Germline genetic regulation of the colorectal tumor immune microenvironment.

OBJECTIVE: To evaluate the contribution of germline genetics to regulating the briskness and diversity of T cell responses in CRC, we conducted a genome-wide association study to examine the associations between germline genetic variation and quantitative measures of T cell landscapes in 2,876 colorectal tumors from participants in the Molecular Epidemiology of Colorectal Cancer Study (MECC). METHODS: Germline DNA samples were genotyped and imputed using genome-wide arrays. Tumor DNA samples were extracted from paraffin blocks, and T cell receptor clonality and abundance were quantified by immunoSEQ (Adaptive Biotechnologies, Seattle, WA). Tumor infiltrating lymphocytes per high powered field (TILs/hpf) were scored by a gastrointestinal pathologist. Regression models were used to evaluate the associations between each variant and the three T-cell features, adjusting for sex, age, genotyping platform, and global ancestry. Three independent datasets were used for replication. RESULTS: We identified a SNP (rs4918567) near RBM20 associated with clonality at a genome-wide significant threshold of 5 × 10- 8, with a consistent direction of association in both discovery and replication datasets. Expression quantitative trait (eQTL) analyses and in silico functional annotation for these loci provided insights into potential functional roles, including a statistically significant eQTL between the T allele at rs4918567 and higher expression of ADRA2A (P = 0.012) in healthy colon mucosa. CONCLUSIONS: Our study suggests that germline genetic variation is associated with the quantity and diversity of adaptive immune responses in CRC. Further studies are warranted to replicate these findings in additional samples and to investigate functional genomic mechanisms.

Heterozygote advantage at HLA class I and II loci and reduced risk of colorectal cancer.

Objective: Reduced diversity at Human Leukocyte Antigen (HLA) loci may adversely affect the host's ability to recognize tumor neoantigens and subsequently increase disease burden. We hypothesized that increased heterozygosity at HLA loci is associated with a reduced risk of developing colorectal cancer (CRC). Methods: We imputed HLA class I and II four-digit alleles using genotype data from a population-based study of 5,406 cases and 4,635 controls from the Molecular Epidemiology of Colorectal Cancer Study (MECC). Heterozygosity at each HLA locus and the number of heterozygous genotypes at HLA class -I (A, B, and C) and HLA class -II loci (DQB1, DRB1, and DPB1) were quantified. Logistic regression analysis was used to estimate the risk of CRC associated with HLA heterozygosity. Individuals with homozygous genotypes for all loci served as the reference category, and the analyses were adjusted for sex, age, genotyping platform, and ancestry. Further, we investigated associations between HLA diversity and tumor-associated T cell repertoire features, as measured by tumor infiltrating lymphocytes (TILs; N=2,839) and immunosequencing (N=2,357). Results: Individuals with all heterozygous genotypes at all three class I genes had a reduced odds of CRC (OR: 0.74; 95% CI: 0.56-0.97, p= 0.031). A similar association was observed for class II loci, with an OR of 0.75 (95% CI: 0.60-0.95, p= 0.016). For class-I and class-II combined, individuals with all heterozygous genotypes had significantly lower odds of developing CRC (OR: 0.66, 95% CI: 0.49-0.87, p= 0.004) than those with 0 or one heterozygous genotype. HLA class I and/or II diversity was associated with higher T cell receptor (TCR) abundance and lower TCR clonality, but results were not statistically significant. Conclusion: Our findings support a heterozygote advantage for the HLA class-I and -II loci, indicating an important role for HLA genetic variability in the etiology of CRC.

NCCN Guidelines® Insights: Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic, Version 2.2024.

