Nuclear Features Research Articles

Myoepithelial Tumors of Bone With EWSR1::PBX3 Fusion: A Spectrum From Benign to Malignant

The EWSR1::PBX3 fusion gene, commonly associated with cutaneous syncytial myoepitheliomas, is also found in myoepithelial tumors (METs) of bone and soft tissue. These tumors typically demonstrate benign histology and favorable outcomes. This study examines 6 previously unreported intraosseous METs harboring the EWSR1::PBX3 fusion, focusing on their histopathologic characteristics, immunophenotype, clinical and radiographic profiles, and patient outcomes. The cohort comprised 5 men and 1 woman, aged 25 to 65 years (median age: 31 years), with tumors located in the proximal tibia (3 cases), distal radius (2 cases), and ilium (1 case) and sizes between 3.2 and 12.2 cm (median size: 3.9 cm). Imaging showed osteolytic lesions with varying degrees of cortical involvement and soft tissue extension in 3 cases. Histologically, 4 tumors showed mainly uniform oval-to-spindled cells in syncytial or fascicular arrangements within a collagenous matrix, displaying either bland nuclear features or mild atypia, and low to slightly elevated mitotic activity (≤1 per 10 high-power fields in 3 cases and 6 per 10 high-power fields in 1), classifying them as benign or atypical METs. In contrast, 2 tumors exhibited pronounced nuclear atypia with ovoid, spindled, epithelioid and round cells, hyperchromatic nuclei, inconspicuous nucleoli, increased N/C ratios, high mitotic rates (17 and 19 per 10 high-power fields), and extensive necrosis. Both tumors behaved aggressively—one patient underwent amputation after neoadjuvant chemotherapy and radiation, and the other died within 7 months with the disease still present. Immunohistochemically, the tumors consistently expressed epithelial membrane antigen and S100 but lacked keratin (AE1/AE3) expression. Our study demonstrated that bone METs with EWSR1::PBX3 fusions encompass a histologic continuum from benign to malignant, with benign/atypical METs mirroring their cutaneous analogs in morphology, and malignant variants distinguished by heterogeneous cytologic and architectural features, pronounced nuclear atypia, and high mitotic rates.

P16 Expression in Multinucleated Stromal Cells of Fibroepithelial Polyps of the Anus (FEPA): A Comprehensive Review and Our Experience

Fibroepithelial polyps of the anus (FEPA) are a common benign polypoid proliferation of the stroma covered by squamous epithelium. They are also an often-overlooked part of pathological practice. Currently, immunohistochemistry (IHC) for p16 is the only recommended test for anal intraepithelial neoplasia, but the expression of p16 in stromal multinucleated atypical cells in FEPA has not been described. We aimed to evaluate the expression of p16 in stromal multinucleated atypical cells in FEPA and its role as a diagnostic biomarker to determine the origin of the atypical multinucleated cells in the stroma of FEPA and to rule out the possibility of a neoplastic process. Therefore, we researched a series of 15 FEPA in middle-aged patients histologically and by IHC. Examination of the subepithelial connective tissue from the FEPA showed bizarre, multinucleated cells, while their causal relationship with human papillomavirus (HPV) infection was rejected. In all cases, these cells showed mild to moderate atypical nuclear features and positive expression for p16, while the overlying squamous epithelium was negative. We concluded that FEPA are benign lesions in the stroma where mononuclear and multinucleated (sometimes atypical) cells showing fibroblastic and myofibroblastic differentiation can be found. Nevertheless, we believe that these cells have a practical diagnostic significance, although sometimes the presence of giant cells is difficult to establish, especially in the inflammatory context. The histological similarity between FEPA and normal anal mucosa supports the hypothesis that FEPA may represent the reactive hyperplasia of subepithelial fibrous connective tissue of the anal mucosa.

Focal adhesion and actin orientation regulated by cellular geometry determine stem cell differentiation via mechanotransduction

