Multi-region exome sequencing reveals the intratumoral heterogeneity of surgically resected small cell lung cancer

Molecular landscape and multi-omic measurements of heterogeneity in fetal adenocarcinoma of the lung

Fetal adenocarcinoma of the lung (FLAC) is a rare form of lung adenocarcinoma and was divided into high-grade (H-FLAC) and low-grade (L-FLAC) subtypes. Despite the existence of some small case series studies, a comprehensive multi-omics study of FLAC has yet to be undertaken. In this study, we depicted the multi-omics landscapes of this rare lung cancer type by performing multi-regional sampling on 20 FLAC cases. A comparison of multi-omics profiles revealed significant differences between H-FLAC and L-FLAC in a multi-omic landscape. Two subtypes also showed distinct relationships between multi-layer intratumor heterogeneity (ITH). We discovered that a lower genetic ITH was significantly associated with worse recurrence-free survival and overall survival in FLAC patients, whereas higher methylation ITH in H-FLAC patients suggested a short survival. Our findings highlight the complex interplay between genetic and transcriptional heterogeneity in FLAC and suggest that different types of ITH may have distinct implications for patient prognosis.

GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status

Noninvasively and accurately predicting the epidermal growth factor receptor (EGFR) mutation status is a clinically vital problem. Moreover, further identifying the most suspicious area related to the EGFR mutation status can guide the biopsy to avoid false negatives. Deep learning methods based on computed tomography (CT) images may improve the noninvasive prediction of EGFR mutation status and potentially help clinicians guide biopsies by visual methods. Inspired by the potential inherent links between EGFR mutation status and invasiveness information, we hypothesized that the predictive performance of a deep learning network can be improved through extra utilization of the invasiveness information. Here, we created a novel explainable transformer network for EGFR classification named gated multiple instance learning transformer (GMILT) by integrating multi-instance learning and discriminative weakly supervised feature learning. Pathological invasiveness information was first introduced into the multitask model as embeddings. GMILT was trained and validated on a total of 512 patients with adenocarcinoma and tested on three datasets (the internal test dataset, the external test dataset, and The Cancer Imaging Archive (TCIA) public dataset). The performance (area under the curve (AUC) =0.772 on the internal test dataset) of GMILT exceeded that of previously published methods and radiomics-based methods (i.e., random forest and support vector machine) and attained a preferable generalization ability (AUC =0.856 in the TCIA test dataset and AUC =0.756 in the external dataset). A diameter-based subgroup analysis further verified the efficiency of our model (most of the AUCs exceeded 0.772) to noninvasively predict EGFR mutation status from computed tomography (CT) images. In addition, because our method also identified the "core area" of the most suspicious area related to the EGFR mutation status, it has the potential ability to guide biopsies.

HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data

Bulk DNA sequencing of multiple samples from the same tumor is becoming common, yet most methods to infer copy-number aberrations (CNAs) from this data analyze individual samples independently. We introduce HATCHet2, an algorithm to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 extends the earlier HATCHet method by improving identification of focal CNAs and introducing a novel statistic, the minor haplotype B-allele frequency (mhBAF), that enables identification of mirrored-subclonal CNAs. We demonstrate HATCHet2's improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 10 prostate cancer patients reveals previously unreported mirrored-subclonal CNAs affecting cancer genes.

RESILIENT Part 2: A Randomized, Open-Label Phase III Study of Liposomal Irinotecan Versus Topotecan in Adults With Relapsed Small Cell Lung Cancer

PURPOSE

The phase III RESILIENT trial compared second-line liposomal irinotecan with topotecan in patients with small cell lung cancer (SCLC).

PATIENTS AND METHODS

Patients with SCLC and progression on or after first-line platinum-based chemotherapy were randomly assigned (1:1) to intravenous (IV) liposomal irinotecan (70 mg/m2 every 2 weeks in a 6-week cycle) or IV topotecan (1.5 mg/m2 daily for 5 consecutive days, every 3 weeks in a 6-week cycle). The primary end point was overall survival (OS). Key secondary end points included progression-free survival (PFS) and objective response rate (ORR).

