Abstract A038: Evaluating DCIS progression: A comparative analysis of CNA predictive power derived from lpWGS and WES data
Notice bibliographique
Résumé
Abstract Ductal carcinoma in situ (DCIS) is a very common non-life threatening, pre-invasive form of breast cancer constituting 25% of all new breast cancer diagnoses in the USA, and is normally treated with invasive measures. However, only 20 to 30% of DCIS cases will progress to life-threatening invasive breast cancer in their lifetime. There is a need for patient stratification to decrease treatment burden and focus resources on patients who actually require treatment. To ameliorate this issue, we aim to develop evolutionary biomarkers that predict both DCIS recurrence and its progression to invasive breast cancer. We conducted an observational longitudinal study in which two DCIS samples from each primary tumor were acquired prior to treatment—to account for intratumor heterogeneity—and followed up with (median event-free patient observation time: 106 months) to classify each patient’s outcome into its respective cohort category of [1] no DCIS recurrence, [2] DCIS recurrence, or [3] invasive ductal carcinoma (IDC) recurrence (progression). Each of the three cohort groups comprises approximately 30 patients, for which our primary data includes low-pass whole genome sequencing (lpWGS) and whole exome sequencing (WES) of the two primary DCIS samples, in addition to WES data for normal tissue control, clinical and epidemiological covariates, and time to recurrence. Preliminary estimates of copy number alterations (CNAs) generated using lpWGS data show that CNA burden, representing the proportion of altered genome, has statistically significant predictive capability for DCIS progression (Univariate Cox regression: p=0.014, HR=1.5). CNA divergence, representing the proportion of non-overlapping altered genome, has predictive capabilities of both recurrence and progression (Univariate Cox regression: Recurrence: p=0.021, HR=0.74; Progression: p=0.024, HR=0.67). In this work, our primary objective is to test if CNAs generated utilizing WES data are comparable in their predictive power to those yielded by lpWGS data for the same human DNA samples. It is not expected that the copy number alteration (CNA) estimates will be identical between the two methods, but rather that mutational burden and intratumor heterogeneity estimated using different data sources are strongly correlated and will have similar predictive capabilities. If we successfully confirm that CNAs estimated with both lpWGS and WES data have similar predictive power of DCIS progression, we will then be able to validate our evolutionary biomarkers using a different cohort for which we only possess WES data. This would also expand the applicability of our evolutionary biomarkers to a larger number of pre-existing datasets. We ultimately aim to develop strong evolutionary biomarkers that, when utilized alongside evolutionary therapies, will revolutionize the clinical management of cancer with the potential to improve both patient prognoses and quality-of-life outcomes. Citation Format: Manasa Iyer, Diego Mallo, Carlo C. Maley, Angelo Fortunato, Luis Cisneros, Lorraine M. King, Marc D. Ryser, Joseph Y. Lo, Allison Hall, Jeffrey R. Marks, Shelley Hwang. Evaluating DCIS progression: A comparative analysis of CNA predictive power derived from lpWGS and WES data [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A038.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».