Transcriptomic Immune-related Signature Predictive of Chemoradiotherapy Response in Anal Squamous Cell Carcinoma

Transcriptomic Immune-related Signature Predictive of Chemoradiotherapy Response in Anal Squamous Cell Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Transcriptomic Immune-related Signature Predictive of Chemoradiotherapy Response in Anal Squamous Cell Carcinoma Soledad Iseas, Mariano Golubicki, Ezequiel Lacunza, Diego Prost, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8147801/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Anal squamous cell carcinoma (ASCC) is a rare malignancy associated with high-risk HPV, with rising incidence among younger adults. While immunotherapy has improved outcomes in metastatic ASCC, treatment for localized disease remains largely unchanged, with high recurrence rates. This study provides comprehensive exome and transcriptome profiling of 40 stage I-III non-metastatic ASCC patients treated with curative chemoradiotherapy (CRT) to identify predictors of treatment response and progression-free survival. Transcriptomic analysis revealed 350 differentially expressed genes between complete responders (CR) and non-complete responders (NCR) (p-value 2). CR was associated with modulation of immune-related pathways, cytokine production, epidermis development, cell differentiation, and signaling pathways associated with TNFA/NFkB and epithelial to mesenchymal transition. Immune infiltrate analysis showed significant enrichment of CD8 + central memory T cells (p = 0.008) in CR cases, correlating with increased tertiary lymphoid structure and improved overall (p = 0.0026) and disease-free survival (p = 0.0098). Exome-seq identified alterations in novel and known cancer driver genes without association to CRT response, despite high tumor mutational burden (TMB) was significantly associated with shorter overall (p = 0.03) and disease-free survival (p = 0.027) compared with low TMB cases. These findings highlight the potential of incorporating gene expression signatures (e.g., FDCSP , ALDOB , ADGRB1 , SPINK7 ) alongside immune-related markers into clinical practice to enhance the prediction of treatment response and guide personalized therapies in ASCC. A robust and functionally active immune microenvironment—characterized by specific T and B cell populations and the presence of tertiary lymphoid structures—emerges as a hallmark of complete response and improved survival in ASCC patients undergoing chemoradiotherapy. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Health sciences/Oncology ASCC chemoradiotherapy exome transcriptome immune infiltrate Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Anal squamous cell carcinoma (ASCC) is a rare but increasingly common malignancy strongly associated with high-risk human papillomavirus (HPV) infections that have a higher incidence among younger adults lately ( 1 , 2 ) Strategies such as HPV vaccination and screening in high-risk populations are being adopted to mitigate this trend, emphasizing the impact of these preventive measures ( 3 , 4 ). Additional risk factors, though less strongly associated, include HIV infection, tobacco use, immunosuppression, and autoimmune conditions such as Crohn's disease, which may contribute through alternative pathogenic mechanisms ( 5 , 6 ). Recently, new therapeutic alternatives based on immunotherapy have expanded the treatment landscape. The combination of carboplatin-paclitaxel with retifanlimab has shown superiority over chemotherapy alone in first-line treatment for metastatic or recurrent ASCC, considered as a new first-line standard ( 7 ). Before this, promising results from early-phase trials of nivolumab and pembrolizumab in chemotherapy-refractory patients have reported objective responses in 11–24% of cases, suggesting new therapeutic possibilities and diversifying treatment strategies ( 8 – 11 ). While novel combinations of chemotherapy, immunotherapy and radiotherapy are ongoing being explored ( 12 ), the standard treatment for the curative setting has remained largely unchanged since the 1990s , and recurrence rates remain high (25–40%) particularly in locally advanced stages ( 12 , 13 ). Efforts to elucidate the molecular and immune mechanisms driving HPV-associated ASCC are continuously emerging. However, current evidence predominantly stems from retrospective studies often lacking associated clinical and treatment parameters. Despite these limitations, significant advances have been hypothetically outlined across multiple omics levels. Genomic and epigenomic alterations, including PIK3CA mutations and DNA methylation patterns, have emerged as prognostic and predictive biomarkers, guiding targeted therapies and enabling disease monitoring ( 14 )( 15 ). On the other hand, the tumor microenvironment in HPV-associated ASCC plays a pivotal role in immune evasion through the actions of Tregs and myeloid-derived suppressor cells (MDSCs), which suppress immune responses and promote T-cell exhaustion. Immune-related signatures show promise in enhancing the efficacy of immune checkpoint blockade therapies ( 15 )( 16 ). HPV-driven ASCC fosters an immunosuppressive microenvironment through regulatory T cells (Tregs) and PD-L1 expression, hindering antitumor immunity ( 16 )( 17 ). Collaborative research remains crucial to advancing personalized therapies and improving patient outcomes in this rare malignancy. In this study, we analyzed a cohort of NM-ASCC patients to identify mutational, transcriptomic, and immune-based biomarkers associated with chemoradiotherapy outcomes. Our goal was to gain a deeper molecular understanding of the mechanisms underlying this rare malignancy and to uncover prognostic and predictive markers of individual treatment response. 2. MATERIALS & METHODS 2.1 Anal cancer cohort This retrospective study comprised 97 consecutive eligible non-metastatic anal cancer patients recruited between 2010 and 2017 who were treated with curative intent at the oncology unit of Hospital Paris Saint Joseph (HPSJ) in Paris, France. The protocol was approved by the ethics committee of the HPSJ institution. Patients had to provide informed consent for their data collection according to the recommendation of the ethics committee and in accordance with the European Union General Data Protection Regulation (GDPR). Inclusion criteria were at least 18 years old, available pretreatment formalin-fixed paraffin-embedded (FFPE) biopsy, histologically confirmed squamous cell carcinoma, clinical stage cTNM I-III disease, and completed definitive CRT as their primary therapeutic approach. Patients with anal adenocarcinoma, in situ squamous cell carcinoma or other histological cancer subtypes were excluded. Clinical and pathologic data were retrieved from the electronic charts. Initial clinical staging was based on anoscopy and digital anal examination, thorax–abdomen computed tomography (CT) scan, pelvic magnetic resonance imaging (MRI), and FDG-PET/TC. 2.2 Treatment and Follow-up Radiotherapy was performed using either 3D conformal or intensity-modulated techniques (IMRT), with a median dose of 54 Gy in 30 daily fractions over 5.5 weeks. The three chemotherapy regimens delivered concomitantly with radiotherapy were: 1) mitomycin 12 mg/m2 IV bolus, day 1–29 (maximum dose 20 mg) with 5-FU 1,000 mg/m2 on days 1–4 and 29–32 by continuous 24-h IV infusion; 2) mitomycin 12 mg/m2 IV bolus, only day 1 (maximum dose 20 mg) with capecitabine 825 mg/m2 twice daily on each radiotherapy treatment day, and 3) cisplatin 60 mg/m2 on days 1 and 29, with 5-FU 1000 mg/m2 on days 1–4 and 29–32 by continuous 24-h IV infusion. All HIV-positive patients simultaneously received highly active antiretroviral therapy (HAART). Those patients with bulky tumors and very symptomatic at presentation received induction chemo before CRT. All cases were discussed in a multidisciplinary team (MDT). The choice of chemotherapy regimen was per the physician's discretion. After completing treatment, Complete Response (CR) was determined according to RECIST v1.1 from clinical, anorectoscopy and radiological images at 24 weeks. CR was defined as clinical (on anal inspection and examination), radiological (CT and MRI of the pelvis) and rectoscopy (no evidence of disease; suspected lesions were confirmed by a biopsy) disappearance of the disease. Abdominal perineal resection was performed in non-CR patients. Clinical follow-up included digital anal examination, anoscopy, and a clinical exam, monthly for 3 months, then every 3 months for the first two years and every 6 months for 3 to 5 years. A thorax CT and abdominal-pelvic MRI imaging were performed every 6 months during the follow-up period. Forty out of the ninety-seven FFPE cases were selected based on quality and quantity of their purified DNA and RNA for further genomic and transcriptomic analysis. These 40 cases were represented by 22 patients with a complete clinical response and 18 patients with no complete clinical response after chemoradiotherapy. 2.3 Exome sequencing and bioinformatics analysis Genomics DNA was isolated from FFPE samples using the "ReliaPrep™ FFPE gDNA Miniprep System" (Promega) according to the manufacturer's instructions. The Nanodrop ND−1000 spectrophotometer (Thermo Scientific) was used to measure concentrations and the absorbance ratios at 260 nm and 280 nm for each sample, with the latter serving as an indicator of sample purity. All samples showed an acceptable 260/280 ratio value (greater than 1.78). The Qubit® 2.0 fluorometer (Thermo Scientific) was used to accurately measure the amount of DNA extracted from FFPE. Sample concentrations were determined using the "DNA BR Assay" kit (Thermo Scientific). The libraries were prepared using the Library Preparation Kit with Enzymatic Fragmentation 2.0 and the Exome Enrichment Kit 2.0 Plus (Twist Biosciences). The resultant libraries were also measured with the Q ubit and DNA BR Assay kit (Thermo Scientific). The indexed libraries were circularized using the "Element Adept Library Compatibility v1.1" kit . Sequencing was performed with paired-end reads of 150 base pairs according to the recommendations provided by Element Biosciences AVITI™ platform at Helixio facility (Saint-Beauzire - France). The number of sequences ranged from 25 to 58 million. The GC content percentage was between 50% and 61%, and the duplication percentage also ranged from 13% to 30%. All these quality conditions have produced a validated dataset. All short-read files from 40 patients (R1 and R2 fastq) were aligned against GRCh.38 with the BWA-MEM algorithm. A GATK standard pipeline, with Mutect2 caller, for “tumor-only” samples was applied. The mean coverage reached was 122x (ranging from 44 to 184x). The resulting raw VCFs were filtered to ensure that the variants obtained met the 'PASS' quality criteria and were of somatic origin. For annotation steps an up to date pipeline with SnpEff and dbNSFP resources were used. For tumor mutational burden (TMB) only non-synonymous variants (predicted with SnpEff as High or Moderate impact) were taken into account, and they also will have at least > = 10 reads for the alternative allele and also > = 5% for variant allele frequency (VAF). A driver discovery tool dNdScv was used to detect driver genes that could be under a positive selection in this cohort ( 17 ). 