Definition Of Subtypes Research Articles

Subtyping of hepatocellular adenoma: a machine learning-based approach.

Subtyping of hepatocellular adenoma (HCA) is an important task in practice as different subtypes may have different clinical outcomes and management algorithms. Definitive subtyping is currently dependent on immunohistochemical and molecular testing. The association between some morphologic/clinical features and HCA subtypes has been reported; however, the predictive performance of these features has been controversial. In this study, we attempted machine learning based methods to select an efficient and parsimonious set of morphologic/clinical features for differentiating a HCA subtype from the others, and then assessed the performance of the selected features in identifying the correct subtypes. We first examined 50 liver HCA resection specimens collected at the University of Washington and Kobe University/Kings College London, including HNF1α-mutated HCA (H-HCA) (n = 16), inflammatory HCA (I-HCA) (n = 20), beta-catenin activated HCA (β-HCA) (n = 8), and unclassified HCA (U-HCA) (n = 6). Twenty-six morphologic/clinical features were assessed. We used LASSO (least absolute shrinkage and selection operator) to select key features that could differentiate a subtype from the others. We further performed SVM (support vector machine) analysis to assess the performance (sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy) of the selected features in HCA subtyping in an independent cohort of liver resection samples (n = 20) collected at the University of Wisconsin-Madison. With some overlap, different combinations of morphologic/clinical features were selected for each subtype. Based on SVM analysis, the selected features classified HCA into correct subtypes with an overall accuracy of at least 80%. Our findings are useful for initial diagnosis and subtyping of HCA, especially in clinical settings without access to immunohistochemical and molecular assays.

Identification of Childhood-Onset Inflammatory Bowel Disease in Swedish Healthcare Registers: A Validation Study.

PurposeThe Swedish National Patient Register (NPR) is often used in observational studies of childhood-onset inflammatory bowel disease (IBD) (<18 years of age) and its subtypes, but the validity of previously used register-based algorithms for capturing childhood-onset IBD has never been examined.MethodsWe identified a random sample of 233 individuals with at least two first ever diagnostic listings of IBD in the NPR between 2002 and 2014. We calculated the test characteristics for different register-based definitions of IBD and its subtypes using the Copenhagen criteria and the revised Porto criteria as gold standard, both based on medical chart review. We made assumptions of the occurrence of undiagnosed IBD in the general child population based on available literature.ResultsOut of 233 individuals with at least two diagnostic listings of IBD, 216 had true IBD, resulting in a positive predictive value (PPV) = 93% (95% confidence interval (CI) 89–96), sensitivity = 88% (95% CI 83–92), specificity = 100% (95% CI 100–100), and negative predictive value (NPV) = 100% (95% CI 100–100). The PPV for the NPR-based definitions of IBD subtypes at time of first IBD diagnosis and at end of follow-up were 78% (95% CI 69–86) and 88% (95% CI 80–94), respectively, for Crohn’s disease and 74% (95% CI 63–83) and 71% (95% CI 60–80), respectively, for ulcerative colitis.ConclusionThe validity of register-based definitions of childhood-onset IBD in the Swedish NPR is high and can be used to identify patients in observational research.

Hemorrhagic Transformation in Acute Ischemic Stroke: A Quantitative Systematic Review.

The prevalence and risk factors of hemorrhagic transformation (HT) after acute ischemic stroke HT have not been adequately delineated. We performed a systematic review and meta-analysis to identify English-language prospective observational MEDLINE and EMBASE-listed reports of acute ischemic stroke with HT published from 1985–2017. Studies that used the ECASS-2 definitions of hemorrhagic transformation subtypes, hemorrhagic infarction (HI), and parenchymal hematoma (PH) were included. Patients treated with intravenous thrombolysis with tissue plasminogen activator (IV-tPA) were compared with those who did not receive thrombolysis. A total of 65 studies with 17,259 patients met inclusion criteria. Overall, HT prevalence was 27%; 32% in patients receiving IV-tPA vs. 20% in those without. Overall PH prevalence was 9%; 12% in IV-tPA treated patients vs. 5% in those without. HT was associated with a history of atrial fibrillation (OR 2.94) and use of anticoagulants (OR 2.47). HT patients had higher NIHSS (Hedge’s-G 0.96) and larger infarct volume (diffusion-weighted MRI, Hedge’s-G 0.8). In IV-tPA treated patients, PH correlated with antiplatelet (OR 3) and statin treatment (OR 4). HT (OR 3) and PH (OR 8) were associated with a poor outcome at 90-day (mRS 5–6). Hemorrhagic transformation is a frequent complication of acute ischemic stroke and is associated with poor outcome. Recognition of risk factors for HT and PH may reduce their incidence and severity.