RESULTS

Among 461 randomly assigned patients, 229 received liposomal irinotecan and 232 received topotecan. The median follow-up was 18.4 months. The median OS was 7.9 months with liposomal irinotecan versus 8.3 months with topotecan (hazard ratio [HR], 1.11 [95% CI, 0.90 to 1.37]; P = .31). The median PFS per blinded independent central review (BICR) was 4.0 months with liposomal irinotecan and 3.3 months with topotecan (HR, 0.96 [95% CI, 0.77 to 1.20]; nominal P = .71); ORR per BICR was 44.1% (95% CI, 37.6 to 50.8) and 21.6% (16.4 to 27.4), respectively. Overall, 42.0% and 83.4% of patients receiving liposomal irinotecan and topotecan, respectively, experienced grade ≥3 related treatment-emergent adverse events (TEAEs). The most common grade ≥3 related TEAEs were diarrhea (13.7%), neutropenia (8.0%), and decreased neutrophil count (4.4%) with liposomal irinotecan and neutropenia (51.6%), anemia (30.9%), and leukopenia (29.1%) with topotecan.

CONCLUSION

Liposomal irinotecan and topotecan demonstrated similar median OS and PFS in patients with relapsed SCLC. Although the primary end point of OS was not met, liposomal irinotecan demonstrated a higher ORR than topotecan. The safety profile of liposomal irinotecan was consistent with its known safety profile; no new safety concerns emerged.

The intricate dance of tumor evolution: Exploring immune escape, tumor migration, drug resistance, and treatment strategies

Recent research has unveiled fascinating insights into the intricate mechanisms governing tumor evolution. These studies have illuminated how tumors adapt and proliferate by exploiting various factors, including immune evasion, resistance to therapeutic drugs, genetic mutations, and their ability to adapt to different environments. Furthermore, investigations into tumor heterogeneity and chromosomal aberrations have revealed the profound complexity that underlies the evolution of cancer. Emerging findings have also underscored the role of viral influences in the development and progression of cancer, introducing an additional layer of complexity to the field of oncology. Tumor evolution is a dynamic and complex process influenced by various factors, including immune evasion, drug resistance, tumor heterogeneity, and viral influences. Understanding these elements is indispensable for developing more effective treatments and advancing cancer therapies. A holistic approach to studying and addressing tumor evolution is crucial in the ongoing battle against cancer. The main goal of this comprehensive review is to explore the intricate relationship between tumor evolution and critical aspects of cancer biology. By delving into this complex interplay, we aim to provide a profound understanding of how tumors evolve, adapt, and respond to treatment strategies. This review underscores the pivotal importance of comprehending tumor evolution in shaping effective approaches to cancer treatment.

Comparison of clinicopathological features and survival analysis between esophageal neuroendocrine carcinoma and esophageal squamous cell carcinoma based on the SEER database, alongside nomogram analysis for esophageal neuroendocrine carcinoma

Background

Esophageal neuroendocrine carcinoma (ENEC) is a rare subtype of esophageal cancer (EC). It presents distinctive clinical and pathological features in comparison to esophageal squamous cell carcinoma (ESCC). To better elucidate the disparities between the two and establish a prognostic prediction model for ENEC, we conducted this study.

Methods

Data of ENEC and ESCC patients (1975 to 2016) were extracted from the Surveillance, Epidemiology and End Results (SEER) database. Patients with a confirmed pathological diagnosis of ENEC and ESCC were enrolled in the study. The Chi-square test was employed to compare categorical variables, and the median survival time was analyzed using the Kaplan-Meier curve. Training and validation groups were randomly assigned at a ratio of 7:3. Factors with a significance level of <0.05 in the multifactor regression model as well as age were integrated into the nomogram model. Concordance index (C-index), calibration curves, and decision curve analyses (DCA) were generated for model validation.

