- Research Article
- Open Access

# Combination therapy for melanoma with BRAF/MEK inhibitor and immune checkpoint inhibitor: a mathematical model

- Xiulan Lai
^{1}Email author and - Avner Friedman
^{2}

**11**:70

https://doi.org/10.1186/s12918-017-0446-9

© The Author(s) 2017

**Received:**22 February 2017**Accepted:**11 July 2017**Published:**19 July 2017

## Abstract

### Background

The B-raf gene is mutated in up to 66% of human malignant melanomas, and its protein product, BRAF kinase, is a key part of RAS-RAF-MEK-ERK (MAPK) pathway of cancer cell proliferation. BRAF-targeted therapy induces significant responses in the majority of patients, and the combination BRAF/MEK inhibitor enhances clinical efficacy, but the response to BRAF inhibitor and to BRAF/MEK inhibitor is short lived. On the other hand, treatment of melanoma with an immune checkpoint inhibitor, such as anti-PD-1, has lower response rate but the response is much more durable, lasting for years. For this reason, it was suggested that combination of BRAF/MEK and PD-1 inhibitors will significantly improve overall survival time.

### Results

This paper develops a mathematical model to address the question of the correlation between BRAF/MEK inhibitor and PD-1 inhibitor in melanoma therapy. The model includes dendritic and cancer cells, CD 4^{+} and CD 8^{+} T cells, MDSC cells, interleukins IL-12, IL-2, IL-6, IL-10 and TGF- *β*, PD-1 and PD-L1, and the two drugs: BRAF/MEK inhibitor (with concentration *γ*
_{
B
}) and PD-1 inhibitor (with concentration *γ*
_{
A
}). The model is represented by a system of partial differential equations, and is used to develop an efficacy map for the combined concentrations (*γ*
_{
B
},*γ*
_{
A
}). It is shown that the two drugs are positively correlated if *γ*
_{
B
} and *γ*
_{
A
} are at low doses, that is, the growth of the tumor volume decreases if either *γ*
_{
B
} or *γ*
_{
A
} is increased. On the other hand, the two drugs are antagonistic at some high doses, that is, there are zones of (*γ*
_{
B
},*γ*
_{
A
}) where an increase in one of the two drugs will increase the tumor volume growth, rather than decrease it.

### Conclusions

It will be important to identify, by animal experiments or by early clinical trials, the zones of (*γ*
_{
B
},*γ*
_{
A
}) where antagonism occurs, in order to avoid these zones in more advanced clinical trials.

## Keywords

- Melanoma
- Mathematical modeling
- BRAF/MEK inhibitor
- PD-1 inhibitor
- Combination therapy

## Background

PD-1 is an immunoinhibitory receptor predominantly expressed on activated T cells [1, 2]. Its ligand PD-L1 is upregulated on the same activated T cells, and is also expressed by myeloid-derived suppressor cells (MDSCs) [2–5] and in some human cancer cells, including melanoma, lung cancer, colon cancer, and leukemia [2, 3]. The complex PD-1-PD-L1 is known to inhibit T cell function [1]. Immune checkpoints are regulatory pathways in the immune system that inhibit its active response against specific targets. In the case of cancer, the complex PD-1-PD-L1 functions as an immune checkpoint for anti-tumor T cells. There has been much progress in recent years in developing checkpoint inhibitors, primarily anti-PD-1 and anti-PD-L1 [6].

The B-raf gene is mutated in up to 66% of human malignant melanomas, and its protein product, BRAF kinase, is a key part of the RAS-RAF-MEK-ERK (MAPK) pathway of cancer cell proliferation [7]. BRAF-targeted therapy induces significant response in the majority of patients but the response is short lived (about 6 months) [7–9]. Initial clinical trials indicate that concurrent inhibition of BRAF with MEK decreases MAPK-driven acquired resistance, resulting in enhanced clinical efficacy and decreased toxicity [10, 11]. This provides a rational for using combined BRAF/MEK inhibition instead of BRAF inhibition alone [11]. Treatment of melanoma with immune checkpoint inhibitors has a lower response rate compared to treatment with BRAF/MEK inhibitors, but the response tends to be more durable, lasting for years [11–13]. It was therefore suggested that BRAF/MEK-targeted therapy may synergize with the PD-1 pathway blockade to enhance anti-tumor immunity [4, 11, 14, 15]. Meta-Analysis of randomized clinical trials show that compared with other treatments of advanced BRAF-mutated melanoma, combined BRAF/MEK and PD-1 inhibitions significantly improved overall survival time [16].

In this paper we develop a mathematical model to address the efficacy of the combination of BRAF/MEK inhibitor (BRAF/MEKi) and anti-PD-1 (e.g. nivolumab). The model includes several types of T cells, MDSCs, and dendritic cells, as well as signaling molecules involved in the crosstalks among these cells.

Melanoma-derived factors alter the maturation and activation of tissue-resident dendritic cells, thus favoring tumor immune escape [17]. In BRAF mutant melanoma, BRAF inhibitor restores the compromised dendritic cells function, and, in particular, the production of IL-12 by dendritic cells [18]. Although MEK inhibitor (e.g. trametinib), as single agent, negatively impacts DC function, when combined with BRAF inhibitor (e.g. vemurafenib or dabrafenib), the functionality of DCs is restored, as well as their production of IL-12 [18, 19].

Dendritic cell-derived IL-12 activates effector T cells (Th1 and CD 8^{+} T cells) [20, 21]. Th1 produces IL-2 which further promotes proliferation of effector T cells. CD 4^{+} T cells (Th1) can kill cancer cell directly, for example, through FAS- or TRAIL-dependent pathway [22–25], while CD 8^{+} T cell is more effective in killing cancer cells [26]. Cancer cells suppress the functions of effector T cells by producing immunosuppressor cytokines TGF- *β*, IL-6, CCL2 and IL-10 [27]. IL-10 inhibits the activation of Th1 and CD 8^{+} T cells [27]. IL-6 and CCL2 recruit MDSCs into tumor [19, 28, 29], and MDSCs produce TGF- *β* and IL-10. IL-6 and CCL2 also recruit T regulatory T cells (Tregs) [15, 28, 29]. TGF- *β* is produced not only by cancer cells and MDSCs, but also by Tregs [30], and Tregs become activated by TGF- *β* [30, 31]. Tregs modulate Th1 and CD 8^{+} T cells [30], thus promoting tumor growth.

One of the checkpoints on T cells is the membrane protein PD-1. Its ligand PD-L1 is expressed on activated effector T cells, on MDSCs and on cancer cells [2–5]. The complex PD-1-PD-L1 inhibits the function of effector T cells [1], but enhances the activation of Tregs [32] and thus promoting cancer.

## Methods

### Mathematical model

List of variables (in units of g/ cm^{3})

Notation | Description |
---|---|

| HMGB-1 concentration |

| Density of necrotic cancer cells |

| Density of DCs |

| Density of activated CD 4 |

| Density of activated CD 8 |

| Density of activated Treg cells |

| Density of activated MDSCs |

| Density of cancer cells |

| IL-12 concentration |

| IL-2 concentration |

| TGF- |

| IL-6 concentration |

| IL-10 concentration |

| PD-1 concentration |

| PD-L1 concentration |

| PD-1-PD-L1 concentration |

| Anti-PD-1 concentration |

| BRAF/MEK inhibitor concentration |

We assume that the density of debris of dead cells from necrosis or apoptosis is constant. We also assume that the densities of immature dendritic cells and naive CD 4^{+} and CD 8^{+} T cells remain constant throughout the tumor tissue. Under the assumption (1), proliferation of cancer cells and immigration of immune cells into the tumor, give rise to internal pressure which results in cells movement. We assume that all the cells move with the same velocity, **u**; **u** depends on space and time and will be taken in unit of cm/day. We also assume that all the cells undergo dispersion (i.e., diffusion), and that all the cytokines and anti-tumor drugs are diffusing within the tumor.

#### Equation for DCs (*D*)

*N*

_{ C }) we mean cancer cells undergoing the process of necrosis. Necrotic cancer cells release HMGB-1 (

*H*) [33]. We model the dynamics of

*N*

_{ C }and

*H*by the following equations:

where \(\lambda _{N_{C}C}\) is the rate at which cancer cells become necrotic, *d*
_{
N
} is the rate at which necrotic cells turn into debris, and \(\lambda _{HN_{C}}\) is the rate at which necrotic cells produce HMGB-1. We note that although molecules like HMGB-1, or other proteins, may be affected by the velocity **u**, their diffusion coefficients are several order of magnitude larger than the diffusion coefficients of cells, hence their velocity term may be neglected. The degradation of HMGB-1 is fast (∼0.01/day) [34], and we assume that the process of necrosis is also fast. We may then approximate the two dynamical equations by the steady staten\(\lambda _{N_{C}C}C-d_{N_{C}}N_{C}=0\) and \(\lambda _{HN_{C}}N_{C}-d_{H}H=0\), so that *H* is proportional to *C*.

*D*

_{0}, is proportional to \(D_{0}\frac {H}{K_{H}+H}\), or to \(D_{0}\frac {C}{K_{C}+C}\), since

*H*is proportional to

*C*. Here, the Michaelis-Menten law is used to account for the limited rate of receptor recycling time which takes place in the process of DCs activation. Hence, the dynamics of DCs is given by

where *δ*
_{
D
} is the diffusion coefficient and *d*
_{
D
} is the death rate of DCs.

#### Equation for CD 4^{+} T cells (*T*
_{1})

^{+}T cells differentiate into Th1 cells (

*T*

_{1}) under IL-12 (

*I*

_{12}) environment [20, 21], while IL-10 and Tregs inhibit the differentiation of naive CD 4

^{+}T cells into

*T*

_{1}cells [27, 30]. The proliferation of activated

*T*

_{1}cells is enhanced by IL-2. Both processes of activation and proliferation of

*T*

_{1}are assumed to be inhibited by PD-1-PD-L1 (

*Q*) by a factor \(\frac {1}{1+Q/K_{TQ}}\). Hence

*T*

_{1}satisfies the following equation:

where *T*
_{10} is the density of the naive CD 4^{+} T cells.

