Abstract
Liposomal chemotherapy offers several advantages over conventional therapies, including high intratumoral drug delivery, reduced side effects, prolonged circulation time, and the possibility to dose higher. The efficient delivery of liposomal chemotherapeutics relies, however, on the enhanced permeability and retention (EPR) effect, which refers to the ability of macromolecules to extravasate leaky tumor vessels and accumulate in the tumor tissue. Using a panel of human xenograft tumors, we evaluated the influence of the EPR effect on liposomal distribution in vivo by injection of pegylated liposomes radiolabeled with 111In. Liposomal accumulation in tumors and organs was followed over time by SPECT/CT imaging. We observed that fast-growing xenografts, which may be less representative of tumor development in patients, showed higher liposomal accumulation than slow-growing xenografts. Additionally, several other parameters known to influence the EPR effect were evaluated, such as blood and lymphatic vessel density, intratumoral hypoxia, and the presence of infiltrating macrophages. The investigation of various parameters showed a few correlations. Although hypoxia, proliferation, and macrophage presence were associated with tumor growth, no hard conclusions or predictions could be made regarding the EPR effect or liposomal uptake. However, liposomal uptake was significantly correlated with tumor growth, with fast-growing tumors showing a higher uptake, although no biological determinants could be elucidated to explain this correlation.
Almost all nanocarriers, including liposomes (1), and many other anticancer drugs rely on the enhanced permeability and retention (EPR) effect for accumulation in tumor tissue. The EPR effect is defined as the process of extravasation of large molecules from leaky tumor vasculature, leading to accumulation in tumor tissue (2). The EPR effect is dependent on many biological parameters, with the development of the abnormal tumor vasculature playing a major role, although other parameters such as the composition of the surrounding stroma, absence of functional lymphatics, and presence of tumor infiltrating macrophages also play an important role (3). Abnormally upregulated growth factors affect the vasculature of the tumor (4) and lead to large endothelial junctions at the luminal surface, resulting in a leaky vasculature (5). In addition, vessels lack smooth muscle cell layers and supporting cells (6), and the fast recruitment of blood vessels results in tumor neovasculature that is not hierarchically organized, causing a heterogeneous spatial distribution (4,7,8). Finally, tumors overexpress many permeability-enhancing factors, which contribute to an enhanced EPR effect (9). These observations suggest that most tumors may be susceptible to nanoparticle treatment, but thus far such particles show only a limited effect in vivo due to various barriers such as the mononuclear phagocyte system, extracellular matrix, low pH, low oxygenation, and high interstitial fluid pressure (10–12). The intra- and intertumoral heterogeneity are major factors influencing the EPR effect, especially in patients, where tumor growth and vessel development are slower (13).
The Food and Drug Administration–approved liposomal doxorubicin (Doxil or Caelyx; Janssen Products, LP) is one of the most successful nanoparticle drugs for several cancer types (14–17). Unfortunately, Doxil has a limited effect on overall survival when compared with conventional chemotherapy (3), as has been shown in various clinical trials (17–19). This and previous results suggest that tumor physiology influences the efficacy of nanoparticle drugs, possibly due to variations in the EPR effect (20).
Because the EPR effect is essential for the efficacy and mode of action of liposomes in vivo, the aim of this study was to investigate major parameters influencing EPR and their effects on liposomal uptake in tumor xenografts. To this end, we injected unloaded, long-circulating, pegylated Doxil-like liposomes to determine liposomal tumor accumulation. Various human xenograft tumor models, including squamous cell carcinoma, breast cancer, and pancreatic cancer, were selected on the basis of tumor growth rate, taking into consideration that slow-growing tumors are more representative of tumor growth in patients. Our results show that it is difficult to elucidate single determinants of the EPR effect and we therefore suggest that liposomal uptake and distribution in tumors is a multifactorial process involving various physiological and morphological parameters.
MATERIALS AND METHODS
Preparation of Unloaded Doxil Liposomes
Unloaded Doxil-like liposomes were prepared as described in the supplemental information (supplemental materials are available at http://jnm.snmjournals.org).
Cell Lines
All cell lines were obtained from the American Type Culture Collection and routinely cultured as described in the supplemental information.
