Strategies to decrease inequalities in cancer therapeutics, care and prevention: Proceedings on a conference organized by the Pontifical Academy of Sciences and the European Academy of Cancer Sciences, Vatican City, February 23-24, 2023.

Analyses of inequalities related to prevention and cancer therapeutics/care show disparities between countries with different economic standing, and within countries with high Gross Domestic Product. The development of basic technological and biological research provides clinical and prevention opportunities that make their implementation into healthcare systems more complex, mainly due to the growth of Personalized/Precision Cancer Medicine (PCM). Initiatives like the USA-Cancer Moonshot and the EU-Mission on Cancer and Europe's Beating Cancer Plan are initiated to boost cancer prevention and therapeutics/care innovation and to mitigate present inequalities. The conference organized by the Pontifical Academy of Sciences in collaboration with the European Academy of Cancer Sciences discussed the inequality problem, dependent on the economic status of a country, the increasing demands for infrastructure supportive of innovative research and its implementation in healthcare and prevention programs. Establishing translational research defined as a coherent cancer research continuum is still a challenge. Research has to cover the entire continuum from basic to outcomes research for clinical and prevention modalities. Comprehensive Cancer Centres (CCCs) are of critical importance for integrating research innovations to preclinical and clinical research, as for ensuring state-of-the-art patient care within healthcare systems. International collaborative networks between CCCs are necessary to reach the critical mass of infrastructures and patients for PCM research, and for introducing prevention modalities and new treatments effectively. Outcomes and health economics research are required to assess the cost-effectiveness of new interventions, currently a missing element in the research portfolio. Data sharing and critical mass are essential for innovative research to develop PCM. Despite advances in cancer research, cancer incidence and prevalence is growing. Making cancer research infrastructures accessible for all patients, considering the increasing inequalities, requires science policy actions incentivizing research aimed at prevention and cancer therapeutics/care with an increased focus on patients' needs and cost-effective healthcare.

Breeze 2.0: an interactive web-tool for visual analysis and comparison of drug response data.

Functional precision medicine (fPM) offers an exciting, simplified approach to finding the right applications for existing molecules and enhancing therapeutic potential. Integrative and robust tools ensuring high accuracy and reliability of the results are critical. In response to this need, we previously developed Breeze, a drug screening data analysis pipeline, designed to facilitate quality control, dose-response curve fitting, and data visualization in a user-friendly manner. Here, we describe the latest version of Breeze (release 2.0), which implements an array of advanced data exploration capabilities, providing users with comprehensive post-analysis and interactive visualization options that are essential for minimizing false positive/negative outcomes and ensuring accurate interpretation of drug sensitivity and resistance data. The Breeze 2.0 web-tool also enables integrative analysis and cross-comparison of user-uploaded data with publicly available drug response datasets. The updated version incorporates new drug quantification metrics, supports analysis of both multi-dose and single-dose drug screening data and introduces a redesigned, intuitive user interface. With these enhancements, Breeze 2.0 is anticipated to substantially broaden its potential applications in diverse domains of fPM.

The Porto European Cancer Research Summit 2021.

Key stakeholders from the cancer research continuum met in May 2021 at the European Cancer Research Summit in Porto to discuss priorities and specific action points required for the successful implementation of the European Cancer Mission and Europe's Beating Cancer Plan (EBCP). Speakers presented a unified view about the need to establish high-quality, networked infrastructures to decrease cancer incidence, increase the cure rate, improve patient's survival and quality of life, and deal with research and care inequalities across the European Union (EU). These infrastructures, featuring Comprehensive Cancer Centres (CCCs) as key components, will integrate care, prevention and research across the entire cancer continuum to support the development of personalized/precision cancer medicine in Europe. The three pillars of the recommended European infrastructures - namely translational research, clinical/prevention trials and outcomes research - were pondered at length. Speakers addressing the future needs of translational research focused on the prospects of multiomics assisted preclinical research, progress in Molecular and Digital Pathology, immunotherapy, liquid biopsy and science data. The clinical/prevention trial session presented the requirements for next-generation, multicentric trials entailing unified strategies for patient stratification, imaging, and biospecimen acquisition and storage. The third session highlighted the need for establishing outcomes research infrastructures to cover primary prevention, early detection, clinical effectiveness of innovations, health-related quality-of-life assessment, survivorship research and health economics. An important outcome of the Summit was the presentation of the Porto Declaration, which called for a collective and committed action throughout Europe to develop the cancer research infrastructures indispensable for fostering innovation and decreasing inequalities within and between member states. Moreover, the Summit guidelines will assist decision making in the context of a unique EU-wide cancer initiative that, if expertly implemented, will decrease the cancer death toll and improve the quality of life of those confronted with cancer, and this is carried out at an affordable cost.

