Biomedical subjects

Stefan Michiels

Publications and source records attributed to Stefan Michiels.

4 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 74·1 months (IQR 68·3-75·4). 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 0·84 [95% CI 0·79-0·88]). Concordance between manual and automated methods was modest. AI-based scoring (AI percentage lymphocytes) reclassified 120 (11·6%) 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] 0·41-0·93). Pertuzumab was associated with improved invasive disease-free survival at higher sTIL levels across all measurement approaches (HRs 0·36-0·48), 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 (≥70·0%; mean absolute improvement 12·1 percentage points [SD 2·8]). 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<0·010). 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 0·375-0·473) 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 0·73 [95% CI 0·66-0·82]; q<0·0001, distant disease-free survival was 0·70 [0·61-0·79]; q<0·0001, and overall survival was 0·72 [0·63-0·82]; q<0·0001 for sTIL scores and 0·80 [0·73-0·89]; q<0·0001, 0·77 [0·69-0·86]; q<0·0001, and 0·79 [0·70-0·88]; q=0·0002, 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↗

PubMed2025

Molecular analysis of lung adenocarcinomas from the SAFIR02-Lung cohort reveals new metastasis-associated copy-number alterations including frequent mutant-specific KRAS-allelic imbalance and identifies CDKN2A homozygous deletions as an independent biomarker of poor prognosis.

BACKGROUND: Identifying molecular alterations specific to advanced lung adenocarcinomas could provide insights into tumour progression and dissemination mechanisms. METHOD: We analysed tumour samples, either from locoregional lesions or distant metastases, from patients with advanced lung adenocarcinoma from the SAFIR02-Lung trial by targeted sequencing of 45 cancer genes and comparative genomic hybridisation array and compared them to early tumours samples from The Cancer Genome Atlas. RESULTS: Differences in copy-number alterations frequencies suggest the involvement in tumour progression of LAMB3, TNN/KIAA0040/TNR, KRAS, DAB2, MYC, EPHA3 and VIPR2, and in metastatic dissemination of AREG, ZNF503, PAX8, MMP13, JAM3, and MTURN. Conversely, no meaningful difference was found in pathogenic single-nucleotide variant frequencies, reinforcing the notion that they are early events in tumorigenesis. CDKN2A homozygous deletion was linked to poor clinical outcome in patients with early tumours (overall survival hazard ratio 2.17, 95% CI: 1.43-3.28, corrected p-value = 0.01). Furthermore, we found that KRAS mutant allele specific imbalance, i.e. focal amplification of the mutant allele, is more prevalent in locoregional or distant samples of metastatic patients than in early lesions (8.4%, 13% and 2.8% respectively). This observation was replicated in three public cohorts. Tumours with KRAS mutant allele specific imbalance show specific patterns of co-occurrence and mutual exclusion with alterations in key cancer genes like CDKN2A, TP53, STK11 and NKX2-1, often in a tumour type dependent manner. CONCLUSION: Advanced LUAD tumours exhibit higher copy-number alteration burden, with distinct alterations associated with tumour progression and metastasis. CDKN2A homozygous deletions predict poor prognosis in early disease, while KRAS mutant allele-specific imbalance is enriched in advanced tumours.

Humans↗

PubMed2025

Multimodal CustOmics: A unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology.

Characterizing cancer presents a delicate challenge as it involves deciphering complex biological interactions within the tumor's microenvironment. Clinical trials often provide histology images and molecular profiling of tumors, which can help understand these interactions. Despite recent advances in representing multimodal data for weakly supervised tasks in the medical domain, achieving a coherent and interpretable fusion of whole slide images and multi-omics data is still a challenge. Each modality operates at distinct biological levels, introducing substantial correlations between and within data sources. In response to these challenges, we propose a novel deep-learning-based approach designed to represent multi-omics & histopathology data for precision medicine in a readily interpretable manner. While our approach demonstrates superior performance compared to state-of-the-art methods across multiple test cases, it also deals with incomplete and missing data in a robust manner. It extracts various scores characterizing the activity of each modality and their interactions at the pathway and gene levels. The strength of our method lies in its capacity to unravel pathway activation through multimodal relationships and to extend enrichment analysis to spatial data for supervised tasks. We showcase its predictive capacity and interpretation scores by extensively exploring multiple TCGA datasets and validation cohorts. The method opens new perspectives in understanding the complex relationships between multimodal pathological genomic data in different cancer types and is publicly available on Github.

Deep Learning↗