2.4 RNA sequencing and bioinformatics analysis Total RNA was isolated from ASCC FFPE samples using the ReliaPrep™ FFPE Total RNA Miniprep System (Promega) following standard manufacturer's protocol. RNA concentration and integrity were measured on an Agilent 2100 Bioanalyzer (Agilent Technologies). RNA samples with RNA integrity number (RIN) over 5 were considered for RNA sequencing. The RNA samples were processed for directional RNA-seq library construction using the NEBNext® rRNA Depletion v2 (Human/Mouse/Rat) module and the NEBNext® UltraTM II Directional RNA Library Prep kit (New England Biolabs) according to the manufacturer's protocol. We performed 150 nt paired-end sequencing using an Element Biosciences AVITI™ platform at Helixio facility (Saint-Beauzire - France) and obtained ~ 30 million clusters per sample with 92% >Q30. The RNAseq raw data has been submitted to the NCBI Gene Expression Omnibus database with the accession number GSE312258 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE312258 ). The raw short-read sequences were quality-checked and trimmed to remove adapters and low-quality bases using the Rfastp R/Bioconductor package. The preprocessed reads were then aligned and mapped to the human genome reference GRCh38 using the Subread aligner algorithm provided by the Rsubread R/Bioconductor package. The aligned reads (BAM files) from each sample were used to calculate gene expression abundance at the whole-genome level using the featureCounts function provided by the Rsubread package. To identify differentially expressed genes between NCR and CR ASCC, we computed fold changes and adjusted p-values using the edgeR R/Bioconductor package based on the normalized log2-based count per million values. Genes showing a log-fold change greater than 1 and an adjusted p-value below 0.05 were considered significantly differentially expressed. Functional enrichment analysis and Gene Set Enrichment Analysis (GSEA) of differentially expressed genes will be performed with the clusterProfiler R package. Tumor immune cell infiltration and T cell dysfunction/exclusion scores were estimated using five algorithms provided by the immunedeconv R/Bioconductor package ( https://github.com/omnideconv/immunedeconv ) on normalized count matrices. In addition, we used the Estimate Systems Immune Response (EaSIeR) R/Bioconductor package to characterize the tumor-immune microenvironment of ASCC based on RNAseq profiles ( 18 ). Immune response scores predicted by EaSIer were further compared among CR and NCR cases using the Rank Product test implemented in the RankProd R/Bioconductor package. Unsupervised hierarchical clustering analysis and heatmaps representations were performed with the MultiExperimentViewer (MeV 4.9.0) software. 2.5 HPV metatranscriptomic and p16 immunohistochemistry analyses For metatranscriptomics, the obtained RNA-Seq data were processed using the bioBakery suite of tools: KneadData was used to separate the human and the nonhuman reads; taxonomic profiling was performed using MetaPhlAn to identify and quantify microbial taxa at species level present in the anal samples ( 19 ). Formalin-fixed and paraffin-embedded (FFPE) tissue sections were cut and reviewed by a specialist pathologist (JA) to confirm the presence of invasive ASCC. Freshly cut slides were stained for p16 as a surrogate marker of HPV infection, using a monoclonal anti-p16 antibody on an automated Leica Bond III IHC platform. Slides were categorized as p16 positive or negative by a specialized pathologist blinded to clinical outcomes. p16 was considered positive in case of a diffuse, nuclear and cytoplasmic, moderate to strong staining of tumor cells. Negative cases had < 10% tumor cells stained at any intensity. 2.6 Statistical analysis of clinicopathological and follow-up data The Chi-square test was used to compare categorical data between groups, while Wilcoxon rank-sum test was used for continuous data. Kaplan-Meier curves and the log-rank test were used to analyze DFS and OS data. DFS was measured from the first day of CRT to clinical or radiological recurrence or death from any cause. OS was measured from treatment initiation to death from any cause, as previously reported ( 13 ). Two-tailed p-values were calculated and p-values < 0.05 were considered as significant. 3. RESULTS AND DISCUSSION 3.1 Patient cohort and treatment response Ninety-seven FFPE samples of non-metastatic ASCC patients with treatment response and follow-up data after definitive chemoradiotherapy were recruited for further mutational and transcriptome based analysis. Clinical and demographic data are summarized in Table 1 . Eighty-two patients were treated with mitomycin−5-FU (85%), 5 patients received cisplatin−5-FU (5%), and 10 patients received mitomycin-capecitabine (10%), concomitantly with 3D-pelvic radiotherapy. Sixty-nine patients reported complete response (CR = 71%) at 6 months after initiation of CRT, while partial or stable response was reported in 28 patients (NCR = 29%). No significant differences were found between CRT regimens according to CR rate and follow-up (p > 0.05). Tumor stage and lymph node status were associated with treatment response (p = 0.008 and p = 0.043, respectively) and disease-free survival (p < 0.0001 and p = 0.0083, respectively). Table 1 Clinicopathological characteristics of the ASCC cohort. Characteristic Total Complete Response Non-complete response p-value Sample size 97 69 28 — Median age (range) 62 (46–89) 62 (46–86) 60 (47–89) p > 0.05 Sex at birth Female 61 46 15 p > 0.05 Male 36 23 13 T stage cT1-T2 39 34 5 p = 0.008 cT3-T4 58 35 23 Nodes Negative 34 29 5 p = 0.043 Positive 63 40 23 HPV Negative 2 1 1 p > 0.05 Positive 95 68 27 HIV Negative 70 52 18 p > 0.05 Positive 27 17 10 Location Margin 6 3 3 p > 0.05 Canal 91 66 25 Treatment MMC−5FU 82 58 24 p > 0.05 CDDP−5FU MMC-Capecitabine 5 10 5 6 0 4 3.2 HPV infection among CRT responder and non-responder ASCC Ninety-eight percent of the ASCC (95 out of 97) were HPV-positive cases according to p16 IHC analysis ( Table 1 ). Virome analysis of the 40 ASCC samples profiled by RNAseq showed that 36 out of 38 HPV-positive cases were infected by high-risk oncogenic subtypes ( Fig. 1 ). Consistent with p16 IHC results (38/40), Alphapapillomavirus−9 (comprising genotypes HPV16, 31, 33, 52, and 58) was detectable in the majority of samples (33/40). Additionally, Alphapapillomavirus−7 (HPV18, 39, 59, 68, 45, 70), Alphapapillomavirus−5 (HPV26, 51, 69, 82), and Alphapapillomavirus−10, which includes the low-risk genotypes HPV6 and HPV11, were also detected in a subset of ASCC. Non-significant associations with CRT response or other clinicopathological variables were detected for HPV infection as determined by p16 or RNAseq virome analysis (p > 0.05). 3.3 Transcriptome profile of responder and non-responder ASCC Statistical analysis of RNA-Seq data revealed 350 differentially expressed genes (DEGs) between CR and NCR cases (p-value 2), 87% were coding RNAs and 13% were ncRNAs. Among the deregulated genes, 261 were up-modulated and 89 down-modulated genes in CR compared with NCR cases ( Fig. 2 A and Supplementary Table S1 ). Functional enrichment analysis of 350 DEGs revealed specific functional bioprocess strongly related to adaptive immunity, epidermis development, cell differentiation, cytokine production ( Fig. 2 B ) and signaling pathways associated with TNFA/NFkB, epithelial to mesenchymal transition, KRAS and IL6-JAK-STAT3 signaling (Supplementary Fig. 1). Interestingly, we identify several multifaceted tumor suppressor related genes involved with the modulation of the TP53 pathway and the immune response among the most significant CR up-modulated genes compared to NCR cases such as FDCSP , ALDOB , ADGRB1 and SPINK7 ( Fig. 2 C-D, Supplementary Table 1). Several CR up-modulated genes were significantly associated with longer DFS and OS outcomes ( Fig. 2 D, Supplementary Table 1). Among the most DEGs, the Follicular dendritic cell secreted protein (FDCSP) has recently emerged as a significant biomarker in various cancer contexts, particularly in relation to immune responses and cancer prognosis. FDCSP is highly expressed in HPV-positive head and neck squamous carcinoma (HNSC) and is associated with a favorable prognosis. Its expression correlates with increased infiltration of T follicular helper cells, which are crucial for effective immune responses. FDCSP's function is linked to chemokine pathways, particularly CXCL13, suggesting its role in modulating immune responses in HPV + HNSC ( 20 ). As described, several of the most significantly CR up-modulated genes act as tumor suppressors by regulating key pathways involved in cancer progression. Aldolase B (ALDOB), a glycolytic enzyme, suppresses tumor growth in hepatocellular and gastric cancers by inhibiting the Akt pathway and modulating the immune microenvironment ( 21 , 22 ). ALDOB downregulation leads to increased TGF-β, immune evasion, and impaired CD8 + T cell function ( 23 ). Adhesion G protein-coupled receptors B1 (ADGRB1) prevents p53 degradation by inhibiting Mdm2, maintaining p53-mediated tumor suppression; its loss results in lower p53 levels and enhanced tumor proliferation ( 24 , 25 ). SPINK7 (also known as ECRG2) encodes a serine protease inhibitor and p53 target, limits cancer cell proliferation, migration, and invasion; its absence is linked to chemoresistance and increased malignancy including squamous esophageal and oral squamous cell carcinoma ( 26 ). Loss of SPINK7 expression can lead to resistance against DNA-damaging anticancer drugs, highlighting its potential as a therapeutic target ( 27 ). NLRC3 functions as a negative regulator of signaling pathways activated by Toll-like receptors (TLRs) and the DNA sensor STING in response to viral infections ( 28 ). NLRC3 associates with PI3Ks, inhibiting the activation of the PI3K-dependent kinase AKT following the binding of growth factor receptors or TLR4. These findings underscore NLRC3 as an inhibitor of the mTOR pathway, an immune regulator, and a tumor suppressor gene ( 28 , 29 ). This study showed that FDCSP , ALDOB , ADGRB1 , SPINK7 genes, and others shown in Supplementary Table S1 , are deregulated in NM-ASCC in association with clinical response to CRT treatment. Importantly, a recent study demonstrates that activation of inflammatory pathways such as IFNγ, IFNα, TNFα signaling via NF-κB, and EMT were significantly enriched in ASCC tumors that respond poorly to chemoradiotherapy ( 31 ). Elevated expression of interferon-induced transmembrane protein 1 (IFITM1), increased regulatory T-cells, and higher levels of the chemokine CXCL2 in blood were also associated with reduced freedom from locoregional failure and distant metastasis after CRT ( 31 ). Further studies need to be performed among independent cohorts to corroborate the relevance of these transcripts as prognostic and predictive biomarkers and its application in clinical settings, particularly in enhancing the effectiveness of CRT in NM-ASCC patients. 