Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations.

We report the structure-based pathogenicity relationship identifier (SPRI), a novel computational tool for accurate evaluation of pathological effects of missense single mutations and prediction of higher-order spatially organized units of mutational clusters. SPRI can effectively extract properties determining pathogenicity encoded in protein structures, and can identify deleterious missense mutations of germ line origin associated with Mendelian diseases, as well as mutations of somatic origin associated with cancer drivers. It compares favorably to other methods in predicting deleterious mutations. Furthermore, SPRI can discover spatially organized pathogenic higher-order spatial clusters (patHOS) of deleterious mutations, including those of low recurrence, and can be used for discovery of candidate cancer driver genes and driver mutations. We further demonstrate that SPRI can take advantage of AlphaFold2 predicted structures and can be deployed for saturation mutation analysis of the whole human proteome.

Identification of potential driver mutations in glioblastoma using machine learning.

Glioblastoma is a fast and aggressively growing tumor in the brain and spinal cord. Mutation of amino acid residues in targets proteins, which are involved in glioblastoma, alters the structure and function and may lead to disease. In this study, we collected a set of 9386 disease-causing (drivers) mutations based on the recurrence in patient samples and experimentally annotated as pathogenic and 8728 as neutral (passenger) mutations. We observed that Arg is highly preferred at the mutant sites of drivers, whereas Met and Ile showed preferences in passengers. Inspecting neighboring residues at the mutant sites revealed that the motifs YP, CP and GRH, are preferred in drivers, whereas SI, IQ and TVI are dominant in neutral. In addition, we have computed other sequence-based features such as conservation scores, Position Specific Scoring Matrices (PSSM) and physicochemical properties, and developed a machine learning-based method, GBMDriver (GlioBlastoma Multiforme Drivers), for distinguishing between driver and passenger mutations. Our method showed an accuracy and AUC of 73.59% and 0.82, respectively, on 10-fold cross-validation and 81.99% and 0.87 in a blind set of 1809 mutants. The tool is available at https://web.iitm.ac.in/bioinfo2/GBMDriver/index.html. We envisage that the present method is helpful to prioritize driver mutations in glioblastoma and assist in identifying therapeutic targets.

Efficacy of chemotherapy plus immune checkpoint inhibitors in patients with non-small cell lung cancer who have rare oncogenic driver mutations: a retrospective analysis.

BACKGROUND: Targeted therapy is now the standard of care in driver-oncogene-positive non-small cell lung cancer (NSCLC). Its initial clinical effects are remarkable. However, almost all patients experience treatment resistance to targeted therapy. Hence, chemotherapy is considered a subsequent treatment option. In patients with driver-oncogene-negative NSCLC, combined immune checkpoint inhibitors (ICIs) and chemotherapy as the first-line therapy has been found to be beneficial. However, the efficacy of ICI plus chemotherapy against driver-oncogene-positive NSCLC other than epidermal growth factor receptor mutation and anaplastic lymphoma kinase fusion is unclear. METHODS: Using the hospital medical records, we retrospectively reviewed advanced or recurrent NSCLC patients who were treated with chemotherapy with or without ICIs at Aichi Cancer Center Hospital between January 2014 and January 2023. Patients with druggable rare mutations such as KRAS-G12C, MET exon 14 skipping, HER2 20 insertion, BRAF-V600E mutations, and ROS1 and RET rearrangements were analyzed. RESULTS: In total, 61 patients were included in this analysis. ICI plus chemotherapy was administered in 36 patients (the ICI-chemo group) and chemotherapy in 25 patients (the chemo group). The median progression-free survival (PFS) rates were 14.0 months in the ICI-chemo group and 4.8 months in the chemo group (hazard ratio [HR] = 0.54, 95% confidence interval [CI] = 0.28-1.01). The median overall survival rates were 31.3 and 21.7 months in the ICI-chemo and chemo groups, respectively (HR = 0.70, 95% CI = 0.33-1.50). Multivariate Cox regression analysis of PFS revealed that HER2 exon 20 insertion mutation was significantly associated with a poorer PFS (HR: 2.39, 95% CI: 1.19-4.77, P = 0.014). Further, ICI-chemo treatment was significantly associated with a better PFS (HR: 0.48, 95% CI: 0.25-0.91, P = 0.025). CONCLUSION: ICI plus chemotherapy improves treatment efficacy in rare driver-oncogene-positive NSCLC.

