Trial Registration: Nivolumab and Ipilimumab and Radiation Therapy in Microsatellite Stable (MSS) and Microsatellite Instability (MSI) High Colorectal and Pancreatic Cancer. Registration #NCT03104439. https://clinicaltrials.gov/ct2/show/NCT03104439?term=17-021&rank=1.
Patient Selection
Metastatic MSS Colorectal Cancer
Eligibility criteria included; at least 18 years old; histologically or cytologically confirmed adenocarcinoma of colorectal documented as MSS by PCR and/or IHC; Eastern Cooperative Oncology Group performance status £1; life expectancy of greater than 3 months; adequate hematologic function (absolute neutrophil count ³1000/mcL, white blood count ³2000/mcL, platelets ³75,000/mcL, hemoglobin ³7.5 g/dL); adequate renal function (serum creatinine £1.5 the upper limit of normal or creatinine clearance ³ 40ml/min); adequate hepatic function (serum total bilirubin £1.5 the upper limit of normal, AST and ALT £3 the upper limit of normal or £ 5 the upper limit of normal in patients with liver metastases; adequate coagulation (International Normalized Ratio or Prothrombin Time (PT) ≤1.5 X ULN and Activated Partial Thromboplastin Time (aPTT) ≤2.5 X ULN; participants must have been on a stable dose of dexamethasone 2 mg or less for 7 days prior to initiation of treatment. Participants were also required to have one previously unirradiated lesion to serve as the radiotherapy target lesion amenable to a prescribed dose of 8 Gy x 3 which would meet dose constraints, and another unirradiated measurable lesion > 1 cm in size outside the radiation field that could be used as measurable disease; documentation of microsatellite status; patients must have received prior fluorouracil (5FU), irinotecan and oxaliplatin (any combination) or have a contraindication to receiving these agents.
Exclusion criteria include participants who had received chemotherapy, targeted small molecule therapy or study therapy within 14 days of protocol treatment, or those who have not recovered [i.e., ≤ Grade 1 (or ≤ Grade 2 for neuropathy) or at baseline] from adverse events due to agents administered more than 2 weeks earlier. Subjects with major surgery must have recovered adequately; participants currently receiving any other investigational agents; known or suspected autoimmune disease other than vitiligo, type I diabetes mellitus, residual hypothyroidism due to autoimmune condition only requiring hormone replacement, psoriasis not requiring systemic treatment, or conditions not expected to recur in the absence of an external trigger condition requiring systemic treatment with either corticosteroids (> 10 mg daily prednisone equivalents) or other immunosuppressive medications within 14 days of study drug administration); prior systemic treatment with an anti-CTLA4 antibody, anti-PD1 or PDL1 antibody; known history of active TB, HBV, HCV or HIV; uncontrolled intercurrent illness or psychiatric illness/social situations that would limit compliance with study requirements; pregnant or breastfeeding; known additional malignancy that is progressing or requires active treatment (excluding basal cell carcinoma of the skin and squamous cell carcinoma of the skin that has undergone potentially curative therapy or in situ cervical cancer); known history of, or any evidence of active, non-infectious pneumonitis; active infection requiring systemic therapy; received a live vaccine within 30 days of planned start of study therapy; history of allergy to study drug components; history of severe hypersensitivity reaction to any monoclonal antibody; uncontrolled brain metastases (patients treated with radiation > 4 weeks prior with follow up imaging showing control were eligible).
Metastatic MSS Pancreatic Ductal Adenocarcinoma
Inclusion criteria was identical to MSS CRC cohort except for the following differences: histologically or cytologically confirmed adenocarcinoma of pancreas, patients could receive treatment after progressing on one or more lines of therapy, and documentation of microsatellite status was required but did not preclude eligibility.
Exclusion criteria also were identical to the MSS cohort of included participants except in the PDAC cohort, patients could have received prior PD-1 or PDL-1 inhibitors.
