Jonas Krag Viken

Background: Tumor invasion and malignant transformation are spatiotemporally coordinated processes that shape clinical behavior, diagnostic classification, and treatment response. Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive tumors that can arise sporadically or in people with neurofibromatosis type 1 (NF-1), yet how distinct spatial niches underlie MPNST intratumor heterogeneity, local invasion, or malignant transformation from benign precursors is not fully understood. Methods: We integrate spatial analysis of 50,896 transcriptomes from 15 human MPNSTs with deep learning image analysis to define the interplay between genetic alterations, the immune microenvironment, and epigenetic cell fate regulation in MPNST invasion and transformation. Results: Spatial transcriptomics reveal distinct infiltrating immune cells and immunomodulatory spatial clusters in sporadic compared to NF-1–associated MPNSTs. Local invasion is marked by spatially defined immunomodulatory and dedifferentiation programs. Analysis of 2 NF-1–associated MPNSTs with contiguous morphologically defined neurofibroma and MPNST components demonstrates that NF-1–associated tumors exhibit a similar malignant transformation process associated with dynamic immune infiltration, chromosome 9p loss, and dedifferentiation concomitant with increased cell proliferation. Finally, deep learning morphology encoders were trained on spatial transcriptomic data to predict spatial transcriptomic cluster identity and chromosome 9p loss from hematoxylin and eosin–stained images although the generalizability of such encoders remains a challenge. Conclusions: Our data provide a spatial framework for peripheral nerve sheath tumor transformation although the limited sample size of the present cohort will require additional validation of the proposed model.

Physical activity is effective for preventing and treating type 2 diabetes, but some individuals do not achieve metabolic benefits from exercise (“non-responders”). We investigated non-responders in terms of insulin sensitivity changes following a 12-week supervised strength and endurance exercise program. We used a hyperinsulinaemic euglycaemic clamp to measure insulin sensitivity among 26 men aged 40–65, categorizing them into non-responders or responders based on their insulin sensitivity change scores. The exercise regimen included VO₂max, muscle strength, whole-body MRI scans, muscle and fat biopsies, and serum samples. mRNA sequencing was performed on biopsies and Olink proteomics on serum samples. Non-responders showed more visceral and intramuscular fat and signs of dyslipidaemia and low-grade inflammation at baseline and did not improve in insulin sensitivity following exercise, although they showed gains in VO₂max and muscle strength. Impaired IL6-JAK-STAT3 signalling in non-responders was suggested by serum proteomics analysis, and a baseline serum proteomic machine learning (ML) algorithm predicted insulin sensitivity responses with high accuracy, validated across two independent exercise cohorts. The ML model identified 30 serum proteins that could forecast exercise-induced insulin sensitivity changes.