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Journal articles on the topic 'Molecular subclassification'

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Author: Grafiati

Published: 11 March 2023

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1

Tsukasaki, Kunihiro, Olivier Hermine, Ali Bazarbachi, Lee Ratner, Juan Carlos Ramos, William Harrington, Deirdre O’Mahony, et al. "Definition, Prognostic Factors, Treatment, and Response Criteria of Adult T-Cell Leukemia-Lymphoma: A Proposal From an International Consensus Meeting." Journal of Clinical Oncology 27, no. 3 (January 20, 2009): 453–59. http://dx.doi.org/10.1200/jco.2008.18.2428.

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Adult T-cell leukemia-lymphoma (ATL) is a distinct peripheral T-lymphocytic malignancy associated with a retrovirus designated human T-cell lymphotropic virus type I (HTLV-1). The diversity in clinical features and prognosis of patients with this disease has led to its subclassification into the following four categories: acute, lymphoma, chronic, and smoldering types. The chronic and smoldering subtypes are considered indolent and are usually managed with watchful waiting until disease progression, analogous to the management of some patients with chronic lymphoid leukemia (CLL) or other indolent histology lymphomas. Patients with aggressive ATL generally have a poor prognosis because of multidrug resistance of malignant cells, a large tumor burden with multiorgan failure, hypercalcemia, and/or frequent infectious complications as a result of a profound T-cell immunodeficiency. Under the sponsorship of the 13th International Conference on Human Retrovirology: HTLV, a group of ATL researchers joined to form a consensus statement based on established data to define prognostic factors, clinical subclassifications, and treatment strategies. A set of response criteria specific for ATL reflecting a combination of those for lymphoma and CLL was proposed. Clinical subclassification is useful but is limited because of the diverse prognosis among each subtype. Molecular abnormalities within the host genome, such as tumor suppressor genes, may account for these diversities. A treatment strategy based on the clinical subclassification and prognostic factors is suggested, including watchful waiting approach, chemotherapy, antiviral therapy, allogeneic hematopoietic stem-cell transplantation (alloHSCT), and targeted therapies.

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Köbel, Martin, and Eun Young Kang. "The Evolution of Ovarian Carcinoma Subclassification." Cancers 14, no. 2 (January 14, 2022): 416. http://dx.doi.org/10.3390/cancers14020416.

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The phenotypically informed histotype classification remains the mainstay of ovarian carcinoma subclassification. Histotypes of ovarian epithelial neoplasms have evolved with each edition of the WHO Classification of Female Genital Tumours. The current fifth edition (2020) lists five principal histotypes: high-grade serous carcinoma (HGSC), low-grade serous carcinoma (LGSC), mucinous carcinoma (MC), endometrioid carcinoma (EC) and clear cell carcinoma (CCC). Since histotypes arise from different cells of origin, cell lineage-specific diagnostic immunohistochemical markers and histotype-specific oncogenic alterations can confirm the morphological diagnosis. A four-marker immunohistochemical panel (WT1/p53/napsin A/PR) can distinguish the five principal histotypes with high accuracy, and additional immunohistochemical markers can be used depending on the diagnostic considerations. Histotypes are further stratified into molecular subtypes and assessed with predictive biomarker tests. HGSCs have recently been subclassified based on mechanisms of chromosomal instability, mRNA expression profiles or individual candidate biomarkers. ECs are composed of the same molecular subtypes (POLE-mutated/mismatch repair-deficient/no specific molecular profile/p53-abnormal) with the same prognostic stratification as their endometrial counterparts. Although methylation analyses and gene expression and sequencing showed at least two clusters, the molecular subtypes of CCCs remain largely elusive to date. Mutational and immunohistochemical data on LGSC have suggested five molecular subtypes with prognostic differences. While our understanding of the molecular composition of ovarian carcinomas has significantly advanced and continues to evolve, the need for treatment options suitable for these alterations is becoming more obvious. Further preclinical studies using histotype-defined and molecular subtype-characterized model systems are needed to expand the therapeutic spectrum for women diagnosed with ovarian carcinomas.

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Effendi, Kathryn, Wit Thun Kwa, Akihisa Ueno, and Michiie Sakamoto. "The role of molecular pathology in the precision diagnosis and subclassification of hepatocellular carcinoma." Universa Medicina 41, no. 2 (June 13, 2022): 194–206. http://dx.doi.org/10.18051/univmed.2022.v41.194-206.

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Hepatocellular carcinoma (HCC) remains a leading cause of cancer death worldwide despite recent advances in surveillance and therapeutic management. The outcomes for HCC patients remain poor, often as a result of late diagnosis or lack of effective treatments. Early detection and precise diagnosis are evidently crucial in improving the prognosis of HCC. However, HCC is a highly heterogeneous cancer with various clinical backgrounds and altered molecular pathways; these factors make its precise diagnosis more difficult. Approximately 25% of HCCs harbor actionable mutations, which are yet to be translated into clinical practice. In the era of precision medicine, molecular or genomic information are indispensable for HCC diagnosis and prognosis. Exploring genomic alterations has become a requirement for identifying the molecular subtypes of HCC. Recent studies have introduced molecular markers to help identify early HCC and to clarify its multistep process of carcinogenesis. The subclassification of tumors into proliferation class and nonproliferation class HCCs gives pointers to the HCC phenotype and facilitates the selection of appropriate treatments. In this review, we broadly summarize some of the latest insights into HCC subclassification from the perspective of molecular pathology. Immunohistochemistry-based subclassification allows improved characterization of HCC in daily clinical practice. Moreover, analysis of the immune microenvironment, intra-tumoral morphological heterogeneity, and imaging features gives additional information regarding the classification of HCC. Combinations of these approaches are expected to inform and advance the precision diagnosis and management of HCC.

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Hytiroglou, Prodromos, Paulette Bioulac-Sage, Neil D. Theise, and Christine Sempoux. "Etiology, Pathogenesis, Diagnosis, and Practical Implications of Hepatocellular Neoplasms." Cancers 14, no. 15 (July 28, 2022): 3670. http://dx.doi.org/10.3390/cancers14153670.

