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Ovarian carcinoma has the highest mortality of all female reproductive cancers and current treatment has become histotype-specific. Pathologists diagnose five common histotypes by microscopic examination, however, histotype determination is not straightforward, with only moderate interobserver agreement between general pathologists (Cohen's kappa 0.54–0.67). We hypothesized that machine learning (ML)-based image classification models may be able to recognize ovarian carcinoma histotype sufficiently well that they could aid pathologists in diagnosis. We trained four different artificial intelligence (AI) algorithms based on deep convolutional neural networks to automatically classify hematoxylin and eosin-stained whole slide images. Performance was assessed through cross-validation on the training set (948 slides corresponding to 485 patients), and on an independent test set of 60 patients from another institution. The best-performing model achieved a diagnostic concordance of 81.38% (Cohen's kappa of 0.7378) in our training set, and 80.97% concordance (Cohen's kappa 0.7547) on the external dataset. Eight cases misclassified by ML in the external set were reviewed by two subspecialty pathologists blinded to the diagnoses, molecular and immunophenotype data, and ML-based predictions. Interestingly, in 4 of 8 cases from the external dataset, the expert review pathologists rendered diagnoses, based on blind review of the whole section slides classified by AI, that were in agreement with AI rather than the integrated reference diagnosis. The performance characteristics of our classifiers indicate potential for improved diagnostic performance if used as an adjunct to conventional histopathology.

A problem that impedes the progress in Brain-Computer Interface (BCI) research is the difficulty in reproducing the results of different papers. Comparing different algorithms at present is very difficult. Some improvements have been made by the use of standard datasets to evaluate different algorithms. However, the lack of a comparison framework still exists. In this paper, we construct a new general comparison framework to compare different algorithms on several standard datasets. All these datasets correspond to sensory motor BCIs, and are obtained from 21 subjects during their operation of synchronous BCIs and 8 subjects using self-paced BCIs. Other researchers can use our framework to compare their own algorithms on their own datasets. We have compared the performance of different popular classification algorithms over these 29 subjects and performed statistical tests to validate our results. Our findings suggest that, for a given subject, the choice of the classifier for a BCI system depends on the feature extraction method used in that BCI system. This is in contrary to most of publications in the field that have used Linear Discriminant Analysis (LDA) as the classifier of choice for BCI systems.

Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) is assigned after exclusion of the defining features of the other three molecular subtypes and includes patients with heterogeneous clinical outcomes. In this study, we employ artificial intelligence (AI)-powered histopathology image analysis to differentiate between p53abn and NSMP EC subtypes and consequently identify a sub-group of NSMP EC patients that has markedly inferior progression-free and disease-specific survival (termed ‘p53abn-like NSMP’), in a discovery cohort of 368 patients and two independent validation cohorts of 290 and 614 from other centers. Shallow whole genome sequencing reveals a higher burden of copy number abnormalities in the ‘p53abn-like NSMP’ group compared to NSMP, suggesting that this group is biologically distinct compared to other NSMP ECs. Our work demonstrates the power of AI to detect prognostically different and otherwise unrecognizable subsets of EC where conventional and standard molecular or pathologic criteria fall short, refining image-based tumor classification. This study’s findings are applicable exclusively to females. Endometrial cancer (EC) has four molecular subtypes; of these, the No Specific Molecular Profile (NSMP) subtype encompasses patients with heterogeneous outcomes. Here, the authors use artificial intelligence and histopathology images to differentiate p53abn and NSMP subtypes in EC, and identify one distinct subgroup within NSMP with unfavourable outcome.

Artificial intelligence (AI)-based models can potentially reduce workload and assist general pathologists in reaching genitourinary pathologists’ performance. A recent large-scale competition to develop generalizable AI models for prostate cancer detection and grading has shown success; implementation of such models positions them beyond hype and as today’s reality.

Sohrab Shah, Samuel Aparicio and colleagues analyze whole genomes and single cells from ovarian cancers in the peritoneal cavity to establish patterns of disease spread. They determine the clonal relationships between multiple tumor sites and characterize the migratory potential of genomically diverse clones. We performed phylogenetic analysis of high-grade serous ovarian cancers (68 samples from seven patients), identifying constituent clones and quantifying their relative abundances at multiple intraperitoneal sites. Through whole-genome and single-nucleus sequencing, we identified evolutionary features including mutation loss, convergence of the structural genome and temporal activation of mutational processes that patterned clonal progression. We then determined the precise clonal mixtures comprising each tumor sample. The majority of sites were clonally pure or composed of clones from a single phylogenetic clade. However, each patient contained at least one site composed of polyphyletic clones. Five patients exhibited monoclonal and unidirectional seeding from the ovary to intraperitoneal sites, and two patients demonstrated polyclonal spread and reseeding. Our findings indicate that at least two distinct modes of intraperitoneal spread operate in clonal dissemination and highlight the distribution of migratory potential over clonal populations comprising high-grade serous ovarian cancers.

Chordoma is a malignant, often incurable bone tumour showing notochordal differentiation. Here, we defined the somatic driver landscape of 104 cases of sporadic chordoma. We reveal somatic duplications of the notochordal transcription factor brachyury ( T ) in up to 27% of cases. These variants recapitulate the rearrangement architecture of the pathogenic germline duplications of T that underlie familial chordoma. In addition, we find potentially clinically actionable PI3K signalling mutations in 16% of cases. Intriguingly, one of the most frequently altered genes, mutated exclusively by inactivating mutation, was LYST (10%), which may represent a novel cancer gene in chordoma. Chordoma is a rare often incurable malignant bone tumour. Here, the authors investigate driver mutations of sporadic chordoma in 104 cases, revealing duplications in notochordal transcription factor brachyury ( T ), PI3K signalling mutations, and mutations in LYST, a potential novel cancer gene in chordoma.

