Journal of Cancer and Tumor International

The Tumour Microbiome and Response to Immune Checkpoint Blockade: A Critical Appraisal of Compartment-Specific Evidence

Kashim Ibrahim Muhammad — 2026-10-17

Microbial communities associated with malignant tissue and with the gastrointestinal tract have been proposed as determinants of response to immune checkpoint inhibitors, and the proposition has moved rapidly from murine models into randomised clinical testing. The speed of that translation has outpaced agreement on what the underlying evidence actually supports. This critical narrative review examines the relationship between microbiota and checkpoint-inhibitor outcomes by keeping two ecologically and methodologically distinct compartments separate: the intestinal microbiome, which acts at a distance through systemic immune and metabolic conditioning, and the intratumoural microbiome, which occupies the tumour microenvironment itself at very low biomass. Evidence published between 2015 and August 2026 was assembled from biomedical literature databases and open scholarly indexes, appraised qualitatively, and synthesised around mechanisms, reproducibility, interventional design and methodological risk rather than by individual study. Three conclusions emerge with differing confidence. Causal contribution of intestinal commensals to checkpoint-inhibitor efficacy is well supported by germ-free and gnotobiotic experiments, by interventional faecal microbiota transplantation trials and, most recently, by randomised placebo-controlled testing, although effect sizes remain modest and endpoints inconsistently met. Predictive taxonomic signatures, by contrast, have repeatedly failed to replicate across geographically distinct cohorts, and the field has accordingly shifted towards ecological, functional and metabolite-based scoring. Claims for a pan-cancer intratumoural microbiome have been substantially revised: the most influential pan-cancer analysis has been retracted, and independent decontaminated reanalyses of large whole-genome datasets indicate that robust tumour-resident communities are concentrated in orodigestive sites rather than distributed across all cancers. Unresolved problems include the absence of validated causal biomarkers, uncertain donor selection logic for microbial therapeutics, poorly characterised interactions with antibiotics and diet, and marked geographical narrowness of the evidence. Progress now depends less on additional descriptive profiling than on contamination-controlled study design, mechanistically anchored endpoints and adequately powered randomised trials.

Insights into Cancer Modelling: Analysis of Some Cancer Parameters in Romania for Public Health

Noureddine Ouerfelli — 2026-10-01

Background: Cancer represents a major public health burden in Romania. Using GLOBOCAN 2020 estimates, this study provides an updated empirical examination of the relationships among cancer incidence, mortality, rank, cumulative risk, and selected age-standardised indicators, with the aim of providing an empirical quantitative description of the Romanian cancer profile and comparing selected characteristics with global patterns.

Methods: GLOBOCAN 2020 estimates for Romania were systematically analysed across 35 cancer sites. The relationships between the number of new cases (Nc) and the number of deaths (Nd) were investigated using linear and log-transformed/power-law regressions. Additional empirical models were applied to describe the dependence of incidence and mortality on cancer rank through a derived severity parameter, and selected results were compared with World 2020 estimates.

Results: Romania recorded an estimated 98,886 new cancer cases and 54,486 cancer-related deaths in 2020. Lung cancer ranked first for both incidence and mortality, whereas breast and prostate cancers showed high incidence with comparatively lower Nd/Nc ratios. A linear relationship constrained through the origin yielded Nd=0.5565Nc, while the log-transformed analysis produced a power-law relationship characterised by α=1.01291 and k=0.4372. Rank-based modelling showed a strong concentration of incidence and mortality among a limited number of leading cancer sites. Comparisons with World 2020 estimates highlighted differences in the relative distribution of major cancers and in selected empirical parameters.

Conclusion: The proposed empirical framework provides a quantitative description of the incidence–mortality structure of cancer in Romania in 2020 and generates interpretable, empirically grounded parameters that may facilitate comparisons across cancer sites, countries, and future GLOBOCAN datasets. Because the analysis is based on estimated cross-sectional data, the observed relationships should be interpreted as empirical associations rather than as direct evidence of biological or clinical causation. Further validation using longitudinal cancer-registry data and uncertainty analysis would strengthen the predictive and comparative value of the proposed models.

A Markov-Chain Model of Colorectal Cancer Treatment-Stage Transitions: A Single-Center Study from Sibiu, Romania

Nicolae Daniel Pop — 2026-10-13

Colorectal cancer (CRC) is the most frequently diagnosed cancer in Romania, according to the most recent GLOBOCAN 2022 estimates. We developed a homogeneous Markov-chain model describing the treatment-stage transitions of a cohort of 100 CRC patients undergoing cytostatic (chemotherapy) treatment at a hospital in Sibiu, Romania, distributed across four states of decreasing severity (S₁–S₄), with S₄ representing patients no longer requiring cytostatic treatment. Transition probabilities, estimated from the observed data conditioned on Body Mass Index via Bayes' formula, define an absorbing Markov chain: numerical evolution of the state distribution over 100 treatment doses shows the cohort converging steadily toward S₄, with over 99% of patients reaching this state by the 100^th dose. We derive semi-empirical exponential approximations that closely reproduce this recursion in closed form for two of the three transient states across the full dose range, and for the third once early transient effects have decayed (n≳30), providing readers with an accessible pedagogical bridge from matrix recursion to closed-form approximation. Building on these empirically grounded results, we additionally propose, as methodological frameworks rather than empirically validated findings, two forward-looking extensions: a quality-of-life-weighted formulation compatible with the existing transition matrix, and a constrained-optimization framework identifying an optimal dose count n₀ that balances treatment benefit against accelerating patient toxicity and financial cost, illustrated numerically with hypothetical weights. As a single-center study with a modest sample size (N=100) and no external validation of the estimated transition probabilities, the results should be read as an approximate, illustrative picture of Romanian clinical practice rather than a nationally representative or externally validated estimate. The present work provides a transparent, reproducible modeling framework - including a didactic Appendix - intended both as a practical tool and as a teaching resource for graduate students in mathematical epidemiology.