The NCCN Guidelines for Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic focus primarily on assessment of pathogenic/likely pathogenic (P/LP) variants associated with increased risk of breast, ovarian, pancreatic, and prostate cancer, including BRCA1, BRCA2, CDH1, PALB2, PTEN, and TP53, and recommended approaches to genetic counseling/testing and care strategies in individuals with these P/LP variants. These NCCN Guidelines Insights summarize important updates regarding: (1) a new section for transgender, nonbinary and gender diverse people who have a hereditary predisposition to cancer focused on risk reduction strategies for ovarian cancer, uterine cancer, prostate cancer, and breast cancer; and (2) testing criteria and management associated with TP53 P/LP variants and Li-Fraumeni syndrome.

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise.

Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.

Integrating Early-Stage Drug Development with Clinical Networks; Challenges and Opportunities: The City of Hope Developing Experience.

Recent data suggest that patients with advanced cancer who participate in biomarker/genomically informed early-stage clinical trials experience clinical benefit. While most early-stage clinical trials are conducted in major academic centers, the majority of cancer patients in the United States are treated in community practices. Here, we describe ongoing efforts at the City of Hope Cancer Center to integrate our network community oncology clinical practices into our academic, centralized biomarker/genomic-driven, early-stage clinical trial program to build an understanding of the approaches that provide the benefits of early-stage clinical trial participation to community patients. Our efforts include three key initiatives: the development of a virtual "Refractory Disease" phase 1 trial matching televideo clinic, the construction of infrastructure to support the expansion of phase 1 clinical trials to a distant regional clinical satellite hub, and the implementation of an enterprise-wide precision medicine, germline, and somatic testing program. Our work at City of Hope may serve as an example to facilitate similar efforts at other institutions.

Critical evaluation of the use of artificial data for machine learning based de novo peptide identification.

Proteins are essential components of all living cells and so the study of their in situ expression, proteomics, has wide reaching applications. Peptide identification in proteomics typically relies on matching high resolution tandem mass spectra to a protein database but can also be performed de novo. While artificial spectra have been successfully incorporated into database search pipelines to increase peptide identification rates, little work has been done to investigate the utility of artificial spectra in the context of de novo peptide identification. Here, we perform a critical analysis of the use of artificial data for the training and evaluation of de novo peptide identification algorithms. First, we classify the different fragment ion types present in real spectra and then estimate the number of spurious matches using random peptides. We then categorise the different types of noise present in real spectra. Finally, we transfer this knowledge to artificial data and test the performance of a state-of-the-art de novo peptide identification algorithm trained using artificial spectra with and without relevant noise addition. Noise supplementation increased artificial training data performance from 30% to 77% of real training data peptide recall. While real data performance was not fully replicated, this work provides the first steps towards an artificial spectrum framework for the training and evaluation of de novo peptide identification algorithms. Further enhanced artificial spectra may allow for more in depth analysis of de novo algorithms as well as alleviating the reliance on database searches for training data.

The establishment of winter wheat root system architecture in field soils: The effect of soil type on root development in a temperate climate.

Winter wheat (Triticum aestivum L.) is an important cereal crop in the temperate climates of western Europe. Root system architecture is a significant contributor to resource capture and plant resilience. However, the impact of soil type on root system architecture (RSA) in field structured soils is yet to be fully assessed. This work studied the development of root growth using deep cultivation (250 mm) during the tillering phase stage (Zadock stage 25) of winter wheat across three soil types. The three sites of contrasting soil types covered a geographical area in the UK and Ireland in October 2018. Root samples were analysed using two methods: X-ray computed tomography (CT) which provides 3D images of the undisturbed roots in the soil, and a WinRHIZO™ scanner used to generate 2D images of washed roots and to measure further root parameters. Important negative relationships existed between soil bulk density and root properties (root length density, root volume, surface area and length) across the three sites. The results revealed that despite reduced root growth, the clay (Southoe) site had a significantly higher crop yield irrespective of root depth. The loamy sand (Harper Adams) site had significantly higher root volume, surface area and root length density compared with the other sites. However, a reduction in grain yield of 2.42 Mt ha-1 was incurred compared with the clay site and 1.6 Mt ha-1 compared with the clay loam site. The significantly higher rooting characteristics found in the loamy sand site were a result of the significantly lower soil bulk density compared with the other two sites. The loamy sand site had a lower soil bulk density, but no significant difference in macroporosity between sites (p > 0.05). This suggests that soil type and structure directly influence crop yield to greater extent than root parameters, but the interactions between both need simultaneous assessment in field sites.

