PathwayTMB: A pathway-based tumor mutational burden analysis method for predicting the clinical outcome of cancer immunotherapy.

Immunotherapy has become one of the most promising therapy methods for cancer, but only a small number of patients are responsive to it, indicating that more effective biomarkers are urgently needed. This study developed a pathway analysis method, named PathwayTMB, to identify genomic mutation pathways that serve as potential biomarkers for predicting the clinical outcome of immunotherapy. PathwayTMB first calculates the patient-specific pathway-based tumor mutational burden (PTMB) to reflect the cumulative extent of mutations for each pathway. It then screens mutated survival benefit-related pathways to construct an immune-related prognostic signature based on PTMB (IPSP). In a melanoma training set, IPSP-high patients presented a longer overall survival and a higher response rate than IPSP-low patients. Moreover, the IPSP showed a superior predictive effect compared with TMB. In addition, the prognostic and predictive value of the IPSP was consistently validated in two independent validation sets. Finally, in a multi-cancer dataset, PathwayTMB also exhibited good performance. Our results indicate that PathwayTMB could identify the mutation pathways for predicting immunotherapeutic survival, and their combination may serve as a potential predictive biomarker for immune checkpoint inhibitor therapy.

Immune signatures predict development of autoimmune toxicity in patients with cancer treated with immune checkpoint inhibitors.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are among the most promising treatment options for melanoma and non-small cell lung cancer (NSCLC). While ICIs can induce effective anti-tumor responses, they may also drive serious immune-related adverse events (irAEs). Identifying biomarkers to predict which patients will suffer from irAEs would enable more accurate clinical risk-benefit analysis for ICI treatment and may also shed light on common or distinct mechanisms underpinning treatment success and irAEs. METHODS: In this prospective multi-center study, we combined a multi-omics approach including unbiased single-cell profiling of over 300 peripheral blood mononuclear cell (PBMC) samples and high-throughput proteomics analysis of over 500 serum samples to characterize the systemic immune compartment of patients with melanoma or NSCLC before and during treatment with ICIs. FINDINGS: When we combined the parameters obtained from the multi-omics profiling of patient blood and serum, we identified potential predictive biomarkers for ICI-induced irAEs. Specifically, an early increase in CXCL9/CXCL10/CXCL11 and interferon-γ (IFN-γ) 1 to 2 weeks after the start of therapy are likely indicators of heightened risk of developing irAEs. In addition, an early expansion of Ki-67+ regulatory T cells (Tregs) and Ki-67+ CD8+ T cells is also likely to be associated with increased risk of irAEs. CONCLUSIONS: We suggest that the combination of these cellular and proteomic biomarkers may help to predict which patients are likely to benefit most from ICI therapy and those requiring intensive monitoring for irAEs. FUNDING: This work was primarily funded by the European Research Council, the Swiss National Science Foundation, the Swiss Cancer League, and the Forschungsförderung of the Kantonsspital St. Gallen.

Recent insights into the use of immune checkpoint inhibitors in gastric cancer.

Gastric cancer (GC) is the fifth most incident and the fourth deadliest cancer worldwide. GC is a heterogeneous disease from the histological and molecular standpoints. This malignancy is mostly diagnosed at advanced stages of the disease, where the available therapeutic interventions are not effective. The emergence of immunotherapy has transformed the landscape of cancer treatment, including GC, and currently immune checkpoint inhibitors have been approved for the treatment of patients with recurrent/metastatic GC. This review summarizes the main clinical trials evaluating the use of immune checkpoint inhibitors in GC. It also highlights the potential of biomarkers for patient selection for GC immune checkpoint inhibition therapy, including programmed cell death ligand 1 expression and tumor mutational burden, and characteristics of the GC molecular classification, such as microsatellite instability status and Epstein-Barr virus infection, as predictors of response to blockade of the programmed cell death 1/programmed cell death ligand 1 axis.

Combination of Whole-Body Baseline CT Radiomics and Clinical Parameters to Predict Response and Survival in a Stage-IV Melanoma Cohort Undergoing Immunotherapy.

BACKGROUND: This study investigated whether a machine-learning-based combination of radiomics and clinical parameters was superior to the use of clinical parameters alone in predicting therapy response after three months, and overall survival after six and twelve months, in stage-IV malignant melanoma patients undergoing immunotherapy with PD-1 checkpoint inhibitors and CTLA-4 checkpoint inhibitors. METHODS: A random forest model using clinical parameters (demographic variables and tumor markers = baseline model) was compared to a random forest model using clinical parameters and radiomics (extended model) via repeated 5-fold cross-validation. For this purpose, the baseline computed tomographies of 262 stage-IV malignant melanoma patients treated at a tertiary referral center were identified in the Central Malignant Melanoma Registry, and all visible metastases were three-dimensionally segmented (n = 6404). RESULTS: The extended model was not significantly superior compared to the baseline model for survival prediction after six and twelve months (AUC (95% CI): 0.664 (0.598, 0.729) vs. 0.620 (0.545, 0.692) and AUC (95% CI): 0.600 (0.526, 0.667) vs. 0.588 (0.481, 0.629), respectively). The extended model was not significantly superior compared to the baseline model for response prediction after three months (AUC (95% CI): 0.641 (0.581, 0.700) vs. 0.656 (0.587, 0.719)). CONCLUSIONS: The study indicated a potential, but non-significant, added value of radiomics for six-month and twelve-month survival prediction of stage-IV melanoma patients undergoing immunotherapy.

Molecular characterization of Kita-Kyushu lung cancer antigen (KK-LC-1) expressing carcinomas.

Cancer/testis antigens (CTAs) are strongly expressed in some solid tumors but minimally expressed in normal tissue, making them appealing therapeutic targets. KK-LC-1 (CXorf61) has cytoplasmic expression in gastric, breast, and lung cancer. We characterized the molecular subtypes of non-small cell lung cancer (NSCLC) expressing KK-LC-1 to inform rational clinical trials of T-cell receptor therapy (TCR-T) targeting KK-LC-1. 9790 NSCLC tumors that underwent whole transcriptome sequencing (Illumina NovaSeq) and NextGen DNA sequencing (NextSeq, 592 Genes and NovaSEQ, WES) at Caris Life Sciences (Phoenix, AZ) were analyzed. Tumors were split into quartiles based on KK-LC-1 expression and pathological and molecular differences were investigated. Adenocarcinoma had significantly higher KK-LC-1 expression than squamous cell carcinoma (median, 3.25 vs. 1.17 transcripts per million (TPM), p < 0.0001). Tumors with the highest quartile of KK-LC-1 expression had a greater proportion of tumors with high tumor mutation burden (TMB) (≥10 mutations per megabase; 44% vs. 28% in Q1, p < 0.001). Increased KK-LC-1 expression was associated with increased M1 macrophage abundance. Higher levels of KK-LC-1 expression were seen in pan-wild type and KRAS mutated tumors and associated with high TMB. TCR-T therapy directed against KK-LC-1 should be considered in patients whose clinical features reflect these characteristics.