Differences in the molecular organisation of tumours along the colon are linked to interactions within the tumour ecosystem.

Tumours exhibit significant heterogeneity in their molecular profiles across patients, largely influenced by the tissue of origin, where certain driver gene mutations are predominantly associated with specific cancer types. Here, we unveil an additional layer of complexity: some cancer types display anatomic location-specific mutation profiles akin to tissue-specificity. To better understand this phenomenon, we concentrate on colon cancer. While prior studies have noted changes of the frequency of molecular alterations along the colon, the underlying reasons and whether those changes occur rather gradual or are distinct between the left and right colon, remain unclear. Developing and leveraging stringent statistical models on molecular data from 522 colorectal tumours from The Cancer Genome Atlas, we reveal disparities in molecular properties between the left and right colon affecting many genes. Interestingly, alterations in genes responsive to environmental cues and properties of the tumour ecosystem, including metabolites which we quantify in a cohort of 27 colorectal cancer patients, exhibit continuous trends along the colon. Employing network methodologies, we uncover close interactions between metabolites and genes, including drivers of colon cancer, showing continuous abundance or alteration profiles. This underscores how anatomic biases in the composition and interactions within the tumour ecosystem help explaining gradients of carcinogenesis along the colon.

Longitudinal gut microbiome changes in immune checkpoint blockade-treated advanced melanoma.

Multiple clinical trials targeting the gut microbiome are being conducted to optimize treatment outcomes for immune checkpoint blockade (ICB). To improve the success of these interventions, understanding gut microbiome changes during ICB is urgently needed. Here through longitudinal microbiome profiling of 175 patients treated with ICB for advanced melanoma, we show that several microbial species-level genome bins (SGBs) and pathways exhibit distinct patterns from baseline in patients achieving progression-free survival (PFS) of 12 months or longer (PFS ≥12) versus patients with PFS shorter than 12 months (PFS <12). Out of 99 SGBs that could discriminate between these two groups, 20 were differentially abundant only at baseline, while 42 were differentially abundant only after treatment initiation. We identify five and four SGBs that had consistently higher abundances in patients with PFS ≥12 and <12 months, respectively. Constructing a log ratio of these SGBs, we find an association with overall survival. Finally, we find different microbial dynamics in different clinical contexts including the type of ICB regimen, development of immune-related adverse events and concomitant medication use. Insights into the longitudinal dynamics of the gut microbiome in association with host factors and treatment regimens will be critical for guiding rational microbiome-targeted therapies aimed at enhancing ICB efficacy.

Circulating biomarkers in familial cerebral cavernous malformation.

BACKGROUND: Cerebral Cavernous Malformation (CCM) is a rare cerebrovascular disease, characterized by the presence of multiple vascular malformations that may result in intracerebral hemorrhages (ICHs), seizure(s), or focal neurological deficits (FND). Familial CCM (fCCM) is due to loss of function mutations in one of the three independent genes KRIT1 (CCM1), Malcavernin (CCM2), or Programmed Cell death 10 (PDCD10/CCM3). The aim of this study was to identify plasma protein biomarkers of fCCM to assess the severity of the disease and predict its progression. METHODS: Here, we have investigated plasma samples derived from n = 71 symptomatic fCCM patients (40 female/31 male) and n = 17 healthy donors (HD) (9 female/8 male) of the Phase 1/2 Treat_CCM trial, using multiplexed protein profiling approaches. FINDINGS: Biomarkers as sCD14 (p = 0.00409), LBP (p = 0.02911), CXCL4 (p = 0.038), ICAM-1 (p = 0.02013), ANG2 (p = 0.026), CCL5 (p = 0.00403), THBS1 (p = 0.0043), CRP (p = 0.0092), and HDL (p = 0.027), were significantly different in fCCM compared to HDs. Of note, sENG (p = 0.011), THBS1 (p = 0.011) and CXCL4 (p = 0.011), were correlated to CCM genotype. sROBO4 (p = 0.014), TM (p = 0.026) and CRP (p = 0.040) were able to predict incident adverse clinical events, such as ICH, FND or seizure. GDF-15, FLT3L, CXCL9, FGF-21 and CDCP1, were identified as predictors of the formation of new MRI-detectable lesions over 2-year follow-up. Furthermore, the functional relevance of ang2, thbs1, robo4 and cdcp1 markers was validated by zebrafish pre-clinical model of fCCM. INTERPRETATION: Overall, our study identifies a set of biochemical parameters to predict CCM progression, suggesting biological interpretations and potential therapeutic approaches to CCM disease. FUNDING: Italian Medicines Agency, Associazione Italiana per la Ricerca sul Cancro (AIRC), ERC, Leducq Transatlantic Network of Excellence, Swedish Research Council.