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Circulating tumor cells from patients with small-cell lung cancer can form tumors in mice, and their derived explants recapitulate the patients' response to chemotherapy. Small-cell lung cancer (SCLC), an aggressive neuroendocrine tumor with early dissemination and dismal prognosis, accounts for 15–20% of lung cancer cases and ∼200,000 deaths each year. Most cases are inoperable, and biopsies to investigate SCLC biology are rarely obtainable. Circulating tumor cells (CTCs), which are prevalent in SCLC, present a readily accessible 'liquid biopsy'. Here we show that CTCs from patients with either chemosensitive or chemorefractory SCLC are tumorigenic in immune-compromised mice, and the resultant CTC-derived explants (CDXs) mirror the donor patient's response to platinum and etoposide chemotherapy. Genomic analysis of isolated CTCs revealed considerable similarity to the corresponding CDX. Most marked differences were observed between CDXs from patients with different clinical outcomes. These data demonstrate that CTC molecular analysis via serial blood sampling could facilitate delivery of personalized medicine for SCLC. CDXs are readily passaged, and these unique mouse models provide tractable systems for therapy testing and understanding drug resistance mechanisms.

Computational prediction of protein structure from amino acid sequence alone has been achieved with unprecedented accuracy, yet the prediction of protein-protein interactions remains a challenge. Here, we assess the ability of protein language models (PLMs), routinely applied to protein folding, to be retrained for protein-protein interaction prediction. Existing models that exploit PLMs use a pre-trained PLM feature set, ignoring that the proteins are physically interacting. We propose PLM-interact, which goes beyond single proteins by jointly encoding protein pairs to learn their relationships, analogous to the next-sentence prediction task from natural language processing. This approach achieves state-of-the-art performance in a widely adopted cross-species protein-protein interaction prediction benchmark: trained on human data and tested on mouse, fly, worm, E. coli and yeast. In addition, we develop a fine-tuning method for PLM-interact to detect mutation effects on interactions. Finally, we report that the model outperforms existing approaches in predicting virus-host interaction at the protein level. Our work demonstrates that large language models can be extended to learn the intricate relationships among biomolecules from their sequences alone. Protein structure can be predicted from amino acid sequences with unprecedented accuracy, yet the prediction of protein–protein interactions remains a challenge. Here, authors present a sequence-based model that jointly encodes protein pairs, achieving state-of-the-art cross-species and virus-host PPI prediction and mutation effects analysis.