111In Labeling of Liposomes
Liposomes contained 0.1 mol% 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-diethylenetriaminepentaacetic acid (DTPA-PE) lipid (Avanti Polar Lipids, Inc.), which enabled conjugation with 111InCl (Mallinckrodt Medical B.V.). Approximately 30 MBq of 111In were incubated per μmol of liposomes for 15 min at room temperature; 2.5 M sodium acetate was used to set the pH at 5.0. Instant thin-layer chromatography silica gel (silica gel–coated paper [Varian Inc.]; 0.1 M sodium citrate, mobile phase) was performed to ascertain complete labeling (21). Because of the molar excess of DTPA lipid over 111In, lipid labeling efficiencies of greater than 99% were achieved. Before injections, the volume was adjusted to 200 μL per μmol of liposomes with N-(2-hydroxyethyl)piperazine-N-(2-ethanesulfonic acid) (HEPES)–buffered saline (10 mM HEPES, 135 mM NaCl, pH 7.4).
In Vivo Biodistribution Studies
Tumor inoculation of NMRI nu/nu mice is described in the supplemental information.
Mice were injected intravenously (tail) with 1 μmol of liposomes labeled with 111In. Immediately after injection, the animal was scanned (t = 0), using the following settings: SPECT (nanoSPECT/CT; Mediso Medical Imaging Systems) static scan with 20 projections, 60 s/projection, and a quality factor of 0.8. APT1 apertures were used with 1.4 mm diameter pinholes (FOV 24 + 16 mm). The following were the CT scan settings: 240 projections, 45 kVp tube voltage, and 500 ms exposure. The t = 0 scan was followed by scans at 4, 8, 12, and 24 h after injection. SPECT/CT analysis was performed using InVivoScope/VivoQuant software (inviCRO). Three-dimensional regions of interest were drawn over the heart (blood pool) and tumor to calculate uptake of 111In liposomes at the selected time points, after which they were corrected for volume (%ID/cm3). Representative figures were made with the same software after correction for 111In decay.
Ex Vivo Biodistribution
Tumors and tissue samples (blood [cardiac puncture], heart, lung, liver, spleen, pancreas, kidney, stomach, duodenum, caecum, colon, tail, and muscle) were harvested and weighed. Radioactivity was determined with a γ-counter (Perkin Elmer), and liposomal accumulation was calculated as percentage injected dose per gram of tissue (%ID/g) and corrected for radioactive decay of 111In (half-life, 2.81 d). The injected dose was calculated by measuring the syringe before and after injection.
Immunohistochemical Staining
After the final scan, pimonidazole (60 mg/kg of body weight) was injected intraperitoneally and allowed to circulate for 4 h before dissection of tumor and organs. Harvested tumors were snap-frozen in liquid nitrogen, and frozen tumor sections were used for immunohistochemical staining as described in the supplemental information. Sections were stained for blood vessels (CD31), pegylated liposomes (PEG), macrophages (CD11b), lymphatic vessels (LYVE-1), vessel integrity (collagen IV), and hypoxia (pimonidazole).
Tile scans of fluorescently stained tumor sections were acquired using an LSM 510 Meta confocal microscope (Carl Zeiss B.V.) with a Plan-Neofluar 10× objective. Quantifications were performed with ImageJ (National Institutes of Health), using manual thresholding (range, 0–255) for density analysis and a colocalization threshold tool to ascertain the ratio between PEG and CD31, and collagen IV and CD31. To avoid regional bias, all quantifications were performed on whole tile scans of tumor sections.
Statistical Analysis
Statistical analyses were performed using GraphPad Prism (version 5.01; GraphPad Software). The differences between fast-, intermediate-, and slow-growing xenografts were evaluated using 1-way ANOVA with nonparametric Kruskal–Wallis H test and Dunn’s multiple comparison post-test. Comparisons between the high- and low-uptake groups were performed using the 2-tailed Mann–Whitney U test. Correlation analysis between the various parameters evaluated in this study was performed using the Spearman rho test. All statistical tests were 2-sided, and a P value of lower than 0.05 was considered statistically significant.
RESULTS
Characterization of Liposomes
Liposomal characteristics, such as size and polydispersity, were confirmed to be similar to Doxil and are described in detail in the supplemental information.
