed interpretation of the results. Our approach is transferable to other preclinical models of brain tumors and further neurologic disorders. The method can be used to decipher the structural, cellular, and molecular basis of MRI signal characteristics. Ultimately, information derived from such analyses could strengthen the neuroradiological evaluation of glioblastoma as they enhance the interpretation of MRI data.

Deciphering the Morphology of Tumor-Stromal Features in Invasive Breast Cancer Using Artificial Intelligence

Tumor-associated stroma in breast cancer (BC) is complex and exhibits a high degree of heterogeneity. To date, no standardized assessment method has been established. Artificial intelligence (AI) could provide an objective morphologic assessment of tumors and stroma, with the potential to identify new features not discernible by visual microscopy. In this study, we used AI to assess the clinical significance of (1) stroma-to-tumor ratio (S:TR) and (2) the spatial arrangement of stromal cells, tumor cell density, and tumor burden in BC. Whole-slide images of a large cohort (n = 1968) of well-characterized luminal BC cases were examined. Region and cell-level annotation was performed, and supervised deep learning models were applied for automated quantification of tumor and stromal features. S:TR was calculated in terms of surface area and cell count ratio, and the S:TR heterogeneity and spatial distribution were also assessed. Tumor cell density and tumor size were used to estimate tumor burden. Cases were divided into discovery (n = 1027) and test (n = 941) sets for validation of the findings. In the whole cohort, the stroma-to-tumor mean surface area ratio was 0.74, and stromal cell density heterogeneity score was high (0.7/1). BC with high S:TR showed features characteristic of good prognosis and longer patient survival in both the discovery and test sets. Heterogeneous spatial distribution of S:TR areas was predictive of worse outcome. Higher tumor burden was associated with aggressive tumor behavior and shorter survival and was an independent predictor of worse outcome (BC-specific survival; hazard ratio: 1.7, P = .03, 95% CI, 1.04-2.83 and distant metastasis-free survival; hazard ratio: 1.64, P = .04, 95% CI, 1.01-2.62) superior to absolute tumor size. The study concludes that AI provides a tool to assess major and subtle morphologic stromal features in BC with prognostic implications. Tumor burden is more prognostically informative than tumor size.

In vivo characterization of the human glioblastoma infiltrative edge with label-free intraoperative fluorescence lifetime imaging.

Challenges in identifying a glioblastoma's infiltrative edge during neurosurgical procedures result in rapid recurrence. A label-free fluorescence lifetime imaging (FLIm) device was used to evaluate glioblastoma's infiltrative edge in vivo in 15 patients (89 samples). FLIm data were analyzed according to tumor cell density, infiltrating tissue type (gray and white matter), and diagnosis history (new or recurrent). Infiltrations in white matter from new glioblastomas showed decreasing lifetimes and a spectral red shift with increasing tumor cell density. Areas of high versus low tumor cell density were separated through a linear discriminant analysis with a ROC-AUC=0.74. Current results support the feasibility of intraoperative FLIm for real-time in vivo brain measurements and encourage refinement to predict glioblastoma infiltrative edge, underscoring the ability of FLIm to optimize neurosurgical outcomes.