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Genomic Variations and Immune-Related Features of TMB, PD-L1 Expression and CD8+ T Cell Infiltration in Chinese Pulmonary Sarcomatoid Carcinoma

Background Pulmonary sarcomatoid carcinoma (PSC) is a rare and distinct subtype of lung cancer characterized by its aggressiveness and dismal prognosis. However, genomic landscape and immune contexture have not been fully elucidated among PSC patients. Methods In the present study, whole-exome-sequencing (WES) analyses were performed to depict genomic landscape of 38 independent PSC samples. Tumor mutation burden (TMB) was calculated with the total number of non-synonymous SNVs and indel variants per megabase of coding regions. PD-L1 expression and CD8+ T cell density were evaluated by immunohistochemistry in PSC samples. Their associations with genomic mutation were further assessed in genes with most frequent mutation. Overall survival (OS) of PSC patients with top mutated genes and high and low TMB, PD-L1 and CD8+ TIL expressions were further compared. Subgroup analyses of OS stratified by morphology and pathological type were conducted. Their correlation with TMB, PD-L1 and CD8+ T cell were further assessed. Results We identified a cohort of genomic and somatic mutation in PSC patients. Subgroup patients with distinct clinicopathological features were found to harbor different genomic mutations and immunologic features. Besides, genomic profiles influenced outcomes, with SARS mutation associated with worsened prognosis. Conclusion Through the mapping of genetic and immunologic landscape, we find the heterogeneity among the subgroups of PSC. Our findings may provide opportunities for therapeutic susceptibility among Chinese PSC patients.

Introduction both cancerous and sarcoma or sarcoma-like components, is generally resistant to conventional chemotherapy. [2][3][4] Recently, mutations found in TP53, MET, EGFR and KRAS as well as high PD-L1 expressions in PSC have rendered targeted therapy and immunotherapies preferable choices for PSC patients. [5][6][7] However, genomic analyses of Chinese PSC patients are relatively few. Therefore, a comprehensive analysis of the genomic profile of Chinese PSC patients is of vital importance, which may be helpful in providing instructive treatment strategies.
Recent studies have found that tumor mutation burden (TMB), PD-L1 expression, tumor-infiltrating lymphocyte (TIL) infiltration are associated with clinical outcomes in response to immunotherapies. [8][9][10][11] It has been commonly recognized that tumors with high expressions of TMB, PD-L1 and TIL are associated with good response to immunotherapies. [12][13][14] Tumors have generally been categorized into four different types according to CD8 + TIL and PD-L1 expression. Patients with high PD-L1 and CD8 + TIL expression have been associated with benefit from immunotherapies. [15][16][17] In addition, previous studies have found that genomic status could influence the immunotherapy efficacy by harnessing the potent cytotoxic T lymphocyte. However, the association between genomic mutation and immune-associated markers has not been fully defined in PSC.
To address the limited knowledge, we performed this study in patients with surgically resected PSC to evaluate the landscape of genomic variations and the profile of immune-related factors. We analyzed association between the genomic alterations and TMB, PD-L1 and CD8 + TIL, as well as the clinical relevance of genomic variations, TMB, PD-L1 and CD8 + TIL, overall survival including the distinct subgroup of PSC, and other clinicopathologic features. These results might provide important insights into the genomic features and biology and a roadmap to inform us of genome and immuno-guided personalized treatment for Chinese PSC patients.

Sample Collection
We retrospectively identified patients who were pathologically confirmed with PSC from January 2014 to April 2019. These patients were recruited from Shandong Cancer Hospital and Institute, Shandong Provincial Hospital, Liaocheng People's Hospital, Linyi People's Hospital. Their pathological types were classified into two types: either pure sarcomatoid component or mix type. All specimens of eligible case must have a confirmed diagnosis of PSC and had at least 80% sarcomatoid cellularity. Both tumor and non-tumor tissues of each patient were collected. All diagnoses were further independently confirmed by two experienced pathologists. The histologic subtypes of pure PSC were confirmed by immunohistochemistry (IHC), in which adenocarcinoma or squamous carcinoma components were not found. Non-pure SPC refers to the subtype in which adenocarcinoma or squamous carcinoma components were found. As you have kindly suggested, we have defined pure SPC and non-pure SPC in the methods section, as indicated in our revised manuscript. A total of 43 PSC patients were collected, leaving a final cohort of 38 patients after exclusion of two polluted samples and three non-tumor tissues without sufficient DNA. This study was approved by the ethics committee of Shandong Cancer Hospital and Institute. All included patients in this study offered written informed consent. Overall survival (OS) was defined as the interval between diagnosis and death or between diagnosis and the last observation point. For surviving patients, the data were censored at the last follow-up.

