Oncogenic activation of fibroblast growth factor receptor 2 (FGFR2) drives multiple cancers and represents a broad therapeutic opportunity, yet selective targeting of FGFR2 has not been achieved. Although the clinical efficacy of pan-FGFR inhibitors (pan-FGFRi) validates FGFR2 driver status in FGFR2 fusion–positive intrahepatic cholangiocarcinoma, their benefit is limited by incomplete target coverage due to FGFR1- and FGFR4-mediated toxicities (hyperphosphatemia and diarrhea, respectively) and the emergence of FGFR2 resistance mutations. RLY-4008 is a highly selective, irreversible FGFR2 inhibitor designed to overcome these limitations. In vitro, RLY-4008 demonstrates >250- and >5,000-fold selectivity over FGFR1 and FGFR4, respectively, and targets primary alterations and resistance mutations. In vivo, RLY-4008 induces regression in multiple xenograft models—including models with FGFR2 resistance mutations that drive clinical progression on current pan-FGFRi—while sparing FGFR1 and FGFR4. In early clinical testing, RLY-4008 induced responses without clinically significant off-isoform FGFR toxicities, confirming the broad therapeutic potential of selective FGFR2 targeting. Significance: Patients with FGFR2-driven cancers derive limited benefit from pan-FGFRi due to multiple FGFR1–4-mediated toxicities and acquired FGFR2 resistance mutations. RLY-4008 is a highly selective FGFR2 inhibitor that targets primary alterations and resistance mutations and induces tumor regression while sparing other FGFRs, suggesting it may have broad therapeutic potential. See related commentary by Tripathi et al., p. 1964. This article is featured in Selected Articles from This Issue, p. 1949

The unexplained rise of young-onset CRC (yoCRC, age 60) has not been fully investigated. 16S rRNA amplicon sequencing was performed in tumor and paired adjacent non-malignant fresh frozen tissue specimens prospectively collected from 136 yoCRC and 140 aoCRC patients. Phyloseq, microbiomeSeq, metagenomeSeq, and NetComi were utilized for bioinformatics analysis. Statistical tests included Fisher's exact test, ANOVA, PERMANOVA with Bonferroni correction, linear regression, and Wilcoxon test. p-value <0.05 was considered statistically significant. yoCRC patients were more likely to have left-sided (72.8 vs. 54.3%), rectal (36.7% vs. 25%), and stage IV (28% vs. 15%) tumors. yoCRC tumors had significantly higher microbial alpha diversity (p = 1.5 × 10^-5) and varied beta diversity (R2 = 0.31, p = 0.013) than aoCRC tumors. yoCRC tumors were enriched with Akkermansia and Bacteroides, whereas aoCRC tumors showed greater relative abundances of Bacillus, Staphylococcus, Listeria, Enterococcus, Pseudomonas, Fusobacterium, and Escherichia/Shigella. Akkermansia had a predominantly negative correlation with the microbial communities in yoCRC tumors. yoCRC and aoCRC tumors had distinct microbial profiles associated with tumor location, sidedness, stage, and obesity. Fusobacterium (R2 = -0.23, p = 0.001) and Akkermansia (R2 = 0.05, p = 0.001) abundance correlated with overall survival in yoCRC. Our study provides a comprehensive understanding of the microbial perturbations in yoCRC tumors. We identify microbial candidates that may highlight a distinct pathogenesis of yoCRC and serve as preventive, diagnostic, and therapeutic targets. Sondra and Stephen Hardis Chair in Oncology Research (A.A.K.).

The incidence of early-onset colorectal cancer (eoCRC) is rising, and its pathogenesis is not completely understood. We hypothesized that machine learning utilizing paired tissue microbiome and plasma metabolome features could uncover distinct host-microbiome associations between eoCRC and average-onset CRC (aoCRC). Individuals with stages I–IV CRC (n = 64) were categorized as eoCRC (age ≤ 50, n = 20) or aoCRC (age ≥ 60, n = 44). Untargeted plasma metabolomics and 16S rRNA amplicon sequencing (microbiome analysis) of tumor tissue were performed. We fit DIABLO (Data Integration Analysis for Biomarker Discovery using Latent variable approaches for Omics studies) to construct a supervised machine-learning classifier using paired multi-omics (microbiome and metabolomics) data and identify associations unique to eoCRC. A differential association network analysis was also performed. Distinct clustering patterns emerged in multi-omic dimension reduction analysis. The metabolomics classifier achieved an AUC of 0.98, compared to AUC 0.61 for microbiome-based classifier. Circular correlation technique highlighted several key associations. Metabolites glycerol and pseudouridine (higher abundance in individuals with aoCRC) had negative correlations with Parasutterella, and Ruminococcaceae (higher abundance in individuals with eoCRC). Cholesterol and xylitol correlated negatively with Erysipelatoclostridium and Eubacterium, and showed a positive correlation with Acidovorax with higher abundance in individuals with eoCRC. Network analysis revealed different clustering patterns and associations for several metabolites e.g.: urea cycle metabolites and microbes such as Akkermansia. We show that multi-omics analysis can be utilized to study host-microbiome correlations in eoCRC and demonstrates promising biomarker potential of a metabolomics classifier. The distinct host-microbiome correlations for urea cycle in eoCRC may offer opportunities for therapeutic interventions.

Background: The incidence of colorectal cancer (CRC) in patients under age 50 is rising for unclear reasons. We examined the effects of socioeconomic factors on outcomes for patients with early‐onset CRC compared to late‐onset CRC.Methods: Patients with CRC from 2004 to 2015 in the National Cancer Database were included and categorized by age (under or over 50 years). Differences in demographic and socioeconomic factors, disease characteristics, and survival outcomes between early‐onset versus late‐onset CRC patients were assessed by Chi‐squared test and Cox models.Results: The study population included 1,061,204 patients, 108,058 (10.2%) of whom were under age 50. The proportion of patients diagnosed under age 50 increased over time: 9.4% in 2004–2006, 10.1% in 2007–2009, 10.5% in 2010–2012, and 10.7% in 2013–2015 (p < 0.0001). Early‐onset CRC patients were more likely to be Black (15.1% vs. 11.3%) or Hispanic (8.6% vs. 4.6%) and to present with stage 4 disease (24.9% vs. 17.0%), p < 0.0001 for all. Black patients had the worst median OS (58.3 months) compared to White (67.0 months), Hispanic (91.6 months), or Asian (104.9 months) patients, p < 0.0001. Within the subgroup of early‐onset CRC patients with private insurance, Black patients had worse OS compared to White patients, even in communities with higher income and education status.Conclusions: Early‐onset CRC continues to increase. Patients with early‐onset CRC are more likely to be Black or Hispanic and to present with stage 4 cancer. Early‐onset Black patients showed worse OS compared to White patients in all income subgroups, even with private insurance.

Arshiya Mariam, BS, and colleagues report the findings of a large meta-analysis assessing the ability of various biomarkers to predict responses to immune checkpoint inhibition.