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Primary liver cancer represents a major health problem. It comprises hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), which differ markedly with regards to their morphology, metastatic potential and responses to therapy. However, the regulatory molecules and tissue context that commit transformed hepatic cells towards HCC or ICC are largely unknown. Here we show that the hepatic microenvironment epigenetically shapes lineage commitment in mosaic mouse models of liver tumorigenesis. Whereas a necroptosis-associated hepatic cytokine microenvironment determines ICC outgrowth from oncogenically transformed hepatocytes, hepatocytes containing identical oncogenic drivers give rise to HCC if they are surrounded by apoptotic hepatocytes. Epigenome and transcriptome profiling of mouse HCC and ICC singled out Tbx3 and Prdm5 as major microenvironment-dependent and epigenetically regulated lineage-commitment factors, a function that is conserved in humans. Together, our results provide insight into lineage commitment in liver tumorigenesis, and explain molecularly why common liver-damaging risk factors can lead to either HCC or ICC. The tumour microenvironment determines which type of liver cancer develops, with transformed hepatocytes giving rise to intrahepatic cholangiocarcinoma or hepatocellular carcinoma depending or whether they are surrounded by cells undergoing necroptosis or apoptosis.

Background Aneuploidy, an abnormal number of chromosomes within a cell, is a hallmark of cancer. Patterns of aneuploidy differ across cancers, yet are similar in cancers affecting closely related tissues. The selection pressures underlying aneuploidy patterns are not fully understood, hindering our understanding of cancer development and progression. Results Here, we apply interpretable machine learning methods to study tissue-selective aneuploidy patterns. We define 20 types of features corresponding to genomic attributes of chromosome-arms, normal tissues, primary tumors, and cancer cell lines (CCLs), and use them to model gains and losses of chromosome arms in 24 cancer types. To reveal the factors that shape the tissue-specific cancer aneuploidy landscapes, we interpret the machine learning models by estimating the relative contribution of each feature to the models. While confirming known drivers of positive selection, our quantitative analysis highlights the importance of negative selection for shaping aneuploidy landscapes. This is exemplified by tumor suppressor gene density being a better predictor of gain patterns than oncogene density, and vice versa for loss patterns. We also identify the importance of tissue-selective features and demonstrate them experimentally, revealing KLF5 as an important driver for chr13q gain in colon cancer. Further supporting an important role for negative selection in shaping the aneuploidy landscapes, we find compensation by paralogs to be among the top predictors of chromosome arm loss prevalence and demonstrate this relationship for one paralog interaction. Similar factors shape aneuploidy patterns in human CCLs, demonstrating their relevance for aneuploidy research. Conclusions Our quantitative, interpretable machine learning models improve the understanding of the genomic properties that shape cancer aneuploidy landscapes.

Image-guided radiofrequency ablation (RFA) is used to treat focal tumors in the liver and other organs. Despite potential advantages over surgery, hepatic RFA can promote local and distant tumor growth by activating pro-tumorigenic growth factor and cytokines. Thus, strategies to identify and suppress pro-oncogenic effects of RFA are urgently required to further improve the therapeutic effect. Here, the proliferative effect of plasma of Hepatocellular carcinoma or colorectal carcinoma patients 90 min post-RFA was tested on HCC cell lines, demonstrating significant cellular proliferation compared to baseline plasma. Multiplex ELISA screening demonstrated increased plasma pro-tumorigenic growth factors and cytokines including the FGF protein family which uniquely and selectively activated HepG2. Primary mouse and immortalized human hepatocytes were then subjected to moderate hyperthermia in-vitro, mimicking thermal stress induced during ablation in the peri-ablational normal tissue. Resultant culture medium induced proliferation of multiple cancer cell lines. Subsequent non-biased protein array revealed that these hepatocytes subjected to moderate hyperthermia also excrete a similar wide spectrum of growth factors. Recombinant FGF-2 activated multiple cell lines. FGFR inhibitor significantly reduced liver tumor load post-RFA in MDR2-KO inflammation-induced HCC mouse model. Thus, Liver RFA can induce tumorigenesis via the FGF signaling pathway, and its inhibition suppresses HCC development.