#### Equation for activated CD 8^{+} T cells (*T*
_{8})

^{+}T cells are activated by IL-12 [20, 21], and this process is resisted by IL-10 and Tregs [27, 30]. IL-2 enhances the proliferation of activated CD 8

^{+}T cells. Similarly to the equation for

*T*

_{1},

*T*

_{8}satisfies the following equation:

where *T*
_{80} is the density of the inactive CD 8^{+} T cells.

#### Equation for activated Tregs (*T*
_{
r
})

^{+}T cells differentiate into Tregs (

*T*

_{ r }) under activation by Fox3+ transcription factor. The complex PD-1-PD-L1 enhances the expression of PTEN, which results in upregulation of Fox3+, and hence in increased production of Tregs [32]. The production of

*T*

_{ r }is also enhanced by TGF-

*β*(

*T*

_{ β }) [30, 31]. The activated Tregs are recruited into tumor by tumor-derived immunosuppressive cytokine IL-6 (and CCL2)[15, 28, 29]. Representing this chemoattraction by ∇·(

*χ*

*T*

_{ r }∇

*I*

_{6}), we get the following equation for

*T*

_{ r }:

#### Equation for activated MDSCs (*M*)

where *M*
_{0} is the source/influx of macrophages from the blood.

#### Equation for tumor cells (*C*)

*T*

_{1}and

*T*

_{8}cells. We assume a logistic growth with carrying capacity (

*C*

_{ M }) in order to account for competition for space among cancer cells. BRAF/MEK inhibitor (

*B*), for example vemurafenib/dabrafenib, is used for treatment of advanced melanoma. Its mechanism of action involves selective inhibition of the mutated BRAF kinase that leads to reduced signaling through the aberrant RAS-RAF-MEK-ERK (MAPK) pathway. We assume that BRAF/MEK inhibitor suppresses the abnormal proliferation of tumor cells by a factor \(\frac {1}{1+B/K_{CB}}\). Then, the equation for

*C*takes the form:

where *η*
_{1} and *η*
_{8} are the killing rates of cancer cells by *T*
_{1} and *T*
_{8}, and *d*
_{
C
} is the natural death rate of cancer cells.

#### Equation for IL-12 (*I*
_{12})

*I*

_{12}then takes the form:

#### Equation for IL-2 (*I*
_{2})

^{+}T cells (

*T*

_{1}) [21]. Hence,

#### Equation for TGF- *β* (*T*
_{
β
})

*β*is produced by tumor cells [27], MDSCs [31, 41, 42] and Tregs [30]:

#### Equation for IL-6 (*I*
_{6})

#### Equation for IL-10 (*I*
_{10})

#### Equation for PD-1 (*P*), PD-L1 (*L*) and PD-1-PD-L1 (*Q*)

^{+}T cells, activated CD 8

^{+}T cells and Tregs. We assume that the number of PD-1 per cell is the same for

*T*

_{1}and

*T*

_{8}cells, but is smaller, by a factor

*ε*

_{ T }, for

*T*

_{ r }cells. If we denote by

*ρ*

_{ P }the ratio between the mass of one PD-1 protein to the mass of one T cell, then

*ρ*

_{ P }is constant when no anti-PD-1 drug is administered. And in this case, to a change in

*T*=

*T*

_{1}+

*T*

_{8}+

*ε*

_{ T }

*T*

_{ r }, given by \(\frac {\partial T}{\partial t}\), there corresponds a change of

*P*, given by \(\rho _{P}\frac {\partial T}{\partial t}\). For the same reason, ∇·(

**u**

*P*)=

*ρ*

_{ P }∇·(

**u**

*T*) and ∇

^{2}

*P*=

*ρ*

_{ P }∇

^{2}

*T*when no anti-PD-1 drug is injected. Hence,

*P*satisfies the equation

*T*

_{1},

*T*

_{8}and

*T*

_{ r }, we get

*A*) is applied, PD-1 is depleted (or blocked) by

*A*. In this case, the ratio \(\frac {P}{T_{1}+T_{8}+\varepsilon _{T}T_{r}}\) may change. In order to include in the model both cases of with and without anti-PD-1, we replace

*ρ*

_{ P }in the previous equation by \(\frac {P}{T_{1}+T_{8}+\varepsilon _{T}T_{r}}\). Hence,

where *μ*
_{
PA
} is the depletion rate of PD-1 by anti-PD-1.

^{+}T cells, activated CD 8

^{+}T cells, MDSCs, and tumor cells. We assume that the number of PD-L1 per cell is the same for

*T*

_{1},

*T*

_{8}and

*M*cells, and denote the ratio between the mass of one PD-L1 protein to the mass of one cell by

*ρ*

_{ L }. Then

where *ε*
_{
C
} depends on the specific type of tumor.

*Q*) on the T cells [2, 3]. Denoting the association and disassociation rates of

*Q*by

*α*

_{ PL }and

*d*

_{ Q }, respectively, we can write

*Q*is less then 1 second (i.e. 1.16×10

^{−5}day) [43], so that

*d*

_{ Q }is very large. Hence we may approximate the dynamical equation for

*Q*by the steady state equation,

*α*

_{ PL }

*P*

*L*=

*d*

_{ Q }

*Q*, or

where *σ*=*α*
_{
PL
}/*d*
_{
Q
}.

#### Equation for anti-PD-1 (*A*)

*γ*

_{ A }is the effective level of the drug; although the level of the drug varies between injections, for simplicity we take it to be constant. The drug

*A*is depleted in the process of blocking PD-1. Hence,

#### Equation for BRAF/MEK inhibitor (*B*)

*C*at a rate \(\mu _{BC}C\frac {B}{K_{B}+B}\), we get the following equation for

*B*:

#### Equation for cells velocity (**u**)

^{3}), cancer cells (approximately,

*C*=0.4 g/cm

^{3}) and MDSCs (approximately,

*M*=0.2 g/cm

^{3}). We assume (in the section of parameter estimation) that the densities of the immune cells

*D*,

*T*

_{1},

*T*

_{8}and

*T*

_{ r }are approximately 4×10

^{−4}, 2×10

^{−3}, 1×10

^{−3}g/ cm

^{3}and 5×10

^{−4}g/ cm

^{3}, respectively, and, for consistency, take the constant in Eq. (1) to be 0.6039. We further assume that all cells have approximately the same volume and surface area, so that the diffusion coefficients of all the cells are the same. Adding Eqs. (2)-(7), we then get

To simplify the computations, we assume that the tumor is spherical and denote its radius by *r*=*R*(*t*). We also assume that all the densities and concentrations are radially symmetric, that is, functions of (*r*,*t*), where 0≤*r*≤*R*(*t*). In particular, **u**=*u*(*r*,*t*)**e**
_{
r
}, where **e**
_{
r
} is the unit radial vector.

#### Equation for free boundary (*R*)

*r*=

*R*(

*t*) moves with the velocity of cells, so that

#### Boundary conditions

^{+}T cells and inactive CD 8

^{+}T cells that migrated from the lymph nodes into the tumor microenvironment have constant densities \(\hat T_{1}\) and \(\hat T_{8}\) at the tumor boundary, and that

*T*

_{1}and

*T*

_{8}are activated by IL-12 upon entering the tumor. We then have the following flux conditions at the tumor boundary:

where \(\sigma _{T}(I_{12})=\sigma _{0} \frac {I_{12}}{I_{12}+K_{I_{12}}}\).

It is tacitly assumed here that the receptors PD-1 and ligands PD-L1 become active only after the T cells are already inside the tumor.

#### Initial conditions

^{3}):

Note that the initial conditions for the cells satisfy Eq. (1).

*B*=0 and

*A*=0, and take the initial condition for

*I*

_{12},

*I*

_{2},

*T*

_{ β },

*I*

_{6},

*I*

_{10}and

*P*to be close to their steady state values, which are computed in the section on parameter estimation. One choice of initial conditions is given as follows (in unit of g/ cm

^{3}):

However, other choices of these initial conditions do not affect the simulations of the model after a few days.

## Results and discussions

The simulations of the model were performed by Matlab based on the moving mesh method for solving partial differential equations with free boundary [45] (see the section on computational method).

*λ*

_{0}(used in estimating some parameters of Eq. (7)). It is interesting to note that the initial increase in TGF-

*β*more than compensates for the initial decrease in

*P*and

*L*, as evident by the initial increase in

*T*

_{ r }. This initial increase of

*T*

_{ r }results in initial decrease in the

*T*

_{1}and

*T*

_{8}cells. We also note that the initial increase in cancer cells results in an increase in the

*D*cells.

