Predict Immunotherapy Response Research Articles

Tumor lymphangiogenesis index reveals the immune landscape and immunotherapy response in lung adenocarcinoma.

Lymphangiogenesis (LYM) has an important role in tumor progression and is strongly associated with tumor metastasis. However, the clinical application of LYM has not progressed as expected. The potential value of LYM needs to be further developed in lung adenocarcinoma (LUAD) patients. The Sequencing data and clinical characteristics of LUAD patients were downloaded from The Cancer Genome Atlas and GEO databases. Multiple machine learning algorithms were used to screen feature genes and develop the LYM index. Immune cell infiltration, immune checkpoint expression, Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and drug sensitivity analysis were used to explore the correlation of LYM index with immune profile and anti-tumor therapy. We screened four lymphangiogenic feature genes (PECAM1, TIMP1, CXCL5 and PDGFB) to construct LYM index based on multiple machine learning algorithms. We divided LUAD patients into the high LYM index group and the low LYM index group based on the median LYM index. LYM index is a risk factor for the prognosis of LUAD patients. In addition, there was a significant difference in immune profile between high LYM index and low LYM index groups. LUAD patients in the low LYM index group seemed to benefit more from immunotherapy based on the results of TIDE algorithm. Overall, we confirmed that the LYM index is a prognostic risk factor and a valuable predictor of immunotherapy response in LUAD patients, which provides new evidence for the potential application of LYM.

Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response

BackgroundAberrant DNA methylation is a major characteristic of cancer genomes. It remains unclear which biological processes determine epigenetic reprogramming and how these processes influence the variants in the cancer methylome, which can further impact cancer phenotypes.MethodsWe performed pairwise permutations of 381,900 loci in 569 paired DNA methylation profiles of cancer tissue and matched normal tissue from The Cancer Genome Atlas (TCGA) and defined conserved differentially methylated positions (DMPs) based on the resulting null distribution. Then, we derived independent methylation signatures from 2,465 cancer-only methylation profiles from the TCGA and 241 cell line-based methylation profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) cohort using nonnegative matrix factorization (NMF). We correlated DNA methylation signatures with various clinical and biological features, including age, survival, cancer stage, tumor immune microenvironment factors, and immunotherapy response. We inferred the determinant genes of these methylation signatures by integrating genomic and transcriptomic data and evaluated the impact of these signatures on cancer phenotypes in independent bulk and single-cell RNA/methylome cohorts.ResultsWe identified 7,364 differentially methylated positions (2,969 Hyper-DMPs and 4,395 Hypo-DMPs) in nine cancer types from the TCGA. We subsequently retrieved three highly conserved, independent methylation signatures (Hyper-MS1, Hypo-MS1, and Hypo-MS4) from cancer tissues and cell lines based on these Hyper and Hypo-DMPs. Our data suggested that Hypo-MS4 activity predicts poor survival and is associated with immunotherapy response and distant tumor metastasis, and Hypo-MS4 activity is related to TP53 mutation and FOXA1 binding specificity. In addition, we demonstrated a correlation between the activities of Hypo-MS4 in cancer cells and the fractions of regulatory CD4 + T cells with the expression levels of immunological genes in the tumor immune microenvironment.ConclusionsOur findings demonstrated that the methylation signatures of distinct biological processes are associated with immune activity in the cancer microenvironment and predict immunotherapy response.

New knowledge on anti-IgLON5 disease.

Anti-IgLON5 disease is characterized by a distinctive sleep disorder, associated with a heterogeneous spectrum of neurological symptoms. Initial autopsies showed a novel neuronal tauopathy predominantly located in the tegmentum of the brainstem. Recently, new diagnostic red flags, biomarkers predictors of response to immunotherapy, and novel insights into the autoimmune pathogenesis of the disease have been reported. Patients with diagnosis of neurodegenerative dementia, progressive supranuclear palsy (PSP) or with motor-neuron disease (MND)-like syndrome have been reported to have IgLON5 antibodies, which are the hallmark of anti-IgLON5 disease. Second, low levels of neurofilament light chain in serum and cerebrospinal fluid of patients at disease onset could be a predictor of immunotherapy response. Recent neuropathological studies indicate that the neuronal tau deposits occur late in the course of the disease. Moreover, IgLON5 antibodies induce cytoskeletal changes in cultured hippocampal neurons suggesting that the tauopathy could be secondary of the IgLON5 antibody effects. Anti-IgLON5 disease can mimic and should be considered in atypical presentations of MND, neurodegenerative dementia and PSP. Neurofilament light chain levels seem promising biomarker for disease prognosis. Finally, the neuropathological and in vitro experimental studies strengthen the autoimmune hypothesis of the disease.