Genetic Risk Score for Serum 25-Hydroxyvitamin D Concentration Helps to Guide Personalized Vitamin D Supplementation in Healthy Finnish Adults.

BACKGROUND: Genetic factors modify serum 25-hydroxyvitamin D [25(OH)D] concentration and can affect the optimal intake of vitamin D. OBJECTIVES: We aimed to personalize vitamin D supplementation by applying knowledge of genetic factors affecting serum 25(OH)D concentration. METHODS: We performed a genome-wide association study of serum 25(OH)D concentration in the Finnish Health 2011 cohort (n = 3339) using linear regression and applied the results to develop a population-matched genetic risk score (GRS) for serum 25(OH)D. This GRS was used to tailor vitamin D supplementation for 96 participants of a longitudinal Digital Health Revolution (DHR) Study. The GRS, serum 25(OH)D concentrations, and personalized supplementation and dietary advice were electronically returned to participants. Serum 25(OH)D concentrations were assessed using immunoassays and vitamin D intake using FFQs. In data analyses, cross-sectional and repeated-measures statistical tests and models were applied as described in detail elsewhere. RESULTS: GC vitamin D-binding protein and cytochrome P450 family 2 subfamily R polypeptide 1 genes showed genome-wide significant associations with serum 25(OH)D concentration. One single nucleotide polymorphism from each locus (rs4588 and rs10741657) was used to develop the GRS. After returning data to the DHR Study participants, daily vitamin D supplement users increased from 32.6% to 60.2% (P = 6.5 × 10-6) and serum 25(OH)D concentration from 64.4 ± 20.9 nmol/L to 68.5 ± 19.2 nmol/L (P = 0.006) between August and November. Notably, the difference in serum 25(OH)D concentrations between participants with no risk alleles and those with 3 or 4 risk alleles decreased from 20.7 nmol/L to 8.0 nmol/L (P = 0.0063). CONCLUSIONS: We developed and applied a population-matched GRS to identify individuals genetically predisposed to low serum 25(OH)D concentration. We show how the electronic return of individual genetic risk, serum 25(OH)D concentrations, and factors affecting vitamin D status can be used to tailor vitamin D supplementation. This model could be applied to other populations and countries.

Breeze: an integrated quality control and data analysis application for high-throughput drug screening.

SUMMARY: High-throughput screening (HTS) enables systematic testing of thousands of chemical compounds for potential use as investigational and therapeutic agents. HTS experiments are often conducted in multi-well plates that inherently bear technical and experimental sources of error. Thus, HTS data processing requires the use of robust quality control procedures before analysis and interpretation. Here, we have implemented an open-source analysis application, Breeze, an integrated quality control and data analysis application for HTS data. Furthermore, Breeze enables a reliable way to identify individual drug sensitivity and resistance patterns in cell lines or patient-derived samples for functional precision medicine applications. The Breeze application provides a complete solution for data quality assessment, dose-response curve fitting and quantification of the drug responses along with interactive visualization of the results. AVAILABILITY AND IMPLEMENTATION: The Breeze application with video tutorial and technical documentation is accessible at https://breeze.fimm.fi; the R source code is publicly available at https://github.com/potdarswapnil/Breeze under GNU General Public License v3.0. CONTACT: swapnil.potdar@helsinki.fi. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comprehensive Drug Testing of Patient-derived Conditionally Reprogrammed Cells from Castration-resistant Prostate Cancer.

