Search

BACKGROUND & AIMS: Mouse models of lineage tracing have helped to describe the important subpopulations of hepatocytes responsible for liver regeneration. However, conflicting results have been obtained from different models. Here we aimed to reconcile these conflicting reports by repeating a key lineage tracing study from pericentral hepatocytes and characterised this Axin2CreERT2 model in detail.METHODS: We performed detailed characterisation of the labelled population in the Axin2CreERT2 model. We lineage traced this cell population, quantifying the labelled population over 1 year and performed in depth phenotypic comparison including transcriptomics, metabolomics and analysis of protein through immunohistochemistry of Axin2CreERT2 mice to WT counterparts.RESULTS: We find that after careful definition of a baseline population there is marked differences in labelling between male and female mice. Upon induced lineage tracing there was no expansion of the labelled hepatocyte population in Axin2CreERT2 mice. We find substantial evidence of disrupted homeostasis in Axin2CreERT2 mice. Offspring are born with sub-Mendelian ratios and adult mice have perturbations of hepatic Wnt/β-catenin signalling and related metabolomic disturbance.CONCLUSIONS: We find no evidence of predominant expansion of the pericentral hepatocyte population during liver homeostatic regeneration. Our data highlight the importance of detailed preclinical model characterisation and the pitfalls which may occur when comparing across sexes and backgrounds of mice and the effects of genetic insertion into native loci.

Beyond tertiary lymphoid structures, a significant number of immune-rich areas without germinal center-like structures are observed in non–small cell lung cancer. Here, we integrated transcriptomic data and digital pathology images to study the prognostic implications, spatial locations, and constitution of immune rich areas (immune hotspots) in a cohort of 935 patients with lung cancer from The Cancer Genome Atlas. A high intratumoral immune hotspot score, which measures the proportion of immune hotspots interfacing with tumor islands, was correlated with poor overall survival in lung squamous cell carcinoma but not in lung adenocarcinoma. Lung squamous cell carcinomas with high intratumoral immune hotspot scores were characterized by consistent upregulation of B-cell signatures. Spatial statistical analyses conducted on serial multiplex IHC slides further revealed that only 4.87% of peritumoral immune hotspots and 0.26% of intratumoral immune hotspots were tertiary lymphoid structures. Significantly lower densities of CD20+CXCR5+ and CD79b+ B cells and less diverse immune cell interactions were found in intratumoral immune hotspots compared with peritumoral immune hotspots. Furthermore, there was a negative correlation between the percentages of CD8+ T cells and T regulatory cells in intratumoral but not in peritumoral immune hotspots, with tertiary lymphoid structures excluded. These findings suggest that the intratumoral immune hotspots reflect an immunosuppressive niche compared with peritumoral immune hotspots, independent of the distribution of tertiary lymphoid structures. A balance toward increased intratumoral immune hotspots is indicative of a compromised antitumor immune response and poor outcome in lung squamous cell carcinoma.Significance:Intratumoral immune hotspots beyond tertiary lymphoid structures reflect an immunosuppressive microenvironment, different from peritumoral immune hotspots, warranting further study in the context of immunotherapies.

The relationship among B-cell, T-cell percentages and CD8+/CD4+FOXP3+ ratio in individual intratumoral IH and peritumoral IH without TLS or LAG. A, Percentages of B-cell subsets in individual intratumoral (n = 262) and peritumoral IH (n = 392) classified by CD8+/CD4+FOXP3+ ratio. The high and low groups were determined by the cut-off of median CD8+/CD4+FOXP3+ ratio in intratumoral and peritumoral IH, respectively. B, Percentages of CD8+ T cells and CD4+FOXP3+ Treg cells in individual intratumoral (n = 262) and peritumoral IH (n = 392) classified by the percentage of B cells. This B-cell percentage was calculated as the total count of all B-cell subsets divided by the total count of all 6 immune cell types. C, Percentages of CD8+ T cells in individual intratumoral (n = 262) and peritumoral IH (n = 392) classified by CD4+FOXP3+ Treg-cell percentages. Statistical significance was evaluated by the Wilcoxon rank sum test and adjusted by the Benjamini–Hochberg method.

