Cancer Molecular Subtypes Research Articles

Molecular classification for predicting the efficacy of neoadjuvant tislelizumab combining nab-paclitaxel in Chinese bladder cancer.

e14563 Background: Bladder cancer is a molecularly diverse disease with heterogeneous clinical outcomes. Transcriptome-based molecular subtypes of muscle invasive bladder cancer (MIBC) and non-muscle invasive bladder cancer (NMIBC) have been shown to be both prognostic and predictive. However, its predictive role in Chinese bladder cancer remains unclear. The aim of this study was to identify the predictive role of molecular subtypes in China bladder cancer with neoadjuvant therapy. Methods: The study was conducted from May 2020 to August 2021. Patients who were age 18 years or older, were diagnosed with NMIBC or MIBC, and neoadjuvant tislelizumab combining nab-paclitaxel followed by surgery were included. Multigenomic sequencing was performed. Molecular subtypes were identified by published consensus. Results: We prospectively recruited 30 patients for neoadjuvant tislelizumab combining nab-paclitaxel, including 14 patients with NMIBC and 16 patients with MIBC. For bladder cancer, 73.3% (22/33) patients responded to neoadjuvant tislelizumab combining nab-paclitaxel. In NMIBC groups, 64.3% (9/14) patients were pCR. The objective remission rate ( ORR) was 71.4%. 4 molecular classes were identified in Chinese NMIBC, including class 1 (7.1%), class 2a (35.7%), class 2b (50%), class 3 (7.1%). Interesting, ORR was significantly higher for patients with class 2b (5/7, 71.4%) as compared to class 2a (3/5,60%) before neoadjuvant. Molecular classes changed in 35.7% (5/14) of patients before and after neoadjuvant. After neoadjuvant, 80% of class 2b patients responded to neoadjuvant, and all class 2a patients did not respond to neoadjuvant. In MIBC groups, a pPR was achieved by 37.5% patients (6/16). The ORR was 75%. We found 4 molecular classes in Chinese MIBC: basal/squamous (43.8%), luminal unstable (25%), luminal papillary (25%), and stroma-rich (6.2%). Patients with basal/squamous (85.7%) and luminal unstable (75%) had significantly higher ORR than luminal papillary (50%) before neoadjuvant. Molecular classes changed in 44.4% (4/9) of patients before and after neoadjuvant. After neoadjuvant, all patients with stroma-rich responded to neoadjuvant. Conclusions: Molecular classification could predict the efficacy of neoadjuvant tislelizumab combining nab-paclitaxel in Chinese bladder cancer, which may be preferable when studying biomarkers of bladder cancer in the future.

Consensus molecular subtypes (CMS) as prognostic and predictive biomarkers of panitumumab (Pmab), fluorouracil and folinic acid (FU/FA) or FU/FA maintenance therapy following Pmab-FOLFOX induction in RAS wildtype metastatic colorectal cancer (mCRC): PANAMA trial (AIO-KRK-0212).

3537 Background: Consensus molecular subtypes (CMS1-4) of colorectal cancer were evaluated as prognostic and predictive biomarkers in the PANAMA trial. PANAMA compared maintenance therapy with panitumumab (Pmab) and fluorouracil/folinic acid (FU/FA) vs. FU/FA alone after Pmab-FOLFOX induction therapy in RAS wildtype mCRC. Methods: Gene expression was measured after mRNA isolation in 179 of 248 patients of the full analysis set. The analysis was conducted using a customized Nanostring PanCancer Progression Panel. The original CMS classifier was re-derived for Nanostring data using a multinomial regression analysis.Median progression-free (PFS) and overall survival (OS) since start of maintenance were estimated by Kaplan-Meier-method and Cox-regression, using the log rank test. Objective response rates (ORR) of maintenance therapy were compared by Chi-square-test. Results: Prevalence of CMS was: CMS1, n = 15 (8.4 %); CMS2, n = 82 (45.8 %); CMS3, n = 20 (11.2 %) and CMS4, n = 62 (34.6 %). A prognostic impact of CMS regardless of treatment was not evident for PFS (p = 0.245) and OS (p = 0.169), but for ORR (p = 0.022), with CMS1 and CMS3 being associated with unfavourable efficacy during maintenance therapy. Potential predictive effects of CMS were observed in patients with CMS2 and CMS4 tumours. In CMS2 and CMS4 tumours, ORR was significantly higher when treated with Pmab-FU/FA in maintenance therapy (CMS2: 56.5% vs 30.6%, p = 0.026; CMS4: 55.6% vs. 28.6%, p = 0.040). In patients with CMS2 mCRC, this translated into a significant effect on PFS (Hazard ratio: 0.61 (95% CI 0.38 – 0.99) p = 0.046 (Table). Conclusions: CMS have limited prognostic impact for pmab-based maintenance therapy. However, CMS2 and CMS4 are positively associated with Pmab efficacy during maintenance therapy in the PANAMA trial. Further trials are necessary to confirm these results. [Table: see text]