Results

This study encompassed a total of 737 ENEC patients and 29,420 ESCC. Compared to ESCC, ENEC patients had higher probability of liver metastasis (13.8% vs. 1.9%, P<0.001), poor differentiation (68.0% vs. 37.1%, P<0.001), and late SEER stage (52.8% vs. 26.9%, P<0.001). Patients who received either surgery, radiotherapy (RT), or chemotherapy had a significantly longer disease-specific survival (DSS) and overall survival (OS) (all P<0.001). After propensity score matching (PSM), ENEC patients were associated with shorter DSS (7.0 months vs. not reached, P<0.0001) and OS (7.0 vs. 12.0 months, P<0.0001) compared to ESCC. Race, SEER stage, surgery, RT, and chemotherapy were identified as predictors of DSS and were incorporated into the nomogram model together with age. The validation of the model using C-index (0.751 and 0.706, respectively) and calibration curves reflected the better discrimination power of the model. In addition, DCA supported the favorable potential clinical effect of the predictive model. Lastly, a risk classification based on the nomogram also verified the reliability of the model.

Conclusions

ENEC and ESCC exhibit distinct clinicopathological features. Patients with ENEC experience significantly poorer survival outcomes compared to those with ESCC. Surgical intervention, radiation therapy, and chemotherapy significantly improve OS and DSS for ENEC patients. The nomogram prediction model, constructed based on age, race, stage, and treatment regimen, demonstrates accurate and effective predictive capabilities for prognostic factors in ENEC patients.

Molecular subtype expression and genomic profiling differ between surgically resected pure and combined small cell lung carcinoma

A new molecular subtype classification method has been proposed for small cell lung carcinoma (SCLC). However, little is known about the differences between the pure (P-SCLC) and combined subtypes (C-SCLC). We aimed to compare the molecular subtype expression and genomic profiling in terms of clinical relevance between the two groups. 154 surgically resected SCLCs were analyzed for protein expression of four subtypes (ASCL1, NEUROD1, POU2F3, and YAP1) and two predictive markers (DLL3 and MYC) by immunohistochemistry (IHC). We also performed whole exome sequencing of 60 samples to examine genomic profiles. A total of 113 patients with P-SCLC and 41 with C-SCLC were included. In P-SCLC and C-SCLC, the expression of these markers was 78.8% and 41.5%, 98.2% and 97.6%, 42.5% and 51.2%, 38.9% and 85.4%, 85.0% and 68.3%, and 24.8% and 34.1%, respectively. ASCL1 and DLL3 were highly expressed in P-SCLC (p = 0.000 and p = 0.021, respectively), and YAP1 expression was significantly enriched in C-SCLC (p = 0.000). NGS results, including 45 P-SCLCs and 15 C-SCLCs, indicated that EGFR gene mutations were mostly observed in C-SCLCs (p = 0.000). C-SCLC showed higher CNA burden and wGII than P-SCLC (p < 0.01 and p < 0.05); conversely, P-SCLC had higher TMB burden and SDI (p < 0.05 and p < 0.05). YAP1 expression was associated with poor prognosis in P-SCLC but with favorable prognosis in C-SCLC. P-SCLC and C-SCLC are heterogeneous diseases characterized by different molecular subtype expressions and genomic profiles. Our data provide a basis for adopting histological subtype-based treatments, and further prospective studies are required to confirm our conclusions.

Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity

Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant cells display variations in their gene transcription profiles and mutation spectra even when originating from a single progenitor cell. Single-cell and spatial transcriptomics sequencing have recently emerged as key technologies for unravelling tumour heterogeneity. Single-cell sequencing promotes individual cell-type identification through transcriptome-wide gene expression measurements of each cell. Spatial transcriptomics facilitates identification of cell-cell interactions and the structural organization of heterogeneous cells within a tumour tissue through associating spatial RNA abundance of cells at distinct spots in the tissue section. However, extracting features and analyzing single-cell and spatial transcriptomics data poses challenges. Single-cell transcriptome data is extremely noisy and its sparse nature and dropouts can lead to misinterpretation of gene expression and the misclassification of cell types. Deep learning predictive power can overcome data challenges, provide high-resolution analysis and enhance precision oncology applications that involve early cancer prognosis, diagnosis, patient survival estimation and anti-cancer therapy planning. In this paper, we provide a background to and review of the recent progress of deep learning frameworks to investigate tumour heterogeneity using both single-cell and spatial transcriptomics data types.

Intratumoral Heterogeneity of Molecular Subtypes in Muscle-invasive Bladder Cancer-An Extensive Multiregional Immunohistochemical Analysis

BACKGROUND

Molecular bladder cancer (BC) subtypes define distinct biological entities and were shown to predict treatment response in neoadjuvant and adjuvant settings. The extent of intratumoral heterogeneity (ITH) might affect subtyping of individual patients.