Diversity of lymphocyte interactions in intratumoral and peritumoral IH without TLS or LAG. A, Examples of lymphocyte interactions in individual peritumoral and intratumoral IH. Lymphocytes identified on the serial IHC slides were connected by the Delaunay triangle graph. Lymphocyte subsets are represented by dots of different colors. Interactions significant in the univariate logistic regression are denoted by red, otherwise blue. B, Shannon diversity of lymphocyte densities and interactions in individual peritumoral (n = 392) and intratumoral IH (n = 262). Statistical significance was evaluated by the Wilcoxon signed-rank test. C, Univariate logistic regression predicting peritumoral and intratumoral IH with individual lymphocyte interactions as variables, n = 654. The coefficients of the significant variables were all negative, suggesting a higher frequency of the interactions in peritumoral IH compared with intratumoral IH. P values were adjusted by the Benjamini–Hochberg method. Regions identified as TLS or LAG were excluded from the above analyses.

Characteristics of the included patients and their association with the Sintra/immune, Sintra/cancer, and Sintra/tissue.

Lymphocyte composition and the presence of TLS at intratumoral and peritumoral IH. A, Example regions showing annotations of TLS at the IH and pathologically defined lymphoid aggregates without a prominent germinal center (LAG) at the intratumoral IH. B, Lymphocyte subset densities at intratumoral (n = 10) and peritumoral IH (n = 10). Cell densities were measured as the cell counts divided by the total area of IHs. C, Pie chart displaying the average percentages of peritumoral IH and intratumoral IH identified as TLS, LAG, and non-TLS/LAG IH. D, Densities of TLS and LAG at intratumoral (n = 10) and peritumoral IH (n = 10). E, Densities of B- and T-cell subsets in TLS (n = 10), LAG (n = 10), and non-TLS/LAG IH (n = 10). Statistical significance was evaluated by the Wilcoxon signed-rank test, followed by the Benjamini–Hochberg method for P value adjustment. *, P < 0.05; **, P < 0.01. The median is marked by the horizontal line; the first and third quartiles are shown as box edges; whiskers indicate the range of error.

Lists of TCGA LUSC and LUAD cases in the discovery and validation cohort

Table S3 and Fig. S1-S10

Immune hotspots in LUSC. A, Illustrative examples of automatic cell classification results and hotspot distribution with zoomed-in regions showing areas enriched with immune cells and cancer–immune coclustering. Cancer hotspots (red) and IHs were identified based on the local clustering of cancer and lymphocytes. Intratumoral IH (green) was determined as regions enriched with both immune and cancer cells, while peritumoral IH (blue) were regions enriched with only immune cells. B, Schematic representations of tissue compartments including cancer hotspots, peritumoral IH, intratumoral IH based on automated spatial analysis, and pathologically defined TLSs and lymphoid aggregates without clear separation of T- and B-cell zones (LAG). The three spatial scores Sintra/immune, Sintra/cancer, and Sintra/tissue were determined as the proportion of intratumoral IH in the immune-rich area (intratumoral IH and peritumoral IH), cancer-rich area (intratumoral IH and cancer hotspots), and the entire tissue, respectively. C, Kaplan–Meier curves to illustrate the difference in OS in LUSC patients stratified by Sintra/immune, with the dichotomization optimized for the discovery cohort (n = 231) and assessed in the validation cohort (n = 231). D, Forest plots to show the prognostic value of Sintra/immune in multivariate models adjusted for stage, age, and smoking pack years in the validation cohort. E, Spearman correlation between lymphocyte percentage and Sintra/immune in the TCGA LUSC cohort (n = 462). F, Kaplan–Meier curve to illustrate the difference in OS in LUSC patients stratified by both Sintra/immune and lymphocyte percentage, with the stratification of the entire cohort shown (n = 462).

