BACKGROUND: Currently, no effective measures are available to predict the curative efficacy of small-cell lung cancer (SCLC) chemotherapy. We expect to develop a method for effectively predicting the SCLC chemotherapy efficacy and prognosis in clinical practice in order to offer more pertinent therapeutic protocols for individual patients.
METHODS: We adopted matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinPro Tools system to detect serum samples from 154 SCLC patients with different curative efficacy of standard chemotherapy and analyze the different peptides/proteins of SCLC patients to discover predictive tumor markers related to chemotherapy efficacy. Ten peptide/protein peaks were significantly different in the two groups.
RESULTS: A genetic algorithm model consisting of four peptides/proteins was developed from the training group to separate patients with different chemotherapy efficacies. Among them, three peptides/proteins (m/z 3323.35, 6649.03 and 6451.08) showed high expression in the disease progression group, whereas the peptide/protein at m/z 4283.18 was highly expressed in the disease response group. The classifier exhibited an accuracy of 91.4% (53/58) in the validation group. The survival analysis showed that the median progression-free survival (PFS) of 30 SCLC patients in disease response group was 9.0 months; in 28 cases in disease progression group, the median PFS was 3.0 months, a statistically significant difference (χ2 = 46.98, P < 0.001). The median overall survival (OS) of the two groups was 13.0 months and 7.0 months, a statistically significant difference (χ2 = 40.64, P < 0.001).
CONCLUSIONS: These peptides/proteins may be used as potential biological markers for prediction of the curative efficacy and prognosis for SCLC patients treated with standard regimen chemotherapy.

Anaplastic lymphoma kinase-tyrosine kinase inhibitors (ALK-TKIs) has demonstrated remarkable therapeutic effects in ALK-positive non-small cell lung cancer (NSCLC) patients. Identifying prognostic biomarkers can enhance the clinical efficacy of relapsed or refractory patients.
We profiled 737 plasma proteins from 159 pre-treatment and on-treatment plasma samples of 63 ALK-positive NSCLC patients using data-independent acquisition-mass spectrometry (DIA-MS). The consensus clustering algorithm was used to identify subtypes with distinct biological features. A plasma-based prognostic model was constructed using the LASSO-Cox method. We performed the Mfuzz analysis to classify the patterns of longitudinal changes in plasma proteins during treatment. 52 baseline plasma samples from another independent ALK-TKI treatment cohort were collected to validate the potential prognostic markers using ELISA.
We identified three subtypes of ALK-positive NSCLC with distinct biological features and clinical efficacy. Patients in subgroup 1 exhibited activated humoral immunity and inflammatory responses, increased expression of positive acute-phase response proteins, and the worst prognosis. Then we constructed and verified a prognostic model that predicts the efficacy of ALK-TKI therapy using the expression levels of five plasma proteins (SERPINA4, ATRN, APOA4, TF, and MYOC) at baseline. Next, we explored the longitudinal changes in plasma protein expression during treatment and identified four distinct change patterns (Clusters 1-4). The longitudinal changes of acute-phase proteins during treatment can reflect the treatment status and tumor progression of patients. Finally, we validated the prognostic efficacy of baseline plasma CRP, SAA1, AHSG, SERPINA4, and TF in another independent NSCLC cohort undergoing ALK-TKI treatment.
This study contributes to the search for prognostic and drug-resistance biomarkers in plasma samples for ALK-TKI therapy and provides new insights into the mechanism of drug resistance and the selection of follow-up treatment.

OBJECTIVE: DNA damage response (DDR) genes have low mutation rates, which may restrict their clinical applications in predicting the outcomes of immune checkpoint inhibitor (ICI) treatment. Thus, a systemic analysis of multiple DDR genes is needed to identify potential biomarkers of ICI efficacy.
METHODS: A total of 39,631 patients with mutation data were selected from the cBioPortal database. A total of 155 patients with mutation data were obtained from the Fudan University Shanghai Cancer Center (FUSCC). A total of 1,660 patients from the MSK-IMPACT cohort who underwent ICI treatment were selected for survival analysis. A total of 249 patients who underwent ICI treatment from the Dana-Farber Cancer Institute (DFCI) cohort were obtained from a published dataset. The Cancer Genome Atlas (TCGA) level 3 RNA-Seq version 2 RSEM data for gastric cancer were downloaded from cBioPortal.
RESULTS: Six MMR and 30 DDR genes were included in this study. Six MMR and 20 DDR gene mutations were found to predict the therapeutic efficacy of ICI, and most of them predicted the therapeutic efficacy of ICI, in a manner dependent on TMB, except for 4 combined DDR gene mutations, which were associated with the therapeutic efficacy of ICI independently of the TMB. Single MMR/DDR genes showed low mutation rates; however, the mutation rate of all the MMR/DDR genes associated with the therapeutic efficacy of ICI was relatively high, reaching 10%-30% in several cancer types.
CONCLUSIONS: Coanalysis of multiple MMR/DDR mutations aids in selecting patients who are potential candidates for immunotherapy.

Anti-programmed cell death protein 1 (PD1) antibodies are in wide use for the treatment of various cancers. PD1 antibody-based immunotherapy, co-administration of nivolumab and ipilimumab, is one of the optimal immunotherapies, especially in advanced melanoma with high tumor mutation burden. Since this combined therapy leads to a high frequency of serious immune-related adverse events (irAEs) in patients with advanced melanoma, biomarkers are needed to evaluate nivolumab efficacy to avoid serious irAEs caused by ipilimumab. This study analyzed baseline serum levels of CXCL5, CXCL10, and CCL22 in 46 cases of advanced cutaneous melanoma treated with nivolumab. Baseline serum levels of CXCL5 were significantly higher in responders than in non-responders. In contrast, there were no significant differences in baseline serum levels of CXCL10 and CCL22 between responders and non-responders. These results suggest that baseline serum levels of CXCL5 may be useful as a biomarker for identifying patients with advanced cutaneous melanoma most likely to benefit from anti-melanoma immunotherapy.

Antibodies against programmed cell death protein 1, such as nivolumab and pembrolizumab, are widely used for treating various cancers, including advanced melanoma. Nivolumab significantly prolongs survival in patients with metastatic melanoma, and sequential administration with lipilimumab may improve outcomes when switched at the appropriate time. Biomarkers are therefore needed to evaluate nivolumab efficacy soon after first administration. This study analyzed serum levels of soluble cluster of differentiation 163 (sCD163) in 59 cases of advanced cutaneous melanoma and 16 cases of advanced mucosal melanoma treated using nivolumab. Serum levels of sCD163 were significantly increased after 6 weeks in responders compared to non-responders after initial administration of nivolumab for cutaneous melanoma. In contrast, no significant difference between responders and non-responders was seen among patients with non-cutaneous melanoma. These results suggest that sCD163 may be useful as a biomarker for selecting patients with advanced cutaneous melanoma most likely to benefit from anti-melanoma immunotherapy.