Mitochondrial ribosomal protein S24 is associated with immunosuppressive microenvironment and cold tumor in lung adenocarcinoma

ObjectiveMRPS24 (Mitochondrial Ribosomal Protein S24) belongs to the mitochondrial ribosomal protein family, which participates in the protein synthesis of the mitochondrion. However, the relationship of MRPS24 with lung adenocarcinoma (LUAD) remained unknown. We aimed to identify its immunological and functional mechanisms in LUAD. MethodsThe analysis of MRPS24 expression, clinical features, diagnosis, prognosis, function analysis, genetic alteration, copy number variations, methylation, and tumor microenvironment was investigated by the TCGA, UCSC Xena, GEO, HPA, GEPIA, cBioPortal, MethSurv, TIMER, TIMER2.0, and TISIDB databases. ResultsMRPS24 was found to be more abundant in LUAD tumor tissue than in normal tissue. High levels of MRPS24 expression were found to be an independent prognostic factor by multivariate analysis. Functional analysis revealed that MRPS24 expression was associated with the immune, cell cycle and methylation. MRPS24 methylation level was inversely linked with its expression (p < 0.001). Patients with low MRPS24 methylation had a worse prognosis than those with high methylation (p < 0.05). In addition, the result revealed that the MRPS24 expression was inversely linked to the immune cell infiltration in LUAD. Finally, the validations of the expression level, prognosis, and immune cell infiltration of MRPS24 were in accordance with our previous results. ConclusionsThis study systematically explored that MRPS24 expression was significantly correlated with prognosis, tumorigenesis, genetic alteration, copy number variations, methylation, and immune cell infiltration in LUAD. MRPS24 might be a potential immune-related biomarker in the development and treatment of LUAD, thereby acting as a promising predictor of immunotherapy response in LUAD.

Correlation analysis of disulfidptosis-related gene signatures with clinical prognosis and immunotherapy response in sarcoma

Disulfidptosis, a newly discovered type of programmed cell death, could be a mechanism of cell death controlled by SLC7A11. This could be closely associated with tumor development and advancement. Nevertheless, the biological mechanism behind disulfidptosis-related genes (DRGs) in sarcoma (SARC) is uncertain. This study identified three valuable genes (SLC7A11, RPN1, GYS1) associated with disulfidptosis in sarcoma (SARC) and developed a prognostic model. The multiple databases and RT-qPCR data confirmed the upregulated expression of prognostic DRGs in SARC. The TCGA internal and ICGC external validation cohorts were utilized to validate the predictive model capacity. Our analysis of DRG riskscores revealed that the low-risk group exhibited a more favorable prognosis than the high-risk group. Furthermore, we observed a significant association between DRG riskscores and different clinical features, immune cell infiltration, immune therapeutic sensitivity, drug sensitivity, and RNA modification regulators. In addition, two external independent immunetherapy datasets and clinical tissue samples were collected, validating the value of the DRGs risk model in predicting immunotherapy response. Finally, the SLC7A11/hsa-miR-29c-3p/LINC00511, and RPN1/hsa-miR-143-3p/LINC00511 regulatory axes were constructed. This study provided DRG riskscore signatures to predict prognosis and response to immunotherapy in SARC, guiding personalized treatment decisions.

Constructing a Novel Amino Acid Metabolism Signature: A New Perspective on Pheochromocytoma Diagnosis, Immune Landscape, and Immunotherapy.

Pheochromocytoma/paraganglioma (PGPG) is a rare neuroendocrine tumor. Amino acid metabolism is crucial for energy production, redox balance, and metabolic pathways in tumor cell proliferation. This study aimed to build a risk model using amino acid metabolism-related genes, enhancing PGPG diagnosis and treatment decisions. We analyzed RNA-sequencing data from the PCPG cohort in the GEO dataset as our training set and validated our findings using the TCGA dataset and an additional clinical cohort. WGCNA and LASSO were utilized to identify hub genes and develop risk prediction models. The single-sample gene set enrichment analysis, MCPCOUNTER, and ESTIMATE algorithm calculated the relationship between amino acid metabolism and immune cell infiltration in PCPG. The TIDE algorithm predicted the immunotherapy efficacy for PCPG patients. The analysis identified 292 genes with differential expression, which are involved in amino acid metabolism and immune pathways. Six genes (DDC, SYT11, GCLM, PSMB7, TYRO3, AGMAT) were identified as crucial for the risk prediction model. Patients with a high-risk profile demonstrated reduced immune infiltration but potentially higher benefits from immunotherapy. Notably, DDC and SYT11 showed strong diagnostic and prognostic potential. Validation through quantitative Real-Time Polymerase Chain Reaction and immunohistochemistry confirmed their differential expression, underscoring their significance in PCPG diagnosis and in predicting immunotherapy response. This study's integration of amino acid metabolism-related genes into a risk prediction model offers critical clinical insights for PCPG risk stratification, potential immunotherapy responses, drug development, and treatment planning, marking a significant step forward in the management of this complex condition.

