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Supplementary Figure S1. Transcriptomic analyses for molecular subtypes Supplementary Figure S2. Identification of the subtypes in LUSC TCGA cohort (n=431) Supplementary Figure S3. The genomic difference across subtypes in LUSC. Supplementary Figure S4. Estimation of Stromal and immune score and distribution of infiltrating immune cells for tumor and noncancer control sample in LUSC Supplementary Figure S5. The immune landscape and micro-environmental signature in LUSC TCGA cohort (n=431) Supplementary Figure S6. The dual roles of stroma are described by distinct gene expression patterns in tumor(subtype A and B) and noncancer control samples. Supplementary Figure S7. Distribution of infiltrating immune cells for tumor and noncancer control sample in LUSC Supplementary Figure S8. Distinct expression pattern of M1 and M2 signature genes in LUSC subtypes Supplementary Figure S9. Comparison of immune contents between subtypes in our LUSC cohort (n=101) and TCGA LUSC cohort (n=431) Supplementary Figure S10. Immune related pathways enriched for genes which have copy number variants. Supplementary Figure S11. Comparison of the LUSC expression subtypes with the previously defined subtypes Supplementary Figure S12. Clinical association analysis across subtypes Supplementary Figure S13. Survival analysis across subtypes

The clinical features of 101 LUSC patients enrolled in this study.

The immune microenvironment in lung squamous cell carcinoma (LUSC) is not well understood, with interactions between the host immune system and the tumor, as well as the molecular pathogenesis of LUSC, awaiting better characterization. To date, no molecularly targeted agents have been developed for LUSC treatment. Identification of predictive and prognostic biomarkers for LUSC could help optimize therapy decisions. We sequenced whole exomes and RNA from 101 tumors and matched noncancer control Korean samples. We used the information to predict subtype-specific interactions within the LUSC microenvironment and to connect genomic alterations with immune signatures. Hierarchical clustering based on gene expression and mutational profiling revealed subtypes that were either immune defective or immune competent. We analyzed infiltrating stromal and immune cells to further characterize the tumor microenvironment. Elevated expression of macrophage 2 signature genes in the immune competent subtype confirmed that tumor-associated macrophages (TAM) linked inflammation and mutation-driven cancer. A negative correlation was evident between the immune score and the amount of somatic copy-number variation (SCNV) of immune genes (r = −0.58). The SCNVs showed a potential detrimental effect on immunity in the immune-deficient subtype. Knowledge of the genomic alterations in the tumor microenvironment could be used to guide design of immunotherapy options that are appropriate for patients with certain cancer subtypes. Cancer Immunol Res; 6(7); 848–59. ©2018 AACR.

Background We characterized the microbiome composition from various samples in pregnant women and their neonates and evaluated the association between the distribution of microbiome and obstetric factors. Results A prospective study was performed in live births delivered at a single tertiary center between March 2020 and January 2021. Samples collected from pregnant women and their newborns included maternal cervicovaginal discharge (VD), amniotic fluid (AF), umbilical cord blood (CB), neonatal gastric liquid (GL), and meconium. Next-generation sequencing was used to analyze the composition of the microbiome from the samples. We identified 19,597,239 bacterial sequences from the 641 samples. The alpha diversity of the negative control group was slightly higher than that of the VD group. The samples were separated well based on the microbiome pattern unique to their body sites despite the significant batch effects present within our dataset. We observed a higher relative abundance of Lactobacillus at the genus level but lower abundances of Aerococcus, Fusobacterium, Gardnerella, Peptoniphilus, Porphyromonas, and Prevotella in VD. In evaluating the association with clinical data, Staphylococcus in M showed a strong association with preterm birth. The lists of bacterial taxa were connected to the weights of newborn babies in the CB group. The microbiome pattern of GL was similar to that of AF, but not technically identical. Twins did not demonstrate significant differences in the composition of microbiomes from neonate samples. Conclusions In this study, samples known to be traditionally sterile, such as AF and CB, showed specific bacterial taxa although they were extremely close to negative controls. The microbiome patterns were grouped according to the body compartments where the samples had been obtained, not according to the mother-fetus/infant pairs. Further studies lowering the possibility of contamination and redeeming the low-biomass specimens are necessary to determine the relationship between microbiomes and specific pathological complications during pregnancy.