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Mutations in TP53 lead to a defective G1 checkpoint and the dependence on checkpoint kinase 1 (Chk1) for G2 or S phase arrest in response to DNA damage. In preclinical studies, Chk1 inhibition resulted in enhanced cytotoxicity of several chemotherapeutic agents. The high frequency of TP53 mutations in triple negative breast cancer (TNBC: negative for estrogen receptor, progesterone receptor, and HER2) make Chk1 an attractive therapeutic target. UCN-01, a non-selective Chk1 inhibitor, combined with irinotecan demonstrated activity in advanced TNBC in our Phase I study. The goal of this trial was to further evaluate this treatment in women with TNBC. Patients with metastatic TNBC previously treated with anthracyclines and taxanes received irinotecan (100–125 mg/m 2 IV days 1, 8, 15, 22) and UCN-01 (70 mg/m 2 IV day 2, 35 mg/m 2 day 23 and subsequent doses) every 42-day cycle. Peripheral blood mononuclear cells (PBMC) and tumor specimens were collected. Twenty five patients were enrolled. The overall response (complete response (CR) + partial response (PR)) rate was 4 %. The clinical benefit rate (CR + PR + stable disease ≥6 months) was 12 %. Since UCN-01 inhibits PDK1, phosphorylated ribosomal protein S6 (pS6) in PBMC was assessed. Although reduced 24 h post UCN-01, pS6 levels rose to baseline by day 8, indicating loss of UCN-01 bioavailability. Immunostains of γH2AX and pChk1 S296 on serial tumor biopsies from four patients demonstrated an induction of DNA damage and Chk1 activation following irinotecan. However, Chk1 inhibition by UCN-01 was not observed in all tumors. Most tumors were basal-like (69 %), and carried mutations in TP53 (53 %). Median overall survival in patients with TP53 mutant tumors was poor compared to wild type (5.5 vs. 20.3 months, p  = 0.004). This regimen had limited activity in TNBC. Inconsistent Chk1 inhibition was likely due to the pharmacokinetics of UCN-01. TP53 mutations were associated with a poor prognosis in metastatic TNBC.

Extensive genomic characterization of human cancers presents the problem of inference from genomic abnormalities to cancer phenotypes. To address this problem, we analysed proteomes of colon and rectal tumours characterized previously by The Cancer Genome Atlas (TCGA) and perform integrated proteogenomic analyses. Somatic variants displayed reduced protein abundance compared to germline variants. Messenger RNA transcript abundance did not reliably predict protein abundance differences between tumours. Proteomics identified five proteomic subtypes in the TCGA cohort, two of which overlapped with the TCGA ‘microsatellite instability/CpG island methylation phenotype’ transcriptomic subtype, but had distinct mutation, methylation and protein expression patterns associated with different clinical outcomes. Although copy number alterations showed strong cis - and trans -effects on mRNA abundance, relatively few of these extend to the protein level. Thus, proteomics data enabled prioritization of candidate driver genes. The chromosome 20q amplicon was associated with the largest global changes at both mRNA and protein levels; proteomics data highlighted potential 20q candidates, including HNF4A (hepatocyte nuclear factor 4, alpha), TOMM34 (translocase of outer mitochondrial membrane 34) and SRC (SRC proto-oncogene, non-receptor tyrosine kinase). Integrated proteogenomic analysis provides functional context to interpret genomic abnormalities and affords a new paradigm for understanding cancer biology. Proteome analysis of The Cancer Genome Atlas (TCGA) colorectal cancer specimens reveals that DNA- or RNA-level measurements cannot reliably predict protein abundance, colorectal tumours can be separated into distinct proteotypes, and that copy number alterations drive mRNA abundance changes but few extend to protein-level changes. Proteomics/genomics of colorectal tumours A team from the Clinical Proteomics Tumor Analysis Consortium has now analysed the proteomes of 95 colon and rectal tumours previously characterized by the Cancer Genome Atlas project. Integration of the proteomics with the original genomic data demonstrates that protein abundance cannot be reliably predicted from DNA- or RNA-level measurements, and that mRNA and protein levels are modestly correlated. Proteomics identified five colorectal cancer subtypes that reflect known biological characteristics, yet capture differences that are not evident at the transcriptome level. Integrated proteogenomic analysis of this type can provide functional context to interpret genomic abnormalities in terms of cancer biology.

