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Jan Budczies

Researcher at University Hospital Heidelberg

Publications -  185
Citations -  16222

Jan Budczies is an academic researcher from University Hospital Heidelberg. The author has contributed to research in topics: Cancer & Breast cancer. The author has an hindex of 48, co-authored 166 publications receiving 12762 citations. Previous affiliations of Jan Budczies include German Cancer Research Center & Humboldt University of Berlin.

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Journal ArticleDOI

Spatial profiling of the microenvironment reveals low intratumoral heterogeneity and STK11-associated immune evasion in therapy-naïve lung adenocarcinomas.

TL;DR: In this paper , the authors performed multi-region sampling (2-4 samples per tumor; total of 55 samples) from a cohort of 19 untreated stage IA-IIIB lung adenocarcinomas (n = 11 KRAS mutant, n = 1 ERBB2 mutant and n = 7 KRAS wildtype).
Book ChapterDOI

Introduction on Genome-wide Expression Profiling from Formalin-Fixed Paraffin-Embedded Tissues Using Microarrays

TL;DR: New protocols for RNA processing of FFPE specimens revealed similar results as compared to the frozen tissue samples, which appears to be surprising given the fact that formalin fixation produces significant chemical modification of the RNA that depends on fixation conditions and times.
Journal ArticleDOI

Homogenous TP53mut-associated tumor biology across mutation and cancer types revealed by transcriptome analysis

TL;DR: In this paper , a comprehensive mRNA expression analysis in 24 cancer types of TCGA was performed to extract a consensus expression signature shared across TP53 mutation types and cancer types, and differential gene expression patterns between tumors harboring different TP53 mutations such as loss of function, gain of function or dominant negative mutations, and cancer-type specific patterns of gene expression and immune infiltration.
Journal ArticleDOI

Von der Paneldiagnostik zu umfassenden genomischen Analysen

TL;DR: Key aspects and applications of each method are summarized and discussed, and all approved predictive biomarkers and molecular targets, including gene fusions and copy number alterations, can be identified depending on panel design and method applied.