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Low-grade serous carcinoma of the ovary (LGSOC) is relatively resistant to chemotherapy. Given its biological parallels to hormone receptor-positive breast cancer, including responsiveness to anti-estrogen therapies, we conducted a pilot phase II study to assess clinical benefit of neoadjuvant fulvestrant and abemaciclib in women with advanced, unresectable LGSOC (NCT03531645). Imaging assessments were performed every 8 weeks until resectable. The primary endpoint was clinical benefit rate (CBR). Exploratory objectives included evaluation of safety profile, accessing biomarkers related to clinical benefit, and description of tumor phenotypic changes. Fifteen patients were enrolled and evaluable for efficacy. CBR was 100%, with 1/15 patients (7%) achieving complete response (CR), 8/15 patients (53%) achieving partial response (PR), and 6/15 (40%) exhibiting stable disease (SD). Interval cytoreductive surgery (ICS) was performed in 9 patients (60%), with 7 (78%) achieving complete or optimal resection. Fourteen tumor samples underwent transcriptomic and proteomic profiling. Tumors from long-term survivors showed significantly higher baseline expression of cell-cycle-related programs and estrogen signaling-related genes, both suppressed upon treatment. Neoadjuvant fulvestrant and abemaciclib was well tolerated, achieved high response rates, and enabled optimal surgical resection in most patients. Tumor proliferative activity and estrogen signaling dependency may predict therapy response.

BackgroundAnalysis of tissue architecture in 3D is critical for understanding disease biology, particularly in immuno-oncology and pathologically similar human ovarian cancer subtypes such as Mesonephric Adenocarcinoma (MA), Mesonephric-like Adenocarcinoma (MLA), Endometrioid Carcinoma (EC). However, traditional tools often struggle with capturing and quantifying volumetric, large scale datasets. We present AlpenInsight 3D, a scalable, explainable pipeline designed for discovery-driven spatial tissue analysis using 3D volumetric images captured with light-sheet microscopy of tissues stained with nuclear and eosin structural biomarkers.MethodsHuman ovarian tumor FFPE tissue biopsies (n=9) were deparaffinized and processed before being stained with a nuclear dye, TO-PRO-3, and Eosin, a cytoplasmic/structural dye. Samples were optically cleared using a modified iDISCO+ protocol. Entire samples were imaged at 4 µm/pixel resolution with a 3Di™ hybrid open-top light-sheet microscope. Light-sheet imaging allowed for non-destructive high-throughput 3D imaging of large of the intact biopsies. Next we adapted 3D Local Binary Patterns (LBP), a texture-based computer vision technique, to extract spatial features from the captured volumetric images. These structural features were integrated with intensity-based signals from Eosin and To-PRO-3 channels. The combined feature sets were then subjected to unsupervised clustering to segment tissue into spatially coherent domains, without the need for predefined labels or supervised training data.ResultsThe resulting clusters reveal distinct spatial domains within tissue volumes. This unsupervised segmentation approach has demonstrated robustness in exploratory contexts, successfully identifying biological heterogeneity across three human ovarian cancer subtypes. While all three MLA samples consistently clustered together, forming a distinct and cohesive group, the MA and EC samples divided into three subgroups: one enriched for MA, one enriched for EC, and a third containing one MA and one EC sample, suggesting overlapping or transitional expression patterns. This indicates that while MLA exhibits a stable signature, MA and EC may share emerging or intermediate profiles.ConclusionsAlpenInsight 3D enables automated exploration of whole-biopsy tissue organization in 3D. By combining unsupervised learning with explainable, biologically grounded features, this Methods provides a powerful tool for hypothesis generation and discovery in immuno-oncology and spatial biology. It is particularly well-suited for scalable analysis of volumetric datasets where conventional approaches may fail.AcknowledgementsThis research was performed in the Flow Cytometry & Cellular Imaging Core Facility, which is supported in part by the National Institutes of Health through M. D. Anderson’s Cancer Center Support Grant P30 CA016672, the NCI’s Research Specialist 1 R50 CA243707-01A1, and a Shared Instrumentation Award from the Cancer Prevention Research Institution of Texas (CPRIT), RP121010.Ethics ApprovalWSIs of H&E-stained treatment-naïve tumor sections and clinicopathological characteristics from the MDACC dataset were obtained from the ovarian cancer repository of the Department of Gynecologic Oncology and Reproductive Medicine under protocols approved by the University of Texas MD Anderson’s Institutional Review Board. Written informed consent from the patients were obtained by front desk personnel, and the studies were conducted in accordance with recognized ethical guidelines