Pan-cancer and single-cell analyses identify CD44 as an immunotherapy response predictor and regulating macrophage polarization and tumor progression in colorectal cancer.

Cluster of differentiation (CD) 44 is a non-kinase cell surface transmembrane glycoprotein critical for tumor maintenance and progression. We conducted a systematic analysis of the expression profile and genomic alteration profile of CD44 in 33 types of cancer. The immune characteristics of CD44 were comprehensively explored by TIMER2.0 and CIBERSORT. In addition, the CD44 transcriptional landscape was examined at the single-cell level. Then, Pseudotime trajectory analysis of CD44 gene expression was performed using Monocle 2, and CellChat was utilized to compare the crosstalk differences between CD44+monocytes and CD44- monocytes. Tumor immune dysfunction and exclusion (TIDE) was used to evaluate the predictive ability of CD44 for immune checkpoint blockade (ICB) responses. The effects of CD44 on colorectal cancer (CRC) and macrophage polarization were investigated by knocking down the expression of CD44 in HCT-116 cell and macrophages in vitro. The expression of CD44 elevated in most cancers, predicting unfavorable prognosis. In addditon, CD44 was correlation with immune cell infiltration and key immune regulators. CD44+ monocytes had a higher information flow intensity than CD44- monocytes. CD44 had good predictive ability for immune checkpoint blockade responses. Knockdown of CD44 inhibited the proliferation, migration, and invasion of HCT-116 cell in vitro. Knockdown of CD44 inhibited M2 macrophage polarization. These findings suggest that CD44 is involved in regulating tumor development, macrophage polarization, and has certain predictive value for patient clinical prognosis and response to immunotherapy.

Abstract 1154: Spatial profiling of the tumor microenvironment in head and neck squamous cell carcinoma revealed immune cell type infiltration into the TME as a predictor of immunotherapy response

Abstract Background: Immunotherapy has shown promising results in advanced head and neck cancer patients, however, only a subset of patients are responsive to immune checkpoint blockade therapy. The molecular and cellular characterization of the tumor microenvironment (TME) may reveal clues as to why some head and neck tumors are responsive or resistant to the therapy. Methods: Formalin-fixed paraffin-embedded (FFPE) tissue samples were collected from 34 patients at two major Queensland Hospitals with advanced head and neck squamous cell carcinoma (HNSCC), prior to immunotherapy. To investigate the cellular and molecular characteristics of the HNSCC TME, we used the Nanostring GeoMx Digital Spatial Profiler (DSP) spanning modules in an immune-oncology panel, which included immune cell typing and profiling, cell death, PI3K/AKT signaling, drug target, immune activation, and pan tumor protein biomarkers. A deeper structural analysis was performed using the Nanostring whole transcriptome atlas (WTA) panel of tertiary lymphoid structures (TLSs) and germinal centers (GCs). Results: Out of 34 patients, 3 had a complete response (CR), 7 had a partial response (PR), 5 had stable disease (SD), and 19 had progressive disease (PD), based on the Response Evaluation Criteria in Solid Tumors (RECIST). When we compared patient responders (patients with CR, PR, and SD) to patient non-responders (patients with PD), we found that the former had a higher infiltration of immune cell type markers representatives of CD8+ T cells, dendritic cells, and macrophages, into their tumor microenvironment than the latter. Then, to investigate differentially expressed genes between patients with complete response and patients with partial response to immunotherapy, we performed DE analyses and we found that patients with PR had higher Ki-67 expression than those with CR. Spatial transcriptomic mapping of TLS’s (peripheral, intra-tumoral) and normal germinal centers revealed interferon pathway enrichment in the TLS’s. In a subset, these appear to be linked with HPV viral infection. Conclusion: In this study, we found that immune cell type infiltration into the tumor microenvironment of head and neck cancers could be predictive of response to immunotherapy. Furthermore, it was revealed that patients with a high expression of Ki-67 might be less likely to respond completely to immunotherapy. Spatially resolved whole transcriptome analysis of HNSCC tissues revealed the presence of tertiary lymphoid structures with a higher enrichment of interferon pathway genes. Citation Format: Habib Sadeghirad, Chin Wee Tan, Ning Liu, James Monkman, Caroline Cooper, Ken O’Byrne, Melissa Davis, Brett Hughes, Arutha Kulasinghe. Spatial profiling of the tumor microenvironment in head and neck squamous cell carcinoma revealed immune cell type infiltration into the TME as a predictor of immunotherapy response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1154.