OBJECTIVE

To comprehensively assess the ITH of molecular subtypes in a cohort of muscle-invasive BC.

DESIGN, SETTING, AND PARTICIPANTS

A total of 251 patients undergoing radical cystectomy were screened. Three cores of the tumor center (TC) and three cores of the invasive tumor front (TF) of each patient were assembled in a tissue microarray. Molecular subtypes were determined employing 12 pre-evaluated immunohistochemical markers (FGFR3, CCND1, RB1, CDKN2A, KRT5, KRT14, FOXA1, GATA3, TUBB2B, EPCAM, CDH1, and vimentin). A total of 18 072 spots were evaluated, of which 15 002 spots were assessed based on intensity, distribution, or combination.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Allocation to one of five different molecular subtypes-urothelial like, genomically unstable, small-cell/neuroendocrine like, basal/squamous cell carcinoma like, and mesenchymal like-was conducted for each patient for the complete tumor, individual cores, TF, and TC separately. The primary objective was to assess the ITH between the TF and TC (n = 208 patients). The secondary objective was the evaluation of multiregion ITH (n = 191 patients). An analysis of the composition of ITH cases, association with clinicopathological parameters, and prognosis was conducted.

RESULTS AND LIMITATIONS

ITH between the TF and TC was seen in 12.5% (n = 26/208), and ITH defined by at least two different subtypes of any location was seen in 24.6% (n = 47/191). ITH was more frequent in locally confined (pT2) versus advanced (pT ≥3) BC stages (38.7% vs 21.9%, p = 0.046), and pT4 BC presented with significantly more basal subtypes than pT2 BC (26.2% vs 11.5%, p = 0.049). In our cohort, there was no association of subtype ITH with prognosis or accumulation of specific molecular subtypes in ITH cases. The key limitations were missing transcriptomic and mutational genetic validation as well as investigation of ITH beyond subtypes.

CONCLUSIONS

Several molecular subtypes can be found in nearly every fourth case of muscle-invasive BC, when using immunohistochemistry. ITH must be given due consideration for subtype-guided strategies in BC. Genomic validation of these results is needed.

PATIENT SUMMARY

Different molecular subtypes can be found in many cases of muscle-invasive bladder cancer. This might have implications for individualized, subtype-based therapeutic approaches.

LRBmat: A novel gut microbial interaction and individual heterogeneity inference method for colorectal cancer

The gut microbial community has been shown to play a significant role in various diseases, including colorectal cancer (CRC), which is a major public health concern worldwide. The accurate diagnosis and etiological analysis of CRC are crucial issues. Numerous methods have utilized gut microbiota to address these challenges; however, few have considered the complex interactions and individual heterogeneity of the gut microbiota, which are important issues in genetics and intestinal microbiology, particularly in high-dimensional cases. This paper presents a novel method called Binary matrix based on Logistic Regression (LRBmat) to address these concerns. The binary matrix in LRBmat can directly mitigate or eliminate the influence of heterogeneity, while also capturing information on gut microbial interactions with any order. LRBmat is highly adaptable and can be combined with any machine learning method to enhance its capabilities. The proposed method was evaluated using real CRC data and demonstrated superior classification performance compared to state-of-the-art methods. Furthermore, the association rules extracted from the binary matrix of the real data align well with biological properties and existing literature, thereby aiding in the etiological analysis of CRC.

The signature of cuproptosis-related immune genes predicts the tumor microenvironment and prognosis of prostate adenocarcinoma

Background

Cuproptosis plays a crucial role in cancer, and different subtypes of cuproptosis have different immune profiles in prostate adenocarcinoma (PRAD). This study aimed to investigate immune genes associated with cuproptosis and develop a risk model to predict prognostic characteristics and chemotherapy/immunotherapy responses of patients with PRAD.