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Hepatocellular carcinoma (HCC), a major global contributor of cancer death, usually arises in a background of chronic liver disease, as a result of molecular changes that deregulate important signal transduction pathways. Recent studies have shown that certain molecular changes of hepatocarcinogenesis are associated with clinicopathologic features and prognosis, suggesting that subclassification of HCC is practically useful. On the other hand, subclassification of hepatocellular adenomas (HCAs), a heterogenous group of neoplasms, has been well established on the basis of genotype–phenotype correlations. Histologic examination, aided by immunohistochemistry, is the gold standard for the diagnosis and subclassification of HCA and HCC, while clinicopathologic correlation is essential for best patient management. Advances in clinico-radio-pathologic correlation have introduced a new approach for the diagnostic assessment of lesions arising in advanced chronic liver disease by imaging (LI-RADS). The rapid expansion of knowledge concerning the molecular pathogenesis of HCC is now starting to produce new therapeutic approaches through precision oncology. This review summarizes the etiology and pathogenesis of HCA and HCC, provides practical information for their histologic diagnosis (including an algorithmic approach), and addresses a variety of frequently asked questions regarding the diagnosis and practical implications of these neoplasms.

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Akhtar, Mohammed, Issam A. Al-Bozom, Mohamed Ben Gashir, and Noheir M. Taha. "Intrinsic Molecular Subclassification of Urothelial Carcinoma of the Bladder." Advances In Anatomic Pathology 26, no. 4 (July 2019): 251–56. http://dx.doi.org/10.1097/pap.0000000000000235.

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Li, Dong, Shuho Semba, Ming Wu, and Hiroshi Yokozaki. "Molecular pathological subclassification of mucinous adenocarcinoma of the colorectum." Pathology International 55, no. 12 (December 2005): 766–74. http://dx.doi.org/10.1111/j.1440-1827.2005.01903.x.

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Tsuiimoto, Gozoh. "α1-adrenoceptor (α1AR) subclassification by pharmacology and molecular cloning." Japanese Journal of Pharmacology 67 (1995): 15. http://dx.doi.org/10.1016/s0021-5198(19)46039-3.

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Thompson, Emily F., Lynn Hoang, Anne Kathrin Höhn, Andrea Palicelli, Karen L. Talia, Nairi Tchrakian, Janine Senz, et al. "Molecular subclassification of vulvar squamous cell carcinoma: reproducibility and prognostic significance of a novel surgical technique." International Journal of Gynecologic Cancer 32, no. 8 (June 28, 2022): 977–85. http://dx.doi.org/10.1136/ijgc-2021-003251.

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ObjectivesVulvar squamous cell carcinoma is subclassified into three prognostically relevant groups: (i) human papillomavirus (HPV) associated, (ii) HPV independent p53 abnormal (mutant pattern), and (iii) HPV independent p53 wild type. Immunohistochemistry for p16 and p53 serve as surrogates for HPV viral integration and TP53 mutational status. We assessed the reproducibility of the subclassification based on p16 and p53 immunohistochemistry and evaluated the prognostic significance of vulvar squamous cell carcinoma molecular subgroups in a patient cohort treated by vulvar field resection surgery.MethodsIn this retrospective cohort study, 68 cases treated by vulvar field resection were identified from the Leipzig School of Radical Pelvic Surgery. Immunohistochemistry for p16 and p53 was performed at three different institutions and evaluated independently by seven pathologists and two trainees. Tumors were classified into one of four groups: HPV associated, HPV independent p53 wild type, HPV independent p53 abnormal, and indeterminate. Selected cases were further interrogated by (HPV RNA in situ hybridization, TP53 sequencing).ResultsFinal subclassification yielded 22 (32.4%) HPV associated, 41 (60.3%) HPV independent p53 abnormal, and 5 (7.3%) HPV independent p53 wild type tumors. Interobserver agreement (overall Fleiss’ kappa statistic) for the four category classification was 0.74. No statistically significant differences in clinical outcomes between HPV associated and HPV independent vulvar squamous cell carcinoma were observed.ConclusionInterobserver reproducibility of vulvar squamous cell carcinoma subclassification based on p16 and p53 immunohistochemistry may support routine use in clinical practice. Vulvar field resection surgery showed no significant difference in clinical outcomes when stratified based on HPV status.

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Liau, Jau-Yu, Jia-Huei Tsai, Ray-Hwang Yuan, Chih-Ning Chang, Hsin-Jung Lee, and Yung-Ming Jeng. "Morphological subclassification of intrahepatic cholangiocarcinoma: etiological, clinicopathological, and molecular features." Modern Pathology 27, no. 8 (January 10, 2014): 1163–73. http://dx.doi.org/10.1038/modpathol.2013.241.

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Nagasaka, Toru, Masaharu Gunji, Noboru Hosokai, Kumiko Hayashi, Hiroshi Ikeda, Masafumi Ito, and Suguru Inao. "FISH 1p/19q deletion/imbalance for molecular subclassification of glioblastoma." Brain Tumor Pathology 24, no. 1 (May 25, 2007): 1–5. http://dx.doi.org/10.1007/s10014-006-0209-6.

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Huse, Jason T., Heidi S. Phillips, and Cameron W. Brennan. "Molecular subclassification of diffuse gliomas: Seeing order in the chaos." Glia 59, no. 8 (March 28, 2011): 1190–99. http://dx.doi.org/10.1002/glia.21165.

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Fukumoto, Kota, Sou Nakaji, Yasuhito Suehara, Manabu Fujisawa, Keisuke Seike, Masafumi Fukaya, Hiroki Sugihara, Masami Takeuchi, and Kosei Matsue. "Endoscopic Ultrasound-Guided Fine Needle Aspiration Biopsy for Diagnosis of Intra-Abdominal Lymphoma without Accessible Peripheral Lymphadenopathy." Blood 124, no. 21 (December 6, 2014): 5380. http://dx.doi.org/10.1182/blood.v124.21.5380.5380.