Four-Dimensional Machine Learning Radiomics for the Pretreatment Assessment of Breast Cancer Pathologic Complete Response to Neoadjuvant Chemotherapy in Dynamic Contrast-Enhanced MRI.

Breast cancer response to neoadjuvant chemotherapy (NAC) is typically evaluated through the assessment of tumor size reduction after a few cycles of NAC. In case of treatment ineffectiveness, this results in the patient suffering potentially severe secondary effects without achieving any actual benefit. To identify patients achieving pathologic complete response (pCR) after NAC by spatio-temporal radiomic analysis of dynamic contrast-enhanced (DCE) MRI images acquired before treatment. Single-center, retrospective. A total of 251 DCE-MRI pretreatment images of breast cancer patients. 1.5 T/3T, T1-weighted DCE-MRI. Tumor and peritumoral regions were segmented, and 348 radiomic features that quantify texture temporal variation, enhancement kinetics heterogeneity, and morphology were extracted. Based on subsets of features identified through forward selection, machine learning (ML) logistic regression models were trained separately with all images and stratifying on cancer molecular subtype and validated with leave-one-out cross-validation. Feature significance was assessed using the Mann-Whitney U-test. Significance of the area under the receiver operating characteristics (ROC) curve (AUC) of the ML models was assessed using the associated 95% confidence interval (CI). Significance threshold was set to 0.05, adjusted with Bonferroni correction. Nine features related to texture temporal variation and enhancement kinetics heterogeneity were significant in the discrimination of cases achieving pCR vs. non-pCR. The ML models achieved significant AUC of 0.707 (all cancers, n= 251, 59 pCR), 0.824 (luminal A, n= 107, 14 pCR), 0.823 (luminal B, n= 47, 15 pCR), 0.844 (HER2 enriched, n= 25, 11 pCR), 0.803 (triple negative, n= 72, 19 pCR). Differences in imaging phenotypes were found between complete and noncomplete responders. Furthermore, ML models trained per cancer subtype achieved high performance in classifying pCR vs. non-pCR cases. They may, therefore, have potential to help stratify patients according to the level of response predicted before treatment, pending further validation with larger prospective cohorts. 4 TECHNICAL EFFICACY: Stage 4.

Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.

Clinical implications of lipid peroxides levels in plasma and tumor tissue in breast cancer patients

Oxidative stress can promote the oxidation of lipoproteins and polyunsaturated fatty acids present in cell membranes; an event known as lipid peroxidation (LPO). LPO has been associated with carcinogenesis and cancer progression, however, its meaning concerning the clinicopathological aspects of human breast cancer is not clear. This study investigated LPO profiles in tumor and plasma samples from breast cancer patients (n = 140) considering their clinicopathological features (age at diagnosis, menopausal status, body mass index, tumor histological grade, tumor size, ki-67 proliferation index, presence of metastasis, chemotherapy response, the molecular subtype of cancer and overall survival status). LPO levels were estimated by tert-butyl hydroperoxide-initiated chemiluminescence. High LPO levels were found regarding poor prognosis parameters as young age at diagnosis (p = 0.006 in tissue), premenopausal patients (p = 0.012 in tissue), high-grade tumors (p = 0.010 in tissue and p = 0.002 in plasma), metastatic disease (p = 0.046 in tissue), chemoresistant tumors (p = 0.041 in tissue), disease relapse (p = 0.018 in tissue and p = 0.009 in plasma) and overall survival status (p = 0.001 in plasma). Our findings point out the clinical meaning of LPO and highlight it as an oxidative stress event linked to poor prognosis and disease aggressiveness in breast cancer patients.

Immune Subtype Profiling and Establishment of Prognostic Immune-Related lncRNA Pairs in Human Ovarian Cancer.