Molecular profiling leading to personalized treatment in breast cancer

SummaryWith the development of various targeted therapies in breast cancer, detection of biomarkers for predicting treatment efficacy gains importance. With the definition of intrinsic subtypes, breast cancer has paved the way for personalized treatment. Already known and recently recognized targets play an important role both in metastatic and early breast cancer. As a result, early molecular profiling is becoming a part of early diagnostic work-up. Notwithstanding, further treatment targets and agents are needed, particularly in the most aggressive subtype encompassing heterogenous diseases under the group of triple negative breast cancer.

Concordance of Alzheimer's Disease Subtypes Produced from Different Representative Morphological Measures: A Comparative Study.

Background: Gray matter (GM) density and cortical thickness (CT) obtained from structural magnetic resonance imaging are representative GM morphological measures that have been commonly used in Alzheimer’s disease (AD) subtype research. However, how the two measures affect the definition of AD subtypes remains unclear. Methods: A total of 180 AD patients from the ADNI database were used to identify AD subgroups. The subtypes were identified via a data-driven strategy based on the density features and CT features, respectively. Then, the similarity between the two features in AD subtype definition was analyzed. Results: Four distinct subtypes were discovered by both density and CT features: diffuse atrophy AD, minimal atrophy AD (MAD), left temporal dominant atrophy AD (LTAD), and occipital sparing AD. The matched subtypes exhibited relatively high similarity in atrophy patterns and neuropsychological and neuropathological characteristics. They differed only in MAD and LTAD regarding the carrying of apolipoprotein E ε2. Conclusions: The results verified that different representative morphological GM measurement methods could produce similar AD subtypes. Meanwhile, the influences of apolipoprotein E genotype, asymmetric disease progression, and their interactions should be considered and included in the AD subtype definition. This study provides a valuable reference for selecting features in future studies of AD subtypes.

Brave New World: Harnessing the promise of biomarkers to help solve the epigenetic puzzle

The term "schizophrenia" should be used to refer to a group of neurodevelopmentally mediated cognitive disorders. Although these disorders may have different etiologies, the final causal pathway is characterized by a cognitive triad of (working memory, corollary discharge, and inhibition) deficits. Biomarkers can be used to lend insights regarding underlying pathophysiology, and lead to more precise definitions of schizophrenia subtypes. Use of endophenotypes (i.e., traitlike heritable markers of liability) that are specific to schizophrenia would aid us in our search for these biomarkers by providing a means to classify more homogeneous groups of schizophrenia patients.

Automatic Mapping and Characterisation of Linear Depositional Bedforms: Theory and Application Using Bathymetry from the North West Shelf of Australia

Bedforms are key components of Earth surfaces and yet their evaluation typically relies on manual measurements that are challenging to reproduce. Several methods exist to automate their identification and calculate their metrics, but they often exhibit limitations where applied at large scales. This paper presents an innovative workflow for identifying and measuring individual depositional bedforms. The workflow relies on the identification of local minima and maxima that are grouped by neighbourhood analysis and calibrated using curvature. The method was trialed using a synthetic digital elevation model and two bathymetry surveys from Australia’s northwest marine region, resulting in the identification of nearly 2000 bedforms. The comparison of the metrics calculated for each individual feature with manual measurements show differences of less than 10%, indicating the robustness of the workflow. The cross-comparison of the metrics resulted in the definition of several sub-types of bedforms, including sandwaves and palaeoshorelines, that were then correlated with oceanic conditions, further corroborating the validity of the workflow. Results from this study support the idea that the use of automated methods to characterise bedforms should be further developed and that the integration of automated measurements at large scales will support the development of new classification charts that currently rely solely on manual measurements.

