551 research outputs found
Molekulargenetische Charakterisierung von Sarkomen zur Identifizierung prognostischer Risikogruppen und potentieller therapeutischer Angriffspunkte
Sarkome sind seltene Tumore, die sich durch eine erhebliche HeterogenitÀt auf
histologischer, molekularer und genetischer Ebene auszeichnen. Trotz aller Fortschritte
in der modernen Krebsbehandlung haben Sarkom-Patienten im fortgeschrittenen
Stadium weiterhin begrenzte therapeutische Möglichkeiten und eine
ungĂŒnstige Prognose. Da die Untersuchung des genetischen Profils nicht nur die
Identifizierung prognostischer, sondern auch therapierelevanter VerÀnderungen
bei heterogenen Erkrankungen ermöglicht, sind genetische Analysen ein unverzichtbarer
Bestandteil der modernen Krebsbehandlung geworden.
In dieser Studie analysierten wir retrospektiv das genetische Profil einer real-life
Kohorte von 53 Sarkom-Patienten anhand eines 720-Gen-Panels.
In Anbetracht der HeterogenitÀt von Sarkomen wurden mehrere histopathologische
Subtypen analysiert, wobei das Leiomyosarkom (17 %) am hÀufigsten vorkam.
Das Durchschnittsalter der Patienten zum Zeitpunkt der Analyse betrug 49
Jahre. Die durchschnittliche Zeitspanne von der Erstdiagnose bis zur genetischen
Analyse betrug 46,8 Monate. Das GesamtĂŒberleben betrug im Durchschnitt
55,9 Monate.
Jeder Patient erhielt eine Tumorgenomsequenzierung mit einem 720-Gene-Panel.
Bei 76,9% der Patienten wurde ein niedriger TMB-Wert festgestellt. Keiner
der Patienten wurde als mikrosatelliteninstabil identifiziert. 25% der Patienten
wiesen einen Mangel an der FunktionalitÀt der homologen Rekombination (HRD)
auf. Bei 30,8% wurde ein Fusionsgen nachgewiesen, wobei EWSR1-FLI1 und
EWSR1- WT1 am hÀufigsten waren. Insgesamt wurden 38 KopienzahlverÀnderungen
(CNAs) gefunden, was auf eine erhebliche genomische InstabilitÀt hinweist.
Bei 15 Patienten wurden Keimbahnmutationen gefunden, die alle behandlungsrelevant
sind, wobei die Mutation im MUTYH-Gen die hÀufigste ist. Therapierelevante
somatische Mutationen wurden bei 47 Patienten gefunden (3,2 Mutationen/
Patient). Die am hÀufigsten betroffenen Gene waren TP53, CDKN2A-C,
CDK4, RB1 und ATRX.
93
Auf der Grundlage der NGS-Ergebnisse erhielten 39,6 % der Patienten eine personalisierte
Antitumortherapie. Das mediane GesamtĂŒberleben (OS) der Patienten
mit einer gemÀà den Daten der NGS-Analyse ausgerichteten Behandlung
betrug 43 gegenĂŒber 33 Monaten bei Patienten ohne zielgerichtete Therapien.
