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Torsten O Nielsen

Publications and source records attributed to Torsten O Nielsen.

8 recordsLinked to original sources
PubMed2026

Manual, digital, and AI tumour-infiltrating lymphocyte scoring: a secondary analysis of the APHINITY randomised trial.

BACKGROUND: Stromal tumour-infiltrating lymphocytes (sTILs) are prognostic in early-stage HER2-positive breast cancer, but their role in the context of dual HER2 blockade remains undefined. We evaluated manual, digital, and artificial intelligence (AI)-based sTIL quantification, together with AI-derived spatial metrics, for prognostic and treatment-benefit stratification using tumour samples from the phase 3 APHINITY trial. METHODS: In the APHINITY trial, 4805 patients were randomly assigned to receive chemotherapy plus trastuzumab with pertuzumab or chemotherapy plus trastuzumab with placebo. Median follow-up was 74&#xb7;1 months (IQR 68&#xb7;3-75&#xb7;4). We analysed 4262 haematoxylin and eosin-stained images using manual assessment, an automated digital approach, AI-based lymphocyte quantification (AI percentage lymphocytes), and two AI-derived spatial features (AI-TIL and immune hotspot). Interobserver reproducibility was assessed in 262 randomly chosen tumour samples scored independently by five pathologists. Multivariable Cox models were used to assess associations between TIL levels and invasive disease-free survival (primary outcome in APHINITY), distant recurrence-free interval, and overall survival. The heterogeneity of pertuzumab benefit was evaluated using subgroup analyses, subpopulation treatment effect pattern plot analyses, and nested Cox models with treatment-by-biomarker interaction terms. FINDINGS: Manual scoring showed high interobserver reproducibility (intraclass correlation coefficient 0&#xb7;84 [95% CI 0&#xb7;79-0&#xb7;88]). Concordance between manual and automated methods was modest. AI-based scoring (AI percentage lymphocytes) reclassified 120 (11&#xb7;6%) of 1035 node-positive tumours from immune-low (by manual scoring) to immune-high; this subgroup of patients showed greater separation of 5-year invasive disease-free survival curves between pertuzumab and placebo groups compared with patients whose tumours were concordantly classified as immune-low by both manual and AI-based approaches. Higher levels of TILs were associated with improved invasive disease-free survival for all sTIL measurement approaches and spatial measurements (hazard ratios [HRs] 0&#xb7;41-0&#xb7;93). Pertuzumab was associated with improved invasive disease-free survival at higher sTIL levels across all measurement approaches (HRs 0&#xb7;36-0&#xb7;48), but was not associated with higher values of spatial measures. The largest 6-year absolute improvements with pertuzumab were observed in patients with node-positive disease whose tumours scored in the highest level of immune infiltration of manual sTIL scoring (&#x2265;70&#xb7;0%; mean absolute improvement 12&#xb7;1 percentage points [SD 2&#xb7;8]). In nested prognostic and predictive models, AI-based immune hotspot scores provided the most consistent additional information when combined with any sTIL measurement (all p<0&#xb7;010). INTERPRETATION: Standardised manual sTIL scoring was reproducible, and digital and AI-based methods showed consistent prognostic stratification and potential for treatment-benefit stratification despite only modest correlation between platforms. AI spatial metrics provided complementary information beyond sTIL density and could support more scalable immune assessment. Future studies are needed to validate these approaches in independent cohorts and to clarify their clinical utility for stratifying contemporary HER2-directed therapies. FUNDING: None.

Humans↗
PubMed2026

Artificial intelligence-based tumour infiltrating lymphocyte quantification in patients with triple-negative breast cancer: an independent validation study.