The development of a custom RNA-sequencing panel for the identification of predictive and diagnostic biomarkers in glioma.

PURPOSE: Various molecular profiles are needed to classify malignant brain tumors, including gliomas, based on the latest classification criteria of the World Health Organization, and their poor prognosis necessitates new therapeutic targets. The Todai OncoPanel 2 RNA Panel (TOP2-RNA) is a custom-target RNA-sequencing (RNA-seq) using the junction capture method to maximize the sensitivity of detecting 455 fusion gene transcripts and analyze the expression profiles of 1,390 genes. This study aimed to classify gliomas and identify their molecular targets using TOP2-RNA. METHODS: A total of 124 frozen samples of malignant gliomas were subjected to TOP2-RNA for classification based on their molecular profiles and the identification of molecular targets. RESULTS: Among 55 glioblastoma cases, gene fusions were detected in 11 cases (20%), including novel MET fusions. Seven tyrosine kinase genes were found to be overexpressed in 15 cases (27.3%). In contrast to isocitrate dehydrogenase (IDH) wild-type glioblastoma, IDH-mutant tumors, including astrocytomas and oligodendrogliomas, barely harbor fusion genes or gene overexpression. Of the 34 overexpressed tyrosine kinase genes, MDM2 and CDK4 in glioblastoma, 22 copy number amplifications (64.7%) were observed. When comparing astrocytomas and oligodendrogliomas in gene set enrichment analysis, the gene sets related to 1p36 and 19q were highly enriched in astrocytomas, suggesting that regional genomic DNA copy number alterations can be evaluated by gene expression analysis. CONCLUSIONS: TOP2-RNA is a highly sensitive assay for detecting fusion genes, exon skipping, and aberrant gene expression. Alterations in targetable driver genes were identified in more than 50% of glioblastoma. Molecular profiling by TOP2-RNA provides ample predictive, prognostic, and diagnostic biomarkers that may not be identified by conventional assays and, therefore, is expected to increase treatment options for individual patients with glioma.

Tissue Elasticity as a Diagnostic Marker of Molecular Mutations in Morphologically Heterogeneous Colorectal Cancer.

The presence of molecular mutations in colorectal cancer (CRC) is a decisive factor in selecting the most effective first-line therapy. However, molecular analysis is routinely performed only in a limited number of patients with remote metastases. We propose to use tissue stiffness as a marker of the presence of molecular mutations in CRC samples. For this purpose, we applied compression optical coherence elastography (C-OCE) to calculate stiffness values in regions corresponding to specific CRC morphological patterns (n = 54). In parallel to estimating stiffness, molecular analysis from the same zones was performed to establish their relationships. As a result, a high correlation between the presence of KRAS/NRAS/BRAF driver mutations and high stiffness values was revealed regardless of CRC morphological pattern type. Further, we proposed threshold stiffness values for label-free targeted detection of molecular alterations in CRC tissues: for KRAS, NRAS, or BRAF driver mutation-above 803 kPa (sensitivity-91%; specificity-80%; diagnostic accuracy-85%), and only for KRAS driver mutation-above 850 kPa (sensitivity-90%; specificity-88%; diagnostic accuracy-89%). To conclude, C-OCE estimation of tissue stiffness can be used as a clinical diagnostic tool for preliminary screening of genetic burden in CRC tissues.

Benefits of NGS in Advanced Lung Adenocarcinoma Vary by Populations and Timing of Examination.