Clinical implications of conflicting variant interpretations in the cancer genetics clinic.

PURPOSE: The aim of this study was to describe the clinical impact of commercial laboratories issuing conflicting classifications of genetic variants. METHODS: Results from 2000 patients undergoing a multigene hereditary cancer panel by a single laboratory were analyzed. Clinically significant discrepancies between the laboratory-provided test reports and other major commercial laboratories were identified, including differences between pathogenic/likely pathogenic and variant of uncertain significance (VUS) classifications, via review of ClinVar archives. For patients carrying a VUS, clinical documentation was assessed for evidence of provider awareness of the conflict. RESULTS: Fifty of 975 (5.1%) patients with non-negative results carried a variant with a clinically significant conflict, 19 with a pathogenic/likely pathogenic variant reported in APC or MUTYH, and 31 with a VUS reported in CDKN2A, CHEK2, MLH1, MSH2, MUTYH, RAD51C, or TP53. Only 10 of 28 (36%) patients with a VUS with a clinically significant conflict had a documented discussion by a provider about the conflict. Discrepant counseling strategies were used for different patients with the same variant. Among patients with a CDKN2A variant or a monoallelic MUTYH variant, providers were significantly more likely to make recommendations based on the laboratory-reported classification. CONCLUSION: Our findings highlight the frequency of variant interpretation discrepancies and importance of clinician awareness. Guidance is needed on managing patients with discrepant variants to support accurate risk assessment.

Application of a Novel Hybrid CNN-GNN for Peptide Ion Encoding.

Almost all state-of-the-art de novo peptide sequencing algorithms now use machine learning models to encode fragment peaks and hence identify amino acids in mass spectrometry (MS) spectra. Previous work has highlighted how the inherent MS challenges of noise and missing peptide peaks detrimentally affect the performance of these models. In the present research we extracted and evaluated the encoding modules from 3 state-of-the-art de novo peptide sequencing algorithms. We also propose a convolutional neural network-graph neural network machine learning model for encoding peptide ions in tandem MS spectra. We compared the proposed encoding module to those used in the state-of-the-art de novo peptide sequencing algorithms by assessing their ability to identify b-ions and y-ions in MS spectra. This included a comprehensive evaluation in both real and artificial data across various levels of noise and missing peptide peaks. The proposed model performed best across all data sets using two different metrics (area under the receiver operating characteristic curve (AUC) and average precision). The work also highlighted the effect of including additional features such as intensity rank in these encoding modules as well as issues with using the AUC as a metric. This work is of significance to those designing future de novo peptide identification algorithms as it is the first step toward a new approach.

Development and comparative analysis of ANN and SVR-based models with conventional regression models for predicting spray drift.

As monitoring of spray drift during application can be expensive, time-consuming, and labor-intensive, drift predicting models may provide a practical complement. Several mechanistic models have been developed as drift prediction tool for various types of application equipment. Nevertheless, mechanistic models are quite often intricate and complex with a large number of input parameters required. Quite often, the detailed data needed for such models are not readily available. In this study, two advanced machine learning models (artificial neural network (ANN) and support vector regression (SVR)) were developed for pesticide drift prediction and compared with three conventional regression-based models: multiple linear regression (MLR), generalized linear model (GLM), and generalized nonlinear least squares (GNLS). The models were evaluated in fivefold cross-validation and by external validation using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute bias (MAB). From regression-based models, GLM and GNLS models performed very well when evaluated by cross-validation with R2 = 0.96 and 0.95 and RMSE = 0.70 and 0.82 respectively, while MLR performed less with R2 of 0.65 and RMSE of 2.25. Simultaneously, ANN and SVR models performed very well with R2 = 0.98 and 0.97 and RMSE = 0.58 and 0.71 respectively. Overall, ANN model performed best compared to the other four models followed by SVR. A comparison was also made between the high-performing model, ANN, and two previously published empirical models. The ANN model outperformed the two previously published empirical models and can be used to predict pesticide drift. Therefore, the ANN model is a potentially promising new approach for predicting ground drift that merits further study. In conclusion, our work demonstrated that the new approach, ANN and SVR-based models, for pesticide drift modeling has better predictive power than conventional regression models. Their ability to model complex relationships is a clear benefit in pesticide drift modeling where the variability in pesticide drift is often affected by a number of variables and the relationships between drift and predictors are very complicated. We believe such insights will pave better way for the application of machine learning towards spray drift modeling.

