Correction: A simplified, two-factor clinical prognostic scoring system for patients with newly diagnosed Hodgkins Lymphoma
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Long-term adaptation of lymphoma cell lines to hypoxia is mediated by diverse molecular mechanisms that are targetable with specific inhibitors
A large body of evidence suggests that hypoxia drives aggressive molecular features of malignant cells irrespective of cancer type. Non-Hodgkin lymphomas (NHL) are the most common hematologic malignancies characterized by frequent involvement of diverse hypoxic microenvironments. We studied the impact of long-term deep hypoxia (1% O2) on the biology of lymphoma cells. Only 2 out of 6 tested cell lines (Ramos, and HBL2) survived ≥ 4 weeks under hypoxia. The hypoxia-adapted (HA)b Ramos and HBL2 cells had a decreased proliferation rate accompanied by significant suppression of both oxidative phosphorylation and glycolytic pathways. Transcriptome and proteome analyses revealed marked downregulation of genes and proteins of the mitochondrial respiration complexes I and IV, and mitochondrial ribosomal proteins. Despite the observed suppression of glycolysis, the proteome analysis of both HA cell lines showed upregulation of several proteins involved in the regulation of glucose utilization including the active catalytic component of prolyl-4-hydroxylase P4HA1, an important druggable oncogene. HA cell lines demonstrated increased transcription of key regulators of auto-/mitophagy, e.g., neuritin, BCL2 interacting protein 3 (BNIP3), BNIP3-like protein, and BNIP3 pseudogene. Adaptation to hypoxia was further associated with deregulation of apoptosis, namely upregulation of BCL2L1/BCL-XL, overexpression of BCL2L11/BIM, increased binding of BIM to BCL-XL, and significantly increased sensitivity of both HA cell lines to A1155463, a BCL-XL inhibitor. Finally, in both HA cell lines AKT kinase was hyperphosphorylated and the cells showed increased sensitivity to copanlisib, a pan-PI3K inhibitor. In conclusion, our data report on several shared mechanisms of lymphoma cell adaptation to long-term hypoxia including: 1. Upregulation of proteins responsible for glucose utilization, 2. Degradation of mitochondrial proteins for potential mitochondrial recycling (by mitophagy), and 3. Increased dependence on BCL-XL and PI3K-AKT signaling for survival. In translation, inhibition of glycolysis, BCL-XL, or PI3K-AKT cascade may result in targeted elimination of HA lymphoma cells.
STAT6 mutations compensate for CREBBP mutations and hyperactivate IL4/STAT6/RRAGD/mTOR signaling in follicular lymphoma
Activating mutations in STAT6 are common in Follicular Lymphoma (FL) and transformed FL and various other B cell lymphomas. Here, we report RNA-seq based gene expression data on normal human lymph node derived B lymphocytes (NBC; N = 6), and primary human FL WT (N = 11) or mutant (N = 4) for STAT6 before and after ex vivo stimulation with IL4. We found that STAT6 mutants result in broad based augmentation of IL4-induced gene expression. Unexpectedly, in FL with WT STAT6 we measured reduced baseline and IL4-induced gene expression levels when compared with NBC lymphocytes or FL with STAT6 mutations. We tracked the attenuated IL4/JAK/STAT6 response to co-existing CREBBP mutations and experimentally verified that intact CREBBP is required for the induction of many IL4-induced genes. One of the IL4-induced genes here identified is RRAGD, a small G-protein involved in lysosomal mTOR activation. We show that IL4 treatment induced RRAGD expression, that RRAGD is required for mTOR activation in lymphoma cells and that IL4-enhanced BCR signaling induced mTOR activation. The IL4 and BCR-induced mTOR activation was reduced by CREBBP mutants and augmented by mutant STAT6, establishing a link between STAT6 mutations and mTOR regulated pro-growth pathways in lymphoma.