B-cell dominance of high-Sintra/immune group in LUSC. A, Gene Ontology (GO) biological processes found to be enriched in the DEGs according to patient groups stratified by Sintra/immune. The red line denotes the ratio of the number of DEGs in a pathway to the total number of genes in that pathway. FDR, false discovery rate. B, Volcano plot showing DEGs in the high-Sintra/immune group compared with the low group. Genes with log-fold change (logFC) smaller than 1 were excluded. Genes involved in immune responses are highlighted in yellow. C, Consistently upregulated B-cell signatures in high-Sintra/immune group across seven bioinformatics methods using the TCGA LUSC cohort, n = 459. Statistical significance was evaluated by the Wilcoxon rank sum test and adjusted by the Benjamini–Hochberg method.

All supplemental figures and legends

KRAS-mutant colorectal cancers are resistant to therapeutics, presenting a significant problem for ∼40% of cases. Rapalogs, which inhibit mTORC1 and thus protein synthesis, are significantly less potent in KRAS-mutant colorectal cancer. Using Kras-mutant mouse models and mouse- and patient-derived organoids, we demonstrate that KRAS with G12D mutation fundamentally rewires translation to increase both bulk and mRNA-specific translation initiation. This occurs via the MNK/eIF4E pathway culminating in sustained expression of c-MYC. By genetic and small-molecule targeting of this pathway, we acutely sensitize KRASG12D models to rapamycin via suppression of c-MYC. We show that 45% of colorectal cancers have high signaling through mTORC1 and the MNKs, with this signature correlating with a 3.5-year shorter cancer-specific survival in a subset of patients. This work provides a c-MYC–dependent cotargeting strategy with remarkable potency in multiple Kras-mutant mouse models and metastatic human organoids and identifies a patient population that may benefit from its clinical application.Significance:KRAS mutation and elevated c-MYC are widespread in many tumors but remain predominantly untargetable. We find that mutant KRAS modulates translation, culminating in increased expression of c-MYC. We describe an effective strategy targeting mTORC1 and MNK in KRAS-mutant mouse and human models, pathways that are also commonly co-upregulated in colorectal cancer.This article is highlighted in the In This Issue feature, p. 995

To improve treatment outcomes in non–small cell lung cancer (NSCLC), preclinical models that can better predict individual patient response to novel therapies are urgently needed. Using freshly resected tumor tissue, we describe an optimized ex vivo explant culture model that enables concurrent evaluation of NSCLC response to therapy while maintaining the tumor microenvironment. We found that approximately 70% of primary NSCLC specimens were amenable to explant culture with tissue integrity intact for up to 72 hours. Variations in cisplatin sensitivity were noted with approximately 50% of cases responding ex vivo. Notably, explant responses to cisplatin correlated significantly with patient survival (P = 0.006) irrespective of tumor stage. In explant tissue, cisplatin-resistant tumors excluded platinum ions from tumor areas in contrast to cisplatin-sensitive tumors. Intact TP53 did not predict cisplatin sensitivity, but a positive correlation was observed between cisplatin sensitivity and TP53 mutation status (P = 0.003). Treatment of NSCLC explants with the targeted agent TRAIL revealed differential sensitivity with the majority of tumors resistant to single-agent or cisplatin combination therapy. Overall, our results validated a rapid, reproducible, and low-cost platform for assessing drug responses in patient tumors ex vivo, thereby enabling preclinical testing of novel drugs and helping stratify patients using biomarker evaluation. Cancer Res; 77(8); 2029–39. ©2017 AACR.

Table S1. Summary of patient, tumour and explant characteristics. Figure S1. LA-ICP-MS analysis of FFPE sections to determine Pt ion distribution. Figure S2. Characteristics of viable and non�viable tumours. Figure S3. Individual explant responses to cisplatin. Figure S4. Identification of cisplatin sensitive/resistant samples. Figure S5. LA-ICP-MS analysis of cisplatin sensitive and resistant explants.