This study collected immune-related genes (IRGs) and used gene expression data from TCGA database to construct a molecular subtype of ovarian cancer (OV) based on immune-related lncRNA gene pairs (IRLnc_GPs). The relationships between molecular subtypes and prognosis and clinical characteristics were further explored. IRGs were acquired from the ImmPort database, and round-robin pairing of immune-related lncRNAs was performed. The NMF algorithm was used to identify molecular subtypes, and the immune score of a single sample was calculated through ESTIMATE, TIMER, ssGSEA, MCPcounter, and CIBERSORT. The relationship between molecular subtypes and immune microenvironments was identified. A hypergeometric test was used to test the lncRNA pairs among the OV molecular subtypes (C1 and C2 subtypes). The BH method was used to screen the different lncRNA pairs, and a predictive risk model was constructed and verified. Finally, correlation analysis between the risk model, immune checkpoint genes, and chemotherapy drugs was carried out. Based on IRLnc_GP to classify 373 OV samples of TCGA, the samples were divided into two subtypes, and the prognosis between the subtypes showed significant differences. The C1 subtype with a poor prognosis was more related to the pathways of tumor occurrence and development. We identified 180 differential lncRNA pairs between subtypes and constructed a prognostic risk model based on 8 IRLnc_GPs. In the independent dataset, the distribution of subtypes in functional modules was different and highly repeatable. There were significant differences in the molecular and clinical characteristics of the subtypes and the drug sensitivity of immunotherapy/chemotherapy. In conclusion, the risk model established based on IRLnc_GP can better evaluate the prognosis of OV samples and can also assess the effects of different drug treatments in the high- and low-risk groups, providing new insights and ideas for the treatment of OV.

Predictive Biomarkers for a Personalized Approach in Resectable Pancreatic Cancer.

The mainstay treatment for patients with immediate resectable pancreatic cancer remains upfront surgery, which represents the only potentially curative strategy. Nevertheless, the majority of patients surgically resected for pancreatic cancer experiences disease relapse, even when a combination adjuvant therapy is offered. Therefore, aiming at improving disease free survival and overall survival of these patients, there is an increasing interest in evaluating the activity and efficacy of neoadjuvant and perioperative treatments. In this view, it is of utmost importance to find biomarkers able to select patients who may benefit from a preoperative therapy rather than upfront surgical resection. Defined genomic alterations and a dynamic inflammatory microenvironment are the major culprits for disease recurrence and resistance to chemotherapeutic treatments in pancreatic cancer patients. Signal transduction pathways or tumor immune microenvironment could predict early recurrence and response to chemotherapy. In the last decade, distinct molecular subtypes of pancreatic cancer have been described, laying the bases to a tailored therapeutic approach, started firstly in the treatment of advanced disease. Patients with homologous repair deficiency, in particular with mutant germline BRCA genes, represent the first subgroup demonstrating to benefit from specific therapies. A fraction of patients with pancreatic cancer could take advantage of genome sequencing with the aim of identifying possible targetable mutations. These genomic driven strategies could be even more relevant in a potentially curative setting. In this review, we outline putative predictive markers that could help in the next future in tailoring the best therapeutic strategy for pancreatic cancer patients with a potentially curable disease.

Molecular Subtyping of Cancer Based on Distinguishing Co-Expression Modules and Machine Learning.

Molecular subtyping of cancer is recognized as a critical and challenging step towards individualized therapy. Most existing computational methods solve this problem via multi-classification of gene-expressions of cancer samples. Although these methods, especially deep learning, perform well in data classification, they usually require large amounts of data for model training and have limitations in interpretability. Besides, as cancer is a complex systemic disease, the phenotypic difference between cancer samples can hardly be fully understood by only analyzing single molecules, and differential expression-based molecular subtyping methods are reportedly not conserved. To address the above issues, we present here a new framework for molecular subtyping of cancer through identifying a robust specific co-expression module for each subtype of cancer, generating network features for each sample by perturbing correlation levels of specific edges, and then training a deep neural network for multi-class classification. When applied to breast cancer (BRCA) and stomach adenocarcinoma (STAD) molecular subtyping, it has superior classification performance over existing methods. In addition to improving classification performance, we consider the specific co-expressed modules selected for subtyping to be biologically meaningful, which potentially offers new insight for diagnostic biomarker design, mechanistic studies of cancer, and individualized treatment plan selection.

Sample-Specific Perturbation of Gene Interactions Identifies Pancreatic Cancer Subtypes.