BackgroundOvarian cancer is the most lethal gynecologic malignancy worldwide, yet factors leading to therapy resistance are poorly understood, partly hindered by the heterogeneous tumor microenvironment (TME) and cancer phenotypes. This study aims to construct a single-cell atlas of epithelial ovarian cancer, to comprehensively characterize the TME ecotypes and cancer phenotypic states across the disease spectrum, thereby identifying mechanisms related to chemotherapy resistance and providing insights for more effective individualized therapies.MethodsA total of 394 samples profiled with single-cell RNA sequencing were collected. Following data integration and unsupervised clustering, TME cell states were identified. Subsequently, immune ecotypes were defined based on samples with intact CD45+ compartments. Non-negative matrix factorization was performed on tumor cells to identify cancer metaprograms. The association of immune ecotypes, stromal subsets, and cancer metaprograms and their correlation with clinical parameters were dissected. Additionally, external spatial datasets were applied for validations.ResultsA total of 89 different TME cell types and states were identified, including 33 T/NK, 15 B/plasma, 18 myeloid, and 23 stromal cell types and states. These TME cells showed distinct distribution patterns among samples of different pathological types, stages, metastatic sites, and treatment statuses. Subsequently, based on the abundance of immune cell subsets, samples were categorized into six immune ecotypes, including naïve-like lymphocytes-enriched (Ecotype 1), primary tumor-enriched (Ecotype 2), metastatic tumor-enriched (Ecotype 3), post-chemotherapy-enriched (Ecotype 4), stress response (Ecotype 5), and immune-suppressive (Ecotype 6). Analysis of the cancer cell compartment led to identification of ten distinct ovarian cancer metaprograms, including antigen presentation, cell cycle, stress response, classical, partial epithelial-mesenchymal transition, interferon response, ciliary-like, inflammatory, hypoxia and angiogenesis, as well as ribosome and protein synthesis. Strong associations among immune ecotypes, stromal subsets, and cancer cell states were observed. Specifically, cancer cells expressing the classical metaprogram was highly enriched in chemotherapy non-responders, and co-localize with immune ecotypes 2, 3 and 6, along with multiple types of cancer-associated fibroblasts. Validations in independent spatial transcriptomic datasets of ovarian cancer indicate that the classical metaprogram was progressively upregulated during the course of chemotherapy, and highly enriched in minimal residual disease.ConclusionsIn summary, this study provided a comprehensive single-cell landscape of epithelial ovarian cancer, highlighting the heterogeneity of TME and cancer phenotypic states across patients. A cancer metaprogram related to chemotherapy response were identified, which may serve as novel predictive biomarkers and provide basis for developing therapeutics targeting chemo-resistant diseases.

ObjectiveTo compare the performance of patients with mild–moderate Alzheimer's disease (AD) and vascular dementia (VaD) on tests of information processing and attention.MethodPatients with AD (n=75) and VaD (n=46) were recruited from a memory clinic along with dementia-free participants (n=28). They underwent specific tests of attention from the Cognitive Drug Research battery, and pen and paper tests including Colour Trails A and B and Stroop. All patients had a CT brain scan that was independently scored for white-matter change/ischaemia.ResultsAttention was impaired in both AD and VaD patients. VaD patients had more impaired choice reaction times and were less accurate on a vigilance test measuring sustained attention. Deficits in selective and divided attention occurred in both patient groups and showed the strongest correlations with Mini Mental State Examination scores.ConclusionThis study demonstrates problems with the attentional network in mild–moderate AD and VaD. The authors propose that attention should be tested routinely in a memory clinic setting.