Sample Processing and Genomic DNA Extraction
After surgical resection, tumor tissues were fixed with formalin, subsequently embedded in paraffin (FFPE). Corresponding non-tumor tissues adjacent to tumors were set as controls. Genomic DNA was extracted from each FFPE sample using the GeneRead DNA FFPE Kit (Qiagen, USA) and those extracted from non-tumor tissues were also served as controls.

DNA Library Construction and Whole Exome Sequencing (WES)
Genomic DNA was digested into fragments with the size of 200 bp using enzymatic method (5X WGS Fragmentation Mix, Qiagen, USA). T-adapters were added to both ends after end repairing and A tailing. For the library construction, the purified DNA was amplified by ligation-mediated PCR, and subsequently the final sequencing libraries were

Sequence Alignment and Variants Detecting
The raw data were preprocessed by FASTP to trim adaptor sequences, 18 and subsequently, the clean reads in Fast Q format were aligned to the reference human genome (hg19/GRCh37) by Burrows-Wheeler Aligner (BWA, v0.7.15). 19 SAM tools and Picard (2.12.1) (http://picard.sourceforge.net/) were used to sort the mapped BAM files and process PCR duplicates. To compute the sequencing coverage and depth, the final BAM files were generated by GATK (the Genome Analysis Toolkit 3.8) for local realignment and base quality recalibration. 20 The somatic single nucleotide variations (SNVs) were identified by MuTect and somatic small insertions and deletions (InDels) were detected by GATK Somatic Indel Detector. ANNOVAR software was performed for variant annotation based on multiple databases, 21 including variant description (HGVS), population frequency databases (1000 Genomes Project, dbSNP, ExAC), variant functional prediction databases (PolyPhen-2 and SIFT), and phenotype or disease databases (OMIM, COSMIC, ClinVar). After annotation, the retained non-synonymous SNVs were screened with variant allele frequency (VAF) (cutoff ≥3%) or with VAF (cutoff ≥1%) for cancer hotspots from disease databases for the further analysis. Tumor mutation burden (TMB) was calculated with the total number of non-synonymous SNVs and indel variants per megabase of coding regions. Significant driver genes were identified by combining two methods of MutsigCV and dNdScv as described in the previous studies, 22,23 with a false discovery rate (FDR, cutoff <10%).
Copy number variations (CNVs) were identified using the Genome Identification of Significant Targets in Cancer (GISTIC) 2.0 algorithm. 24 At chromosomal arm-level, significant amplification or deletion were screened with FDR (cutoff <10%) for further analyses. At focal-level, significant amplification was screened with FDR (cutoff <5%) and G-score (cutoff >0.3), and significant deletion was screened with FDR (cutoff <5%) and G-score (cutoff <−0.2) for further analyses.
Mutational signatures were analyzed by R Foundation for Somatic Signatures. 25 We further compared mutational signatures with the Sanger signatures in the Catalogue of Somatic Mutations in Cancer (COSMIC) database.

Immunohistochemical Staining
Immunohistochemical staining was conducted using Enhance Labelled Polymer System (ELPS). Briefly, the PSC tissues were stained with anti-PD-L1 (CST, 13684, 1:100), anti-CD8+ (CST, 85336, 1:100) at 4°C overnight, then washed with PBS for three times, with 5 minutes per time. Corresponding secondary antibody was used to stain the tissues at 37°C for 30 minutes, after which they were then washed with PBS for three times with 5 minutes per time. Further staining with 3,3-diaminobenzidine (DAB), after which washed with the distilled water. Next, hematoxylin staining was conducted, followed by dehydration, clearing and mounting with neutral gums. Images of the stained tissues were captured by Digital Pathology Slide Scanner (KF-PRO-120, KF-BIO).