The trial protocol is provided in Supplement 1. All procedures were conducted in accordance with the Declaration of Helsinki and the International Conference on Harmonization Guidelines for Good Clinical Practice.35 The protocol and all amendments were reviewed by the scientific review committee and institutional review board at the Dana Farber Cancer Institute/Harvard Cancer Center. All patients provided written informed consent prior to enrollment.
Study Design and Treatment
This was an open-label, single-arm, Phase 2 clinical trial conducted at the Massachusetts General Hospital (MGH) Cancer Center in Boston, MA. Patients enrolled between 07/2017 to 12/2018. On cycle 1, day 1, patients received one dose of nivolumab 240 mg and ipilimumab 1 mg/kg. Nivolumab was administered first as a 30-minute IV infusion followed by ipilimumab, as a 30-min IV infusion, 30 minutes after completion of the nivolumab infusion. Patients went on to receive nivolumab 240 mg once every two weeks, on days 15, 29 of a 42-day cycle. On cycle 2, day 1, patients again received nivolumab 240 mg and ipilimumab 1 mg/kg but also started radiation. Patients received 24 Gy total given as 3 fractions of 8 Gy administered every other day or 2 days as needed. All treatments were administered at either the Clark Center for Radiation Oncology or the Francis H. Burr Proton Center at MGH. After radiation, treatment continued with receive nivolumab 240 mg days every two weeks, on days 15, 29 of a 42-day cycle. For cycle 3 and beyond, patients continued treatment with ipilimumab 1 mg/kg on day 1 with nivolumab 240 mg and then nivolumab every two weeks for a 42 day cycle until disease progression defined according to Response Evaluation Criteria in Solid Tumors version 1.136, unacceptable toxicity, or withdrawal. Dose interruptions and management of immunologic toxicities were in accordance with the protocol.
Correlative Science
Optional biopsies of the tumor site being radiated were done prior to treatment, immediately prior to radiation (weeks 2-5) and within 2 weeks after radiation completion. Whole blood was obtained from patients for DNA germline control. RNA and genomic DNA was extracted from fresh frozen biopsies after homogenization with a TissueLyser (2x4 minutes at 20Hz), using the Qiagen AllPrep DNA/RNA/Protein mini kit (Cat.No.80004, Qiagen) according to manufacturer instructions. Germline genomic DNA was extracted from whole blood samples with the use of the DNAzol BD reagent (10974020, Invitrogen).
Whole Exome Sequencing (WES)
A total of 41 tumor samples with paired germline DNA from 17 patients from the per protocol cohort were analyzed by WES. The Nextera DNA Exome kit (20020617, Illumina) was used to prepare pooled libraries enriched in exonic regions. A genomic DNA input of 50 ng was used per sample. After library preparation and amplification (10 cycles), up to 8 dual indexed libraries were pooled together for the downstream enrichment steps. Pooled samples were sequenced using on a HiSeq X using 150-150 paired end reads.
Whole Exome Sequencing analysis
We called single nucleotide variations (SNVs) and indel mutations from paired-end whole-exome sequencing reads, for which read lengths were 150 base pairs. We downloaded the Broad Institute’s GATK b37 resource bundle [1] as reference data for read processing. We pre-processed sequencing reads according to GATK Best Practices recommendations [2, 3].
We first aligned the sequencing reads to the human_g1k_v37_decoy reference genome (GRCh37) using bwa-0.7.17 mem [4] and samtools-1.6 [5]. Duplicates were marked with picard-2.11.0 MarkDuplicates [6]. Indel realignments were done with the Genome Analysis toolkit (GenomeAnalysisTK-3.8-1-0-gf15c1c3ef) RealignerTargetCreator and IndelRealigner [7] using the 1000 genome phase1 indel (1000G_phase1.indels.b37.vcf) and Mills indel calls (Mills_and_1000G_gold_standard.indels.b37.vcf) as references. Base calls were recalibrated with BaseRecalibrator [7] and dbSNP version 138.
MuTect 1.1.7 [8] and Strelka 1.0.15 [9] were used to call SNVs and indels on pre-processed sequencing data. For the MuTect calls, dbSNP 138 and CosmicCodingMuts.vcf version 86 [10] were used as reference files. For the Strelka calls, we set “isSkipDepthFilters = 1” to prevent filtering-out of mutation calls from exome sequencing due to exome-sequencing mapping breadth.
