Genomic and epigenomic evolution of acquired resistance to combination therapy in esophageal squamous cell carcinoma

Impacts of tumor microenvironment during neoadjuvant chemotherapy in patients with esophageal squamous cell carcinoma

With the advent of immune checkpoint inhibitors (ICIs), a better understanding of tumor microenvironment (TME) is becoming crucial in managing esophageal squamous cell carcinoma (ESCC) patients. We investigated the survival impact of TME status and changes in patients with ESCC who underwent neoadjuvant chemotherapy (NAC) followed by surgery (n = 264). We examined immunohistochemical status (CD4+, CD8+, CD20+, Foxp3+, HLA class-1+, CD204+, and programmed death ligand-1 [PD-L1+]) on 264 pre-NAC and 204 paired post-NAC specimens. Patients were classified by their pre- and post-NAC immune cell status and their changes following NAC. Our findings showed that pre-NAC TME status was not significantly associated with survival outcomes. In contrast, post-NAC TME status, such as low level of T cells, CD4+ T cells, and high PD-L1 combined positive score (CPS), were significantly associated with poor overall survival (OS). Notably, TME changes through NAC exerted significant survival impacts; patients with consistently low levels of T cells, low levels of CD4+ T cells, or high levels of PD-L1 (CPS) had very poor OS (3-year OS: 35.5%, 40.2%, and 33.3%, respectively). Tumor microenvironment changes of consistently low T cells, low CD4+ T cells, and high PD-L1 were independent predictors of poor OS in multivariate Cox hazards analyses, while factors indicating post-NAC status (T cells, CD4+, and PD-L1 [CPS]) alone were not. Therefore, we suggest that the consistently low T/high PD-L1 group could benefit from additional therapies, such as ICIs, and the importance of stratification by the TME, which has recently been recognized.

Genomic characterization and risk stratification of esophageal squamous dysplasia

Objectives

The majority of esophageal squamous dysplasia (ESD) patients progress slowly, while a subset of patients can undergo recurrence rapidly or progress to invasive cancer even after proper treatment. However, the molecular mechanisms underlying these clinical observations are still largely unknown.

Methods

By sequencing the genomic data of 160 clinical samples from 49 tumor-free ESD patients and 88 esophageal squamous cell carcinoma (ESCC) patients, we demonstrated lower somatic mutation and copy number alteration (CNA) burden in ESD compared with ESCC.

Results

Cross-species screening and functional assays identified ACSM5 as a novel driver gene for ESD progression. Furthermore, we revealed that miR-4292 promoted ESD progression and could serve as a non-invasive diagnostic marker for ESD.

Conclusions

These findings largely expanded our understanding of ESD genetics and tumorigenesis, which possessed promising significance for improving early diagnosis, reducing overtreatment, and identifying high-risk ESD patients.

SLC7A8 overexpression inhibits the growth and metastasis of lung adenocarcinoma and is correlated with a dismal prognosis

BACKGROUND

Overexpression of solute carrier family 7 member 8 (SLC7A8) has been shown to relate to the survival time and tumor progression in cancer patients. However, the role of SLC7A8 in lung adenocarcinoma (LUAD) is still obscure.

METHOD

The relationships between SLC7A8 expression in LUAD tissues and clinical values as well as immune infiltration were explored through bioinformatics. The functions and pathways of SLC7A8 in LUAD were investigated using Kyoto Encyclopedia of Genes and Genomes enrichment analysis, Gene Set Enrichment Analysis, Western blotting, and other methods.

RESULTS

We found that the expression of SLC7A8 was decreased significantly in LUAD tissues compared with normal tissues, which was related to the dismal survival time and disease progression. Moreover, it carried diagnostic value in LUAD and was a risk factor for dismal prognosis. Receiver operating characteristic curve analysis indicated that the expression level of SLC7A8 carried significant diagnostic value in LUAD. Overexpression of SLC7A8 inhibited the proliferation, invasion, and migration of LUAD cells, likely through a mechanism involving the cell cycle. SLC7A8 expression in LUAD was significantly correlated with the infiltration of immune cells, especially B cells, interstitial dendritic cells, mast cells, CD56 bright cells, natural killer cells, plasmacytoid dendritic cells, T follicular helper cells, T helper 2 and 17 cells, and immune factors.

CONCLUSION

The downregulation of SLC7A8 was related to a dismal prognosis and immune cell infiltration in LUAD. Increasing the expression of SLC7A8 inhibited the growth and migration of LUAD cells, thereby improving the prognosis of patients.