Despite the widespread application of next-generation sequencing (NGS) in advanced lung adenocarcinoma, its impact on survival and the optimal timing for the examination remain uncertain. This cohort study included advanced lung adenocarcinoma patients who underwent NGS testing. We categorized patients into four groups: Group 1: treatment-naïve, upfront NGS; Group 2: Treatment-naïve, exclusionary EGFR/ALK/ROS1; Group 3: post-treatment, no known EGFR/ALK/ROS1; Group 4: known driver mutation and post-TKI treatment. A total of 424 patients were included. There were 128, 126, 90, and 80 patients in Groups 1, 2, 3, and 4, respectively. In Groups 1, 2, 3, and 4, targetable mutations were identified in 76.6%, 49.2%, 41.1%, and 33.3% of the patients, respectively (p < 0.001). Mutation-targeted treatments were applied in 68.0%, 15.1%, 27.8%, and 22.5% of the patients, respectively (p < 0.001). In the overall population, patients receiving mutation-targeted treatments exhibited significantly longer overall survival (OS) (aHR 0.54 [95% CI 0.37-0.79], p = 0.001). The most profound benefit was seen in the Group 1 patients (not reached vs. 40.4 months, p = 0.028). The median OS of patients with mutation-targeted treatments was also significantly longer among Group 2 patients. The median post-NGS survival of patients receiving mutation-targeted treatments was numerically longer in Group 3 and Group 4 patients. In conclusion, mutation-targeted therapy is associated with a favorable outcome. However, the opportunities of NGS-directed treatment and the survival benefits of mutation-targeted treatment were various among different populations.

Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network.

BACKGROUND: In cancer genomic medicine, finding driver mutations involved in cancer development and tumor growth is crucial. Machine-learning methods to predict driver missense mutations have been developed because variants are frequently detected by genomic sequencing. However, even though the abnormalities in molecular networks are associated with cancer, many of these methods focus on individual variants and do not consider molecular networks. Here we propose a new network-based method, Net-DMPred, to predict driver missense mutations considering molecular networks. Net-DMPred consists of the graph part and the prediction part. In the graph part, molecular networks are learned by a graph neural network (GNN). The prediction part learns whether variants are driver variants using features of individual variants combined with the graph features learned in the graph part. RESULTS: Net-DMPred, which considers molecular networks, performed better than conventional methods. Furthermore, the prediction performance differed by the molecular network structure used in learning, suggesting that it is important to consider not only the local network related to cancer but also the large-scale network in living organisms. CONCLUSIONS: We propose a network-based machine learning method, Net-DMPred, for predicting cancer driver missense mutations. Our method enables us to consider the entire graph architecture representing the molecular network because it uses GNN. Net-DMPred is expected to detect driver mutations from a lot of missense mutations that are not known to be associated with cancer.

Experimental analysis of bladder cancer-associated mutations in EP300 identifies EP300-R1627W as a driver mutation.

BACKGROUND: Bladder cancer (BCa) is the most common malignant tumor of the urinary system, with transitional cell carcinoma (TCC) being the predominant type. EP300 encodes a lysine acetyltransferase that regulates a large subset of genes by acetylating histones and non-histone proteins. We previously identified several bladder cancer-associated mutations in EP300 using high-throughput sequencing; however, the functional consequences of these mutations remain unclear. METHODS: Bladder cancer cells T24 and TCC-SUP were infected with shEP300 lentiviruses to generate stable EP300 knockdown cell lines. The expression levels of EP300, p16 and p21 were detected by real-time PCR and western blots. The transcriptional activity of p16 and p21 were detected by dual luciferase assay. Cell proliferation assay, flow cytometric analyses of cell cycle, invasion assay and xenograft tumor model were used to measure the effect of EP300-R1627W mutation in bladder cancer. Immunoprecipitation was used to explore the relationship between EP300-R1627W mutation and p53. Structural analysis was used to detect the structure of EP300-R1627W protein compared to EP300-wt protein. RESULTS: we screened the mutations of EP300 and found that the EP300-R1627W mutation significantly impairs EP300 transactivation activity. Notably, we demonstrated that the R1627W mutation impairs EP300 acetyltransferase activity, potentially by interfering with substrate binding. Finally, we show that EP300-R1627W is more aggressive in growth and invasion in vitro and in vivo compared to cells expressing EP300-wt. We also found that the EP300-R1627W mutation occurs frequently in seven different types of cancers. CONCLUSION: In summary, our work defines a driver role of EP300-R1627W in bladder cancer development and progression.

PredCID: prediction of driver frameshift indels in human cancer.