Radiofrequency thermal ablation (RFA) of hepatic and renal tumors can be accompanied by non-desired tumorigenesis in residual, untreated tumor. Here, we studied the use of micelle-encapsulated siRNA to suppress IL-6-mediated local and systemic secondary effects of RFA. We compared standardized hepatic or renal RFA (laparotomy, 1 cm active tip at 70 ± 2 °C for 5 min) and sham procedures without and with administration of 150 nm micelle-like nanoparticle (MNP) anti-IL6 siRNA (DOPE-PEI conjugates, single IP dose 15 min post-RFA, C57Bl mouse:3.5 ug/100ml, Fisher 344 rat: 20 ug/200 ul), RFA/scrambled siRNA, and RFA/empty MNPs. Outcome measures included: local periablational cellular infiltration (α-SMA+ stellate cells), regional hepatocyte proliferation, serum/tissue IL-6 and VEGF levels at 6-72 hr, and distant tumor growth, tumor proliferation (Ki-67) and microvascular density (MVD, CD34) in subcutaneous R3230 and MATBIII breast adenocarcinoma models at 7 days. For liver RFA, adjuvant MNP anti-IL6 siRNA reduced RFA-induced increases in tissue IL-6 levels, α-SMA+ stellate cell infiltration, and regional hepatocyte proliferation to baseline (p < 0.04, all comparisons). Moreover, adjuvant MNP anti-IL6- siRNA suppressed increased distant tumor growth and Ki-67 observed in R3230 and MATBIII tumors post hepatic RFA (p<0.01). Anti-IL6 siRNA also reduced RFA-induced elevation in VEGF and tumor MVD (p < 0.01). Likewise, renal RFA-induced increases in serum IL-6 levels and distant R3230 tumor growth was suppressed with anti-IL6 siRNA (p < 0.01). Adjuvant nanoparticle-encapsulated siRNA against IL-6 can be used to modulate local and regional effects of hepatic RFA to block potential unwanted pro-oncogenic effects of hepatic or renal RFA on distant tumor.

In this Article, the pCaMIN construct consisted of ‘mouse MYC and mouse Nras G12V ’ instead of ‘mouse Myc and human NRAS G12V ; and the pCAMIA construct consisted of ‘mouse Myc and human AKT1 ’ instead of ‘mouse Myc and Akt1 ’ this has been corrected online.

Aneuploidy, an abnormal number of chromosomes within a cell, is considered a hallmark of cancer. Patterns of aneuploidy differ across cancers, yet are similar in cancers affecting closely-related tissues. The selection pressures underlying aneuploidy patterns are not fully understood, hindering our understanding of cancer development and progression. Here, we applied interpretable machine learning (ML) methods to study tissue-selective aneuploidy patterns. We defined 20 types of features of normal and cancer tissues, and used them to model gains and losses of chromosome-arms in 24 cancer types. In order to reveal the factors that shape the tissue-specific cancer aneuploidy landscapes, we interpreted the ML models by estimating the relative contribution of each feature to the models. While confirming known drivers of positive selection, our quantitative analysis highlighted the importance of negative selection for shaping the aneuploidy landscapes of human cancer. Tumor-suppressor gene density was a better predictor of gain patterns than oncogene density, and vice-versa for loss patterns. We identified the contribution of tissue-selective features and demonstrated them experimentally for chr13q gain in colon cancer. In line with an important role for negative selection in shaping the aneuploidy landscapes, we found compensation by paralogs to be a top predictor of chromosome-arm loss prevalence, and demonstrated this relationship for one such paralog interaction. Similar factors were found to shape aneuploidy patterns in human cancer cell lines, demonstrating their relevance for aneuploidy research. Overall, our quantitative, interpretable ML models improve the understanding of the genomic properties that shape cancer aneuploidy landscapes.

We present Jamba-1.5, new instruction-tuned large language models based on our Jamba architecture. Jamba is a hybrid Transformer-Mamba mixture of experts architecture, providing high throughput and low memory usage across context lengths, while retaining the same or better quality as Transformer models. We release two model sizes: Jamba-1.5-Large, with 94B active parameters, and Jamba-1.5-Mini, with 12B active parameters. Both models are fine-tuned for a variety of conversational and instruction-following capabilties, and have an effective context length of 256K tokens, the largest amongst open-weight models. To support cost-effective inference, we introduce ExpertsInt8, a novel quantization technique that allows fitting Jamba-1.5-Large on a machine with 8 80GB GPUs when processing 256K-token contexts without loss of quality. When evaluated on a battery of academic and chatbot benchmarks, Jamba-1.5 models achieve excellent results while providing high throughput and outperforming other open-weight models on long-context benchmarks. The model weights for both sizes are publicly available under the Jamba Open Model License and we release ExpertsInt8 as open source.