Pancreatic cancer is a highly fatal disease and an increasing common cause of cancer mortality. Mounting evidence now indicates that molecular heterogeneity in pancreatic cancer significantly impacts its clinical features. However, the dynamic nature of gene expression pattern makes it difficult to rely solely on gene expression alterations to estimate disease status. By contrast, biological networks tend to be more stable over time under different situations. In this study, we used a gene interaction network from a new point of view to explore the subtypes of pancreatic cancer based on individual-specific edge perturbations calculated by relative gene expression value. Our study shows that pancreatic cancer patients from the TCGA database could be separated into four subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of pancreatic cancer exhibited substantial heterogeneity in many aspects, including prognosis, phenotypic traits, genetic mutations, the abundance of infiltrating immune cell, and predictive therapeutic efficacy (chemosensitivity and immunotherapy efficacy). The new network-based subtypes were closely related to previous reported molecular subtypes of pancreatic cancer. This work helps us to better understand the heterogeneity and mechanisms of pancreatic cancer from a network perspective.

Integrated profiling of human pancreatic cancer organoids reveals chromatin accessibility features associated with drug sensitivity

Chromatin accessibility plays an essential role in controlling cellular identity and the therapeutic response of human cancers. However, the chromatin accessibility landscape and gene regulatory network of pancreatic cancer are largely uncharacterized. Here, we integrate the chromatin accessibility profiles of 84 pancreatic cancer organoid lines with whole-genome sequencing data, transcriptomic sequencing data and the results of drug sensitivity analysis of 283 epigenetic-related chemicals and 5 chemotherapeutic drugs. We identify distinct transcription factors that distinguish molecular subtypes of pancreatic cancer, predict numerous chromatin accessibility peaks associated with gene regulatory networks, discover regulatory noncoding mutations with potential as cancer drivers, and reveal the chromatin accessibility signatures associated with drug sensitivity. These results not only provide the chromatin accessibility atlas of pancreatic cancer but also suggest a systematic approach to comprehensively understand the gene regulatory network of pancreatic cancer in order to advance diagnosis and potential personalized medicine applications.

Heterogeneity of neuroendocrine transcriptional states in metastatic small cell lung cancers and patient-derived models

Molecular subtypes of small cell lung cancer (SCLC) defined by the expression of key transcription regulators have recently been proposed in cell lines and limited number of primary tumors. The clinical and biological implications of neuroendocrine (NE) subtypes in metastatic SCLC, and the extent to which they vary within and between patient tumors and in patient-derived models is not known. We integrate histology, transcriptome, exome, and treatment outcomes of SCLC from a range of metastatic sites, revealing complex intra- and intertumoral heterogeneity of NE differentiation. Transcriptomic analysis confirms previously described subtypes based on ASCL1, NEUROD1, POU2F3, YAP1, and ATOH1 expression, and reveal a clinical subtype with hybrid NE and non-NE phenotypes, marked by chemotherapy-resistance and exceedingly poor outcomes. NE tumors are more likely to have RB1, NOTCH, and chromatin modifier gene mutations, upregulation of DNA damage response genes, and are more likely to respond to replication stress targeted therapies. In contrast, patients preferentially benefited from immunotherapy if their tumors were non-NE. Transcriptional phenotypes strongly skew towards the NE state in patient-derived model systems, an observation that was confirmed in paired patient-matched tumors and xenografts. We provide a framework that unifies transcriptomic and genomic dimensions of metastatic SCLC. The marked differences in transcriptional diversity between patient tumors and model systems are likely to have implications in development of novel therapeutic agents.

Molecular classification of hormone-sensitive and castration-resistant prostate cancer, using nonnegative matrix factorization molecular subtyping of primary and metastatic specimens.