The discrimination of driver from passenger mutations has been a hot topic in the field of cancer biology. Although recent advances have improved the identification of driver mutations in cancer genomic research, there is no computational method specific for the cancer frameshift indels (insertions or/and deletions) yet. In addition, existing pathogenic frameshift indel predictors may suffer from plenty of missing values because of different choices of transcripts during the variant annotation processes. In this study, we proposed a computational model, called PredCID (Predictor for Cancer driver frameshift InDels), for accurately predicting cancer driver frameshift indels. Gene, DNA, transcript and protein level features are combined together and selected for classification with eXtreme Gradient Boosting classifier. Benchmarking results on the cross-validation dataset and independent dataset showed that PredCID achieves better and robust performance compared with existing noncancer-specific methods in distinguishing cancer driver frameshift indels from passengers and is therefore a valuable method for deeper understanding of frameshift indels in human cancer. PredCID is freely available for academic research at http://bioinfo.ahu.edu.cn:8080/PredCID.

Disease characterization using a partial correlation-based sample-specific network.

A single-sample network (SSN) is a biological molecular network constructed from single-sample data given a reference dataset and can provide insights into the mechanisms of individual diseases and aid in the development of personalized medicine. In this study, we proposed a computational method, a partial correlation-based single-sample network (P-SSN), which not only infers a network from each single-sample data given a reference dataset but also retains the direct interactions by excluding indirect interactions (https://github.com/hyhRise/P-SSN). By applying P-SSN to analyze tumor data from the Cancer Genome Atlas and single cell data, we validated the effectiveness of P-SSN in predicting driver mutation genes (DMGs), producing network distance, identifying subtypes and further classifying single cells. In particular, P-SSN is highly effective in predicting DMGs based on single-sample data. P-SSN is also efficient for subtyping complex diseases and for clustering single cells by introducing network distance between any two samples.

Identification of putative noncanonical driver mutations in patients with essential thrombocythemia.

Essential thrombocythemia (ET) cases without canonical JAK2, CALR, or MPL mutations, that is, triple-negative (TN) ET, have been found in 10%-20% of ET cases. Owing to the limited number of TN ET cases, its clinical significance remains unclear. This study evaluated TN ET's clinical characteristics and identified novel driver mutations. Among 119 patients with ET, 20 (16.8%) had no canonical JAK2/CALR/MPL mutations. Patients with TN ET tended to be younger and had lower white blood cell counts and lactate dehydrogenase values. We identified putative driver mutations in 7 (35%): MPL S204P, MPL L265F, JAK2 R683G, and JAK2 T875N were previously reported as candidate driver mutations in ET. Moreover, we identified a THPO splicing site mutation, MPL*636Wext*12, and MPL E237K. Four of the seven identified driver mutations were germline. Functional studies on MPL*636Wext*12 and MPL E237K revealed that they are gain-of-function mutants that increase MPL signaling and confer thrombopoietin hypersensitivity with very low efficiency. Patients with TN ET tended to be younger, although this was thought to be due to the inclusion of germline mutations, hereditary thrombocytosis. Accumulating the genetic and clinical characteristics of noncanonical mutations may help future clinical interventions in TN ET and hereditary thrombocytosis.

Multi-institution analysis of tumor mutational burden and outcomes in pediatric central nervous system tumor patients.

BACKGROUND: Pediatric central nervous system (CNS) tumors are the leading cause of pediatric cancer mortality. Research addressing genomic biomarkers and clinical outcomes is needed to inform therapeutic decision-making. METHODS: We conducted a retrospective analysis of pediatric patients (age <21) diagnosed with a primary CNS tumor at four upstate New York hospitals from 2008 to 2021. Clinical and histopathologic data were identified from each patient, including genomic analysis of somatic mutations and tumor mutational burden (TMB) where available. These variables were each compared with overall survival using Cox regression analyses. Multivariable analysis was conducted to identify patient characteristics that may independently predict survival. RESULTS: We identified 119 patients. Common tumor types included low-grade glioma (N = 51), high-grade glioma (N = 29), and medulloblastoma (N = 11). Common driver mutations included TP53 inactivation (N = 16), BRAF-KIAA1549 fusion (N = 16), FGFR1 amplification (N = 12), BRAF V600E mutation (N = 12), NF1 loss (N = 12), and H3F3A K28M mutation (N = 6). Median TMB was one mutation/megabase (mut/Mb, range = 0-132). Overall survival was 79.9%. Variables associated with poorer survival on univariable analysis were higher TMB (p = .002, HR 4.97), high-grade tumors (p = .009, HR 84.3), and high-grade glioma histology (p = .021, HR 3.14). Multivariable analyses further identified TMB (p = .011, HR 4.46) and high-grade histology (p = .015, HR 5.28) as independently predictive of worse survival. Tumor progression was more common in high-TMB (N = 15, 44%) than in low-TMB tumors (N = 19, 35%). CONCLUSIONS: High TMB is correlated with higher rates of progression and death as compared to low-TMB tumors. These findings may help identify patients who may benefit from alternative treatments, such as immunotherapies.