BACKGROUND: Tumour-infiltrating lymphocytes (TILs) are a robust prognostic marker in patients with triple-negative breast cancer. Artificial intelligence (AI)-derived computational tools assessing TILs could improve efficiency, but require independent validation against clinical outcomes. We aimed to compare the prognostic performance of AI-derived TIL scores with pathologist-scored TILs in a large, prospectively collected dataset pooled from randomised controlled trials. METHODS: CATALINA was an independent, external validation study using prospectively collected long-term clinical outcome data pooled from seven randomised clinical trials conducted at multiple sites. We independently evaluated two previously validated AI pipelines that generate five computationally assessed tumour-infiltrating lymphocyte (cTIL) scores by masked, independent deployment of locked models. cTIL scores were correlated with the mean of the pathologist-scored stromal TILs (sTILs) in 220 digitised haematoxylin and eosin whole slide images in a cohort of patients with early-stage triple-negative or HER-2 positive breast cancer, previously scored by trained pathologists in a TIL-reproducibility study. Prognostic performance was assessed in a separate cohort of patients with early triple-negative breast cancer pooled from seven prospective, randomised adjuvant trials. Multivariable Cox regression models adjusted for clinicopathological factors and study heterogeneity assessed associations of cTIL score and sTIL score with invasive disease-free survival, distant disease-free survival, and overall survival. 5-year discrimination was estimated using time-dependent area under the receiver operating characteristic curve (AUC). FINDINGS: Individual data were collated from 1759 patients, of whom 1356 had complete clinicopathological data, pathologist sTIL scores, and cTIL scores available. Modest correlation (r 0&#xb7;375-0&#xb7;473) was observed between cTIL scores and the mean pathologist sTIL score. Both sTIL and cTIL were independently associated with 5-year invasive disease-free survival, distant disease-free survival, and overall survival after adjustment for clinicopathological factors (hazard ratio for invasive disease-free survival was 0&#xb7;73 [95% CI 0&#xb7;66-0&#xb7;82]; q<0&#xb7;0001, distant disease-free survival was 0&#xb7;70 [0&#xb7;61-0&#xb7;79]; q<0&#xb7;0001, and overall survival was 0&#xb7;72 [0&#xb7;63-0&#xb7;82]; q<0&#xb7;0001 for sTIL scores and 0&#xb7;80 [0&#xb7;73-0&#xb7;89]; q<0&#xb7;0001, 0&#xb7;77 [0&#xb7;69-0&#xb7;86]; q<0&#xb7;0001, and 0&#xb7;79 [0&#xb7;70-0&#xb7;88]; q=0&#xb7;0002, respectively, for percentage_lymphocyte scores). In models adjusted for clinicopathological variables and sTIL score, cTIL score did not maintain a statistically significant prognostic association. Both sTIL and cTIL scores improved the 5-year AUC over clinicopathological variables alone, while cTIL score did not significantly further improve AUC when combined with clinicopathological variables and sTIL score. INTERPRETATION: Two cTIL models deployed entirely without retraining or modification provided statistically significant prognostic information and improved risk discrimination compared with clinicopathological variables alone in this large, platform-based, independent validation study. Although cTIL score did not incrementally improve prognostication compared with models combining clinicopathological variables with sTIL score, these findings support the application of cTILs as a reproducible prognostic biomarker, particularly in settings where routine or widespread pathologist assessment is unavailable. FUNDING: Breast Cancer Research Foundation (USA).

Humans↗
PubMed2004

Expression of the insulin-like growth factor I receptor and urokinase plasminogen activator in breast cancer is associated with poor survival: potential for intervention with 17-allylamino geldanamycin.

Urokinase plasminogen activator (uPA) expression in breast cancer is associated with relapse and a reduction in disease-specific survival. Thus, efforts are under way to identify uPA inhibitors. By screening a chemical library of >1000 compounds, 17-allyaminogeldanamycin (17AAG) was identified as a potent inhibitor of uPA by the National Cancer Institute and is now in Phase I clinical trials. At this time, it remains unclear how 17AAG blocks uPA; one possibility is through disruption of the insulin-like growth factor I receptor (IGF-IR) pathway. This would be consistent with studies from our laboratory showing that activation of IGF-IR results in the induction of uPA protein. In the study described herein, we observed that IGF-IR and uPA were highly expressed in 87 and 55% of breast cancer by screening tumor tissue microarrays representing 930 cases. A significant proportion (52.1% = 354 of 680 cases, P < 0.0001) of the patients had tumors expressing both proteins. uPA alone (P = 0.033) or in combination with IGF-IR (P = 0.0104) was indicative of decreased disease-specific survival. Next, we demonstrated that treating MDA-MB-231 cells with increasing concentrations of 17AAG resulted in IGF-IR degradation (IC(50) = 1.0 micro M) and blocked signal transduction through the Akt and mitogen-activated protein kinase pathways. Finally, we found that 17AAG had a robust inhibitory effect on the production of uPA mRNAand protein in the presence of IGF-I. Thus, our study raises the possibility that 17AAG could prove to be an effective therapeutic agent for a large number of breast cancer patients by inhibiting the IGF-IR and ultimately uPA.