BackgroundDespite the rapidly evolving therapeutic landscape, immunotherapy has demonstrated limited activity in prostate cancer. A greater understanding of the molecular landscape, particularly the expression of immune‐related pathways, will inform future immunotherapeutic strategies. Consensus nonnegative matrix factorization (cNMF) is a novel model of molecular classification analyzing gene expression data, focusing on biological interpretation of metagenes and selecting meaningful clusters.ObjectiveWe aimed to identify molecular subtypes of prostate cancer using cNMF and correlate these with existing biomarkers to inform future immunotherapeutic strategies.MethodsA cohort of archival tumor specimens from hormone‐sensitive and castration‐resistant disease was studied. Whole transcriptomic profiles were generated using TruSeq RNA Access technology and subjected to cNMF. Comprehensive genomic profiling was performed with the FoundationOne assay. NMF subtypes were characterized by gene expression pathways, genomic alterations and correlated with clinical data, then applied to The Cancer Genome Atlas data set.ResultsWe studied 164 specimens, including 52 castration‐resistant and 13 paired primary/metastatic specimens. cNMF identified four distinct subtypes. NMF1 (19%) is enriched for immune‐related and stromal‐related pathways with transforming growth factor β (TGFβ) signature. NMF2 (36%) is associated with FOXO‐mediated transcription signature and AKT signaling, NMF3 (26%) is enriched for ribosomal RNA processing, while NMF4 (19%) is enriched for cell cycle and DNA‐repair pathways. The most common gene alterations included TMPRSS22 (42%), TP53 (23%), and DNA‐repair genes (19%), occurring across all subtypes. NMF4 is significantly enriched for MYC and Wnt‐signaling gene alterations. TMB, CD8 density, and PD‐L1 expression were low overall. NMF1 and NMF4 were NMF2 was associated with superior overall survival.ConclusionsUsing cNMF, we identified four molecularly distinct subtypes which may inform treatment selection. NMF1 demonstrates the most inflammatory signature with asuppressive TGFβ signature, suggesting potential benefit with immunotherapy combination strategies targeting TGFβ and PD‐(L)1. Prospective studies are required to evaluate the use of this novel model to molecularly stratify patients for optimal treatment selection.

Interspatial Distribution of Tumor and Immune Cells in Correlation with PD-L1 in Molecular Subtypes of Gastric Cancers.

Simple SummaryThe majority of gastric cancers are negative for the Epstein–Barr virus (EBV) and mismatch repair-proficient, or microsatellite stable. Molecular subtyping currently does not influence clinical decision making, and accurate response prediction towards immunotherapy remains a major challenge. We therefore analyzed PD-L1 expression, tumor-infiltrating lymphocytes, and their spatial relationship to tumor cells in EBV-negative mismatch repair-proficient gastric cancers compared to EBV-positive and mismatch repair-deficient tumors to identify an immunogenic phenotype that is susceptible to immunotherapy in this large group. We demonstrated a close relationship between the total number of tumor-infiltrating lymphocytes, their proximity to tumor cells, and the expression of PD-L1 across all subtypes, including the EBV-negative and mismatch repair-proficient cancers. However, we also identified a subgroup of PD-L1-negative, EBV-negative, and mismatch repair-proficient cancers with high numbers of tumor-associated CD8+ lymphocytes. This is indicative of an immunoreactive phenotype in a subgroup of gastric cancers along or independent of PD-L1 status and molecular type.(1) Background: EBV-positive and mismatch repair-deficient (MMRd) gastric cancers (GCs) show higher levels of tumor-infiltrating lymphocytes (TILs) and PD-L1 expression and thus a more profound response to immunotherapy. However, the majority of GCs are EBV-negative (EBV−) and MMR proficient (MMRp). We analyzed PD-L1 expression and TILs in EBV-MMRpGCs in comparison to EBV-positive (EBV+) and MMRdGCs to identify an immunogenic phenotype susceptible to immunotherapy. (2) Methods: A next-generation tissue microarray of 409 primary resected GCs was analyzed by Epstein-Barr encoding region (EBER) in situ hybridization for MSH1, PMS2, MSH2, MSH6, PD-L1, and CD8 immunohistochemistry. PD-L1 positivity was defined as a combined positive score (CPS) of ≥1. CD8+ TILs and their proximity to cancer cells were digitally analyzed on the HALO™ image analysis platform. (3) Results: Eleven cases were EBV+, 49 cases MMRd, and 349 cases EBV-MMRpGCs. The highest rate of PD-L1 positivity was seen in EBV+GCs, followed by MMRdGCs and EBV-MMRpGCs (81.8%, 73.5%, and 27.8%, respectively). EBV+ and MMRdGCs also demonstrated increased numbers and proximity of CD8+ TILs to tumor cells compared to EBV-MMRpGCs (p < 0.001 each). PD-L1 status positively correlated with the total numbers of CD8+ TILs and their proximity to tumor cells in all subtypes, including EBV-MMRpGCs (p < 0.001 each). A total of 28.4% of EBV-MMRpGCs showed high CD8+ TILs independent of PD-L1. (4) Conclusions: PD-L1 and CD8 immunohistochemistry, supplemented by digital image analysis, may identify EBV-MMRpGCs with high immunoreactivity indices, indicating susceptibility to immunotherapy.