To perform image-guided delivery, the liposomes were labeled with 111In for SPECT imaging. The radiolabeling efficiency with 111In after 15 min labeling at room temperature was greater than 99%. The 111In-conjugated liposomes were tested for stability for up to 96 h as shown in Supplemental Figure 1. On average the liposomes were labeled with 34.54 ± 1.04 MBq of 111In per μmol of liposomes.
Tumor Characterization
The supplemental information and Supplemental Figure 2 show the subdivision of tumor types based on growth rate.
In Vivo Localization of 111In-Labeled Liposomes in Human Tumor Xenografts
All mice were injected intravenously with 1 μmol of liposomes labeled with 111In for SPECT imaging. A volume of interest was drawn to quantify liposomal accumulation in the tumor and heart (blood pool). Figure 1A shows accumulation of liposomes in the tumor over time (0, 4, 8, 12, and 24 h after injection) as a percentage injected dose corrected for tumor volume (%ID/cm3). The EPR effect ensured accumulation of liposomes in the tumors over time, whereas the heart showed clearance of liposomes from circulation. In the heart, liposomes still accounted for 6.28 ± 1.50 %ID/cm3 (SD) after 24 h, which was confirmed by t1/2(β) of 16.99 ± 1.81 h (SEM). Figure 1B shows representative mice depicting high liposomal uptake (HPAF-II tumor) and low liposomal uptake (PANC-1 tumor). The SPECT/CT scans show radiolabeled liposomes in circulation, visible in the heart, aortic arch, aorta, and continuing downstream into axillary, hepatic, splenic, renal, and femoral arteries. The liposomes in circulation diminished over time and accumulated in the liver, spleen, and, to varying degrees, the tumor. The liposomes were slowly cleared via the liver and spleen (mononuclear phagocytic system), whereas they were retained in the tumor because of the EPR effect (22). Spleen macrophages and hepatic Kupfer cells can ingest liposomes and are responsible for liposome accumulation in these organs. Liver fenestrations (100 nm) and spleen lumina (up to 5 μm) will further contribute to this accumulation (23).
(A) Average tumor uptake in %ID/cm3 was calculated and is depicted at 0, 4, 8, 12, and 24 h after injection. (B) Representative SPECT/CT scans, adjusted for 111In half-life, of HPAF-II or PANC-1 tumor (white circle)–bearing mice at different time intervals after injection with 111In-labeled liposomes, showing uptake of liposomes over time to a higher (HPAF-II) and lesser (PANC-1) degree.
Supplemental Figure 3 shows all studied tumor types 12 h after injection with radiolabeled liposomes. In addition to differences in total uptake, early variations in intratumoral localization were clearly visible and were probably caused by perfusion differences (24,25). For example, MDA-MB-231 showed a clear uptake in certain regions of the tumor periphery, whereas other regions remained clear. Other tumors such as CFPAC-1 showed a more homogeneous distribution of liposomes. Importantly, the circulation of liposomes and accumulation in liver and spleen were comparable between different tumor models, as confirmed later by the biodistribution data. Liposomes are degraded in the liver after 12–24 h, causing a release of free 111In-DTPA, as detected by low levels of radioactivity in the kidney and bladder, because free 111In-DTPA is rapidly cleared from circulation (26).
Ex Vivo Organ Biodistribution
Radioactivity uptake was measured after resection of the tumors and other relevant tissues and is shown per tumor type in Figure 2 and in more detail for all analyzed organs, including the tumor, in Supplemental Figure 4. The tumors are again arranged from fast- to slow-growing, with levels of uptake ranging from PANC-1 (1.51 ± 0.11 %ID/g) on the low end to CFPAC-1 (7.99 ± 3.59 %ID/g) on the high end. A trend was observed between tumor growth and liposomal uptake (Table 1, P = 0.0104), although outliers, such as A431, BxPC-3, and MDA-MB-468, indicate that other factors are important and may be influencing liposomal accumulation. The reason for this was investigated in more detail using immunohistochemical stainings, but it is, for example, known that the presence of necrotic or poorly vascularized tumor cores in fast-growing tumors may inhibit liposomal uptake (27). Most of the liposomes accumulate in the liver, spleen, and tumor, after which they are cleared. Biodistribution data up to 96 h after injection are shown in Supplemental Figure 5, showing clearance of liposomes from most organs. Liposomal uptake in vivo was compared with other modalities used to detect liposomal uptake and the morphological parameters thought to influence the EPR effect and nanoparticle accumulation. To this end, the tumors were subdivided into high- and low-uptake groups; the high-uptake group included CFPAC-1, HPAF-II, and MDA-MB-231 xenografts. Table 1 shows that the difference in uptake between these groups can be detected with all tested modalities: in vivo SPECT/CT imaging, ex vivo biodistribution, and the anti-PEG antibody staining (P value < 0.0001, < 0.0001, and = 0.0018, respectively). In addition, there was a significant difference in tumor growth, that is, the time required for the tumors to reach 200 mm3 (P = 0.0104). We did not observe a statistically significant correlation with any other morphological parameter evaluated.