High-dose melphalan treatment significantly increases mutational burden at relapse in multiple myeloma

High-dose melphalan (HDM) improves progression-free survival in multiple myeloma (MM), yet melphalan is a DNA-damaging alkylating agent; therefore, we assessed its mutational effect on surviving myeloma cells by analyzing paired MM samples collected at diagnosis and relapse in the IFM 2009 study. We performed deep whole-genome sequencing on samples from 68 patients, 43 of whom were treated with RVD (lenalidomide, bortezomib, and dexamethasone) and 25 with RVD + HDM. Although the number of mutations was similar at diagnosis in both groups (7137 vs 7230; P = .67), the HDM group had significantly more mutations at relapse (9242 vs 13 383, P = .005). No change in the frequency of copy number alterations or structural variants was observed. The newly acquired mutations were typically associated with DNA damage and double-stranded breaks and were predominantly on the transcribed strand. A machine learning model, using this unique pattern, predicted patients who would receive HDM with high sensitivity, specificity, and positive prediction value. Clonal evolution analysis showed that all patients treated with HDM had clonal selection, whereas a static progression was observed with RVD. A significantly higher percentage of mutations were subclonal in the HDM cohort. Intriguingly, patients treated with HDM who achieved complete remission (CR) had significantly more mutations at relapse yet had similar survival rates as those treated with RVD who achieved CR. This similarity could have been due to HDM relapse samples having significantly more neoantigens. Overall, our study identifies increased genomic changes associated with HDM and provides rationale to further understand clonal complexity.

Towards artificial intelligence to multi-omics characterization of tumor heterogeneity in esophageal cancer

Esophageal cancer is a unique and complex heterogeneous malignancy, with substantial tumor heterogeneity: at the cellular levels, tumors are composed of tumor and stromal cellular components; at the genetic levels, they comprise genetically distinct tumor clones; at the phenotypic levels, cells in distinct microenvironmental niches acquire diverse phenotypic features. This heterogeneity affects almost every process of esophageal cancer progression from onset to metastases and recurrence, etc. Intertumoral and intratumoral heterogeneity are major obstacles in the treatment of esophageal cancer, but also offer the potential to manipulate the heterogeneity themselves as a new therapeutic strategy. The high-dimensional, multi-faceted characterization of genomics, epigenomics, transcriptomics, proteomics, metabonomics, etc. of esophageal cancer has opened novel horizons for dissecting tumor heterogeneity. Artificial intelligence especially machine learning and deep learning algorithms, are able to make decisive interpretations of data from multi-omics layers. To date, artificial intelligence has emerged as a promising computational tool for analyzing and dissecting esophageal patient-specific multi-omics data. This review provides a comprehensive review of tumor heterogeneity from a multi-omics perspective. Especially, we discuss the novel techniques single-cell sequencing and spatial transcriptomics, which have revolutionized our understanding of the cell compositions of esophageal cancer and allowed us to determine novel cell types. We focus on the latest advances in artificial intelligence in integrating multi-omics data of esophageal cancer. Artificial intelligence-based multi-omics data integration computational tools exert a key role in tumor heterogeneity assessment, which will potentially boost the development of precision oncology in esophageal cancer.

Rational combinations of targeted cancer therapies: background, advances and challenges

Over the past two decades, elucidation of the genetic defects that underlie cancer has resulted in a plethora of novel targeted cancer drugs. Although these agents can initially be highly effective, resistance to single-agent therapies remains a major challenge. Combining drugs can help avoid resistance, but the number of possible drug combinations vastly exceeds what can be tested clinically, both financially and in terms of patient availability. Rational drug combinations based on a deep understanding of the underlying molecular mechanisms associated with therapy resistance are potentially powerful in the treatment of cancer. Here, we discuss the mechanisms of resistance to targeted therapies and how effective drug combinations can be identified to combat resistance. The challenges in clinically developing these combinations and future perspectives are considered.

Molecular features and predictive models identify the most lethal subtype and a therapeutic target for osteosarcoma

Background

Osteosarcoma is the most common primary malignant bone tumor. The existing treatment regimens remained essentially unchanged over the past 30 years; hence the prognosis has plateaued at a poor level. Precise and personalized therapy is yet to be exploited.

Methods

One discovery cohort (n=98) and two validation cohorts (n=53 & n=48) were collected from public data sources. We performed a non-negative matrix factorization (NMF) method on the discovery cohort to stratify osteosarcoma. Survival analysis and transcriptomic profiling characterized each subtype. Then, a drug target was screened based on subtypes' features and hazard ratios. We also used specific siRNAs and added a cholesterol pathway inhibitor to osteosarcoma cell lines (U2OS and Saos-2) to verify the target. Moreover, PermFIT and ProMS, two support vector machine (SVM) tools, and the least absolute shrinkage and selection operator (LASSO) method, were employed to establish predictive models.

Results

We herein divided osteosarcoma patients into four subtypes (S-I ~ S-IV). Patients of S- I were found probable to live longer. S-II was characterized by the highest immune infiltration. Cancer cells proliferated most in S-III. Notably, S-IV held the most unfavorable outcome and active cholesterol metabolism. SQLE, a rate-limiting enzyme for cholesterol biosynthesis, was identified as a potential drug target for S-IV patients. This finding was further validated in two external independent osteosarcoma cohorts. The function of SQLE to promote proliferation and migration was confirmed by cell phenotypic assays after the specific gene knockdown or addition of terbinafine, an inhibitor of SQLE. We further employed two machine learning tools based on SVM algorithms to develop a subtype diagnostic model and used the LASSO method to establish a 4-gene model for predicting prognosis. These two models were also verified in a validation cohort.