Improving the specificity of E. coli acetate/propionate exclusion biosensors via iterative engineering.

Real-time monitoring of key performance indicator analytes such as acetate and propionate within anaerobic digestors (AD) is required for optimum biogas production. In this paper the further refinement of acetate and propionate whole cell (E. coli) exclusion biosensors is reported following an iterative process in which key metabolites that might interfere with O2-uptake measurements are identified and genes required for their catabolism are knocked out (exclusion). Analysis of biological leachate from an AD reactor treating lignocellulosic material revealed the presence of formate, which was subsequently shown to elicit a response in previously developed E. coli biosensor strains. P1 phage transduction was employed to delete two genes encoding formate dehydrogenase, fdoH and fdnH, to eliminate formate catabolism. Deletion of these genes from the propionate biosensor strain W:ldgyepak abolished interference from formate and enabled accurate determination of propionate concentrations in biological leachate. However, the acetate biosensing strain E1/pGDR11-acs, despite not having any response to formate, responded to propionate. It was likely that this was a result of the promiscuity of the wild type acetyl CoA synthetase, which was replaced with Acs2 from Saccharomyces cerevisiae, resolving the problem and enabling acetate determination with the biosensor. Acetate and propionate concentrations in authentic leachate influent were estimated to be 26.5 mM and 65.5 mM, respectively, using the biosensor, and 26.6 and 70 mM, respectively, by HPLC, demonstrating the accuracy and specificity of the refined biosensor.

Discovery and Optimization of a Synthetic Class of Nectin-4-Targeted CD137 Agonists for Immuno-oncology.

CD137 (4-1BB) is a co-stimulatory receptor on immune cells and Nectin-4 is a cell adhesion molecule that is overexpressed in multiple tumor types. Using a series of poly(ethylene glycol) (PEG)-based linkers, synthetic bicyclic peptides targeting CD137 were conjugated to Bicycles targeting Nectin-4. The resulting bispecific molecules were potent CD137 agonists that require the presence of both Nectin-4-expressing tumor cells and CD137-expressing immune cells for activity. A multipronged approach was taken to optimize these Bicycle tumor-targeted immune cell agonists by exploring the impact of chemical configuration, binding affinity, and pharmacokinetics on CD137 agonism and antitumor activity. This effort resulted in the discovery of BT7480, which elicited robust CD137 agonism and maximum antitumor activity in syngeneic mouse models. A tumor-targeted approach to CD137 agonism using low-molecular-weight, short-acting molecules with high tumor penetration is a yet unexplored path in the clinic, where emerging data suggest that persistent target engagement, characteristic of biologics, may lead to suboptimal immune response.

The impact of noise and missing fragmentation cleavages on de novo peptide identification algorithms.