Abstract 2516: Influence of pre-treatment nutrition and inflammation on immunotherapy response in non-small cell lung cancer

Abstract Purpose: Immune checkpoint inhibitor therapy (ICI) is effective in non-small-cell lung cancer (NSCLC) but only a minority respond, likely due in part to the gut microbiome. Given the coupling of host nutritional status and gut microbiota composition, we investigated the pre-treatment associations of nutritional status and biomarkers with ICI treatment response and survivals. Methods: We recruited stage III-IV NSCLC patients. Pre-ICI treatment clinical information and blood samples were obtained. Clinician assessed ICI response and survival data were recorded. The nutritional/inflammatory indicators were compared based on treatment response and survivals. ICI Clinical Benefit was defined as 12-month progression free survival. Pearson Chi-square or Fisher’s Exact test was used to compare categorical metrics; non-parametric analysis compared continuous metrics by treatment response. Overall survivals were assessed using multivariable Cox proportional hazards models to estimate associations. Results: Of the 76 patients recruited, all were ECOG performance status 0 (14.5%) or 1 (85.5%). 36 (47%) had Clinical Benefit with median survival 21.3 mo. vs. 6.6 mo. among the No Clinical Benefit patients (p&amp;lt;0.01). Non-responders had significantly more often weight loss, lower serum albumin, elevated red cell distribution index (RDW) and worse scores for Frailty Index, Geriatric Nutritional Risk Index and Mini-Nutritional Assessment (MNA). Non-responders had significantly more inflammation with a higher Systemic Immune Response Index (SIRI) and RDW. Overall survival was negatively correlated with: 1. weight loss; 2. low serum albumin; 3. low serum sodium; 4. high RDW; 5. elevated total neutrophils, total white blood cells, total monocytes, neutrophil to lymphocyte ratio, and monocyte to lymphocyte ratio; 6. adverse MNA and Geriatric Nutritional Risk Index; 7. elevated SIRI and Systemic Immune Inflammation Index (SII). ICI response prediction accuracy using a solely MNA-based model was significantly high in terms of AUROC of 0.85 [95% CI= (0.76, 0.93); p&amp;lt;0.05]. In multivariable analysis, the predictor of immunotherapy response and survival was the 6-measure MNA which strongly correlates with weight loss, low serum albumin and depressed Frailty Index. Conclusions: An adverse nutritional status with accompanying chronic inflammation is strong predictor of non-response to ICI. Since lung cancer patients with depressed pre-treatment nutrition have a low likelihood of an ICI response, they should be promptly referred for dietician consultation for intensive nutritional therapy which may improve their clinical outlook. Considering the negative association of malnutrition/inflammation with response to ICI treatment in NSCLC patients who still qualify as ECOG performance status 0-1 patients, prospective clinical trials should also stratify their results by nutritional status. Citation Format: Lary A. Robinson, Youngchul Kim, Acadia Buro, Stephanie R. Hogue, Alexis Bailey, Gina M. DeNicola, Christine Pierce, Mark Umbarger, Doratha A. Byrd. Influence of pre-treatment nutrition and inflammation on immunotherapy response in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2516.

Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response

Tertiary lymphoid structures (TLSs) have been associated with favorable immunotherapy responses and prognosis in various cancers. Despite their significance, their quantification using multiplex immunohistochemistry (mIHC) staining of T and B lymphocytes remains labor-intensive, limiting its clinical utility. To address this challenge, we curated a dataset from matched mIHC and H&E whole-slide images (WSIs) and developed a deep learning model for automated segmentation of TLSs. The model achieved Dice coefficients of 0.91 on the internal test set and 0.866 on the external validation set, along with intersection over union (IoU) scores of 0.819 and 0.787, respectively. The TLS ratio, defined as the segmented TLS area over the total tissue area, correlated with B lymphocyte levels and the expression of CXCL13, a chemokine associated with TLS formation, in 6140 patients spanning 16 tumor types from The Cancer Genome Atlas (TCGA). The prognostic models for overall survival indicated that the inclusion of the TLS ratio with TNM staging significantly enhanced the models’ discriminative ability, outperforming the traditional models that solely incorporated TNM staging, in 10 out of 15 TCGA tumor types. Furthermore, when applied to biopsied treatment-naïve tumor samples, higher TLS ratios predicted a positive immunotherapy response across multiple cohorts, including specific therapies for esophageal squamous cell carcinoma, non-small cell lung cancer, and stomach adenocarcinoma. In conclusion, our deep learning-based approach offers an automated and reproducible method for TLS segmentation and quantification, highlighting its potential in predicting immunotherapy response and informing cancer prognosis.