Unbiased normal/tumor read counts for each SNV and indel call were then assigned with the bam-readcount software (0.8.0-unstable-6-963acab-dirty (commit 963acab-dirty)) [11]. A minimum base quality filter of “-b 15” was used for all mutations except for possible KRAS mutations, which instead had no such filter. This was to capture all possible driver KRAS mutations. The reads were counted in an insertion-centric way with the “-i” flag, so that reads overlapping with insertions were not included in the per-base read counts. of The union of all mutation calls were annotated with the snpEff.v4.3t software [12] using the following code:
$java_path -jar $snpeff_path ann \
-noStats \
-strict \
-hgvs1LetterAa \
-hgvs \
-canon \
-fastaProt $output_path/"$vcf_name"".fasta" \
GRCh37.75 \
$input_vcf \
> $output_path/"$vcf_name""_ann.vcf"
Only annotations without “WARNING” or “ERROR” were kept.
Common mutations in KRAS known to be pathogenic were then manually curated (chromosome 12, bases 25378562, 25380275, 25398281, 25398282, 25398284, and 25398285) corresponding to the top ten coding mutations in KRAS denoted in the Genomic Data Commons Database [13] if the sample already did not already contain a KRAS mutation. Five additional KRAS mutations were added in this way.
Tumor mutational burden (TMB) was computed for: (1) all mutations, (2) nonsynonymous SNV mutations, and (3) missense mutations. Coding mutations with variant allele frequencies greater than 10% were reported as “high-quality”. All KRAS mutations were reported.
[1] https://gatk.broadinstitute.org/hc/en-us/articles/360035890811-Resource-bundle
[2] DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, Del Angel G, Rivas MA, Hanna M, McKenna A. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature genetics. 2011 May;43(5):491.
[3] Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy‐Moonshine A, Jordan T, Shakir K, Roazen D, Thibault J, Banks E. From FastQ data to high‐confidence variant calls: the genome analysis toolkit best practices pipeline. Current protocols in bioinformatics. 2013 Oct;43(1):11-0.
[4] Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv:1303.3997. 2013 Mar 16.
[5] Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The sequence alignment/map format and SAMtools. Bioinformatics. 2009 Aug 15;25(16):2078-9.
[6] http://broadinstitute.github.io/picard
[7] McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome research. 2010 Sep 1;20(9):1297-303.
[8] Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Gabriel S, Meyerson M, Lander ES, Getz G. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature biotechnology. 2013 Mar;31(3):213-9.
[9] Saunders CT, Wong WS, Swamy S, Becq J, Murray LJ, Cheetham RK. Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics. 2012 Jul 15;28(14):1811-7.
[10] Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N, Boutselakis H, Cole CG, Creatore C, Dawson E, Fish P. COSMIC: the catalogue of somatic mutations in cancer. Nucleic acids research. 2019 Jan 8;47(D1):D941-7.
[11] https://github.com/genome/bam-readcount
[12] Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. 2012 Apr 1;6(2):80-92.
[13] Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR, Kibbe WA, Staudt LM. Toward a shared vision for cancer genomic data. New England Journal of Medicine. 2016 Sep 22;375(12):1109-12.
Total RNA sequencing (RNA-seq)
The Smarter Stranded Total RNA-Seq kit v2 (634413, Takara) was used with 10 ng RNA input and 4 minutes fragmentation time, according to the manufacturer instructions to generate dual-indexed libraries for total RNA sequencing. After qPCR-based quantification (KAPA library quantification kit, 07960140001, Roche), libraries were pooled and sequenced on the Illumina NextSeq 500 platform using a 150 cycles kit with paired end read mode.
RNA-seq computational analysis
Raw Illumina reads were quality-filtered as follows. First, ends of the reads were trimmed to remove N’s and bases with quality less than 20. After that, the quality scores of the remaining bases were sorted, and the quality at the 20th percentile was computed. If the quality at the 20th percentile was less than 15, the whole read was discarded. Also, reads shorter than 40 bases after trimming were discarded. If at least one of the reads in the pair failed the quality check and had to be discarded, we discarded the mate as well.