SRIQ clustering: A fusion of Random Forest, QT clustering, and KNN concepts

Gene expression profiling together with unsupervised analysis methods, typically clustering methods, has been used extensively in cancer research to unravel, e.g., new molecular subtypes that hold promise of disease refinement that may ultimately benefit patients. However, many of the commonly used methods require a prespecified number of clusters to extract and frequently require some type of feature pre-selection, e.g. variance filtering. This introduces subjectivity to the process of cluster discovery and the definition of putative novel tumor subtypes. Here, we introduce SRIQ, a novel unsupervised clustering method that could circumvent some of the issues in commonly used unsupervised analysis methods. SRIQ incorporates concepts from random forest machine learning as well as quality threshold- and k-nearest neighbor clustering. It is implemented as a Java and Python pipeline including data pre-processing, differential expression analysis, and pathway analysis. Using 434 lung adenocarcinomas profiled by RNA sequencing, we demonstrate the technical reproducibility of SRIQ and benchmark its performance compared to the commonly used consensus clustering method. Based on differential gene expression analysis and auxiliary molecular data we show that SRIQ can define new tumor subsets that appear biologically relevant and consistent compared and that these new subgroups seem to refine existing transcriptional subtypes that were defined using consensus clustering. Together, this provides support that SRIQ may be a useful new tool for unsupervised analysis of gene expression data from human malignancies.

Residual cancer burden after neoadjuvant chemotherapy and long-term survival outcomes in breast cancer: a multicentre pooled analysis of 5161 patients.

SummaryBackgroundPrevious studies have independently validated the prognostic relevance of residual cancer burden (RCB) after neoadjuvant chemotherapy. We used results from several independent cohorts in a pooled patient-level analysis to evaluate the relationship of RCB with long-term prognosis across different phenotypic subtypes of breast cancer, to assess generalisability in a broad range of practice settings.MethodsIn this pooled analysis, 12 institutes and trials in Europe and the USA were identified by personal communications with site investigators. We obtained participant-level RCB results, and data on clinical and pathological stage, tumour subtype and grade, and treatment and follow-up in November, 2019, from patients (aged ≥18 years) with primary stage I–III breast cancer treated with neoadjuvant chemotherapy followed by surgery. We assessed the association between the continuous RCB score and the primary study outcome, event-free survival, using mixed-effects Cox models with the incorporation of random RCB and cohort effects to account for between-study heterogeneity, and stratification to account for differences in baseline hazard across cancer subtypes defined by hormone receptor status and HER2 status. The association was further evaluated within each breast cancer subtype in multivariable analyses incorporating random RCB and cohort effects and adjustments for age and pretreatment clinical T category, nodal status, and tumour grade. Kaplan-Meier estimates of event-free survival at 3, 5, and 10 years were computed for each RCB class within each subtype.FindingsWe analysed participant-level data from 5161 patients treated with neoadjuvant chemotherapy between Sept 12, 1994, and Feb 11, 2019. Median age was 49 years (IQR 20–80). 1164 event-free survival events occurred during follow-up (median follow-up 56 months [IQR 0–186]). RCB score was prognostic within each breast cancer subtype, with higher RCB score significantly associated with worse event-free survival. The univariable hazard ratio (HR) associated with one unit increase in RCB ranged from 1·55 (95% CI 1·41–1·71) for hormone receptor-positive, HER2-negative patients to 2·16 (1·79–2·61) for the hormone receptor-negative, HER2-positive group (with or without HER2-targeted therapy; p<0·0001 for all subtypes). RCB score remained prognostic for event-free survival in multivariable models adjusted for age, grade, T category, and nodal status at baseline: the adjusted HR ranged from 1·52 (1·36–1·69) in the hormone receptor-positive, HER2-negative group to 2·09 (1·73–2·53) in the hormone receptor-negative, HER2-positive group (p<0·0001 for all subtypes).InterpretationRCB score and class were independently prognostic in all subtypes of breast cancer, and generalisable to multiple practice settings. Although variability in hormone receptor subtype definitions and treatment across patients are likely to affect prognostic performance, the association we observed between RCB and a patient’s residual risk suggests that prospective evaluation of RCB could be considered to become part of standard pathology reporting after neoadjuvant therapy.

Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study.

Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy. Unsupervised learning of emphysema subtypes on computed tomography (CT) opens the way to new definitions of emphysema subtypes and eliminates the need of thorough manual labeling. However, CT-based emphysema subtypes have been limited to texture-based patterns without considering spatial location. In this work, we introduce a standardized spatial mapping of the lung for quantitative study of lung texture location and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs) that represent novel emphysema subtype candidates. Exploiting two cohorts of full-lung CT scans from the MESA COPD (n = 317) and EMCAP (n = 22) studies, we first show that our spatial mapping enables population-wide study of emphysema spatial location. We then evaluate the characteristics of the sLTPs discovered on MESA COPD, and show that they are reproducible, able to encode standard emphysema subtypes, and associated with physiological symptoms.

Renewing an old interest: Pituitary folliculostellate cells.

Anterior pituitary folliculostellate (FS) cells, first described almost 50years ago, have a wide range of functions with respect to supporting and coordinating endocrine cell function, in particular through paracrine and gap junction-mediated signalling. Our previous studies identified the morphological organisation of FS cells, which mediates coordinated calcium activity throughout the homotypic FS network and allows signalling across the whole pituitary gland. It is also clear that FS cells can modify endocrine output and feedback on pituitary axes over a range of timescales. Recently, several studies have defined FS cells as a source of anterior pituitary endocrine cell renewal, which has resulted in a renaming of FS cells as "Sox2+ve stem cells". Here, we highlight the broader potential of the FS cell population in fine-tuning and coordinating pituitary axes function. In addition, we identify a need for: the definition of the possible subtypes of FS cell and their relationship with the stem cell population; the potential role of FS cells in pulsatile hormone secretion and coordination of heterotypic cell networks; and the roles that FS cells may play in both early-life programming of pituitary axes and in memory, or anticipation, of demand. Further studies of FS cells may demonstrate the fundamental importance of this cell type and its potential as a therapeutic target to correct pituitary gland dysfunction, one of which is stem cell therapy. Clearly, a thorough understanding of all of these interactions and relationships of FS and endocrine cells is required whatever therapeutic use is suggested by their various roles.

Transcriptional and anatomical diversity of medium spiny neurons in the primate striatum

Medium spiny neurons (MSNs) constitute the vast majority of striatal neurons and the principal interface between dopamine reward signals and functionally diverse cortico-basal ganglia circuits. Information processing in these circuits is dependent on distinct MSN types: cell types that are traditionally defined according to their projection targets or dopamine receptor expression. Single-cell transcriptional studies have revealed greater MSN heterogeneity than predicted by traditional circuit models, but the transcriptional landscape in the primate striatum remains unknown. Here, we set out to establish molecular definitions for MSN subtypes in Rhesus monkeys and to explore the relationships between transcriptionally defined subtypes and anatomical subdivisions of the striatum. Our results suggest at least nine MSN subtypes, including dorsal striatum subtypes associated with striosome and matrix compartments, ventral striatum subtypes associated with the nucleus accumbens shell and olfactory tubercle, and an MSN-like cell type restricted to μ-opioid receptor rich islands in the ventral striatum. Although each subtype was demarcated by discontinuities in gene expression, continuous variation within subtypes defined gradients corresponding to anatomical locations and, potentially, functional specializations. These results lay the foundation for achieving cell-type-specific transgenesis in the primate striatum and provide a blueprint for investigating circuit-specific information processing.

Molecular Targets for Gastric Cancer Treatment and Future Perspectives from a Clinical and Translational Point of View.

Simple SummaryDuring the last several years, gastric cancer has been extensively studied on the molecular level and distinct molecular subtypes have been defined accordingly. Here, we review the current data on targeted treatment approaches for advanced or metastatic gastric cancer based on molecular alterations or immunological features, ranging from clinically approved treatment strategies to more innovative concepts which are currently in preclinical or clinical development. Information on the translational context and methodology is also provided, holding promise for the further improved development of personalized treatment strategies in the future.Gastric cancer is a leading cause of cancer death worldwide. Systemic treatment comprising chemotherapy and targeted therapy is the standard of care in advanced/metastatic gastric cancer. Comprehensive molecular characterization of gastric adenocarcinomas by the TCGA Consortium and ACRG has resulted in the definition of distinct molecular subtypes. These efforts have in parallel built a basis for the development of novel molecularly stratified treatment approaches. Based on this molecular characterization, an increasing number of specific genomic alterations can potentially serve as treatment targets. Consequently, the development of promising compounds is ongoing. In this review, key molecular alterations in gastric and gastroesophageal junction cancers will be addressed. Finally, the current status of the translation of targeted therapy towards clinical applications will be reviewed.