Tumor liposome accumulation of 111In in %ID/g at 24 h after intravenous injection, ordered from fast- to slow-growing tumor xenografts.
Comparison of Liposomal Uptake, Tumor Growth, and Morphology in High- Versus Low-Uptake Tumors
Immunohistochemistry
To further investigate the underlying mechanisms of the EPR effect and to find possible explanations for the observed variations in liposomal tumor uptake, several morphological parameters were investigated using immunohistochemistry (Table 1). In Figure 3A, the percentage of total tumor areas positive for liposomal content was calculated using an anti-PEG antibody. This shows a significant correlation with the in vivo and ex vivo quantifications (Supplemental Table 1; P = 0.007 and < 0.001, respectively). Unfortunately, the aforementioned subdivision into slow-, intermediate-, and fast-growing xenografts did not result in significant correlations (Supplemental Table 2). Figure 3B shows HPAF-II and PANC-1 frames to demonstrate the difference of liposome localization in tumors with high and low uptake, respectively. Additionally, the intratumoral localization of the liposomes was determined in relation to blood vessels (CD31) showing that most liposomes had indeed extravasated from the vessels (Fig. 3C), although the distance traveled from the vessels is minimal. The percentage of extravasated liposomes was calculated by subtracting the amount of CD31-colocalized liposomes from the total amount of liposomes present in the area. This is depicted in Figure 3D, in which an example is given of a CD31 staining (green), total PEG staining (red), and the calculated colocalized liposomes (PEG in yellow), which shows liposomes within and extravasated from these vessels (shown in PEG composite in yellow and red, respectively). Although nanoparticle distribution and accumulation is undeniably related to the presence of blood vessels (28), Figure 4A shows that the number of vessels (depicted in percentage mean vessel density) cannot be used to predict the degree of liposomal uptake. For example, AsPC-1 and UACC-893 have a similar uptake of liposomes, depicted with in vivo, ex vivo, and immunohistochemical quantifications, but the mean vessel density of UACC-893 is 3 times higher than that of AsPC-1. Representative scans of the aforementioned stainings are shown in Supplemental Figure 6. As mentioned before, the factors influencing the EPR effect and the uptake of liposomes are more complex. The mononuclear phagocytic system also has a major influence on the delivery of liposomes, although detection by the mononuclear phagocytic system may be reduced by pegylation (29,30). Monocytes internalize liposomes, after which the drug may be released, resulting in toxic effects to the monocytes. Figure 4B shows that the percentage of macrophages in the tumor tissue is in general relatively low, with the exception of MDA-MB-468 (5.70% ± 2.26%). This might explain why a slow-growing tumor (5 mo) with a poor mean vessel density (1.79% ± 0.42%) still has a considerable liposomal uptake (2.87% ± 1.05%). Hypoxia is usually correlated with poor vascularization and indicative of poor drug uptake. Because of unrestrained tumor growth, cells move beyond the distance over which oxygen can diffuse (∼150 μm), and drugs targeting fast-dividing cells will usually be less effective (31,32). All tumors show a comparable degree of hypoxia (Fig. 4C), suggesting that this aspect plays a minor role in these tumor models. The functionality of both blood and lymphatic vessels is thought to be of great importance to the EPR effect and eventual uptake of macromolecules (2). We therefore incorporated a lymphatic marker (LYVE-1) and a collagen IV antibody to measure lymphatic vessel density and collagen IV support of blood vessels. The lymphatic vessels (Figs. 5A and 5B) are important for drainage of the tissue, and the result suggests that the high presence of these vessels caused lower accumulation in BxPC-3 tumors. The intermediate-growing BxPC-3 has a high lymphatic vessel density, 1.52% ± 0.86%, and a lymphatic–to–blood vessel ratio of 0.35 ± 0.11, with a well-developed mean vessel density, 5.19% ± 0.92%, and still showed a relatively poor liposomal accumulation. In addition, BxPC-3 tumor cells grew in tight clusters, which may be an additional factor contributing to the lower uptake, because the tissue is less permeable to liposomes. Most tumors have blood vessels supported with collagen IV matrix, apart from MDA-MB-468 tumors (Figs. 5C and 5D). To decipher possible associations between liposomal uptake and the various morphological parameters tested, we performed a correlation analysis (Supplemental Table 1). Although there was a significant correlation between the percentage PEG and the liposomal uptake in vivo, no further significant correlations related to other tested morphological parameters could be found. A multivariate analysis requiring a much larger dataset may in the future enable elucidation of multiple factors contributing to the EPR effect.