Antineoplastic Agents↗
PubMed2003

Tissue microarray validation of epidermal growth factor receptor and SALL2 in synovial sarcoma with comparison to tumors of similar histology.

Histological diagnosis of synovial sarcoma can be difficult. Genome-wide expression profiling has identified a number of genes expressed at higher levels in synovial sarcoma than in other soft tissue tumors, representing excellent candidates for diagnostic immunohistochemical markers. A tissue microarray comprising 77 sarcomas, including 46 synovial sarcomas, was constructed to validate identified markers and investigate their expression in tumors in the differential diagnosis of synovial sarcoma. Immunostaining was performed for two such markers, epidermal growth factor receptor and SAL (drosophila)-like 2 (SALL2), and for fifteen established markers used in the differential diagnosis of sarcomas. As predicted by expression profiling, epidermal growth factor receptor (a potential therapeutic target) and SALL2 stained most cases of synovial sarcoma; staining was significantly less common among other tested sarcomas. Hierarchical clustering analysis applied to immunostaining results for all 18 antibodies showed that synovial sarcomas, leiomyosarcomas, hemangiopericytomas, and solitary fibrous tumors cluster distinctly, and assigned one case with indeterminate histology as a Ewing sarcoma. Digital images from over 2500 immunostained cores analyzed in this study were captured and are made accessible through the accompanying website: http://microarray-pubs.stanford.edu/tma_portal/synsarc.

Biomarkers↗
PubMed2003

Gene expression patterns and gene copy number changes in dermatofibrosarcoma protuberans.

Dermatofibrosarcoma protuberans (DFSP) is an aggressive spindle cell neoplasm. It is associated with the chromosomal translocation, t(17:22), which fuses the COL1A1 and PDGFbeta genes. We determined the characteristic gene expression profile of DFSP and characterized DNA copy number changes in DFSP by array-based comparative genomic hybridization (array CGH). Fresh frozen and formalin-fixed, paraffin-embedded samples of DFSP were analyzed by array CGH (four cases) and DNA microarray analysis of global gene expression (nine cases). The nine DFSPs were readily distinguished from 27 other diverse soft tissue tumors based on their gene expression patterns. Genes characteristically expressed in the DFSPs included PDGF beta and its receptor, PDGFRB, APOD, MEOX1, PLA2R, and PRKCA. Array CGH of DNA extracted either from frozen tumor samples or from paraffin blocks yielded equivalent results. Large areas of chromosomes 17q and 22q, bounded by COL1A1 and PDGF beta, respectively, were amplified in DFSP. Expression of genes in the amplified regions was significantly elevated. Our data shows that: 1) DFSP has a distinctive gene expression profile; 2) array CGH can be applied successfully to frozen or formalin-fixed, paraffin-embedded tumor samples; 3) a characteristic amplification of sequences from chromosomes 17q and 22q, demarcated by the COL1A1 and PDGF beta genes, respectively, was associated with elevated expression of the amplified genes.

Adult↗
PubMed2002

Molecular characterisation of soft tissue tumours: a gene expression study.

