Evidence map›Paper›PMID 40617936›Full record

ArticleNPJ precision oncology2025

Unsupervised detection of rare events in liquid biopsy assays.

Javier Murgoitio-Esandi, Dean Tessone, Amin Naghdloo, Stephanie N Shishido, Brian Zhang, Haofeng Xu, Agnimitra Dasgupta, Jeremy Mason, Rajiv M Nagaraju, George Courcoubetis and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Javier Murgoitio-EsandiDepartment of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
Dean TessoneConvergent Science Institute for Cancer, Michelson Center, University of Southern California, Los Angeles, CA, USA.
Amin NaghdlooDepartment of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
Stephanie N ShishidoConvergent Science Institute for Cancer, Michelson Center, University of Southern California, Los Angeles, CA, USA.
Brian ZhangConvergent Science Institute for Cancer, Michelson Center, University of Southern California, Los Angeles, CA, USA.
Haofeng XuDepartment of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
Agnimitra DasguptaDepartment of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
Jeremy MasonConvergent Science Institute for Cancer, Michelson Center, University of Southern California, Los Angeles, CA, USA.
Rajiv M NagarajuConvergent Science Institute for Cancer, Michelson Center, University of Southern California, Los Angeles, CA, USA.
George CourcoubetisConvergent Science Institute for Cancer, Michelson Center, University of Southern California, Los Angeles, CA, USA.
James HicksConvergent Science Institute for Cancer, Michelson Center, University of Southern California, Los Angeles, CA, USA.
Peter KuhnDepartment of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
Assad A OberaiDepartment of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA. aoberai@usc.edu.

Funding

Multi-modal Liquid Biopsy Early Assessment of Breast Cancer, Pancreatic Cancer, and Multiple MyelomaU01CA285013 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI KUHN, PETER · 2023 to 2025
$3.4M
Breast Cancer Research Foundation BCRF-23-089NCI NIH HHS U01 CA285013NCI NIH HHS U01CA285013
6 · The paper itself

Abstract

The use of liquid biopsies in the detection, diagnosis and treatment monitoring of different types of cancers and other diseases often requires identifying and enumerating instances of analytes that are rare. Most current techniques that aim to computationally isolate these rare instances or events first learn the signature of the event, and then scan the appropriate biological assay for this signature. While such techniques have proven to be very useful, they are limited because they must first establish what signature to look for, and only then identify events that are consistent with this signature. In contrast to this, in this study, we present an automated approach that does not require the knowledge of the signature of the rare event. It works by breaking the assay into a sequence of components, learning the probability distribution of these components, and then isolating those that are rare. This is done with the help of deep generative algorithms in an unsupervised manner, meaning without a-priori knowledge of the rare event associated with an analyte. In this study, this approach is applied to immunofluorescence microscopy images of peripheral blood, where it is shown that it successfully isolates biologically relevant events in blood from normal donors spiked with cancer-related cells and in blood from patients with late-stage breast cancer.

Identifiers

PMID40617936
PMCPMC12228807

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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.