(A) Percentage of liposomal uptake calculated from immunohistochemical staining of PEG, which shows comparable uptake to in vivo/ex vivo quantifications. (B) Representative images of tumor periphery and center indicate that uptake in periphery is higher in both high- and low-uptake tumors. (C and D) Extravasated liposomes calculated for all tumors based on total PEG (red) minus PEG colocalized (yellow) with blood vessels (CD31, green), leading to final composite figure showing colocalized (yellow) and extravasated (red) liposomes. Degree of extravasation is relatively high in all tumors. All quantifications were performed on whole tile scans of tumor sections. Scale bars = 100 μm.
(A) Mean vessel density in tumors based on anti-CD31 staining. (B) Macrophages are stained with anti-CD11b antibody, and a high percentage is seen in MDA-MB-468 tumors. (C) Hypoxia percentage in different tumors, which was calculated on the basis of presence of pimonidazole after intraperitoneal injection. Degree of hypoxia is comparable for all tumors. All quantifications were performed on whole tile scans of tumor sections.
(A) LYVE-1 staining indicates presence of lymphatic vessels in the different tumors, which can be used to interpret possible drainage of tumor. (B) Lymphatic vessel (LYVE-1)–to–blood vessel (CD31) ratio is indicative of supply and drainage of tumor tissue. (C) Collagen IV is important for support of blood vessels and is used as a marker for mature blood vessels. Degree of collagen is comparable between tumors. (D) Colocalization of collagen IV and blood vessels (CD31) supports observation that most tumor vessels are mature and supported by collagen, with exception of MDA-MB-468. All quantifications were performed on whole tile scans of tumor sections.
DISCUSSION
The EPR effect, first described by Matsumura and Maeda in 1986 (2), shows tumor-specific accumulation of large molecules and is dependent on the following parameters: the size of the molecules should be larger than the renal clearance threshold (7 nm) to prevent fast clearance; the molecules should have characteristics that ensure a long circulation time to increase the chance for extravasation; and the nature of the tumor has to be such that it will ensure retention of these molecules, usually ranging from days to weeks, leading to enhanced efficacy. These properties distinguish the EPR effect needed for therapeutic purposes from targeting of low-molecular-weight molecules with a short half-life and fast clearance, which are more suited for imaging purposes (33). By utilizing the EPR effect, pegylated liposomes such as Doxil have been successful in the treatment of certain cancers.
This study has consolidated that pegylated liposomes can accumulate in various solid tumors, although considerable differences in uptake were observed. Uptake levels ranged from PANC-1 to CFPAC-1 at the low to high end, respectively. There was a trend between liposome accumulation and the speed of tumor growth. Although tumors, such as squamous cell carcinoma A431, showed very fast tumor growth but had limited liposomal accumulation in the tumor. This could be explained by the high growth rate and relatively large size of these tumors. Angiogenesis is increased during tumor growth (34) and the tumor will grow exponentially until sufficient vascularization can no longer be sustained. This leads to hypoxic or necrotic tumor cores, which are more difficult to reach with anticancer drugs. Before this point is reached, the chaotic development of tumor vasculature together with the obstruction and collapse of lymphatic vessels at the tumor core will lead to an enhanced EPR effect and possibly an increased accumulation of nanoparticles (2). Lymphatic vessel density evaluation revealed that most tumors had an impaired lymphatic system aiding liposome accumulation. The exception was the intermediate-growing BxPC-3, which had a high lymphatic vessel density and a well-developed mean vessel density, resulting in a relatively poor liposomal accumulation. Although it has been hypothesized that normalization of the vasculature, extracellular matrix, and lymphatic vessels would lead to better delivery of drugs (35), this may not always hold true for liposomal drugs. Normalization of vessels will lead to a less leaky vasculature, which might impair the EPR effect.
