Abstract PD8-05: Single-cell analysis of breast cancer metastasis using patient-derived xenograft model reveals clonal relationship between primary tumor and its metastases
Notice bibliographique
Résumé
Abstract Introduction Breast cancer is the most common cancer in female and triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer which shows high rate of recurrence and metastasis. Malignant cells that comprise primary tumor are heterogeneous and during disease progression selection of tumor cells occur as a mean to adapt and survive. Tumor heterogeneity can be studied by grouping cells as clones which refer to a group of cells related to each other by descent from a unitary origin. Understanding the mechanism of clonal dynamics and selection during cancer evolution is important to develop new therapeutic strategies for cancer metastasis. Moreover, it is imperative to be able to detect rare subclones that are responsible for metastasis. Thus, single-cell analysis is critical to identify cellular heterogeneity of cancer and molecular basis of metastatic phenotype in depth. Measuring genome and transcriptome in single-cell level will enable us to discover clonal dynamics during cancer metastasis and infer molecular determinants of metastasis fitness. Here, we propose to investigate the clonal dynamics, genomic, and transcriptomic profiles of human breast tumor metastasis using TNBC patient-derived xenograft (PDX) model to understand the mechanism of breast cancer progression and metastasis. Methods Tumor cells from previously established PDXs were transplanted into mammary fat pad of mice. Tumors were removed when it reached maximum allowed endpoint size (1,000mm3) and mice were monitored and allowed to grow metastasis. PDXs that developed primary tumor and metastasis were selected for subsequent single cell analysis. Single-cell whole-genome sequencing (scWGS) was performed using direct library preparation (DLP+) method which is preamplification-free single-cell genome sequencing approach. scWGS data was used to call cell-specific copy number events, which allows us to cluster cells and identify clones. Hierarchical clustering was used to identify clonal structure of each sample. Phylogenetic tree was computed using copy number data of samples from each PDX. Results Nine different TNBC PDX lines were tested and 6 PDX lines developed metastasis. We focused on 2 PDX lines of which metastatic tissues were available for single cell sequencing. SA919 developed metastatic mass near cervical or lumbar spine area. SA535 developed metastasis to lung, axillary or inguinal area and tumor recurrence at mammary fat pad site. scWGS for SA919 and SA535 was carried out and we generated libraries from single cells of primary tumor and its metastases. Hierarchical clustering using copy number data revealed clonal structure of primary tumor and metastases. SA919 showed oligoclonal primary tumor and metastasis whereas SA535 showed polyclonal metastasis from polyclonal primary tumor. scWGS data of primary tumor and its metastases in each PDX were merged to compute phylogenetic trees. Phylogenetic analysis revealed that not all clones of primary tumor contributed to metastasis in both PDXs. Different metastatic lesions of SA535 harbored different clones from primary tumor. For example, clones consisting left axillary metastasis and right axillary metastasis in SA535 were different from each other, however they both originated from primary tumor clones. Metastatic lesions from SA919 and SA535 also had newly appearing clones that were not present in primary tumor inferring clonal evolution during metastasis. Conclusion We were able to capture different patterns of metastasis in several PDXs. Phylogenetic analysis using scWGS data revealed clonal relationship between primary tumor and metastases. Further analysis of single cell genomic and transcriptomic profiles will provide deeper understanding of metastasis in breast cancer. Citation Format: Hakwoo Lee, Farhia Kabeer, Ciara O'Flanagan, Jazmine Brimhall, Justina Biele, Biexi Wang, Teresa Ruiz Algara, So Ra Lee, Daniel Lai, Michael Yuen, Simong Song, Patricia Ye, Jenifer Pham, Richard Moore, Andy J Mungall, Sohrab P Shah, Samuel Aparicio. Single-cell analysis of breast cancer metastasis using patient-derived xenograft model reveals clonal relationship between primary tumor and its metastases [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD8-05.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».