Tuning cell adhesion geometry can affect cytoskeleton organization and the distribution of cytoskeleton forces, which play critical roles in controlling cell functions. To elucidate the geometrical relationship with cytoskeleton force distribution, it is necessary to control cell morphology. In this study, a series of dextral vortex micropatterns were prepared to precisely control cell morphology for investigating the influence of the curvature degree of adhesion curves on intracellular force distribution and stem cell differentiation at a sub-cellular level. Peripherial actin filaments of micropatterned cells were assembled along the adhesion curves and showed different orientations, filament thicknesses and densities. Focal adhesion and cytoskeleton force distribution were dependent on the curvature degree. Intracellular force distribution was also regulated by adhesion curves. The cytoskeleton and force distribution affected the osteogenic differentiation of mesenchymal stem cells through a YAP/TAZ-mediated mechanotransduction process. Thus, regulation of cell adhesion curvature, especially at cytoskeletal filament level, is critical for cell function manipulation. Statement of significanceIn this study, a series of dextral micro-vortexes were prepared and used for the culture of human mesenchymal stem cells (hMSCs) to precisely control adhesive curvatures (0°, 30°, 60°, and 90°). The single MSCs on the micropatterns had the same size and shape but showed distinct focal adhesion (FA) and cytoskeleton orientations. Cellular nanomechanics were observed to be correlated with the curvature degrees, subsequently influencing nuclear morphological features. As a consequence, the localization of the mechanotransduction sensor and activator-YAP/TAZ was affected, influencing osteogenic differentiation. The results revealed the pivotal role of adhesive curvatures in the manipulation of stem cell differentiation via the machanotransduction process, which has rarely been investigated.

Diagnostic-therapeutic management in patients with non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP)

Diagnostic-therapeutic management in patients with non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP)

Abstract PO1-07-11: Radiogenomic-based imaging intratumor heterogeneity model predicts breast cancer prognosis and unveils therapeutic targets

Abstract Background: Intratumor heterogeneity (ITH) refers to variations observed among cancer cells within a single tumor, posing significant challenges in clinical practice due to its contribution to treatment resistance and unfavorable outcome. However, the inconvenience of multi-region biopsy and limitations of genomic sequencing based on limited tissue impede the practical detection of ITH. Consequently, there is an urgent need for a non-invasive method that comprehensively captures ITH from a holistic perspective of the entire tumor. Methods: We utilized a large multicenter dataset comprising dynamic contrast-enhanced magnetic resonance images from breast cancer patients (n = 1423) along with matched multiomics data (n = 468) to develop a non-invasive machine learning approach for measuring ITH. We extracted quantitative radiomic features from both the entire tumor and peritumor regions, with a specific focus on features associated with imaging heterogeneity. Using these radiomic features, we established an imaging ITH (IITH) model. The robustness of IITH evaluation was determined by assessing its correlation with genomic ITH (employing the mutant-allele tumor heterogeneity [MATH] algorithm) and pathological cellular ITH (analyzing variation in quantitative nuclear features extracted through digital pathology). Prognostic power was evaluated using Kaplan-Meier and multivariate Cox proportional hazards analyses. Integrated multiomics analyses were performed to investigate the molecular basis of different IITH subgroups. Results: We developed a non-invasive radiomic signature to quantify imaging ITH (IITH) in the FUSCC cohort. Breast cancer patients were retrospectively categorized into high and low IITH groups. Multivariate Cox analysis identified high IITH as an independent predictor of poor prognosis in breast cancer patients, even after adjusting for clinical risk factors such as tumor size, positive lymph nodes, clinical subtype and lymphovascular invasion status. These findings were further validated in the DUKE cohort, confirming the prognostic value of IITH. Moreover, we substantiated the robustness of IITH by demonstrating its association with genomic and pathological ITH. Multiomics analysis revealed the activation of oncogenic pathways and metabolic dysregulation in high-IITH tumors. Intriguingly, our investigation also highlighted ferroptosis as a vulnerability and potential therapeutic target in high-IITH tumors, which was further supported by evidence from the TCGA cohort. Conclusion: Radiomic-based assessment of ITH provides a non-invasive approach to comprehensively capture ITH and predict the prognosis of breast cancer patients. Targeting ferroptosis may hold promise as a treatment strategy for patients with high IITH. Citation Format: Guan-Hua Su, Yi Xiao, Chao You, Ren-Cheng Zheng, Shen Zhao, He Wang, Yi-Zhou Jiang, Ya-Jia Gu, Zhi-Ming Shao. Radiogenomic-based imaging intratumor heterogeneity model predicts breast cancer prognosis and unveils therapeutic targets [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 PO1-07-11.

Atypical glandular cells and predictive features of malignancy in Pap smears: A retrospective monocentric study.