Systematic prediction of degrons and E3 ubiquitin ligase binding via deep learning.

BACKGROUND: Degrons are short linear motifs, bound by E3 ubiquitin ligase to target protein substrates to be degraded by the ubiquitin-proteasome system. Mutations leading to deregulation of degron functionality disrupt control of protein abundance due to mistargeting of proteins destined for degradation and often result in pathologies. Targeting degrons by small molecules also emerges as an exciting drug design strategy to upregulate the expression of specific proteins. Despite their essential function and disease targetability, reliable identification of degrons remains a conundrum. Here, we developed a deep learning-based model named Degpred that predicts general degrons directly from protein sequences. RESULTS: We showed that the BERT-based model performed well in predicting degrons singly from protein sequences. Then, we used the deep learning model Degpred to predict degrons proteome-widely. Degpred successfully captured typical degron-related sequence properties and predicted degrons beyond those from motif-based methods which use a handful of E3 motifs to match possible degrons. Furthermore, we calculated E3 motifs using predicted degrons on the substrates in our collected E3-substrate interaction dataset and constructed a regulatory network of protein degradation by assigning predicted degrons to specific E3s with calculated motifs. Critically, we experimentally verified that a predicted SPOP binding degron on CBX6 prompts CBX6 degradation and mediates the interaction with SPOP. We also showed that the protein degradation regulatory system is important in tumorigenesis by surveying degron-related mutations in TCGA. CONCLUSIONS: Degpred provides an efficient tool to proteome-wide prediction of degrons and binding E3s singly from protein sequences. Degpred successfully captures typical degron-related sequence properties and predicts degrons beyond those from previously used motif-based methods, thus greatly expanding the degron landscape, which should advance the understanding of protein degradation, and allow exploration of uncharacterized alterations of proteins in diseases. To make it easier for readers to access collected and predicted datasets, we integrated these data into the website http://degron.phasep.pro/ .

The role of brain radiotherapy for EGFR- and ALK-positive non-small-cell lung cancer with brain metastases: a review.

Non-small cell lung cancer (NSCLC) is frequently complicated by central nervous system (CNS) metastases affecting patients' life expectancy and quality. At the present clinical trials including neurosurgery, radiotherapy (RT) and systemic treatments alone or in combination have provided controversial results. CNS involvement is even more frequent in NSCLC patients with EGFR activating mutations or ALK rearrangement suggesting a role of target therapy in the upfront treatment in place of loco-regionals treatments (i.e. RT and/or surgery). So far clinical research has not explored the potential role of accurate brain imaging (i.e. MRI instead of the routine total-body contrast CT and/or PET/CT staging) to identify patients that could benefit of local therapies. Moreover, for patients who require concomitant RT there are no clear guidelines on the timing of intervention with respect to innovative precision medicine approaches with Tyrosine Kinase Inhibitors, ALK-inhibitors and/or immuno-oncological therapies. On this basis the present review describes the therapeutic strategies integrating medical and radiation oncology in patients with metastatic NSCLC (mNSCLC) adenocarcinoma with CNS involvement and EGFR activating mutations or ALK rearrangement.

[Chinese expert consensus on immunotherapy for advanced non-small lung cancer with oncogenic driver mutations (2023 edition)].