BACKGROUND: Soft-tissue tumours are derived from mesenchymal cells such as fibroblasts, muscle cells, or adipocytes, but for many such tumours the histogenesis is controversial. We aimed to start molecular characterisation of these rare neoplasms and to do a genome-wide search for new diagnostic markers. METHODS: We analysed gene-expression patterns of 41 soft-tissue tumours with spotted cDNA microarrays. After removal of errors introduced by use of different microarray batches, the expression patterns of 5520 genes that were well defined were used to separate tumours into discrete groups by hierarchical clustering and singular value decomposition. FINDINGS: Synovial sarcomas, gastrointestinal stromal tumours, neural tumours, and a subset of the leiomyosarcomas, showed strikingly distinct gene-expression patterns. Other tumour categories--malignant fibrous histiocytoma, liposarcoma, and the remaining leiomyosarcomas--shared molecular profiles that were not predicted by histological features or immunohistochemistry. Strong expression of known genes, such as KIT in gastrointestinal stromal tumours, was noted within gene sets that distinguished the different sarcomas. However, many uncharacterised genes also contributed to the distinction between tumour types. INTERPRETATION: These results suggest a new method for classification of soft-tissue tumours, which could improve on the method based on histological findings. Large numbers of uncharacterised genes contributed to distinctions between the tumours, and some of these could be useful markers for diagnosis, have prognostic significance, or prove possible targets for treatment.

Gene Expression Profiling↗
PubMed2002

Tissue microarrays are an effective quality assurance tool for diagnostic immunohistochemistry.

There has been considerable variability in the reported results of immunohistochemical staining for some diagnostically relevant antigens. Our objectives in this study were to (1) use a multitumor tissue microarray with tissue from 351 cases received in our department, representing 16 normal tissues and 47 different tumor types, to compare immunohistochemical staining results in our laboratory with published data, using a panel of 22 antibodies; (2) assess interlaboratory variability of immunohistochemical staining for S-100 using this microarray; and (3) test the ability of hierarchical clustering analysis to group tumors by primary site, based on their immunostaining profile. Tissue microarrays consisting of duplicate 0.6-mm cores from blocks identified in the hospital archives were constructed and stained according to our usual protocols. Antibodies directed against the following antigens were used: B72.3, bcl-2, carcinoembryonic antigen, c-kit, pankeratin, CD 68, CD 99, CK 5/6, CK 7, CK 8/18, CK19, CK 20, CK 22, epithelial membrane antigen, estrogen receptor, melan-A, p53, placental alkaline phosphatase, S-100, synaptophysin, thyroid transcription factor-1, and vimentin. Staining results on the array cases were compared with published results, and hierarchical clustering analysis was performed based on the immunohistochemical staining results. Unstained slides of the multitumor tissue microarray were sent to five other diagnostic immunohistochemistry laboratories and stained for S-100 protein. The staining results from the different laboratories were compared. Staining results using our current methods and samples from our laboratory were compatible with those described in the literature for most antigens. Placental alkaline phosphatase staining was not specific with our protocol, showing staining of a broad spectrum of different tumors; this finding initiated a review of our recent requests for placental alkaline phosphatase immunostaining and revealed two instances in which placental alkaline phosphatase positivity was incorrectly interpreted as evidence of a germ cell tumor. S-100 staining was less sensitive but more specific for the diagnosis of melanoma or neural tumor in our laboratory, compared to some published reports. Assessment of interlaboratory variability of S-100 immunostaining showed that there was more frequent staining of carcinomas in some laboratories, resulting in decreased specificity of S-100 staining in distinguishing melanoma from carcinoma. Hierarchical clustering analysis showed a strong trend for tumors to cluster by tissue of origin, but there were significant exceptions. We conclude that multiple-tumor microarrays are an efficient method for assessing the sensitivity and specificity of staining with any antibody used diagnostically. As a tool for quality assurance, they offer the advantage of taking into account local differences in tissue fixation, processing, and staining. They also allow cost-effective assessment of interlaboratory variability in immunohistochemical staining. Results of hierarchical clustering analysis show the potential for panels of immunohistochemical stains to identify the primary site of metastatic carcinomas but also confirm the limitations of currently available antibodies in giving unequivocal tissue-specific staining patterns.

Biomarkers, Tumor↗