The Calculation of PD-L1
For the evaluation of PD-L1 expression, tumor proportion score (TPS) was defined as the number of PD-L1-staining tumor cells divided by the total number of viable tumor cells multiplied by 100; combined positive score (CPS) was defined as the number of PD-L1-staining cells divided by the total number of viable tumor cells multiplied by 100. PD-L1 staining of the tonsil had been adopted to ensure eligibility of the enrolled specimens. The qualified staining should be strong positivity for PD-L1 in intratonsillar cleft epithelia and negative staining for PD-L1 in lymphocytes (mantle zone and germinal center B cells) and superficial epithelial cells. The cutoff TPS/CPS value was 1.0% for positivity of PD-L1.

The Calculation of CD8 + T Cell
We observed CD8 + T cell distribution in the tumor stroma at a 200-fold magnification. If they were equally distributed, they were calculated in three randomly chosen areas (0.1 mm 2 per area). If unequally distributed, corresponding areas would be selected according to the percentage of CD8 + T cells in areas of various densities (0.1 mm 2 per area). CD8 + T cell infiltration was calculated as CD8 + T cell count/0.1 mm 2 × 10, namely CD8 + T cell count/mm 2 . For each sample, TMB was defined as the total number of nonsynonymous SNVs/Indels of coding area of a tumor genome based on whole-exome sequencing (WES). TNB was determined as the number of all putative neoantigens per megabase of genome. No staining of CD8 + T cell infiltration was defined as negative and otherwise positive.

Statistical Analysis
R Foundation for Statistics Computing, Package (R package, version 3.3.3) was used to perform the statistics analysis. Fisher's exact test and Wilcoxon test were used to analyze the relationship between TMB and clinical indexes. Kaplan-Meier method was used to estimate overall survival. Hazard ratios (HR) was analyzed by Cox regression analysis. p < 0.05 was defined as statistically significant.

Patient Characteristics
Thirty-eight surgically resected PSC samples were collected and successfully sequenced. Baseline characteristics of the enrolled patients with PSC are listed in Table 1. The median age was 61 years (range 39-82 years). Thirty patients (78.9%) were male. The percentage of patients with AJCC stage I-II (63.2%) was higher than that with AJCC stage III (36.8%). The percentage of smokers (60.5%) was higher than that of the non-smokers (34.2%), and two patients were unknown in smoking status. We classified the 38 PSC patients into the following categories: spindle-like (84.2%) and non-spindle-like (15.8%) as defined by morphology. The proportion of pure and non-pure PSC patients was 57.9% and 42.1%, respectively. The threshold of PD-L1 and CD8 + T cell was set to 1% and 5%, respectively, according to previous studies. [26][27][28] The number of patients with PD-L1 higher than 1% and lower than 1% was 21 and 17. And those whose CD8 + T higher than 5% accounted for 73.7% whereas those with CD8 + T lower than 5% accounted for 26.3%.

Mutational Features of PSC Patients
We further analyzed the mutational features of each PSC patient in our cohort using the nonnegative matrix factorization (NMF) algorithm, which identified four independent mutational signatures (S1-S4) that matched four of the Sanger signatures in the Catalogue of Somatic Mutations in Cancer (COSMIC) database, including deficient mismatch repair (dMMR) (signature 6), dMMR (signature 15), smoking and an unknown feature (Figure 2A and B). The type of base-pair substitutions of each sample and mutational spectrum are demonstrated (Figure 2A). Each mutation status of the 38 PSC patients as defined by the four mutational signatures, sex, morphology, stage, survival, smoker, age, pathological subtype was shown ( Figure 2C). There was a significant disparity between dMMR (signature 6) and dMMR (signature 15) (p<0.0001), between dMMR (signature 6) and smoking (p=0.00051), between dMMR (signature 15) and smoking (p<0.0001) ( Figure 2D).