Supplementary Table and Figure Legends

The glycocalyx component and sialomucin podocalyxin (PODXL) is required for normal tissue development by promoting apical membranes to form between cells, triggering lumen formation. Elevated PODXL expression is also associated with metastasis and poor clinical outcome in multiple tumor types. How PODXL presents this duality in effect remains unknown. We identify an unexpected function of PODXL as a decoy receptor for galectin-3 (GAL3), whereby the PODXL-GAL3 interaction releases GAL3 repression of integrin-based invasion. Differential cortical targeting of PODXL, regulated by ubiquitination, is the molecular mechanism controlling alternate fates. Both PODXL high and low surface levels occur in parallel subpopulations within cancer cells. Orthotopic intraprostatic xenograft of PODXL-manipulated cells or those with different surface levels of PODXL define that this axis controls metastasis in vivo. Clinically, interplay between PODXL-GAL3 stratifies prostate cancer patients with poor outcome. Our studies define the molecular mechanisms and context in which PODXL promotes invasion and metastasis.

Altered eIF4A1 activity promotes translation of highly structured, eIF4A1-dependent oncogene mRNAs at root of oncogenic translational programmes. It remains unclear how these mRNAs recruit and activate eIF4A1 unwinding specifically to facilitate their preferential translation. Here, we show that single-stranded RNA sequence motifs specifically activate eIF4A1 unwinding allowing local RNA structural rearrangement and translation of eIF4A1-dependent mRNAs in cells. Our data demonstrate that eIF4A1-dependent mRNAs contain AG-rich motifs within their 5’UTR which specifically activate eIF4A1 unwinding of local RNA structure to facilitate translation. This mode of eIF4A1 regulation is used by mRNAs encoding components of mTORC-signalling and cell cycle progression, and renders these mRNAs particularly sensitive to eIF4A1-inhibition. Mechanistically, we show that binding of eIF4A1 to AG-rich sequences leads to multimerization of eIF4A1 with eIF4A1 subunits performing distinct enzymatic activities. Our structural data suggest that RNA-binding of multimeric eIF4A1 induces conformational changes in the RNA resulting in an optimal positioning of eIF4A1 proximal to the RNA duplex enabling efficient unwinding. Our data proposes a model in which AG-motifs in the 5’UTR of eIF4A1-dependent mRNAs specifically activate eIF4A1, enabling assembly of the helicase-competent multimeric eIF4A1 complex, and positioning these complexes proximal to stable localised RNA structure allowing ribosomal subunit scanning.

Beyond tertiary lymphoid structures, a significant number of immune-rich areas without germinal center-like structures are observed in non–small cell lung cancer. Here, we integrated transcriptomic data and digital pathology images to study the prognostic implications, spatial locations, and constitution of immune rich areas (immune hotspots) in a cohort of 935 patients with lung cancer from The Cancer Genome Atlas. A high intratumoral immune hotspot score, which measures the proportion of immune hotspots interfacing with tumor islands, was correlated with poor overall survival in lung squamous cell carcinoma but not in lung adenocarcinoma. Lung squamous cell carcinomas with high intratumoral immune hotspot scores were characterized by consistent upregulation of B-cell signatures. Spatial statistical analyses conducted on serial multiplex IHC slides further revealed that only 4.87% of peritumoral immune hotspots and 0.26% of intratumoral immune hotspots were tertiary lymphoid structures. Significantly lower densities of CD20+CXCR5+ and CD79b+ B cells and less diverse immune cell interactions were found in intratumoral immune hotspots compared with peritumoral immune hotspots. Furthermore, there was a negative correlation between the percentages of CD8+ T cells and T regulatory cells in intratumoral but not in peritumoral immune hotspots, with tertiary lymphoid structures excluded. These findings suggest that the intratumoral immune hotspots reflect an immunosuppressive niche compared with peritumoral immune hotspots, independent of the distribution of tertiary lymphoid structures. A balance toward increased intratumoral immune hotspots is indicative of a compromised antitumor immune response and poor outcome in lung squamous cell carcinoma. Significance: Intratumoral immune hotspots beyond tertiary lymphoid structures reflect an immunosuppressive microenvironment, different from peritumoral immune hotspots, warranting further study in the context of immunotherapies.

