| 1. |
- Figiel, Sandy, et al.
(författare)
-
Spatial transcriptomic analysis of virtual prostate biopsy reveals confounding effect of tissue heterogeneity on genomic signatures
- 2023
-
Ingår i: Molecular Cancer. - : Springer Nature. - 1476-4598. ; 22:1, s. 162-
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Genetic signatures have added a molecular dimension to prognostics and therapeutic decision-making. However, tumour heterogeneity in prostate cancer and current sampling methods could confound accurate assessment. Based on previously published spatial transcriptomic data from multifocal prostate cancer, we created virtual biopsy models that mimic conventional biopsy placement and core size. We then analysed the gene expression of different prognostic signatures (OncotypeDx®, Decipher®, Prostadiag®) using a step-wise approach with increasing resolution from pseudo-bulk analysis of the whole biopsy, to differentiation by tissue subtype (benign, stroma, tumour), followed by distinct tumour grade and finally clonal resolution. The gene expression profile of virtual tumour biopsies revealed clear differences between grade groups and tumour clones, compared to a benign control, which were not reflected in bulk analyses. This suggests that bulk analyses of whole biopsies or tumour-only areas, as used in clinical practice, may provide an inaccurate assessment of gene profiles. The type of tissue, the grade of the tumour and the clonal composition all influence the gene expression in a biopsy. Clinical decision making based on biopsy genomics should be made with caution while we await more precise targeting and cost-effective spatial analyses.
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
- Chelebian, Eduard, et al.
(författare)
-
Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer
- 2021
-
Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 13:19
-
Tidskriftsartikel (refereegranskat)abstract
- Simple Summary Prostate cancer has very varied appearances when examined under the microscope, and it is difficult to distinguish clinically significant cancer from indolent disease. In this study, we use computer analyses inspired by neurons, so-called 'neural networks', to gain new insights into the connection between how tissue looks and underlying genes which program the function of prostate cells. Neural networks are 'trained' to carry out specific tasks, and training requires large numbers of training examples. Here, we show that a network pre-trained on different data can still identify biologically meaningful regions, without the need for additional training. The neural network interpretations matched independent manual assessment by human pathologists, and even resulted in more refined interpretation when considering the relationship with the underlying genes. This is a new way to automatically detect prostate cancer and its genetic characteristics without the need for human supervision, which means it could possibly help in making better treatment decisions. Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H & E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H & E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out.
|
|
| 6. |
- Erickson, Andrew, et al.
(författare)
-
The spatial landscape of clonal somatic mutations in benign and malignant tissue
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Defining the transition from benign to malignant tissue is fundamental to improve early diagnosis of cancer. Here, we provide an unsupervised approach to study spatial genome integrity in situ to gain molecular insight into clonal relationships. We employed spatially resolved transcriptomics to infer spatial copy number variations in >120 000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of an unsupervised approach to capture the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.
|
|
| 7. |
|
|
| 8. |
- Joffrin, E., et al.
(författare)
-
Overview of the JET preparation for deuterium-tritium operation with the ITER like-wall
- 2019
-
Ingår i: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 59:11
-
Forskningsöversikt (refereegranskat)abstract
- For the past several years, the JET scientific programme (Pamela et al 2007 Fusion Eng. Des. 82 590) has been engaged in a multi-campaign effort, including experiments in D, H and T, leading up to 2020 and the first experiments with 50%/50% D-T mixtures since 1997 and the first ever D-T plasmas with the ITER mix of plasma-facing component materials. For this purpose, a concerted physics and technology programme was launched with a view to prepare the D-T campaign (DTE2). This paper addresses the key elements developed by the JET programme directly contributing to the D-T preparation. This intense preparation includes the review of the physics basis for the D-T operational scenarios, including the fusion power predictions through first principle and integrated modelling, and the impact of isotopes in the operation and physics of D-T plasmas (thermal and particle transport, high confinement mode (H-mode) access, Be and W erosion, fuel recovery, etc). This effort also requires improving several aspects of plasma operation for DTE2, such as real time control schemes, heat load control, disruption avoidance and a mitigation system (including the installation of a new shattered pellet injector), novel ion cyclotron resonance heating schemes (such as the three-ions scheme), new diagnostics (neutron camera and spectrometer, active Alfven eigenmode antennas, neutral gauges, radiation hard imaging systems...) and the calibration of the JET neutron diagnostics at 14 MeV for accurate fusion power measurement. The active preparation of JET for the 2020 D-T campaign provides an incomparable source of information and a basis for the future D-T operation of ITER, and it is also foreseen that a large number of key physics issues will be addressed in support of burning plasmas.
|
|
| 9. |
- Linder, Nina, et al.
(författare)
-
Deep learning for detecting tumour-infiltrating lymphocytes in testicular germ cell tumours
- 2019
-
Ingår i: Journal of Clinical Pathology. - : BMJ Publishing Group Ltd. - 0021-9746 .- 1472-4146. ; 72:2, s. 157-164
-
Tidskriftsartikel (refereegranskat)abstract
- AIMS: To evaluate if a deep learning algorithm can be trained to identify tumour-infiltrating lymphocytes (TILs) in tissue samples of testicular germ cell tumours and to assess whether the TIL counts correlate with relapse status of the patient.METHODS: TILs were manually annotated in 259 tumour regions from 28 whole-slide images (WSIs) of H&E-stained tissue samples. A deep learning algorithm was trained on half of the regions and tested on the other half. The algorithm was further applied to larger areas of tumour WSIs from 89 patients and correlated with clinicopathological data.RESULTS: A correlation coefficient of 0.89 was achieved when comparing the algorithm with the manual TIL count in the test set of images in which TILs were present (n=47). In the WSI regions from the 89 patient samples, the median TIL density was 1009/mm2. In seminomas, none of the relapsed patients belonged to the highest TIL density tertile (>2011/mm2). TIL quantifications performed visually by three pathologists on the same tumours were not significantly associated with outcome. The average interobserver agreement between the pathologists when assigning a patient into TIL tertiles was 0.32 (Kappa test) compared with 0.35 between the algorithm and the experts, respectively. A higher TIL density was associated with a lower clinical tumour stage, seminoma histology and lack of lymphovascular invasion.CONCLUSIONS: Deep learning-based image analysis can be used for detecting TILs in testicular germ cell cancer more objectively and it has potential for use as a prognostic marker for disease relapse.
|
|