Follicular lymphoma (FL) is an indolent, yet incurable B cell malignancy. A subset of patients experience an increased mortality rate driven by two distinct clinical end points: histological transformation and early progression after immunochemotherapy. The nature of tumor clonal dynamics leading to these clinical end points is poorly understood, and previously determined genetic alterations do not explain the majority of transformed cases or accurately predict early progressive disease. We contend that detailed knowledge of the expansion patterns of specific cell populations plus their associated mutations would provide insight into therapeutic strategies and disease biology over the time course of FL clinical histories. Using a combination of whole genome sequencing, targeted deep sequencing, and digital droplet PCR on matched diagnostic and relapse specimens, we deciphered the constituent clonal populations in 15 transformation cases and 6 progression cases, and measured the change in clonal population abundance over time. We observed widely divergent patterns of clonal dynamics in transformed cases relative to progressed cases. Transformation specimens were generally composed of clones that were rare or absent in diagnostic specimens, consistent with dramatic clonal expansions that came to dominate the transformation specimens. This pattern was independent of time to transformation and treatment modality. By contrast, early progression specimens were composed of clones that were already present in the diagnostic specimens and exhibited only moderate clonal dynamics, even in the presence of immunochemotherapy. Analysis of somatic mutations impacting 94 genes was undertaken in an extension cohort consisting of 395 samples from 277 patients in order to decipher disrupted biology in the two clinical end points. We found 12 genes that were more commonly mutated in transformed samples than in the preceding FL tumors, including TP53, B2M, CCND3, GNA13, S1PR2, and P2RY8. Moreover, ten genes were more commonly mutated in diagnostic specimens of patients with early progression, including TP53, BTG1, MKI67, and XBP1. Our results illuminate contrasting modes of evolution shaping the clinical histories of transformation and progression. They have implications for interpretation of evolutionary dynamics in the context of treatment-induced selective pressures, and indicate that transformation and progression will require different clinical management strategies.

Mutation signatures in cancer genomes reflect endogenous and exogenous mutational processes, offering insights into tumour etiology, features for prognostic and biologic stratification and vulnerabilities to be exploited therapeutically. We present a novel machine learning formalism for improved signature inference, based on multi-modal correlated topic models (MMCTM) which can at once infer signatures from both single nucleotide and structural variation counts derived from cancer genome sequencing data. We exemplify the utility of our approach on two hormone driven, DNA repair deficient cancers: breast and ovary (n = 755 samples total). We show how introducing correlated structure both within and between modes of mutation can increase accuracy of signature discovery, particularly in the context of sparse data. Our study emphasizes the importance of integrating multiple mutation modes for signature discovery and patient stratification, and provides a statistical modeling framework to incorporate additional features of interest for future studies.

Background/Objectives: Neuroendocrine neoplasms (NENs) are uncommon neoplasms. Grading informs the prognosis and treatment decision of NENs and is determined by cell proliferation, which is measured by mitotic count and Ki-67 index. These measurements present challenges for pathologists as they suffer inter- and intra-observer variability and are cumbersome to quantify. To address these challenges, we developed a machine learning pipeline for identifying tumor areas, proliferating cells, and grading NENs. Methods: Our study includes 385 samples of gastroenteropancreatic NENs from across British Columbia with two stains (247 H&E and 138 Ki-67 images). Labels for these cases are at the patient-level, and there are 186 patients. We systematically investigated three settings for our study: H&E, H&E with Ki-67, and pathologist-reviewed and corrected cases. Results: Our H&E framework achieved a three-fold balanced accuracy of 77.5% in NEN grading. The H&E with Ki-67 framework yields a performance improvement to 83.0% on grading. We provide survival and multivariate analysis with a c-index of 0.65. Grade 1 NENs misclassified by the model were reviewed by a pathologist to assess reasons. Analysis of our AI-graded NENs for the subset of pathologist-assessed G1s demonstrated a significant (p-value = 0.007) survival difference amongst samples the algorithm assigned to a higher grade (n = 20; median survival 4.22 years) compared to concordant G1 samples (n = 60; median survival 10.13 years). Conclusions: Our model identifies NEN grades with high accuracy and identified some grade 1 tumors as prognostically unique, suggesting potential improvements to standard grading. Further studies are needed to determine if this discordant group is a different clinical entity.

Sarcomatoid mesothelioma is an aggressive malignancy that can be challenging to distinguish from benign spindle cell mesothelial proliferations based on biopsy, and this distinction is crucial to patient treatment and prognosis. A novel deep learning based classifier may be able to aid pathologists in making this critical diagnostic distinction. SpindleMesoNET was trained on cases of malignant sarcomatoid mesothelioma and benign spindle cell mesothelial proliferations. Performance was assessed through cross-validation on the training set, on an independent set of challenging cases referred for expert opinion (‘referral’ test set), and on an externally stained set from outside institutions (‘externally stained’ test set). SpindleMesoNET predicted the benign or malignant status of cases with AUC's of 0.932, 0.925, and 0.989 on the cross-validation, referral and external test sets, respectively. The accuracy of SpindleMesoNET on the referral set cases (92.5%) was comparable to the average accuracy of 3 experienced pathologists on the same slide set (91.7%). We conclude that SpindleMesoNET can accurately distinguish sarcomatoid mesothelioma from benign spindle cell mesothelial proliferations. A deep learning system of this type holds potential for future use as an ancillary test in diagnostic pathology.