3.4 Immune infiltrate populations within distinct tumor treatment response Given the aforementioned strong association of the transcriptome changes with immune-related themes, we performed a computational evaluation of tumor immune infiltrate among CR and NCR cases using CIBERSORT, xCell, MCPcounter, Quantiseq and TIMER algorithms. Supporting the GO and functional annotation observations, tumor immune infiltrate analysis based on RNA-seq data identified the enrichment of different subpopulations of T and B-cells in CR cases compared to NCR ( Fig. 3 A ). The CD8 + central memory T cells was the most significantly enriched immune type among CR cases (p = 0.008) associated with longer and DFS (p = 0.005) and OS (p = 0.003) ( Fig. 3 C, D ). The CD4 + memory resting B cells were also enriched in CR cases compared to NCR (p = 0.01) in association with longer DFS (p = 0.021) and OS (p = 0.002). The remaining immune cell infiltrates enriched in CR cases were not significantly associated with outcomes (p > 0.05). Interestingly, T cell CD4 + Th1 and Macrophage M1 were significantly depleted among CR compared with NCR cases (p = 0.044 and p = 0.01) ( Fig. 3 A ). In addition, T cell CD4 + memory resting (p = 0.041), Tregs (p = 0.008) and myeloid dendritic cell activated (p = 0.011) were significantly depleted among HIV-positive compared with HIV-negative cases ( Fig. 3 B ). We further employed the Estimate Systems Immune Response (EaSIeR) tool to generate a high-level representation of the anti-tumor immune responses in the tumor microenvironment of CR and NCR cases. Briefly EaSIer computes immune response scores based on gene expression signatures including immune cytolytic activity, chemokine, IFNy, T-cell inflamed, immune resistance program and tertiary lymphoid structures (TLS) signatures among others. Interestingly, the TLS (FC = 4.4; p = 0.0012) and cytolytic activity (FC = 1.7; p = 0.04) scores were significantly increased among CR compared with NCR cases ( Fig. 3 D ). The immune cytolytic activity score represents the level of two cytolytic effectors, granzyme A and perforin, which are overexpressed upon CD8 + T cell activation. The TLS score is derived from differentially expressed genes in tumors with TLS. The enrichment of B cells, T cells, particularly T central memory cells, and dendritic cells among CR cases could be explained by TLS associated with CR cases. TLS are privileged sites of lymphoid neogenesis within tumors for the recruitment and activation of central-memory T and B cells that circulate and limit cancer progression ( 32 ). TLS has been associated with a favorable prognosis in various cancers such as non-small cell lung cancer, colorectal, gastric, pancreatic, and esophageal cancer among others ( 33 ). Recently, Wang et al. showed that TLS predicts the response to neoadjuvant therapy and recurrence-free survival of patients with locally advanced rectal cancer ( 34 ). Overall, these findings suggest that CR cases had a significant enrichment of TLS with T CD8 + central memory cells that could facilitate an increased CD8 + regional memory T cell in CR compared to NCR ASCC cases. CD8 + regional memory T cells have been consistently associated with favorable prognosis in multiple cancer types, including lung cancer, endometrial adenocarcinoma, bladder urothelial carcinoma, cervical cancer, and gastric cancer ( 35 , 36 ). High densities of these cells within tumors correlate with improved survival rates and better clinical outcomes ( 37 – 39 ). Further studies need to be performed to corroborate the CD103 + CD8 + TILs among NM-ASCC in association with CRT response. 3.5 Mutational profile of responder and non-responder ASCC Exome-seq was performed in pretreatment biopsies from 40 patients using the Exome Enrichment Kit 2.0 Plus (Twist Biosciences), The mean coverage for all samples was 122.4X (min 43.9X and max 184.6X) that allow the identification of 172 somatic mutations across 21 cancer driver genes. Missense variants accounted for 70% of the detected mutations, while nonsense mutations represented 14%. Frameshift mutations and in-frame indels each constituted approximately 2% and 14% of the total, respectively. We combined a bioinformatics approach, using the dNdScv algorithm, with a thorough literature review to identify cancer driver genes ( 40 ). In this sense, we selected genes with significant driver mutations (q global < 0.1) and those mutated genes previously associated with ASCC. Among these, we detected mutations affecting SLAMF7 (65%), GOLGA6L9, ZNF208 and ZNF429 (43%), RBM38 (40%), ZNF430 (35%) and MTCH2 (32.5%) and for the genes that was previously associated to ASCC we found that KMT2C (18%), KMT2D (13%), PIK3CA, FBXW7, ATM, RB1 and PTEN account for (10%) and finally EP300, BRCA2, APC, CDKN2A (8%) and JAK2, NOTCH1 and, TP53 (5%) ( Fig. 4 A ). The mean Tumor Mutational Burden (TMB) for all s a mples was 6 mut/Mb ranging from 2.6 to 18.2. Patients with a higher number of mutations per megabase showed a trend towards complete response to treatment, although this difference was not statistically significant (p = 0.07) ( Fig. 4 B ). In addition, patients with high TMB showed a shorter DFS (p = 0.027) and OS (p = 0.03) compared with low TMB cases ( Fig. 4 C ). High TMB is often regarded as a marker of increased immunogenicity and a predictor of response to immune checkpoint inhibitors in various cancers. However, in patients receiving standard treatments such as chemoradiotherapy (CRT), tumors with high TMB may carry numerous driver mutations that contribute to more aggressive tumor biology and greater resistance to DNA-damaging therapies ( 41 ). Non-significant associations were detected for any of the mutational variants identified with the CRT response (p > 0.05). Study limitations Despite our comprehensive analysis of clinical, mutational, and transcriptomic characteristics, the data on treatment and clinical responses are derived from a single-center cohort, which may not fully represent the variability found across different clinical environments. Moreover, the retrospective design introduces potential biases, including variability in treatment regimens, treatment adherence, and patient comorbidities, which could influence the results. Additionally, while exome sequencing and transcriptome analysis are robust methods, the absence of validation of some identified biomarkers and genes in independent cohorts is a significant limitation. The findings should be viewed as preliminary until confirmed in larger, more diverse studies. While we observed interesting associations between the transcriptomic profile and treatment response, the limitations in immune infiltrate analysis, particularly in identifying specific T and B cell subpopulations, require further investigation. Additional methods, such as a more detailed characterization of the tumor microenvironment, could enhance our understanding of the mechanisms driving treatment response in ASCC. 4. Conclusions Comprehensive characterization of non-metastatic ASCC at mutational, transcriptomic and immune levels allowed us to identify the most relevant changes in the context of CRT response and survival outcomes. A comparison of the mutational profile identified in this non-metastatic cohort revealed novel putative cancer driver genes frequently altered in ASCC such as SLAMF7 and GOLGA6L9 and previously reported genes but not associated with clinical response to CRT treatment. Our study highlights key molecular and immune markers that could improve the clinical management of ASCC patients. We identified a gene expression signature expressed in CR cases (e.g. FDCSP, ALDOB, ADGRB1, SPINK7) and downregulated in NCR, which are associated with good prognosis and that may serve as potential biomarkers of CRT response. Tumor-immune infiltrate analysis revealed that responders to CRT exhibited enrichment of T and B cell subpopulations, particularly CD8 + central memory T cells and CD4 + resting memory B cells, which correlated with improved survival outcomes and the presence of tertiary lymphoid structures likely plays a role in this immune enrichment environment. Together, these findings underscore the potential of integrating molecular and immune markers into clinical practice to better predict treatment response and guide personalized therapies for ASCC patients. Further validation in independent cohorts is necessary to confirm the clinical relevance of these biomarkers and their application in therapeutic decision-making. Declarations Author Contributions: All the authors have directly participated in the preparation of this manuscript and have read and approved the final version submitted and declare no ethical conflicts of interest. SI and MCA conceived the study, performed formal analysis, and wrote the article. GM, EL, DP, SB, CL, NB, ER and JA were responsible for methodology, research assistance, genomics data analysis, and clinical data curation of participants. Informed consent and patient details This retrospective stuty was approved by the Hospital Paris Saint Joseph (HPSJ) ethics committee. All patients provided informed consent for their data collection according to the recommendation of the ethics committee and in accordance with the European Union General Data Protection Regulation (GDPR). Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments We thank the patients who participated in this research and their relatives for their time, altruism, and generosity. We extend our heartfelt gratitude to Dr. Esteban Cvitkovic for his invaluable contributions and unwavering support to this study and previous research efforts in rare cancer malignancies. Funding Declaration This research was funded by Foundation Nelia and Amadeo Barletta (FNAB) (SI) and the National University of La Plata M250 I+D grant (MCA). References Islami, F., Ferlay, J., Lortet-Tieulent, J., Bray, F. & Jemal, A. International trends in anal cancer incidence rates. Int. J. Epidemiol. 46 (3), 924–938 (2017). Clifford, G. M. et al. A meta-analysis of anal cancer incidence by risk group: Toward a unified anal cancer risk scale. Int. J. 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Anti-telomerase CD4 + Th1 immunity and monocytic-myeloid-derived-suppressor cells are associated with long-term efficacy achieved by docetaxel, cisplatin, and 5-fluorouracil (DCF) in advanced anal squamous cell carcinoma: Translational study of Epitopes-HPV01 and 02 trials. Int. J. Mol. Sci. 21 (18), 6838 (2020). Lechien, J. R. et al. HPV Involvement in the Tumor Microenvironment and Immune Treatment in Head and Neck Squamous Cell Carcinomas. Cancers [Internet]. ;12(5). (2020). Available from: http://dx.doi.org/10.3390/cancers12051060 Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171 (5), 1029–41e21 (2017). Lapuente-Santana, Ó., van Genderen, M., Hilbers, P. A. J., Finotello, F. & Eduati, F. Interpretable systems biomarkers predict response to immune-checkpoint inhibitors. Patterns (N Y) . 2 (8), 100293 (2021). McIver, L. J. et al. bioBakery: a meta’omic analysis environment. Bioinformatics 34 (7), 1235–1237 (2018). Wu, Q. et al. 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(Weinh) . 11 (38), e2402329 (2024). Pennacchiotti, G. et al. SPINK7 expression changes accompanied by HER2, P53 and RB1 can be relevant in predicting oral squamous cell carcinoma at a molecular level. Sci. Rep. 11 (1), 6939 (2021). Patel, H., Sheikh, M. S. & Huang, Y. ECRG2/SPINK7 tumor suppressor as modulator of DNA damage response. Int. J. Mol. Sci. 25 (11), 5854 (2024). Zhang, L. et al. NLRC3, a member of the NLR family of proteins, is a negative regulator of innate immune signaling induced by the DNA sensor STING. Immunity 40 (3), 329–341 (2014). Karki, R. et al. NLRC3 is an inhibitory sensor of PI3K-mTOR pathways in cancer. Nature 540 (7634), 583–587 (2016). Kanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. & Ishiguro-Watanabe, M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 53 , D672–D677 (2025). Martin, D. et al. Inflammatory pathways confer resistance to chemoradiotherapy in anal squamous cell carcinoma. NPJ Precis Oncol. 8 (1), 93 (2024). Dieu-Nosjean, M. C. et al. Tertiary lymphoid structures, drivers of the anti-tumor responses in human cancers. Immunol. Rev. 271 (1), 260–275 (2016). Sautès-Fridman, C. et al. Tertiary Lymphoid Structures and B cells: Clinical impact and therapeutic modulation in cancer. Semin Immunol. 