Non-small cell lung cancer (NSCLC) with oncogenic driver mutations was previously deemed " forbidden territory" for immunotherapy. With the growing understanding of the impact of target drugs on the immune microenvironment and the continuous generation of clinical evidence, immunotherapy is expected to bring new hope for the NSCLC with oncogenic driver mutations. This consensus is updated based on the Chinese expert consensus on immunotherapy for advanced non-small lung cancer with oncogenic driver mutations (2022 edition), and developed by the consensus expert panel through symposiums, combining the latest medical evidence and clinical practice. After thorough discussion, the expert panel reached new consensuses on 3 clinical questions: in patients with ALK fusion who are progressing on tyrosine kinase inhibitor(TKI) therapy, immune checkpoint inhibitors (ICIs)-based treatment is not recommended; ICIs-based treatment is recommended for patients with HER-2 mutations; ICIs-based treatment is recommended for NSCLC patients with MET exon 14 skipping after resistance to the targeted therapy. At the same time, with the continuous accumulation of clinical evidence, the recommendation levels of the three consensus opinions were adjusted in this update: the recommendation of ICIs combined with anti-angiogenesis therapy for patients with extensive progression after EGFR-TKIs resistance was adjusted to the level of strong; the ICIs recommendations for patients with advanced KRAS mutant and BRAF mutant NSCLC were adjusted to the level of consistent and strong, respectively. This updated consensus, combined with the latest evidence and clinical experience widely recognized by the expert panel in the immunotherapy of driver gene mutation advanced NSCLC, aims to provide standardized guidance for the clinical practice in China.

Constitutional POLE variants causing a phenotype reminiscent of constitutional mismatch repair deficiency.

Heterozygous POLE or POLD1 germline pathogenic variants (PVs) cause polymerase proofreading associated polyposis (PPAP), a constitutional polymerase proofreading deficiency that typically presents with colorectal adenomas and carcinomas in adulthood. Constitutional mismatch-repair deficiency (CMMRD), caused by germline bi-allelic PVs affecting one of four MMR genes, results in a high propensity for the hematological, brain, intestinal tract, and other malignancies in childhood. Nonmalignant clinical features, such as skin pigmentation alterations, are found in nearly all CMMRD patients and are important diagnostic markers. Here, we excluded CMMRD in three cancer patients with highly suspect clinical phenotypes but identified in each a constitutional heterozygous POLE PV. These, and two additional POLE PVs identified in published CMMRD-like patients, have not previously been reported as germline PVs despite all being well-known somatic mutations in hyper-mutated tumors. Together, these five cases show that specific POLE PVs may have a stronger "mutator" effect than known PPAP-associated POLE PVs and may cause a CMMRD-like phenotype distinct from PPAP. The common underlying mechanism, that is, a constitutional replication error repair defect, and a similar tumor spectrum provide a good rationale for monitoring these patients with a severe constitutional polymerase proofreading deficiency according to protocols proposed for CMMRD.

A recurrent somatic missense mutation in GNAS gene identified in familial thyroid follicular cell carcinomas in German longhaired pointer dogs.

BACKGROUND: We previously reported a familial thyroid follicular cell carcinoma (FCC) in a large number of Dutch German longhaired pointers and identified two deleterious germline mutations in the TPO gene associated with disease predisposition. However, the somatic mutation profile of the FCC in dogs has not been investigated at a genome-wide scale. RESULTS: Herein, we comprehensively investigated the somatic mutations that potentially contribute to the inherited tumor formation and progression using high depth whole-genome sequencing. A GNAS p.A204D missense mutation was identified in 4 out of 7 FCC tumors by whole-genome sequencing and in 20 out of 32 dogs' tumors by targeted sequencing. In contrast to this, in the human TC, mutations in GNAS gene have lower prevalence. Meanwhile, the homologous somatic mutation in humans has not been reported. These findings suggest a difference in the somatic mutation landscape between TC in these dogs and human TC. Moreover, tumors with the GNAS p.A204D mutation had a significantly lower somatic mutation burden in these dogs. Somatic structural variant and copy number alterations were also investigated, but no potential driver event was identified. CONCLUSION: This study provides novel insight in the molecular mechanism of thyroid carcinoma development in dogs. German longhaired pointers carrying GNAS mutations in the tumor may be used as a disease model for the development and testing of novel therapies to kill the tumor with somatic mutations in the GNAS gene.

FGFR1 promotes tumor immune evasion via YAP-mediated PD-L1 expression upregulation in lung squamous cell carcinoma.