Parvimonas micra is associated with tumour immune profiles in molecular subtypes of colorectal cancer

The importance of the tumour microbiome in different aspects of colorectal cancer (CRC) has been increasingly recognised, but many questions remain. The aim of this study was to explore the effect of specific CRC associated microbes on the tumour immune response, which has a considerable prognostic value in CRC. We applied specific qPCR to detect Parvimonas micra and Fusobacterium nucleatum in tumour tissues from an immunologically well-characterised cohort of 69 CRC patients. This cohort included detailed analyses of immune profiles based on flow cytometry and transcriptomics in tumour tissue and blood, along with comprehensive analyses of molecular subtypes. P. micra and F. nucleatum were detected in 24% and 64% of tumour tissues, respectively. We found a significant association of P. micra with high-grade tumours and tumours of CMS1 subtype. F. nucleatum was significantly associated with right-sided tumours, microsatellite instability, and CMS1 tumours. The immunological analyses revealed significant associations of P. micra with activated CD69+ T lymphocytes and increased antigen-presenting HLA-DR+ B lymphocytes. P. micra was also positively associated with M1 and M2 macrophage traits. The impact of P. micra tumour colonisation on the immune response was further assessed using transcriptomics in validation of our findings. No associations were found between F. nucleatum and immune profiles in this study. Our findings support novel associations between P. micra and the immune response in CRC. A better understanding of these interactions might help to identify important predictive and prognostic tools as well as new targets for therapy.

The Prognostic Value and Immunological Role of STEAP1 in Pan-Cancer: A Result of Data-Based Analysis.

Purpose This study is aimed at systematically analyzing the expression, function, and prognostic value of six transmembrane epithelial antigen of the prostate 1 (STEAP1) in various cancers. Methods The expressions of STEAP1 between normal and tumor tissues were analyzed using TCGA and GTEx. Clinicopathologic data was collected from GEPIA and TCGA. Prognostic analysis was conducted by Cox proportional hazard regression and Kaplan-Meier survival. DNA methylation, mutation features, and molecular subtypes of cancers were also investigated. The top-100 coexpressed genes with STEAP1 were involved in functional enrichment analysis. ESTIMATE algorithm was used to analyze the correlation between STEAP1 and immunity value. The relationships of STEAP1 and biomarkers including tumor mutational burden (TMB), microsatellite instability (MSI), and stemness score as well as chemosensitivity were also illustrated. Results Among 33 cancers, STEAP1 was overexpressed in 19 cancers such as cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colon adenocarcinoma, and lymphoid neoplasm diffuse large B cell lymphoma while was downregulated in 5 cancers such as adrenocortical carcinoma, breast invasive carcinoma (BRCA), and kidney chromophobe renal cell carcinoma. STEAP1 has significant prognostic relationships in multiple cancers. 15 cancers exhibited differences of DNA methylation including bladder urothelial carcinoma, BRCA, and CESC. STEAP1 expression was positively correlated to immune molecules especially in thyroid carcinoma and negatively especially in uveal melanoma. STEAP1 was associated with TMB and MSI in certain cancers. In addition, STEAP1 was connected with increased chemosensitivity of drugs such as trametinib and pimasertib. Conclusions STEAP1 was an underlying target for prognostic prediction in different cancer types and a potential biomarker of TMB, MSI, tumor microenvironment, and chemosensitivity.

Transcriptomic Profiling of MSI-H/dMMR Gastrointestinal Tumors to Identify Determinants of Responsiveness to Anti-PD-1 Therapy.

Transcriptomic profiling was performed for microsatellite instability-high (MSI-H)/mismatch repair-deficient (dMMR) gastrointestinal tumors to determine the predictors of response to PD-1 blockade. Thirty-six patients with MSI-H/dMMR gastrointestinal tumors, including gastric cancer, colorectal cancer, cholangiocarcinoma, small intestine cancer, and pancreatic cancer, being treated with PD-1 blockade were analyzed. We conducted the transcriptomic analysis of gastrointestinal tumors using RNA sequencing data, including the consensus molecular subtypes (CMS) of colorectal cancer. Gene set enrichment analysis (GSEA) demonstrated that non-responders had upregulations of epithelial-mesenchymal transition, angiogenesis, hypoxia, mTORC1, TNF-α, KRAS, Wnt/β-catenin, TGF-β, and various metabolism-related signaling pathways. Meanwhile, the IFNγ pathway was enriched in responders. On the basis of the leading-edge analysis of GSEA, VEGF-A was significantly correlated with enriched pathways in non-responders. Patients with high VEGF-A expression, compared with those with low expression, had significantly shorter progression-free survival [PFS; median 4.8 months vs. not reached (NR), P = 0.032] and overall survival (median 11.1 months vs. NR, P = 0.045). Among 13 patients with colorectal cancer evaluable for CMS classification, the objective response rate was 100%, 0%, 0%, and 16.7% in CMS1, CMS2, CMS3, and CMS4, respectively. Patients with CMS1 had significantly longer PFS (NR vs. 4.8 months, P = 0.017) than those with CMS2, CMS3, or CMS4. Several transcriptomic features, including CMS classification and related genes, were associated with response to PD-1 blockade in MSI-H/dMMR gastrointestinal tumors. These findings can help develop predictive biomarkers or combination immunotherapies.