48 (101406), 101406 (2020). Wang, Q. et al. Tertiary lymphoid structures predict survival and response to neoadjuvant therapy in locally advanced rectal cancer. NPJ Precis Oncol. 8 (1), 61 (2024). Komdeur, F. L. et al. CD103 + tumor-infiltrating lymphocytes are tumor-reactive intraepithelial CD8 + T cells associated with prognostic benefit and therapy response in cervical cancer. Oncoimmunology 6 (9), e1338230 (2017). Li, R. et al. Identification and validation of an immunogenic subtype of gastric cancer with abundant intratumoural CD103 + CD8 + T cells conferring favourable prognosis. Br. J. Cancer . 122 (10), 1525–1534 (2020). Djenidi, F. et al. CD8 + CD103 + tumor-infiltrating lymphocytes are tumor-specific tissue-resident memory T cells and a prognostic factor for survival in lung cancer patients. J. Immunol. 194 (7), 3475–3486 (2015). Workel, H. H. et al. CD103 defines intraepithelial CD8 + PD1 + tumour-infiltrating lymphocytes of prognostic significance in endometrial adenocarcinoma. Eur. J. Cancer . 60 , 1–11 (2016). Corgnac, S. et al. CD103 + CD8 + TRM cells accumulate in tumors of anti-PD–1-responder lung cancer patients and are tumor-reactive lymphocytes enriched with Tc17. Cell. Rep. Med. 1 (7), 100127 (2020). Iseas, S. et al. Unraveling emerging anal cancer clinical biomarkers from current immuno-oncogenomics advances. Mol. Diagn. Ther. 28 (2), 201–214 (2024). Nikanjam, M. et al. Tumor mutational burden is not predictive of cytotoxic chemotherapy response. Oncoimmunology 9 (1), 1781997 (2020). Additional Declarations No competing interests reported. Supplementary Files SupplementaryData1.xlsx SupplementaryTable1.docx SupplementaryFigure1.png Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviews received at journal 01 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers invited by journal 31 Mar, 2026 Editor assigned by journal 31 Mar, 2026 Editor invited by journal 09 Dec, 2025 Submission checks completed at journal 07 Dec, 2025 First submitted to journal 07 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8147801","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":616673561,"identity":"24cae17f-401b-430a-815e-74eee88d5695","order_by":0,"name":"Soledad Iseas","email":"","orcid":"","institution":"Hôpital Paris Saint-Joseph","correspondingAuthor":false,"prefix":"","firstName":"Soledad","middleName":"","lastName":"Iseas","suffix":""},{"id":616673562,"identity":"7f46bdc9-dea1-46c6-99e6-69785aebfacf","order_by":1,"name":"Mariano Golubicki","email":"","orcid":"","institution":"Gastroenterology Hospital “Dr. Carlos Bonorino Udaondo”","correspondingAuthor":false,"prefix":"","firstName":"Mariano","middleName":"","lastName":"Golubicki","suffix":""},{"id":616673563,"identity":"eaf523a5-6ed3-4c22-9a59-f69a7d860b98","order_by":2,"name":"Ezequiel Lacunza","email":"","orcid":"","institution":"National University of La Plata","correspondingAuthor":false,"prefix":"","firstName":"Ezequiel","middleName":"","lastName":"Lacunza","suffix":""},{"id":616673564,"identity":"10347900-b5d5-42cc-b944-e166c6e9864c","order_by":3,"name":"Diego Prost","email":"","orcid":"","institution":"Hôpital Paris Saint-Joseph","correspondingAuthor":false,"prefix":"","firstName":"Diego","middleName":"","lastName":"Prost","suffix":""},{"id":616673565,"identity":"50e9e093-283a-4c00-8fd5-ffb670141ff0","order_by":4,"name":"Sarah Bouchereau","email":"","orcid":"","institution":"Hôpital Paris Saint-Joseph","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Bouchereau","suffix":""},{"id":616673566,"identity":"579cb712-1bc0-453b-adcf-5d903d1f5d22","order_by":5,"name":"Chloe Lahaie","email":"","orcid":"","institution":"Hôpital Paris Saint-Joseph","correspondingAuthor":false,"prefix":"","firstName":"Chloe","middleName":"","lastName":"Lahaie","suffix":""},{"id":616673567,"identity":"9ddb6ca0-adb1-41df-8daa-0e90ab314d56","order_by":6,"name":"Nabil Baba-Hamed","email":"","orcid":"","institution":"Hôpital Paris Saint-Joseph","correspondingAuthor":false,"prefix":"","firstName":"Nabil","middleName":"","lastName":"Baba-Hamed","suffix":""},{"id":616673568,"identity":"da807604-4eb4-4029-bb7c-3c58911ac3c7","order_by":7,"name":"Eric Raymond","email":"","orcid":"","institution":"Hôpital Paris Saint-Joseph","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Raymond","suffix":""},{"id":616673569,"identity":"f736ae8f-4c2a-4756-a1be-f8b1cee2b9f8","order_by":8,"name":"Julien Adam","email":"","orcid":"","institution":"Institut Gustave Roussy","correspondingAuthor":false,"prefix":"","firstName":"Julien","middleName":"","lastName":"Adam","suffix":""},{"id":616673570,"identity":"f5ad03cd-22b7-4c3d-aeb5-2edf4173049b","order_by":9,"name":"Martin Abba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYFACHjYGhgogfQCIHzBAGYS1nIGqTCBaC2MbKVp0288ee/Bz3jY5vgM8Zg8SdzDI8d1IYPxcgUeL2Zm8dMPebbeNJQ/wmBsknmEwlryRwCx5Bp+WAzlmErzbbiduANoikdjGkLjhRgKDZAM+LeffmEn+nXO7HqalHqiF+SdeLTdyzKR5G24nGEC1JBjcSGDDb8uNd+nGMsduG848zFYmkXhGwnDmmYdtlvgdlnvs4Zua2/J8x5u3SXzcYQNkJB++iU8LAjADMWODBJgkSgMEkKR4FIyCUTAKRgwAAOQVVAXNN/bIAAAAAElFTkSuQmCC","orcid":"","institution":"National University of La Plata","correspondingAuthor":true,"prefix":"","firstName":"Martin","middleName":"","lastName":"Abba","suffix":""}],"badges":[],"createdAt":"2025-11-18 16:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8147801/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8147801/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106299067,"identity":"c38fbb6b-1ab6-42ad-9186-809752d47a86","added_by":"auto","created_at":"2026-04-07 09:08:18","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":274576,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of relative abundances of Alphapapillomavirus species detected in 40 ASCC samples, as assessed by metatranscriptomic analysis. Below the heatmap, a tile plot aligns samples with their clinicopathological variables including CRT response, progression, lymph node status and HIV and HPV status based on p16 IHC.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8147801/v1/41259e44126f97fb8cb6097c.jpeg"},{"id":106299062,"identity":"283e9f8b-7c89-415b-ab1e-a89a99a1e6ec","added_by":"auto","created_at":"2026-04-07 09:08:18","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":964532,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptome changes among 40 non-metastatic ASCC according to CRT treatment response. \u003cstrong\u003eA) \u003c/strong\u003eVolcano plot of 350 differentially expressed genes among CR and NCR ASCC patients (p\u0026lt;0.01; FC\u0026gt;2). \u003cstrong\u003eB) \u003c/strong\u003eFunctional enrichment analysis of the differentially expressed genes based on Gene Ontology Biological process, KEGG and Reactome pathway databases (30). \u003cstrong\u003eC) \u003c/strong\u003eHeatmap of top 20 most significantly deregulated genes in CR cases. \u003cstrong\u003eD) \u003c/strong\u003eRepresentative Kaplan-Meier curves of a coding (ALDOB), a non-coding (LINC00861), and two immune-related genes (CD2 and CD6) up-modulated in CR cases and associated with ASCC disease-free survival.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8147801/v1/eee15b89318ef1396170e1d6.jpeg"},{"id":106299108,"identity":"3afd2394-6be2-481b-ad73-52cf98b5daa2","added_by":"auto","created_at":"2026-04-07 09:08:19","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1224524,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptome-based predictors of the tumor immune infiltrate and immune response among ASCC patients according to the clinical response to treatment. \u003cstrong\u003eA) \u003c/strong\u003eTumor immune infiltrate fractions (TILs) differentially enriched among responder and non-responder patients (p\u0026lt;0.05) as estimated by CIBERSORT, xCell, MCPcounter, Quantiseq and TIMER algorithms. \u003cstrong\u003eB) \u003c/strong\u003eTILs differentially enriched among HIV-negative and HIP-positive cases (p\u0026lt;0.05). \u003cstrong\u003eC) \u003c/strong\u003eBoxplot of T cell CD8+ central memory tumor infiltrates among patients with CR and NCR response (p=0.008). \u003cstrong\u003eD) \u003c/strong\u003eSurvival analysis of the T cell CD8+ central memory tumor infiltrates among NM-ASCC patients. \u003cstrong\u003eE) \u003c/strong\u003eImmune response scores as estimated by the EaSIeR R/Bioconductor package. Increased tertiary lymphoid structure (TLS) and cytolytic activity were significantly detected among CR cases compared to NCR (p=0.0012 and p=0.05, respectively).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8147801/v1/39ba3d028226468901f6bf43.jpeg"},{"id":106403682,"identity":"7d9edcde-92b0-4145-9102-c4845c19ed25","added_by":"auto","created_at":"2026-04-08 09:14:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":320611,"visible":true,"origin":"","legend":"\u003cp\u003eMutational profile of NM-ASCC according to the CRT response. \u003cstrong\u003eA) \u003c/strong\u003eTile plot showing recurrent altered cancer driver genes and the significant genes that were under positive selection based on dNdScv algorithm among 40 non-metastatic ASCC. Non-significant associations with CRT response were detected for any of the mutational variants identified (p\u0026gt;0.05). \u003cstrong\u003eB) \u003c/strong\u003eTumor Mutational Burden (TMB) among patients with complete (CR) and non-complete (NCR) response to CRT. \u003cstrong\u003eC) \u003c/strong\u003eSurvival analysis of the TMB among NM-ASCC patients. Tumors with high TMB were associated with shorter DFS (p=0.027) and OS (p=0.03) compared with low TMB tumors.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8147801/v1/55a6700dbd1fe008122a0563.png"},{"id":106405780,"identity":"1f0ed3de-7c27-4646-aaa1-6e30b42fd0ed","added_by":"auto","created_at":"2026-04-08 09:28:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3699255,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8147801/v1/9c28fd9e-0bd5-4528-856b-a5d6f39ad13d.pdf"},{"id":106299056,"identity":"d3fea6c5-7d40-48e0-81bd-fa5a02069745","added_by":"auto","created_at":"2026-04-07 09:08:13","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":50739,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8147801/v1/11b08a1dbb5340421f84f402.xlsx"},{"id":106299059,"identity":"8a527e04-95f0-44a7-b8e4-e3cb594c0902","added_by":"auto","created_at":"2026-04-07 09:08:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9512,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8147801/v1/91724694f0f6564c71a7b6a9.docx"},{"id":106299104,"identity":"165cbf5b-1b4f-4941-ba0e-392dc8b671f3","added_by":"auto","created_at":"2026-04-07 09:08:19","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1197864,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8147801/v1/ff8b4d4dc13f982f251d8b5a.