Alejandro A. Schäffer

Qombucha: Reconstructing unobserved progenitor methylation profiles reveals distinct developmental programs in glioblastoma

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

Proteomic Profiling Identifies CLDN3 as a Tumor-Selective Therapeutic Target in Small Cell Lung Cancer

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

Identifying Robust Subclonal Structures through Tumor Progression Tree Alignment

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

A machine learning framework for supervised treatment response prediction from tumor transcriptomics: A large-scale pan-cancer study

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.

Fair molecular feature selection unveils universally tumor lineage-informative methylation sites in colorectal cancer

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

Single-cell-guided identification of logic-gated antigen combinations for designing effective and safe CAR therapy

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.

TMPRSS2-ERG confers resistance to antiandrogens: mechanism and therapeutic implications

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.

IMMClock reveals immune aging and T cell function at single-cell resolution

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.

Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models

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.

Characterizing the landscape of viral expression in cancer by deep learning

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.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

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

Strain Level Microbial Detection and Quantification with Applications to Single Cell Metagenomics

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.

Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors

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.

Pan-cancer analysis of patient tumor single-cell transcriptomes identifies promising selective and safe CAR targets in head and neck cancer

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: ribosomal RNA sequence analysis for GenBank submissions and database curation

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

Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data

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

Ancestral Haplotype Reconstruction in Endogamous Populations using Identity-By-Descent

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.

Deconvolving clinically relevant cellular immune crosstalk from bulk gene expression using CODEFACS and LIRICS

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: A Fast Branch and Bound Algorithm for the Perfect Tumor Phylogeny Reconstruction Problem

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: validation and annotation of virus sequence submissions to GenBank

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

Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data

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 Landscape of Receptor-Mediated Precision Cancer Combination Therapy: A Single-Cell Perspective

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

Beyond synthetic lethality: charting the landscape of clinically relevant genetic interactions in cancer

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.