The introduction of cytological screening with the Papanicolau smear significantly reduced cervical cancer mortality. However, Pap smear examination can be challenging, being based on the observer ability to decode different cytological and architectural features. This study aims to evaluate the malignancy rate of AGC (atypical glandular cells) category, investigating the relationships between cytological and histological diagnosis. Eighty-nine patients, diagnosed as AGC at cytological evaluation and followed up with biopsy or surgical procedure at Policlinico Gemelli Hospital, Rome, Italy, were included in the study. The cytopathological architectural (feathering, rosette formation, overlapping, loss of polarity, papillary formation, three-dimensional formation) and nuclear (N/C ratio, nuclear enlargement and hyperchromasia, mitoses, nuclei irregularity, evident nucleoli) features of AGC were evaluated. Statistical analyses were performed to assess cyto-histological correlation and determine the relevance of architectural and nuclear features in the diagnosis of malignancy. Of the 89 AGC patients, 48 cases (53.93%) were diagnosed as AGC-NOS and 41 (46.07%) were diagnosed as AGC-FN, according to the Bethesda classification system. The follow-up biopsies or surgical resections revealed malignancy in 46 patients (51.69%). The rates of malignancy for AGC-NOS and AGC-FN were 35.41% and 70.73% respectively. Furthermore, analysing cytopathological features, we found that both architectural and nuclear criteria were statistically significant (p < 0.05). Only overlapping, nuclear irregularity and increased N/C ratio were not found to be statistically significant for detecting malignancy. Cytological diagnosis of glandular lesions remains a valid tool, when appropriate clinical correlation and expert evaluation are available.

Clinicopathologic Features and Cytologic Correlation of ALK-Rearranged Papillary Thyroid Carcinoma: A Series of Eight Cases

Anaplastic lymphoma kinase (ALK) gene fusions are rare in papillary thyroid carcinoma (PTC) but may serve as a therapeutic target. This study aims to evaluate the preoperative cytologic findings and clinicopathologic features of a series of eight ALK-rearranged PTCs from our pathology archives and consultations. All cases were confirmed by ALK D5F3 immunohistochemistry and six with additional targeted RNA-based next-generation sequencing (NGS). The original fine-needle aspiration (FNA) cytology diagnosis included the Bethesda System (TBS) category II in three (37.5%), TBS III in two (25%), TBS V in two (25%), and TBS VI in one (12.5%). Six cases had available FNA cytology and were reviewed. The cytologic features showed microfollicular architecture as well as limited or reduced nuclear elongation and chromatin alterations in all six. Nuclear grooves and pseudoinclusions were absent in two cases, rarely or focally noted in three, and frequently found in one. Two cases initially diagnosed as TBS II, showing microfollicular architecture without well-developed nuclear features, were revised to TBS III (with architectural atypia only). For histologic correlations, four were infiltrative follicular variant PTCs, three as classic subtype PTC with predominant follicular growth, and one as solid/trabecular subtype PTC. All eight cases demonstrated reduced PTC nuclear features with respect to nuclear elongation and chromatin alterations compared to those typically identified in “BRAF-like” PTCs. The NGS testing revealed EML4::ALK fusion in three, STRN::ALK fusion in two, and ITSN2::ALK fusion in one. In conclusion, although ALK-rearranged PTCs have been associated with neutral gene expression profile from a BRAF-RAS scoring perspective, the “RAS-like” nuclear features were more commonly identified in this series, resulting in frequent indeterminate diagnosis of preoperative FNA.

NIFTP-adjusted risk estimation of Bethesda thyroid cytology categories should consider the indication for FNA according to TIRADS

BackgroundNon-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) was firstly described in 2016. Since NIFTP is thought a non-malignant tumor, the Bethesda system for thyroid cytology proposes two estimations of risk of malignancy of the diagnostic categories, one considering NIFTP as cancer and another one considering it as a benign neoplasm. The present study aimed to review NIFTPs in a single center, re-assess them across categories of three Thyroid Imaging Reporting and Data Systems (TIRADSs), and define the indication for biopsy according to the category-specific size cut-offs.MethodsThe study period was from 2017 to 2023. The institutional database was searched for histologically proven NIFTPs with preoperative ultrasound images. NIFTPs were re-assessed according to the American College of Radiology (ACR), European (EU), and Korean (K) TIRADSs. The indication for biopsy was defined according to TIRADS category-specific size threshold.ResultsTwenty NIFTPs from 19 patients were included. The median size of the NIFTPs was 23 mm. According to ultrasound, 80–85% of NIFTPs were at low-intermediate risk and 5–15% at high risk without significant difference among the tree TIRADSs (p = 0.91). The indication for FNA, according to three TIRADSs, was found in 52–58% of cases with no significant difference among systems (p = 0.96).ConclusionNIFTPs have heterogeneous presentation according to TIRADSs with very low indication rate for FNA.