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptomic Immune-related Signature Predictive of Chemoradiotherapy Response in Anal Squamous Cell Carcinoma","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAnal squamous cell carcinoma (ASCC) is a rare but increasingly common malignancy strongly associated with high-risk human papillomavirus (HPV) infections that have a higher incidence among younger adults lately\u003c/span\u003e (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eStrategies such as HPV vaccination and screening in high-risk populations are being adopted to mitigate this trend, emphasizing the impact of these preventive measures\u003c/span\u003e (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAdditional risk factors, though less strongly associated, include HIV infection, tobacco use, immunosuppression, and autoimmune conditions such as Crohn's disease, which may contribute through alternative pathogenic mechanisms\u003c/span\u003e (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eRecently, new therapeutic alternatives based on immunotherapy have expanded the treatment landscape. The combination of carboplatin-paclitaxel with retifanlimab has shown superiority over chemotherapy alone in first-line treatment for metastatic or recurrent\u003c/span\u003e ASCC, \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003econsidered as a new first-line standard\u003c/span\u003e (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBefore this, promising results from early-phase trials of nivolumab and pembrolizumab in chemotherapy-refractory patients have reported objective responses in 11\u0026ndash;24% of cases, suggesting new therapeutic possibilities and diversifying treatment strategies\u003c/span\u003e (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eWhile novel combinations of chemotherapy, immunotherapy and radiotherapy are ongoing being explored\u003c/span\u003e (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ethe standard treatment for the curative setting has remained largely unchanged since the 1990s\u003c/span\u003e, \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eand recurrence rates remain high (25\u0026ndash;40%) particularly in locally advanced stages\u003c/span\u003e (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eEfforts to elucidate the molecular and immune mechanisms driving HPV-associated ASCC are continuously emerging. However, current evidence predominantly stems from retrospective studies often lacking associated clinical and treatment parameters. Despite these limitations, significant advances have been hypothetically outlined across multiple omics levels. Genomic and epigenomic alterations, including\u003c/span\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003ePIK3CA\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003emutations and DNA methylation patterns, have emerged as prognostic and predictive biomarkers, guiding targeted therapies and enabling disease monitoring\u003c/span\u003e (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). On the other hand, \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ethe tumor microenvironment in HPV-associated ASCC plays a pivotal role in immune evasion through the actions of Tregs and myeloid-derived suppressor cells (MDSCs), which suppress immune responses and promote T-cell exhaustion. Immune-related signatures show promise in enhancing the efficacy of immune checkpoint blockade therapies\u003c/span\u003e (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eHPV-driven ASCC fosters an immunosuppressive microenvironment through regulatory T cells (Tregs) and PD-L1 expression, hindering antitumor immunity\u003c/span\u003e (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCollaborative research remains crucial to advancing personalized therapies and improving patient outcomes in this rare malignancy.\u003c/span\u003e\u003c/p\u003e \u003cp\u003eIn this study, we analyzed a cohort of NM-ASCC patients to identify mutational, transcriptomic, and immune-based biomarkers associated with chemoradiotherapy outcomes. Our goal was to gain a deeper molecular understanding of the mechanisms underlying this rare malignancy and to uncover prognostic and predictive markers of individual treatment response.\u003c/p\u003e"},{"header":"2. MATERIALS \u0026 METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Anal cancer cohort\u003c/h2\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThis retrospective study comprised 97 consecutive eligible non-metastatic anal cancer patients recruited between 2010 and 2017 who were treated with curative intent at the oncology unit of\u003c/span\u003e Hospital \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eParis Saint Joseph (HPSJ) in Paris, France. The protocol was approved by the ethics committee of the HPSJ institution. Patients had to provide informed consent for their data collection according to the recommendation of the ethics committee and in accordance with the European Union General Data Protection Regulation (GDPR).\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eInclusion criteria were at least 18 years old, available pretreatment formalin-fixed paraffin-embedded (FFPE) biopsy, histologically confirmed squamous cell carcinoma, clinical stage cTNM I-III disease, and completed definitive CRT as their primary therapeutic approach. Patients with anal adenocarcinoma, in situ squamous cell carcinoma or other histological cancer subtypes were excluded. Clinical and pathologic data were retrieved from the electronic charts. Initial clinical staging was based on anoscopy and digital anal examination, thorax\u0026ndash;abdomen computed tomography (CT) scan, pelvic magnetic resonance imaging (MRI), and FDG-PET/TC.\u003c/span\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Treatment and Follow-up\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eRadiotherapy was performed using either 3D conformal or intensity-modulated techniques (IMRT), with a median dose of 54 Gy in 30 daily fractions over 5.5 weeks. The three chemotherapy regimens delivered concomitantly with radiotherapy were: 1) mitomycin 12 mg/m2 IV bolus, day 1\u0026ndash;29 (maximum dose 20 mg) with 5-FU 1,000 mg/m2 on days 1\u0026ndash;4 and 29\u0026ndash;32 by continuous 24-h IV infusion; 2) mitomycin 12 mg/m2 IV bolus, only day 1 (maximum dose 20 mg) with capecitabine 825 mg/m2 twice daily on each radiotherapy treatment day, and 3) cisplatin 60 mg/m2 on days 1 and 29, with 5-FU 1000 mg/m2 on days 1\u0026ndash;4 and 29\u0026ndash;32 by continuous 24-h IV infusion. All HIV-positive patients simultaneously received highly active antiretroviral therapy (HAART). Those patients with bulky tumors and very symptomatic at presentation received induction chemo before CRT. All cases were discussed in a multidisciplinary team (MDT). The choice of chemotherapy regimen was per the physician's discretion.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAfter completing treatment, Complete Response (CR) was determined according to RECIST v1.1 from clinical, anorectoscopy and radiological images at 24 weeks. CR was defined as clinical (on anal inspection and examination), radiological (CT and MRI of the pelvis) and rectoscopy (no evidence of disease; suspected lesions were confirmed by a biopsy) disappearance of the disease. Abdominal perineal resection was performed in non-CR patients. Clinical follow-up included digital anal examination, anoscopy, and a clinical exam, monthly for 3 months, then every\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e3 months for the first two years and every 6 months for 3 to 5 years. A thorax CT and abdominal-pelvic MRI imaging were performed every 6 months during the follow-up period.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eForty out of the ninety-seven FFPE cases were selected based on quality and quantity of their purified DNA and RNA for further genomic and transcriptomic analysis. These 40 cases were represented by 22 patients with a complete clinical response and 18 patients with no complete clinical response after chemoradiotherapy.\u003c/span\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Exome sequencing and bioinformatics analysis\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eGenomics DNA was isolated from FFPE samples using the \"ReliaPrep\u0026trade; FFPE gDNA Miniprep System\" (Promega) according to the manufacturer's instructions. The Nanodrop ND\u0026minus;1000 spectrophotometer (Thermo Scientific) was used to measure concentrations and the absorbance ratios at 260 nm and 280 nm for each sample, with the latter serving as an indicator of sample purity. All samples showed an acceptable 260/280 ratio value (greater than 1.78). The Qubit\u0026reg; 2.0 fluorometer (Thermo Scientific) was used to accurately measure the amount of DNA extracted from FFPE. Sample concentrations were determined using the \"DNA BR Assay\" kit (Thermo Scientific). The libraries were prepared using the Library Preparation Kit with Enzymatic Fragmentation 2.0 and the Exome Enrichment Kit 2.0 Plus (Twist Biosciences). The resultant libraries were also measured with the\u003c/span\u003e Q\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eubit and DNA BR Assay kit (Thermo Scientific). The indexed libraries were circularized using the \"Element Adept Library Compatibility v1.1\" kit\u003c/span\u003e. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSequencing was performed with paired-end reads of 150 base pairs according to the recommendations provided by Element Biosciences AVITI\u0026trade; platform at Helixio facility (Saint-Beauzire - France). The number of sequences ranged from 25 to 58\u0026nbsp;million. The GC content\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003epercentage was between 50% and 61%, and the duplication percentage also ranged from 13% to 30%. All these quality conditions have produced a validated dataset.\u003c/span\u003e \u003c/p\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAll short-read files from 40 patients (R1 and R2 fastq) were aligned against GRCh.38\u003c/span\u003e with the BWA-MEM \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ealgorithm. A GATK standard pipeline, with Mutect2 caller, for \u0026ldquo;tumor-only\u0026rdquo; samples was applied. The mean coverage reached was 122x (ranging from 44 to 184x). The resulting raw VCFs were filtered to ensure that the variants obtained met the 'PASS' quality criteria and were of somatic origin. For annotation steps an up to date pipeline with SnpEff and dbNSFP resources were used. For tumor mutational burden (TMB) only non-synonymous variants (predicted with SnpEff as High or Moderate impact) were taken into account, and they also will have at least\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;10 reads for the alternative allele and also\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;5% for variant allele frequency (VAF). A driver discovery tool dNdScv was used to detect driver genes that could be under a positive selection in this cohort\u003c/span\u003e (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 RNA sequencing and bioinformatics analysis\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTotal RNA was isolated from ASCC FFPE samples using the ReliaPrep\u0026trade; FFPE Total RNA Miniprep System (Promega) following standard manufacturer's protocol. RNA concentration and integrity were measured on an Agilent 2100 Bioanalyzer (Agilent Technologies). RNA samples with RNA integrity number (RIN) over 5 were considered for RNA sequencing. The RNA samples were processed for directional RNA-seq library construction using the NEBNext\u0026reg; rRNA Depletion v2 (Human/Mouse/Rat) module and the NEBNext\u0026reg; UltraTM II Directional RNA Library Prep kit (New England Biolabs) according to the manufacturer's protocol. We performed 150 nt paired-end sequencing using an Element Biosciences AVITI\u0026trade; platform at Helixio facility (Saint-Beauzire - France) and obtained\u0026thinsp;~\u0026thinsp;30\u0026nbsp;million clusters per sample with 92% \u0026gt;Q30. The RNAseq raw data\u003c/span\u003e has \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ebeen submitted to the NCBI Gene Expression Omnibus database with the accession number GSE312258 (\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE312258\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE312258\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"UnderlineSmallCaps\" class=\"UnderlineSmallCaps\" name=\"Emphasis\"\u003e).