BACKGROUND: Technology development to enable the culture of human prostate cancer (PCa) progenitor cells is required for the identification of new, potentially curative therapies for PCa. OBJECTIVE: We established and characterized patient-derived conditionally reprogrammed cells (CRCs) to assess their biological properties and to apply these to test the efficacies of drugs. DESIGN, SETTING, AND PARTICIPANTS: CRCs were established from seven patient samples with disease ranging from primary PCa to advanced castration-resistant PCa (CRPC). The CRCs were characterized by genomic, transcriptomic, protein expression, and drug profiling. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The phenotypic quantification of the CRCs was done based on immunostaining followed by image analysis with Advanced Cell Classifier using Random Forest supervised machine learning. Copy number aberrations (CNAs) were called from whole-exome sequencing and transcriptomics using in-house pipelines. Dose-response measurements were used to generate multiparameter drug sensitivity scores using R-statistical language. RESULTS AND LIMITATIONS: We generated six benign CRC cultures which all had an androgen receptor-negative, basal/transit-amplifying phenotype with few CNAs. In three-dimensional cell culture, these cells could re-express the androgen receptor. The CRCs from a CRPC patient (HUB.5) displayed multiple CNAs, many of which were shared with the parental tumor. We carried out high-throughput drug-response studies with 306 emerging and clinical cancer drugs. Using the benign CRCs as controls, we identified the Bcl-2 family inhibitor navitoclax as the most potent cancer-specific drug for the CRCs from a CRPC patient. Other drug efficacies included taxanes, mepacrine, and retinoids. CONCLUSIONS: Comprehensive cancer pharmacopeia-wide drug testing of CRCs from a CRPC patient highlighted both known and novel drug sensitivities in PCa, including navitoclax, which is currently being tested in clinical trials of CRPC. PATIENT SUMMARY: We describe an approach to generate patient-derived cancer cells from advanced prostate cancer and apply such cells to discover drugs that could be applied in clinical trials for castration-resistant prostate cancer.

Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose-response data.

MOTIVATION: Most data analysis tools for high-throughput screening (HTS) seek to uncover interesting hits for further analysis. They typically assume a low hit rate per plate. Hit rates can be dramatically higher in secondary screening, RNAi screening and in drug sensitivity testing using biologically active drugs. In particular, drug sensitivity testing on primary cells is often based on dose-response experiments, which pose a more stringent requirement for data quality and for intra- and inter-plate variation. Here, we compared common plate normalization and noise-reduction methods, including the B-score and the Loess a local polynomial fit method under high hit-rate scenarios of drug sensitivity testing. We generated simulated 384-well plate HTS datasets, each with 71 plates having a range of 20 (5%) to 160 (42%) hits per plate, with controls placed either at the edge of the plates or in a scattered configuration. RESULTS: We identified 20% (77/384) as the critical hit-rate after which the normalizations started to perform poorly. Results from real drug testing experiments supported this estimation. In particular, the B-score resulted in incorrect normalization of high hit-rate plates, leading to poor data quality, which could be attributed to its dependency on the median polish algorithm. We conclude that a combination of a scattered layout of controls per plate and normalization using a polynomial least squares fit method, such as Loess helps to reduce column, row and edge effects in HTS experiments with high hit-rates and is optimal for generating accurate dose-response curves. CONTACT: john.mpindi@helsinki.fi. AVAILABILITY AND IMPLEMENTATION: Supplementary information: R code and Supplementary data are available at Bioinformatics online.

Critical comparison of virtual screening methods against the MUV data set.

In the current work, we measure the performance of seven ligand-based virtual screening tools--five similarity search methods and two pharmacophore elucidators--against the MUV data set. For the similarity search tools, single active molecules as well as active compound sets clustered in terms of their chemical diversity were used as templates. Their score was calculated against all inactive and active compounds in their target class. Subsequently, the scores were used to calculate different performance metrics including enrichment factors and AUC values. We also studied the effect of data fusion on the results. To measure the performance of the pharmacophore tools, a set of active molecules was picked either random- or chemical diversity-based from each target class to build a pharmacophore model which was then used to screen the remaining compounds in the set. Our results indicate that template sets selected by their chemical diversity are the best choice for similarity search tools, whereas the optimal training sets for pharmacophore elucidators are based on random selection underscoring that pharmacophore modeling cannot be easily automated. We also suggest a number of improvements for future benchmark sets and discuss activity cliffs as a potential problem in ligand-based virtual screening.

Comparison of structure fingerprint and molecular interaction field based methods in explaining biological similarity of small molecules in cell-based screens.