Unsere NGS-Daten aus einer heterogenen Kohorte von 53 Sarkom-Patienten
deuten darauf hin, dass personalisierte Therapien, die auf den Ergebnissen einer
720 Gen-Panel-Sequenzierung basieren, zu verbesserten klinischen Ergebnissen
bei Sarkom-Patienten fĂŒhren könnten
CANCER TREATMENT BY TARGETING HDAC4 TRANSLOCATION INDUCED BY MICROSECOND PULSED ELECTRIC FIELD EXPOSURE: MECHANISTIC INSIGHTS THROUGH KINASES AND PHOSPHATASES
Epigenetic modifications, arising from sub-cellular shifts in histone deacetylase (HDAC) activity and localization, present promising strategies for diverse cancer treatments. HDACs, enzymes responsible for post-translational histone modifications, induce these epigenetic changes by removing acetyl groups from Δ-N-acetyl-lysine residues on histones, thereby suppressing gene transcription. Within the HDAC group, class IIa HDACs are notable for their responsiveness to extracellular signals, bridging the gap between external stimuli, plasma membrane, and genome through nuclear-cytoplasmic translocation. This localization offers two significant mechanisms for cancer treatment: nuclear accumulation of HDACs represses oncogenic transcription factors, such as myocyte-specific enhancer factor 2C (MEF2C), triggering various cell death pathways. Conversely, cytoplasmic HDAC accumulation acts similarly to HDAC inhibitors by silencing genes. My dissertation introduces an innovative approach for glioblastoma and breast cancer treatment by investigating the application of microsecond pulsed electric fields. It particularly focuses on HDAC4, a class IIa HDAC overexpressed in these cancers. Beyond demonstrating HDAC4 translocation, my research delves into the intricate roles of kinases and phosphatases, shedding light on the underlying factors governing HDAC4 translocation
Idiopathic inflammatory myopathies and cancer : familial risk, genetics and consequences
Idiopathic inflammatory myopathies (IIMs) are a group of rare rheumatic inflammatory
diseases (RIDs), characterised by a diverse range of clinical, serological and
histopathological characteristics, with muscle weakness as a shared hallmark. While
advancements in disease management have improved the survival rates of patients with IIM,
the mortality rate among patients with IIM is still higher than the general population, mainly
due to association with comorbidities such as cancer. The pathogenesis of IIM, the
pathological link between IIM and cancer and the impact of cancer on the survival of patients
with IIM remain a subject of uncertainty. The rarity and heterogeneity inherent in IIM pose
significant challenges in filing these knowledge gaps. This thesis encompasses five studies,
which aimed at addressing research questions concerning the genetic contribution to IIM and
its link with other autoimmune diseases and cancer, as well as the disease burden in the
context of cancer in a large representative population of patients with IIM.
Study I was a population-based case-control family study including 7,615 first-degree
relatives of 1,620 patients with IIM diagnosis between 1997 and 2016 and 37,309 first-degree
relatives of 7,797 matched comparators without IIM. Patients with IIM were four times more
likely to have at least one first-degree relative affected by IIM compared to matched
comparators without IIM. The heritability of IIM, a proportion of the phenotypic variance
that can be explained by additive genetic variance, was 22% in the Swedish population.
Study II, with the same study population as in Study I, analysed the familial associations
between IIM and a variety of autoimmune diseases under a causal framework. We found
shared familial factors between IIM and other RIDs, inflammatory bowel diseases,
autoimmune thyroid diseases and celiac disease.
Study III, with a similar study population and analytical approach as in Study II,
comprehensively investigated the familial co-aggregation of IIM and cancer. We did not
observe a familial association between IIM and cancer overall but modification effect by sex
was noted: there was a modest familial association (adjusted odds ratio=1.39) with cancer in
male first-degree relatives of patients with IIM. We also found that offspring of patients with
IIM were more likely to have a cancer diagnosis at age younger than 50 years compared to
those of matched comparators without IIM. In the exploratory analysis by specific cancer
types, findings suggest that IIM shared familial factors with myeloid malignancies and liver
cancer.
Study IV explored genetic correlation between IIM and B-cell lymphomas via a cross-trait
secondary analysis using summary statistics from genome-wide associations studies of IIM
and four common B-cell lymphoma subtypes including diffuse large B-cell lymphoma,
follicular lymphoma, chronic lymphocytic leukaemia and marginal zone lymphoma. We
detected a limited number of genomic loci, predominantly within the human leukocyte
antigen region, demonstrating significant genetic correlations between IIM and common Bcell
lymphoma subtypes.
Study V, a cohort study, followed 1,826 patients to (first and second) cancer and death
(overall and cause-specific death) events since IIM diagnosis for more than 20 years.