Ultrasound and cytopathological characteristics of thyroid tumours of uncertain malignant potential - from diagnosis to treatment.

The latest World Health Organization (WHO) classification from 2022 distinguishes the division of low-risk thyroid neoplasms such as non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), follicular tumour of uncertain malignant potential (FT-UMP), and well-differentiated tumour of uncertain malignant potential (WDT-UMP). The final diagnosis is made postoperatively according to histopathologic results. The aim of the study was the assessment of ultrasonographic and cytopathological features of borderline lesions to predict low-risk tumours preoperatively and plan the optimal treatment for that group of patients. A total of 35 patients (30 women; 5 men), aged 20-81 years with a mean age of 49 years, were enrolled in the study. The study evaluated 35 focal lesions of the thyroid gland, classified as low-risk neoplasms according to the WHO 2022 classification: FT-UMP (n = 21), NIFTP (n = 7), and WDT-UMP (n = 7). Ultrasonographic features of nodules including contrast-enhanced ultrasound (CEUS) and elastography were assessed by 2 specialists, and the risk of malignancy was evaluated according to EU-TIRADS-PL classification. Of the 35 focal thyroid lesions, most were categorised as low or intermediate risk of malignancy according to EU-TIRADS-PL, with dominant category 3 [n = 13 (37.2%)] and category 4 [n = 15 (42.8%)]. High-risk category 5 was assessed in 7 lesions (20%). In cytopathology nodules were categorised as follows (Bethesda System TBSRTC 2023): Bethesda II (n = 4), Bethesda III (n = 2), Bethesda IV (n = 25), Bethesda V (n = 3), and Bethesda VI (n = 1). In the CEUS study, contrasting patterns dominated compared to the surrounding parenchyma, such as enhancement equal to the parenchyma (66.6%) or intense (28.5%), heterogeneous (61.9%), centripetal (42.8%), or diffuse (57.1%) with fast (33.3%) or compared to parenchyma contrast wash-in (42.8%) and its fast (33.3%) or comparable to thyroid parenchyma wash-out (52.3%). The study indicates that lesions with uncertain malignant potential typically present features suggesting low to intermediate risk of malignancy based on EU-TIRADS-PL classification, with dominant cytopathologic Bethesda IV category. However, 20% of lesions were assessed tas EU-TIRADS-PL category 5. Low-risk tumours, including NIFTP, FT-UMP, and WDT-UMP, require careful observation and monitoring post surgical treatment due to their potential for recurrence and metastasis. The preoperatively prediction of borderline tumour may play an important role in proper treatment and follow-up.

Papillae, psammoma bodies, and/or many nuclear pseudoinclusions are helpful criteria but should not be required for a definitive cytologic diagnosis of papillary thyroid carcinoma: An institutional experience of 207 cases with surgical follow up.

Noninvasive follicular thyroid neoplasm with papillary-like features (NIFTP) was introduced in 2016 replacing noninvasive follicular variant of papillary thyroid carcinoma, with recommendations to label them "noncancer." To avoid reducing risk of malignancy (ROM) and overdiagnosing NIFTP as malignant, some authors required restricted cytologic criteria (RC) for a definitive diagnosis of papillary thyroid carcinoma (PTC), including papillae, psammoma bodies. or≥3 nuclear pseudoinclusions. Since then, NIFTP criteria have been revised, biologic behavior better understood, and incidence reported to be much lower than initially anticipated. This study examines the impact of RC on PTC cytologic diagnoses, ROM, and detection of clinically significant carcinomas (CSC). A total of 207 thyroid FNAs originally diagnosed as PTC and suspicious for PTC (SPTC) with surgical follow-up were evaluated. RC were retrospectively applied to cases as a requirement for diagnosing PTC, and cases that did not meet RC were reclassified as SPTC. ROMs and diagnostic accuracies of pre- and post-RC diagnoses were correlated with followup CSC. RC were met in 118/142 (83%) and 20/65 (31%) of cases originally diagnosed as PTC and SPTC, respectively. Post-RC, 29% (19/65) of CSC originally diagnosed as SPTC were upgraded to PTC, and 17% (24/142) of CSC originally diagnosed as PTC were downgraded to SPTC. No NIFTPs were diagnosed as malignant. RC should not be required for a definitive diagnosis of PTC when other nuclear features of PTC are diffuse and overt. Applying RC, however, helps the pathologist arrive at a more definitive diagnosis of PTC in suspicious cases.