White-Matter Hyperintensity Load and Differences in Resting-State Network Connectivity Based on Mild Cognitive Impairment Subtype.

“Mild cognitive impairment” (MCI) is a diagnosis characterised by deficits in episodic memory (aMCI) or in other non-memory domains (naMCI). Although the definition of subtypes is helpful in clinical classification, it provides little insight on the variability of neurofunctional mechanisms (i.e., resting-state brain networks) at the basis of symptoms. In particular, it is unknown whether the presence of a high load of white-matter hyperintensities (WMHs) has a comparable effect on these functional networks in aMCI and naMCI patients. This question was addressed in a cohort of 123 MCI patients who had completed an MRI protocol inclusive of T1-weighted, fluid-attenuated inversion recovery (FLAIR) and resting-state fMRI sequences. T1-weighted and FLAIR images were processed with the Lesion Segmentation Toolbox to quantify whole-brain WMH volumes. The CONN toolbox was used to preprocess all fMRI images and to run an independent component analysis for the identification of four large-scale haemodynamic networks of cognitive relevance (i.e., default-mode, salience, left frontoparietal, and right frontoparietal networks) and one control network (i.e., visual network). Patients were classified based on MCI subtype (i.e., aMCI vs. naMCI) and WMH burden (i.e., low vs. high). Maps of large-scale networks were then modelled as a function of the MCI subtype-by-WMH burden interaction. Beyond the main effects of MCI subtype and WMH burden, a significant interaction was found in the salience and left frontoparietal networks. Having a low WMH burden was significantly more associated with stronger salience-network connectivity in aMCI (than in naMCI) in the right insula, and with stronger left frontoparietal-network connectivity in the right frontoinsular cortex. Vice versa, having a low WMH burden was significantly more associated with left-frontoparietal network connectivity in naMCI (than in aMCI) in the left mediotemporal lobe. The association between WMH burden and strength of connectivity of resting-state functional networks differs between aMCI and naMCI patients. Although exploratory in nature, these findings indicate that clinical profiles reflect mechanistic interactions that may play a central role in the definition of diagnostic and prognostic statuses.

World Heart Day 2021: COVID-19, digital health, and tackling cardiovascular disease

World Heart Day 2021: COVID-19, digital health, and tackling cardiovascular disease

Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer.

Simple SummaryIt is already known that DNA alterations do not fully recapitulate the complex nature of a tumor or its potential interaction with specific treatments. Therefore, in order to establish more precise and effective therapeutic approaches for non-small cell lung cancer, tumors will have to be characterized in a more accurate and comprehensive way. In this regard, transcription profiling has already demonstrated its utility in further stratifying patients in a much more refined way than genomic alterations. Examples of this include the definition of intrinsic subtypes in colorectal cancer, breast, or non-small cell lung cancer tumors based on their expression patterns. Moreover, the characterization of the activity levels of the pathways involved in tumor progression and development is bound to better predict the specific response to a certain therapy than isolated biomarkers such as specific DNA alterations or the expression of single genes. This is especially relevant in the context of patients not harboring targetable alterations or those developing resistance after treatment. Recent technological advances and the application of high-throughput mutation and transcriptome analyses have improved our understanding of cancer diseases, including non-small cell lung cancer. For instance, genomic profiling has allowed the identification of mutational events which can be treated with specific agents. However, detection of DNA alterations does not fully recapitulate the complexity of the disease and it does not allow selection of patients that benefit from chemo- or immunotherapy. In this context, transcriptional profiling has emerged as a promising tool for patient stratification and treatment guidance. For instance, transcriptional profiling has proven to be especially useful in the context of acquired resistance to targeted therapies and patients lacking targetable genomic alterations. Moreover, the comprehensive characterization of the expression level of the different pathways and genes involved in tumor progression is likely to better predict clinical benefit from different treatments than single biomarkers such as PD-L1 or tumor mutational burden in the case of immunotherapy. However, intrinsic technical and analytical limitations have hindered the use of these expression signatures in the clinical setting. In this review, we will focus on the data reported on molecular classification of non-small cell lung cancer and discuss the potential of transcriptional profiling as a predictor of survival and as a patient stratification tool to further personalize treatments.