Immunological Landscape of PSC Patients
Previous reports have shown that the high degree of TMB, PD-L1 expressions and CD8 + T infiltration are correlated with the immunogenic features of the tumor and reliably predict a good response to immunotherapy. High TMB, related to tumor neoepitope burden, indicating a T-cell-inflamed tumor microenvironment (TME), act as predictive biomarkers for immunotherapy. In the present study, we assessed TMB, PD-L1 and CD8 + T infiltration comprising the immunological milieu of TME among PSC patients. We analyzed the association between TMB, PD-L1 expression and CD8 + T cell infiltration. As demonstrated in Figure 3A, there was a positive correlation between CD8 + T cell and PD-L1 expression (p<0.01). However, there was no significant association between TMB versus CD8 + T cell, TMB versus PD-L1. Among the high frequently mutated genes, we detected higher TMB in patients with mutations in ZFHX4, TTN, TP53, KRAS, FAT3, PKHD1L1, CDKN2A, CSMD2, DNAH5 and DST as compared with their corresponding wild type (p<0.01 for   Figure 3B). As for PD-L1 expression, we observed lower expression of PD-L1 in PSC with mutation in RBM38 than those with wild type (p<0.05) ( Figure 3C). High level of CD8 + T cell infiltration was found to be in mutations in USH2A and TP53 genes than PSC patients with wild-type USH2A and TP53 (p<0.05) ( Figure 3D). To the contrary, we shifted out genes whose mutational status may affect TMB, PD-L1 and CD8 + T cell expression. Results have shown that among the 38 PSC patients, those with higher TMB tend to associate with a series of genes mutations such as TP53, ZFHX4, CSMD2, DNAH5, PKHD1L1, CSMD1, PTPRT, WDR87, ADAMTS12, ATP2B2, CHD5, DMD, IPO9, KMT2D, LRP1B, PLXNB2, SLITRK2, TNXB (p<0.05 for all) ( Figure 3E). Mutations vary between those with high PD-L1 and lower PD-L1 expression. For samples whose PD-L1 expressions higher than 1%, RYR3 is shown to be reported whereas PTPRO tends to be mutated in samples with relatively lower PD-L1 expressions (<1%) (p<0.05) ( Figure 3F). For specimen with CD8 + T cell infiltration more than 5%, TP53 mutation was detected. However, ZNF43, ADGRV1, ANKS1B and CAMK2G were proven to be mutated in samples with CD8 + T cell infiltration less than 5% (p<0.05) ( Figure 3G).

Association of Genomic Variations and Immunological Factors with Clinicopathological Features
Considering the possible impact of clinical variables such as sex, age, tumor stage, smoking status, morphology, pathological type and living status on genetic mutations, we further analyzed the association between gene mutation and clinical variables. As shown in Figure 4A, higher frequencies of AASDH and PTPRK mutations were found in female PSC patients compared with male patients (p<0.05). NLRP7, AKAP2, ALMS1, PALM2, SOGA3, SPTBN1 mutations were found to be more in PSC patients over 65-year-old compared with those in younger patients ( Figure 4B). PSC patients with stage III were more likely to harbor PCDH15, STON1, CHD8, COL12A1, DRC1, HLTF, PPFIA2, SEMA6D, TMEM260 mutations compared with those with stage I/II (p<0.05) ( Figure 4C). There were more smokers in PSC patients with mutations in TTN, ZFHX4, DNAH5, PKHD1L1 compared with nonsmokers. And EGFR, AASDH, HLTF, PTPPK and ZFAND mutations were more likely to be found in nonsmoker PSC patients compared with their smoker counterpart ( Figure 4D). Non-spindle cell PSC showed a significantly higher frequency of VPS13B, ANAPC2, BMP7, C9orf3, CCDC70, CHD3, DHX32, DSG2, FRMPD4, LRRC26, LRRC8A, LZTS1, NDUFV3, OSBP2, PIAS3, PKP1, SEC24A, SFMBT2, ZHX3, ZNF148 mutations than spindle cell PSC ( Figure 4E). Mix PSC demonstrated higher rate of frequency of ADGRV1 and ARHGAP8 mutations compared with pure PSC (p<0.05) ( Figure 4F). Besides, higher frequency of SIGLEC1 mutations tend to result in death in our cohort (p<0.05) ( Figure 4G). Further analyses revealed that the level of TMB was higher in male compared with female among PSC patients (p<0.05) ( Figure 4H). And smokers also boosted higher TMB value than non-smokers among PSC patients (p<0.01) ( Figure 4I).
To ascertain the association between morphology and gene mutation, we next analyzed the gene mutation in spindle cell and non-spindle cell PSC. Non-spindle PSC patients were found to be mutated at a higher frequency in VPS13B ,  ANAPC2, BMP7, C9orf3, CCDC70, CHD3, DHX32, DSG2, FRMPD4, LRRC26, LRRC8A, LZST1, NDUFV3, OSBP2, PIAS3, PKPI, SEC24A, SFMBT2, ZHX3 and ZNF148 compared with spindle subgroup. Similarly, PSC of mix pathological type had significantly higher frequency of ADGRV1 and ARHGAP8 mutations than that of pure PSC ( Figure 5). Considering the association with morphology and pathological type with gene mutation among PSC patients, we further analyzed OS and immunological landscape of PSC patients with different morphology and pathological subtype. No significant difference in OS as well as immunological landscape as featured by TMB, PD-L1 and CD8 + T cell infiltration has been found between spindle-cell and non-spindle cell carcinoma. Likewise, PSC patients with mix and pure pathological subtype also witnessed no significant difference in OS, TMB and PD-L1, as demonstrated in Supplementary Figure 2.