Comprehensive evaluation of cytomorphologic, histologic, and molecular features of DICER1-altered thyroid lesions on FNA: A multipractice experience.

DICER1 mutations, though infrequent, are encountered on preoperative molecular testing of indeterminate adult and pediatric thyroid fine-needle aspiration (FNA) specimens. Yet, published cytomorphologic features of DICER1-altered thyroid lesions are limited. Cytomorphological features of DICER1-altered thyroid lesions were examined in a multipractice FNA cohort with clinical, radiological, and histologic data. The cohort comprised 18 DICER1-altered thyroid FNAs, with 14 having slides available and eight having corresponding surgical resections. Smears, ThinPrep, and formalin-fixed cell block slides were reviewed and correlated with histology, when available. Clinical and radiologic data were obtained from the medical record. Most DICER1-altered FNAs were classified as atypia of undetermined significance (94.4%). DICER1 mutations occurred in codons 1709 (50%), 1810 (27.8%), and 1813 (22.2%). One patient had an additional DICER1 p.D1822N variant in both of their FNAs. Lesions were often hypoechoic (35.3%) and solid (47.1%) on ultrasound. Notable cytomorphologic features include mixed but prominent microfollicular or crowded component, variable colloid, and insignificant nuclear atypia. On resection (n=10), histologic diagnoses ranged from benign follicular adenoma and low-risk follicular thyroid carcinoma to high-grade follicular-derived nonanaplastic thyroid carcinoma. Subcapsular infarct-type change was the most common histologic change. There was no evidence of recurrence or metastasis in eight patients on limited follow-up. DICER1-altered thyroid lesions occurred frequently in young females and FNAs show RAS-like cytomorphology including crowded, mixed macro-/microfollicular pattern, and bland nuclear features. On resection, DICER1-altered thyroid lesions include benign (50%), low-risk lesions (30%), or high-risk malignancies (20%).

Abstract 7669: Use of computational pathology to predict biochemical recurrence in prostate cancer (Pca) (Rp) patients following radical prostatectomy: Evaluation in low decipher risk category

Abstract Background: Current tools for predicting prostate cancer biochemical recurrence (BCR) after RP often depend on parameters determined by pathologists, such as tumor grade, which is known to vary between reviewers. Genomic risk classifiers provide useful information but require substantial tissue, are costly, and are not commonly accessible in many medical centers. The study presents an artificial intelligence (AI) model for automated risk assessment of biochemical recurrence (BCR) by analyzing nuclear morphologic patterns from Pca archival H&amp;E slides. Nuclear morphometric features are essential for disease diagnosis and gaining insights into cellular functions. Specifically, we sought to evaluate whether the computational pathology approach could provide additional granular risk stratification within the low-risk group identified by Decipher. Methods: The study employed two cohorts from the University of Pennsylvania D1 and D2. D1 comprised 180 patients, while D2 comprised 175 patients, including 63 individuals from the Decipher low-risk group. None of the patients in either cohort had received any pre-operative treatments. Within the Decipher cohort, a tumor segmentation model was created using the U-net deep learning network. The HoVer-Net model was used to both segment and classify nuclei. The feature extracted included a combination of nuclear texture and quantitative measurements of the spatial arrangement of nuclei. The eleven most significant features, selected via the least absolute shrinkage and selection operator (LASSO), were utilized to build a Cox regression model for predicting the risk of BCR in D1. In D2, patients were classified as either low-risk or high-risk for BCR based on the median risk score derived from D1. Results: Patients from D1 identified as high risk by the AI model experienced shorter survival, with a median BCR time of 27 months, in contrast to 55 months for those classified as low risk. The AI model demonstrated significant prognostic value for BCR in both D1 (HR = 6.82, 95% confidence interval [CI]: 3.54-13.12, p &amp;lt; 0.0019), and D2 (HR = 2.72, 95% CI: 1.53-4.82, p &amp;lt; 0.0032). Furthermore, the model effectively stratified the Decipher low-risk group in D2 into distinct low and high-risk categories (HR = 4.52, 95% confidence interval [CI]: 1.70-12.05, p &amp;lt; 0.0085). The most prognostic features identified included five related to nuclei shape diversity (Minor Axis Length, Eccentricity, Equivalent Diameter, Solidity, and Circularity) in conjunction with nine nuclear texture features. Conclusions: The AI model's findings suggest that a digital image-based prognostic classifier for prostate cancer could serve as an alternative or complementary method to molecular-based companion risk tests. Additional, independent multi-site and prospective validation of these findings are warranted. Citation Format: Kamal Hammouda, Tilak Pathak, Tuomas Mirtti, Priti Lal, Shilpa Gupta, Anant Madabhushi. Use of computational pathology to predict biochemical recurrence in prostate cancer (Pca) (Rp) patients following radical prostatectomy: Evaluation in low decipher risk category [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7669.