Proteomics aims to characterise system-wide protein expression and typically relies on mass-spectrometry and peptide fragmentation, followed by a database search for protein identification. It has wide ranging applications from clinical to environmental settings and virtually impacts on every area of biology. In that context, de novo peptide sequencing is becoming increasingly popular. Historically its performance lagged behind database search methods but with the integration of machine learning, this field of research is gaining momentum. To enable de novo peptide sequencing to realise its full potential, it is critical to explore the mass spectrometry data underpinning peptide identification. In this research we investigate the characteristics of tandem mass spectra using 8 published datasets. We then evaluate two state of the art de novo peptide sequencing algorithms, Novor and DeepNovo, with a particular focus on their performance with regard to missing fragmentation cleavage sites and noise. DeepNovo was found to perform better than Novor overall. However, Novor recalled more correct amino acids when 6 or more cleavage sites were missing. Furthermore, less than 11% of each algorithms' correct peptide predictions emanate from data with more than one missing cleavage site, highlighting the issues missing cleavages pose. We further investigate how the algorithms manage to correctly identify peptides with many of these missing fragmentation cleavages. We show how noise negatively impacts the performance of both algorithms, when high intensity peaks are considered. Finally, we provide recommendations regarding further algorithms' improvements and offer potential avenues to overcome current inherent data limitations.

BT7480, a novel fully synthetic Bicycle tumor-targeted immune cell agonist™ (Bicycle TICA™) induces tumor localized CD137 agonism.

BACKGROUND: CD137 (4-1BB) is an immune costimulatory receptor with high therapeutic potential in cancer. We are creating tumor target-dependent CD137 agonists using a novel chemical approach based on fully synthetic constrained bicyclic peptide (Bicycle®) technology. Nectin-4 is overexpressed in multiple human cancers that may benefit from CD137 agonism. To this end, we have developed BT7480, a novel, first-in-class, Nectin-4/CD137 Bicycle tumor-targeted immune cell agonist™ (Bicycle TICA™). METHODS: Nectin-4 and CD137 co-expression analyses in primary human cancer samples was performed. Chemical conjugation of two CD137 Bicycles to a Nectin-4 Bicycle led to BT7480, which was then evaluated using a suite of in vitro and in vivo assays to characterize its pharmacology and mechanism of action. RESULTS: Transcriptional profiling revealed that Nectin-4 and CD137 were co-expressed in a variety of human cancers with high unmet need and spatial proteomic imaging found CD137-expressing immune cells were deeply penetrant within the tumor near Nectin-4-expressing cancer cells. BT7480 binds potently, specifically, and simultaneously to Nectin-4 and CD137. In co-cultures of human peripheral blood mononuclear cells and tumor cells, this co-ligation causes robust Nectin-4-dependent CD137 agonism that is more potent than an anti-CD137 antibody agonist. Treatment of immunocompetent mice bearing Nectin-4-expressing tumors with BT7480 elicited a profound reprogramming of the tumor immune microenvironment including an early and rapid myeloid cell activation that precedes T cell infiltration and upregulation of cytotoxicity-related genes. BT7480 induces complete tumor regressions and resistance to tumor re-challenge. Importantly, antitumor activity is not dependent on continuous high drug levels in the plasma since a once weekly dosing cycle provides maximum antitumor activity despite minimal drug remaining in the plasma after day 2. BT7480 appears well tolerated in both rats and non-human primates at doses far greater than those expected to be clinically relevant, including absence of the hepatic toxicity observed with non-targeted CD137 agonists. CONCLUSION: BT7480 is a highly potent Nectin-4-dependent CD137 agonist that produces complete regressions and antitumor immunity with only intermittent drug exposure in syngeneic mouse tumor models and is well tolerated in preclinical safety species. This work supports the clinical investigation of BT7480 for the treatment of cancer in humans.

Regulatory and Technical Constraints: An Overview of the Technical Possibilities and Regulatory Limitations of Vehicle Telematic Data.