In the case of TNBC, the complexity of the genomic structure and the molecular heterogeneity present a significant challenge in the evaluation of targeted therapeutics, and convincing data have yet to be generated proving the utility of PARP inhibitors. If synthetic lethality is to be exploited in TNBC with PARP inhibitors, a way is needed to identify those patients who have defects in the BRCA pathway or the homologous recombination-directed DNA repair (HRR) pathway in order to narrow down the target population. However, to date there is not a validated assay that can detect HRR deficiency using archival tumor specimens. Functional assays, such as that which detects the formation of DNA repair protein foci following radiation in ex vivo breast cancer biopsy specimens have been explored, but these assays are difficult to employ in the clinical setting. [7] Rios and Puhalla have reviewed the possibility of using PARP expression level, array CGH patterns, gene expression signature or BRCAl mRNA expression, and PTEN loss, but none of these approaches has been validated. Perhaps another way to attack the problem is at the genomic level. McCabe et al reported that deficiencies in HRR pathway genes- including RAD51, RAD54, DSSl, RPAl, NBSl, ATR, ATM, CHKl, CHK2, FANCD2, FANCA, and FANCC-dso induced sensitivity to PARP inhibition. [8] In another study by Turner et al, a synthetic lethal SiRNA screen identified additional genes that mediate sensitivity to a PARP inhibitor, including cyclin-dependent kinase 5 (CDK5) and other genes. [9] Perhaps a gene mutation panel could be created to predict PARP inhibitor sensitivity?

cAMP, through the activation of cAMP-dependent protein kinase (PKA), is involved in transcriptional regulation. In eukaryotic cells, cAMP is not considered to alter the binding affinity of CREB/ATF to cAMP-responsive element (CRE) but to induce serine phosphorylation and consequent increase in transcriptional activity. In contrast, in prokaryotic cells, cAMP enhances the DNA binding of the catabolite repressor protein to regulate the transcription of several operons. The structural similarity of the cAMP binding sites in catabolite repressor protein and regulatory subunit of PKA type II (RII) suggested the possibility of a similar role for RII in eukaryotic gene regulation. Herein we report that RIIβ subunit of PKA is a transcription factor capable of interacting physically and functionally with a CRE. In contrast to CREB/ATF, the binding of RIIβ to a CRE was enhanced by cAMP, and in addition, RIIβ exhibited transcriptional activity as a Gal4-RIIβ fusion protein. These experiments identify RIIβ as a component of an alternative pathway for regulation of CRE-directed transcription in eukaryotic cells.

New DNA sequencing platforms have revolutionized human genome sequencing. The dramatic advances in genome sequencing technologies predict that the $1,000 genome will become a reality within the next few years. Applied to cancer, the availability of cancer genome sequences permits real-time decision-making with the potential to affect diagnosis, prognosis, and treatment, and has opened the door towards personalized medicine. A promising strategy is the identification of mutated tumor antigens, and the design of personalized cancer vaccines. Supporting this notion are preliminary analyses of the epitope landscape in breast cancer suggesting that individual tumors express significant numbers of novel antigens to the immune system that can be specifically targeted through cancer vaccines.

Fibroblasts are poorly characterised cells that variably impact tumour progression. Here, we use single cell RNA-sequencing, multiplexed immunohistochemistry and digital cytometry (CIBERSORTx) to identify and characterise three major fibroblast subpopulations in human non-small cell lung cancer: adventitial, alveolar and myofibroblasts. Alveolar and adventitial fibroblasts (enriched in control tissue samples) localise to discrete spatial niches in histologically normal lung tissue and indicate improved overall survival rates when present in lung adenocarcinomas (LUAD). Trajectory inference identifies three phases of control tissue fibroblast activation, leading to myofibroblast enrichment in tumour samples: initial upregulation of inflammatory cytokines, followed by stress-response signalling and ultimately increased expression of fibrillar collagens. Myofibroblasts correlate with poor overall survival rates in LUAD, associated with loss of epithelial differentiation, TP53 mutations, proximal molecular subtypes and myeloid cell recruitment. In squamous carcinomas myofibroblasts were not prognostic despite being transcriptomically equivalent. These findings have important implications for developing fibroblast-targeting strategies for cancer therapy. Fibroblast heterogeneity is a prominent but poorly understood feature of solid tumours. Here three major fibroblast subpopulations in non-small cell lung cancer are identified and characterised through single cell RNA-sequencing, multiplexed immunohistochemistry and digital cytometry.