BACKGROUND: Variations in FGFR1 are common driver mutations of LSQCC. And immune checkpoint inhibitors targeting PD-1 and PD-L1 are powerful anticancer weapons. Activation of FGFR1 leads to tumorigenesis through multiple downstream molecules, including YAP, but whether and how FGFR1 regulates tumor immune evasion remain largely unclear. METHODS: LSQCC cells were modified to increase or decrease the expression of FGFR1, YAP and PD-L1, as assessed by molecular assays. After FGFR1 knockdown, cancer cells were assessed after cocultured with Jurkat T cells in vitro, and the tumor microenvironment were analyzed in C57BL/6 mice. The effect of the combination of FGFR1 knockdown and PD-1 blockade was also explored. RESULTS: In human LSQCC, activation of FGFR1 was positively correlated with transcription of PD-L1. In H520 and HCC95 cells, FGFR1 upregulated PD-L1 expression via YAP, and YAP initiated the transcription of PD-L1 after binding to its promoter region. FGFR1 knockdown decreased tumor growth, reduced immune escape and induced reactivation of CD8+ T cells. The combination of FGFR1 knockdown and PD-1 blockade synergistically exerted antitumor effects. CONCLUSIONS: The FGFR1/YAP/PD-L1 regulatory axis mediates tumor-associated immune suppression in lung squamous cell carcinoma, and FGFR1 knockdown reactivates T cells in the tumor microenvironment. Synergistic inhibition of both FGFR1 and PD-1/PD-L1 pathways may be a possible treatment for lung cancer patients.

Association of recurrent APOBEC3B alterations with the prognosis of gastric-type cervical adenocarcinoma.

OBJECTIVE: Gastric-type cervical adenocarcinoma (GCA) is a rare and aggressive type of endocervical adenocarcinoma (ECA) with distinct histopathologic features and unfavorable treatment outcomes, but no genomic prognostic factor has been revealed. We aimed to systematically investigate the somatic alterations of GCA at genome-wide level and evaluate their prognostic value. METHODS: We performed whole-exome sequencing (WES) on 25 pairs of tumor and matched normal samples to characterize the genomic features of Chinese patients with GCA and investigated their relations to histopathological characterizations and prognosis. The prognostic value of the genomic alterations was evaluated in a total of 58 GCA patients. RESULTS: Mutations were commonly observed in reported GCA-related driver genes, including TP53 (32%), CDKN2A (20%), SKT11 (20%), BRCA2 (12%), SMAD4 (12%), and ERBB2 (12%). Recurrent novel trunk mutations were also observed in PBRM1 (12%), FRMPD4 (12%), and NOP2 (8%) with high variant allele frequency. Moreover, enrichment of the APOBEC signature was attributed to frequent gain of somatic copy number alteration (SCNA) of APOBEC3B (20%), which perfectly matched the nuclear-positive staining of APOBEC3B through immunohistochemistry. In contrast, APOBEC3B alteration was absent in patients with conventional type of ECA (N = 52). Notably, positive APOBEC3B was consistently enriched in patients with favorable prognosis in both the discovery cohort and an additional 33 GCA patients, thus indicating a significant association with lower relapse risk of GCA independent of cancer stage (P = 0.02). CONCLUSION: Our results can aid understanding of the molecular basis of GCA in the Chinese population by providing genomic profiles and highlighting the potential prognostic value of APOBEC3B for GCA through routine clinical IHC.

Struggle within: evolution and ecology of somatic cell populations.

The extent to which normal (nonmalignant) cells of the body can evolve through mutation and selection during the lifetime of the organism has been a major unresolved issue in evolutionary and developmental studies. On the one hand, stable multicellular individuality seems to depend on genetic homogeneity and suppression of evolutionary conflicts at the cellular level. On the other hand, the example of clonal selection of lymphocytes indicates that certain forms of somatic mutation and selection are concordant with the organism-level fitness. Recent DNA sequencing and tissue physiology studies suggest that in addition to adaptive immune cells also neurons, epithelial cells, epidermal cells, hematopoietic stem cells and functional cells in solid bodily organs are subject to evolutionary forces during the lifetime of an organism. Here we refer to these recent studies and suggest that the expanding list of somatically evolving cells modifies idealized views of biological individuals as radically different from collectives.