In this work, we calculated the pair wise chemical similarity for a subset of small molecules screened against the NCI60 cancer cell line panel. Four different compound similarity calculation methods were used: Brutus, GRIND, Daylight and UNITY. The chemical similarity scores of each method were related to the biological similarity data set. The same was done also for combinations of methods. In the end, we had an estimate of biological similarity for a given chemical similarity score or combinations thereof. The data from above was used to identify chemical similarity ranges where combining two or more methods (data fusion) led to synergy. The results were also applied in ligand-based virtual screening using the DUD data set. In respect to their ability to enrich biologically similar compound pairs, the ranking of the four methods in descending performance is UNITY, Daylight, Brutus and GRIND. Combining methods resulted always in positive synergy within a restricted range of chemical similarity scores. We observed no negative synergy. We also noted that combining three or four methods had only limited added advantage compared to combining just two. In the virtual screening, using the estimated biological similarity for ranking compounds produced more consistent results than using the methods in isolation.

Thymidylate synthase expression predicts the response to 5-fluorouracil-based adjuvant therapy in pancreatic cancer.

PURPOSE: Thymidylate synthase (TS) is the target enzyme for 5-fluorouracil (5-FU), and TS expression may determine clinical response and survival after therapy with 5-FU in colorectal cancer. 5-FU is also widely used in the adjuvant therapy of pancreatic cancer. Therefore, we explored the hypothesis that TS expression was associated with patient prognosis and the response to adjuvant therapy in pancreatic cancer. EXPERIMENTAL DESIGN: Cylindrical tissue cores from a large retrospective, nonrandomized series covering 132 resected patients were used to build a pancreatic cancer tissue microarray. TS expression was determined using immunohistochemistry. RESULTS: High intratumoral TS expression and low intratumoral TS expression were present in 83 of 132 (63%) and 49 of 132 (37%) tumors, respectively. Median survival among patients with low intratumoral TS expression (18 months) was longer than that among patients with high TS expression (12 months). In multivariate analysis, more advanced pathological stage [risk ratio (RR) = 1.70; P = 0.015], poorly differentiated histology (RR = 1.71; P = 0.015), management with adjuvant therapy (RR = 0.49; P = 0.011), and high TS expression [RR = 1.66; 95% confidence interval (CI) = 1.05-2.63; P = 0.029] were independent predictors of mortality. The risk of death was significantly reduced by any adjuvant therapy (RR = 0.40; 95% CI = 0.18-0.90; P = 0.001) among patients with high TS expression. This difference in survival among patients with low- and high-TS-expressing tumors became more significant when the analysis was restricted to the 73 patients receiving 5-FU-based adjuvant therapy (RR = 0.37; 95% CI = 0.16-0.86; P = 0.0006). In contrast, 5-FU-based adjuvant therapy did not influence survival among patients with low-TS-expressing pancreatic cancer. CONCLUSIONS: High TS expression is a marker of poor prognosis in resected pancreatic cancer. Patients with high intratumoral TS expression benefit from adjuvant therapy.

A genomic map of a 6-Mb region at 13q21-q22 implicated in cancer development: identification and characterization of candidate genes.

Chromosomal region 13q21-q22 harbors a putative breast cancer susceptibility gene and has been implicated as a common site for somatic deletions in a variety of malignant tumors. We have built a complete physical clone contig for a region between D13S1308 and AFM220YE9 based on 18 yeast artificial chromosome and 81 bacterial artificial chromosome (BAC) clones linked together by 22 genetic markers and 61 other sequence tagged sites. Combining data from 47 sequenced BACs (as of June 2001), we have assembled in silico an integrated 5.7-Mb genomic map with 90% sequence coverage. This area contains eight known genes, two hypothetical proteins, 24 additional Unigene clusters, and approximately 100 predicted genes and exons. We have determined the cDNA and genomic sequence, and tissue expression profiles for the KIAA1008 protein (homologous to the yeast mitotic control protein dis3+), KLF12 (AP-2 repressor), progesterone induced blocking factor 1, zinc finger transcription factor KLF5, and LIM domain only-7, and for the hypothetical proteins FLJ22624 and FLJ21869. Mutation screening of the five known genes in 19 breast cancer families has revealed numerous polymorphisms, but no deleterious mutations. These data provide a basis and resources for further analyses of this chromosomal region in the development of cancer.