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Purpose To analyze concordance rates between individual foci of bifocal BC for histological grade, type and intrinsic subtype based on immunohistochemical (IHC) and mRNA-testing using MammaTyper. Methods We evaluated histological grade and type as well as intrinsic subtype based on IHC status for estrogen and progesterone receptors, HER2 and the mitotic activity index in 158 individual foci of 79 bifocal BC. A subgroup of 31 cases additionally underwent mRNA-based subtyping using the MammaTyper (MT) test. We calculated concordance rates between individual foci, as well as Cohen’s Kappa ( k ). Results For 79 bifocal BC, concordance rates between individual foci for grade, histological type, and IHC-based subtype were 69.6% ( k =0.53), 92.4% ( k =0.81), and 74.7% ( k =0.62), respectively. In the MT subgroup of 31 bifocal BC, concordance rates between individual foci for grade, histological type, IHC-based and mRNA-based intrinsic subtype were 87.1% ( k =0.78), 90.3% ( k =0.73), 87.1% ( k =0.82), and 87.1% ( k =0.7), respectively. Overall concordance between IHC- and mRNA-based subtype in the MT subgroup was 79% ( k =0.7). In 6/79 cases (7.6%), testing of the smaller focus added clinically relevant information either on IHC- or mRNA-level: four cases showed high hormonal receptor expression while the expression in the larger focus was negative or low, warranting additional endocrine treatment; two cases presented with higher proliferative activity in the smaller focus, warranting additional chemotherapy. Conclusion In bifocal BC, intertumoral heterogeneity on the morphological, immunohistochemical and molecular level is common, with discordant intrinsic subtype in up to 25% between individual foci, with about 8% clinically relevant discordances.

Context: Primary aldosteronism (PA) is one of the leading causes of secondary hypertension, and its diagnostic subtyping consistently presents a clinical challenge. Objective: This study aimed to investigate the potential of [.sup.68]Ga-Pentixafor positron emission tomography/computed tomography (PET/CT) in PA classification and its applicability in guiding the development of clinical treatment plans by increasing the sample size. Methods: We prospectively enrolled 120 patients with either PA or nonfunctional adenoma (NFA) for analysis. All patients underwent [.sup.68]Ga-Pentixafor PET/CT. Of these, 11 patients underwent adrenal venous sampling (AVS), 77 underwent adrenalectomy, 76 received pathological diagnoses, and 71 underwent immunohistochemical detection of aldosterone synthase (CYP11B2). Immunohistochemistry for C-X-C chemokine receptor 4 (CXCR4) was performed in 62 cases. Follow-up was conducted for all patients. Results: Among the 120 patients, 66 were diagnosed with aldosterone-producing adenoma (APA), 33 with idiopathic hyperaldosteronism (IHA), and 21 with NFA. For APA patients, the sensitivity, specificity, and accuracy of visual analysis using [.sup.68]Ga-Pentixafor PET/CT were 92.40%, 94.40%, and 93.33%, respectively. Furthermore, for APA patients with a nodule greater than 1 cm in diameter, when the maximum standard uptake value was 7.3 or greater, the specificity was 100%; and for APA patients with a nodule less than 1 cm in diameter, [.sup.68]Ga-Pentixafor PET/CT also exhibited high sensitivity. AVS was successfully performed in 5 patients. Among the 5 patients, the concordance rate between the AVS and [.sup.68]Ga-Pentixafor PET/CT for PA subtyping was 60%. In the 77 patients who underwent adrenalectomy, 61 PET/CT scans displayed positive lesions, all of which benefited from the surgery. Additionally, the concordance rate between [.sup.68]Ga-Pentixafor PET/CT imaging and CYP11B2 was 81.69%. Conclusion: [.sup.68]Ga-Pentixafor PET/CT is a reliable and noninvasive functional imaging technique that demonstrates high accuracy in classifying PA and provides valuable guidance for clinical treatment decision-making. Key Words: primary aldosteronism, [.sup.68]Ga-Pentixafor PET/CT, adrenal venous sampling, subtyping, treatment Abbreviations: ACTH, adrenocorticotropin; APA, aldosterone-producing adenoma; ARR, aldosterone/renin ratio; AUROC, area under the receiver operating characteristic curve; AVS, adrenal venous sampling; BMI, body mass index; CXCR4, C-X-C chemokine receptor 4; IHA, idiopathic hyperaldosteronism; LAR, lesion-to-normal adrenal ratio; LI, lateralization index; LLR, lesion-to-liver ratio; NFA, nonfunctional adenoma; PA, primary aldosteronism; PAC, plasma aldosterone concentration; PET/CT, positron emission tomography/computed tomography; ROC, receiver operating characteristic; SUVmax, maximum standard uptake value.