Dissection of molecular and histological subtypes of papillary thyroid cancer using alternative splicing profiles

Despite growing evidence of the relevance of alternative splicing (AS) to cancer development and progression, the biological implications of AS for tumor behaviors, including papillary thyroid cancer (PTC), remain elusive. With the aim of further understanding the molecular and histological subtypes of PTC, we in this study explored whether AS events might act as new molecular determinants. For this purpose, AS profiles were analyzed in RNA-sequencing data from The Cancer Genome Atlas (TCGA) and from a Korean patient dataset. A total of 23 distinct exon-skipping (ES) events that correlated significantly with PTC oncogenic activity and differentiation scores were identified. The two top-ranked ES events, NUMA1_17515 in exon 18 of NUMA1 and TUBB3_38175 in exon 6 of TUBB3, showed high correlations with oncogenic activities and discriminated histological and molecular subtypes of PTC. Furthermore, two novel intron-retention (IR) events for TUBB3 were uncovered. All ES and IR events for the TUBB3 gene were predicted to induce nonsense-mediated mRNA decay. The relative abundances of intron reads in the PTC dataset from TCGA showed IR levels to differ significantly among PTC subtypes, possibly reflecting their different tumor behaviors. This study provides a landscape of AS changes among PTC subtypes and identified two significant AS events, NUMA1_17515 and TUBB3_38175, as potential AS biomarkers for PTC subclassification and characterization. The AS events identified in this study may be involved in the development of phenotypic differences underlying the functional characteristics and histological differentiation of PTCs.

Clinical characteristics and patient outcomes of molecular subtypes of small cell lung cancer (SCLC)

BackgroundRecent studies have shown that according to the expression levels of achaete-scute homolog 1 (ASCL1), neurogenic differentiation factor 1 (NEUROD1), and POU class 2 homeobox 3 (POU2F3), small cell lung cancer (SCLC) can be divided into four subtypes: SCLC-A (ASCL1-dominant), SCLC-N (NEUROD1-dominant), SCLC-P (POU2F3-dominant), and SCLC-I (triple negative or SCLC-inflamed). However, there are limited data on the clinical characteristics and prognosis of molecular subtypes of SCLC.MethodsImmunohistochemistry (IHC) was used to detect the expression levels of ASCL1, NEUROD1, and POU2F3 in 53 patient samples of resectable SCLC. The subtype was defined by the differential expression of the transcription factors for ASCL1, NEUROD1, and POU2F3 or the low expression of all three factors with an inflamed gene signature (SCLC-A, SCLC-N, SCLC-P, and SCLC-I, respectively). The clinicopathological characteristics, immunological features (programmed death ligand 1 [PD-L1] expression and CD8+ tumor infiltrating lymphocyte [TIL] density), and patient outcomes of the four subtypes of SCLC were analyzed.ResultsPositive ASCL1, NEUROD1, and POU2F3 staining was detected in 43 (79.2%), 27 (51.0%), and 17 (32.1%) SCLC specimens by IHC. According to the results of IHC analysis, SCLC was divided into four subtypes: SCLC-A (39.6%), SCLC-N (28.3%), SCLC-P (17.0%), and SCLC-I (15.1%). The 5-year overall survival (OS) rates of these four subtypes were 61.9%, 69.3%, 41.7%, and 85.7%, respectively (P=0.251). There were significant differences in smoking status among different subtypes of SCLC (P= 0.031). However, we did not confirm the correlation between subtypes of SCLC and other clinicopathological factors or immune profiles. Cox multivariate analysis showed that N stage (P=0.025), CD8+ TILs (P=0.024), Ki-67 level (P=0.040), and SCLC-P (P=0.023) were independent prognostic factors for resectable SCLC.ConclusionsOur IHC-based study validated the proposed classification of SCLC using the expression patterns of key transcriptional regulatory factors. We found that SCLC-P was associated with smokers and was one of the poor prognostic factors of limited-stage SCLC. In addition, no correlation was found between PD-L1 expression or CD8+ TIL density and SCLC subtypes.