Thymomas: Analysis of Histological Subtypes and Staging in Patients with Surgical Treatment in Two Reference Centers in Argentina

Background: Thymomas are a heterogeneous group of tumors which represent the most frequent tumor of the anterior mediastinum. Aims: To describe the clinical, histological, surgical and oncological characteristics of a cohort of patients with a diagnosis of thymoma surgically treated in two centers in Argentina and to evaluate the possibility of retrospectively implementing the 8th edition of TNM staging. Materials and Methods: 180 patients with thymoma surgically treated over a period of 41 years were studied. The following variables were analysed: age, sex, presence of myasthenia gravis at diagnosis, Masaoka staging (1994), TNM staging of thymus tumors, Histological classification (WHO 2015), neoadjuvant treatment with chemotherapy, post-operative radiation treatment and clinical evolution of myasthenia gravis defined according to the modified Osserman classification. Results: 96 men and 84 women were analysed. Median age 51 years (range 13-85). 85% of the patients analysed came from the public sphere. When analysing the institutional distribution by Masaoaka-Koga stage and TNM, a higher proportion of stages I was observed for both staging systems. Most myasthenic patients belonged to the WHO B2 histological classification (49%, p=0.04) and 15 patients received neoadjuvant treatment prior to surgery to improve the chances of resection, most of them classified as stages III of Masaoka (p=0.002) or IIIa of the TNM stage (p=0.001). 74 (46%) cases received post-operative RT when they presented Masaoka Koga stages II (p=0.000) and IIIa or more advanced TNM staging (p=0.000). 76% of the patients presented remission or stability of symptoms after surgical treatment and only 3/6 died due to myasthenic crisis in the immediate post-operative period. Conclusion: As reported in the literature, we have observed a higher frequency of B2 thymomas and their association with Myasthenia gravis. The histological criteria of the WHO 2015 classification, based on the ITMIG recommendations, favour precision in the definition of subtypes. The retrospective implementation of the 8th edition of TNM staging highlights the need to standardize protocols for pathological and surgical studies.

European guidelines (S3) on diagnosis and management of mucous membrane pemphigoid, initiated by the European Academy of Dermatology and Venereology - Part I.

This guideline on mucous membrane pemphigoid (MMP) has been elaborated by the Task Force for Autoimmune Blistering Diseases of the European Academy of Dermatology and Venereology (EADV) with a contribution of physicians from all relevant disciplines and patient organizations. It is a S3 consensus‐based guideline encompassing a systematic review of the literature until June 2019 in the MEDLINE and EMBASE databases. This first part covers methodology, the clinical definition of MMP, epidemiology, MMP subtypes, immunopathological characteristics, disease assessment and outcome scores. MMP describes a group of autoimmune skin and mucous membrane blistering diseases, characterized by a chronic course and by predominant involvement of the mucous membranes, such as the oral, ocular, nasal, nasopharyngeal, anogenital, laryngeal and oesophageal mucosa. MMP patients may present with mono‐ or multisite involvement. Patients’ autoantibodies have been shown to be predominantly directed against BP180 (also called BPAG2, type XVII collagen), BP230, laminin 332 and type VII collagen, components of junctional adhesion complexes promoting epithelial stromal attachment in stratified epithelia. Various disease assessment scores are available, including the Mucous Membrane Pemphigoid Disease Area Index (MMPDAI), the Autoimmune Bullous Skin disorder Intensity Score (ABSIS), the ‘Cicatrising Conjunctivitis Assessment Tool’ and the Oral Disease Severity Score (ODSS). Patient‐reported outcome measurements (PROMs), including DLQI, ABQOL and TABQOL, can be used for assessment of quality of life to evaluate the effectiveness of therapeutic interventions and monitor disease course.