Summary of parameter values

Notation | Description | Value used | References |
---|---|---|---|

| Diffusion coefficient of DCs | 8.64×10 | [65] |

| Diffusion coefficient of T cells | 8.64×10 | [65] |

| Diffusion coefficient of MDSCs | 8.64×10 | [65] |

| Diffusion coefficient of tumor cells | 8.64×10 | [65] |

\(\delta _{I_{12}}\) | Diffusion coefficient of IL-12 | 6.05×10 | Estimated |

\(\delta _{I_{2}}\) | Diffusion coefficient of IL-2 | 9.58×10 | Estimated |

\(\delta _{T_{\beta }}\) | Diffusion coefficient of TGF- | 8.52×10 | Estimated |

\(\delta _{I_{6}}\) | Diffusion coefficient of IL-6 | 9.03×10 | Estimated |

\(\delta _{I_{10}}\) | Diffusion coefficient of IL-10 | 9.11×10 | Estimated |

| Diffusion coefficient of anti-PD-1 | 7.85×10 | Estimated |

| Diffusion coefficient of BRAF/MEKi | 3.16×10 | Estimated |

| Flux rate of | 1 cm | [65] |

| Chemoattraction coefficient of IL-6 | 10 cm | |

| Activation rate of DCs by tumor cells | 4 g/cm | [65] |

\(\lambda _{T_{1}I_{12}}\) | Activation rate of CD 4 | 18.64 day | Estimated |

\(\lambda _{T_{1}I_{2}}\) | Activation rate of CD 4 | 0.25 day | [65] |

\(\lambda _{T_{8}I_{12}}\) | Activation rate of CD 8 | 16.6 day | Estimated |

\(\lambda _{T_{8}I_{2}}\) | Activation rate of CD 8 | 0.25 day | [65] |

\(\lambda _{T_{r}T_{\beta }}\) | Activation rate of Tregs by TGF- | 0.415 day | Estimated |

\(\lambda _{T_{r}Q}\) | Activation rate of Tregs by PD-1-PD-L1 | 0.083 day | Estimated |

| Activation rate of MDSCs | 1.05 day | [40] |

| Growth rate of cancer cells | 0.616 day | Estimated |

| Growth rate of cancer cells uninhibited (by immune cells) | 0.069 day | Estimated |

\(\lambda _{I_{12}D}\) | Production rate of IL-12 by DCs | 2.76×10 | Estimated |

\(\lambda _{I_{12}B}\) | Promotion of IL-12 production by BRAF/MEKi | 1 | Estimated |

\(\lambda _{I_{2}T_{1}}\) | Production rate of IL-2 by CD 4 | 2.82×10 | Estimated |

\(\lambda _{T_{\beta } C}\) | Production rate of TGF- | 2.18×10 | Estimated |

\(\lambda _{T_{\beta } T_{r}}\) | Production rate of TGF- | 5.57×10 | [75] |

\(\lambda _{T_{\beta } M}\) | Production rate of TGF- | 2.18×10 | Estimated |

\(\lambda _{I_{6} C}\) | Production rate of IL-6 by cancer cells | 3.54×10 | Estimated |

\(\lambda _{I_{10} C}\) | Production rate of IL-10 by cancer cells | 9.10×10 | Estimated |

\(\lambda _{I_{10} M}\) | Production rate of IL-10 by MDSCs | 1.82×10 | Estimated |

| Killing rate of tumor cells by CD 4 | 11.5 day | Estimated |

| Killing rate of tumor cells by CD 8 | 46 day | Estimated |

| Blocking rate of PD-1 by anti-PD-1 | 6.04×10 | Estimated |

| Absorbtion rate of BRAF/MEKi by cancer cells | 6.17×10 | Estimated |

| Expression of PD-1 in T cells | 2.49×10 | Estimated |

| Expression of PD-L1 in T cells | 5.22×10 | Estimated |

| Expression of PD-L1 in tumor cells | 0.01 | [84] |

| Expression of PD-L1 in MDSCs | 0.005 | Estimated |

| Death rate of DCs | 0.1 day | [65] |

\(d_{T_{1}}\) | Death rate of CD 4 | 0.197 day | [65] |

\(d_{T_{8}}\) | Death rate of CD 8 | 0.18 day | [65] |

\(d_{T_{r}}\) | Death rate of Tregs | 0.2 day | [75] |

| Death rate of MDSCs | 0.03 day | [40] |

| Death rate of tumor cells | 0.17 day | [65] |

\(d_{I_{12}}\) | Degradation rate of IL-12 | 1.38 day | [65] |

\(d_{I_{2}}\) | Degradation rate of IL-2 | 2.376 day | [65] |

\(d_{T_{\beta }}\) | Degradation rate of TGF- | 499.066 day | Estimated |

\(d_{I_{6}}\) | Degradation rate of IL-6 | 4.16 day | Estimated |

\(d_{I_{10}}\) | Degradation rate of IL-10 | 8.32 day | Estimated |

| Degradation rate of anti-PD-1 | 0.046 day | [87] |

| Degradation rate of BRAF/MEKi | 1.66day | Estimated |

| Density of inactive DCs | 2×10 | [65] |

| Density of naive CD 4 | 4×10 | Estimated |

| Density of naive CD 8 | 2×10 | Estimated |

| Carrying capacity of cancer cells | 0.8 g/cm | [65] |

\(\hat T_{1}\) | Density of CD 4 | 4×10 | Estimated |

\(\hat T_{8}\) | Density of CD 8 | 2×10 | Estimated |

Summary of parameter values

Notation | Description | Value used | References |
---|---|---|---|

\(K_{T_{1}}\) | Half-saturation of CD 4 | 2×10 | Estimated |

\(K_{T_{8}}\) | Half-saturation of CD 8 | 1×10 | Estimated |

\(K_{T_{r}}\) | Half-saturation of Tregs | 5×10 | [65] |

| Half-saturation of tumor cells | 0.4 g/cm | [65] |

\(K_{I_{12}}\) | Half-saturation of IL-12 | 8×10 | Estimated |

\(K_{I_{2}}\) | Half-saturation of IL-2 | 2.37×10 | [65] |

\(K_{T_{\beta }}\) | Half-saturation of TGF- | 2.68×10 | Estimated |

\(K_{I_{6}}\) | Half-saturation of IL-6 | 3.4×10 | Estimated |

\(K_{I_{10}}\) | Half-saturation of IL-10 | 8.75×10 | Estimated |

\(K^{\prime }_{Q}\) | Half-saturation of PD-1-PD-L1 | 3.54×10 | Estimated |

| Half-saturation of BRAF/MEKi | 6.69×10 | Estimated |

\(K^{\prime }_{TQ}\) | Inhibition of function of T cells by PD-1-PD-L1 | 1.77×10 | Estimated |

| Inhibition of proliferation of cancer cells by BRAF/MEKi | 3.06×10 | Estimated |

*γ*

_{ B },

*γ*

_{ A }), by the formula:

*R*

_{60}=

*R*

_{60}(

*γ*

_{ B },

*γ*

_{ A }) is computed at day 60;

*R*

_{60}(0,0) is the radius at day 60 in the control case (no drugs). The efficacy is a positive number, and its value lies between 0 and 1 (or between 0 and 100%). Figure 4 is the efficacy map of the combined therapy, with

*γ*

_{ B }in the range of 0−5×10

^{−9}g/cm

^{3}·day and

*γ*

_{ A }in the range of 0−1.4×10

^{−9}g/cm

^{3}·day. The color column shows the efficacy for any pair of (

*γ*

_{ B },

*γ*

_{ A }); the maximum efficacy is 0.97 (97%).

As the number of cancer cells increases, the tumor radius increases. Hence, if *T*
_{1} and *T*
_{8} were monotone increasing functions of *γ*
_{
A
}(or of *γ*
_{
B
}), then we should see that *R*
_{60}(*γ*
_{
B
},*γ*
_{
A
}) is a decreasing function of *γ*
_{
A
}(or of *γ*
_{
B
}), and *E*(*γ*
_{
B
},*γ*
_{
A
}) would then also be an increasing function of *γ*
_{
A
}(or of *γ*
_{
B
}). But Fig. 4 shows that this is not generally the case; indeed there are small oscillations in “northeast” corner of the figure. This means that the functions *T*
_{1} and *T*
_{8} cannot be monotone increasing with respect to *γ*
_{
B
} for fixed *γ*
_{
A
}>0.5×10^{−9} g/cm^{3}·day, and also cannot be monotone increasing in *γ*
_{
A
} for fixed *γ*
_{
B
}>1.5×10^{−9} g/cm^{3}·day. Indeed, for example, Fig. 5a shows that the average densities of *T*
_{1} and *T*
_{8} are decreasing functions of *γ*
_{
B
}, for fixed *γ*
_{
A
}=1.26×10^{−9} g/cm^{3}·day; however, for smaller values of *γ*
_{
A
}, *T*
_{1} and *T*
_{8} may become monotone increasing, as seen, for example, in Fig. 5b with *γ*
_{
A
}=0.14×10^{−9} g/cm^{3}·day. Similarly, Fig. 6a shows that, for fixed *γ*
_{
B
}=3×10^{−9} g/cm^{3}·day, there is a *γ*
_{
A
}-interval where *T*
_{1} and *T*
_{8} are decreasing as *γ*
_{
A
} increases. The *γ*
_{
A
}-interval where *T*
_{1} and *T*
_{8} are decreasing may shrink as we take a smaller fixed *γ*
_{
B
}, as seen, for example, in Fig. 6b with *γ*
_{
B
}=0.1×10^{−9} g/cm^{3}·day.

*γ*

_{ B }increases, the population of cancer cells decreases, and then, by Eqs. (2)-(4) and (8), so does the signal to activate T cells by dendritic cells-derived IL-12 (since the number of activated dendritic cells decrease with decreased cancer cell density) and thus the densities of

*T*

_{1}and

*T*

_{8}decrease. As for Fig. 6a, when

*γ*

_{ A }begins to increase,

*T*

_{1}and

*T*

_{8}also begin to increase, which results in a decrease of cancer cells. Then, as explained in the case of Fig. 5a, this leads to a decrease in dendritic cells-derived IL-12 and, hence, the density of activated

*T*

_{1}and

*T*

_{8}cells will begin to decrease as

*γ*

_{ A }continues to increase for a while.

If we inject IL-12 directly into tumor (as an additional drug), the influence of dendritic cells-secreted IL-12 diminishes, and the antagonism between BRAF/MEKi and anti-PD-1 also diminishes and it disappears already at very small amount of injection, e.g., an injection of order of magnitude 10^{−14} gcm^{3}·day.