Survival Curves for PSC Patients with Different Mutation Status and Immunological Landscape
We further explore the impact of different gene mutations on OS. It is observed that SARS status has significant influence on OS. The presence of SARS mutation seems to be associated with significantly worse OS, suggesting the prognostic effect of SARS in the prediction of poor survival (log-rank p=0.04) ( Figure 6). As shown in Supplementary Figure 3A, although OS was improved by a minimal degree, we did not observe significantly longer OS in PSC patients with higher TMB, PD-L1 expression, CD8 + T infiltration (log-rank p = 0.6431, log-rank p = 0.1794, log-rank p = 0.06168). Furthermore, we found that the combination of TMB, PD-L1 and CD8 + T cell could not significantly discriminate the population with different OS. In the present study, the threshold for TMB, PD-L1 and CD8 + T cell infiltration has been opted for 2.95, 1% and 5%, respectively. For PD-L1, those above the threshold have been deemed positive whereas those below the threshold have been deemed negative. For TMB and CD8+ T cell, those above the threshold have been deemed higher whereas those below the threshold have been deemed lower. Prolonged OS was not observed in patients with TMB ≥2.95 plus PD-L1 ≥1% compared with others (p=0.3065). Similarly, OS was not significantly longer for those with TMB ≥2.95 plus CD8 + T cell infiltration ≥5% compared with other groups (p=0.4763). And for those with PD-L1 ≥1% plus CD8 + T cell infiltration ≥5%, they also did not boost significantly prolonged OS in comparison with other groups (p=0.06192) (Supplementary Figure 3B). For PSC patients with relatively higher TMB, PD-L1 and CD8 + T cell infiltration, OS was also not significantly elevated compared with other groups (p=0.3562) (Supplementary Figure 3B). Hazard ratio was numerically elevated in subgroups of TMB+ and PDL1+, TMB-and PDL1+, TMB+ and CD8-, TMB-and CD8+, PDL1+ and CD8+, PDL1+ and CD8-as compared with other groups (Supplementary Figure 3C). We next analyzed OS of patients with frequent mutation and found no significant difference in OS between patients with mutation in TP53, TTN, ZFHX4, USH2A, FAT3, MUC16, PKHD1L1, CSMD2, DST, DNAH5, CDKN2A, KRAS, RBM38, MDN1 and their wildtype counterpart (Supplementary Figure 3D).