Over the course of an individual’s lifetime, normal human cells accumulate mutations 1 . Here we compare the mutational landscape in 29 cell types from the soma and germline using multiple samples from the same individuals. Two ubiquitous mutational signatures, SBS1 and SBS5/40, accounted for the majority of acquired mutations in most cell types, but their absolute and relative contributions varied substantially. SBS18, which potentially reflects oxidative damage 2 , and several additional signatures attributed to exogenous and endogenous exposures contributed mutations to subsets of cell types. The rate of mutation was lowest in spermatogonia, the stem cells from which sperm are generated and from which most genetic variation in the human population is thought to originate. This was due to low rates of ubiquitous mutational processes and may be partially attributable to a low rate of cell division in basal spermatogonia. These results highlight similarities and differences in the maintenance of the germline and soma. The authors report the mutational landscape of 29 cell types from microdissected biopsies from 19 organs and explore the mechanisms underlying mutation rates in normal tissues.

A series of 725 patients underwent attempted implantation of a leadless transcatheter pacemaker. At 6 months, 96.0% of patients had no major device-related complications, and 98.3% had a low and stable pacing capture threshold. For more than half a century, permanent cardiac pacing for symptomatic bradycardia has been achieved with systems that consist of a surgically implanted subcutaneous electrical generator connected to one or more transvenous leads that deliver the pacing therapy to the heart. Although these devices are effective, approximately one in eight patients has an early complication, frequently related to the lead or leads or to the subcutaneous “pocket.” 1 Complications include problems with the subcutaneous pocket, such as hematomas and infections; lead-insertion problems, such as pneumothoraxes and hemothoraxes; lead dislodgements and integrity problems; infections, including septicemia and endocarditis; vascular obstructions; and reduced . . .

In materio computing offers the potential for widespread embodied intelligence by leveraging the intrinsic dynamics of complex systems for efficient sensing, processing, and interaction. While individual devices offer basic data processing capabilities, networks of interconnected devices can perform more complex and varied tasks. However, designing such networks for dynamic tasks is challenging in the absence of physical models and accurate characterization of device noise. We introduce the Noise-Aware Dynamic Optimization (NADO) framework for training networks of dynamical devices, using Neural Stochastic Differential Equations (Neural-SDEs) as differentiable digital twins to capture both the dynamics and stochasticity of devices with intrinsic memory. Our approach combines backpropagation through time with cascade learning, enabling effective exploitation of the temporal properties of physical devices. We validate this method on networks of spintronic devices across both temporal classification and regression tasks. By decoupling device model training from network connectivity optimization, our framework reduces data requirements and enables robust, gradient-based programming of dynamical devices without requiring analytical descriptions of their behaviour. Dynamic systems show promise for physical neural networks, but gradient based optimization requires mathematical models. Here, the authors present a data-driven framework for optimizing networks of arbitrary dynamic systems which is robust to noise, and enables tasks such as neuroprosthetic control.

Frequent loss-of-function mutations in KEAP1 , a master regulator of the NRF2 antioxidant pathway, accelerate mutant KRAS driven lung carcinogenesis, but also impose a dependency of these tumors on glutaminolysis. Using a precision medicine–based approach, this work uncovers a metabolic vulnerability of KRAS – KEAP1 -mutant lung cancers that can be therapeutically exploited using currently available glutaminase inhibitors and provides a scientific rationale for patient selection in clinical trials. Treating KRAS -mutant lung adenocarcinoma (LUAD) remains a major challenge in cancer treatment given the difficulties associated with directly inhibiting the KRAS oncoprotein 1 . One approach to addressing this challenge is to define mutations that frequently co-occur with those in KRAS , which themselves may lead to therapeutic vulnerabilities in tumors. Approximately 20% of KRAS -mutant LUAD tumors carry loss-of-function mutations in the KEAP1 gene encoding Kelch-like ECH-associated protein 1 (refs. 2 , 3 , 4 ), a negative regulator of nuclear factor erythroid 2-like 2 ( NFE2L2 ; hereafter NRF2 ), which is the master transcriptional regulator of the endogenous antioxidant response 5 , 6 , 7 , 8 , 9 , 10 . The high frequency of mutations in KEAP1 suggests an important role for the oxidative stress response in lung tumorigenesis. Using a CRISPR–Cas9-based approach in a mouse model of KRAS-driven LUAD, we examined the effects of Keap1 loss in lung cancer progression. We show that loss of Keap1 hyperactivates NRF2 and promotes KRAS-driven LUAD in mice. Through a combination of CRISPR–Cas9-based genetic screening and metabolomic analyses, we show that Keap1 - or Nrf2 -mutant cancers are dependent on increased glutaminolysis, and this property can be therapeutically exploited through the pharmacological inhibition of glutaminase. Finally, we provide a rationale for stratification of human patients with lung cancer harboring KRAS / KEAP1 - or KRAS / NRF2 -mutant lung tumors as likely to respond to glutaminase inhibition.