Analysis of response-related endpoints in trials of first-line medical treatment of metastatic colorectal cancer

What makes TMB an ambivalent biomarker for immunotherapy? A subtle mismatch between the sample-based design of variant callers and real clinical cohort

Tumor mutation burden (TMB) is a widely recognized biomarker for predicting the efficacy of immunotherapy. However, its use still remains highly controversial. In this study, we examine the underlying causes of this controversy based on clinical needs. By tracing the source of the TMB errors and analyzing the design philosophy behind variant callers, we identify the conflict between the incompleteness of biostatistics rules and the variety of clinical samples as the critical issue that renders TMB an ambivalent biomarker. A series of experiments were conducted to illustrate the challenges of mutation detection in clinical practice. Additionally, we also discuss potential strategies for overcoming these conflict issues to enable the application of TMB in guiding decision-making in real clinical settings.

TMBserval: a statistical explainable learning model reveals weighted tumor mutation burden better categorizing therapeutic benefits

A high tumor mutation burden (TMB) is known to drive the response to immune checkpoint inhibitors (ICI) and is associated with favorable prognoses. However, because it is a one-dimensional numerical representation of non-synonymous genetic alterations, TMB suffers from clinical challenges due to its equal quantification. Since not all mutations elicit the same antitumor rejection, the effect on immunity of neoantigens encoded by different types or locations of somatic mutations may vary. In addition, other typical genomic features, including complex structural variants, are not captured by the conventional TMB metric. Given the diversity of cancer subtypes and the complexity of treatment regimens, this paper proposes that tumor mutations capable of causing various degrees of immunogenicity should be calculated separately. TMB should therefore, be segmented into more exact, higher dimensional feature vectors to exhaustively measure the foreignness of tumors. We systematically reviewed patients' multifaceted efficacy based on a refined TMB metric, investigated the association between multidimensional mutations and integrative immunotherapy outcomes, and developed a convergent categorical decision-making framework, TMBserval (Statistical Explainable machine learning with Regression-based VALidation). TMBserval integrates a multiple-instance learning concept with statistics to create a statistically interpretable model that addresses the broad interdependencies between multidimensional mutation burdens and decision endpoints. TMBserval is a pan-cancer-oriented many-to-many nonlinear regression model with discrimination and calibration power. Simulations and experimental analyses using data from 137 actual patients both demonstrated that our method could discriminate between patient groups in a high-dimensional feature space, thereby rationally expanding the beneficiary population of immunotherapy.

TMBcat: A multi-endpoint p-value criterion on different discrepancy metrics for superiorly inferring tumor mutation burden thresholds

Tumor mutation burden (TMB) is a widely recognized stratification biomarker for predicting the efficacy of immunotherapy; however, the number and universal definition of the categorizing thresholds remain debatable due to the multifaceted nature of efficacy and the imprecision of TMB measurements. We proposed a minimal joint p-value criterion from the perspective of differentiating the comprehensive therapeutic advantages, termed TMBcat, optimized TMB categorization across distinct cancer cohorts and surpassed known benchmarks. The statistical framework applies to multidimensional endpoints and is fault-tolerant to TMB measurement errors. To explore the association between TMB and various immunotherapy outcomes, we performed a retrospective analysis on 78 patients with non-small cell lung cancer and 64 patients with nasopharyngeal carcinomas who underwent anti-PD-(L)1 therapy. The stratification results of TMBcat confirmed that the relationship between TMB and immunotherapy is non-linear, i.e., treatment gains do not inherently increase with higher TMB, and the pattern varies across carcinomas. Thus, multiple TMB classification thresholds could distinguish patient prognosis flexibly. These findings were further validated in an assembled cohort of 943 patients obtained from 11 published studies. In conclusion, our work presents a general criterion and an accessible software package; together, they enable optimal TMB subgrouping. Our study has the potential to yield innovative insights into therapeutic selection and treatment strategies for patients.

Carcinoembryonic Antigen-related Tumor Kinetics After Eight Weeks of Chemotherapy is Independently Associated With Overall Survival in Patients With Metastatic Colorectal Cancer

BACKGROUND

Carcinoembryonic antigen (CEA) best reduction after chemotherapy in patients with metastatic colorectal cancer (mCRC) has been reported as a prognostic factor. The study aims to evaluate whether serum CEA kinetics after 8 weeks of chemotherapy was prognostic in patients with mCRC.

PATIENTS AND METHODS

A retrospective analysis of patients with mCRC, who received chemotherapy and for whom CEA determinations were available at baseline and after 8 weeks, was performed. A Cox model was built including all variables with a significant correlation with overall survival (OS) after bivariate analysis.

RESULTS

Of 200 screened patients with mCRC, 83 were eligible and were enrolled for the analysis. Eighteen variables were tested in bivariate analysis with OS, and a Cox model was built up with 7 of them. Two of 5 CEA kinetics-related variables reported an independent effect on OS when included in the previous Cox model: the CEA response rate after 8 weeks (hazard ratio, 2.02; 95% confidence interval, 1.13-3.59) and the CEA-specific growth rate after 8 weeks (hazard ratio, 1.86; 95% confidence interval, 1.03-3.37).

CONCLUSIONS

After 8 weeks from the beginning of chemotherapy, CEA reduction rate of 50% and CEA-specific growth lower than -0.5%/day are effective prognostic factors among patients with high serum CEA levels and could become useful intermediate endpoints of clinical trials.