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Abstract Introduction: Endoscopic ultrasound-guided fine needle aspiration biopsy (EUS-FNAB) is considered the procedure of choice for the diagnosis and staging of intra-abdominal non-Hodgkin’s lymphoma (NHL) without accessible peripheral lymphadenopathy. However, diagnosis and subclassification lymphoma by FNAB is often challenging due to variable cellularity and lack of architecture. Recent advances of ancillary techniques such as immunohistochemical staining, flowcytometry (FCM), fluorescence in situ hybridization (FISH) analysis and molecular analysis allowed classify lymphoma more precisely, although sufficient information can be obtained through this procedure remained undetermined. The present study was performed to evaluate the yield of EUS-FNAB using a standard 19 or 22-gauge needle for diagnosis and subclassification of lymphoma, assessing the feasibility of immunohistological, FCM, molecular and cytogenetic assessments. Methods: Between April 2008 and July 2014, 90 patients with malignant lymphoma who had an intra-abdominal mass without accessible peripheral lymphadenopathy underwent EUS-guided fine needle aspiration biopsy at our hospital. All patients received positron emission tomography/computed tomography and had 2-deoxy-2-(18F)fluoro-D-glucose-avid lesions in abdomen before examination. The aspirated materials were processed for flowcytometry (FCM), molecular analysis of immunoglobulin heavy (IgH) and T-cell receptor (TCR) gene rearrangement, cytogenetic analysis by conventional G banding, and FISH analysis, in addition to standard histopathological studies. Patients’ baseline data, including age, sex, laboratory examinations, imaging studies, and final diagnosis of lymphoma, were collected and examined to determine the feasibility and sensitivity for diagnosis and subclassification of lymphoma. Results: The mean of the diameter of mass was 37mm (9.7-149mm). Among the 90 patients, conventional G banding, FCM analysis, standard cytogenetic analysis, and FISH were successfully performed in 67 (74%), 78 (87%), 44 (49%), and 58 (64%) cases, respectively. G banding analysis were successful in 45 patients (67%) that showed normal karyotype in 5 cases (7%), t(14;18)(q32;q21) in 7 cases (10%), t(8;14)(q24;q32) in one case, and complex abnormality in 32 cases (48%), respectively. FCM analysis showed immunoglobulin light chain restriction in 48 cases (62%) and were diagnosed as B-cell lymphoma. FCM could not determine the T-cell clonality. Molecular analyses for TCR and/or IgH receptor rearrangements were successful in 35 patients (83%), 31 rearranged in IgH and 4 rearranged in TCR, respectively. There were 32 cases with IgH/Bcl2 fusions by FISH analysis, 26 cases in follicular lymphoma (FL) and 6 cases in diffuse large B-cell lymphoma (DLBCL), respectively. IgH/Bcl6 fusion was seen in 2 case of DLBCL and IgH/C-myc fusion was seen in 1 case of Burkitt lymphoma (BL). Finally, our cohort included 82 B-cell lymphomas (91%) and 8 T-cell lymphomas (9%). Subclassification of lymphoma in accordance with WHO system included 40 cases of FL, 39 cases of DLBCL, one case of BL, 2 cases of lymphoblastic lymphoma, 7 cases of peripheral T cell lymphoma not specified, and one case of angioimmunoblastic lymphoma. Although all of the outpatients were hospitalized until the day after biopsy, there were no serious complications related to this procedure like bleeding, perforation, ileus, and infection. Conclusions: EUS-FNAB using a standard 19 or 22-gauge needle is safe and feasible and has high diagnostic value for subclassification of intra-abdominal lymphoma without accessible peripheral lymphadenopathy. With the use of simultaneous immunophenotyping, molecular, and cytogenetic studies, lymphoma subclassification was possible in most of the cases. Disclosures No relevant conflicts of interest to declare.

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Kobayashi, Yasuhito, Yoshio Tokuchi, Takehisa Hashimoto, Moriaki Hayashi, Hitoshi Nishimura, Yuichi Ishikawa, Ken Nakagawa, Yukitoshi Sato, Atsushi Takahashi, and Eiju Tsuchiya. "Molecular markers for reinforcement of histological subclassification of neuroendocrine lung tumors." Cancer Science 95, no. 4 (April 2004): 334–41. http://dx.doi.org/10.1111/j.1349-7006.2004.tb03212.x.

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Díaz-Martín, Juan, Michele Biscuola, Jonatan Benoit, David Marcilla, Gema Civantos, and Enrique de Álava. "What's in a name? Molecular subclassification of sarcomas creates fresh challenges." Journal of Pathology 247, no. 4 (January 16, 2019): 409–12. http://dx.doi.org/10.1002/path.5206.

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Wu, Yilin, Eric Zander, Andrew Ardeleanu, Ryan Singleton, and Barnabas Bede. "An Overview of Mathematical Models for RNA Sequence-based Glioblastoma Subclassification." Artificial Intelligence in Oncology 3, no. 1 (June 1, 2021): 001–7. http://dx.doi.org/10.52454/aio.v3i1.11.

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Molecular marker-based glioblastoma (GBM) subclassification is emerging as a key factor in personalized GBM treatment planning. Multiple genetic alterations, including methylation status and mutations, have been proposed in GBM subclassification. RNA-Sequence (RNA-Seq)-based molecular profiling of GBM is widely implemented and readily quantifiable. Machine learning (ML) algorithms have been reported as an applicable method that can consistently subgroup GBM. In this study, we systematically studied the applicability of the commonly used ML algorithms based on The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset and cross-validated in the Chinese Glioma Genome Atlas (CGGA) dataset. ML algorithms studied include Binomial and multinomial Logistic Regression, Linear discriminant analysis, Decision trees, K-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machines, Gradient Boosting, Voting Ensemble, Multi-Layer Perceptron. RNA-Seq data of 44 biomarkers were passed through the algorithms for performance evaluation. We found ML algorithms Support Vector Machines, Multi-Layer Perceptron s, and Voting Ensemble are best equipped in assigning GBM to correct molecular subgroups of GBM without histological studies.

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Rossi, Maura, Maria Antonella Laginestra, Anna Gazzola, Maria Rosaria Sapienza, Stefano A. Pileri, and Pier Paolo Piccaluga. "Molecular Profiling of Aggressive Lymphomas." Advances in Hematology 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/464680.

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In the last years, several studies of molecular profiling of aggressive lymphomas were performed. In particular, it was shown that DLBCL can be distinguished in two different entities according to GEP. Specifically, ABC and GCB subtypes were characterized by having different pathogenetic and clinical features. In addition, it was demonstrated that DLBCLs are distinct from BL. Indeed, the latter is a unique molecular entity. However, relevant pathological differences emerged among the clinical subtypes. More recently, microRNA profiling provided further information concerning BL-DLBCL distinction as well as for their subclassification. In this paper, the authors based on their own experience and the most updated literature review, the main concept on molecular profiling of aggressive lymphomas.

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Kelleher, Fergal C., Andrew J. Colebatch, and Aparna Rao. "New Molecular Targets in Lung Adenocarcinoma." Oncology & Hematology Review (US) 09, no. 02 (2013): 122. http://dx.doi.org/10.17925/ohr.2013.09.2.122.

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Lung cancer is designated as either non-small-cell lung cancer (NSCLC) or small-cell lung cancer. There are three subtypes of NSCLC: adenocarcinoma (48 %), squamous cell carcinoma (28 %), and large-cell carcinoma (24 %). Epidermal growth factor receptor(EGFR)mutations, anaplastic lymphoma kinase(ALK)rearrangements, andROS1rearrangements are co-associated with lung adenocarcinoma in never-smokers. Histologically, lung adenocarcinoma is sub-divided into papillary, acinar, bronchioalveolar, and solid subtypes. A superseding molecular subclassification is emerging with important therapeutic implications. Secondary resistance to medications targeting these molecular abnormalities does invariably occur. It is anticipated that strategies including drugs with increased receptor binding affinity, altered medication pharmacodynamic profiles, and combinatorial approaches will emerge.