Abstract 7620: Immuno-transcriptomic profiling of blood and tumor tissue identifies gene signatures associated with immunotherapy response in metastatic bladder cancer

Abstract Muscle-invasive bladder cancer (MIBC) accounts for 25% of bladder cancer cases. Despite the broader usage of immune checkpoint blockade targeting the PD-1/PD-L1 axis, the response rate and survival of patients remain low for this disease. Redirecting swiftly these patients toward alternative therapeutic strategies upon immunotherapy failure should be a priority. So far, no marker allows to precisely determine early outcome to the treatment.Blood-based biomarkers represent ideal candidates for the development of non-invasive immuno-oncology-based assays. However, to date, no blood biomarker has been validated to predict clinical response to immunotherapy. In this study, we used next generation sequencing (RNAseq) on bulk RNA extracted from whole blood and tumor samples in a pre-clinical MIBC mouse model. We aimed at identifying biomarkers associated with immunotherapy response and to assess the potential application of simple non-invasive blood biomarkers as therapeutic decision-making assay compared to tissue-based biomarkers. We established that circulating immune cells and the tumor microenvironment (TME) display highly organ-specific transcriptional responses to ICI. Interestingly, a common lymphocytic activation signature can be identified in both compartments associated with the efficient response to immunotherapy, including a blood specific CD8+ T cell activation/proliferation signature which predicts immunotherapy response. Citation Format: Pedro J. Romero, Emma Desponds, Davide Croci, Victoria Wosika, Noushin Hadadi, Sara Fonseca Cost, Laura Ciarloni, Marco Ongaro, Hana Zdimerova, Marine M. Leblond, Sahar Hosseinian-Ehrensberger, Grégory Verdeil. Immuno-transcriptomic profiling of blood and tumor tissue identifies gene signatures associated with immunotherapy response in metastatic bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7620.

Abstract 910: Clinical inference and biological dissection of tumor ploidy and heterogeneity in cutaneous melanoma for immunotherapy response using deep learning

Abstract Background: Tumor ploidy and heterogeneity demonstrated to be pivotal in predicting immunotherapy response in cutaneous melanoma in several independent cohorts (Liu NatMed2019, Tarantino BioRXiv2022). Their estimation can guide more personalized and rational utilization of these immunotherapies. However, 1) the biology underpinning ploidy and heterogeneity is unknown; and 2) these findings were derived in patients using retrospective research tumor-normal paired whole exome sequencing which is not performed for clinical management. Methods: This study addresses this gap by employing deep learning models to predict these crucial markers using routinely available clinical assays, including H&amp;E images. Attention-based computer vision models enable the identification of key morphological features in H&amp;E slides. Segmentation of tumor tissue to perform automated masking, enhances ploidy inference.Moreover, biologically informed neural networks (P-Net, Elmarakeby Nature2021) uncover transcriptional and genomic features linked to ploidy and tumor heterogeneity. Our models are trained on publicly available data (e.g., Liu et al Nature Medicine 2019; TCGA SKCM) from melanoma cohorts and further validated in independent cohorts to ensure robustness. Results: We developed and validated automated tumor tissue masking, enabling the prediction of Whole Genome Doubling (WGD) from H&amp;E Slides with an AUC &amp;gt; 0.75. Attention-based models identify distinct Tumor Microenvironment (TME) structures predictive of high tumor heterogeneity. P-Net application revealed the Calmodulin pathway, previously associated with regulating proliferation in cancer and targeted with chemotherapy, as intricately linked to high tumor heterogeneity, providing valuable insights into underlying mechanisms. Conclusion: In conclusion, our study strategically harnesses and integrates existing datasets to rigorously test, refine, and validate hypotheses concerning the biological and therapeutic implications of genomic heterogeneity and ploidy. Notably, our predictive models with automated tumor masking demonstrate a remarkable AUC &amp;gt;0.75 for biomarkers traditionally challenging to derive from clinical assays. This breakthrough opens avenues for novel therapeutic strategies targeting genomic heterogeneity and ploidy, providing a transformative potential to enhance patient care and outcomes. Citation Format: Marc Glettig, Giuseppe Tarantino, Tyler Aprati, Haitham Elmarakeby, David Liu. Clinical inference and biological dissection of tumor ploidy and heterogeneity in cutaneous melanoma for immunotherapy response using deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 910.