\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThe raw short-read sequences were quality-checked and trimmed to remove adapters and low-quality bases using the Rfastp R/Bioconductor package. The preprocessed reads were then aligned and mapped to the human genome reference GRCh38 using the Subread aligner algorithm provided by the Rsubread R/Bioconductor package. The aligned reads (BAM files) from each sample were used to calculate gene expression abundance at the whole-genome level using the featureCounts function provided by the Rsubread package. To identify differentially expressed genes between NCR and CR ASCC, we computed fold changes and adjusted p-values using the edgeR R/Bioconductor package based on the normalized log2-based count per million values. Genes showing a log-fold change greater than 1 and an adjusted p-value below 0.05 were considered significantly differentially expressed. Functional enrichment analysis and Gene Set Enrichment Analysis (GSEA) of differentially expressed genes will be performed with the clusterProfiler R package. Tumor immune cell infiltration and T cell dysfunction/exclusion scores were estimated using five algorithms provided by the immunedeconv R/Bioconductor package (\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/omnideconv/immunedeconv\u003c/span\u003e\u003cspan address=\"https://github.com/omnideconv/immunedeconv\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"UnderlineSmallCaps\" class=\"UnderlineSmallCaps\" name=\"Emphasis\"\u003e)\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eon normalized count matrices. In addition, we used the Estimate Systems Immune Response (EaSIeR) R/Bioconductor package to characterize the tumor-immune microenvironment of ASCC based on RNAseq profiles\u003c/span\u003e (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eImmune response scores predicted by EaSIer were further compared among CR and NCR cases using the Rank Product test implemented in the RankProd R/Bioconductor package. Unsupervised hierarchical clustering analysis and heatmaps representations were performed with the MultiExperimentViewer (MeV 4.9.0) software.\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 HPV metatranscriptomic and p16 immunohistochemistry analyses\u003c/h2\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e For metatranscriptomics, the obtained RNA-Seq data were processed using the bioBakery suite of tools: KneadData was used to separate the human and the nonhuman reads; taxonomic profiling was performed using MetaPhlAn to identify and quantify microbial taxa at species level\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003epresent in the anal samples\u003c/span\u003e (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eFormalin-fixed and paraffin-embedded (FFPE) tissue sections were cut and reviewed by a specialist pathologist\u003c/span\u003e (JA) \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eto confirm the presence of invasive ASCC. Freshly cut slides were stained for p16 as a surrogate marker of HPV infection, using a monoclonal anti-p16 antibody on an automated Leica Bond III IHC platform. Slides were categorized as p16 positive or negative by a specialized pathologist blinded to clinical outcomes. p16 was considered positive in case of a diffuse, nuclear and cytoplasmic, moderate to strong staining of tumor cells. Negative cases had \u0026lt;\u003c/span\u003e 10% \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003etumor cells stained at any intensity.\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis of clinicopathological and follow-up data\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThe Chi-square test was used to compare categorical data between groups, while Wilcoxon rank-sum test was used for continuous data. Kaplan-Meier curves and the log-rank test were used to analyze DFS and OS data. DFS was measured from the first day of CRT to clinical or radiological recurrence or death from any cause. OS was measured from treatment initiation to death from any cause, as previously reported\u003c/span\u003e (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTwo-tailed p-values were calculated and p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered as significant.\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient cohort and treatment response\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNinety-seven FFPE samples of non-metastatic ASCC patients with treatment response and follow-up data after definitive chemoradiotherapy were recruited for further mutational and transcriptome based analysis. Clinical and demographic data are summarized in\u003c/span\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eEighty-two patients were treated with mitomycin\u0026minus;5-FU (85%), 5 patients received cisplatin\u0026minus;5-FU (5%), and 10 patients received mitomycin-capecitabine (10%), concomitantly with 3D-pelvic radiotherapy. Sixty-nine patients reported complete response (CR\u0026thinsp;=\u0026thinsp;71%) at 6 months after initiation of CRT, while partial or stable response was reported in 28 patients (NCR\u0026thinsp;=\u0026thinsp;29%). No significant differences were found between CRT regimens according to CR rate and follow-up (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Tumor stage and lymph node status were associated with treatment response (p\u0026thinsp;=\u0026thinsp;0.008 and p\u0026thinsp;=\u0026thinsp;0.043, respectively) and disease-free survival (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 and p\u0026thinsp;=\u0026thinsp;0.0083, respectively).\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eClinicopathological characteristics of the ASCC cohort.\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCharacteristic\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTotal\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eComplete\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eResponse\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNon-complete\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eresponse\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ep-value\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSample size\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e97\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e69\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e28\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e\u0026mdash;\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMedian age (range)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e62 (46\u0026ndash;89)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e62 (46\u0026ndash;86)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e60 (47\u0026ndash;89)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSex at birth\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eFemale\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e61\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e46\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e15\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMale\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e36\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e23\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e13\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eT stage\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ecT1-T2\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e39\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e34\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ep\u0026thinsp;=\u0026thinsp;0.008\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ecT3-T4\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e58\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e35\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e23\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNodes\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNegative\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e34\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e29\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ep\u0026thinsp;=\u0026thinsp;0.043\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ePositive\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e63\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e40\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e23\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eHPV\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNegative\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ePositive\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e95\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e68\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e27\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eHIV\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNegative\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e70\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e52\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e18\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ePositive\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e27\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e17\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e10\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eLocation\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMargin\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e6\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCanal\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e91\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e66\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e25\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTreatment\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMMC\u0026minus;5FU\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e82\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e58\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e24\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eCDDP\u0026minus;5FU\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMMC-Capecitabine\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e10\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e6\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e0\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e4\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 HPV infection among CRT responder and non-responder ASCC\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNinety-eight percent of the ASCC (95 out of 97) were HPV-positive cases according to p16 IHC analysis (\u003c/span\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e). Virome analysis of the 40 ASCC samples profiled by RNAseq showed that\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e36 out of 38 HPV-positive cases were infected by high-risk oncogenic subtypes (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e). Consistent with p16 IHC results (38/40), Alphapapillomavirus\u0026minus;9 (comprising genotypes HPV16, 31, 33, 52, and 58) was detectable in the majority of samples (33/40). Additionally, Alphapapillomavirus\u0026minus;7 (HPV18, 39, 59, 68, 45, 70), Alphapapillomavirus\u0026minus;5 (HPV26, 51, 69, 82), and Alphapapillomavirus\u0026minus;10, which includes the low-risk genotypes HPV6 and HPV11, were also detected in a subset of ASCC. Non-significant associations with CRT response or other clinicopathological variables were detected for HPV infection as determined by p16 or RNAseq virome analysis (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Transcriptome profile of responder and non-responder ASCC\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eStatistical analysis of RNA-Seq data revealed 350 differentially expressed genes (DEGs) between CR and NCR cases (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01; FC\u0026thinsp;\u0026gt;\u0026thinsp;2), 87% were coding RNAs and 13% were ncRNAs. Among the deregulated genes, 261 were up-modulated and 89 down-modulated genes in CR compared with NCR cases (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eand Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Functional enrichment analysis of 350 DEGs revealed specific functional bioprocess strongly related to adaptive immunity, epidermis development, cell differentiation, cytokine production (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e) and signaling pathways associated with TNFA/NFkB, epithelial to mesenchymal transition, KRAS and IL6-JAK-STAT3 signaling (Supplementary Fig.