## Sensitivity analysis

*R*at day 60 in the control case, with respect to some of the production parameters of the system (2)-(16), namely,

*λ*

_{ DC }, \(\lambda _{T_{1}I_{12}}\), \(\lambda _{T_{8}I_{12}}\), \(\lambda _{T_{r}T_{\beta }}\), \(\lambda _{T_{\beta } C}\), \(\lambda _{I_{6}C}\), \(\lambda _{I_{10}C}\),and the parameters

*K*

_{ TQ },

*η*

_{1}and

*η*

_{8}which play important role in the dynamics of

*C*. Following the method of [46], we performed Latin hypercube sampling and generated 1000 samples to calculate the partial rank correlation coefficients (PRCC) and the

*p*-values with respect to the tumor radius at day 60. In sampling all the parameters, we took the range of each from 1/2 to twice its values in Tables 2 and 3. The results are shown in Fig. 8.

We see that the production/activation rates that promote effector T cells, namely, *λ*
_{
DC
}, \(\lambda _{T_{1}I_{12}}\) and \(\lambda _{T_{8}I_{12}}\), are negatively correlated to the tumor radius, while the production/activation rates of the effector T cell-suppressors, such as \(\lambda _{T_{r}T_{\beta }}\), \(\lambda _{I_{10}C}\), \(\lambda _{T_{\beta } C}\) and \(\lambda _{I_{6}C}\), are positively correlated to the tumor radius. The killing rate of effector T cells, *η*
_{1} and *η*
_{8} are negatively correlated to the tumor radius, and the correlation with *η*
_{8} is higher than with *η*
_{1}.

## Conclusion

BRAF mutation occurs in up to 66% of human malignant melanomas and for this reason BRAF has been one of the primary targets in melanoma therapy. Treatment with BRAF inhibitors (such as vemurafenib or dabrafenib) encounters MAPK-driven resistance, but combining it with MEK inhibitor (e.g. trametinib) significantly reduces this resistance as well as toxicity. While the response to the combined BRAF/MEK inhibitor is significant, it is short lived. On the other hand, PD-1 antibody (nivolumab) has lower response rate but a far greater durability. It was therefore suggested that BRAF/MEK inhibitor should positively correlate with anti-PD-1.

In the present paper we developed a mathematical model to test this hypothesis, *in silico*, by computing the efficacy of the combined therapy. The model is represented by a system of partial differential equations within the tumor tissue. The model includes immune cells (Th1 and CD 8^{+} T cells, Tregs, MDSCs and dendritic cells), cytokines (IL-12, IL-2, IL-6, IL-10 and TGF- *β*), and PD-1, PD-L1 and the complex PD-1-PD-L1. We simulated the model with combination of drugs, BRAF/MEK inhibitor at the ‘level’ *γ*
_{
B
} and PD-1 antibody at the ‘level’ *γ*
_{
A
}, and computed the tumor radius *R*
_{60}=*R*
_{60}(*γ*
_{
A
},*γ*
_{
B
}) at day 60, and the efficacy \(E(\gamma _{B},\gamma _{A})=\frac {R_{60}(0,0)-R_{60}(\gamma _{B},\gamma _{A})}{R_{60}(0,0)}\); the efficacy is an expression that quantifies the reduction in tumor size compared to the control case (no drugs).

The efficacy map in Fig. 4 shows that for low levels of *γ*
_{
B
} and *γ*
_{
A
}, the two drugs are positively correlated, in the sense that tumor volume decreases as each of the drugs is increased. However, in the ‘northeast’ corner of Fig. 4 we see that for higher levels of *γ*
_{
B
} and *γ*
_{
A
} there are zones where the drugs are antagonistic in the sense that when *γ*
_{
B
} and *γ*
_{
A
} in these zones are increased, the efficacy actually decreases. The antagonism between the combined drugs can be explained by the pathway shown in Fig. 7. An increase in the number of effector T cells (Th1 and CD 8^{+}) results in decrease in cancer cells and necrotic cancer cells, hence in decreased signals to activate dendritic cells. This results in a decrease in IL-12 production by dendritic cells, and hence in a decrease in effector T cells.

The parameter \(\lambda _{I_{12}B}\) may be viewed as the immune system response to BRAF/MEK inhibitor. When this parameter is increased, the antagonism in the combined therapy is reduced, but it does not completely disappear (not shown here).

- (i)
In order to focus on the combined therapy of a BRAF/MEK inhibitor and an anti-PD-1 drug, we did not include in the model the effect of angiogenesis, thus assuming that the tumor is avascular. We tacitly assumed that the effect of this omission is not significant in comparing the results of therapy to no therapy.

- (ii)
We assumed that the densities of immature, or naive, immune cells remain constant throughout the progression of the cancer and that density of debris of dead cells is constant.

- (iii)
We assumed that the process of necrosis is fast, and that the density of cancer cells undergoing necrosis is at steady state.

- (iv)
In estimating parameters we made a steady state assumption in some of the differential equations.

- (v)
We did not make any direct connection between drugs administered to the patient, and their ‘effective strengths’

*γ*_{ B }and*γ*_{ A }, as they appear in the differential equations, since these data are not available.

A general study of synergistic and antagonistic networks in drug combinations appeared in [47]. Clinical records on combination therapy show that the number of drugs that are synergistic far exceeds the number of drugs that are antagonistic [48].

In our model, the combination (*γ*
_{
B
},*γ*
_{
A
}) is antagonistic when the drugs are administered in high doses, but not in low doses. For this reason it will be important to identify more carefully the zones of antagonism, by animal experiments or by early clinical trials, in order to avoid those zones in more advanced clinical trials.

## Appendix

### Parameter estimation

#### Half-saturation

In an expression of the form \(Y\frac {X}{K_{X}+X}\) where *Y* is activated by *X*, the half-saturation parameter *K*
_{
X
} is taken to be the approximate steady state concentration of species *X*.

#### Diffusion coefficients

*p*:

*M*

_{ V }and

*δ*

_{ V }are respectively the molecular weight and diffusion coefficient of VEGF,

*M*

_{ p }is the molecular weight of

*p*, and

*M*

_{ V }=24kDa [50] and

*δ*

_{ V }=8.64×10

^{−2}cm

^{2}day

^{−1}[51]. Since, \(M_{I_{2}}=17.6\)kDa [52], \(M_{I_{12}}=70\)kDa [53], \(M_{T_{\beta }}=25\)kDa [54], \(M_{I_{6}}=21\)kDa [55, 56], \(M_{I_{10}}=20.5\)kDa [57],

*M*

_{ A }=32kDa [58] and

*M*

_{ B }=489.93Da [59], we get \(\delta _{I_{2}}=9.58\times 10^{-2}\ \text {cm}^{2}\:\text {day}^{-1}\), \(\delta _{I_{12}} =6.05\times 10^{-2}\ \text {cm}^{2}\:\text {day}^{-1}\), \(\delta _{T_{\beta }}=8.52\times 10^{-2}\ \text {cm}^{2}\:\text {day}^{-1}\), \(\delta _{I_{6}}=9.03\times 10^{-2}\ \text {cm}^{2}\:\text {day}^{-1}\), \(\delta _{I_{10}}=9.11\times 10^{-2}\ \text {cm}^{2}\:\text {day}^{-1}\),

*δ*

_{ A }=7.85×10

^{−2}cm

^{2}day

^{−1}and

*δ*

_{ B }=3.16×10

^{−1}cm

^{2}day

^{−1}.

#### Equation (2)

The number of DCs in various organs (heart, kidney, pancreas and liver) in mouse varies from 1.1×10^{6} cells/ cm^{3} to 6.6×10^{6} cells/ cm^{3} [60]. In the dermal tissue, the number of DCs is larger (600-1500 cells/ mm^{2}) [61, 62], but we do not specify where the melanoma cancer is located; it may be at its initial dermal tissue or in another organ where it metastasized. Mature DCs are approximately 10 to 15 *μ*m in diameter [63]. Accordingly, we estimate the steady state of DCs to be *K*
_{
D
}=4×10^{−4} g/cm^{3}. We assume that there are always immature dendritic cells, some coming from the blood as tumor infiltrating dendritic cells (TID) [20, 21, 64]. We also assume that the density of immature DCs to be smaller than the density of active DCs, and take \(D_{0}=\frac {1}{20}K_{D}=2\times 10^{-5}\ \mathrm {g}/\text {cm}^{3}\). From the steady statenof Eq. (2), we get *λ*
_{
DC
}=2*d*
_{
D
}
*D*/*D*
_{0}=4/day, since *d*
_{
D
}=0.1/day [65]. We take *K*
_{
C
}=0.4 g/cm^{3}.

#### Equation (3)

*μ*m in diameter. Assuming that the number of Th1 cells is 1/4 the number of lymphocytes, we estimate steady state density of Th1 cells to be \(K_{T_{1}}=2\times 10^{-3}\ \mathrm {g}/\text {cm}^{3}\). We assume that the density of naive CD 4

^{+}T cells to be less than the density of Th1, and take \(T_{10}=\frac {1}{5}K_{T}=4\times 10^{-4}\ \mathrm {g}/\text {cm}^{3}\). As in [65], we choose \(K_{TT_{r}}\) to be half-saturation of

*T*

_{ r }, that is, \(K_{TT_{r}}=5\times 10^{-4}\ \mathrm {g}/\text {cm}^{3}\), and as in [66], we choose \(K_{TI_{10}}\) to be half-saturation of

*I*

_{10}, namely, \(K_{TI_{10}}=2\times 10^{-7}\ \mathrm {g}/\text {cm}^{3}\). We assume that in steady state,

*Q*/

*K*

_{ TQ }=2 (the value of

*K*

_{ TQ }is derived in the estimates of Eqs. (13)-(15)). From the steady state of Eq. (3), we get

*T*

_{10}=4×10

^{−4}g/cm

^{3}and \(T_{1}=K_{T_{1}}=2\times 10^{-3}\ \mathrm {g}/\text {cm}^{3}\). Hence \(\lambda _{T_{1}I_{12}}=18.64/\text {day}\).