Compared to patients with no cancer diagnosis after IIM, patients with a first cancer diagnosis
after IIM faced a greater five-year mortality (22% versus 49%). This excessive risk was due
to an increased risk of death from cancer. In patients with a first cancer diagnosis after IIM,
the one-year risk of having a second primary cancer was 11% and having a second cancer
diagnosis slightly increased the risk of death. We also reported several prognostic factors
associated with increased risks of cancer and death (overall, from cancer and from other
causes).
This thesis offers useful insight into the role of genetics in IIM pathogenesis and its
connections with other autoimmune diseases and cancer, as well as the impact of cancer on
the survival of patients with IIM. The observed familial aggregation of IIM and familial
associations between IIM and other autoimmune diseases suggest genetic involvement in the
development of IIM. Family history of IIM, other RIDs, inflammatory bowel diseases,
autoimmune thyroid diseases and celiac disease may serve as indicators pointing towards an
IIM diagnosis. Missing heritability is suggested by the discrepancy between our family-based
heritability and the SNP-based heritability, implying yet-to-be discovered genetic variants
associated with IIM. The acquired knowledge of shared familial factors between IIM and
other autoimmune diseases may inform future genetic studies aiming to uncover novel IIMassociated
genetic variants. There is a limited shared familial/genetic susceptibility between
IIM and cancer. The human leukocyte antigen region plays an important role in the limited
shared genetic susceptibility between IIM and common B-cell lymphoma subtypes. IIM
concomitant with cancer leads to a substantial increase in mortality, mainly due to cancer.
Future research should focus on reducing cancer-related disease burden in patients with IIM
Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea
ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy actionâs effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (â10% per year) trend after adoption and enforcement of these laws (ÎČ2â=â0.18, p-valueâ=â0.07; ÎČ3â=ââ0.10, p-valueâ=â0.02). SHS exposure at home (ÎČ2â=â0.10, p-valueâ=â0.09; ÎČ3â=ââ0.03, p-valueâ=â0.14) and the primary cigarette smoking rate (ÎČ2â=â0.03, p-valueâ=â0.10; ÎČ3â=â0.008, p-valueâ=â0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK
Multi-omics of AML
Acute myeloid leukemia (AML) is one of the most aggressive hematopoietic malignancies and has been
recognized as a heterogeneous disease due to a lack of unifying characteristics. It is driven by different
genome aberrations, gene expression changes, and epigenomic dysregulations. Therefore a multi-omics
approach is needed to unravel the complex biology of this disease. This thesis deals with the challenges of
identifying driver events that account for differences in clinical phenotypes and responses to treatment.
The work presented here investigates the driver events of AML and epigenetics drug response profiles.
The thesis consists of three main projects. The first study identifies recurrent mutations in AML carrying
t(8;16)(p11;p13), a rare abnormality. The second project is identifying prospective drivers of mutation-
negative nkAML. The third project concentrates on epigenetic changes after AML drugs.
t(8;16) AML is a rare and distinguishable clinicopathological entity. Some previous reports that rep-
resented the characteristics of patients with this type of AML suggest that the t(8;16) translocation
could be sufficient to induce hematopoietic cell transformation to AML without acquiring other genetic
alterations. Therefore here I evaluate the frequently mutated genes and compare them with the most
frequent mutated genes in AML in general and AML carrying t(8;16) translocation. FLT3 mutation was
found in 3 patients of my cohort, a potential target for therapy with tyrosine kinase inhibitors. However,
exciting finding was the mutations in EYS, KRTAP9-1, PSIP1, and SPTBN5 that were depicted earlier
in AML.
Elucidating different layers of aberrations in normal karyotype no-driver acute myeloid leukemia pro-
vides better biology insight and may impact risk-group stratification and new potential driver events.
