Abstract PR009: Bugs in Space: Spatial Analysis of the Immune-Microbial Axis in Rectal Cancer Reveals a Unique Interface in Young Onset Rectal Cancers
Notice bibliographique
Résumé
Abstract Several hypotheses have been postulated to define the mechanism behind the rising incidence of young onset rectal cancer (YORC). These hypotheses often relate to differences in a population’s exposome and back to a patient’s gut or tumor microbiome. We previously demonstrated a unique tumoral microbiome in YORC (<50 years old) versus late-onset (≥50 years old, LORC) rectal cancers through metagenomic quantification of intra-tumoral bacteria. Here we further compare tumoral immune signatures in response to the presence of tumoral bacteria between YORC and LORC. We build on previous bulk sequencing analyses to correlate tumoral microbial presence with rectal cancer outcomes and tumor immune infiltrates with the hypothesis that tumoral bacteria lead to derangements in local host immune response as a mechanism to affect response rates to neoadjuvant therapy. Pretreatment biopsies from rectal adenocarcinomas were studied using NanoString GeoMx digital spatial profiling using the Whole Human Transcriptome Atlas and 16S rRNA probes. We initially studied the impact of bacterial presence in these tumors and noted distinct immune profiles when comparing sterile (Baclow) with bacterially enriched (Bachigh) regions of the tumor. In Bachigh regions, a higher proportion of memory B-cells (p=0.042) and M1 macrophages (p=0.023) and lower abundance of naïve B cells (p=0.046) were noted – suggesting a decrease in adaptive immunity in Bachigh regions of the tumor. Importantly lower bacterial density and increased B cell activity each correlated with higher rates of pathologic response. Differential expression of genes previously associated with deleterious microbes and tumor progression such as CXCL8 (p<0.001), VIM (p=0.010), TGFBI (p=0.004), and SPP1 (p=0.018) were noted in Bachigh tumor regions. While CXCL8 (p=0.046), MYC (p=0.024), VEGFA (p=0.008), and ICAM1 (p=0.005) were upregulated in Bachigh stromal regions. This underscores the important role tumoral bacteria play in rectal cancer derangement of the host immune response and the associated evasion of tumor response to cytotoxic neoadjuvant therapy resulting in poorly controlled progression of these tumors. When comparing the effect of bacteria on these immune infiltrates in YORC and LORC, we see B cell maturation suppressed in the Bachigh regions of YORC with a decrease in memory B cell populations (p=0.005) and a concomitant increase in naïve B cells (p=0.004). While LORC Bachigh regions were associated with an immune landscape skewed toward innate inactivation and an increase in neutrophils (p=0.091), dendritic cells (p=0.011), and inactivated NK cells (p=0.026) and an increase in extracellular matrix (ECM) organization, proteoglycans, and degradation. Ongoing studies will confirm whether this differential response between YORC and LORC patients is secondary to age, specific bacterial populations, or a combination of factors. Ultimately, however, these bacteria provide intriguing targets for clearance as being studied (NCT06569368) during neoadjuvant therapy for locally advanced rectal cancers. Citation Format: Ryan Morgan, Reed Ayabe, Brenda Melendez, Taylor Neilson, Laurence Diggs, Norman Galbraith, Ashish Damania, Bharat Singh, Matthew Wong, Pranoti Sahasrabhojane, Yasmine Hoballah, Melissa Taggart, Alexander Lazar, Khalida Wani, Davis Ingram, Diana Shamsutdinova, Jumanah Alshenaifi, Zuzana Lutter-Berka, David Menter, Ramy Behman, Paula M. Smith, George J. Chang, Nadim Ajami, Susan Bullman, Christopher Johnston, Scott Kopetz, J. Joshua. Smith, Jennifer A. Wargo, Y. Nancy. You, Michael G. White. Bugs in Space: Spatial Analysis of the Immune-Microbial Axis in Rectal Cancer Reveals a Unique Interface in Young Onset Rectal Cancers [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr PR009.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».