Abstract 3660: Plasma cell free DNA hydroxymethylation profiling reveals anti-PD1 treatment response and resistance biology in non-small cell lung cancer

Abstract Background: Treatment with immune checkpoint inhibitors (ICIs) targeting programmed death-1 (PD-1) can yield durable anti-tumor responses, yet not all patients respond to ICIs. Current approaches to select patients who may benefit from anti-PD-1 treatment are insufficient. 5-hydroxymethylation (5hmC) analysis of plasma-derived cell free DNA (cfDNA) presents a novel non-invasive approach for identification of therapy response biomarkers which can tackle challenges associated with tumor biopsies such as tumor heterogeneity and serial sample collection. Methods: 151 blood samples were collected from 31 non-small cell lung cancer (NSCLC) patients before therapy start and at multiple timepoints whilst on therapy. Blood samples were processed to obtain plasma-derived cfDNA, followed by enrichment of 5hmC-containing cfDNA fragments through biotinylation via a two-step chemistry and binding to streptavidin coated beads. 5hmC-enriched cfDNA and whole genome libraries were prepared in parallel and sequenced to obtain whole hydroxymethylome and whole genome plasma profiles, respectively. Results: Comparison of on-treatment timepoint to matched pre-treatment samples from same patients revealed that anti-PD-1 treatment induced distinct changes in plasma cfDNA 5hmC profiles of responders, as judged by RECIST, relative to non-responders. In responders, 5hmC accumulated over genes involved in immune activation such as IFNγ and IFNα response, inflammatory response, and TNFα signaling, whereas in non-responders 5hmC increased over epithelial to mesenchymal transition genes. The Molecular Response to anti-PD-1 treatment, as measured by 5hmC changes in plasma cfDNA profiles were observed early, starting with the first cycle of treatment. Comparison of pre-treatment plasma samples revealed that anti-PD-1 treatment response- and resistance-associated genes can be captured by 5hmC profiling of plasma-derived cfDNA. Conclusions: These results demonstrate that 5hmC profiling can identify response and resistance associated biological pathways in plasma samples, offering a novel method for non-invasive prediction and monitoring of immunotherapy response in NSCLC. Citation Format: Gulfem D. Guler, Yuhong Ning, Ceyda Coruh, Tierney Phillips, Maryam Nabiyouni, Kyle Hazen, Aaron Scott, Wayne Volkmuth, Samuel Levy. Plasma cell free DNA hydroxymethylation profiling reveals anti-PD1 treatment response and resistance biology in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3660.

Abstract 7530: Harmonization radiomics models to predict tumor response in non-small cell lung cancer (NSCLC) patients treated with immunotherapy

Abstract Background: Precise prediction of immunotherapy response in non-small cell lung cancer (NSCLC) is crucial for personalized treatment. This study investigates the application of pretreatment CT-based radiomics and use of harmonization model in predicting these outcomes in NSCLC patients receiving immunotherapy. Methods: This retrospective study examined data from 152 stage III-IV NSCLC patients undergoing immunotherapy. Patient responses were categorized using immune-related RECIST (irRECIST) criteria into durable responders (complete response [CR], partial response [PR], or stable disease [SD]) for at least 24 weeks and non-responders. Tumor segmentation was performed using LIFEx software (IMIV/CEA, Orsay, France). 3D-radiomic features were extracted from both the tumor and surrounding 1 cm thick peritumoral regions. The Random Forest (RF) algorithm was employed to develop a classification model to differentiate between responders and non-responders. A harmonization model was deployed to account for the scanner-related differences by using spleen and normal lung signals. The dataset was divided into a training set (75%) and a test set (25%). Bootstrapping with 1,000 iterations was performed to estimate the model's performance by calculating the median and 95% confidence interval (CI) estimate. The accuracy of the model's predictions was evaluated by creating a confusion matrix. The model's performance was assessed by calculating the sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and area under the ROC curve (AUC) for response prediction. Results: A total of 152 patients were analyzed. 87 (57.23%) were female and 65 (42.76%) were male. The median age was 59 years. Histology types included were: 111 Adenocarcinoma (73%), 27 Squamous cell carcinoma (17.8%), and 14 other types (9.2%). The model achieved a sensitivity of 0.63, a specificity of 0.59, a PPV of 0.51, and NPV of 0.7 for response prediction of NSCLC patients receiving immunotherapy. The AUC of 0.61 indicates that the model may discriminate between responders and non-responders with an accuracy of 61%. Conclusion: This study demonstrates the potential of radiomics and the use of harmonization models to predict immunotherapy responses in NSCLC patients, offering insights into personalized treatment approaches. Larger studies are needed to validate our findings and the utility of harmonization models in predicting a tumor response. Citation Format: Monica Yadav, Jeeyeon Lee, Peter Haseok Kim, Seyoung Lee, Taegyu Um, Salie Lee, Maria Jose Chuchuca, Trie Arni Djunadi, Liam Il-Young Chung, Jisang Yu, Darren Rodrigues, Nicolo Gennaro, Leeseul Kim, Myungwoo Nam, Youjin Oh, Sungmi Yoon, Zunairah Shah, Yuchan Kim, Ilene Hong, Jessica Jang, Grace Kang, Amy Cho, Soowon Lee, Timothy Hong, Cecilia Nam, Yury S Velichko, Young Kwang Chae. Harmonization radiomics models to predict tumor response in non-small cell lung cancer (NSCLC) patients treated with immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7530.

