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Enregistrement W3140079447 · doi:10.1158/1538-7445.am2013-sy05-01

Abstract SY05-01: Genetic and non-genetic mechanisms contribute to long-term clonal growth dynamics and therapy resistance.

2013· article· en· W3140079447 sur OpenAlexaff
John E. Dick

Notice bibliographique

RevueCancer Research · 2013
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCancer Genomics and Diagnostics
Établissements canadiensPrincess Margaret Cancer CentreOntario Institute for Cancer Research
Organismes subventionnairesnon disponible
Mots-clésCancer stem cellBiologySomatic evolution in cancerGenetic heterogeneityStem cellEpigeneticsCancerCancer researchGeneticsGenePhenotype

Résumé

récupéré en direct d'OpenAlex

Abstract Individual cells of a tumor vary in many hallmarks of cancer and this heterogeneity can contribute to therapy failure and disease recurrence. The cellular and molecular basis for intra-tumoral heterogeneity is poorly understood. Individual tumor cells can exhibit genetic diversity due to mutations and clonal evolution resulting in intra-tumoral functional heterogeneity. Thus, a single tumor can be composed of genetically distinct subclones, each with distinct functional properties. However, non-genetic determinants also can drive heterogeneity. Often proposed as mutually exclusive to genetic models, the cancer stem cell (CSC) model postulates that tumors are cellular hierarchies, where subpopulations of self-renewing cancer stem cells (CSC) sustain long-term clonal maintenance (i.e. long term tumor propagation only derives from CSC). CSC and non-CSC are considered to be genetically identical (although evidence is very limited), therefore developmental pathways and epigenetic modifications govern the stemness state and drive this functional heterogeneity. There is strong evidence for the CSC model in AML and some solid tumors. By fractionating primary AML into functionally defined leukemia stem cells (LSC) and non-LSC populations and subjecting these to mRNA and miRNA profiling, we have identified an LSC specific gene signature. The LSC signature is highly similar to normal HSC-specific signatures revealing a common and shared stemness program that governs the stem cell state. Critically, the LSC signature and the common stemness signature were shown to be strong independent predictors of patient survival in large clinical databases; bulk blasts and non-LSC fractions lacked prognostic power (Eppert Nature Med 2011). Thus, determinants of stemness influence clinical outcome of AML establishing the clinical relevance of LSC are and not artifacts of xenotransplantation. This work and other recent reports of stem cell prognostic power establishes clinical relevance and helps to place the entire CSC concept onto a stronger footing. In an effort to determine if the clonal evolution genetic model and the CSC model of cancer can be unified, we have carried out three different studies examining CSC from both the functional and the genetic level. To conclusively establish that human acute leukemia possess functionally distinct genetic subclones we focused on Ph+ B-ALL. Genome sequencing is clearly showing that genetic diversity exists in leukemia and solid tumors, however an in silico demonstration of diversity cannot establish how the cellular properties of the tumor cells are influenced by specific gene abnormalities. We found that diagnostic patient samples possessed extensive subclonal genetic diversity and through the use of xenotransplant assays, we found that this diversity originated from within the leukemia initiating cells (L-IC). Individual mice derived from the same primary sample were repopulated with genetically distinct subclones and the growth properties, L-IC frequency, etc were distinct for each subclone. Reconstruction of their genetic ancestry showed that multiple L-IC subclones were related through a complex branching evolutionary process indicating that genetic and functional heterogeneity are closely connected (Notta Nature 2011). Direct clonal evidence that an individual L-IC can evolve came from our prior study of human B-ALL initiated in an experimental leukemogenesis model where human progenitors were transformed with an MLL-ENL vector (Barabe Science 2007). Serial xenografts of B-ALL showed evolution at the Ig locus, yet the viral insertion confirmed the disease was initiated with a single L-IC. Thus the evolution and CSC models need not be mutually exclusive: CSC are not static and they are subject to acquisition of new mutations and subsequent evolution during disease progression. New additional evidence on changes in LSC during disease progression will be presented where we have examined the genetic diversity of paired diagnostic and relapse sample of AML, combined with parallel xenografting and LSC studies. Collectively our study points to the need to develop effective therapies to eradicate all genetic subclones to prevent further evolution and recurrence. Our third functional/genetic study involved colorectal cancer (CRC) and directly tested functional repopulation behaviour of single cells. In parallel the genetic diversity of the single clones that propagated in xenogafts was also ascertained enabling an evaluation of the relative contribution of genetic and non-genetic determinants to variable growth properties. Our lab developed a highly reliable xenograft assay for primary CRC. By combining DNA copy number alteration (CNA) profiling, targeted and exome sequencing and lentiviral lineage tracing, we followed the repopulation dynamics of many single lentivirus-marked lineages from colorectal cancers through serial xenograft passages. The xenografts were genetically stable on serial transplantation. Despite this genetic stability, the proliferation, persistence, and chemotherapy tolerance of lentivirally marked lineages was variable within each clone. Not all tumor-initiating cells (T-IC) cells are contributing to tumor growth continuously: some are held in reserve, while others have the ability to oscillate between periods of dormancy and activity. This distinct intraclonal behavior affected response to conventional chemotherapy, as actively proliferating progeny were preferentially eliminated, while the relatively dormant CRC cells became dominant during tumor re-initiation following chemotherapy. The intraclonal diversity in genetically identical, single cell functional behavior of primary human CRC cells in vivo has the net effect of contributing to tumor growth during both homeostasis and therapy response (Kreso Science 2013). Our findings reveal another layer of complexity, beyond genetic diversity, that drives intratumoral heterogeneity of CRC. The prospect of understanding how genetic and non-genetic determinants interact to influence the functional diversity and therapy response for other cancers should drive future cancer research. Citation Format: John E. Dick. Genetic and non-genetic mechanisms contribute to long-term clonal growth dynamics and therapy resistance. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr SY05-01. doi:10.1158/1538-7445.AM2013-SY05-01

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,030

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0090,003

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,310
Écart entre enseignants0,293 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2013
Routes d'admission1
Résumé présentoui

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