We conducted a systematic study of top susceptibility variants from a genome-wide association (GWA) study of bipolar disorder to gain insight into the functional consequences of genetic variation influencing disease risk. We report here the results of experiments to explore the effects of these susceptibility variants on DNA methylation and mRNA expression in human cerebellum samples. Among the top susceptibility variants, we identified an enrichment of cis regulatory loci on mRNA expression (eQTLs), and a significant excess of quantitative trait loci for DNA CpG methylation, hereafter referred to as methylation quantitative trait loci (mQTLs). Bipolar disorder susceptibility variants that cis regulate both cerebellar expression and methylation of the same gene are a very small proportion of bipolar disorder susceptibility variants. This finding suggests that mQTLs and eQTLs provide orthogonal ways of functionally annotating genetic variation within the context of studies of pathophysiology in brain. No lymphocyte mQTL enrichment was found, suggesting that mQTL enrichment was specific to the cerebellum, in contrast to eQTLs. Separately, we found that using mQTL information to restrict the number of single-nucleotide polymorphisms studied enhances our ability to detect a significant association. With this restriction a priori informed by the observed functional enrichment, we identified a significant association ( rs12618769 , P bonferroni <0.05) from two other GWA studies (TGen+GAIN; 2191 cases and 1434 controls) of bipolar disorder, which we replicated in an independent GWA study (WTCCC). Collectively, our findings highlight the importance of integrating functional annotation of genetic variants for gene expression and DNA methylation to advance the biological understanding of bipolar disorder.

Although a highly heritable and disabling disease, bipolar disorder's (BD) genetic variants have been challenging to identify. We present new genotype data for 1,190 cases and 401 controls and perform a genome-wide association study including additional samples for a total of 2,191 cases and 1,434 controls. We do not detect genome-wide significant associations for individual loci; however, across all SNPs, we show an association between the power to detect effects calculated from a previous genome-wide association study and evidence for replication (P = 1.5×10(-7)). To demonstrate that this result is not likely to be a false positive, we analyze replication rates in a large meta-analysis of height and show that, in a large enough study, associations replicate as a function of power, approaching a linear relationship. Within BD, SNPs near exons exhibit a greater probability of replication, supporting an enrichment of reproducible associations near functional regions of genes. These results indicate that there is likely common genetic variation associated with BD near exons (±10 kb) that could be identified in larger studies and, further, provide a framework for assessing the potential for replication when combining results from multiple studies.

To identify bipolar disorder (BD) genetic susceptibility factors, we conducted two genome-wide association (GWA) studies: one involving a sample of individuals of European ancestry (EA; n =1001 cases; n =1033 controls), and one involving a sample of individuals of African ancestry (AA; n =345 cases; n =670 controls). For the EA sample, single-nucleotide polymorphisms (SNPs) with the strongest statistical evidence for association included rs5907577 in an intergenic region at Xq27.1 ( P =1.6 × 10 −6 ) and rs10193871 in NAP5 at 2q21.2 ( P =9.8 × 10 −6 ). For the AA sample, SNPs with the strongest statistical evidence for association included rs2111504 in DPY19L3 at 19q13.11 ( P =1.5 × 10 −6 ) and rs2769605 in NTRK2 at 9q21.33 ( P =4.5 × 10 −5 ). We also investigated whether we could provide support for three regions previously associated with BD, and we showed that the ANK3 region replicates in our sample, along with some support for C15Orf53 ; other evidence implicates BD candidate genes such as SLITRK2 . We also tested the hypothesis that BD susceptibility variants exhibit genetic background-dependent effects. SNPs with the strongest statistical evidence for genetic background effects included rs11208285 in ROR1 at 1p31.3 ( P =1.4 × 10 −6 ), rs4657247 in RGS5 at 1q23.3 ( P =4.1 × 10 −6 ), and rs7078071 in BTBD16 at 10q26.13 ( P =4.5 × 10 −6 ). This study is the first to conduct GWA of BD in individuals of AA and suggests that genetic variations that contribute to BD may vary as a function of ancestry.

Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable psychiatric disorders. Associated genetic and gene expression changes have been identified, but many have not been replicated and have unknown functions. We identified groups of genes whose expressions varied together, that is co-expression modules, then tested them for association with SCZ. Using weighted gene co-expression network analysis, we show that two modules were differentially expressed in patients versus controls. One, upregulated in cerebral cortex, was enriched with neuron differentiation and neuron development genes, as well as disease genome-wide association study genetic signals; the second, altered in cerebral cortex and cerebellum, was enriched with genes involved in neuron protection functions. The findings were preserved in five expression data sets, including sets from three brain regions, from a different microarray platform, and from BD patients. From those observations, we propose neuron differentiation and development pathways may be involved in etiologies of both SCZ and BD, and neuron protection function participates in pathological process of the diseases.

The use of clinical features to define subtypes of a disorder may aid in gene identification for complex diseases. In particular, clinical subtypes of mania may distinguish phenotypic subgroups of bipolar subjects that may also differ genetically. To assess this possibility, we performed a genome-wide association study using genotype data from the Bipolar Genome Study (BiGS) and subjects that were categorized as having either irritable or elated mania during their most severe episode. A bipolar case-only analysis in the GAIN bipolar sample identified several genomic regions that differed between irritable and elated subjects, the most significant of which was for 33 SNPs on chromosome 13q31 (peak p = 2×10(-7)). This broad peak is in a relative gene desert over an unknown EST and between the SLITRK1 and SLITRK6 genes. Evidence for association to this region came predominantly from subjects in the sample that were originally collected as part of a family-based bipolar linkage study, rather than those collected as bipolar singletons. We then genotyped an additional sample of bipolar singleton cases and controls, and the analysis of irritable vs. elated mania in this new sample did not replicate our previous findings. However, this lack of replication is likely due to the presence of significant differences in terms of clinical co-morbity that were identified between these singleton bipolar cases and those that were selected from families segregating the disorder. Despite these clinical differences, analysis of the combined sample provided continued support for 13q31 and other regions from our initial analysis. Though genome-wide significance was not achieved, our results suggest that irritable mania results from a distinct set of genes, including a region on chromosome 13q31.

Research suggests that clinical symptom dimensions may be more useful in delineating the genetics of bipolar disorder (BD) than standard diagnostic models. To date, no study has applied this concept to data from genome-wide association studies (GWAS). We performed a GWAS of factor dimensions in 927 clinically well-characterized BD patients of German ancestry. Rs9875793, which is located in an intergenic region of 3q26.1 and in the vicinity of the solute carrier family 2 (facilitated glucose transporter), member 2 gene ( SLC2A2 ), was significantly associated with the factor analysis-derived dimension ‘negative mood delusions’ ( n =927; P =4.65 × 10 −8 , odds ratio (OR)=2.66). This dimension was comprised of the symptoms delusions of poverty, delusions of guilt and nihilistic delusions. In case–control analyses, significant association with the G allele of rs9875793 was only observed in the subgroup of BD patients who displayed symptoms of ‘negative mood delusions’ (allelic χ 2 model: P G =0.0001, OR=1.92; item present, n =89). Further support for the hypothesis that rs9875793 is associated with BD in patients displaying ‘negative mood delusions’ symptom, such as delusions of guilt, was obtained from an European American sample (GAIN/TGEN), which included 1247 BD patients and 1434 controls ( P EA =0.028, OR=1.27).