scholarly journals Sex Differences in Cancer Immunotherapy Efficacy, Biomarkers, and Therapeutic Strategy

Molecules ◽  
2019 ◽  
Vol 24 (18) ◽  
pp. 3214 ◽  
Author(s):  
Shixiang Wang ◽  
Li An Cowley ◽  
Xue-Song Liu
Keyword(s):  
Sex Differences ◽  
Cancer Immunotherapy ◽  
Cancer Patients ◽  
Checkpoint Inhibitors ◽  
Research Progress ◽  
Lung Cancer Patients ◽  
Female Patients ◽  
Tumor Mutational Burden ◽  
Male Patients ◽  
Cancer Germline

Sex differences in innate and adaptive immune responses are known, and women generally mount a stronger immune response than men. Cancer immunotherapy, represented by immune checkpoint inhibitors (ICIs), has revolutionized the treatment of cancer, and sex differences in cancer immunotherapy are just starting to be revealed. Here, we summarize recent research progress concerning sex differences in cancer immunotherapy efficacy. On their own, ICIs tend to be more effective in male cancer patients compared with female patients, while ICIs combined with chemotherapy tend to be more effective in female patients than male patients. Male tumors are usually more antigenic than female tumors, and this is reflected by their increased number of tumor mutations and cancer germline antigens. The biomarker tumor mutational burden (TMB), which reflects tumor antigenicity, is more effective at predicting immunotherapy response for female lung cancer patients than for male patients. In this review, we propose different therapeutic strategies for the different sexes: For male cancer patients, the immune environment should be enhanced, whereas for female cancer patients, tumor antigenicity should be enhanced.

scholarly journals Circulating regulatory T cells predict efficacy and atypical responses in lung cancer patients treated with PD-1/PD-L1 inhibitors

2021 ◽  
Author(s):  
Da Hyun Kang ◽  
Chaeuk Chung ◽  
Pureum Sun ◽  
Da Hye Lee ◽  
Song-I Lee ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Advanced Nsclc ◽  
Checkpoint Inhibitors ◽  
Standard Of Care ◽  
Treg Cells ◽  
University Hospital ◽  
X Rays ◽  
Lung Cancer Patients ◽  
National University

Abstract Background Immune checkpoint inhibitors (ICIs) have become the standard of care for a variety of cancers, including non-small cell lung cancer (NSCLC). In this study, we investigated the frequency of pseudoprogression and hyperprogression in lung cancer patients treated with ICIs in the real world and aimed to discover a novel candidate marker to distinguish pseudoprogression from hyperprogression soon after ICI treatment. Methods This study included 74 patients with advanced NSCLC who were treated with PD-1/PD-L1 inhibitors at Chungnam National University Hospital (CNUH) between January 2018 and August 2020. Chest X-rays were examined on day 7 after the first ICI dose to identify changes in the primary mass, and the response was assessed by computed tomography (CT). We evaluated circulating regulatory T (Treg) cells using flow cytometry and correlated the findings with clinical outcomes. Results The incidence of pseudoprogression was 13.5%, and that of hyperprogression was 8.1%. On day 7 after initiation of treatment, the frequency of CD4+CD25+CD127loFoxP3+ Treg cells was significantly decreased compared with baseline (P = 0.038) in patients who experienced pseudoprogression and significantly increased compared with baseline (P = 0.024) in patients who experienced hyperprogression. In the responder group, the frequencies of CD4+CD25+CD127loFoxP3+ Treg cells and PD-1+CD4+CD25+CD127loFoxP3+ Treg cells were significantly decreased 7 days after commencement of treatment compared with baseline (P = 0.034 and P < 0.001, respectively). Conclusion Circulating Treg cells represent a promising potential dynamic biomarker to predict efficacy and differentiate atypical responses, including pseudoprogression and hyperprogression, after immunotherapy in patients with NSCLC.

scholarly journals Higher CD4/CD8 ratio of pleural effusion predicts better survival for lung cancer patients receiving immune checkpoint inhibitors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Po-Hsin Lee ◽  
Tsung-Ying Yang ◽  
Kun-Chieh Chen ◽  
Yen-Hsiang Huang ◽  
Jeng-Sen Tseng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Pleural Effusion ◽  
Cancer Patients ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Lung Cancer Patients ◽  
Predicting Factor ◽  
Type 3

AbstractPleural effusion is a rare immune-related adverse event for lung cancer patients receiving immune checkpoint inhibitors (ICIs). We enrolled 281 lung cancer patients treated with ICIs and 17 were analyzed. We categorized the formation of pleural effusion into 3 patterns: type 1, rapid and massive; type 2, slow and indolent; and type 3, with disease progression. CD4/CD8 ratio of 1.93 was selected as the cutoff threshold to predict survival. Most patients of types 1 and 2 effusions possessed pleural effusion with CD4/CD8 ratios ≥ 1.93. The median OS time in type 1, 2, and 3 patients were not reached, 24.8, and 2.6 months, respectively. The median PFS time in type 1, 2, and 3 patients were 35.5, 30.2, and 1.4 months, respectively. The median OS for the group with pleural effusion CD4/CD8 ≥ 1.93 and < 1.93 were not reached and 2.6 months. The median PFS of those with pleural effusion CD4/CD8 ≥ 1.93 and < 1.93 were 18.4 and 1.2 months. In conclusion, patients with type 1 and 2 effusion patterns had better survival than those with type 3. Type 1 might be interpreted as pseudoprogression of malignant pleural effusion. CD4/CD8 ratio ≥ 1.93 in pleural effusion is a good predicting factor for PFS.

scholarly journals A machine learning approach for somatic mutation discovery

2018 ◽  
Vol 10 (457) ◽  
pp. eaar7939 ◽  
Author(s):  
Derrick E. Wood ◽  
James R. White ◽  
Andrew Georgiadis ◽  
Beth Van Emburgh ◽  
Sonya Parpart-Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Patients ◽  
Somatic Mutation ◽  
Mutation Detection ◽  
Checkpoint Inhibitors ◽  
False Negative ◽  
The Cancer Genome Atlas ◽  
High Quality ◽  
Lung Cancer Patients ◽  
Mutation Discovery

Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning that outperformed existing methods in identifying experimentally validated tumor alterations (sensitivity of 97% versus 90 to 99%; positive predictive value of 98% versus 34 to 92%). Analysis of paired tumor-normal exome data from 1368 TCGA (The Cancer Genome Atlas) samples using this method revealed concordance for 74% of mutation calls but also identified likely false-positive and false-negative changes in TCGA data, including in clinically actionable genes. Determination of high-quality somatic mutation calls improved tumor mutation load–based predictions of clinical outcome for melanoma and lung cancer patients previously treated with immune checkpoint inhibitors. Integration of high-quality machine learning mutation detection in clinical next-generation sequencing (NGS) analyses increased the accuracy of test results compared to other clinical sequencing analyses. These analyses provide an approach for improved identification of tumor-specific mutations and have important implications for research and clinical management of cancer patients.

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