It has also been shown that mean vessel density correlates with the degree of liposome accumulation (28), but we did not observe this correlation in our study. The liposomes were still in circulation after 24 h. Various degrees of colocalization of blood vessels and liposomes were observed, but most liposomes had extravasated from tumor vessels. Unfortunately, high levels of extravasation do not always result in high efficacy, due to the poor penetration characteristics of liposomes. The liposomes did not penetrate further than the perivascular space, confirming that smaller particles (12 nm) can penetrate a tumor heterogeneously up to 80 μm, but particles of 60 nm and larger do not leave the perivascular space or even the vessel (36). Several additional barriers such as high interstitial fluid pressure, low oxygenation, and the extracellular matrix need to be overcome (12).
Various other variables can influence the uptake of liposomes and the EPR effect in addition to the important role of the vasculature. For example, associated inflammation (37) and interactions with monocytes (38) can play a major role to increase the liposomal uptake in the tumor. This may explain why MDA-MB-468 had an intermediate liposomal uptake even though the mean vessel density was low. The monocyte staining to determine the involvement of the immune system showed a high presence of macrophages in MDA-MB-468 as opposed to the other tumors.
This study supports the notion that the EPR effect is a highly complex, multifactorial, heterogeneous phenomenon, which is possibly much larger in animal tumors than in human tumors (13). Because tumors are usually faster-growing in animal models, it is to be expected that they will have a higher degree of vascularization and a lesser developed vascular environment, leading to a high EPR effect. For this reason, we also investigated slow-growing tumors with a lower vessel density to explore pegylated liposomes in a more clinically relevant setting. Especially the pancreatic tumor models are more representative for the clinical setting, in which human tumors grow over long periods of time (39). Slow-growing tumors, such as prostate and pancreatic tumors, are known to have more normalized vessels and are usually difficult to treat with nanomedicine. Here, we showed that even within the pancreatic tumor models, there is a wide range in liposomal uptake and EPR effect, which accentuates the importance of selecting the appropriate tumor model for preclinical studies. In addition, it is crucial to include an imaging modality in studies investigating the effects of nanoparticle accumulation in vivo and to intervene if the therapy is inhibited by poor access to the tumor. Fortunately, an increasing number of methods to enhance the EPR effect in tumors have been developed, both in preclinical and clinical settings, including the use of heat to increase vessel permeability and induce extravasation of nanomedicine (40–43).
CONCLUSION
The EPR effect can be used to predict liposomal accumulation into tumors or explain limited uptake. In this study, we investigated several parameters and our results suggest that the EPR effect, and thus liposomal uptake, is a complex, multifactorial, and heterogeneous phenomenon. This is caused by tumor (microenvironment) variability, which therefore should be taken into account when considering liposomes as an anticancer therapy.
DISCLOSURE
The costs of publication of this article were defrayed in part by the payment of page charges. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734. No potential conflict of interest relevant to this article was reported.
Acknowledgments
We thank Jan de Swart for technical support for the nanoSPECT/CT and Gert van Cappellen for advice on imaging and quantification of the immunohistochemical stainings.
Footnotes
Published online Dec. 30, 2015.
- © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.
REFERENCES
- Received for publication August 31, 2015.
- Accepted for publication November 25, 2015.
Jump to section
Related Articles
Cited By...
- Assessing Tumor Microenvironment Characteristics and Stratifying EPR with a Nanobubble Companion Nanoparticle via Contrast-Enhanced Ultrasound Imaging
- MRI techniques for immunotherapy monitoring
- Spatial Transcriptomics-based Identification of Molecular Markers for Nanomedicine Distribution in Tumor Tissue
- An Antibody-Tumor Necrosis Factor Fusion Protein that Synergizes with Oxaliplatin for Treatment of Colorectal Cancer
- Tumor Intrinsic Efficacy by SHP2 and RTK Inhibitors in KRAS-Mutant Cancers