To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.

Histological subtyping of pulmonary adenocarcinoma has recently been updated based on predominant pattern, but data on reproducibility are required for validation. This study first assesses reproducibility in subtyping adenocarcinomas and then assesses further the distinction between invasive and non-invasive (wholly lepidic) pattern of adenocarcinoma, among an international group of pulmonary pathologists. Two ring studies were performed using a micro-photographic image-based method, evaluating selected images of lung adenocarcinoma histologic patterns. In the first study, 26 pathologists reviewed representative images of typical and ‘difficult’ histologic patterns. A total number of scores for the typical patterns combined (n=94) and the difficult cases (n=21) were 2444 and 546, respectively. The mean kappa score (±s.d.) for the five typical patterns combined and for difficult cases were 0.77±0.07 and 0.38±0.14, respectively. Although 70% of the observers identified 12–65% of typical images as single pattern, highest for solid and least for micropapillary, recognizing the predominant pattern was achieved in 92–100%, of the images except for micropapillary pattern (62%). For the second study on invasion, identified as a key problem area from the first study, 28 pathologists submitted and reviewed 64 images representing typical as well as ‘difficult’ examples. The kappa for typical and difficult cases was 0.55±0.06 and 0.08±0.02, respectively, with consistent subdivision by the same pathologists into invasive and non-invasive categories, due to differing interpretation of terminology defining invasion. In pulmonary adenocarcinomas with classic morphology, which comprise the majority of cases, there is good reproducibility in identifying a predominant pattern and fair reproducibility distinguishing invasive from in-situ (wholly lepidic) patterns. However, more precise definitions and better education on interpretation of existing terminology are required to improve recognition of purely in-situ disease, this being an area of increasing importance.

The genomics-based concept of precision medicine began to emerge following the completion of the Human Genome Project. In contrast to evidence-based medicine, precision medicine will allow doctors and scientists to tailor the treatment of different subpopulations of patients who differ in their susceptibility to specific diseases or responsiveness to specific therapies. The current precision medicine model was proposed to precisely classify patients into subgroups sharing a common biological basis of diseases for more effective tailored treatment to achieve improved outcomes. Precision medicine has become a term that symbolizes the new age of medicine. In this review, we examine the history, development, and future perspective of precision medicine. We also discuss the concepts, principles, tools, and applications of precision medicine and related fields. In our view, for precision medicine to work, two essential objectives need to be achieved. First, diseases need to be classified into various subtypes. Second, targeted therapies must be available for each specific disease subtype. Therefore, we focused this review on the progress in meeting these two objectives.

The accuracy of machine learning methods is often limited by the amount of training data that is available. We proposed to improve machine learning training regimes by augmenting datasets with synthetically generated samples. We present a method for synthesizing gene expression samples and test the system’s capabilities for improving the accuracy of categorical prediction of cancer subtypes. We developed SyntheVAEiser, a variational autoencoder based tool that was trained and tested on over 8000 cancer samples. We have shown that this technique can be used to augment machine learning tasks and increase performance of recognition of underrepresented cohorts.

As the field of translational ‘omics has progressed, refined classifiers at both genomic and proteomic levels have emerged to decipher the heterogeneity of breast cancer in a clinically-applicable way. The integration of ‘omics knowledge at the DNA, RNA and protein levels is further expanding biologic understanding of breast cancer and opportunities for customized treatment, a particularly pressing need in clinically triple negative tumors. For this group of aggressive breast cancers, work from multiple groups has now validated at least four major biologically and clinically distinct omics-based subtypes. While to date most clinical trial designs have considered triple negative breast cancers as a single group, with an expanding arsenal of targeted therapies applicable to distinct biological pathways, survival benefits may be best realized by designing and analyzing clinical trials in the context of major molecular subtypes. While RNA-based classifiers are the most developed, proteomic classifiers proposed for triple negative breast cancer based on new technologies have the potential to more directly identify the most clinically-relevant biomarkers and therapeutic targets. Phospho-proteomic data further identify targetable signalling pathways in a unique subtype-specific manner. Single cell profiling of the tumor microenvironment represents a promising way to allow a better characterization of the heterogeneity of triple negative breast cancer which could be integrated in a spatially resolved context to build an ecosystem-based patient classification. Multi-omic data further allows in silico analysis of genetic and pharmacologic screens to map therapeutic vulnerabilities in a subtype-specific context. This review describes current knowledge about molecular subtyping of triple negative breast cancer, recent advances in omics-based genomics and proteomics diagnostics addressing the diversity of this disease, key advances made through single cell analysis approaches, and developments in treatments including targeted therapeutics being tested in major clinical trials.

