Synergistic effects of complex drug combinations in colorectal cancer cells predicted by logical modelling

Drug combinations have been proposed to combat drug resistance in cancer, but due to the large number of possible drug targets, in vitro testing of all possible combinations of drugs is challenging. Computational models of a disease hold great promise as tools for prediction of response to treatment, and here we constructed a logical model integrating signaling pathways frequently dysregulated in cancer, as well as pathways activated upon DNA damage, to study the effect of clinically relevant drug combinations. By fitting the model to a dataset of pairwise combinations of drugs targeting MEK, PI3K, and TAK1, as well as several clinically approved agents (palbociclib, olaparib, oxaliplatin, and 5FU), we were able to perform model simulations that allowed us to predict more complex drug combinations, encompassing sets of three and four drugs, with potentially stronger effects compared to pairwise drug combinations. All predicted third-order synergies, as well as a subset of non-synergies, were successfully confirmed by in vitro experiments in the colorectal cancer cell line HCT-116, highlighting the strength of using computational strategies to rationalize drug testing

A Study of Models for Prediction of Treatment Response in Cancer

Abstract in Norwegian Kreft kjennetegnes av molekylære forandringer som resulterer i unormal og høy celledeling. Opp gjennom årene har et vesentlig antall kjemoterapier rettet mot å hemme denne celledelingen blitt godkjent for bruk i behandling av kreft. Anvendelsen av disse har ført til generelt forbedrede prognoser for kreftpasienter, men fortsatt er det slik at langt fra alle pasienter responderer på slik behandling, og i tillegg opplever mange bivirkninger. For å øke effekten av kreftbehandling, fokuserer mye av dagens forskning på mulighetene for mer målrettet behandling ved å spesifikt angripe de molekylære endringene som gir opphav til sykdommen. Denne behandlingsformen antas å være gunstig av flere grunner, blant annet ved at bivirkningene blir mindre, og ved at mulighetene for persontilpasset behandling blir større. Selv om målrettet behandling i teorien er en lovende strategi, er det også flere utfordringer. En av disse utfordringene er knyttet til plastisiteten til kreftceller, der endringer i kreftcellenes molekylære signalertrafikk ofte gir opphav til behandlingsresistens. Bruken av medikamentkombinasjoner har vist seg å være en effektiv strategi for å omgå resistens, men på grunn av det astronomiske antallet mulige kombinasjoner som må undersøkes eksperimentelt, har relativt få blitt vurdert, godkjent, og nådd klinisk bruk. I tillegg antas mangler i den biologiske likheten mellom mange av dagens eksperimentelle kreftmodeller og virkelige svulster å føre til lite samsvar mellom eksperimentelle og kliniske responser. Med hovedmål om å øke kunnskapen om hvordan kreftbehandling kan effektiviseres, var arbeidet i denne doktorgraden spesielt rettet mot å undersøke 1) hvordan bruk av mer avanserte eksperimentelle kreftmodeller, med antatt økt klinisk relevans, kan brukes i høykapasitets-utprøving av mange medisiner, og 2) hvordan datamodeller kan brukes som verktøy i søket etter effektive medikamentkombinasjoner. Gjennom en storskala studie som studerte effekten av 21 medikamentkombinasjoner i klassiske (2D) og mer avanserte (3D) eksperimentelle kreftmodeller, ønsket vi å finne forskjellene i medikamentrespons forårsaket av forskjeller i den tredimensjonale oppbyggingen av kreftsvulster. Resultatene fra studien viste signifikante forskjeller i kombinasjonseffekt mellom kreftmodeller, som igjen belyser viktigheten av å nøye vurdere den kliniske relevansen av ulike modeller ved design av eksperimentelle studier. Av de 21 medikamentkombinasjonene som ble testet i studien, ble en betydelig andel funnet å være ineffektiv i begge modellene. For å vise at datakraft kan brukes til å forutsi medikamentrespons og dermed fungere som et verktøy for effektivisering av eksperimentelle studier, designet vi en datamodell basert på medikamentene som er inkludert i kombinasjonsstudien vår. Datamodellen ble oppdatert basert på eksperimentelle funn, og kunne deretter brukes til å identifisere en rekke nye medikamentkombinasjoner med mulig høyere effekt. Alle disse ble bevist riktige i en oppfølgingsstudie, som viser kraften i å bruke datamodeller for å effektivisere eksperimentelle studier av medikamentkombinasjoner. Avslutningsvis, rettet mot å ytterligere øke den kliniske relevansen av eksperimentelle kreftmodeller, utviklet vi en metode for medikamentresponsstudier i primære pasientderiverte kreftmodeller (sfæroider). Denne studien viste klare forskjeller i respons mellom sfæroider fra forskjellige pasienter, som igjen understreker relevansen av persontilpasset behandling av kreft