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Jin, Ming, and Paul E. Wakely Jr. "Endoscopic/Endobronchial Ultrasound-Guided Fine Needle Aspiration and Ancillary Techniques, Particularly Flow Cytometry, in Diagnosing Deep-Seated Lymphomas." Acta Cytologica 60, no. 4 (2016): 326–35. http://dx.doi.org/10.1159/000447253.

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Evaluation of deep-seated lymphomas by fine-needle aspiration (FNA) can be challenging due to their reduced accessibility. Controversy remains as to whether FNA and ancillary techniques can be used to diagnose deep-seated lymphomas reliably and sufficiently for clinical management. Most published studies are favorable that endobronchial ultrasound (EBUS)/endoscopic ultrasound (EUS)-FNA plays an important role in the diagnosis of deep-seated lymphomas. The addition of ancillary techniques, particularly flow cytometry, increases diagnostic yield. While subclassification is possible in a reasonable proportion of cases, the reported rates of successful subclassification are lower than those for lymphoma detection/diagnosis. The diagnostic limitation exists for Hodgkin's lymphoma, grading of follicular lymphoma, and some T-cell lymphomas. The role of FNA in deep-seated lymphomas is much better established for recurrent than primary disease. It remains unclear whether the use of large-sized-needle FNA or a combination of core needle biopsy and FNA improves subclassification. It is important for cytopathologists to have considerable understanding of the WHO lymphoma classification and develop a collaborative working relationship with hematopathologists and oncologists. As EUS/EBUS-FNA techniques advance and sophisticated molecular techniques such as next- generation sequencing become possible, the role of FNA in the diagnosis of deep-seated lymphomas will possibly increase.

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Nakada, M., W. Obuchi, S. Ohtsuki, S. Tanaka, T. Furuta, T. Kitabayashi, H. Sabit, T. Terasaki, and Y. Hayashi. "CS-25 * MOLECULAR SUBCLASSIFICATION OF GLIOBLASTOMA BASED ON THE ABSOLUTE QUANTITATIVE PROTEOMICS." Neuro-Oncology 16, suppl 5 (November 1, 2014): v56. http://dx.doi.org/10.1093/neuonc/nou242.25.

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Kaur, Kavneet, Aanchal Kakkar, Anupam Kumar, Supriya Mallick, Pramod K. Julka, Deepak Gupta, Ashish Suri, Vaishali Suri, Mehar C. Sharma, and Chitra Sarkar. "Integrating Molecular Subclassification of Medulloblastomas into Routine Clinical Practice: A Simplified Approach." Brain Pathology 26, no. 3 (September 9, 2015): 334–43. http://dx.doi.org/10.1111/bpa.12293.

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Takahashi, Masayuki, Ximing J. Yang, Jun Sugimura, Jesper Backdahl, Maria Tretiakova, Chao-Nan Qian, Steven G. Gray, et al. "Molecular subclassification of kidney tumors and the discovery of new diagnostic markers." Oncogene 22, no. 43 (October 2003): 6810–18. http://dx.doi.org/10.1038/sj.onc.1206869.

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Amador, Catalina, Timothy C. Greiner, Tayla B. Heavican, Lynette M. Smith, Karen Tatiana Galvis, Waseem Lone, Alyssa Bouska, et al. "Reproducing the molecular subclassification of peripheral T-cell lymphoma–NOS by immunohistochemistry." Blood 134, no. 24 (December 12, 2019): 2159–70. http://dx.doi.org/10.1182/blood.2019000779.

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One-third of peripheral T-cell lymphomas are “not otherwise specified” (PTCL-NOS), but they have been subdivided into 2 subgroups based on gene expression profiling. Amador and colleagues generated an immunohistochemical algorithm that parallels the molecular separation of PTCL-NOS and provides useful prognostic information.

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Kristensen, Vessela N. "Divide and conquer: the genetic basis of molecular subclassification of breast cancer." EMBO Molecular Medicine 3, no. 4 (March 10, 2011): 183–85. http://dx.doi.org/10.1002/emmm.201100128.

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HAMILTON, C. A. "Imidazoline Receptors, Subclassification, and Drug-Induced Regulation." Annals of the New York Academy of Sciences 763, no. 1 The Imidazoli (July 1995): 57–65. http://dx.doi.org/10.1111/j.1749-6632.1995.tb32390.x.

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MANNHOLD, R., and R. BAYER. "Biochemical subclassification of Ca antagonists-correlation with functionalproperties." Journal of Molecular and Cellular Cardiology 19 (1987): S56. http://dx.doi.org/10.1016/s0022-2828(87)80174-8.

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Hieble, J. Paul. "Adrenoceptor subclassification: an approach to improved cardiovascular therapeutics." Pharmaceutica Acta Helvetiae 74, no. 2-3 (March 2000): 163–71. http://dx.doi.org/10.1016/s0031-6865(99)00030-8.

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Barroca, Helena, and Cristina Marques. "A Basic Approach to Lymph Node and Flow Cytometry Fine-Needle Cytology." Acta Cytologica 60, no. 4 (2016): 284–301. http://dx.doi.org/10.1159/000448679.

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According to the World Health Organization (WHO), the new classification of lymphomas is mainly based on morphological, immunophenotypical, and molecular criteria. Consequently, this new approach has led from the substantial role that architecture played in the past to a secondary panel highlighting the role of fine-needle biopsy (FNB). Applied together with other ancillary techniques, such as flow cytometry (FC), FNB is a potential tool for the diagnosis of lymphomas, and enlarged lymph nodes represent an excellent target for the implementation of this technique. Despite the difficulties inherent in this technology, which might pose problems in differential diagnosis, in the majority of cases this joint work allows an accurate diagnosis of malignancy and even correct subcharacterization in routine lymphomas. Additionally, in selected cases, other molecular techniques like FISH and PCR can also be performed on FNB specimens, helping in the characterization and diagnosis of lymphomas. In this review, we discuss the basic aspects of the combination of FNB cytology and FC in the diagnosis and subclassification of lymphomas. The preanalytical phase is extensively discussed. The advantages, disadvantages, and technical limitations of this joint work are addressed in general and in terms of the accurate subclassification of lymphomas.