A telematics device is a vehicle instrument that comes preinstalled by the vehicle manufacturer or can be added later. The device records information about driving behavior, including speed, acceleration, and turning force. When connected to vehicle computers, the device can also provide additional information regarding the mechanical usage and condition of the vehicle. All of this information can be transmitted to a central database via mobile networks. The information provided has led to new services such as Usage Based Insurance (UBI). A range of consultants, industry commentators and academics have produced an abundance of projections on how telematics information will allow the introduction of services from personalized insurance, bespoke entertainment and advertise and vehicle energy optimization, particularly for Electric Vehicles (EVs). In this paper we examine these potential services against a backdrop of nascent regulatory limitations and against the technical capacity of the devices. Using a case study approach, we examine three applications that can use telematics information. We find that the expectations of service providers will be significantly tempered by regulatory and technical hurdles. In our discussion we detail these limitations and suggest a more realistic rollout of ancillary services.

Anticancer immunity induced by a synthetic tumor-targeted CD137 agonist.

BACKGROUND: In contrast to immune checkpoint inhibitors, the use of antibodies as agonists of immune costimulatory receptors as cancer therapeutics has largely failed. We sought to address this problem using a new class of modular synthetic drugs, termed tumor-targeted immune cell agonists (TICAs), based on constrained bicyclic peptides (Bicycles). METHODS: Phage libraries displaying Bicycles were panned for binders against tumor necrosis factor (TNF) superfamily receptors CD137 and OX40, and tumor antigens EphA2, Nectin-4 and programmed death ligand 1. The CD137 and OX40 Bicycles were chemically conjugated to tumor antigen Bicycles with different linkers and stoichiometric ratios of binders to obtain a library of low molecular weight TICAs (MW <8 kDa). The TICAs were evaluated in a suite of in vitro and in vivo assays to characterize their pharmacology and mechanism of action. RESULTS: Linking Bicycles against costimulatory receptors (e.g., CD137) to Bicycles against tumor antigens (e.g., EphA2) created potent agonists that activated the receptors selectively in the presence of tumor cells expressing these antigens. An EphA2/CD137 TICA (BCY12491) efficiently costimulated human peripheral blood mononuclear cells in vitro in the presence of EphA2 expressing tumor cell lines as measured by the increased secretion of interferon γ and interleukin-2. Treatment of C57/Bl6 mice transgenic for the human CD137 extracellular domain (huCD137) bearing EphA2-expressing MC38 tumors with BCY12491 resulted in the infiltration of CD8+ T cells, elimination of tumors and generation of immunological memory. BCY12491 was cleared quickly from the circulation (plasma t1/2 in mice of 1-2 hr), yet intermittent dosing proved effective. CONCLUSION: Tumor target-dependent CD137 agonism using a novel chemical approach (TICAs) afforded elimination of tumors with only intermittent dosing suggesting potential for a wide therapeutic index in humans. This work unlocks a new path to effective cancer immunotherapy via agonism of TNF superfamily receptors.

Psychosocial outcomes following germline multigene panel testing in an ethnically and economically diverse cohort of patients.

BACKGROUND: Little is known about the psychological outcomes of germline multigene panel testing, particularly among diverse patients and those with moderate-risk pathogenic variants (PVs). METHODS: Study participants (N = 1264) were counseled and tested with a 25- or 28-gene panel and completed a 3-month postresult survey including the Multidimensional Impact of Cancer Risk Assessment (MICRA). RESULTS: The mean age was 52 years, 80% were female, and 70% had cancer; 45% were non-Hispanic White, 37% were Hispanic, 10% were Asian, 3% were Black, and 5% had another race/ethnicity. Approximately 28% had a high school education or less, and 23% were non-English-speaking. The genetic test results were as follows: 7% had a high-risk PV, 6% had a moderate-risk PV, 35% had a variant of uncertain significance (VUS), and 52% were negative. Most participants (92%) had a total MICRA score ≤ 38, which corresponded to a mean response of "never," "rarely," or only "sometimes" reacting negatively to results. A multivariate analysis found that mean total MICRA scores were significantly higher (more uncertainty/distress) among high- and moderate-risk PV carriers (29.7 and 24.8, respectively) than those with a VUS or negative results (17.4 and 16.1, respectively). Having cancer or less education was associated with a significantly higher total MICRA score; race/ethnicity was not associated with the total MICRA score. High- and moderate-risk PV carriers did not differ significantly from one another in the total MICRA score, uncertainty, distress, or positive experiences. CONCLUSIONS: In a diverse population undergoing genetic counseling and multigene panel testing for hereditary cancer risk, the psychological response corresponded to test results and showed low distress and uncertainty. Further studies are needed to assess patient understanding and subsequent cancer screening among patients from diverse backgrounds. LAY SUMMARY: Multigene panel tests for hereditary cancer have become widespread despite concerns about adverse psychological reactions among carriers of moderate-risk pathogenic variants (mutations) and among carriers of variants of uncertain significance. This large study of an ethnically and economically diverse cohort of patients undergoing panel testing found that 92% "never," "rarely," or only "sometimes" reacted negatively to results. Somewhat higher uncertainty and distress were identified among carriers of high- and moderate-risk pathogenic variants, and lower levels were identified among those with a variant of uncertain significance or a negative result. Although the psychological response corresponded to risk, reactions to testing were favorable, regardless of results.