Therefore, this study aimed to detect such anomalies in samples without known driver genetic abnor-
malities using multi-omic molecular profiling. Samples were analyzed using RNA sequencing (n=43),
whole genome sequencing (n=43), and EPIC DNA methylation array (n=42). In 33 of 43 patients, all
three layers of data were available. I developed a pipeline looking for a driver in any layer of data by
connecting the information of all layers of data and utilizing public genomic, transcriptomic, and clinical
data available from TCGA. Genetic alterations of somatic cells can drive malignant clone formation
and promote leukemogenesis. Therefore I first built a mutation prioritization workflow that checks each
patientâs genomic mutation drivers. Here I use the information on the allele frequency of the specific mu-
tation combining information from WGS and RNA sequencing data. Finally, I compared each mutation
on a positional level with AML and other TCGA cancer cohorts to assess the causative genomic muta-
tions. I found potential driver stopgain mutation in genes implicated in chromosome segregation during
mitosis and some tumor suppressor genes. I found new stopgain mutations in cancer genes (NIPBL
and NF1). Since fusions are increasingly acknowledged as oncology therapeutic targets, I investigated
potential driver fusion events by evaluating high-confidence and in-frame cancer-related fusion findings.
As a result, I found specific gene fusion patterns. Kinases activated by gene fusions define a meaningful
class of oncogenes associated with hematopoietic malignancies. I identify several novel and recurrent
fusions involving kinases that potentially play a role in leukemogenesis. I detected previously unreported
fusions involving known cancer-related genes, such as PIM3- RAC2 and PROK2- EIF4E3. In addition,
outliers, such as gene expression levels, can pinpoint potential pathogenic events. Therefore, combining
my AML cohort with a healthy control group, I determined aberrant gene expression levels as possible
pathogenic events using the deep learning method. Finally, I combined the data and looked for a com-
parison to the methylation pattern of each patient. Overall, the analysis uncovered a rich landscape of
potential drivers. In different data layers, I found an altered genomic and transcriptomic signature of
different GTPases, which are known to be involved in many stages of tumorigenesis. My methods and
results demonstrate the power of integrating multi-omics data to study complex driver alterations in
AML and point to future directions of research that aim to bridge gaps in research and clinical applications. Furthermore, I provide in vitro evidence for antileukemic cooperativity and epigenetic activity
between DAC and ATRA. I performed differential methylation analysis on CpG resolution and across
genomic and transposable elements regions, enhancing the resultsâ statistical power and interpretabil-
ity. I demonstrated that single-agent ATRA caused no global demethylation, nor did ATRA improve
the demethylation mediated by DAC. In summary, combining multi-omics profiling is a powerful tool
for studying dysregulated patterns in AML. Furthermore, multi-omics profiling performed on mutation-
negative nkAML reveals several promising drivers. My findings not only go beyond augmenting my
understanding of the heterogeneity landscape of AML but also may have immediate implications for new
targeted therapy studies
An exPADItion for citrullination in the developing hair follicle
During epidermal development, to assure proper tissue structure, highly complex transcriptional networks interact within the stem cell compartments of the epidermis and hair follicles (HFs) to balance the choice between self-renewal or differentiation. The full characterisation of the protein profiles resulting from those transcriptional networks, within the compartments of the HF, remains, however, incomplete. Moreover, the proteins themselves can be regulated via posttranslational modification (PTMs). One such PTM is citrullination, carried out by the peptidylarginine deiminase (PADI) family of enzymes. Although, PADIs have been described in other stem and progenitor cells, their role in hair follicle stem cell (HFSC) and progenitor lineages have remained elusive.
The main objectives of this thesis are to address the functional consequences of PADI expression in HFSCs during development.
Paper I identifies Padi4 expression in the developing HF, where it is found to participate in restricting proliferation and lineage commitment of HF progenitors, as well as playing a role in the central mechanism for translational control, and by doing so altering the distinct sequential events that mark HF differentiation progression. As a result, we identify citrullination as a means to assert regulation of protein function in HFSCs and progenitors.
Paper II identifies alternative isoforms of PADI2 and PADI3, in oligodendrocytes and HF differentiated cells, respectively, and show that the alternative isoforms have an incumbering effect on the enzymatic activity and stability of their conventional counterparts.
Paper III is a review paper in which meta-analysis of published human citrullinomes in health and inflammatory disease reveals that citrullination is a commonplace yet highly dynamic molecular regulator of protein function. A strong case is made for the involvement of PADIs and citrullination in hair follicle stem cell biology and inflammatory alopecia.