Discussion
PSC is a rare subtype of lung cancer with poor prognosis and generally resistant to traditional chemotherapy. The present study summarized the landscape of genomic variations by whole exosome sequencing (WES) and immune profiling by immunohistochemistry.
In the present study, the most frequent mutations in PSC are TP53 (52.6%), TTN (47.4%), ZFHX4 (26.3%), USH2A (23.7%) and FAT3 (23.7%). It is not surprising that mutation in TP53 is the most frequently mutated gene in PSC due to its ubiquitous mutation detected in all types of cancers. The previous study reported the most frequent mutations in PSC were TP53, CDKN2A and KRAS, accounting for 73.6%, 37.6% and 34.4% using comprehensive genomic profiling (CGP). It has to be noted that CGP only covers genetic mutation that has clinical values. 29 In Yang's study, the top five mutated genes were TP53, CDKN2A, MYC, MET and CCND1 in PSC. 30 Though we are not the first to conduct genomic analysis among PSC patients, our WES analysis of PSC patients boosts distinctive merits as a validation of previous findings on the mutational landscape of PSC. More importantly, we exhibited genomic and immune profiling in PSC patients of distinct morphology and pathological type, which has not been previously reported in other studies. It has to be noted that although no difference in TMB, PD-L1, CD8 + T infiltration in PSC patients stratified by the pathological type was found. Mix type PSC demonstrated higher ADGRV1 and ARHGAP8 mutations compared with pure type. ARHGAP8, which is located within a critical region of loss-of- heterozygosity on chromosome 22q13.31, has found to be overexpressed in breast and colorectal carcinomas. 31,32 ADGRV1 has also been reported in a series of biological processes such as myoclonic epilepsy and usher syndrome type II disease. 33,34 Therefore, deeper probing into these two genes may help us better understand the genomic disparity between mix and pure PSC, thus offering optimized treatments.
A cohort of candidate genes also demonstrated enrichment for mutation in our PSC cohort. For instance, somatic mutations in ZFHX4 gene are associated with poor overall survival of Chinese esophageal squamous cell carcinoma patients. Aberrations in MUC16 have been described in ovarian and pancreatic cancers. CSMD2, has been reported to be implicated in the central nervous system of primary lymphoma and systemic melanoma metastasis. Our current findings have emphasized and implicated the potential role of these mutant genes involved in PSC development. Additionally, our results revealed that SARS mutation was indicative of worsened survival among PSC patients. SARS, also known as seryl tRNA synthetase (SerRS), was reported to affect glucose-induced lipid biosynthesis in breast cancer. 35 We therefore assume that the worse OS incurred by SARS mutation in PSC could also be possibly attributed to the dysregulated pathways that may be potentially activated.
Amounts of studies have shown that TMB, PD-L1 and immune cell infiltration are intimately correlated with the efficacy of immunotherapy. [36][37][38] It is still not clear what molecular factors may affect patient responses to immunotherapies and whether PSC of different mutational status exhibit differential immune signatures. To this end, we measured immunological landscape of different mutational background and depicted their differences. We observed that patients with the significantly mutated genes ZFHX4, TTN, TP53, KRAS, FAT3, PKHD1L1, CDKN2A, CSMD2, DNAH5, DST showed higher TMB compared with the wild type. It is possible that neoantigens, resulted from the somatic nonsynonymous mutations, may account for higher TMB. The identification of these mutations among PSC patients opens the way for both targeted and immunotherapies.
Our results showed that PSC patients boosted with higher TMB, PD-L1 and CD8 + T cell infiltration had longer OS, despite being statistically insignificant. In addition, a number of studies have confirmed the association between TMB, PD-L1, CD8 + T infiltration and immunotherapy response, showing their higher intensity may render tumors higher immunogenicity, leading to improved clinical response to immunotherapy. 39,40 Undeniably, there are several limitations in our study. First, the information on the number of patients should be provided who showed recurrence, who received cytotoxic chemotherapy, tyrosine kinase inhibitors, or immune checkpoint inhibitors were not collected. Moreover, it is a pity that the association between these markers and immune response was not explored in the whole PSC population, which warrants further study.

Conclusion
In the present study, first of all, we identified a cohort of gene and chromosomal mutation in PSC. We also analyzed the association between gene mutation at higher frequency and PD-L1, TMB and CD8 + T cell infiltration. Next, by the investigation of prognostic impact of gene mutation and immune landscape on survival, we revealed that a worsened survival could be predicted in PSC patients presenting with SARS mutation. Notably, mutational and immune landscape of PSC based on the morphology and pathological type have been depicted. In an analogous meaning, it has some clinical utility for the morphological and pathological type could be largely speculated according to the genomic mutation and immune landscape of PSC patients. We have demonstrated the genomic alterations of PSC patients. The description of genetic driver mutations has provided a new hope for personalized medicine going forward, bringing PSC into the era of targeted therapy and immunotherapy. However, more clinical trials are needed to further delineate possible genomic status of PSC and to predict clinical benefit from targeted and immunotherapies.

Data Sharing Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.