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Zhu, J., L. Wu, Y. Zhou, R. Wang, S. Chen, S. Yu, S. Zheng, F. Xiao, and J. Li. "POS0833 A RETROSPECTIVE COHORT STUDY IN CHINESE PATIENTS WITH ADULT POLYMYOSITIS AND DERMATOMYOSITIS: RISK OF COMORBIDITIES AND SUBCLASSIFICATION USING MACHINE LEARNING." Annals of the Rheumatic Diseases 80, Suppl 1 (May 19, 2021): 670.1–670. http://dx.doi.org/10.1136/annrheumdis-2021-eular.590.

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Background:Idiopathic inflammatory myopathy (IIM), also known as myositis, refers to a group of heterogeneous disorders including polymyositis (PM), dermatomyositis (DM), inclusion body myositis and immune-mediated necrotising myopathy. Phenotype, pathogenesis, and prognosis vary due to multi-organ involvement and comorbidities. With the clinical application of MSAs, a new classification system for myositis was explored to reduce confusion between subgroups. But it is far from showing the full picture of myositis due to high heterogeneity. Therefore, it is necessary to systematically evaluate the relevant risk factors of myositis for ILD, other rheumatic diseases, and malignancy for better clinical vigilance. And further exploring the subclassification of myositis is critical.Objectives:To identify the risk factors in Chinese patients with adult polymyositis and dermatomyositis for their comorbidities and explore a subclassification system.Methods:Clinical records of 397 patients with idiopathic inflammatory myopathies were retrospectively reviewed. Logistic regression was used to identify potential risk factors for interstitial lung disease (ILD), other rheumatic diseases, and malignancy after bivariate analysis. Hierarchical clustering and decisional tree were utilized to identify subgroups and explore a subclassification system.Results:A total of 119 polymyositis and 191 dermatomyositis patients were included. Anti-PM/Scl, anti-Ro52, anti-aminoacyl-tRNA synthetase and anti-MDA5 (adjusted odds ratios (AOR)=4.779, 1.917, 5.092 and 7.714 respectively) antibodies were risks (p<0.05), whereas overlapping malignancy was protective (AOR=0.107; p=0.002) for ILD across polymyositis, dermatomyositis and the total group. In subgroup models, Raynaud’s phenomenon, arthralgia and semi-quantitative anti-nuclear antibody (AOR=51.233, 4.261, 3.047 respectively) were risks for other overlapping rheumatic diseases (p<0.05). For overlapping malignancy, male and anti-TIF1γ antibodies (AOR=2.533, 16.949) were risks (p<0.05), whereas disease duration and combination of ILD (AOR=0.954, 0.106) were protective in the total group (p<0.05); while anti-NXP2 antibodies were identified as risk factors (AOR=73.152; p=0.038) in polymyositis. Hierarchical clustering suggested a subclassification with 6 subgroups: malignancy overlapping dermatomyositis, classical dermatomyositis, polymyositis with severe muscle involvement, dermatomyositis with ILD, polymyositis with ILD, and overlapping of myositis with other rheumatic diseases according to the characteristics of grouped patients. Accuracy of the classification and regression trees model was 0.768 (95% CI 0.711 to 0.819) on training set and 0.633 (95%CI 0.499 to 0.754) on test set.Conclusion:Accompanying ILD, other rheumatic diseases and malignancy are strongly associated with clinical manifestation and myositis-specific or myositis-associated autoantibodies among Chinese polymyositis and dermatomyositis patients. The subclassification system proposed a more precise phenotype defining toward stratified treatments.Acknowledgements:The study was supported by the Natural Science Foundation of China [No. 81803932] and the Natural Science Foundation of Guangdong Province [No. 2018030310025 and 2017A030313868]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscriptDisclosure of Interests:None declared

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Dijkhuizen, Trijnie, Eva van den Berg, Willemina M. Molenaar, J. Wolter Oosterhuis, Anke Dam, Janneke Wiersema, Heimen Schraffordt Koops, and Bauke de Jong. "Cytogenetics as a tool in the histologic subclassification of chondrosarcomas." Cancer Genetics and Cytogenetics 76, no. 2 (September 1994): 100–105. http://dx.doi.org/10.1016/0165-4608(94)90457-x.

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Chan, CY, AWI Lo, KO Lam, HY Ng, CC Chen, ML Lung, and JMY Ko. "Abstract 5266: Prognostic role of molecular subclassification based on mutational profiling and tumor-associated neutrophil status in stage III colon cancer." Cancer Research 82, no. 12_Supplement (June 15, 2022): 5266. http://dx.doi.org/10.1158/1538-7445.am2022-5266.

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Abstract The traditional prognostic prediction of colon cancer (CC) is based on the tumor-node-metastasis (TNM) system. This, however, is not adequate for clinical decision making due to the existence of inter-patient variations and heterogeneity of underlying molecular pathways. We aim to investigate the prognostic value of molecular subclassification utilizing i) tumor-associated neutrophil (TAN) status by immunohistochemical (IHC) staining; ii) microsatellite instability (MSI) and elevated microsatellite alteration at selected tetranucleotide repeats (EMAST) status by PCR-based microsatellite typing; and iii) next-generation sequencing (NGS) mutational profiling by deep sequencing of a 77-gene oncology panel. In the discovery phase, 130 pairs of archived normal and tumor formalin-fixed paraffin-embedded (FFPE) specimens of stage III CC patients were retrospectively recruited from Queen Mary Hospital. CC patients will be categorized into different molecular subclasses (MSCs) based on the status of TAN/MSI/EMAST/NGS mutational profiling molecular biomarkers. Kaplan-Meier analysis, log rank test, univariate and multivariate Cox proportional hazards regression analysis will be used for estimation of progression-free survival (PFS) and overall survival (OS) between the two groups of CC patients. We expect our data to provide useful information for improved stratification of stage III CC patients into high or low risk of recurrence and survival. Acknowledgement: The Health and Medical Research Fund (07181536) to JMYK. Citation Format: CY Chan, AWI Lo, KO Lam, HY Ng, CC Chen, ML Lung, JMY Ko. Prognostic role of molecular subclassification based on mutational profiling and tumor-associated neutrophil status in stage III colon 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 5266.

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Zhang, Xiaohui, Prerna Rastogi, Bijal Shah, and Ling Zhang. "B lymphoblastic leukemia/lymphoma: new insights into genetics, molecular aberrations, subclassification and targeted therapy." Oncotarget 8, no. 39 (July 15, 2017): 66728–41. http://dx.doi.org/10.18632/oncotarget.19271.

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Jain, Rajan, Laila Poisson, Jayant Narang, David Gutman, Lisa Scarpace, Scott N. Hwang, Chad Holder, et al. "Genomic Mapping and Survival Prediction in Glioblastoma: Molecular Subclassification Strengthened by Hemodynamic Imaging Biomarkers." Radiology 267, no. 1 (April 2013): 212–20. http://dx.doi.org/10.1148/radiol.12120846.