Inhibition of poly(ADP-ribose) polymerase induces synthetic lethality in BRIP1 deficient ovarian epithelial cells.

OBJECTIVE: Pathogenic variations in the homologous recombination (HR) gene, BRCA1 interacting protein C-terminal helicase 1 (BRIP1) increase the risk for ovarian cancer. PARP inhibitors (PARPi) exert a synthetic lethal effect in BRCA-mutated ovarian cancers. Effective HR requires cooperation between BRCA1 and BRIP1; therefore, BRIP1-incompetancy may predict vulnerability to synthetic lethality. Here we investigated the response of ovarian epithelial cells with defective BRIP1 function to PARPi, and compared these cells to those lacking BRCA1 activity. METHODS: We engineered Chinese Hamster ovarian (CHO) epithelial cells to express deficient BRIP1 or BRCA1, and exposed them to olaparib with or without carboplatin or cisplatin. We assessed cellular proliferation and survival; we calculated inhibitory concentrations and combination and reduction drug indices. RESULTS: BRIP1 and BRCA1 inactivation impedes HR activity, decreases cellular proliferation and compromises DNA damage recovery. Platinum agent exposure impairs cellular survival. Olaparib exposure alone decreases cell viability in BRCA1-deficient cells, although has no effect on BRIP1-deficient cells. Combining carboplatin or cisplatin with olaparib synergistically attenuates cellular survival, consistent with synthetic lethality. CONCLUSIONS: BRIP1-deficient ovarian epithelial cells exhibit defective HR, resulting in synthetic lethality when exposed to a platinum agent/PARPi combination. PARPi alone had no effect; this lack of effect may result from distinguishing molecular properties of BRIP1and/or consequences of genomic background. Our study identifies altered BRIP1 as a target for precision medicine-based therapies for ovarian cancers. This investigation supports consideration of the use of a platinum agent/PARPi combination in ovarian cancers depending upon genetic profile and genomic background.

Lymphocytic infiltration in stage II microsatellite stable colorectal tumors: A retrospective prognosis biomarker analysis.