#### Equation (4)

*T*

_{8}to be the half of steady state of

*T*

_{1}, i.e., \(K_{T_{8}}=\frac {1}{2}K_{T_{1}}=1\times 10^{-3}\ \mathrm {g}/\text {cm}^{3}\). From the steady state of Eq. (4), we have

*T*

_{80}=2×10

^{−4}g/cm

^{3}, \(T_{8}=K_{T_{8}}=1\times 10^{-3}\ \mathrm {g}/\text {cm}^{3}\). Hence \(\lambda _{T_{8}I_{12}}=16.6/\text {day}\).

#### Equation (5)

We assume that TGF- *β* activates Tregs more than PD-1-PD-L1 does, and take \(\lambda _{T_{r}T_{\beta }}=5\lambda _{T_{r}Q}\). From the steady state of Eq. (5), we get, \((\lambda _{T_{r}T_{\beta }}\cdot \frac {1}{2}+\lambda _{T_{r}Q}\cdot \frac {1}{2})T_{10}-d_{T_{r}}T_{r}=0\), where *T*
_{10}=1×10^{−3} g/cm^{3}, \(T_{r}=K_{T_{r}}=5\times 10^{-4}\ \mathrm {g}/\text {cm}^{3}\) [65], and \(d_{T_{r}}=0.2\)/day [65]. Hence \(\lambda _{T_{r}Q}=0.083/\text {day}\) and \(\lambda _{T_{r}T_{\beta }}=0.415\)/day.

#### Equation (6)

The density of tumor-associated macrophages in melanoma can be up to 30% of the tumor tissue density [67]; we take MDSC density to be 20% of the tumor tissue density, so that *M*=0.2 g/cm^{3} in steady state. From the steady state of Eq. (6), we get, \(\frac {1}{2}\lambda _{M}(M_{0}-M)=d_{M}M\), where *d*
_{
M
}=0.015/day [40], *λ*
_{
M
}=20/19=1.05 [40], and *M*=*K*
_{
M
}=0.2 g/cm^{3}. Hence, *M*
_{0}=0.21 g/cm^{3}.

#### Equation (7)

*d*

_{ C }=0.17 day

^{−1}and

*C*

_{ M }=0.8 g/cm

^{3}[65]. In the control case (no anti-tumor drugs), the tumor grows according to

We take *λ*
_{0}=0.069/day, and assume that in steady state, *C* is approximately 0.4 g/cm^{3}, so that from Eq. (25) we get \(\frac {1}{2}\lambda _{C}-d_{C}=2\lambda _{0}\), or *λ*
_{
C
}=2(2*λ*
_{0}+*d*
_{
C
})=0.616/day. By comparing Eq. (24) to Eq. (25), we see that *η*
_{1}
*T*
_{1}+*η*
_{8}
*T*
_{8}=*λ*
_{0}. Noting that *T*
_{8} cells kill cancer cells more effectively than *T*
_{1} cells, we take *η*
_{8}=4*η*
_{1}, so that (with \(T_{1}=K_{T_{1}}=2\times 10^{-3}\ \mathrm {g}/\text {cm}^{3}\) and \(T_{8}=K_{T_{8}}=1\times 10^{-3}\ \mathrm {g}/\text {cm}^{3}\)) \(\eta _{1}=\frac {\lambda _{0}}{T_{1}+4T_{8}}=11.5\ \text {cm}^{3}/\mathrm {g}\cdot \text {day}\) and *η*
_{8}=46 cm^{3}/g·day. From Eq. (26), we have \(\frac {1}{2}\lambda _{C}\cdot \frac {1}{1+B/K_{CB}}-(\eta _{1}T_{1}+\eta _{8}T_{8})-d_{C}=\frac {1}{5}\lambda _{0}\). Since *λ*
_{
C
}=2(2*λ*
_{0}+*d*
_{
C
}) and *η*
_{1}
*T*
_{1}+*η*
_{8}
*T*
_{8}=*λ*
_{0}, we get \((2\lambda _{0} +d_{C})\cdot \frac {1}{1+B/K_{CB}}-\lambda _{0}-d_{C}=\frac {1}{5}\lambda _{0}\), so that (with *B*=*K*
_{
B
}=6.69×10^{−10} g/cm^{3}) \(K_{CB}=B\frac {5d_{C}+6\lambda _{0} }{4\lambda _{0}}=3.06\times 10^{-9}\ \mathrm {g}/\text {cm}^{3}\).

#### Equation (8)

The serum level of IL-12 in melanoma patients varies from 7.5×10^{−11}−9.6×10^{−11} g/cm^{3} [71, 72]. We assume that the IL-12 level in tissue is higher, and take \(I_{12}=K_{I_{12}}=8\times 10^{-10}\ \mathrm {g}/\text {cm}^{3}\). In the control case (no drugs), from the steady state of Eq. (8), we get \(\lambda _{I_{12}D}D-d_{I_{12}}I_{12}=0\), where \(d_{I_{12}}=1.38\)/day [65] and *D*=*K*
_{
D
}=4×10^{−4} g/cm^{3}. Hence, \(\lambda _{I_{12}D}=2.76\times 10^{-6}\)/day. In the simulations we take \(\lambda _{I_{12}B}=1\), but simulations do not change qualitatively with smaller or larger values of \(\lambda _{I_{12}B}\).

#### Equation (9)

From the steady state of Eq. (9), we get \(\lambda _{I_{2}T_{1}}T_{1}-d_{I_{2}}I_{2}=0\), where \(d_{I_{2}}=2.376\)/day [65] and \(I_{2}=K_{I_{2}}=2.37\times 10^{-11}\ \mathrm {g}/\text {cm}^{3}\) [65], and \(T_{1}=K_{T_{1}}=2\times 10^{-3}\ \mathrm {g}/\text {cm}^{3}\). Hence, \(\lambda _{I_{2}T_{1}}=2.82\times 10^{-8}\)/day.

#### Equation (10)

The half-life of TGF- *β* is about 2 min [73], that is, *t*
_{1/2}=0.0014 day, so that \(d_{T_{\beta }}=\text {ln}2/t_{1/2}=499.07\ \text {day}^{-1}\). The concentration of serum TGF- *β* in melanoma is 2.68×10^{−14} g/cm^{3} [74]. We assume that the concentration of TGF- *β* in tissue is higher than in serum, and take *T*
_{
β
}=2.68×10^{−13} g/cm^{3}. By [75], \(\lambda _{T_{\beta } T_{r}}=5.57\times 10^{-9}\)/day. According to [27, 42], melanoma cells secrete more TGF- *β* than MDSC, and we assume that \(\lambda _{T_{\beta } C}C=2\lambda _{T_{\beta } M}\)M. Hence, from the steady state of Eq. (10) we have, \(\lambda _{T_{\beta } C}C+\lambda _{T_{\beta } M}M+\lambda _{T_{\beta } T_{r}}T_{r}=d_{T_{\beta }}T_{\beta }\), or \(3\lambda _{T_{\beta } M}M+\lambda _{T_{\beta } T_{r}}T_{r}=d_{T_{\beta }}T_{\beta }\). Thus \(\lambda _{T_{\beta } M}=(d_{T_{\beta }}T_{\beta }-\lambda _{T_{\beta } T_{r}}T_{r})/ (3M)=2.18\times 10^{-10}\)/day, and \(\lambda _{T_{\beta } C}=2\lambda _{T_{\beta } M}M/C= 2.18\times 10^{-10}\)/day.

#### Equation (11)

The half-life of IL-6 is less than 6 hours [76], and we take it to be 4 hours, that is, *t*
_{1/2}=0.17 day, so that \(d_{I_{6}}=\text {ln}2/t_{1/2}=4.16\ \text {day}^{-1}\). The concentration of serum IL-6 in melanoma is 3.4×10^{−12} g/cm^{3} [77]. We assume that the concentration of IL-6 in tissue is higher than in serum, and take *I*
_{6}=3.4×10^{−11} g/cm^{3}. From the steady state of Eq. (11), we get \(\lambda _{I_{6}C}=d_{I_{6}}I_{6}/C=3.54\times 10^{-10}\)/day.

#### Equation (12)

The half-life of IL-10 ranges from 1.1 to 2.6 hours [78]; we take it to be 2 hours, that is, *t*
_{1/2}=0.08 day, so that \(d_{I_{10}}=8.32\ \text {day}^{-1}\). The concentration of serum IL-10 in melanoma is 8.75×10^{−12} g/cm^{3} [79]. We assume that the concentration of IL-10 in tissue is higher than in serum, and take *I*
_{10}=8.75×10^{−11} g/cm^{3}. In melanoma, the tissue concentrations of IL-10 secreted by tumor cells and by macrophages are similar [80], and, accordingly, we assume that \(\lambda _{I_{10}C}C=\lambda _{I_{10}M}M\) in steady state. Hence, from the steady state of Eq. (12) we get, \(2\lambda _{I_{10}C}C-d_{I_{1}0}I_{10}=0\), so that \(\lambda _{I_{10}C}=d_{I_{10}}I_{10}/2C=9.10\times 10^{-10}\)/day, and \(\lambda _{I_{10}M}=\lambda _{I_{10}C}C/M=1.82\times 10^{-9}\)/day.

#### Equations (13)-(15)

In order to estimate the parameters *K*
_{
TQ
} (in Eqs. (3) and (4)) and *K*
_{
Q
} (in Eq. (5)), we need to determine the steady state concentrations of *P* and *L* in the control case (no drugs). To do that, we begin by estimating *ρ*
_{
P
} and *ρ*
_{
L
}.