Abstract 876: TP53 pathway function, estrogen receptor status, and breast cancer risk factors in the Carolina Breast Cancer Study

Abstract Purpose: TP53 and estrogen receptor (ER) both play essential roles in breast cancer development and progression, with recent research revealing crosstalk between TP53 and ER signaling pathways. While many studies have demonstrated heterogeneity of risk factor associations across ER subtypes, TP53 status has been inconsistently linked to breast cancer risk factors. This may be due to few studies evaluating TP53 effects in the context of ER. Additionally, studies have generally classified TP53 status using immunohistochemistry (IHC) staining or DNA sequencing, which are prone to misclassification of TP53 pathway function. RNA-based methods of measuring TP53 pathway activity may reduce misclassification and clarify etiologic associations. Methods: This case-only analysis included 4,466 incident breast cancer cases from the Carolina Breast Cancer Study (1993-2013). Using RNA expression previously quantified using NanoString assays on FFPE tumor samples, tumors were classified for TP53 functional status (mutant-like or wildtype-like) using a validated 52-gene RNA signature. We used a two-stage polytomous logistic regression model to evaluate sources of risk factor heterogeneity due to RNA-based TP53 or ER, adjusting for each other and for PR, HER2, and grade, while accounting for missing tumor marker data. Risk factor heterogeneity was also evaluated for IHC-based TP53 and ER. For each risk factor, joint effects of TP53 and ER were evaluated by allowing for an interaction between the two markers. Results: When classifying TP53 status using the RNA signature, the effects of several hormone-related factors (oral contraceptive use, menopausal status, age at menopause, and pre- and post-menopausal BMI) were heterogeneous across TP53 subtypes, while heterogeneity of reproductive factors (age at first birth and parity) and smoking status was observed across ER subtypes. The effect of number of births was heterogenous across both TP53 and ER-based subtype definitions. Additionally, we observed an interaction between RNA-based TP53 and ER status with family history of breast cancer in a first-degree relative (p=0.05). When classifying TP53 status using IHC, the TP53 effects were not recapitulated but there was minimal change in the ER effects. We observed an interaction between IHC-based TP53 and ER status with age at menarche (p=0.02), smoking status (p=0.05), and alcohol use (p=0.06). Conclusions: This study demonstrates that TP53 and ER are valuable for defining etiologic subtypes of breast cancer, although IHC measures may not have as much value as RNA. Analyses of the joint effects of TP53 and ER with breast cancer risk factors revealed intriguing findings that could lead to new hypotheses. However, larger studies incorporating multiple correlated tumor characteristics will be required to confirm our findings to conclusively define etiologically relevant subtypes of breast cancer. Citation Format: Amber N. Hurson, Mustapha Abubakar, Kathleen Conway-Dorsey, Katherine Hoadley, Michael I. Love, Andrew F. Olshan, Montserrat Garcia-Closas, Melissa A. Troester. TP53 pathway function, estrogen receptor status, and breast cancer risk factors in the Carolina Breast Cancer Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 876.

Abstract 196: Redefining cancer subtypes using multi-omics and deep learning

Abstract Cancer is a heterogeneous collection of diseases traditionally classified by the tissue of origin. The diversity of the molecular profiles of cancers has a big impact on the way patients are diagnosed and treated, how they respond to their prescribed treatments, the duration of survival after diagnosis, and factors such as remission, recurrence, or spread (metastasis) of the disease. While such diagnostic and prognostic outcomes are potentially predictable by taking a closer look into the changes of the genome, epigenome, transcriptome, proteome, and various other omics platforms, the contemporary cancer treatments still predominantly don't make the best use of such multi-omics profiling of patient samples. Therefore, multi-omics profiling of cancers holds great potential to define a molecularly coherent subtype definition of cancers in order to achieve the eventual goal of matching the best possible treatment to the subgroup of patients. However, the current subtypes from consortiums such as TCGA have been defined by heterogeneous methods and molecular markers by different teams. A subset of these studies have not attempted to characterize molecular subtypes, but rather taken histopathologically defined subtypes as the gold standard and tried to characterize molecular features of these subtypes. Here we evaluate TCGA cancer subtypes based on the molecular profile coherence score. This novel metric combines survival statistics, pathways information, tumor purity estimates, and mutational signatures. We expect that subtypes that are patient subgroups should display molecular signature homogeneity. We evaluate TCGA subtypes from 21 cancers using these criteria and compare the subtypes with our own definition using multi-omics data in a deep learning framework. We have refined the several subtypes from multiple cancers towards more molecularly coherent patient subgroups. Citation Format: Altuna Akalin, Bora Uyar, Jonathan Ronen, Vedran Franke. Redefining cancer subtypes using multi-omics and deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 196.