Abstract 2276: Comprehensive analysis of the genomic landscape of thyroid cancer

Abstract Thyroid cancer, the most common endocrine malignancy, has a high incidence worldwide. Meanwhile, the incidence of thyroid cancer ranks first in Korea. To assess the possible influence of ethnicity on the molecular profile of thyroid cancer, we compared the genomic features of South Korean thyroid cancer with European thyroid cancer. In this study, we performed whole genome sequencing profiling (WGS) for 459 Korean thyroid cancer patients, including papillary thyroid carcinoma (PTC, n = 274), noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP, n =18), follicular thyroid carcinoma (FTC, n = 16), follicular thyroid adenoma (FTA, n = 63), Hürthle cell adenoma (HCA, n = 22), and invasive encapsulated follicular variant papillary thyroid carcinoma (IEFVPTC, n = 19). We assessed the single nucleotide variant (SNV) and insertions and deletions using Mutect2, Varscan2, and Strelka2. For mutation signature and copy number alterations analysis, we used the Maftools and GISTIC2.0 packages, respectively. Our results revealed that most somatic mutations were similar between the Korean thyroid cancer dataset and the cancer genome atlas program (TCGA). However, the Korean cohort (1%) had a much lower NRAS mutation frequency than the TCGA cohort (8%). Mutation signature analysis showed that SBS6 and SBS2 were highly associated with the TCGA and Korean cohort. SBS6 is related to defective DNA mismatch repair, and SBS2 is characterized predominantly by CC &amp;gt; TT doublet base substitutions. In addition, we identified the NIFTP-specific somatic mutations such as KEAP1 and ZFP36L2. This study presents insights into the genomic landscape of Korean thyroid cancer and reveals some similarities and differences with European thyroid cancer at the molecular level. Citation Format: SooHyun Im, Jong-Lyul Park, Jae-Yoon Kim, Chan-Kwon Jung, Seon-Young Kim. Comprehensive analysis of the genomic landscape of thyroid cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2276.

Abstract 4340: Profiling the evolutionary history of giant cancer cells in sarcomas: Hopeful monsters or an evolutionary dead end

Abstract Background: As tumors evolve, they accumulate somatic mutations that serve as an archeological record of their evolutionary past. Undifferentiated pleomorphic sarcomas (USARCs) are malignant soft tissue tumors that often contain scattered giant cancer cells, or potential ‘hopeful monster cells’, histologically. As USARCs frequently undergo whole genome doubling (WGD) and chromothripsis (Steele et al, 2019), both mutational events are likely to contribute to the formation of giant cancer cells, which frequently display atypical nuclear features such as polyploidy, micronuclei and extrachromosomal DNA. Giant cancer cells are also a common histological finding in other sarcoma types, such as dedifferentiated liposarcomas. Aims: This study aims to leverage single cell sequencing approaches to determine the evolutionary history and genomic landscape of giant cancer cells in sarcomas. Methods: 112 giant cancer cells identified in 8 USARCs, 1 dedifferentiated liposarcoma (DDLPS) and 1 myxofibrosarcoma by histology (i.e. nuclear size and shape, as well as multi-nucleation) were isolated via laser capture microdissection thereby retaining important spatial and morphological information. Once isolated, single cell DNA was extracted and amplified using the Ampli1 whole genome amplification (WGA) kit and underwent WGS. DNA was also estimated at a single cell level in one USARC using FACS. Once sorted into different ploidy populations, single nuclei also underwent WGS. Allele specific copy number aberrations (CNAs) were inferred in all single cells. Bulk tumour and normal WGS was also performed. Results: LCM isolated giant cancer cells exhibited extreme sub-clonal heterogeneity with some demonstrating de novo chromothripsis events that was not present in bulk WGS. FACS analysis and copy number inference of single cancer cells in one USARC also revealed the presence of three ploidy populations, including a haploid, a near diploid and a WGD population. Single cell WGS and copy number profiling demonstrated that following near genome scale haploidisation, multiple haploid cells underwent successive rounds of WGD. Conclusions: Chromothripsis and WGD emerges as major contributors to intra-tumor heterogeneity in sarcoma giant cancer cells. Single cell LCM and WGS revealed that some giant cancer cells contained de novo chromothripsis events that were not observed in bulk WGS. Multiple sub-clonal WGD events also permitted one USARC to continue to explore its fitness landscape contributing to ongoing tumor heterogeneity and lethality. Citation Format: Amy Bowes, Tom Lesluyes, Sara Waise, Annelien Verfaillie, Adrienne Flanagan, Jonas Demeulemeester, Maxime Tarabichi, Nischalan Pillay, Peter Van Loo. Profiling the evolutionary history of giant cancer cells in sarcomas: Hopeful monsters or an evolutionary dead end [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4340.