Development of a combined In-Cell ELISA and flow cytometry method for quantification of uptake of PEGylated nanoparticles by Raw264.7 and HepG2 cells

Cancer is one of today’s most common causes of human mortality. For long, chemotherapeutics has been a conventional treatment of the disease, but due to its low precision and high frequency of side effects, this treatment has been highly debated. Nanosized particles, so called nanoparticles (NPs), have emerged as a promising tool for cancer treatment, due to their ability of selectively reaching tumor sites. Developing biocompatible NPs has turned out to be challenging, due to the particles’ tendency of interacting with proteins and immune cells present throughout the blood, interactions further leading to removal of the NPs from the bloodstream. This removal strongly reduces the circulation half-life of NPs in the blood, something that in turn reduces the therapeutic efficacy of the particles. Coating of NPs is often used as a tool for increasing the half-life of the particles in the blood. By attaching coating molecules to the surface of the NPs, protein interaction and subsequent removal of the particles from the bloodstream can be reduced. Polyethylene glycol (PEG) is one of the most common polymers used for coating of NPs. The main goal of this project was to study whether coating degree and PEG length of NPs had an impact on the uptake of these particles by Raw264.7 (macrophage cell line) and HepG2 (hepatocyte cell line) cells. To study the impact of coating on cellular uptake, organosilicophosphonate core NPs were coated with PEG of three different lengths, each at two different coating degrees. The coated NPs were characterized with respect to size, pH, charge, coating degree and tendency to aggregate in culture media. Analytical techniques such as gel permeation chromatography (GPC), dynamic light scattering (DLS), zeta potential measurements and inductively coupled plasma optical emission spectroscopy (ICP-OES) were used for characterization of the particles. Uptake of PEG coated as well as bare NPs by Raw264.7 and HepG2 cells was quantified by developing and using a combined method constituting In-Cell enzyme-linked immunosorbent assay (In-Cell ELISA), ELISA and flow cytometry. Finally, the intracellular distribution of NPs in Raw264.7 cells was studied using fluorescence microscopy. Indicatively, coating degree as well as PEG length had an impact on the cellular uptake of NPs. NPs coated with long-length PEG showed a lower uptake, compared to NPs coated with short-length PEG. In addition, NPs coated with a high amount of PEG showed a lower uptake than NPs coated with a low amount of PEG. Preliminary indications of differences in intracellular distribution of PEG coated NPs in Raw264.7 cells were seen.Nanoparticles are small particles that have emerged as a promising tool for cancer treatment. In order for these particles to efficiently treat cancer they have to avoid being eaten by cells from the immune system. This can be done by putting “invisibility jackets” on the particles. Cancer kills 8 million people every year and finding efficient cancer treatments has turned out to be hard. Many of the existing treatments kill cancer cells efficiently, but the problem is that healthy cells are affected as well. This causes unwanted side effects. Nanoparticles are small particles that have emerged as a novel tool for cancer treatment. Nanoparticles have the possibility to reach cancer cells without affecting healthy cells and in this way side effects can be reduced. However, treating cancer with nanoparticles is not completely without problems. One problem that may arise is that the nanoparticles may be removed from the body by cells called immune cells. Immune cells are supposed to protect the body from intruders. Even if the nanoparticles are used for a good reason (to treat the cancer) the immune cells will regard them as intruders. The immune cells will therefore remove the nanoparticles by eating them. This means that the particles will not reach the cancer cells, in turn meaning that they will not treat the cancer. The nanoparticles can however be prevented from being eaten if they become invisible to the immune cells. An “invisibility jacket”, made of molecules called PEG, can for example be put onto the particles. The PEG can be regarded as the textile fibers in the jacket. Like with all clothing, the quality of the invisibility jacket will vary depending on the material in it (i.e. the PEG). Just as with textile fibers PEG molecules can be very different. Some of them will generate good invisibility jackets, which will make the nanoparticles more invisible. Others will generate bad jackets. In an experiment the quality of different invisibility jackets was evaluated. Some of the jackets were made of long PEG, others of short or medium PEG. The number of PEGs in the jackets also varied; some jackets contained many PEG molecules, whereas others contained few. The quality of the different invisibility jackets was evaluated by putting them onto nanoparticles. The nanoparticles were then allowed to meet immune cells. If many nanoparticles were eaten by the immune cells, the invisibility jacket was regarded as bad (it did not make the particles invisible), whereas if few particles were eaten, the jacket was regarded as good (it made the particles more invisible). It was found that jackets made of long PEGs were better (they made the particles more invisible), compared to jackets made of short PEGs. It was also found that jackets containing many PEGs were better compared to jackets containing few PEGs