Paper IV addresses the involvement of transcription factor ID1 in self-renewal and differentiation of epidermal progenitor cells during development. This study describes how ID1 facilitates synchronisation of progenitor proliferation and differentiation via TCF3- binding, and establishes a novel axis of coordination for how BMP-induction of Id1 expression via pSMAD1/5 is supressed by CEBPa.
The combined efforts within this thesis demonstrate the clear and overarching importance of PADIs and citrullination in skin developmental physiology
Stem Cells in Domestic Animals
Stem cells are an attractive tool for cell-based therapies in regenerative medicine, both for humans and animals. The research and review articles published in this first book of the Collection âStem Cells in Domestic Animals: Applications in Health and Productionâ are excellent examples of the recent advances made in the field of stem/stromal cell research in veterinary medicine. In this field, sophisticated and new treatments are now required for improving patientsâ quality of life; in livestock animals, the goal of regenerative medicine is to improve not only animal welfare but also the quality of production, aiming to preserve human health. The contributions collected in this book concern both laboratory research and clinical applications of mesenchymal stem/stromal cells. The increasing knowledge of cell-based therapies may constitute an opportunity for researchers, veterinary practitioners, and animal owners to contribute to animal and human health and well-being
Anwendungen maschinellen Lernens fĂŒr datengetriebene PrĂ€vention auf Populationsebene
Healthcare costs are systematically rising, and current therapy-focused healthcare systems are not sustainable in the long run. While disease prevention is a viable instrument for reducing costs and suffering, it requires risk modeling to stratify populations, identify high- risk individuals and enable personalized interventions. In current clinical practice, however, systematic risk stratification is limited: on the one hand, for the vast majority of endpoints, no risk models exist. On the other hand, available models focus on predicting a single disease at a time, rendering predictor collection burdensome. At the same time, the den- sity of individual patient data is constantly increasing. Especially complex data modalities, such as -omics measurements or images, may contain systemic information on future health trajectories relevant for multiple endpoints simultaneously. However, to date, this data is inaccessible for risk modeling as no dedicated methods exist to extract clinically relevant information. This study built on recent advances in machine learning to investigate the ap- plicability of four distinct data modalities not yet leveraged for risk modeling in primary prevention. For each data modality, a neural network-based survival model was developed to extract predictive information, scrutinize performance gains over commonly collected covariates, and pinpoint potential clinical utility. Notably, the developed methodology was able to integrate polygenic risk scores for cardiovascular prevention, outperforming existing approaches and identifying benefiting subpopulations. Investigating NMR metabolomics, the developed methodology allowed the prediction of future disease onset for many common diseases at once, indicating potential applicability as a drop-in replacement for commonly collected covariates. Extending the methodology to phenome-wide risk modeling, elec- tronic health records were found to be a general source of predictive information with high systemic relevance for thousands of endpoints. Assessing retinal fundus photographs, the developed methodology identified diseases where retinal information most impacted health trajectories. In summary, the results demonstrate the capability of neural survival models to integrate complex data modalities for multi-disease risk modeling in primary prevention and illustrate the tremendous potential of machine learning models to disrupt medical practice toward data-driven prevention at population scale.Die Kosten im Gesundheitswesen steigen systematisch und derzeitige therapieorientierte