Revealing the role of SPP1+ macrophages in glioma prognosis and therapeutic targeting by investigating tumor-associated macrophage landscape in grade 2 and 3 gliomas

BackgroundGlioma is a highly heterogeneous brain tumor categorized into World Health Organization (WHO) grades 1–4 based on its malignancy. The suppressive immune microenvironment of glioma contributes significantly to unfavourable patient outcomes. However, the cellular composition and their complex interplays within the glioma environment remain poorly understood, and reliable prognostic markers remain elusive. Therefore, in-depth exploration of the tumor microenvironment (TME) and identification of predictive markers are crucial for improving the clinical management of glioma patients.ResultsOur analysis of single-cell RNA-sequencing data from glioma samples unveiled the immunosuppressive role of tumor-associated macrophages (TAMs), mediated through intricate interactions with tumor cells and lymphocytes. We also discovered the heterogeneity within TAMs, among which a group of suppressive TAMs named TAM-SPP1 demonstrated a significant association with Epidermal Growth Factor Receptor (EGFR) amplification, impaired T cell response and unfavourable patient survival outcomes. Furthermore, by leveraging genomic and transcriptomic data from The Cancer Genome Atlas (TCGA) dataset, two distinct molecular subtypes with a different constitution of TAMs, EGFR status and clinical outcomes were identified. Exploiting the molecular differences between these two subtypes, we developed a four-gene-based prognostic model. This model displayed strong associations with an elevated level of suppressive TAMs and could be used to predict anti-tumor immune response and prognosis in glioma patients.ConclusionOur findings illuminated the molecular and cellular mechanisms that shape the immunosuppressive microenvironment in gliomas, providing novel insights into potential therapeutic targets. Furthermore, the developed prognostic model holds promise for predicting immunotherapy response and assisting in more precise risk stratification for glioma patients.Graphical abstract

Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index.

Melanoma is a highly aggressive and recurrent form of skin cancer, posing challenges in prognosis and therapy prediction. In this study, we developed a novel TIPRGPI consisting of 20 genes using Univariate Cox regression and the LASSO algorithm. The high and low-risk groups based on TIPRGPI exhibited distinct mutation profiles, hallmark pathways, and immune cell infiltration in the tumor microenvironment. Notably, significant differences in tumor immunogenicity and TIDE were observed between the risk groups, suggesting a better response to immune checkpoint blockade therapy in the low-TIPRGPI group. Additionally, molecular docking predicted 10 potential drugs that bind to the core target, PTPRC, of the TIPRGPI signature. Our findings highlight the reliability of TIPRGPI as a prognostic signature and its potential application in risk classification, immunotherapy response prediction, and drug candidate identification for melanoma treatment. The "TIP genes" guided strategy presented in this study may have implications beyond melanoma and could be applied to other cancer types.

Serum tumor markers for response prediction and monitoring of advanced lung cancer: A review focusing on immunotherapy and targeted therapies.

The value of serum tumor markers (STMs) in the current therapeutic landscape of lung cancer is unclear. This scoping review gathered evidence of the predictive, prognostic, and monitoring value of STMs for patients with advanced lung cancer receiving immunotherapy (IT) or targeted therapy (TT). Literature searches were conducted (cut-off: May 2022) using PubMed and Cochrane CENTRAL databases. Medical professionals advised on the search strategies. Study heterogeneity limited the evidence and inferences from the 36 publications reviewed. While increased baseline levels of serum cytokeratin 19 fragment antigen (CYFRA21-1) and carcinoembryonic antigen (CEA) may predict IT response, results for TT were less clear. For monitoring IT-treated patients, STM panels (including CYFRA21-1, CEA, and neuron-specific enolase) may surpass the power of single analyses to predict non-response. CYFRA21-1 measurement could aid in monitoring TT-treated patients, but the value of CEA in this context requires further investigation. Overall, baseline and dynamic changes in individual or combined STM levels have potential utility to predict treatment outcome and for monitoring of patients with advanced lung cancer. In advanced lung cancer, STMs provide additional relevant clinical information by predicting treatment outcome, but further standardization and validation is warranted.