Conclusion

The molecular classification enhanced our understanding of osteosarcoma; the novel predicting models served as robust prognostic biomarkers; the therapeutic target SQLE opened a new way for treatment. Our results served as valuable hints for future biological studies and clinical trials of osteosarcoma.

SLC7A8 coding for LAT2 is associated with early disease progression in osteosarcoma and transports doxorubicin

Background: Despite (neo) adjuvant chemotherapy with cisplatin, doxorubicin and methotrexate, some patients with primary osteosarcoma progress during first-line systemic treatment and have a poor prognosis. In this study, we investigated whether patients with early disease progression (EDP), are characterized by a distinctive pharmacogenetic profile.

Methods and Findings: Germline DNA from 287 Dutch high-grade osteosarcoma patients was genotyped using the DMET Plus array (containing 1,936 genetic markers in 231 drug metabolism and transporter genes). Associations between genetic variants and EDP were assessed using logistic regression models and associated variants (p <0.05) were validated in independent cohorts of 146 (Spain and United Kingdom) and 28 patients (Australia). In the association analyses, EDP was significantly associated with an SLC7A8 locus and was independently validated (meta-analysis validation cohorts: OR 0.19 [0.06–0.55], p = 0.002). The functional relevance of the top hits was explored by immunohistochemistry staining and an in vitro transport models. SLC7A8 encodes for the L-type amino acid transporter 2 (LAT2). Transport assays in HEK293 cells overexpressing LAT2 showed that doxorubicin, but not cisplatin and methotrexate, is a substrate for LAT2 (p < 0.0001). Finally, SLC7A8 mRNA expression analysis and LAT2 immunohistochemistry of osteosarcoma tissue showed that the lack of LAT2 expression is a prognostic factor of poor prognosis and reduced overall survival in patients without metastases (p = 0.0099 and p = 0.14, resp.).

Conclusion: This study identified a novel locus in SLC7A8 to be associated with EDP in osteosarcoma. Functional studies indicate LAT2-mediates uptake of doxorubicin, which could give new opportunities to personalize treatment of osteosarcoma patients.

Hedgehog-Gli2 Signaling Promotes Chemoresistance in Ovarian Cancer Cells by Regulating MDR1

Background

Cisplatin (DDP) resistance remains a key challenge in improving the clinical outcome of patients with ovarian cancer (OC). Gli2 overexpression can lead to DDP resistance in OC cells, but the specific underlying regulatory mechanism remains unclear. The membrane transporter encoding gene MDR1 positively regulates chemotherapy resistance in various cancer types. We evaluated MDR1 as a potential Gli2 downstream target and the contribution of the Gli2/MDR1 axis in promoting DDP resistance in OC cells.

Methods

To generate drug-resistant SKOV3/DDP cells, SKOV3 cells were grown for six months under continuous induction wherein the DDP concentration was steadily increased. Gli2 expression in OC cells with varying DDP sensitivities was detected using western blot. Cell counting kit-8 assays were used to assess the DDP sensitivity of SKOV3, SKOV3/DDP, A2780, and A2780/DDP cells and reversal of DDP resistance in SKOV3/DDP and A2780/DDP cells. Cell proliferation was analyzed using 5-ethynyl-2′-deoxyuridine (EdU) incorporation assays. The transcriptional regulation of MDR1 by Gli2 was determined using luciferase reporter assays. Finally, xenograft OC tumors were generated in nude mice, which were then treated with intraperitoneal DDP or phosphate-buffered saline (PBS) injections to investigate if Gli2 affected DDP resistance in OC in vivo.

Results

DDP-resistant SKOV3/DDP and A2780/DDP cells showed higher expression of Gli2 and MDR1 as compared with that in DDP-sensitive OC cells. Gli2 knockdown in SKOV3/DDP cells significantly reduced MDR1 expression, whereas it increased DNA damage, thereby sensitizing OC cells to DDP. Similar results were obtained after targeting Gli2 expression with the Gli-antagonist 61 inhibitor (GANT61) in SKOV3/DDP and A2780/DDP cells. In cells stably overexpressing Gli2, treatment with gradient concentrations of verapamil, an MDR1 inhibitor, significantly inhibited MDR1 expression. Our findings indicate that downregulation of MDR1 expression may reverse OC cell resistance to DDP. Moreover, dual-luciferase reporter gene assays confirmed that MDR1 is a direct downstream target of Gli2, with Gli2 positively regulating MDR1 expression. Finally, subcutaneous xenotransplantation in nude mice demonstrated that Gli2 plays a key role in regulating OC drug resistance.

Conclusions

We identified a mechanism by which Hedgehog-Gli signaling regulates OC chemoresistance by modulating MDR1 expression. Hence, Gli2 and MDR1 are potential biomarkers and therapeutic targets in patients with chemoresistant OC.