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Linge, A., B. Tawk, S. Löck, M. Großer, F. Lohaus, V. Gudziol, A. Nowak, et al. "OC-0390 TCGA molecular subclassification is prognostic for LRC of HNSCC after postoperative RCTx." Radiotherapy and Oncology 133 (April 2019): S196. http://dx.doi.org/10.1016/s0167-8140(19)30810-2.

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Alaña, Lide, Idoia Martin-Guerrero, Aurora Navajas, Laura Zaldumbide, Lorena Mosteiro, Edward C. Schwalbe, Debbie Hicks, Steven C. Clifford, and Miguel García-Ariza. "MEDB-65. Molecular subclassification of a national cohort of pediatric medulloblastoma based on methylation profile." Neuro-Oncology 24, Supplement_1 (June 1, 2022): i121. http://dx.doi.org/10.1093/neuonc/noac079.439.

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Abstract INTRODUCTION: Pediatric Medulloblastoma (MB) accounts for approximately 20% of all childhood brain tumors. Molecular subgroups namely WNT, SHH, Group 3 and Group 4, exhibit divergent biology, and clinical outcomes. DNA methylation analysis is a robust option to classify pediatric MB into molecular subgroups, which allows the optimization of diagnosis and stratification of the treatment. We review the first experience of molecular subclassification carried out at the national level in our country. METHODS: Multi-center centralized prospective and retrospective study of frozen tumor samples at diagnosis from pediatric MB patients diagnosed in Spanish hospitals, from April 2021 to December 2021. A registry was created with histology review, immunohistochemical (IHC) subgrouping, and a molecular subgrouping based on the Minimal Methylation Classifier (MIMIC) from Schwalbe et al., 2017. The time from the sample centralized reception to the study result was also collected. RESULTS: 25 frozen MB tumor samples from patients at diagnosis were included. 6 were retrospective and 19 prospective. IHC classified 19 cases (76%) as non-WNT/non-SHH MBs, 3 (12%) as WNT-activated and 3 (12%) as SHH-activated. MIMIC classified, in the non-WNT/non-SHH, 6 tumors (24%) as Group 3 and 13 (52%) as Group 4. 2 cases (8%) were WNT-activated MBs and 3 (12%) were SHH-activated MBs. Only 1 case (4%) was unclassified by MIMIC (WNT using IHC). Comparing both methods (IHC and MIMIC), diagnosis agreed in 96% of cases. The response time ranged from 5 to 10 days. CONCLUSIONS: DNA methylation profiling has proven to be a robust and quick option to classify MB into subgroups and it correlates with the IHC diagnosis. This tool was successfully implemented in our national routine diagnosis, enabling a reliable and rapid molecular subgrouping classification.

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Hirani, Samina, Aswani Thurlapati, Shahzeem Bhayani, Adam Greer, and Glenn Mills. "PATH-43. TARGETED GENOMIC ANALYSIS OF PRIMARY ADULT BRAIN TUMORS: A SINGLE INSTITUTIONAL EXPERIENCE OF 26 CASES." Neuro-Oncology 22, Supplement_2 (November 2020): ii173—ii174. http://dx.doi.org/10.1093/neuonc/noaa215.723.

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Abstract BACKGROUND In the United States, primary brain tumors comprise 2% of all cancers with an alarming 5-year survival rate of 35% [1,2]. To aid histopathological diagnosis, genomic profiling of tumors using next generation sequencing (NGS) has allowed more accurate molecular subclassification of tumors and identification of novel, subtype specific therapies in management [3]. We present single institutional data of molecular gene profiles in patients with adult primary brain tumors. Understanding the genetic landscape of these tumors will help determine molecular targets for currently available drugs and open new avenues for further drug development [4]. METHODS We retrospectively reviewed all primary brain tumor patients seen at LSU Health Science Center Shreveport who underwent NGS from July, 2017 to July, 2019. After reviewing a total of 44 patient charts, 26 were identified to have all the data required for our analysis. Standardized genetic sequencing reports made by molecular pathologists were used. Genetic mapping was performed and descriptive analysis was done using Statistical Analysis System (SAS). RESULTS Demographic characteristics of our patients consist of 50% males, 15% African American with an average age of diagnosis 59. The histopathological types diagnosed include 65% glioblastoma-multiforme and others comprising of gliosarcoma, pilocytic astrocytoma, ganglioma, oligodendroglioma, anaplastic oligodendroglioma, anaplastic ganglioma, meningioma, hemangiopericytoma, and astroblastoma. In 23% of patients, the results of gene sequencing in addition to histopathology impacted the clinical management by improving subclassification and in identifying targetable mutations. 42% of our patients found to have targetable mutations. CONCLUSION In the era of precision oncology, targeted gene sequencing of adult primary brain tumors may aid in diagnosis beyond histopathology by identifying the genetic landscape of tumors and molecular targets for individualized therapies.

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Cammer, Stephen A., Brian T. Hoffman, Jeffrey A. Speir, Mary A. Canady, Melanie R. Nelson, Stacy Knutson, Marijo Gallina, Susan M. Baxter, and Jacquelyn S. Fetrow. "Structure-based Active Site Profiles for Genome Analysis and Functional Family Subclassification." Journal of Molecular Biology 334, no. 3 (November 2003): 387–401. http://dx.doi.org/10.1016/j.jmb.2003.09.062.

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Conroy, Siobhan, Frank A. E. Kruyt, Justin V. Joseph, Veerakumar Balasubramaniyan, Krishna P. Bhat, Michiel Wagemakers, Roelien H. Enting, Annemiek M. E. Walenkamp, and Wilfred F. A. den Dunnen. "Subclassification of Newly Diagnosed Glioblastomas through an Immunohistochemical Approach." PLoS ONE 9, no. 12 (December 29, 2014): e115687. http://dx.doi.org/10.1371/journal.pone.0115687.

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Sean, Brown, Hall Caitlin, Galliera Raffaele, and Bagui Sikha. "Object Detection and Ship Classification Using YOLOv5." BOHR International Journal of Computer Science 1, no. 1 (2021): 124–33. http://dx.doi.org/10.54646/bijcs.017.

