ArticlePLoS computational biology2026
ALFAssay: A feed‑forward neural network for quantitative fragmentomics‑based ctDNA profiling in breast cancer.
Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Pet Imaging as a Biomarker of Everolimus Added Value in Hormone Refractory postmenopausaL Women
Neoadjuvant Biomarker ResearcH Study of Palbociclib Combined With Endocrine Therapy in Estrogen Receptor Positive/HER2 Negative Breast CAncer
A Phase I/II Study of Paclitaxel Plus Carboplatin and Durvalumab (MEDI4736) With or Without Oleclumab (MEDI9447) for Previously Untreated Locally Recurrent Inoperable or Metastatic Triple-negative Breast Cancer
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The analysis of cell‑free DNA (cfDNA) is transforming cancer diagnostics, yet quantifying the fraction of circulating tumour DNA (ctDNA) from shallow whole‑genome sequencing (sWGS) remains challenging in tumours with low copy‑number aberration burden. We introduce ALFAssay, a feed‑forward neural network that estimates ctDNA fraction from fragmentation profiles of cfDNA in breast cancer. Using cfDNA from 896 plasma samples spanning early and metastatic HR + /HER2- and triple‑negative breast cancer, and healthy controls, (id: NCT03616886, NCT02028364, NCT03065621) we extract 204 bin‑level fragmentation features by computing the ratio of short fragments (90-150 bp) to all reads within 5 Mb genomic windows, while accounting for coverage effects by incorporating the total number of reads for each bin as an additional input feature. The resulting 408‑dimensional vectors are input to a fully connected network trained against ichorCNA‑derived ctDNA fractions using five‑fold cross‑validation. ALFAssay demonstrates high sensitivity (0.87) and specificity (0.94) for ctDNA detection and correlates with ichorCNA (r = 0.89) and the fragmentation‑based tool Fragle (r = 0.81). Its ctDNA predictions stratify patients by progression‑free survival and add complementary prognostic value to available tools. ALFAssay thus expands the bioinformatics toolkit for ctDNA quantification and highlights the potential of fragmentation signatures to augment multi‑modal liquid‑biopsy workflows.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.