By [81], the mass of one PD-1 is *m*
_{
P
}=8.3×10^{−8} pg= 8.3×10^{−20} g, and by [1] the mass of one PD-L1 is *m*
_{
L
}=5.8×10^{−8} pg= 5.8×10^{−20} g. We assume that the mass of one T cell is *m*
_{
T
}=10^{−9} g. By [82], there are 3000 PD-1 proteins and 9000 PD-L1 proteins on one T cell (*T*
_{1} or *T*
_{8}). Since *ρ*
_{
P
}
*T* is the density of PD-1 (without anti-PD-1 drug), we get \(\rho _{P}=3000\times \frac {m_{P}}{m_{T}}=\frac {3000\times (8.3\times 10^{-20})}{10^{-9}}=2.49\times 10^{-7}\), and \(\rho _{L}=9000\times \frac {m_{L}}{m_{T}}=\frac {9000\times (5.8\times 10^{-20})}{10^{-9}}=5.22\times 10^{-7}\).

*P*, we assume that the average densities of

*T*

_{1},

*T*

_{8}and

*T*

_{ r }are approximately 2×10

^{−3}, 1×10

^{−3}and 5×10

^{−4}g/ cm

^{3}, respectively. PD-1 is expressed by Tregs at higher or lower level than in

*T*

_{1}and

*T*

_{8}cells depending on the type of the cancer [83]; we assume that

*ε*

_{ T }=0.8. Hence, in steady state,

*ε*

_{ C }in Eq. (14) depends on the type of cancer. We take

*ε*

_{ C }=0.01 [84]. MDSCs express PD-L1 at lower level than tumor cells [85], and accordingly, we assume that \(\varepsilon _{M}M=\frac {1}{4}\varepsilon _{C}C\), so that

*ε*

_{ M }=

*ε*

_{ C }

*C*/4

*M*=

*ε*

_{ C }/2=0.005. Then, by Eq. (14), we get

#### Equations (16)-(17)

In mice experiments [44, 86] different amounts of drugs were injected, and the amount of BRAF/MEK inhibitor was larger than the amount of anti-PD-1. It is difficult to compare the amounts injected into mice with the actual levels of the drugs which appear in Eqs. (16) and (17), since there is no information available on the PK/PD of the drugs. For the choice of *γ*
_{
A
}=0.3×10^{−9} g/cm^{3}·day and *γ*
_{
B
}=0.5×10^{−9} g/cm^{3}·day, we found that the simulations are in qualitative agreement with experiments reported in [44]. We shall accordingly take *γ*
_{
A
} in the range of *n*.4×10^{−9} g/cm^{3}·day and *γ*
_{
B
} in the range of 0−5×10^{−9} g/cm^{3}·day.

*μ*

_{ PA }

*P*

*A*/10

*%*=

*d*

_{ A }

*A*/90

*%*, so that

*B*≥

*γ*

_{ B }/

*d*

_{ B }, and we assume that

*d*

_{ B }=1.66/day. We take

*γ*

_{ B }to be order of magnitude 10

^{−9}g/cm

^{3}·day in the simulations. Hence,

*B*=

*K*

_{ B }=6.69×10

^{−10}g/cm

^{3}in steady state, and

*μ*

_{ BC }=2

*d*

_{ B }

*B*/9

*C*=6.17×10

^{−10}/day.

Eqs. (20): We assume that \(\hat T_{1}\) is larger than \(K_{T_{1}}\) and take \(\hat T_{1}=4\times 10^{-3}\ \mathrm {g}/\text {cm}^{3}\). Similarly, we also assume that \(\hat T_{8}\) is larger than \(K_{T_{8}}\) and take \(\hat T_{8}=2\times 10^{-3}\ \mathrm {g}/\text {cm}^{3}\).

### Computational method

*F*represents the term in the right hand side of Eq. (2). Let \(r_{i}^{k}\) and \(D_{i}^{k}\) denote numerical approximations of i-th grid point and \(D(r_{i}^{k},n\tau)\), respectively, where

*τ*is the size of time-step. The discretization of Eq. (27) is derived by the fully implicit finite difference scheme:

where \(D_{r}=\frac {h_{-1}^{2}D_{i+1}^{k+1}-h_{1}^{2}D_{i-1}^{k+1}-(h_{1}^{2}-h_{-1}^{2})D_{i}^{k+1}}{h_{1}(h_{-1}^{2}-h_{1}h_{-1})}\), \(D_{rr}=2\frac {h_{-1}D_{i+1}^{k+1}-h_{1}D_{i-1}^{k+1}+(h_{1}-h_{-1})D_{i}^{k+1}}{h_{1}(h_{1}h_{-1}-h_{-1}^{2})}\),\(u_{r}=\frac {h_{-1}^{2}u_{i+1}^{k+1}-h_{1}^{2}u_{i-1}^{k+1}-(h_{1}^{2}-h_{-1}^{2})u_{i}^{k+1}}{h_{1}(h_{-1}^{2}-h_{1}h_{-1})}\), \(h_{-1}=r_{i-1}^{k+1}-r_{i}^{k+1}\) and \(h_{1}=r_{i+1}^{k+1}-r_{i}^{k+1}\). The mesh moves by \(r_{i}^{k+1}=r_{i}^{k}+u_{i}^{k+1}\tau \), where \(u_{i}^{k+1}\) is solved by the velocity equation.

## Declarations

### Acknowledgements

This work is supported by the Mathematical Biosciences Institute and the National Science Foundation (Grant DMS 0931642), and by the Renmin University of China and the National Natural Science Foundation of China (Grant No. 11501568), and the International Postdoctoral Exchange Fellowship Program 2016 by the Office of China Postdoctoral Council.

### Availability of data and materials

The dataset supporting the conclusions of this article is included within the article.

### Authors’ contributions

XL and AF developed and simulated the model, and wrote the final manuscript. Both authors read and approved the final manuscript.

### Competing interests

The authors declare that they have no competing interests.

### Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Open Access** This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