Multiomic quantification of the KRAS mutation dosage improves the preoperative prediction of survival and recurrence in patients with pancreatic ductal adenocarcinoma
Most cancer mutation profiling studies are laboratory-based and lack direct clinical application. For clinical use, it is necessary to focus on key genes and integrate them with relevant clinical variables. We aimed to evaluate the prognostic value of the dosage of the KRAS G12 mutation, a key pancreatic ductal adenocarcinoma (PDAC) variant and to investigate the biological mechanism of the prognosis associated with the dosage of the KRAS G12 mutation. In this retrospective cohort study, we analyzed 193 surgically treated patients with PDAC between 2009 and 2016. RNA, whole-exome, and KRAS-targeted sequencing data were used to estimate the dosage of the KRAS G12 mutant. Our prognostic scoring system included the mutation dosage from targeted sequencing ( > 0.195, 1 point), maximal tumor diameter at preoperative imaging ( > 20 mm, 1 point), and carbohydrate antigen 19-9 levels ( > 150 U/mL, 1 point). The KRAS mutation dosage exhibited comparable performance with clinical variables for survival prediction. High KRAS mutation dosages activated the cell cycle, leading to high mutation rates and poor prognosis. According to prognostic scoring systems that integrate mutation dosage with clinical factors, patients with 0 points had superior median overall survival of 97.0 months and 1-year, 3-year, and 5-year overall survival rates of 95.8%, 70.8%, and 66.4%, respectively. In contrast, patients with 3 points had worse median overall survival of only 16.0 months and 1-year, 3-year, and 5-year overall survival rates of 65.2%, 8.7%, and 8.7%, respectively. The incorporation of the KRAS G12 mutation dosage variable into prognostic scoring systems can improve clinical variable-based survival prediction, highlighting the feasibility of an integrated scoring system with clinical significance.
Prognostic impact of expression of CD2, CD25, and/or CD30 in/on mast cells in systemic mastocytosis: a registry study of the European Competence Network on Mastocytosis
Expression of CD2, CD25 and/or CD30 in extracutaneous mast cells (MC) is a minor diagnostic criterion for systemic mastocytosis (SM) in the classification of the World Health Organization and International Consensus Classification. So far, it remains unknown whether expression of these antigens on MC is of prognostic significance in SM. We performed a retrospective multi-center study of patients with SM using the data set of the registry of the European Competence Network on Mastocytosis, including 5034 patients with various MC disorders. The percentage of CD2–, CD25+ and/or CD30+ MC was considerably lower in patients with indolent SM compared to patients with advanced SM, including aggressive SM and MC leukemia. Whereas CD25 and CD30 expression in MC could not be associated with prognosis, we found that lack of CD2 expression in MC is associated with a significantly reduced overall survival (OS) in patients with SM (p < 0.0001). Lack of CD2 was also associated with the presence of extramedullary involvement affecting the spleen, liver, and/or lymph nodes (odds ratio 2.63 compared to SM with CD2+ MC). Together, lack of CD2 expression in MC is a prognostic marker and indicator of reduced OS and extramedullary disease expansion in patients with SM.
Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence
Despite advances in precision oncology, clinical decision-making still relies on limited variables and expert knowledge. To address this limitation, we combined multimodal real-world data and explainable artificial intelligence (xAI) to introduce AI-derived (AID) markers for clinical decision support. We used xAI to decode the outcome of 15,726 patients across 38 solid cancer entities based on 350 markers, including clinical records, image-derived body compositions, and mutational tumor profiles. xAI determined the prognostic contribution of each clinical marker at the patient level and identified 114 key markers that accounted for 90% of the neural network’s decision process. Moreover, xAI enabled us to uncover 1,373 prognostic interactions between markers. Our approach was validated in an independent cohort of 3,288 patients with lung cancer from a US nationwide electronic health record-derived database. These results show the potential of xAI to transform the assessment of clinical variables and enable personalized, data-driven cancer care.
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