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Using a public dataset of images of maritime vessels provided by Analytics Vidhya, manual annotations were made on a subsample of images with Roboflow using the ground truth classifications provided by the dataset. YOLOv5, a prominent open source family of object detection models that comes with an out-of-the-box pre-training on the Common Objects in Context (COCO) dataset, was used to train on annotations of subclassifications of maritime vessels. YOLOv5 provides significant results in detecting a boat. The training, validation, and test set of images trained YOLOv5 in the cloud using Google Colab. Three of our five subclasses, namely, cruise ships, ROROs (Roll On Roll Off, typically car carriers), and military ships, have very distinct shapes and features and yielded positive results. Two of our subclasses, namely, the tanker and cargo ship, have similar characteristics when the cargo ship is unloaded and not carrying any cargo containers. This yielded interesting misclassifications that could be improved in future work. Our trained model resulted in the validation metric of mean Average Precision (mAP@.5) of 0.932 across all subclassification of ships.

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Gilks, C. Blake. "Subclassification of Ovarian Surface Epithelial Tumors Based on Correlation of Histologic and Molecular Pathologic Data." International Journal of Gynecological Pathology 23, no. 3 (July 2004): 200–205. http://dx.doi.org/10.1097/01.pgp.0000130446.84670.93.

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Hori, Yoshifumi, Hidetaka Yamamoto, Yui Nozaki, Takehiro Torisu, Minako Fujiwara, Kenichi Taguchi, Kenichi Nishiyama, Shotaro Nakamura, Takanari Kitazono, and Yoshinao Oda. "Colorectal diffuse large B-cell lymphoma: molecular subclassification and prognostic significance of immunoglobulin gene translocation." Human Pathology 96 (February 2020): 67–78. http://dx.doi.org/10.1016/j.humpath.2019.09.003.

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Lindberg, Olof E., Per Östberg, Bram Zandbelt, Johanna Öberg, Yi Zhang, Lisa Botes, Christian Andersen, Nenad Bogdanovic, and Lars-Olof Wahlund. "O3-06-04: Morphometric subclassification of frontotemporal lobar degeneration." Alzheimer's & Dementia 4 (July 2008): T171. http://dx.doi.org/10.1016/j.jalz.2008.05.453.

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Jaffe, Elaine S., Paul M. Barr, and Sonali M. Smith. "Understanding the New WHO Classification of Lymphoid Malignancies: Why It's Important and How It Will Affect Practice." American Society of Clinical Oncology Educational Book, no. 37 (May 2017): 535–46. http://dx.doi.org/10.1200/edbk_175437.

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Improved delineation of lymphoid malignancy biology has prompted refinement of the 2008 World Health Organization (WHO) classification of hematopoietic and lymphoid tumors with a new framework introduced in 2016. This knowledge has provided valuable insights regarding management. Early clonal proliferations have been set apart given their limited potential for malignant dissemination. Increasing knowledge of molecular drivers of aggressive lymphomas has allowed subclassification and opportunity for clinical investigations to personalize therapy. New insights into T-cell pathophysiology has allowed grouping based on shared molecular and cellular features. This article will summarize the key changes in terms of diagnosis and histopathologic definitions, the impact of these changes on clinical management, and the challenges of future research in this field.

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D’Arcy, Colleen E., Liana Figueiredo Nobre, Anthony Arnaldo, Vijay Ramaswamy, Michael D. Taylor, Lili Naz-Hazrati, and Cynthia E. Hawkins. "Immunohistochemical and nanoString-Based Subgrouping of Clinical Medulloblastoma Samples." Journal of Neuropathology & Experimental Neurology 79, no. 4 (January 30, 2020): 437–47. http://dx.doi.org/10.1093/jnen/nlaa005.

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Abstract The diagnosis of medulloblastoma incorporates the histologic and molecular subclassification of clinical medulloblastoma samples into wingless (WNT)-activated, sonic hedgehog (SHH)-activated, group 3 and group 4 subgroups. Accurate medulloblastoma subclassification has important prognostic and treatment implications. Immunohistochemistry (IHC)-based and nanoString-based subgrouping methodologies have been independently described as options for medulloblastoma subgrouping, however have not previously been directly compared. We describe our experience with nanoString-based subgrouping in a clinical setting and compare this with our IHC-based results. Study materials included FFPE tissue from 160 medulloblastomas. Clinical data and tumor histology were reviewed. Immunohistochemical-based subgrouping using β-catenin, filamin A and p53 antibodies and nanoString-based gene expression profiling were performed. The sensitivity and specificity of IHC-based subgrouping of WNT and SHH-activated medulloblastomas was 91.5% and 99.54%, respectively. Filamin A immunopositivity highly correlated with SHH/WNT-activated subgroups (sensitivity 100%, specificity 92.7%, p < 0.001). Nuclear β-catenin immunopositivity had a sensitivity of 76.2% and specificity of 99.23% for detection of WNT-activated tumors. Approximately 23.8% of WNT cases would have been missed using an IHC-based subgrouping method alone. nanoString could confidently predict medulloblastoma subgroup in 93% of cases and could distinguish group 3/4 subgroups in 96.3% of cases. nanoString-based subgrouping allows for a more prognostically useful classification of clinical medulloblastoma samples.

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Giannikopoulos, Petros, and David M. Parham. "Rhabdomyosarcoma: How Advanced Molecular Methods Are Shaping the Diagnostic and Therapeutic Paradigm." Pediatric and Developmental Pathology 24, no. 5 (June 9, 2021): 395–404. http://dx.doi.org/10.1177/10935266211013621.

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For the past 40 years, progress in rhabdomyosarcoma (RMS) has been focused on understanding its molecular basis and characterizing the mutations that drive its tumorigenesis and progression. Genetic predisposition to RMS has allowed discovery of key genetic pathways and driver mutations. Subclassification of RMS into embryonal (ERMS) and alveolar (ARMS) subtypes has shifted from histology to PAX-FOXO1 fusion status, and new driver mutations have been found in spindle cell RMS. Comprehensive molecular profiling leveraging genome-scale next-generation sequencing (NGS) indicates that the RAS/RAF/PI3K axis is mutated in the majority of ERMS and modulated by downstream effects of PAX-FOXO1 fusions in ARMS. Because of the continued poor outcome of high-risk RMS, a variety of molecular targets have been or are now being tested in current or recent therapy trials. New techniques such as single cell sequencing, spatial multi-omics, and CRISPR/Cas9 genome editing offer potential for further discovery, but a need for clinically annotated specimens persists.

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Scalia, Federica, Antonella Marino Gammazza, Everly Conway de Macario, Alberto J. L. Macario, and Francesco Cappello. "Myelin Pathology: Involvement of Molecular Chaperones and the Promise of Chaperonotherapy." Brain Sciences 9, no. 11 (October 30, 2019): 297. http://dx.doi.org/10.3390/brainsci9110297.

