Correction: A patient-derived xenograft pre-clinical trial reveals treatment responses and a resistance mechanism to karonudib in metastatic melanoma.

On Pubmed, the name of co-author Roger Olofsson Bagge appeared incorrectly as "Bagge RO" instead of "Olofsson Bagge, Roger". This has been corrected in the PDF and HTML versions.

Transcriptomic and genomic profiling of early-stage ovarian carcinomas associated with histotype and overall survival.

Ovarian cancer is the most lethal gynecological malignancy in the western world. Despite recent efforts to characterize ovarian cancer using molecular profiling, few targeted treatment options are currently available. Here, we examined genetic variants, fusion transcripts, SNP genotyping, and gene expression patterns for early-stage (I and II) ovarian carcinomas (n=96) in relation to clinicopathological characteristics and clinical outcome, thereby identifying novel genetic features of ovarian carcinomas. Furthermore, mutation frequencies of specific genetic variants and/or their gene expression patterns were associated with histotype and overall survival, e.g. SLC28A2 (mucinous ovarian carcinoma histotype), ARCN1 (low expression in 0-2 year survival group), and tumor suppressor MTUS1 (mutation status and overall survival). The long non-coding RNA MALAT1 was identified as a highly promiscuous fusion transcript in ovarian carcinoma. Moreover, gene expression deregulation for 23 genes was associated with tumor aggressiveness. Taken together, the novel biomarkers identified here may improve ovarian carcinoma subclassification and patient stratification according to histotype and overall survival.

A patient-derived xenograft pre-clinical trial reveals treatment responses and a resistance mechanism to karonudib in metastatic melanoma.

Karonudib (TH1579) is a novel compound that exerts anti-tumor activities and has recently entered phase I clinical testing. The aim of this study was to conduct a pre-clinical trial in patient-derived xenografts to identify the possible biomarkers of response or resistance that could guide inclusion of patients suffering from metastatic melanoma in phase II clinical trials. Patient-derived xenografts from 31 melanoma patients with metastatic disease were treated with karonudib or a vehicle for 18 days. Treatment responses were followed by measuring tumor sizes, and the models were categorized in the response groups. Tumors were harvested and processed for RNA sequencing and protein analysis. To investigate the effect of karonudib on T-cell-mediated anti-tumor activities, tumor-infiltrating T cells were injected in mice carrying autologous tumors and the mice treated with karonudib. We show that karonudib has heterogeneous anti-tumor effect on metastatic melanoma. Thus, based on the treatment responses, we could divide the 31 patient-derived xenografts in three treatment groups: progression group (32%), suppression group (42%), and regression group (26%). Furthermore, we show that karonudib has anti-tumor effect, irrespective of major melanoma driver mutations. Also, we identify high expression of ABCB1, which codes for p-gp pumps as a resistance biomarker. Finally, we show that karonudib treatment does not hamper T-cell-mediated anti-tumor responses. These findings can be used to guide future use of karonudib in clinical use with a potential approach as precision medicine.

Mutational Signature and Transcriptomic Classification Analyses as the Decisive Diagnostic Tools for a Cancer of Unknown Primary.

PURPOSE: Cancer of unknown primary is a group of metastatic tumors in which the standard diagnostic workup fails to identify the site of origin of the tumor. The potential impact of precision oncology on this group of patients is large, because actionable driver mutations and a correct diagnosis could provide treatment options otherwise not available for patients with these fatal cancers. This study investigated if comprehensive genomic analyses could provide information on the origin of the tumor. PATIENTS AND METHODS: Here we describe a patient whose tumor was misdiagnosed at least three times. Next-generation sequencing, a patient-derived xenograft mouse model, and bioinformatics were used to identify an actionable mutation, predict resistance development to the targeted therapy, and correctly diagnose the origin of the tumor. Transcriptomic classification was benchmarked using The Cancer Genome Atlas (TCGA). RESULTS: Despite the lack of a known primary tumor site and the absence of diagnostic immunohistochemical markers, the origin of the patient's tumor was established using the novel bioinformatic workflow. This included a mutational signature analysis of the sequenced metastases and comparison of their transcriptomic profiles to a pan-cancer panel of tumors from TCGA. We further discuss the strengths and limitations of the latter approaches in the context of three potentially incorrectly diagnosed TCGA lung tumors. CONCLUSION: Comprehensive genomic analyses can provide information on the origin of tumors in patients with cancer of unknown primary.

Clinical responses to adoptive T-cell transfer can be modeled in an autologous immune-humanized mouse model.

Immune checkpoint inhibitors and adoptive cell transfer (ACT) of autologous tumor-infiltrating T cells have shown durable responses in patients with melanoma. To study ACT and immunotherapies in a humanized model, we have developed PDXv2.0 - a melanoma PDX model where tumor cells and tumor-infiltrating T cells from the same patient are transplanted sequentially in non-obese diabetic/severe combined immune-deficient/common gamma chain (NOG/NSG) knockout mouse. Key to T-cell survival/effect in this model is the continuous presence of interleukin-2 (IL-2). Tumors that grow in PDXv2.0 are eradicated if the autologous tumor cells and T cells come from a patient that exhibited an objective response to ACT in the clinic. However, T cells from patients that are non-responders to ACT cannot kill tumor cells in PDXv2.0. Taken together, PDXv2.0 provides the potential framework to further model genetically diverse human cancers for assessing the efficacy of immunotherapies as well as combination therapies.Combining different types of immune therapies might benefit certain patients. Here, the authors develop an autologous immune-humanized melanoma mouse model that allows the preclinical assessment of cancer cell-T cell interactions from each individual patient and the benefits of immunotherapies combinations.