Gesundheitssysteme sind nicht nachhaltig. Angesichts vieler verhinderbarer Krankheiten stellt die PrĂ€vention ein veritables Instrument zur Verringerung von Kosten und Leiden dar. Risikostratifizierung ist die grundlegende Voraussetzung fĂŒr ein prĂ€ventionszentri- ertes Gesundheitswesen um Personen mit hohem Risiko zu identifizieren und MaĂnah- men einzuleiten. Heute ist eine systematische Risikostratifizierung jedoch nur begrenzt möglich, da fĂŒr die meisten Krankheiten keine Risikomodelle existieren und sich verfĂŒg- bare Modelle auf einzelne Krankheiten beschrĂ€nken. Weil fĂŒr deren Berechnung jeweils spezielle Sets an PrĂ€diktoren zu erheben sind werden in Praxis oft nur wenige Modelle angewandt. Gleichzeitig versprechen komplexe DatenmodalitĂ€ten, wie Bilder oder -omics- Messungen, systemische Informationen ĂŒber zukĂŒnftige GesundheitsverlĂ€ufe, mit poten- tieller Relevanz fĂŒr viele Endpunkte gleichzeitig. Da es an dedizierten Methoden zur Ex- traktion klinisch relevanter Informationen fehlt, sind diese Daten jedoch fĂŒr die Risikomod- ellierung unzugĂ€nglich, und ihr Potenzial blieb bislang unbewertet. Diese Studie nutzt ma- chinelles Lernen, um die Anwendbarkeit von vier DatenmodalitĂ€ten in der PrimĂ€rprĂ€ven- tion zu untersuchen: polygene Risikoscores fĂŒr die kardiovaskulĂ€re PrĂ€vention, NMR Meta- bolomicsdaten, elektronische Gesundheitsakten und Netzhautfundusfotos. Pro Datenmodal- itĂ€t wurde ein neuronales Risikomodell entwickelt, um relevante Informationen zu extra- hieren, additive Information gegenĂŒber ĂŒblicherweise erfassten Kovariaten zu quantifizieren und den potenziellen klinischen Nutzen der DatenmodalitĂ€t zu ermitteln. Die entwickelte Me-thodik konnte polygene Risikoscores fĂŒr die kardiovaskulĂ€re PrĂ€vention integrieren. Im Falle der NMR-Metabolomik erschloss die entwickelte Methodik wertvolle Informa- tionen ĂŒber den zukĂŒnftigen Ausbruch von Krankheiten. Unter Einsatz einer phĂ€nomen- weiten Risikomodellierung erwiesen sich elektronische Gesundheitsakten als Quelle prĂ€dik- tiver Information mit hoher systemischer Relevanz. Bei der Analyse von Fundusfotografien der Netzhaut wurden Krankheiten identifiziert fĂŒr deren Vorhersage Netzhautinformationen genutzt werden könnten. Zusammengefasst zeigten die Ergebnisse das Potential neuronaler Risikomodelle die medizinische Praxis in Richtung einer datengesteuerten, prĂ€ventionsori- entierten Medizin zu verĂ€ndern
BET inhibitor trotabresib in heavily pretreated patients with solid tumors and diffuse large B-cell lymphomas
B-cell lymphoma; Cancer therapy; CNS cancerLimfoma de cĂšl·lules B; TerĂ pia del cĂ ncer; CĂ ncer del SNCLinfoma de cĂ©lulas B; Terapia del cĂĄncer; CĂĄncer del SNCBromodomain and extraterminal proteins (BET) play key roles in regulation of gene expression, and may play a role in cancer-cell proliferation, survival, and oncogenic progression. CC-90010-ST-001 (NCT03220347) is an open-label phase I study of trotabresib, an oral BET inhibitor, in heavily pretreated patients with advanced solid tumors and relapsed/refractory diffuse large B-cell lymphoma (DLBCL). Primary endpoints were the safety, tolerability, maximum tolerated dose, and RP2D of trotabresib. Secondary endpoints were clinical benefit rate (complete response [CR]â+âpartial response [PR]â+âstable disease [SD] of â„4 monthsâ duration), objective response rate (CRâ+âPR), duration of response or SD, progression-free survival, overall survival, and the pharmacokinetics (PK) of trotabresib. In addition, part C assessed the effects of food on the PK of trotabresib as a secondary endpoint. The dose escalation (part A) showed that trotabresib was well tolerated, had single-agent activity, and determined the recommended phase 2 dose (RP2D) and schedule for the expansion study. Here, we report long-term follow-up results from part A (Nâ=â69) and data from patients treated with the RP2D of 45âmg/day 4 days on/24 days off or an alternate RP2D of 30âmg/day 3 days on/11 days off in the dose-expansion cohorts (parts B [Nâ=â25] and C [Nâ=â41]). Treatment-related adverse events (TRAEs) are reported in almost all patients. The most common severe TRAEs are hematological. Toxicities are generally manageable, allowing