bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/187526

Methylation-To-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, And Pathologic Complete Response in Multiple Cohorts

Thompson, J. A.·Christensen, B. C.·Marsit, C. J.
View original source ↗Export metadata ↓

Abstract

BackgroundApproaches that capitalize on the benefits of multi-omic data integration in invasive breast carcinoma to define prognostic biomarkers for precision medicine have been slow to emerge. In this work, we examined the efficacy of our methylation-to-expression feature model (M2EFM) approach to combining molecular and clinical predictors as part of a single analysis to create prognostic risk scores for overall survival, distant metastasis, and chemosensitivity.\n\nMethodsGene expression and DNA methylation values as well as clinical variables were integrated via M2EFM to build prognostic models of overall survival using 1028 breast tumor samples and further applied to external validation cohorts of 61 and 327 samples. Data-integrated prognostic models of distant recurrence-free survival and pathologic complete response were built using 306 samples and validated on 182 samples of external validation data. Additionally, we compared the discrimination and calibration of M2EFM models to other approaches.\n\nResultsDespite different populations and assays, M2EFM models validated with good accuracy (C-index or AUC [≥] .7) for all outcomes in all validation data. M2EFM models had the most consistent performance overall and superior calibration, suggesting a greater likelihood of clinical utility. Finally, we demonstrated that M2EFM identifies functionally relevant genes, which could be useful in translating an M2EFM biomarker to the clinic.\n\nConclusionM2EFM uses multiple levels of genomic data to infer disrupted regulatory patterns, thus providing a gene signature that connects loss of regulatory control with cancer prognosis.\n\nFundingThe analyses described in this report were supported by NIH grants R01ES022222, P30CA138292, P30ES019776, and R01DE022772.\n\nConflicts of InterestThe authors declare no potential conflicts of interest.

Source connections

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Thompson, J. A., Christensen, B. C., Marsit, C. J.. 2017-09-12. Methylation-To-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, And Pathologic Complete Response in Multiple Cohorts. https://doi.org/10.1101/187526

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Transcript quantification using context-aware MAPQ modeling

Long-read RNA-sequencing enables accurate transcript-level quantification, but prior static filtering of MAPping Quality (MAPQ) can ignore transcriptome-intrinsic ambiguity, and discarding reads on this basis distorts abundance estimates. We present CALMeR (Context-Aware Long-read MAPQ-based Rescuer), which conditions MAPQ filtering on transcript properties rather than a fixed cutoff. CALMeR fits a zero-one inflated beta regression on various transcript and read-level features to model the expected MAPQ distribution for each alignment. Reads whose MAPQ fits this context, such as low-MAPQ reads from multi-isoform genes and paralog families are retained; reads with anomalously low MAPQ are removed as likely artifacts. Retained reads can be summed per transcript or passed to any downstream quantifier. Compared with a MAPQ [≥] 1 filter and expectation-maximization-based transcript quantification methods, CALMeR recovers more spike-in transcripts and better matches known spike-in concentrations. CALMeR is available at https://github.com/sparthib/CALMeR.

bioinformatics↗
bioRxiv2026

Structure-resolved fold annotation of the medicinal-leech salivary-EV cargo: a Ly-6/uPAR three-finger scaffold and a structurally dark fraction

Many proteins that medicinal leeches package into salivary extracellular vesicles (EVs) carry no interpretable sequence annotation, so the least-characterised part of a pharmacologically important secretome lacks even a fold assignment. A companion Astral-DIA study of Hirudinaria manillensis nominated 17 ultra-enriched candidate EV cargo proteins. Here we ask whether structure prediction and structural search can assign folds where sequence methods fail, and which candidates remain unresolved under prespecified criteria. With an offline pipeline (ESMFold, TM-align, Foldseek against AFDB-SwissProt and PDB100, UniRef90 search, protein-to-genome alignment), the candidates separate into intracellular/membrane machinery and a secreted, cysteine-dense fraction of three proteins with no Pfam domain. The predicted structure of the leech Protein-sleepless homolog g21166 supports its assignment to the Ly-6/uPAR three-finger fold (TM 0.58 vs CD59; best significant PDB match Lynx1); no function is inferred. g436 and g20791 returned no match meeting the criterion (alnTM [≥] 0.5, E [≤] 0.05) in either database, g436 even after homolog-informed modelling. Gene structure and splice junctions support a conserved locus for the two-Trypsin-segment model g8114 (catalytic unit not established). A Boltz-2 thrombin pilot did not support target engagement, and a pre-registered nicotinic test was terminated as undecidable. The results yield a structure-informed shortlist for experimental follow-up.

bioinformatics↗
bioRxiv2026

EpiRank: partner-agnostic surface-patch retrieval for epitope prediction of protein binding interfaces

Structure-based interface predictors are normally reported as a single pooled ROC AUC over a residue test set. For a protein designer the operative question is different and much harder: given one target chain, will the top-ranked surface patch be a real binding site? We built EpiRank, a partner-agnostic epitope predictor, on HumanPPI 29,246 predicted human dimers, 12,296 target chains, 4.18 M residues using only single-chain geometry and physicochemistry (170 features, no partner, no sequence search, no structural alphabet), and evaluated it as a retrieval problem with homology- and partner-graph-controlled splits, on the 3,529 chains that survive an explicit partner-contamination filter. The model places a real epitope in its top-ranked patch 64 % of the time. That number is far less informative than it looks. Compact patches placed at random hit a real epitope 41 % of the time under the same rule, so the model's lift is 1.57 x, not 64 %; 54 % of all predicted patches land on nothing; and when the same chains are scored by models trained on a 2,000-chain and an 11,669-chain superset, a substantial fraction change answer while the aggregate barely moves. We document four specific ways this task invites overstatement partner-graph percolation across splits, single-linkage homology percolation at the conventional 30 % identity threshold, a count-only hit rule that rewards spatial smoothing rather than signal, and hotspot precision conditioned on success, which ranks a random baseline above the trained model. Each was hit during development and each inverted a conclusion we had already drawn. We argue that interface-prediction papers should report lift over a geometry-matched random baseline, patch precision, and per-target stability alongside AUC, and we release the evaluation harness that computes them.

bioinformatics↗