Virtual histological staining of unlabeled autopsy tissue

Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.

Differentiating BRAF V600E- and RAS-like alterations in encapsulated follicular patterned tumors through histologic features: a validation study.

Differentiating BRAF V600E- and RAS-altered encapsulated follicular-patterned thyroid tumors based on morphology remains challenging. This study aimed to validate an 8-score scale nuclear scoring system and investigate the importance of nuclear pseudoinclusions (NPIs) in aiding this differentiation. A cohort of 44 encapsulated follicular-patterned tumors with varying degrees of nuclear atypia and confirmed BRAF V600E or RAS alterations was studied. Nuclear parameters (area, diameter, and optical density) were analyzed using a deep learning model. Twelve pathologists from eight Asian countries visually assessed 22 cases after excluding the cases with any papillae. Eight nuclear features were applied, yielding a semi-quantitative score from 0 to 24. A threshold score of 14 was used to distinguish between RAS- and BRAF V600E-altered tumors. BRAF V600E-altered tumors typically demonstrated higher nuclear scores and notable morphometric alterations. Specifically, the nuclear area and diameter were significantly larger, and nuclear optical density was much lower compared to RAS-altered tumors. Observer accuracy varied, with two pathologists correctly identifying genotype of all cases. Observers were categorized into proficiency groups, with the highest group maintaining consistent accuracy across both evaluation methods. The lower group showed a significant improvement in accuracy upon utilizing the 8-score scale nuclear scoring system, with notably increased sensitivity and negative predictive value in BRAF V600E tumor detection. BRAF V600E-altered tumors had higher median total nuclear scores. Detailed reevaluation revealed NPIs in all BRAF V600E-altered cases, but in only 2 of 14 RAS-altered cases. These results could significantly assist pathologists, particularly those not specializing in thyroid pathology, in making a more accurate diagnosis.

HistoEM: A Pathologist-Guided and Explainable Workflow Using Histogram Embedding for Gland Classification

Pathologists have, over several decades, developed criteria for diagnosing and grading prostate cancer. However, this knowledge has not, so far, been included in the design of convolutional neural networks (CNN) for prostate cancer detection and grading. Further, it is not known whether the features learned by machine-learning algorithms coincide with diagnostic features used by pathologists. We propose a framework that enforces algorithms to learn the cellular and subcellular differences between benign and cancerous prostate glands in digital slides from hematoxylin and eosin–stained tissue sections. After accurate gland segmentation and exclusion of the stroma, the central component of the pipeline, named HistoEM, utilizes a histogram embedding of features from the latent space of the CNN encoder. Each gland is represented by 128 feature-wise histograms that provide the input into a second network for benign vs cancer classification of the whole gland. Cancer glands are further processed by a U-Net structured network to separate low-grade from high-grade cancer. Our model demonstrates similar performance compared with other state-of-the-art prostate cancer grading models with gland-level resolution. To understand the features learned by HistoEM, we first rank features based on the distance between benign and cancer histograms and visualize the tissue origins of the 2 most important features. A heatmap of pixel activation by each feature is generated using Grad-CAM and overlaid on nuclear segmentation outlines. We conclude that HistoEM, similar to pathologists, uses nuclear features for the detection of prostate cancer. Altogether, this novel approach can be broadly deployed to visualize computer-learned features in histopathology images.