Methods

The CIBERSORT algorithm was used to evaluate the immune and stromal scores of patients with PRAD in The Cancer Genome Atlas (TCGA) cohort. Validation of differentially expressed genes DLAT and DLD in benign and malignant tissues by immunohistochemistry, and the immune-related genes of DLAT and DLD were further screened. Univariable Cox regression were performed to select key genes. Least absolute shrinkage and selection operator (LASSO)-Cox regression analyse was used to develop a risk model based on the selected genes. The model was validated in the TCGA, Memorial Sloan-Kettering Cancer Center (MSKCC) and Gene Expression Omnibus (GEO) datasets, as well as in this study unit cohort. The genes were examined via functional enrichment analysis, and the tumor immune features, tumor mutation features and copy number variations (CNVs) of patients with different risk scores were analysed. The response of patients to multiple chemotherapeutic/targeted drugs was assessed using the pRRophetic algorithm, and immunotherapy was inferred by the Tumor Immune Dysfunction and Exclusion (TIDE) and immunophenoscore (IPS).

Results

Cuproptosis-related immune risk scores (CRIRSs) were developed based on PRLR, DES and LECT2. High CRIRSs indicated poor overall survival (OS), disease-free survival (DFS) in the TCGA-PRAD, MSKCC and GEO datasets and higher T stage and Gleason scores in TCGA-PRAD. Similarly, in the sample collected by the study unit, patients with high CRIRS had higher T-stage and Gleason scores. Additionally, higher CRIRSs were negatively correlated with the abundance of activated B cells, activated CD8+ T cells and other stromal or immune cells. The expression of some immune checkpoints was negatively correlated with CRIRSs. Tumor mutational burden (TMB), mutant-allele tumor heterogeneity (MATH) and copy number variation (CNV) scores were all higher in the high-CRIRS group. Multiple chemotherapeutic/targeted drugs and immunotherapy had better responsiveness in the low-CRIRS group.

Conclusion

Overall, lower CRIRS indicated better response to treatment strategies and better prognostic outcomes.

HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data

Multi-region DNA sequencing of primary tumors and metastases from individual patients helps identify somatic aberrations driving cancer development. However, most methods to infer copy-number aberrations (CNAs) analyze individual samples. We introduce HATCHet2 to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 introduces a novel statistic, the mirrored haplotype B-allele frequency (mhBAF), to identify mirrored-subclonal CNAs having different numbers of copies of parental haplotypes in different tumor clones. HATCHet2 also has high accuracy in identifying focal CNAs and extends the earlier HATCHet method in several directions. We demonstrate HATCHet2's improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 50 prostate cancer samples from 10 patients reveals previously-unreported mirrored-subclonal CNAs affecting cancer genes.

Orchestrating smart therapeutics to achieve optimal treatment in small cell lung cancer: recent progress and future directions

Small cell lung cancer (SCLC) is a recalcitrant malignancy with elusive mechanism of pathogenesis and dismal prognosis. Over the past decades, platinum-based chemotherapy has been the backbone treatment for SCLC. However, subsequent chemoresistance after initial effectiveness urges researchers to explore novel therapeutic targets of SCLC. Recent years have witnessed significant improvements in targeted therapy in SCLC. New molecular candidates such as Ataxia telangiectasia and RAD3-related protein (ATR), WEE1, checkpoint kinase 1 (CHK1) and poly-ADP-ribose polymerase (PARP) have shown promising therapeutic utility in SCLC. While immune checkpoint inhibitor (ICI) has emerged as an indispensable treatment modality for SCLC, approaches to boost efficacy and reduce toxicity as well as selection of reliable biomarkers for ICI in SCLC have remained elusive and warrants our further investigation. Given the increasing importance of precision medicine in SCLC, optimal subtyping of SCLC using multi-omics have gradually applied into clinical practice, which may identify more drug targets and better tailor treatment strategies to each individual patient. The present review summarizes recent progress and future directions in SCLC. In addition to the emerging new therapeutics, we also focus on the establishment of predictive model for early detection of SCLC. More importantly, we also propose a multi-dimensional model in the prognosis of SCLC to ultimately attain the goal of accurate treatment of SCLC.