\u0026nbsp;1). Interestingly, we identify several multifaceted tumor suppressor related genes involved with the modulation of the TP53 pathway and the immune response among the most significant CR up-modulated genes compared to NCR cases such as\u003c/span\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eFDCSP\u003c/span\u003e, \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eALDOB\u003c/span\u003e, \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eADGRB1\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eand\u003c/span\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eSPINK7\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e(\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D, \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;1). Several CR up-modulated genes were significantly associated with longer DFS and OS outcomes (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSupplementary Table\u0026nbsp;1).\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAmong the most DEGs, the\u003c/span\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eFollicular dendritic cell secreted protein\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e(FDCSP) has recently emerged as a significant biomarker in various cancer contexts, particularly in relation to immune responses and cancer prognosis. FDCSP is highly expressed in HPV-positive head and neck squamous carcinoma (HNSC) and is associated with a favorable prognosis. Its expression correlates with increased infiltration of T follicular helper cells, which are crucial for effective immune responses. FDCSP's function is linked to chemokine pathways, particularly CXCL13, suggesting its role in modulating immune responses in HPV\u0026thinsp;+\u0026thinsp;HNSC\u003c/span\u003e (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAs described, several of the most significantly CR up-modulated genes act as tumor suppressors by regulating key pathways involved in cancer progression. Aldolase B (ALDOB), a glycolytic enzyme, suppresses tumor growth in hepatocellular and gastric cancers by inhibiting the Akt pathway and modulating the immune microenvironment\u003c/span\u003e (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eALDOB downregulation leads to increased TGF-β, immune evasion, and impaired CD8\u0026thinsp;+\u0026thinsp;T cell function\u003c/span\u003e (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAdhesion G protein-coupled receptors B1 (ADGRB1) prevents p53 degradation by inhibiting Mdm2, maintaining p53-mediated tumor suppression; its loss results in lower p53 levels and enhanced tumor proliferation\u003c/span\u003e (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eSPINK7 (also known as ECRG2) encodes a serine protease inhibitor and p53 target, limits cancer cell proliferation, migration, and invasion; its absence is linked to chemoresistance and increased malignancy including squamous esophageal and oral squamous cell carcinoma\u003c/span\u003e (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eLoss of SPINK7 expression can lead to resistance against DNA-damaging anticancer drugs, highlighting its potential as a therapeutic target\u003c/span\u003e (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNLRC3 functions as a negative regulator of signaling pathways activated by Toll-like receptors (TLRs) and the DNA sensor STING in response to viral infections\u003c/span\u003e (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNLRC3 associates with PI3Ks, inhibiting the activation of the PI3K-dependent kinase AKT following the binding of growth factor receptors or TLR4. These findings underscore NLRC3 as an inhibitor of the mTOR pathway, an immune regulator, and a tumor suppressor gene\u003c/span\u003e (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThis\u003c/span\u003e study showed \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ethat\u003c/span\u003e \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eFDCSP\u003c/span\u003e, \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eALDOB\u003c/span\u003e, \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eADGRB1\u003c/span\u003e, \u003cspan type=\"ItalicSmallCaps\" class=\"ItalicSmallCaps\" name=\"Emphasis\"\u003eSPINK7\u003c/span\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003egenes, and others shown in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, are deregulated in NM-ASCC in association with clinical response to CRT treatment. Importantly, a recent study\u003c/span\u003e demonstrates that activation of inflammatory pathways such as IFNγ, IFNα, TNFα signaling via NF-κB, and EMT were significantly enriched in ASCC tumors that respond poorly to chemoradiotherapy (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Elevated expression of interferon-induced transmembrane protein 1 (IFITM1), increased regulatory T-cells, and higher levels of the chemokine CXCL2 in blood were also associated with reduced freedom from locoregional failure and distant metastasis after CRT (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eFurther studies need to be performed among independent cohorts to corroborate the relevance of these transcripts as prognostic and predictive biomarkers and its application in clinical settings, particularly in enhancing the effectiveness of CRT in NM-ASCC patients.\u003c/span\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Immune infiltrate populations within distinct tumor treatment response\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eGiven the aforementioned strong association of the transcriptome changes with immune-related themes, we performed a computational evaluation of tumor immune infiltrate among CR and NCR cases using CIBERSORT, xCell, MCPcounter, Quantiseq and TIMER algorithms. Supporting the GO and functional annotation observations, tumor immune infiltrate analysis based on RNA-seq data identified the enrichment of different subpopulations of T and B-cells in CR cases compared to NCR (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e). The CD8\u0026thinsp;+\u0026thinsp;central memory T cells was the most significantly enriched immune type among CR cases (p\u0026thinsp;=\u0026thinsp;0.008) associated with longer and DFS (p\u0026thinsp;=\u0026thinsp;0.005) and OS (p\u0026thinsp;=\u0026thinsp;0.003) (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e). The CD4\u0026thinsp;+\u0026thinsp;memory resting B cells were also enriched in CR cases compared to NCR (p\u0026thinsp;=\u0026thinsp;0.01) in association with longer DFS (p\u0026thinsp;=\u0026thinsp;0.021) and OS (p\u0026thinsp;=\u0026thinsp;0.002). The remaining immune cell infiltrates enriched in CR cases were not significantly associated with outcomes (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Interestingly, T cell CD4\u0026thinsp;+\u0026thinsp;Th1 and Macrophage M1 were significantly depleted among CR compared with NCR cases (p\u0026thinsp;=\u0026thinsp;0.044 and p\u0026thinsp;=\u0026thinsp;0.01) (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e). In addition, T cell CD4\u0026thinsp;+\u0026thinsp;memory resting (p\u0026thinsp;=\u0026thinsp;0.041), Tregs (p\u0026thinsp;=\u0026thinsp;0.008) and myeloid dendritic cell activated (p\u0026thinsp;=\u0026thinsp;0.011) were significantly depleted among HIV-positive compared with HIV-negative cases (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eWe further employed the Estimate Systems Immune Response (EaSIeR) tool to generate a high-level representation of the anti-tumor immune responses in the tumor microenvironment of CR and NCR cases. Briefly EaSIer computes immune response scores based on gene expression signatures including immune cytolytic activity, chemokine, IFNy, T-cell inflamed, immune resistance program and tertiary lymphoid structures (TLS) signatures among others. Interestingly, the TLS (FC\u0026thinsp;=\u0026thinsp;4.4; p\u0026thinsp;=\u0026thinsp;0.0012) and cytolytic activity (FC\u0026thinsp;=\u0026thinsp;1.7; p\u0026thinsp;=\u0026thinsp;0.04) scores were significantly increased among CR compared with NCR cases (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e). The immune cytolytic activity score represents the level of two cytolytic effectors, granzyme A and perforin, which are overexpressed upon CD8\u0026thinsp;+\u0026thinsp;T cell activation. The TLS score is derived from differentially expressed genes in tumors with TLS.\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThe enrichment of B cells, T cells, particularly T central memory cells, and dendritic cells among CR cases could be explained by TLS associated with CR cases. TLS are privileged sites of lymphoid neogenesis within tumors for the recruitment and activation of central-memory T and B cells that circulate and limit cancer progression\u003c/span\u003e (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTLS has been associated with a favorable prognosis in various cancers such as non-small cell lung cancer, colorectal, gastric, pancreatic, and esophageal cancer among others\u003c/span\u003e (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eRecently, Wang et al. showed that TLS predicts the response to neoadjuvant therapy and recurrence-free survival of patients with locally advanced rectal cancer\u003c/span\u003e(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eOverall, these findings suggest that CR cases had a significant enrichment of TLS with T CD8\u0026thinsp;+\u0026thinsp;central memory cells that could facilitate an increased CD8\u0026thinsp;+\u0026thinsp;regional memory T cell in CR compared to NCR ASCC cases. CD8\u0026thinsp;+\u0026thinsp;regional memory T cells have been consistently associated with favorable prognosis in multiple cancer types, including lung cancer, endometrial adenocarcinoma, bladder urothelial carcinoma, cervical cancer, and gastric cancer\u003c/span\u003e (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eHigh densities of these cells within tumors correlate with improved survival rates and better clinical outcomes\u003c/span\u003e (\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eFurther studies need to be performed to corroborate the CD103\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;TILs among NM-ASCC in association with CRT response.