Identification of a disulfidptosis-related lncRNA signature for the prognostic and immune landscape prediction in head and neck squamous cell carcinoma

PurposeDisulfidptosis, a newly identified form of cell death, is triggered by disulfide stress. Herein, a unique signature was developed based on disulfidptosis-related lncRNAs (DRlncRNAs) for the prognostic and immune landscape prediction of head and neck squamous cell carcinoma (HNSCC).MethodsTranscriptome, somatic mutation, and clinical data were acquired at The Cancer Genome Atlas database. Individuals were partitioned into training and test cohorts at a 1:1 ratio to facilitate the development of a DRlncRNA signature using the least absolute shrinkage and selection operation method. Based on the median risk score, all HNSCC individuals were stratified into the high-risk group (HRG) and low-risk group (LRG). Kaplan–Meier survival and time-dependent receiver operating characteristic (ROC) analyses were used to estimate the prognostic value, and a nomogram was generated for survival prediction. To provide a more comprehensive assessment, the tumor microenvironment, functional enrichment, immune cell infiltration, and immunotherapeutic sensitivity were explored between LRG and HRG.ResultsA DRlncRNA signature was established with 10 DRlncRNAs. The corresponding values of areas under the ROC curves for 1–, 3–, and 5–year overall survival were 0.710, 0.692, and 0.640. A more favorable prognosis was noted in the patients with lower risk, along with higher immune scores, increased immune-related functions, and immune cell infiltration, as well as improved response to the immunotherapeutic intervention in comparison with individuals at higher risk.ConclusionThese findings demonstrate that the developed DRlncRNA signature holds promise as a reliable prognostic marker and predictor of immunotherapy response in HNSCC patients.

Identifying microRNAs associated with tumor immunotherapy response using an interpretable machine learning model

Predicting clinical responses to tumor immunotherapy is essential to reduce side effects and the potential for sustained clinical responses. Nevertheless, preselecting patients who are likely to respond to such treatments remains highly challenging. Here, we explored the potential of microRNAs (miRNAs) as predictors of immune checkpoint blockade responses using a machine learning approach. First, we constructed random forest models to predict the response to tumor ICB therapy using miRNA expression profiles across 19 cancer types. The contribution of individual miRNAs to each prediction process was determined by employing SHapley Additive exPlanations (SHAP) for model interpretation. Remarkably, the predictive performance achieved by using a small number of miRNAs with high feature importance was similar to that achieved by using the entire miRNA set. Additionally, the genes targeted by these miRNAs were closely associated with tumor- and immune-related pathways. In conclusion, this study demonstrates the potential of miRNA expression data for assessing tumor immunotherapy responses. Furthermore, we confirmed the potential of informative miRNAs as biomarkers for the prediction of immunotherapy response, which will advance our understanding of tumor immunotherapy mechanisms.

PIK3CA mutation-driven immune signature as a prognostic marker for evaluating the tumor immune microenvironment and therapeutic response in breast cancer

PurposeGene mutations drive tumor immune microenvironment (TIME) heterogeneity, in turn affecting prognosis and immunotherapy efficacy. PIK3CA is the most frequently mutated gene in breast cancer (BC), yet its relevance to BC prognosis remains controversial. Herein, we sought to determine the impact of PIK3CA mutation-driven immune genes (PDIGs) on BC prognosis in relation to TIME heterogeneity.MethodsPIK3CA mutation characteristics were compared and verified between the TCGA-BRCA dataset and a patient cohort from our hospital. PIK3CA mutation-driven differentially expressed genes were identified for consensus clustering and weighted gene co-expression network analysis to select the modules most relevant to the immune subtype. Thereafter, the two were intersected to obtain PDIGs. Univariate Cox, LASSO, and multivariate Cox regression analyses were sequentially performed on PDIGs to obtain a PIK3CA mutation-driven immune signature (PDIS), which was then validated using the Gene Expression Omnibus (GEO) database. Differences in functional enrichment, mutation landscape, immune infiltration, checkpoint gene expression, and drug response were compared between different risk groups.ResultsPIK3CA mutation frequencies in the TCGA and validation cohorts were 34.49% and 40.83%, respectively. PIK3CA mutants were significantly associated with ER, PR, and molecular BC subtypes in our hospital cohort. The PDIS allowed for effective risk stratification and exhibited prognostic power in TCGA and GEO sets. The low-risk patients exhibited greater immune infiltration, higher expression of common immune checkpoint factors, and lower scores for tumor immune dysfunction and exclusion.ConclusionThe PDIS can be used as an effective prognostic model for predicting immunotherapy response to guide clinical decision-making.