## Authors’ Affiliations

## References

- Cheng X, Veverka V, Radhakrishnan A, Waters LC, Muskett FW, Morgan SH, et al. Human pd-l1/b7-h1/cd274 protein. Sino Biological Inc. http://www.sinobiological.com/PD-L1-B7-H1-CD274-Protein-g-533.html. Accessed 2002.
- Shi L, Chen S, Yang L, Li Y. The role of pd-1 and pd-l1 in t-cell immune suppression in patients with hematological malignancies. J Hematol Oncol. 2013; 6(74):10–118617568722674.Google Scholar
- Muppidi MR, George S. Immune checkpoint inhibitors in renal cell carcinoma. J Targeted Ther Cancer. 2015; 4:47–52.Google Scholar
- Cooper ZA, Juneja VR, Sage PT, Frederick DT, Piris A, Mitra D, et al. Response to braf inhibition in melanoma is enhanced when combined with immune checkpoint blockade. Cancer Immunol Res. 2014; 2(7):643–54.View ArticlePubMedPubMed CentralGoogle Scholar
- Wilmott JS, Long GV, Howle JR, Haydu LE, Sharma RN, Thompson JF, et al. Selective braf inhibitors induce marked t-cell infiltration into human metastatic melanoma. Clin Cancer Res. 2012; 18(5):1386–94.View ArticlePubMedGoogle Scholar
- He J, Hu Y, Hu M, Li B. Development of pd-1/pd-l1 pathway in tumor immune microenvironment and treatment for non-small cell lung cancer. Sci Rep. 2015; 5(13110):10–103813110.Google Scholar
- Ascierto PA, Kirkwood JM, Grob JJ, Simeone E, Grimaldi AM, Maio M, et al. The role of braf v600 mutation in melanoma. J Transl Med. 2012; 10(85):1–9.PubMedPubMed CentralGoogle Scholar
- Sanchez-Laorden B, Viros A, Girotti MR, Pedersen M, Saturno G, Zambon A, et al. Braf inhibitors induce metastasis in ras mutant or inhibitor-resistant melanoma cells by reactivating mek and erk signaling. Sci Signal. 2014; 7(318):30.View ArticleGoogle Scholar
- Menziesa AM, Long GV. Recent advances in melanoma systemic therapy. braf inhibitors, ctla4 antibodies and beyond. Eur J Cancer. 2013; 49(15):3229–241.View ArticleGoogle Scholar
- Eroglu Z, Ribas A. Combination therapy with braf and mek inhibitors for melanoma: latest evidence and place in therapy. Ther Adv Med Oncol. 2016; 8(1):48–56.View ArticlePubMedPubMed CentralGoogle Scholar
- Ott PA. Combined braf and mek inhibition in brafv600e mutant melanoma: a synergistic and potentially safe combination partner with immunotherapy. Ann Transl Med. 2015; 3(20):313.PubMedPubMed CentralGoogle Scholar
- Topalian SL, Sznol M, McDermott DF, Kluger HM, Carvajal RD, Sharfman WH, et al. Survival, durable tumor remission, and long-term safety in patients with advanced melanoma receiving nivolumab. J Clin Oncol. 2014; 32(10):1020–30.View ArticlePubMedPubMed CentralGoogle Scholar
- Sugiura K, Sugiura M. Immune checkpoint blockade therapy: Merits and demerits. J Clin Exp Dermatol Res. 2016; 7(372):10–4172215595541000372.Google Scholar
- Hu-Lieskovan S, Mok S, Moreno BH, Tsoi J, Faja LR, Goedert L, et al. Improved antitumor activity of immunotherapy with braf and mek inhibitors in brafv600e melanoma. Sci Transl Med. 2016; 7(279):279–41.Google Scholar
- Kawakami Y, Yaguchi T, Sumimoto H, Kudo-Saito C, Iwata-Kajihara T, Nakamura S, et al. Improvement of cancer immunotherapy by combining molecular targeted therapy. Front Oncol. 2013; 3(136):1–7.Google Scholar
- Devji T, Levine O, Neupane B, Beyene J, Xie F. Systemic therapy for previously untreated advanced braf-mutated melanoma: A systematic review and network meta-analysis of randomized clinical trials. JAMA Oncol; 3(3):366–73.Google Scholar
- Hargadon KM, Bishop JD, Brandt JP, Hand ZC, Ararso YT, Forrest OA. Melanoma-derived factors alter the maturation and activation of differentiated tissue-resident dendritic cells. Immunol Cell Biol. 2016; 94(1):24–38.Google Scholar
- Ott PA, Henry T, Baranda SJ, Frleta D, Manches O, Bogunovic D, et al. Inhibition of both braf and mek in braf(v600e) mutant melanoma restores compromised dendritic cell (dc) function while having differential direct effects on dc properties. Cancer Immunol Immunother. 2013; 62(4):811–22.View ArticlePubMedGoogle Scholar
- Ilieva KM, Correa I, Josephs DH, Karagiannis P, Egbuniwe IU, Cafferkey MJ, et al. Effects of braf mutations and braf inhibition on immune responses to melanoma. Mol Cancer Ther. 2014; 13(12):2769–83.View ArticlePubMedPubMed CentralGoogle Scholar
- Janco JMT, Lamichhane P, Karyampudi L, Knutson KL. Tumor-infiltrating dendritic cells in cancer pathogenesis. J Immunol. 2015; 194(7):2985–91.View ArticlePubMed CentralGoogle Scholar
- Ma Y, Shurin1 GV, Peiyuan Z, Shurin MR. Dendritic cells in the cancer microenvironment. J Cancer. 2013; 4(1):36–44.Google Scholar
- Haabeth OAW, Tveita AA, Fauskanger M, Schjesvold F, Lorvik KB, Hofgaard PO, et al. How do cd4(+) t cells detect and eliminate tumor cells that either lack or express mhc class ii molecules?Front Immunol. 2014; 5(174):1–13.Google Scholar
- Kim HJ, Cantor H. Cd4 t-cell subsets and tumor immunity: the helpful and the not-so-helpful. Cancer Immunol Res. 2014; 2(2):91–8.View ArticlePubMedGoogle Scholar
- Thomas WD, Hersey P. Tnf-related apoptosis-inducing ligand (trail) induces apoptosis in fas ligand-resistant melanoma cells and mediates cd4 t cell killing of target cells. J Immunol. 1998; 161(5):2195–200.PubMedGoogle Scholar
- Schattnern EJ, Mascarenhas J, Bishop J, Yoo DH, Chadburn A, Crow M, et al. Cd4+ t-cell induction of fas-mediated apoptosis in burkitt’s lymphoma b cells. Blood. 1996; 88(4):1375–82.Google Scholar
- Martinez-Lostao L, Anel A, Pardo J. How do cytotoxic lymphocytes kill cancer cells?Clin Cancer Res. 2015; 21(22):5047–56.View ArticlePubMedGoogle Scholar
- Perrot CY, Javelaud D, Mauviel A. Insights into the transforming growth factor-beta signaling pathway in cutaneous melanoma. Ann Dermatol. 2013; 25(2):135–44.View ArticlePubMedPubMed CentralGoogle Scholar
- Oelkrug C, Ramage JM. Enhancement of t cell recruitment and infiltration into tumours. Clin Exp Immunol. 2014; 178(1):1–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Jobe NP, Rösel D, Dvořánková B, Kodet O, Lacina L, Mateu R, et al. Simultaneous blocking of il-6 and il-8 is sufficient to fully inhibit caf-induced human melanoma cell invasiveness. Histochem Cell Biol. 2016; 146(2):205–17.View ArticlePubMedGoogle Scholar
- Whiteside TL. The role of regulatory t cells in cancer immunology. Immunotargets Ther. 2015; 4:159–71.View ArticlePubMedPubMed CentralGoogle Scholar
- Umansky V, Blattner C, Gebhardt C, Utikal J. The role of myeloid-derived suppressor cells (mdsc) in cancer progression. Vaccines. 2016; 4(36):1–16.Google Scholar
- Munn DH, Mellor AL. Ido in the tumor microenvironment: Inflammation, counter-regulation, and tolerance. Trends in Immunology. 2016; 37(3):193–207.View ArticlePubMedPubMed CentralGoogle Scholar
- Sims GP, Rowe DC, Rietdijk ST, Herbst R, Coyle AJ. Hmgb1 and rage in inflammation and cancer. Annu Rev Immunol. 2010; 28:367–88.View ArticlePubMedGoogle Scholar
- Zandarashvili L, Sahu D, Lee K, Lee YS, Singh P, Rajarathnam K, et al. Real-time kinetics of high-mobility group box 1 (hmgb1) oxidation in extracellular fluids studied by in situ protein nmr spectroscopy. J Biol Chem. 2013; 288(17):11621–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Palucka J, Banchereau J. Cancer immunotherapy via dendritic cells. Nat Rev Cancer. 2012; 12(4):265–77.View ArticlePubMedPubMed CentralGoogle Scholar
- Saenz R, Futalan D, Leutenez L, Eekhout F, Fecteau JF, Sundelius S, et al. Tlr4-dependent activation of dendritic cells by an hmgb1-derived peptide adjuvant. J Transl Med. 2014; 12(211):1–11.Google Scholar
- Chen D, Roda JM, Marsh CB, Eubank TD, Friedman A. Hypoxia inducible factors-mediated inhibition of cancer by gm-csf: a mathematical model. Bull Math Biol. 2012; 74(11):2752–77.PubMedPubMed CentralGoogle Scholar
- Eubank TD, Roberts RD, Khan M, Curry JM, Nuovo GJ, Kuppusamy P, et al. Granulocyte macrophage colony-stimulating factor inhibits breast cancer growth and metastasis by invoking an anti-angiogenic program in tumor-educated macrophages. Cancer Res. 2009; 69(5):2133–40.View ArticlePubMedPubMed CentralGoogle Scholar
- Frederick DT, Piris A, Cogdill AP, Cooper ZA, Lezcano C, Ferrone CR, et al. Braf inhibition is associated with enhanced melanoma antigen expression and a more favorable tumor microenvironment in patients with metastatic melanoma. Clin Cancer Res. 2013; 19(5):1225–31.View ArticlePubMedPubMed CentralGoogle Scholar
- Hao W, Rovin BH, Friedmanc A. Mathematical model of renal interstitial fibrosis. Proc Natl Acad Sci U S A. 2014; 111(39):14193–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Condamine T, Gabrilovich DI. Molecular mechanisms regulating myeloid-derived suppressor cell differentiation and function. Trends Immunol. 2011; 32(1):19–25.View ArticlePubMedGoogle Scholar
- Cantelli G, Crosas-Molist1 E, Georgouli M, Sanz-Moreno V. Tgfb-induced transcription in cancer. Semin Cancer Biol. 2016; 42:60–9.Google Scholar
- Mautea RL, Gordona SR, Mayere AT, McCrackena MN, Natarajane A, Ring NG, et al. Engineering high-affinity pd-1 variants for optimized immunotherapy and immuno-pet imaging. Proc Natl Acad Sci USA. 2015; 112(47):6506–14.View ArticleGoogle Scholar
- Hu-Lieskovan S, Mok S, Moreno BH, Tsoi J, Faja LR, Goedert L, et al. Improved antitumor activity of immunotherapy with braf and mek inhibitors in braf(v600e) melanoma. Sci Transl Med. 2015; 7(279):1–11.View ArticleGoogle Scholar
- D’Acunto B. Computational Methods for PDE in Mechanics. Series on Advances in Mathematics for Applied Sciences-Vol.67. Singapore: Word Scientific; 2004.View ArticleGoogle Scholar
- Marino S, Hogue I, Ray C, Kirschner D. A methodology for performing global uncertainty and sensitivity analysis in systems biology. J Theor Biol. 2008; 254(1):178–96.View ArticlePubMedPubMed CentralGoogle Scholar