Single cell profiling by genetic, proteomic and imaging methods has expanded the ability to identify programmes regulating distinct cell states. The 3-dimensional (3D) culture of cells or tissue fragments provides a system to study how such states contribute to multicellular morphogenesis. Whether cells plated into 3D cultures give rise to a singular phenotype or whether multiple biologically distinct phenotypes arise in parallel is largely unknown due to a lack of tools to detect such heterogeneity. Here we develop Traject3d (Trajectory identification in 3D), a method for identifying heterogeneous states in 3D culture and how these give rise to distinct phenotypes over time, from label-free multi-day time-lapse imaging. We use this to characterise the temporal landscape of morphological states of cancer cell lines, varying in metastatic potential and drug resistance, and use this information to identify drug combinations that inhibit such heterogeneity. Traject3d is therefore an important companion to other single-cell technologies by facilitating real-time identification via live imaging of how distinct states can lead to alternate phenotypes that occur in parallel in 3D culture. There are currently a lack of tools to detect heterogeneity in 3D cultures. Here the authors report Traject3d as a framework to identify heterogeneous states in 3D culture and to understand how these give rise to distinct phenotypes using label-free multi-day time-lapse imaging.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Background RNA-Seq exploits the rapid generation of gigabases of sequence data by Massively Parallel Nucleotide Sequencing, allowing for the mapping and digital quantification of whole transcriptomes. Whilst previous comparisons between RNA-Seq and microarrays have been performed at the level of gene expression, in this study we adopt a more fine-grained approach. Using RNA samples from a normal human breast epithelial cell line (MCF-10a) and a breast cancer cell line (MCF-7), we present a comprehensive comparison between RNA-Seq data generated on the Applied Biosystems SOLiD platform and data from Affymetrix Exon 1.0ST arrays. The use of Exon arrays makes it possible to assess the performance of RNA-Seq in two key areas: detection of expression at the granularity of individual exons, and discovery of transcription outside annotated loci. Results We found a high degree of correspondence between the two platforms in terms of exon-level fold changes and detection. For example, over 80% of exons detected as expressed in RNA-Seq were also detected on the Exon array, and 91% of exons flagged as changing from Absent to Present on at least one platform had fold-changes in the same direction. The greatest detection correspondence was seen when the read count threshold at which to flag exons Absent in the SOLiD data was set to t <1 suggesting that the background error rate is extremely low in RNA-Seq. We also found RNA-Seq more sensitive to detecting differentially expressed exons than the Exon array, reflecting the wider dynamic range achievable on the SOLiD platform. In addition, we find significant evidence of novel protein coding regions outside known exons, 93% of which map to Exon array probesets, and are able to infer the presence of thousands of novel transcripts through the detection of previously unreported exon-exon junctions. Conclusions By focusing on exon-level expression, we present the most fine-grained comparison between RNA-Seq and microarrays to date. Overall, our study demonstrates that data from a SOLiD RNA-Seq experiment are sufficient to generate results comparable to those produced from Affymetrix Exon arrays, even using only a single replicate from each platform, and when presented with a large genome.

Non-coding RNAs (ncRNAs) are frequent and prevalent across the taxa. Although individual non-coding loci have been assigned a function, most are uncharacterized. Their global biological significance is unproven and remains controversial. Here we investigate the role played by ncRNAs in the stress response of Schizosaccharomyces pombe . We integrate global proteomics and RNA sequencing data to identify a systematic programme in which elevated antisense RNA arising both from ncRNAs and from 3′-overlapping convergent gene pairs is directly associated with substantial reductions in protein levels throughout the genome. We describe an extensive array of ncRNAs with trans associations that have the potential to influence multiple pathways. Deletion of one such locus reduces levels of atf1 , a transcription factor downstream of the stress-activated mitogen-activated protein kinase (MAPK) pathway, and alters sensitivity to oxidative stress. These non-coding transcripts therefore regulate specific stress responses, adding unanticipated information-processing capacity to the MAPK signalling system. Non-coding RNAs are widely expressed, yet their functions remain poorly understood. Here, Leong et al . identify a set of antisense RNAs elevated during the yeast stress response that directly correlate with reduced protein levels, indicating a general regulatory effect of antisense expression.

Intrinsic radiosensitivity is an important factor underlying radiotherapy response, but there is no method for its routine assessment in human tumours. Gene signatures are currently being derived and some were previously generated by expression profiling the NCI-60 cell line panel. It was hypothesised that focusing on more homogeneous tumour types would be a better approach. Two cell line cohorts were used derived from cervix [n = 16] and head and neck [n = 11] cancers. Radiosensitivity was measured as surviving fraction following irradiation with 2 Gy (SF2) by clonogenic assay. Differential gene expression between radiosensitive and radioresistant cell lines (SF2 median) was investigated using Affymetrix GeneChip Exon 1.0ST (cervix) or U133A Plus2 (head and neck) arrays. There were differences within cell line cohorts relating to tissue of origin reflected by expression of the stratified epithelial marker p63. Of 138 genes identified as being associated with SF2, only 2 (1.4%) were congruent between the cervix and head and neck carcinoma cell lines (MGST1 and TFPI), and these did not partition the published NCI-60 cell lines based on SF2. There was variable success in applying three published radiosensitivity signatures to our cohorts. One gene signature, originally trained on the NCI-60 cell lines, did partially separate sensitive and resistant cell lines in all three cell line datasets. The findings do not confirm our hypothesis but suggest that a common transcriptional signature can reflect the radiosensitivity of tumours of heterogeneous origins.