\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Mutational profile of responder and non-responder ASCC\u003c/h2\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eExome-seq was performed in pretreatment biopsies from 40 patients using the Exome Enrichment Kit 2.0 Plus (Twist Biosciences), The mean coverage for all samples was 122.4X (min 43.9X and max 184.6X) that allow the identification of 172 somatic mutations across 21 cancer driver genes. Missense variants accounted for 70% of the detected mutations, while nonsense mutations represented 14%. Frameshift mutations and in-frame indels each constituted approximately 2% and 14% of the total, respectively. We combined a bioinformatics approach, using the dNdScv algorithm, with a thorough literature review to identify cancer driver genes\u003c/span\u003e (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eIn this sense, we selected genes with significant driver mutations (q global\u0026thinsp;\u0026lt;\u0026thinsp;0.1) and those mutated genes previously associated with ASCC. Among these, we detected mutations affecting SLAMF7 (65%), GOLGA6L9, ZNF208 and ZNF429 (43%), RBM38 (40%), ZNF430 (35%) and MTCH2 (32.5%) and for the genes that was previously associated to ASCC we found that KMT2C (18%), KMT2D (13%), PIK3CA, FBXW7, ATM, RB1 and PTEN account for (10%) and finally EP300, BRCA2, APC, CDKN2A (8%) and JAK2, NOTCH1 and, TP53 (5%) (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e). The mean Tumor Mutational Burden (TMB) for all s\u003c/span\u003ea\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003emples was 6 mut/Mb ranging from 2.6 to 18.2. Patients with a higher number of mutations per megabase showed a trend towards complete response to treatment, although this difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.07) (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e). In addition, patients with high TMB showed a shorter DFS (p\u0026thinsp;=\u0026thinsp;0.027) and OS (p\u0026thinsp;=\u0026thinsp;0.03) compared with low TMB cases (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eHigh TMB is often regarded as a marker of increased immunogenicity and a predictor of response to immune checkpoint inhibitors in various cancers. However, in patients receiving standard treatments such as chemoradiotherapy (CRT), tumors with high TMB may carry numerous driver mutations that contribute to more aggressive tumor biology and greater resistance to DNA-damaging therapies\u003c/span\u003e (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNon-significant associations were detected for any of the mutational variants identified with the CRT response (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy limitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eDespite our comprehensive analysis of clinical, mutational, and transcriptomic characteristics, the data on treatment and clinical responses are derived from a single-center cohort, which may not fully represent the variability found across different clinical environments. Moreover, the retrospective design introduces potential biases, including variability in treatment regimens, treatment adherence, and patient comorbidities, which could influence the results. Additionally, while exome sequencing and transcriptome analysis are robust methods, the absence of validation of some identified biomarkers and genes in independent cohorts is a significant limitation. The findings should be viewed as preliminary until confirmed in larger, more diverse studies. While we observed interesting associations between the transcriptomic profile and treatment response, the limitations in immune infiltrate analysis, particularly in identifying specific T and B cell subpopulations, require further investigation. Additional methods, such as a more detailed characterization of the tumor microenvironment, could enhance our understanding of the mechanisms driving treatment response in ASCC.\u003c/span\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eComprehensive characterization of non-metastatic ASCC at mutational, transcriptomic and immune levels allowed us to identify the most relevant changes in the context of CRT response and survival outcomes. A comparison of the mutational profile identified in this non-metastatic cohort revealed novel putative cancer driver genes frequently altered in ASCC such as SLAMF7 and GOLGA6L9 and previously reported genes but not associated with clinical response to CRT treatment. Our study highlights key molecular and immune markers that could improve the clinical management of ASCC patients. We identified a gene expression signature expressed in CR cases (e.g. FDCSP, ALDOB, ADGRB1, SPINK7) and downregulated in NCR, which are associated with good prognosis and that may serve as potential biomarkers of CRT response. Tumor-immune infiltrate analysis revealed that responders to CRT exhibited enrichment of T and B cell subpopulations, particularly CD8\u0026thinsp;+\u0026thinsp;central memory T cells and CD4\u0026thinsp;+\u0026thinsp;resting memory B cells, which correlated with improved survival outcomes and the presence of tertiary lymphoid structures likely plays a role in this immune enrichment environment. Together, these findings underscore the potential of integrating molecular and immune markers into clinical practice to better predict treatment response and guide personalized therapies for ASCC patients. Further validation in independent cohorts is necessary to confirm the clinical relevance of these biomarkers and their application in therapeutic decision-making.\u003c/span\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e\n\u003cp\u003eAll the authors have directly participated in the preparation of this manuscript and have read and approved the final version submitted and declare no ethical conflicts of interest. SI and MCA conceived the study, performed formal analysis, and wrote the article. GM, EL, DP, SB, CL, NB, ER and JA were responsible for methodology, research assistance, genomics data analysis, and clinical data curation of participants.\u003c/p\u003e\n\u003ch2\u003eInformed consent and patient details\u003c/h2\u003e\n\u003cp\u003eThis retrospective stuty was approved by the Hospital Paris Saint Joseph (HPSJ) ethics committee. All patients provided informed consent for their data collection according to the recommendation of the ethics committee and in accordance with the European Union General Data Protection Regulation (GDPR).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eWe thank the patients who participated in this research and their relatives for their time, altruism, and generosity. We extend our heartfelt gratitude to Dr. Esteban Cvitkovic for his invaluable contributions and unwavering support to this study and previous research efforts in rare cancer malignancies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Foundation Nelia and Amadeo Barletta (FNAB) (SI) and the National University of La Plata M250 I+D grant (MCA).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIslami, F., Ferlay, J., Lortet-Tieulent, J., Bray, F. \u0026amp; Jemal, A. International trends in anal cancer incidence rates. \u003cem\u003eInt. J. Epidemiol.\u003c/em\u003e \u003cb\u003e46\u003c/b\u003e (3), 924\u0026ndash;938 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClifford, G. M. et al. A meta-analysis of anal cancer incidence by risk group: Toward a unified anal cancer risk scale. \u003cem\u003eInt. J. 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Tumor mutational burden is not predictive of cytotoxic chemotherapy response. \u003cem\u003eOncoimmunology\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (1), 1781997 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ASCC, chemoradiotherapy, exome, transcriptome, immune infiltrate","lastPublishedDoi":"10.21203/rs.3.rs-8147801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8147801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnal squamous cell carcinoma (ASCC) is a rare malignancy associated with high-risk HPV, with rising incidence among younger adults. While immunotherapy has improved outcomes in metastatic ASCC, treatment for localized disease remains largely unchanged, with high recurrence rates. This study provides comprehensive exome and transcriptome profiling of 40 stage I-III non-metastatic ASCC patients treated with curative chemoradiotherapy (CRT) to identify predictors of treatment response and progression-free survival. Transcriptomic analysis revealed 350 differentially expressed genes between complete responders (CR) and non-complete responders (NCR) (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01; FC\u0026thinsp;\u0026gt;\u0026thinsp;2). CR was associated with modulation of immune-related pathways, cytokine production, epidermis development, cell differentiation, and signaling pathways associated with TNFA/NFkB and epithelial to mesenchymal transition. Immune infiltrate analysis showed significant enrichment of CD8\u0026thinsp;+\u0026thinsp;central memory T cells (p\u0026thinsp;=\u0026thinsp;0.008) in CR cases, correlating with increased tertiary lymphoid structure and improved overall (p\u0026thinsp;=\u0026thinsp;0.0026) and disease-free survival (p\u0026thinsp;=\u0026thinsp;0.0098). Exome-seq identified alterations in novel and known cancer driver genes without association to CRT response, despite high tumor mutational burden (TMB) was significantly associated with shorter overall (p\u0026thinsp;=\u0026thinsp;0.03) and disease-free survival (p\u0026thinsp;=\u0026thinsp;0.027) compared with low TMB cases. These findings highlight the potential of incorporating gene expression signatures (e.g., \u003cem\u003eFDCSP\u003c/em\u003e, \u003cem\u003eALDOB\u003c/em\u003e, \u003cem\u003eADGRB1\u003c/em\u003e, \u003cem\u003eSPINK7\u003c/em\u003e) alongside immune-related markers into clinical practice to enhance the prediction of treatment response and guide personalized therapies in ASCC. A robust and functionally active immune microenvironment\u0026mdash;characterized by specific T and B cell populations and the presence of tertiary lymphoid structures\u0026mdash;emerges as a hallmark of complete response and improved survival in ASCC patients undergoing chemoradiotherapy.\u003c/p\u003e","manuscriptTitle":"Transcriptomic Immune-related Signature Predictive of Chemoradiotherapy Response in Anal Squamous Cell Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 09:08:06","doi":"10.21203/rs.3.rs-8147801/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"64909120585009341358364803870757089193","date":"2026-05-12T07:54:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140160814954026165068860994075175839686","date":"2026-04-01T12:00:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T09:44:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148768999516088835191749261955364045659","date":"2026-04-01T06:36:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-31T14:07:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T14:04:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-09T11:01:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-07T17:55:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-07T17:48:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd63309e-602e-4fe9-9712-07f5ba98f9ff","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"64909120585009341358364803870757089193","date":"2026-05-12T07:54:17+00:00","index":134,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65623493,"name":"Health sciences/Biomarkers"},{"id":65623494,"name":"Biological sciences/Cancer"},{"id":65623495,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":65623496,"name":"Biological sciences/Immunology"},{"id":65623497,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-04-07T09:08:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 09:08:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8147801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8147801","identity":"rs-8147801","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}