Evidence map›Paper›PMID 37165441›Full record

ArticleBreast cancer research : BCR2023

Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer.

Joseph R Peterson, John A Cole, John R Pfeiffer, Gregory H Norris, Yuhan Zhang, Dorys Lopez-Ramos, Tushar Pandey, Matthew Biancalana, Hope R Esslinger, Anuja K Antony and 1 more

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Article in Breast cancer research : BCR, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Joseph R Peterson *SimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA. jrp@simbiosys.com.
John A Cole *SimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA.
John R PfeifferSimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA.
Gregory H NorrisSimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA.
Yuhan ZhangSimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA.
Dorys Lopez-RamosSimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA.
Tushar PandeySimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA.
Matthew BiancalanaSimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA.
Hope R EsslingerDepartment of Radiation Oncology, University of Cincinnati, College of Medicine, Cincinnati, OH, USA.
Anuja K AntonySimBioSys, Inc., 180 N La Salle St. Suite 3250, Chicago, IL, 60601, USA.
Vinita TakiarDepartment of Radiation Oncology, University of Cincinnati, College of Medicine, Cincinnati, OH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGeneralizable population-based studies are unable to account for individual tumor heterogeneity that contributes to variability in a patient's response to physician-chosen therapy. Although molecular characterization of tumors has advanced precision medicine, in early-stage and locally advanced breast cancer patients, predicting a patient's response to neoadjuvant therapy (NAT) remains a gap in current clinical practice. Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform.

methodsA single-blinded study was performed using a retrospective database from a single institution (9/2014-12/2020). Patients included: ≥ 18 years with breast cancer who completed NAT, with pre-treatment dynamic contrast enhanced magnetic resonance imaging. Demographics, chemotherapy, baseline (pre-treatment) MRI and pathologic data were input into the TumorScope Predict (TS) biophysical simulation platform to generate predictions. Primary outcomes included predictions of pathological complete response (pCR) versus residual disease (RD) and final volume for each tumor. For validation, post-NAT predicted pCR and tumor volumes were compared to actual pathological assessment and MRI-assessed volumes. Predicted pCR was pre-defined as residual tumor volume ≤ 0.01 cm

resultsThe cohort consisted of eighty patients; 36 Caucasian and 40 African American. Most tumors were high-grade (54.4% grade 3) invasive ductal carcinomas (90.0%). Receptor subtypes included hormone receptor positive (HR+)/human epidermal growth factor receptor 2 positive (HER2+, 30%), HR+/HER2- (35%), HR-/HER2+ (12.5%) and triple negative breast cancer (TNBC, 22.5%). Simulated tumor volume was significantly correlated with post-treatment radiographic MRI calculated volumes (r = 0.53, p = 1.3 × 10

conclusionWe demonstrated TS ability to simulate and model tumor in vivo conditions in silico and forecast volume response to NAT across breast tumor subtypes.

Indexed as

Breast NeoplasmsTriple Negative Breast NeoplasmsAntineoplastic Combined Chemotherapy ProtocolsErb-b2 Receptor Tyrosine KinasesFemaleHumansNeoadjuvant TherapyPrognosisRetrospective StudiesErb-b2 Receptor Tyrosine KinasesBiophysical simulationBreast cancerMathematical modelingNeoadjuvant therapyTumor volume predictionVirtual twin

Identifiers

PMID37165441
PMCPMC10170712

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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.