Approximately 70% of patients with non–small-cell lung cancer present with late-stage disease and have limited treatment options, so there is a pressing need to develop efficacious targeted therapies for these patients. This remains a major challenge as the underlying genetic causes of ∼50% of non–small-cell lung cancers remain unknown. Here we demonstrate that a targeted genetic dependency screen is an efficient approach to identify somatic cancer alterations that are functionally important. By using this approach, we have identified three kinases with gain-of-function mutations in lung cancer, namely FGFR4, MAP3K9, and PAK5. Mutations in these kinases are activating toward the ERK pathway, and targeted depletion of the mutated kinases inhibits proliferation, suppresses constitutive activation of downstream signaling pathways, and results in specific killing of the lung cancer cells. Genomic profiling of patients with lung cancer is ushering in an era of personalized medicine; however, lack of actionable mutations presents a significant hurdle. Our study indicates that targeted genetic dependency screens will be an effective strategy to elucidate somatic variants that are essential for lung cancer cell viability.

Molecular sequences carry information. Analysis of sequence conservation between homologous loci is a proven approach with which to explore the information content of molecular sequences. This is often done using multiple sequence alignments to support comparisons between homologous loci. These methods therefore rely on sufficient underlying sequence similarity with which to construct a representative alignment. Here we describe a method using a formal metric of information, surprisal, to analyse biological sub-sequences without alignment constraints. We applied our model to the genomes of five different species to reveal similar patterns across a panel of eukaryotes. As the surprisal of a sub-sequence is inversely proportional to its occurrence within the genome, the optimal size of the sub-sequences was selected for each species under consideration. With the model optimized, we found a strong correlation between surprisal and CG dinucleotide usage. The utility of our model was tested by examining the sequences of genes known to undergo splicing. We demonstrate that our model can identify biological features of interest such as known donor and acceptor sites. Analysis across all annotated coding exon junctions in Homo sapiens reveals the information content of coding exons to be greater than the surrounding intron regions, a consequence of increased suppression of the CG dinucleotide in intronic space. Sequences within coding regions proximal to exon junctions exhibited novel patterns within DNA and coding mRNA that are not a function of the encoded amino acid sequence. Our findings are consistent with the presence of secondary information encoding features such as DNA and RNA binding sites, multiplexed through the coding sequence and independent of the information required to define the corresponding amino-acid sequence. We conclude that surprisal provides a complementary methodology with which to locate regions of interest in the genome, particularly in situations that lack an appropriate multiple sequence alignment.

Columnar cell hyperplasia (CCH) is the earliest histologically identifiable breast lesion linked to cancer progression and is characterized by increased proliferation, decreased apoptosis and elevated oestrogen receptor α (ERα) expression. The mechanisms underlying the initiation of these lesions have not been clarified but might involve early and fundamental changes in cancer progression. MiRNAs are key regulators of several biological processes, acting by influencing the post-transcriptional regulation of numerous targets, thus making miRNAs potential candidates in cancer initiation. Here we have defined novel epithelial as well as stromal miRNA signatures from columnar cell hyperplasia lesions compared to normal terminal duct lobular units by using microdissection and miRNA microarrays. Let-7c were among the identified downregulated epithelial miRNAs and its functions were delineated in unique CCH derived cells and breast cancer cell line MCF-7 suggesting anti-proliferative traits potentially due to effects on Myb and ERα. MiR-132 was upregulated in the stroma surrounding CCH compared to stoma surrounding normal terminal duct lobular units (TDLUs), and overexpression of miR-132 in immortalized fibroblasts and in fibroblasts co-cultured with epithelial CCH cells caused substantial expression changes of genes involved in metabolism, DNA damage and cell motility. The miRNA signatures identified in CCH indicate early changes in the epithelial and stromal compartment of CCH and could represent early key alterations in breast cancer progression that potentially could be targeted in novel prevention or treatment schedules.