Integrative Analysis of a Large Real-World Cohort of Small Cell Lung Cancer Identifies Distinct Genetic Subtypes and Insights into Histologic Transformation

Small cell lung cancer (SCLC) is a recalcitrant neuroendocrine carcinoma with dismal survival outcomes. A major barrier in the field has been the relative paucity of human tumors studied. Here we provide an integrated analysis of 3,600 "real-world" SCLC cases. This large cohort allowed us to identify new recurrent alterations and genetic subtypes, including STK11-mutant tumors (1.7%) and TP53/RB1 wild-type tumors (5.5%), as well as rare cases that were human papillomavirus-positive. In our cohort, gene amplifications on 4q12 are associated with increased overall survival, whereas CCNE1 amplification is associated with decreased overall survival. We also identify more frequent alterations in the PTEN pathway in brain metastases. Finally, profiling cases of SCLC containing oncogenic drivers typically associated with NSCLC demonstrates that SCLC transformation may occur across multiple distinct molecular cohorts of NSCLC. These novel and unsuspected genetic features of SCLC may help personalize treatment approaches for this fatal form of cancer.

SIGNIFICANCE

Minimal changes in therapy and survival outcomes have occurred in SCLC for the past four decades. The identification of new genetic subtypes and novel recurrent mutations as well as an improved understanding of the mechanisms of transformation to SCLC from NSCLC may guide the development of personalized therapies for subsets of patients with SCLC. This article is highlighted in the In This Issue feature, p. 1501.

Spatial biology of cancer evolution

The natural history of cancers can be understood through the lens of evolution given that the driving forces of cancer development are mutation and selection of fitter clones. Cancer growth and progression are spatial processes that involve the breakdown of normal tissue organization, invasion and metastasis. For these reasons, spatial patterns are an integral part of histological tumour grading and staging as they measure the progression from normal to malignant disease. Furthermore, tumour cells are part of an ecosystem of tumour cells and their surrounding tumour microenvironment. A range of new spatial genomic, transcriptomic and proteomic technologies offers new avenues for the study of cancer evolution with great molecular and spatial detail. These methods enable precise characterizations of the tumour microenvironment, cellular interactions therein and micro-anatomical structures. In conjunction with spatial genomics, it emerges that tumours and microenvironments co-evolve, which helps explain observable patterns of heterogeneity and offers new routes for therapeutic interventions.

From Samples to Germline and Somatic Sequence Variation: A Focus on Next-Generation Sequencing in Melanoma Research

Next-generation sequencing (NGS) applications have flourished in the last decade, permitting the identification of cancer driver genes and profoundly expanding the possibilities of genomic studies of cancer, including melanoma. Here we aimed to present a technical review across many of the methodological approaches brought by the use of NGS applications with a focus on assessing germline and somatic sequence variation. We provide cautionary notes and discuss key technical details involved in library preparation, the most common problems with the samples, and guidance to circumvent them. We also provide an overview of the sequence-based methods for cancer genomics, exposing the pros and cons of targeted sequencing vs. exome or whole-genome sequencing (WGS), the fundamentals of the most common commercial platforms, and a comparison of throughputs and key applications. Details of the steps and the main software involved in the bioinformatics processing of the sequencing results, from preprocessing to variant prioritization and filtering, are also provided in the context of the full spectrum of genetic variation (SNVs, indels, CNVs, structural variation, and gene fusions). Finally, we put the emphasis on selected bioinformatic pipelines behind (a) short-read WGS identification of small germline and somatic variants, (b) detection of gene fusions from transcriptomes, and (c) de novo assembly of genomes from long-read WGS data. Overall, we provide comprehensive guidance across the main methodological procedures involved in obtaining sequencing results for the most common short- and long-read NGS platforms, highlighting key applications in melanoma research.

Ferroptosis, necroptosis, and pyroptosis in the tumor microenvironment: Perspectives for immunotherapy of SCLC

Small cell lung cancer (SCLC) is an aggressive form of lung cancer characterized by dismal prognosis. Although SCLC may initially respond well to platinum-based chemotherapy, it ultimately relapses and is almost universally resistant to this treatment. Immune checkpoint inhibitors (ICIs) have been approved as the first- and third-line therapeutic regimens for extensive-stage or relapsed SCLC, respectively. Despite this, only a minority of patients with SCLC respond to ICIs partly due to a lack of tumor-infiltrating lymphocytes (TILs). Transforming the immune "cold" tumors into "hot" tumors that are more likely to respond to ICIs is the main challenge for SCLC therapy. Ferroptosis, necroptosis, and pyroptosis represent the newly discovered immunogenic cell death (ICD) forms. Promoting ICD may alter the tumor microenvironment (TME) and the influx of TILs, and combination of their inducers and ICIs plays a synergistical role in enhancing antitumor effects. Nevertheless, the combination of the above two modalities has not been systematically discussed in SCLC therapy. In the present review, we summarize the roles of distinct ICD mechanisms on antitumor immunity and recent advances of ferroptosis-, necroptosis- and pyroptosis-inducing agents, and present perspectives on these cell death mechanisms in immunotherapy of SCLC.