- Yin N, Ma W, Pei J, Ouyang Q, Tang C, Lai L. Synergistic and antagonistic drug combinations depend on network topology. PLoS ONE. 2014; 9(4):93960.View ArticleGoogle Scholar
- Jia J, Zhu F, Ma X, Cao ZW, Li YX, Chen YZ. Mechanisms of drug combinations: interaction and network perspectives. Nat Rev Drug Discov. 2009; 8(2):111–28.View ArticlePubMedGoogle Scholar
- Young ME. Estimation of diffusion coefficients of proteins. Biotech Bioeng. 1980; XXII:947–55.View ArticleGoogle Scholar
- Shui YB, Wang X, Hu JS, Wang SP, Garcia CM, et al. Vascular endothelial growth factor expression and signaling in the lens. Invest Ophthalmol Vis Sci. 2003; 44(9):3911–9.View ArticlePubMedGoogle Scholar
- Liao KL, Bai XF, Friedman A. Mathematical modeling of interleukin-27 induction of anti-tumor t cells response. PLoS ONE. 2014; 9(3):91844.View ArticleGoogle Scholar
- PhosphoSitePlus/IL2(human). http://www.phosphosite.org/proteinAction.action?id=14691%26showAllSites=true. Accessed 2003.
- Hamza T, Barnett JB, Li B. Interleukin 12 a key immunoregulatory cytokine in infection applications. Int J Mol Sci. 2010; 11(3):789–806.View ArticlePubMedPubMed CentralGoogle Scholar
- Poniatowski LA, Wojdasiewicz P, Gasik R, Szukiewicz D. Transforming growth factor beta family: Insight into the role of growth factors in regulation of fracture healing biology and potential clinical applications. Mediat Inflamm. 2015;(Article ID 137823):1–17.Google Scholar
- Abcam/. Recombinant human il6 protein (ab9627). http://www.abcam.com/recombinant-human-il6-protein-ab9627.html.
- Keller ET, Wanagat J, Ershler WB. Stromelysin-1/matrix metalloproteinase-3 (mmp-3) expression accounts for invasive properties of human astrocytoma cell lines. Front Biosci. 1996; 1(1):340–57.View ArticleGoogle Scholar
- PhosphoSitePlus/IL10(human). http://www.phosphosite.org/proteinAction.action?id=2473887%26showAllSites=true. Accessed 2003.
- Abcam/. Anti-pd1 antibody (ab89828). http://www.abcam.com/pd1-antibody-ab89828.html.
- Shah N, Iyer RM, Mair HJ, Choi DS, Tian H, Diodone R, et al. Improved human bioavailability of vemurafenib, a practically insoluble drug, using an amorphous polymer-stabilized solid dispersion prepared by a solvent-controlled coprecipitation process. J Pharm Sci. 2013; 102(3):967–81.View ArticlePubMedGoogle Scholar
- Steptoe RJ, Patel RK, Subbotin VM, Thomson AW. Comparative analysis of dendritic cell density and total number in commonly transplanted organs: morphometric estimation in normal mice. Transpl Immunol. 2000; 8(1):49–56.View ArticlePubMedGoogle Scholar
- Bergstresser PR, Fletcher CR, Streilein JW. Surface densities of langerhans cells in relation to rodent epidermal sites with special immunologic properties. J Invest Dermatol. 1980; 74(2):77–88.View ArticlePubMedGoogle Scholar
- Romani N, Clausen BE, Stoitzner P. Langerhans cells and more: langerin-expressing dendritic cell subsets in the skin. Immunol Rev. 2010; 234(1):120–41.View ArticlePubMedPubMed CentralGoogle Scholar
- Dumortier H, van Mierlo GJD, Egan D, van Ewijk W, Toes REM, Offringa R, et al. Antigen presentation by an immature myeloid dendritic cell line does not cause ctl deletion in vivo, but generates cd8+ central memory-like t cells that can be rescued for full effector function. J Immunol. 2005; 175(2):855–63.View ArticlePubMedGoogle Scholar
- Ataera H, Hyde E, Price KM, Stoitzner P, Ronchese F. Murine melanoma-infiltrating dendritic cells are defective in antigen presenting function regardless of the presence of cd4cd25 regulatory t cells. PLoS ONE. 2011; 6(3):17515.View ArticleGoogle Scholar
- Hao W, Friedman A. The role of exosomes in pancreatic cancer microenvironment. Bull Math Biol. 2017;1–23. doi:10.1007/s11538-017-0254-9.
- Hao W, Schlesinger LS, Friedman A. Modeling granulomas in response to infection in the lung. PLoS ONE. 2016; 11(3):0148738.Google Scholar
- Hussein MR. Tumour-associated macrophages and melanoma tumourigenesis: integrating the complexity. Int J Exp Pathol. 2006; 87(3):163–76.View ArticlePubMedPubMed CentralGoogle Scholar
- Tian H, Shi G, Wang Q, Li Y, Yang Q, Li C, et al. A novel cancer vaccine with the ability to simultaneously produce anti-pd-1 antibody and gm-csf in cancer cells and enhance th1-biased antitumor immunity. Signal Transduct Target Ther. 2016; 1(16025):10–1038201625.Google Scholar
- Li B, VanRoey M, Wang C, Chen TH, Korman A, Jooss K. Anti-programmed death-1 synergizes with granulocyte macrophage colony-stimulating factor–secreting tumor cell immunotherapy providing therapeutic benefit to mice with established tumors. Clin Cancer Res. 2009; 15(5):1623–1634.View ArticlePubMedGoogle Scholar
- Ali OA, Lewin SA, Dranoff G, Mooney DJ. Vaccines combined with immune checkpoint antibodies promote cytotoxic t cell activity and tumor eradication. 2015. http://dx.doi.org/10.1158/2326-6066.CIR-14-0126.
- Chun Y, Wang Y, Vo A, Liu H, Wang D, Gershenwald J, et al. Elevated interleukin-12 is a plasma marker of poor prognosis in stage iii melanoma patients. Am Assosiation for Cancer Res. 2008; 68(9):1.Google Scholar
- Bankhead C. Aacr: Il-12 level may predict melanoma prognosis. Medpage Today. 2008;9177. https://www.medpagetoday.com/meetingcoverage/aacr/9177.
- Kaminska B, Wesolowska A, Danilkiewicz M. Tgf beta signalling and its role in tumour pathogenesis. Acta Biochim Pol. 2005; 52(2):329–37.PubMedGoogle Scholar
- Kyrtsonis MC, Repa C, Dedoussis GV, Mouzaki A, Simeonidis A, Stamatelou M, et al. Serum transforming growth factor-beta 1 is related to the degree of immunoparesis in patients with multiple myeloma. Med Oncol. 1998; 15(2):124–8.View ArticlePubMedGoogle Scholar
- Lo WC, Arsenescu V, Arsenescu RI, Friedman A. Inflammatory bowel disease: How effective is tnf-alpha suppression?PLoS One. 2016; 11(11):0165782.Google Scholar
- Ridker PM, Rifai N, Stampfer MJ, Hennekens CH. Plasma concentration of interleukin-6 and the risk of future myocardial infarction among apparently healthy men. Clin Investig Rep. 2000; 101:1767–72.Google Scholar
- Hoejberg L, Bastholt L, Johansen JS, Christensen JI, Gehl J, Schmidt H. Serum interleukin-6 as a prognostic biomarker in patients with metastatic melanoma. Melanoma Res. 2012; 22(4):287–93.View ArticlePubMedGoogle Scholar
- Mueller C, Braag SA, Martino AT, Tang Q, Campbell-Thompson M, Flotte T. The pros and cons of immunomodulatory il-10 gene therapy with recombinant aav in a cftr-/–dependent allergy mouse model. Gene Therapy. 2009; 16:172–83.View ArticlePubMedGoogle Scholar
- Nemunaitis J, Fong T, Shabe P, Martineau D, Ando D. Comparison of serum interleukin-10 (il-10) levels between normal volunteers and patients with advanced melanoma. Cancer Invest. 2001; 19(3):239–47.View ArticlePubMedGoogle Scholar
- Itakura E, Huang RR, Wen DR, Paul E, Wunsch PH, Cochran AJ. Il-10 expression by primary tumor cells correlates with melanoma progression from radial to vertical growth phase and development of metastatic competence. Mod Pathol. 2011; 24(6):801–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Agata Y, Kawasaki A, Nishimura H, Ishida Y, Tsubat T, Yagita H, et al. Expression of the pd-1 antigen on the surface of stimulated mouse t and b lymphocytes. Int Immunol. 1996; 8(5):765–72.View ArticlePubMedGoogle Scholar
- Cheng X, Veverka V, Radhakrishnan A, Waters LC, Muskett FW, Morgan SH, et al. Structure and interactions of the human programmed cell death 1 receptor. J Biol Chem. 2013; 288(17):11771–85.View ArticlePubMedPubMed CentralGoogle Scholar
- Lowther DE, Goods BA, Lucca LE, Lerner BA, Raddassi K, van Dijk D, et al. Pd-1 marks dysfunctional regulatory t cells in malignant gliomas. JCI Insight. 2016; 1(5):85935.View ArticleGoogle Scholar
- Buttea MJ, Pena-Cruzc V, Kima MJ, Freemanc GJ, Sharpe AH. Interaction of human pd-l1 and b7-1. Mol Immunol. 2008; 45(13):3567–72.View ArticleGoogle Scholar
- Schalper KA, Carvajal-Hausdorf D, McLaughlin J, Velcheti V, Chen L, Sanmamed M, et al. Clinical significance of pd-l1 protein expression on tumor-associated macrophages in lung cancer. J Immunother Cancer. 2015; 3(Suppl 2):415.View ArticleGoogle Scholar
- Lipson EJ, Sharfman WH, Drake CG, Wollner I, Taube JM, Anders RA, et al. Durable cancer regression off-treatment and effective reinduction therapy with an anti-pd-1 antibody. Clin Cancer Res. 2013; 19(2):462–8.View ArticlePubMedGoogle Scholar
- Brahmer JR, Drake CG, Wollner I, Powderly JD, Picus J, Sharfman WH, et al. Phase i study of single-agent anti-programmed death-1 (mdx-1106) in refractory solid tumors: Safety, clinical activity, pharmacodynamics, and immunologic correlates. J Clin Oncol. 2010; 28(19):3167–75.View ArticlePubMedPubMed CentralGoogle Scholar
- Luke JJ, Ott PA. New developments in the treatment of metastatic melanoma - role of dabrafenib-trametinib combination therapy. Drug Healthc Patient Saf. 2014; 6:77–88.View ArticlePubMedPubMed CentralGoogle Scholar
- rey R Infante J, Fecher LA, Falchook GS, Nallapareddy S, Gordon MS, Becerra C, et al. Safety, pharmacokinetic, pharmacodynamic, and effi cacy data for the oral mek inhibitor trametinib: a phase 1 dose-escalation trial. Lancet Oncol. 2012; 13(8):773–81.View ArticleGoogle Scholar
- Kima Y, Lawlerb S, Nowickib MO, Chioccab EA, Friedman A. A mathematical model for pattern formation of glioma cells outside the tumor spheroid core. J Theor Biol. 2009; 260(3):359–71.View ArticleGoogle Scholar
- Mercapide J, Cicco RLD, Castresana JS, Klein-Szanto AJP. Stromelysin-1/matrix metalloproteinase-3 (mmp-3) expression accounts for invasive properties of human astrocytoma cell lines. Int J Cancer. 2003; 106(5):676–82.View ArticlePubMedGoogle Scholar