Köpcentrumturism : En studie om hur ett köpcentrum kan uppfattas som en turistdestination

Syfte: Uppsatsen har som mål att inventera de faktorerna som anses vara väsentliga för att ett köpcentrum ska kunna uppfattas som en turistdestination. Studien har fokus ur ett kundperspektiv. Syftet är att göra en inventering av olika faktorer från teori och empiri för att sedan se vilka faktorer som är väsentliga för att ett köpcentrum ska kunna uppfattas som en turistdestination. Att göra på det sättet får vi ut om ett köpcentrum har de faktorer som behövs för att kunna anta begreppet turistdestination. Forskningsfråga: Vilka faktorer är väsentliga för att ett köpcentrum ska kunna uppfattas som en turistdestination? Metod: Vi har använt oss av en deduktiv metod, vilket gjorde att vi utgick från olika teoretiska begrepp som vi sedan jämförde med enkäter och observationer från empirin. Kvantitativ metod blev det då vi använda oss utav enkäter samt observationer. Observationer, enkäter samt olika teorier ställs mot varandra och diskuteras i texten för att sedan analyseras för att ge en slutsats. Resultat: Resultatet av vår undersökning gav ett nytt begrepp köpcentrumsturism. Då sex olika faktorer om turistdestinationer och köpcentrum från teorin jämförts med empiri från enkäter och observationer inom samma ämne kunde resultatet formas. Ett köpcentrum har de faktorer så som anläggning, service, attraktion, upplevelse, tillgänglighet samt turism som en turistdestination behöver. Där av kan också ett köpcentrum uppfattas som en turistdestination

High-throughput screening reveals higher synergistic effect of MEK inhibitor combinations in colon cancer spheroids

Drug combinations have been proposed to combat drug resistance, but putative treatments are challenged by low bench-to-bed translational efficiency. To explore the effect of cell culture format and readout methods on identification of synergistic drug combinations in vitro, we studied response to 21 clinically relevant drug combinations in standard planar (2D) layouts and physiologically more relevant spheroid (3D) cultures of HCT-116, HT-29 and SW-620 cells. By assessing changes in viability, confluency and spheroid size, we were able to identify readout- and culture format-independent synergies, as well as synergies specific to either culture format or readout method. In particular, we found that spheroids, compared to 2D cultures, were generally both more sensitive and showed greater synergistic response to combinations involving a MEK inhibitor. These results further shed light on the importance of including more complex culture models in order to increase the efficiency of drug discovery pipelines.publishedVersio

Systematic review: predictive value of organoids in colorectal cancer

Abstract While chemotherapy alone or in combination with radiotherapy and surgery are important modalities in the treatment of colorectal cancer, their widespread use is not paired with an abundance of diagnostic tools to match individual patients with the most effective standard-of-care chemo- or radiotherapy regimens. Patient-derived organoids are tumour-derived structures that have been shown to retain certain aspects of the tissue of origin. We present here a systematic review of studies that have tested the performance of patient derived organoids to predict the effect of anti-cancer therapies in colorectal cancer, for chemotherapies, targeted drugs, and radiation therapy, and we found overall a positive predictive value of 68% and a negative predictive value of 78% for organoid informed treatment, which outperforms response rates observed with empirically guided treatment selection

High-throughput screening reveals higher synergistic effect of MEK inhibitor combinations in colon cancer spheroids

Drug combinations have been proposed to combat drug resistance, but putative treatments are challenged by low bench-to-bed translational efficiency. To explore the effect of cell culture format and readout methods on identification of synergistic drug combinations in vitro, we studied response to 21 clinically relevant drug combinations in standard planar (2D) layouts and physiologically more relevant spheroid (3D) cultures of HCT-116, HT-29 and SW-620 cells. By assessing changes in viability, confluency and spheroid size, we were able to identify readout- and culture format-independent synergies, as well as synergies specific to either culture format or readout method. In particular, we found that spheroids, compared to 2D cultures, were generally both more sensitive and showed greater synergistic response to combinations involving a MEK inhibitor. These results further shed light on the importance of including more complex culture models in order to increase the efficiency of drug discovery pipelines