Evolution of small cell lung cancer tumor mutation: from molecular mechanisms to novel viewpoints

Small cell lung cancer (SCLC) is a clinically common malignant tumor originating from the lung neuroendocrine stem cells, which has a poor prognosis and accounts for approximately 15% of all lung cancer cases. However, research on its treatment has been slow, and the 5-year survival rate of patients with SCLC has been < 5% for many years. In recent years, the development and popularization of gene sequencing technology have facilitated the understanding of the gene mutation landscape and tumor evolution of SCLC, thereby leading to a more accurate prediction of the prognosis of SCLC and the development of individualized treatment. In this review, we aimed to discuss the mutation evolution of SCLC from the perspective of a tumor evolution theory and described the sequence of mutation evolution in the occurrence and development of SCLC. In addition, we summarized the existing whole-exome sequencing (WES) data of SCLC cases at our center along with relevant publications on sequencing. Thereafter, we discuss the role of different mutated pathways in the occurrence of SCLC to predict its prognosis more accurately and summarized individualized treatment strategies.

YES1 is a druggable oncogenic target in Small Cell Lung Cancer

RATIONALE

Small cell lung cancer (SCLC) is an extremely aggressive subtype of lung cancer without approved targeted therapies. Here we identified YES1 as a novel targetable oncogene driving SCLC maintenance and metastasis.

OBJECTIVES

To investigate the role of YES1 in SCLC prognosis and evaluate its inhibition as a new therapeutic strategy.

METHODS

Association between YES1 levels and prognosis was evaluated in SCLC clinical samples. In vitro functional experiments for proliferation, apoptosis, cell cycle and cytotoxicity were performed. Genetic and pharmacological inhibition of YES1 was evaluated in vivo in cell-/patient-derived xenografts (PDXs) and in metastasis. YES1 levels were evaluated in mouse and patients' plasma-derived exosomes MEASUREMENTS AND MAIN RESULTS: Overexpression or gain/amplification of YES1 was identified in 31% and 26% of cases, respectively, across molecular subgroups, and was found as an independent predictor of poor prognosis. Genetic depletion of YES1 dramatically reduced cell proliferation, 3D organoid formation, tumor growth and distant metastasis, leading to extensive apoptosis and tumor regressions. Mechanistically, YES1-inhibited cells showed alterations in the replisome and DNA repair processes, that conferred sensitivity to irradiation. Pharmacological blockade with the novel YES1 inhibitor CH6953755 or Dasatinib induced significant anti-tumor activity in organoid models and cell-/patient-derived xenografts. YES1 protein was detected in plasma exosomes from patients and mouse models, with levels matching those of tumors, suggesting that circulating YES1 could represent a biomarker for patient selection/monitoring.

CONCLUSIONS

Our results provide evidence that YES1 is a new druggable oncogenic target and biomarker to advance the clinical management of a subpopulation of SCLC patients.

Somatic Mutation of FAT Family Genes Implicated Superior Prognosis in Patients With Stomach Adenocarcinoma