TROP2 Expression Across Molecular Subtypes of Urothelial Carcinoma and Enfortumab Vedotin-resistant Cells

Sacituzumab govitecan (SG) is an antibody-drug conjugate (ADC) targeting TROP2, which has recently been approved for treatment-refractory metastatic urothelial cancer (UC). However, the variability of TROP2 expression across different bladder cancer (BC) subtypes, as well as after enfortumab vedotin (EV) exposure, remains unknown. Using gene expression data from four clinical cohorts with >1400 patient samples of muscle-invasive BC and a BC tissue microarray, we found that TROP2 mRNA and protein are highly expressed across basal, luminal, and stroma-rich subtypes, but depleted in the neuroendocrine subtype. In addition, TROP2 mRNA levels are correlated with NECTIN4 mRNA but are more highly expressed than NECTIN4 mRNA in patient cohorts and BC cell lines. Moreover, CRISPR/Cas9-mediated knockdown of TROP2 demonstrates that its expression is one factor governing SG sensitivity. After prolonged EV exposure, cells can downregulate NECTIN4, leading to EV resistance, but retain TROP2 expression and remain sensitive to SG, suggesting nonoverlapping resistance mechanisms to these ADCs. While our findings warrant further validation, they have significant implications for biomarker development, patient selection, and treatment sequencing in the clinic as well as clinical trial design and stratification for metastatic BC patients. Patient summaryIn this report, we investigated the expression levels of the drug target TROP2 across different molecular subtypes of bladder cancer in multiple patient cohorts and cell lines. We found high levels of TROP2 in most subtypes except in the neuroendocrine subtype. Overall, TROP2 gene expression is higher than NECTIN4 gene expression, and cells resistant to enfortumab vedotin (EV), a NECTIN4-targeting antibody-drug conjugate, remain sensitive to sacituzumab govitecan (SG). Our findings suggest that SG may be effective across most bladder cancer subtypes, including the bladder cancers previously treated with EV.

Characterizing molecular subtypes of high-risk nonmuscle-invasive bladder cancer in African American patients.

527 Background: Patients with high-risk non-muscle-invasive bladder cancer (NMIBC) have heterogeneous outcomes with African Americans (AAs) having worse survival than European Americans (EAs). It is unknown whether race-based biological differences contribute to this disparity. Methods: We performed a retrospective cohort study including patients from the University of Texas Medical Branch (UTMB) and the Durham VA Health Care System (DVAHCS) from 2010-2020 among treatment naïve, high-risk NMIBC. Profiled gene expressions of high-risk NMIBC by race were performed using the UROMOL classification system. Results: A total of 26 patients (14 AAs and 12 EAs) matched on age and sex were included with no significant difference in clinical stage group (CIS +/- T1 or TaHG vs. TaHG or T1, no CIS), smoking status, or progression. We found a similar racial UROMOL subtype distribution with class 2a being most common. A total of 10 genes were discovered to be commonly upregulated differentially expressed genes (up-DEGs) in AAs vs EAs. EFEMP1, which has been associated with progression to muscle-invasive bladder cancer (MIBC) in vitro, and S100A16 gene expression, which has been implicated with mitomycin C resistance in bladder cancer in vitro, was significantly more common among AAs. We used single nuclei analysis to map the malignant cell heterogeneity in urothelial cancer which five distinct malignant epithelial subtypes whose presence has been associated with different therapeutic response prediction ability. We mapped the expression of the 10 genes commonly up-DEGs by race as a function of the five malignant subtypes. This showed borderline (p = 0.056) differences among the subtypes suggesting AA and EA patients may be expected to have different therapeutic responses to treatments for BC. AAs were enriched with immune-related, inflammatory, and cellular regulation pathways compared to EAs, yet appeared to have reduced levels of the aggressive C3 bladder tumor cell population. Conclusions: In this small sample, we found similar subtype distribution among high-risk NMIBC patients according to race. However, gene expression differs by race, supporting potential novel race-based etiologies for differences in muscle-invasion, response to treatments, and transcriptome pathway regulations. Further biological studies in NMIBC molecular sub-stratification, associated treatment(s), and prognoses in a larger cohort are needed to support these hypotheses.