Acute cardiorespiratory breathlessness accounts for one in eight of all emergency hospitalizations. Early, noninvasive diagnostic testing is a clinical priority that allows rapid triage and treatment. Here, we sought to find and replicate diagnostic breath volatile organic compound (VOC) biomarkers of acute cardiorespiratory disease and understand breath metabolite network enrichment in acute disease, with a view to gaining mechanistic insight of breath biochemical derangements. We collected and analyzed exhaled breath samples from 277 participants presenting acute cardiorespiratory exacerbations and aged-matched healthy volunteers. Topological data analysis phenotypes differentiated acute disease from health and acute cardiorespiratory exacerbation subtypes (acute heart failure, acute asthma, acute chronic obstructive pulmonary disease, and community-acquired pneumonia). A multibiomarker score (101 breath biomarkers) demonstrated good diagnostic sensitivity and specificity (≥80%) in both discovery and replication sets and was associated with all-cause mortality at 2 years. In addition, VOC biomarker scores differentiated metabolic subgroups of cardiorespiratory exacerbation. Louvain clustering of VOCs coupled with metabolite enrichment and similarity assessment revealed highly specific enrichment patterns in all acute disease subgroups, for example, selective enrichment of correlated C5-7 hydrocarbons and C3-5 carbonyls in heart failure and selective depletion of correlated aldehydes in acute asthma. This study identified breath VOCs that differentiate acute cardiorespiratory exacerbations and associated subtypes and metabolic clusters of disease-associated VOCs.

The glycocalyx component and sialomucin Podocalyxin (PODXL) is required for normal tissue development by promoting apical membranes to form between cells, triggering lumen formation. Elevated PODXL expression is also associated with metastasis and poor clinical outcome in multiple tumour types. How PODXL presents this duality in effect remains unknown. We identify an unexpected function of PODXL as a decoy receptor for Galectin-3 (GAL3), whereby the PODXL-GAL3 interaction releases GAL3 repression of integrin-based invasion. Differential cortical targeting of PODXL, regulated by ubiquitination, is the molecular mechanism controlling alternate fates. Both PODXL highversuslow surface levels occur in parallel subpopulations within cancer cells. Orthotopic intraprostatic xenograft of PODXL-manipulated cells or those with different surface levels of PODXL define that this axis controls metastasisin vivo. Clinically, interplay between PODXL-GAL3 stratifies prostate cancer patients with poor outcome. Our studies define the molecular mechanisms and context in which PODXL promotes invasion and metastasis.

Since the 8A Delphi approach was used with two rounds of blinded anonymized analysis of resected non-mucinous lung adenocarcinoma cases with presumed invasive and non-invasive components, followed by one round of reviewer de-anonymized and unblinded review of cases with known outcomes. A digital pathology platform was used for measuring total tumor size and invasive tumor size.The mean coefficient of variation for measuring total tumor size and tumor invasive size was 6.9% (range 1.7-22.3%) and 54% (range 14.7-155%), respectively, with substantial variations in interpretation of the size and location of invasion among pathologists. Following the presentation of the results and further discussion among members at large of the IASLC Pathology Committee, extensive epithelial proliferation (EEP) in areas of collapsed lepidic growth pattern is recognized as a feature likely to be associated with invasive growth. EEP is characterized by multilayered luminal epithelial cell growth, usually with high grade cytological features in several alveolar spaces.Collapsed alveoli and transition zones with EEP were identified by the Delphi process as morphologic features that were a source of interobserver variability. Definition criteria for collapse and EEP are proposed to improve reproducibility of invasion measurement.