FAT family genes encode protocadherin, which regulates tumor cell proliferation and migration. Although transcriptional levels of FAT family members had been reported in multiple malignant tumors, the association between mutation and prognosis of the FAT family in stomach adenocarcinoma (STAD) has not been investigated. Herein, we performed a multi-omics integrative bioinformatics analysis using genomic and mRNA expression data to explore the role of gene mutations across the FAT family on clinical outcomes of STAD. The results showed that FAT mutations occurred in 174 of 435 (40%) of the samples. Patients with FAT mutations possessed significantly better progression-free survival (P = 0.019) and overall survival (P = 0.034) than those with non-FAT mutations, and FAT mutations exhibited significantly higher tumor mutational burden (TMB) and microsatellite instability. Notably, FAT mutations had a greater effect on somatic single-nucleotide variation than copy number variation and resulted in more abundant DNA damage repair (DDR) mutations. Further investigation demonstrated that FAT mutations contributed to an inflammatory tumor microenvironment (TME), as indicated by significantly increased numbers of activated CD4 and CD8 T cells, and significantly decreased numbers of mast cell, plasmacytoid dendritic cell, type 2 T helper cell, and high expression of immune-promoting genes. Moreover, biological process antigen processing and presentation, DNA replication, and DDR-related pathways were significantly upregulated in patients with FAT mutations. Collectively, FAT mutations significantly improved the survival of patients with STAD by enhancing tumor immunogenicity (e.g., TMB and DDR mutations) and an inflamed TME, indicating that the FAT family might be a potential prognostic and therapeutic biomarker for STAD.

Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information

Purpose: To assess the significance of mutation mutual exclusion information in the optimization of radiomics algorithms for predicting gene mutation.

Methods: We retrospectively analyzed 258 non-small cell lung cancer (NSCLC) patients. Patients were randomly divided into training (n = 180) and validation (n = 78) cohorts. Based on radiomics features, radiomics score (RS) models were developed for predicting KRAS proto-oncogene mutations. Furthermore, a composite model combining mixedRS and epidermal growth factor receptor (EGFR) mutation status was developed.

Results: Compared with CT model, the PET/CT radiomics score model exhibited higher AUC for predicting KRAS mutations (0.834 vs. 0.770). By integrating EGFR mutation information into the PET/CT RS model, the AUC, sensitivity, specificity, and accuracy for predicting KRAS mutations were all elevated in the validation cohort (0.921, 0.949, 0.872, 0.910 vs. 0.834, 0.923, 0.641, 0.782). By adding EGFR exclusive mutation information, the composite model corrected 64.3% false positive cases produced by the PET/CT RS model in the validation cohort.

Conclusion: Integrating EGFR mutation status has potential utility for the optimization of radiomics models for prediction of KRAS gene mutations. This method may be used when repeated biopsies would carry unacceptable risks for the patient.

Killing SCLC: insights into how to target a shapeshifting tumor

Small cell lung cancer (SCLC) is a rapidly growing, highly metastatic, and relatively immune-cold lung cancer subtype. Historically viewed in the laboratory and clinic as a single disease, new discoveries suggest that SCLC comprises multiple molecular subsets. Expression of MYC family members and lineage-related transcription factors ASCL1, NEUROD1, and POU2F3 (and, in some studies, YAP1) define unique molecular states that have been associated with distinct responses to a variety of therapies. However, SCLC tumors exhibit a high degree of intratumoral heterogeneity, with recent studies suggesting the existence of tumor cell plasticity and phenotypic switching between subtype states. While SCLC plasticity is correlated with, and likely drives, therapeutic resistance, the mechanisms underlying this plasticity are still largely unknown. Subtype states are also associated with immune-related gene expression, which likely impacts response to immune checkpoint blockade and may reveal novel targets for alternative immunotherapeutic approaches. In this review, we synthesize recent discoveries on the mechanisms of SCLC plasticity and how these processes may impinge on antitumor immunity.

Comprehensive Analysis of Innate Immunophenotyping Based on Immune Score Predicting Immune Alterations and Prognosis in Breast Cancer Patients

Breast cancer is the most common cancer, with the highest mortality rate and the most diagnosed cancer type in women worldwide. To identify the effect innate immune checkpoint for breast cancer immunotherapy, the innate immune prognostic biomarkers were selected through the ICI score model and the risk model in breast cancer patients. Moreover, the reliability and accuracy of the ICI score model and the risk model were further examined through the analysis of breast cancer prognosis and immune cell infiltration. The pan cancer analysis further confirmed and selected CXCL9 as the key innate immune checkpoint for breast cancer immunotherapy and identified three small molecular drugs for target CXCL9 through molecular docking analysis. In summary, CXCL9 significantly correlated with the prognostic of breast cancer and immune cell infiltration and could be innate immune checkpoint for breast cancer immunotherapy.