some patients to remain on treatment for â„2 years, with two patients receiving â„3 years of treatment. Trotabresib monotherapy shows antitumor activity, with an ORR of 13.0% (95% CI, 2.8â33.6) in patients with R/R DLBCL (part B) and an ORR of 0.0% (95% CI, 0.0â8.6) and a CBR of 31.7% (95% CI, 18.1â48.1) in patients with advanced solid tumors (part C). These results support further investigation of trotabresib in combination with other anticancer agents.This study was sponsored by Celgene, a Bristol Myers Squibb Company. The study sponsor was involved in the study design, analysis of data, and writing the manuscript. Medical writing and editorial assistance were provided by Bernard Kerr, PGDipSci, and Agata Shodeke, of Spark, funded by Bristol Myers Squibb
Sarcoma ecosystems : spatial characterization and prognostic significance
Sarcoma is a highly heterogeneous disease with complex biological activities and unique tumor microenvironments (TME) in distinct subtypes. The limited treatment options and inadequate responses to current therapies necessitate a deeper understanding of sarcoma biology and personalized treatment strategies. Our research comprehensively explores the sarcoma TME through advanced spatial analysis and investigates sarcoma's molecular and genetic profiles through transcriptome and genome sequencing.
In paper I, we focused on undifferentiated pleomorphic sarcoma (UPS) using multiplex immunofluorescence (mIF) staining for in-depth spatial analysis of B cell populations and lymphocyte aggregates (LAs). LAs in UPS were found to be associated with longer overall survival (OS) and metastasis-free survival (MFS). Moreover, we unveiled distinct maturation profiles among B cell subsets, indicative of different phenotypes that contribute to functional ecosystems in TME. LA-positive tumors displayed a more well-differentiated B cell profile throughout the entire tumor section, not limited in LA regions. We introduced the B-index, an integrated measurement tool combining B cell abundance and maturity, which demonstrated predictive power for both MFS and OS. Using the TissUUmap tool, we identified B cell desert areas characterized by extremely low B cell infiltration. LA-positive tumors displayed smaller and more fragmented B cell desert areas.
In paper II, we performed double immunohistochemistry to study CD11c-positive antigen-presenting cells (APCs) and CD8- positive cells in 177 soft tissue sarcoma (STS) patients. We found that CD11c-CD8 interactions in the TME were associated with improved MFS and OS. Transcriptomic analysis in The Cancer Genome Atlas (TCGA) sarcoma cohort supported the prognostic significance of combining CD11c with CD8, irrespective of FOXP3 levels and in the presence of CD274 (PD-L1).
In paper III, we conducted transcriptome and targeted DNA sequencing in 91 synovial sarcomas, identifying three distinct Synovial Sarcoma Clusters (SSCs) mirroring histological subtypes. SSC-I was characterized by high cell proliferation and immune evasion with an unfavorable prognosis. SSC-II was dominated by vascularstromal components and correlated with better outcomes. SSC-III displayed biphasic differentiation, genomic complexity, and immune checkpoint-mediated immune suppression, leading to adverse outcomes, even after a good histological response to neoadjuvant treatment.
In paper IV, we analyzed Ewing sarcoma (ES) transcriptome signatures in four previously published cohorts and identified 29 prognostic RNA-binding protein (RBP) genes, from which we constructed and validated an RBP-associated prognostic risk model (RPRM). The RPRM demonstrated stable predictive value for prognosis, with NSUN7 emerging as an independent and favorable prognostic marker.
In summary, our research integrates spatial analysis of the sarcoma TME to identify unique immune features and prognostic markers. Moreover, we use transcriptomic and genomic analyses to categorize specific sarcoma types for more detailed survival stratification. This work provides a deeper insight into the sarcoma TME and suggests an improved grouping strategy, aiming to shape the development of personalized immunotherapy in the future
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