Detection of senescence using machine learning algorithms based on nuclear features

Cellular senescence is a stress response with broad pathophysiological implications. Senotherapies can induce senescence to treat cancer or eliminate senescent cells to ameliorate ageing and age-related pathologies. However, the success of senotherapies is limited by the lack of reliable ways to identify senescence. Here, we use nuclear morphology features of senescent cells to devise machine-learning classifiers that accurately predict senescence induced by diverse stressors in different cell types and tissues. As a proof-of-principle, we use these senescence classifiers to characterise senolytics and to screen for drugs that selectively induce senescence in cancer cells but not normal cells. Moreover, a tissue senescence score served to assess the efficacy of senolytic drugs and identified senescence in mouse models of liver cancer initiation, ageing, and fibrosis, and in patients with fatty liver disease. Thus, senescence classifiers can help to detect pathophysiological senescence and to discover and validate potential senotherapies.

Nuclear morphology explained through digital morphometry: differentiating nuclear features across the three histological grades in cutaneous squamous cell carcinoma.

The accurate grading of cutaneous squamous cell carcinoma (cSCC) is crucial for effective treatment and patient care, but the current method, relying on pathologist visual assessment, can be subjective. This study aimed to quantitatively investigate nuclear morphometrics in cSCC tumor cells, specifically examining potential differences between established histological grades and exploring the utility of digital morphometrics in characterizing nuclear morphology. We analyzed 47 cSCC cases, utilizing whole slide images and CellProfiler software to extract nuclear morphometric features from a total of 519 828 nuclei. The results showed significant differences in nuclear size, shape, and spatial arrangement between different tumor grades. Notably, grade 3 tumors exhibited distinct characteristics compared to grades 1 and 2, suggesting a potential shift towards a simplified, 2-tier grading system. The study highlights the potential of digital morphometrics as a valuable tool for enhancing the accuracy and objectivity of cSCC grading, ultimately improving patient care.

Application of artificial intelligence (AI) features of nuclear morphology from BLASST-1 (Bladder Cancer Signal Seeking Trial) of nivolumab, gemcitabine, and cisplatin in patients with MIBC undergoing cystectomy.

674 Background: BLASST-1 is a multi-center phase II trial evaluating neoadjuvant nivolumab (N) with gemcitabine-cisplatin (GC) for patients (pts) with MIBC undergoing radical cystectomy (RC) (NCT03294304). 41 pts with MIBC (cT2-T4a, N≤1, M0) were enrolled between Feb 2018 and June 2019; (cT2N0 90%, cT3N0 7%, cT4N1 3%). Pts received C (70mg/m2) IV on D1, G (1000mg/m2) on D1, D8, and N (360 mg) IV on D8 every 21 days for 4 cycles followed by RC within 8 wks. The primary endpoint was pathologic downstaging (PaR; ≤pT1N0). Safety, Relapse-free survival (RFS), Progression-free survival (PFS) and biomarker analyses were secondary endpoints. PaR rate was 65.8%, the pCR (≤pT is N0) rate was 49% and there were no safety concerns or delays to RC. Morphometric characteristics of the cell nucleus can be used to assess bladder cancer grading and gain insights into cellular functionalities. In this study, we sought to evaluate the ability of the AI model to identify non-responders to neoadjuvant chemo-immunotherapy. in the BLASST-1 cohort based on computerized image features of nuclear morphology and architecture on pre-treatment transurethral resection of bladder tumor (TURBT) tissues. Methods: Of the 41 pts, we had H&amp;E images available for 34 pts, of which 23 had PaR and 11 did not have PaR and these were classified as responder (R) and non-responder (NR) groups. A machine learning model (U-net) was developed and invoked for tumor segmentation on the H&amp;E images from the BLASST-1 cohort. A second machine learning model (HoVer-Net) was employed to segment and classify individual nuclei. A total of 408 features relating to the textural and spatial arrangement of individual cancer nuclei were extracted. The 17 most significant features, identified through the least absolute shrinkage and selection operator, were used to train a Cox regression model to predict the risk of death using 361 MIBC pts from the Cancer Genome Atlas (TCGA). This Cox model was then applied to assign a risk score to pts in BLASST-1, using a threshold learned from TCGA pts, the individual pts in BLASST-1 were assigned as either low-risk or high-risk. Results: The top identified prognostic features described the textural appearance of individual nuclei with more texture. This model accurately predicted PaR in BLASST-1, with an area under a receiver operating characteristic curve of 0.83. Overall, the pts with the same nuclear angle direction within the tumor tissue had a high chance of response to the neoadjuvant chemo-IO combination in the BLASST-1 trial. Conclusions: A computerized AI model relying on nuclear morphologic and architecture features demonstrated prognostic capability in MIBC within the TCGA dataset and predictive capability for the PaR in the BLASST-1 trial. These findings support further validation studies.