Quality filtered reads were mapped to the human genome (gencode annotation, build 38) and to repbase elements (release 20) using STAR aligner. Aligned reads were assigned to genes using the featureCounts function of the Rsubread package using the external Ensembl annotation. This produced the raw read counts for each gene. Mapping and counting of the reads was done in 2 stages. First, reads were mapped to the human genome, and the counts were determined using the Gencode annotation and the annotation derived from the repeatmasker output. After that, the reads which were not assigned to any feature in either Gencode and repeatmasker annotation were realigned to the repeat consensus sequence (repbase). Counts obtained from repeatmasker and repbase were added together.
Differential Gene Expression Analysis
Differential expression and statistical analysis were performed using DESeq2 in R, with un-normalized raw read counts as the input. A false discovery rate (FDR) adjusted p value < 0.05 was used for the selection of differentially expressed genes. Before plotting, repeat RNA was normalized to total protein coding counts, and protein coding genes were RPM normalized.
Assessments
Participants were seen weekly for clinical assessments including a physical examination with vital signs, performance status, hematology, and biochemistry tests on or within 72 hours before Day 1, then weekly until week 12, and subsequently every two weeks. Participants were evaluated for radiographic response every 12 weeks, noting that the first scan was performed after completion of radiation. In addition to a baseline scan, confirmatory scans were obtained 3 weeks following initial documentation of objective response. Scans could also be obtained prior to every 12 weeks at the clinician’s request. Patients were followed for survival until death, withdrawal of consent for follow-up or up to 5 years. All AEs were monitored from registration until 30 days after treatment and were graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events, Version 4.03. Following disease progression, patients were followed-up within 30 days from the last dose +/- 7 days or coinciding with the date of discontinuation (+/- 7 days) if date of discontinuation was greater than 37 days after last dose with a second follow-up visit 8-10 weeks (+/- 7 days) from follow-up visit 1. After 2 in person follow-up visits, patients were followed with a phone call or in clinic visit every 10-12 weeks for survival.
Study End Points
The primary trial endpoint was disease control rate (response plus stable disease [DCR]) by RECIST 1.1. Responses and stable disease were defined as responses and stability outside of the irradiated field. Secondary endpoints include overall response rate (complete response and partial response [ORR]) in unirradiated lesions, treatment-related adverse event (AE) rates, overall survival (OS) and progression-free survival (PFS). Exploratory objectives included biomarker analysis of serial tumor biopsies and peripheral blood samples.
Statistical Analysis
A two-stage design was used to demonstrate a DCR of 20% under the alternative hypothesis as the minimum level of promising efficacy, while a 5% rate is specified under the null hypothesis to indicate minimal or no activity. In the MSS cohort if at least 2 of the first 20 patients achieved disease control, the cohort proceeded to enroll a total of 40 patients. The two-stage design provided 92% power to accept the protocol treatment is associated with a 20% rate of disease control, while the probability of a type 1 error is 10% if the underlying rate of disease control were truly only 5%. In the mPDAC cohort if at least 1 of the first 15 patients achieved disease control, the cohort proceeded to enroll a total of 25 patients. The two-stage design provided 89% power to accept the protocol treatment is associated with a DCR of 20%, while the probability of a type 1 error is 12% if the underlying DCR were truly only 5%. PFS and OS were measured from the first dose of protocol treatment. PFS was defined as time until the earlier date of either progressive disease or death, or otherwise censored at the date of last follow-up. OS was defined as time to death from any cause or otherwise censored at the date of last follow-up for patients still alive at the time of analysis. OS and PFS curves were estimated by the Kaplan-Meier method, with the 95% confidence interval (95% CI) obtained by the log-log transformation. Analyses were done for the intention-to-treat (ITT) population as well as the per protocol analysis, defined as patients who received radiation. Statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC) and R version 3.3.1 (R Foundation).