Oncogenic translational programmes underpin cancer development and are often driven by dysregulation of oncogenic signalling pathways that converge on the eukaryotic translation initiation (eIF) 4F complex. Altered eIF4F activity promotes translation of oncogene mRNAs that typically contain highly structured 5’UTRs rendering their translation strongly dependent on RNA unwinding by the DEAD-box helicase eIF4A1 subunit of the eIF4F complex. While eIF4A1-dependent mRNAs have been widely investigated, it is still unclear how highly structured mRNAs recruit and activate eIF4A1 unwinding specifically to facilitate their preferential translation.Here, we show that RNA sequence motifs regulate eIF4A1 unwinding activity in cells. Our data demonstrate that eIF4A1-dependent mRNAs contain AG-rich motifs within their 5’UTR which recruit and stimulate eIF4A1 unwinding of localised RNA structure to facilitate mRNA translation. This mode of eIF4A1 regulation is used by mRNAs encoding components of mTORC-signalling and cell cycle progression and renders these mRNAs particularly sensitive to eIF4A1-inhibition. Mechanistically, we show that binding of eIF4A1 to AG-rich sequences leads to multimerization of eIF4A1 with eIF4A1 subunits performing distinct enzymatic activities. Our structural data suggest that RNA-binding of multimeric eIF4A1 induces conformational changes in the RNA substrate resulting in an optimal positioning of eIF4A1 proximal to the RNA duplex region that supports efficient unwinding.Hence, we conclude a model in which mRNAs utilise AG-rich sequences to specifically recruit eIF4A1, enabling assembly of the helicase-active multimeric eIF4A1 complex, and positioning these complexes proximal to stable localised RNA structure allowing ribosomal subunit scanning.

Aims: The decision to consider adjuvant chemotherapy (AC) for non‐small cell lung cancer is currently governed by clinical stage. This study aims to assess other routinely collected pathological variables related to metastasis and survival for their ability to predict the efficacy of AC in lung adenocarcinoma. Methods and results: A retrospective single‐centre series of 620 resected lung non‐mucinous adenocarcinoma cases from 2005 to 2015 was used. Digital images of all slides were subjected to central review, and data on tumour histopathology, AC treatment and patient survival were compiled. A statistical case matching approach was used to counter selection bias. Several high‐risk pathological criteria predict both pathological nodal involvement and early death: positive vascular invasion status (VI+) (HR = 2.10, P < 0.001), positive visceral pleural invasion status (VPI+) (HR = 2.16, P < 0.001), and solid/micropapillary‐predominant WHO tumour type (SPA/MPPA) (HR = 3.29, P < 0.001). Crucially, these criteria also identify patient groups benefiting from AC (VI + HR = 0.69, P = 0.167, VPI + HR = 0.44, P = 0.004, SPA/MPPA HR = 0.36, P = 0.006). Cases showing VI+/VPI+/SPA/MPPA histology in the absence of AC stage criteria were common (170 of 620 total), and 8 had actually received AC. This group showed much better outcomes than equivalent untreated cases in matched analysis (3‐year OS 100.0% versus 31.3%). Inclusion of patients with VI+/VPI+/SPA/MPPA histology would increase AC‐eligible patients from 51.0% to 84.0% of non‐mucinous tumours in our cohort. Conclusions: Our data provide preliminary evidence that the consideration of AC in patients with additional high‐risk pathological indicators may significantly improve outcomes in operable lung adenocarcinoma, and that AC may be currently underused.

Protein–protein interactions (PPIs) are an essential part of correct cellular functionality, making them increasingly interesting drug targets. While Förster resonance energy transfer-based methods have traditionally been widely used for PPI studies, label-free techniques have recently drawn significant attention. These methods are ideal for studying PPIs, most importantly as there is no need for labeling of either interaction partner, reducing potential interferences and overall costs. Already, several different label-free methods are available, such as differential scanning calorimetry and surface plasmon resonance, but these biophysical methods suffer from low to medium throughput, which reduces suitability for high-throughput screening (HTS) of PPI inhibitors. Differential scanning fluorimetry, utilizing external fluorescent probes, is an HTS compatible technique, but high protein concentration is needed for experiments. To improve the current concepts, we have developed a method based on time-resolved luminescence, enabling PPI monitoring even at low nanomolar protein concentrations. This method, called the protein probe technique, is based on a peptide conjugated with Eu3+ chelate, and it has already been applied to monitor protein structural changes and small molecule interactions at elevated temperatures. Here, the applicability of the protein probe technique was demonstrated by monitoring single-protein pairing and multiprotein complexes at room and elevated temperatures. The concept functionality was proven by using both artificial and multiple natural protein pairs, such as KRAS and eIF4A together with their binding partners, and C-reactive protein in a complex with its antibody.