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The process of axon myelination involves various proteins including molecular chaperones. Myelin alteration is a common feature in neurological diseases due to structural and functional abnormalities of one or more myelin proteins. Genetic proteinopathies may occur either in the presence of a normal chaperoning system, which is unable to assist the defective myelin protein in its folding and migration, or due to mutations in chaperone genes, leading to functional defects in assisting myelin maturation/migration. The latter are a subgroup of genetic chaperonopathies causing demyelination. In this brief review, we describe some paradigmatic examples pertaining to the chaperonins Hsp60 (HSPD1, or HSP60, or Cpn60) and CCT (chaperonin-containing TCP-1). Our aim is to make scientists and physicians aware of the possibility and advantages of classifying patients depending on the presence or absence of a chaperonopathy. In turn, this subclassification will allow the development of novel therapeutic strategies (chaperonotherapy) by using molecular chaperones as agents or targets for treatment.

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Bylund, David B., John W. Regan, James E. Faber, J. Paul Hieble, Christopher R. Triggle, and Robert R. Ruffolo Jr. "Vascular α-adrenoceptors: from the gene to the human." Canadian Journal of Physiology and Pharmacology 73, no. 5 (May 1, 1995): 533–43. http://dx.doi.org/10.1139/y95-068.

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Adrenoceptors can be subdivided into three major types, the α1-, α2-, and β-adrenoceptors. Each of these types can be further subdivided into three subtypes, based on pharmacological characteristics. Molecular cloning techniques have supported this subclassification. Recent data now suggest that α-adrenoceptor subtypes identified by pharmacological and molecular techniques correspond well, although species orthologs of several adrenoceptor subtypes have been identified. The secondary structure of the adrenoceptors has been elucidated and correlated with their interaction with second messenger molecules. α1-Adrenoceptors, β-adrenoceptors, and α2-adrenoceptors mediate their actions through stimulation of inositol phosphate release, stimulation of adenylate cyclase, and inhibition of adenylate cyclase, respectively. Site-directed mutagenesis and the preparation of chimeric receptors have located the site of receptor – second messenger interaction to the third intracellular loop for each of these adrenoceptors. While subtypes of each of these classes all interact with the same second messenger, studies with recombinant α2-adrenoceptors show subtype-related differences in receptor – second messenger interaction. Multiple α-adrenoceptor subtypes are expressed in vascular smooth muscle and are involved in various aspects of blood vessel function, including contraction, cellular growth, and proliferation. Various physiological factors can selectively influence responses to a particular subtype, and the relative roles of each subtype can vary between vascular beds and along an individual blood vessel as its caliber changes. Functional studies in blood vessels suggest the presence of additional α-adrenoceptor subtypes not yet identified via molecular techniques. Optimization of the therapeutic profile of an α-adrenoceptor antagonist may be possible via enhancement of selectivity for a particular subtype or by design of a specific profile of affinity for the individual subtypes.Key words: adrenoceptor subclassification, second messenger, G-proteins, vasoconstriction, smooth muscle proliferation.

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Shomaf, Maha, Jamal Masad, Saleh Najjar, and Dana Faydi. "Distribution of breast cancer subtypes among Jordanian women and correlation with histopathological grade: molecular subclassification study." JRSM Short Reports 4, no. 10 (September 13, 2013): 204253331349051. http://dx.doi.org/10.1177/2042533313490516.

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Erdmann, J. I., F. A. L. M. Eskens, C. M. Vollmer, N. F. M. Kok, B. Groot Koerkamp, K. Biermann, and C. H. J. van Eijck. "Histological and Molecular Subclassification of Pancreatic and Nonpancreatic Periampullary Cancers: Implications for (Neo) Adjuvant Systemic Treatment." Annals of Surgical Oncology 22, no. 7 (December 12, 2014): 2401–7. http://dx.doi.org/10.1245/s10434-014-4267-4.

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Tang, Xiaoyu, Yongjun Liu, Amanda Spencer, Setareh Sharzehi, and Negar Rassaei. "77 Balancing Immunohistochemistry for Tumor Subclassification and Tissue Preservation for Molecular Study in Lung Core Biopsies." American Journal of Clinical Pathology 149, suppl_1 (January 2018): S34. http://dx.doi.org/10.1093/ajcp/aqx117.076.

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Fukuoka, Kohei, Jun Kurihara, Makiko Mori, Yuki Arakawa, Ema Yoshioka, Tomoko Shofuda, Yuko Matsushita, et al. "MEDB-30. Subclassification of Group 3/4 medulloblastoma as a potential prognostic biomarker to reduce the dose of craniospinal irradiation in patients with metastatic tumors: A Japanese Pediatric Molecular Neuro-Oncology Group study." Neuro-Oncology 24, Supplement_1 (June 1, 2022): i111. http://dx.doi.org/10.1093/neuonc/noac079.404.

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Abstract BACKGROUND: In patients with medulloblastoma, one of the most significant challenges is to reduce the dose of craniospinal irradiation (CSI) to minimize neurological sequelae in survivors. Molecular characterization of patients treated using lower-dose CSI rather than standard therapy is important for further reducing the treatment burden. METHODS: We conducted DNA methylation analysis using an Illumina Methylation EPIC array to investigate molecular prognostic markers in 38 patients with medulloblastoma who were registered in the Japan Pediatric Molecular Neuro-Oncology Group and were treated using lower-dose CSI rather than standard-dose radiation therapy. RESULTS: Among the patients, 23 were classified as having a “standard-risk” and 15 as having a “high-risk” according to the classic classification based on tumor resection rate and presence of metastasis, respectively. The median follow-up period was 71.5 months. The median CSI dose was 18 Gy in both groups, and 10 patients in the “high-risk” group received a CSI dose of 23.4 Gy or 24 Gy. Molecular subgrouping revealed the “standard-risk” cohort included 5 WNT, 2 SHH, and 16 Group 3/4 cases; all 15 patients in the “high-risk” cohort had Group 3/4 medulloblastoma. Among the patients with Group 3/4 medulloblastoma, 13 of the 16 “standard-risk” patients were subclassified as subtypes I, IV, VI, and VII, which were associated with a good prognosis according to the novel sub-subclassification among Group 3/4 medulloblastomas. However, only 6 of the 15 “high-risk” patients were included in the subtypes. The good prognostic subtype cases among “high-risk” cohort were all survived without recurrence, in contrast to a worse prognosis (5-year progression free survival=33.3%; p=0.01) of the other cases. CONCLUSION: Although these findings require validation in a larger cohort, the present findings suggest that the novel sub-subclassification of Group 3/4 medulloblastoma may be a promising prognostic biomarker for reducing the dose of CSI in patients with metastatic medulloblastoma.

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