Alejandro A. Schäffer

Qombucha infers unobserved progenitor methylation profiles in GBM, revealing subtype-specific developmental programs and improved subtype classification.

The paper identifies CLDN3 as a tumor-selective target in SCLC using proteogenomic analysis and validates its therapeutic potential with a monoclonal antibody.

This paper introduces omlta, an algorithm for aligning clonal trees to compare tumor progression, and applies it to cancer datasets.

This paper develops a machine learning framework, EXPRESSO, to predict treatment response from tumor transcriptomics across multiple cancer types and therapies, showing improved performance over existing methods.

This paper introduces FALAFL, a fair feature selection algorithm for single-cell methylation data, identifying universally tumor lineage-informative CpG sites in colorectal cancer.

This paper introduces LogiCAR designer, a computational method using single-cell transcriptomics to identify logic-gated antigen combinations for CAR therapy, showing improved efficacy and safety compared to existing approaches.

This study shows that TMPRSS2-ERG fusion promotes resistance to antiandrogens by interacting with GR, suggesting combined GR and AR inhibition could benefit tERG-positive prostate cancer patients.

The paper identifies Mif as a regulator of tumor immune evasion in low intra-tumor heterogeneity melanoma by analyzing single-cell RNA sequencing data and validating in patient data.

IMMClock predicts immune age of T cells and NK cells using gene expression, showing immune aging is more linked to cellular aging than differentiation and is accelerated in diseases.

The paper investigates the co-occurrence of chromosome 10 loss and chromosome 7 gain in gliomas, finding functional rescue interactions on chromosome 7 that may compensate for chromosome 10 loss.

This study maps the SCLC tumor microenvironment and identifies cancer-associated fibroblasts as key drivers of neuroendocrine state plasticity and poor prognosis.

This paper introduces Sgootr, a method to infer tumor lineage trees from single-cell methylation data, revealing metastatic progression models and identifying lineage-informative CpG sites.

The paper develops viRNAtrap, a deep learning-based pipeline to identify and characterize viral expression in cancer, revealing new viruses and endogenous viruses linked to poor survival.

ENLIGHT predicts patient response to targeted and immunotherapies using tumor transcriptomes without prior treatment response data, showing better accuracy than existing transcriptomics-based signatures.

CAMMiQ improves microbial strain-level detection and quantification in single-cell metagenomics by using combinatorial optimization and doubly-unique substrings, outperforming existing methods in accuracy.

This paper introduces PERCEPTION, a framework using single-cell transcriptomics to predict cancer treatment response and resistance, outperforming bulk expression-based methods in clinical trials.

The paper analyzes pan-cancer single-cell transcriptomes to evaluate and identify new CAR targets for head and neck cancer, finding five promising targets with superior selectivity and safety.

Ribovore improves GenBank processing of rRNA sequences by validating and classifying them using profile-based methods and HMMs.

The paper develops computational methods combining sequencing and miFISH data to improve accurate inference of clonal copy number evolution in tumors.

This paper introduces an algorithm called thread to reconstruct ancestral haplotypes in endogamous populations using pedigree and genotype data, finding it can reconstruct 79% of haplotypes for 224 ancestral individuals per chromosome.

The paper develops CODEFACS and LIRICS to deconvolve bulk gene expression and identify clinically relevant ligand-receptor interactions in tumors, finding specific T-cell interactions linked to anti-PD1 response.

PhISCS-BnB computes the most likely perfect tumor phylogeny from single-cell sequencing data, offering optimality guarantees and significant speed improvements over existing methods.

VADR validates and annotates viral sequences submitted to GenBank, improving submission speed and annotation quality by using curated RefSeq models and providing deterministic alerts.

The paper develops methods to deconvolve tumor copy number data by integrating bulk and single-cell sequencing data, improving accuracy over bulk data alone.

The paper uses single-cell transcriptomics data to explore optimal combination therapies for cancer by analyzing receptor targets across multiple tumor types.

The paper identifies 71,946 genetic interactions in cancer using a novel method, revealing non-synthetic-lethal interactions that explain driver gene specificity and drug response.