BackgroundProgrammed cell death (PCD) plays a complex and critical role in the progression of colon adenocarcinoma (COAD). Elucidating PCD-related characteristics is expected to provide new insights for tumor subtyping, prognosis assessment, and personalized therapy.MethodsThis study integrated multi-omics data from COAD and employed 10 clustering algorithms for molecular subtyping. Based on PCD-related genes, a Programmed cell death signature (PCDS) predictive model was constructed using 113 machine learning algorithms. The focus then shifted to the core gene of the model, TERT. Single-cell and spatial transcriptomic data were incorporated to decipher its cellular localization and regulatory pathways. Finally, the function of TERT was validated through in vitro experiments.ResultsWe categorized COAD into 5 molecular subtypes with distinct prognostic differences. Subsequently, we successfully developed an 18-gene PCDS. This model effectively predicted patient risk and overall survival in both the training set and multiple independent validation cohorts. The PCDS was closely associated with the tumor microenvironment, mutation burden, and response to immunotherapy. Single-cell and spatial transcriptomic analyses revealed that the core gene, TERT, was specifically highly expressed in malignant epithelial cells. In vitro experiments confirmed that knocking down TERT significantly inhibited the proliferation, migration, invasion, and clonogenic formation abilities of COAD cells. Mechanistically, TERT may inhibit apoptosis, regulate the cell cycle, and promote proliferation potentially through the E2F, G2/M checkpoint, and MYC signaling pathways.ConclusionThis study defines novel molecular subtypes of COAD through multi-omics clustering analysis and develops a robust PCD-related prognostic signature. Furthermore, it reveals the significant value of TERT as a potential therapeutic target in COAD.

Background About 20% of breast cancers in humans are basal-like, a subtype that is often triple-negative and difficult to treat. An effective translational model for basal-like breast cancer is currently lacking and urgently needed. To determine whether spontaneous mammary tumors in pet dogs could meet this need, we subtyped canine mammary tumors and evaluated the dog–human molecular homology at the subtype level. Methods We subtyped 236 canine mammary tumors from 3 studies by applying various subtyping strategies on their RNA-seq data. We then performed PAM50 classification with canine tumors alone, as well as with canine tumors combined with human breast tumors. We identified feature genes for human BLBC and luminal A subtypes via machine learning and used these genes to repeat canine-alone and cross-species tumor classifications. We investigated differential gene expression, signature gene set enrichment, expression association, mutational landscape, and other features for dog–human subtype comparison. Results Our independent genome-wide subtyping consistently identified two molecularly distinct subtypes among the canine tumors. One subtype is mostly basal-like and clusters with human BLBC in cross-species PAM50 and feature gene classifications, while the other subtype does not cluster with any human breast cancer subtype. Furthermore, the canine basal-like subtype recaptures key molecular features (e.g., cell cycle gene upregulation, TP53 mutation) and gene expression patterns that characterize human BLBC. It is enriched in histological subtypes that match human breast cancer, unlike the other canine subtype. However, about 33% of canine basal-like tumors are estrogen receptor negative (ER−) and progesterone receptor positive (PR+), which is rare in human breast cancer. Further analysis reveals that these ER−PR+ canine tumors harbor additional basal-like features, including upregulation of genes of interferon- γ response and of the Wnt-pluripotency pathway. Interestingly, we observed an association of PGR expression with gene silencing in all canine tumors and with the expression of T cell exhaustion markers (e.g., PDCD1 ) in ER−PR+ canine tumors. Conclusions We identify a canine mammary tumor subtype that molecularly resembles human BLBC overall and thus could serve as a vital translational model of this devastating breast cancer subtype. Our study also sheds light on the dog–human difference in the mammary tumor histology and the hormonal cycle.