BACKGROUND: Identifying stage II patients with colorectal cancer (CRC) at higher risk of progression is a clinical priority in order to optimize the advantages of adjuvant chemotherapy while avoiding unnecessary toxicity. Recently, the intensity and the quality of the host immune response in the tumor microenvironment have been reported to have an important role in tumorigenesis and an inverse association with tumor progression. This association is well established in microsatellite instable CRC. In this work, we aim to assess the usefulness of measures of T-cell infiltration as prognostic biomarkers in 640 stage II, CRC tumors, 582 of them confirmed microsatellite stable. METHODS AND FINDINGS: We measured both the quantity and clonality index of T cells by means of T-cell receptor (TCR) immunosequencing in a discovery dataset (95 patients with colon cancer diagnosed at stage II and microsatellite stable, median age 67, 30% women) and replicated the results in 3 additional series of stage II patients from 2 countries. Series 1 and 2 were recruited in Barcelona, Spain and included 112 fresh frozen (FF, median age 69, 44% women) and 163 formalin-fixed paraffin-embedded (FFPE, median age 67, 39% women) samples, respectively. Series 3 included 270 FFPE samples from patients recruited in Haifa, Northern Israel, as part of a large case-control study of CRC (median age 73, 46% women). Median follow-up time was 81.1 months. Cox regression models were fitted to evaluate the prognostic value of T-cell abundance and Simpson clonality of TCR variants adjusting by sex, age, tumor location, and stage (IIA and IIB). In the discovery dataset, higher TCR abundance was associated with better prognosis (hazard ratio [HR] for ≥Q1 = 0.25, 95% CI 0.10-0.63, P = 0.003). A functional analysis of gene expression on these tumors revealed enrichment in pathways related to immune response. Higher values of clonality index (lower diversity) were not associated with worse disease-free survival, though the HR for ≥Q3 was 2.32 (95% CI 0.90-5.97, P = 0.08). These results were replicated in an independent FF dataset (TCR abundance: HR = 0.30, 95% CI 0.12-0.72, P = 0.007; clonality: HR = 3.32, 95% CI 1.38-7.94, P = 0.007). Also, the association with prognosis was tested in 2 independent FFPE datasets. The same association was observed with TCR abundance (HR = 0.41, 95% CI 0.18-0.93, P = 0.03 and HR = 0.56, 95% CI 0.31-1, P = 0.042, respectively, for each FFPE dataset). However, the clonality index was associated with prognosis only in the FFPE dataset from Israel (HR = 2.45, 95% CI 1.39-4.32, P = 0.002). Finally, a combined analysis combining all microsatellite stable (MSS) samples demonstrated a clear prognosis value both for TCR abundance (HR = 0.39, 95% CI 0.26-0.57, P = 1.3e-06) and the clonality index (HR = 2.13, 95% CI 1.44-3.15, P = 0.0002). These associations were also observed when variables were considered continuous in the models (HR per log2 of TCR abundance = 0.85, 95% CI 0.78-0.93, P = 0.0002; HR per log2 or clonality index = 1.16, 95% CI 1.03-1.31, P = 0.016). LIMITATIONS: This is a retrospective study, and samples had been preserved with different methods. Validation series lack complete information about microsatellite instability (MSI) status and pathology assessment. The Molecular Epidemiology of Colorectal Cancer (MECC) study had information about overall survival instead of progression-free survival. CONCLUSION: Results from this study demonstrate that tumor lymphocytes, assessed by TCR repertoire quantification based on a sequencing method, are an independent prognostic factor in microsatellite stable stage II CRC.

The Community Oncology and Academic Medical Center Alliance in the Age of Precision Medicine: Cancer Genetics and Genomics Considerations.

Recent public policy, governmental regulatory and economic trends have motivated the establishment and deepening of community health and academic medical center alliances. Accordingly, community oncology practices now deliver a significant portion of their oncology care in association with academic cancer centers. In the age of precision medicine, this alliance has acquired critical importance; novel advances in nucleic acid sequencing, the generation and analysis of immense data sets, the changing clinical landscape of hereditary cancer predisposition and ongoing discovery of novel, targeted therapies challenge community-based oncologists to deliver molecularly-informed health care. The active engagement of community oncology practices with academic partners helps with meeting these challenges; community/academic alliances result in improved cancer patient care and provider efficacy. Here, we review the community oncology and academic medical center alliance. We examine how practitioners may leverage academic center precision medicine-based cancer genetics and genomics programs to advance their patients' needs. We highlight a number of project initiatives at the City of Hope Comprehensive Cancer Center that seek to optimize community oncology and academic cancer center precision medicine interactions.