Fever‐Inspired Immunotherapy Based on Photothermal CpG Nanotherapeutics: The Critical Role of Mild Heat in Regulating Tumor Microenvironment

Abstract Although there have been more than 100 clinical trials, CpG‐based immunotherapy has been seriously hindered by complications in the immunosuppressive microenvironment of established tumors. Inspired by the decisive role of fever upon systemic immunity, a photothermal CpG nanotherapeutics (PCN) method with the capability to induce an immunofavorable tumor microenvironment by casting a fever‐relevant heat (43 °C) in the tumor region is developed. High‐throughput gene profile analysis identifies nine differentially expressed genes that are closely immune‐related upon mild heat, accompanied by IL‐6 upregulation, a pyrogenic cytokine usually found during fever. When treated with intratumor PCN injection enabling mild heating in the tumor region, the 4T1 tumor‐bearing mice exhibit significantly improved antitumor immune effects compared with the control group. Superb efficacy is evident from pronounced apoptotic cell death, activated innate immune cells, enhanced tumor perfusion, and intensified innate and adaptive immune responses. This work highlights the crucial role of mild heat in modulating the microenvironment in optimum for improved immunotherapy, by converting the tumor into an in situ vaccine.


Detailed information for gene expression sequencing
RNA quantification and qualification: RNA degradation and contamination was monitored on 1% agarose gels. RNA purity was checked using the NanoPhotometer® spectrophotometer (IMPLEN, CA, USA). RNA concentration was measured using Qubit® RNA Assay Kit in Qubit®2.0 Flurometer (Life Technologies, CA, USA). RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA).
Library preparation for Transcriptome sequencing: A total amount of 1 μg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext®Ultra TM RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer's recommendations and index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligoattached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer (5X). First strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase (RNase H-).
Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3' ends of DNA fragments, NEBNext Adaptor with hairpin loop structure were ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 200-250 bp in length, the library fragments were purified with AMPure XP system (Beckman Coulter, Beverly, USA). Then 3 μl USER Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at 37°C for 15 min followed by 5 min at 95°C before PCR. Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. At last, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system.

Clustering and sequencing:
The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v4-cBot-HS (Illumia) according to the manufacturer's instructions. After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform and paired-end reads were generated.

Detailed information for gene expression data analysis
Quality control: Raw data (raw reads) of fastq format were firstly processed through inhouse perl scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing ploy-N and low quality reads from raw data. At the same time, Q20, Q30, GC-content and sequence duplication level of the clean data were calculated.
All the downstream analyses were based on clean data with high quality.
Comparative analysis: The adaptor sequences and low-quality sequence reads were removed from the data sets. Raw sequences were transformed into clean reads after data processing. These clean reads were then mapped to the reference genome sequence. Only reads with a perfect match or one mismatch were further analyzed and annotated based on the reference genome. Tophat2 tools soft were used to map with reference genome.

Differential expression analysis:
For the samples with biological replicates: Differential expression analysis of two conditions/groups was performed using the DESeq R package (1.10.1). DESeq provide statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P values were adjusted using the Benjamini and Hochberg's approach for controlling the false discovery rate. Genes with an adjusted P-value <0.05 found by DESeq were assigned as differentially expressed.
For the samples without biological replicates: Prior to differential gene expression analysis, for each sequenced library, the read counts were adjusted by edgeR program package through one scaling normalized factor. Differential expression analysis of two samples was performed using the DEGseq (2010) R package. P value was adjusted using q value. Q value<0.005 & |log2 (fold change) |≥1 was set as the threshold for significantly differential expression.
GO enrichment analysis: Gene Ontology (GO) enrichment analysis of the differentially expressed genes (DEGs) was implemented by the GOseq R packages based Wallenius noncentral hyper-geometric distribution , which can adjust for gene length bias in DEGs. Figure S1. The agarose gel electrophoretic analysis of PCN compared with CpG. To guarantee the equal CpG amount, no ultra filtration treatment was performed after preparation of PCN.     The survival rate of the mice after different treatments. Statistical analysis was performed by the log-rank test, * p<0.05, ** p<0.01, ***p<0.001. (c) A secondary rechallenge with 4T1 tumor cells was administered to mice 7 days after different treatments. (d)-(f) Cytokine levels in serum from mice 7 days after different treatments. The data are presented as means ± s.d. (n=5).; Statistical analysis was performed by one-way factorial ANOVA. * p<0.05, ** p<0.01, ***p<0.001. Figure S7. The expression of CD8 + T cells in tumor by immunofluorescence 7 days after intratumoral injection of PCN with and without light. All scale bars are 100 μm.