2573 Background: Immune checkpoint inhibitors (ICIs) have demonstrated durable clinical responses and improved survival in patients (pts) across numerous indications. Despite this progress, the benefit of ICIs is limited to a minority of overall metastatic cancer patients. There is a critical need for biomarkers agnostic of tumor type to inform which pts will benefit from nivo alone versus ipi/nivo combination treatment. Both pre-treatment tumoral CD8 + cells and recruitment of CD8 + T cells in response to ICIs are associated with improved clinical outcomes in patients treated with anti-PD-1 therapy. 1,2,3,4 Here we report the final results of a prospective clinical study in which pts with varying advanced solid tumors were assigned to nivo, with or without ipi, based on the percentage of tumoral CD8 cells at the time of treatment. Methods: We performed a prospective, non-randomized, open-label, multicenter study in which pts with tumoral CD8 + cells ≥ 15% (CD8 + high) received nivo 360mg IV Q3W, followed by nivo maintenance 480mg Q4W. Pts with tumoral CD8 + cells < 15% (CD8 + low) received nivo 360 mg IV Q3W, and ipi at 1 mg/kg IV Q3W for 2 doses and then Q6W for 2 doses, followed by nivo maintenance 480 mg IV Q4W until PD or intolerable toxicity. Primary endpoints were Disease Control Rate (DCR: CR, PR, or SD ≥ 6 months) and CD8 low to high conversion (< 15% to ≥ 15%). Baseline and on-treatment tumor, blood and stool samples were collected for multiomic biomarker analyses. This study was not powered for formal statistical analysis. Up to 200 pts could be enrolled to allow for adaptive exploration of response and CD8 changes. Results: N = 79 pts were enrolled:7 in CD8 + high arm (nivo) and 72 in CD8 + low arm (ipi/nivo). The study enrolled a wide variety of primary solid tumors; the most common were gynecological (n = 15), prostate (12), and head and neck (7). DCR was 14% (1/7; 95% CI 1 - 44) and 24% (17/72; 95% CI 15 - 34) in the CD8 high and CD8 low arms, respectively. Of 39 pts in CD8 low arm with an on-treatment biopsy, 14 (36%; 95% CI 22 - 51) had CD8 conversion; 7/14 pts (50%) who converted had DCR. Immune-related AEs (irAEs) were consistent with known safety profile of both drugs. Conclusions: Ipi/nivo demonstrated clinical responses and increased CD8% in a range of “cold” tumors with low tumoral CD8 cells. There may be an association between increasing CD8% and response. Baseline high CD8% alone does not appear to be sufficient as a pan-cancer predictive biomarker of response to nivo monotherapy. CD8 conversion, response, and irAEs associated with circulating and stool-based biomarkers are under evaluation as composite biomarkers may improve their predictive value. Clinical trial information: 03651271.
Abstract Personalized cancer medicine, the matching of therapies to a given patient's somatic alterations, depends on highly accurate and complete identification of patients’ somatic alterations, or their mutome. Advances in sequencing technologies (exome sequencing, RNAseq, and whole genome sequencing) have provided a means to examine large portions of the genetic content of patients’ cancers. Computational tools have arisen that make somatic mutation predictions utilizing particular sequencing assays; however, each sequencing assay has limitations and existing mutation detection tools exhibit less than ideal agreement when analyzing the same data. The task of identifying all somatic mutations in one patient's cancer remains a challenge to personalized cancer medicine. Typically, somatic mutation detection is performed utilizing DNA sequencing. Because RNA sequencing is often a component of genome characterization projects along with DNA sequencing, we sought to evaluate the possible added value of RNA sequencing in somatic mutation detection. We have developed an original computational method, UNCeqR, that makes patient-specific somatic mutation predictions utilizing RNA sequencing combined with DNA sequencing. DNA mutations and RNA mutations are statistically modeled separately and results are combined in a meta-analytic fashion, resulting in up to three predictions for a locus: DNA-only, RNA-only, and DNA+RNA. In addition to de novo genomewide mutation predictions, UNCeqR can query specific a priori mutations. UNCeqR was applied to The Cancer Genome Atlas (TCGA) lung squamous cell carcinoma sequencing data, consisting of Ilumina RNAseq and Illumina exome sequencing. Of annotated exons, 20% had very low to zero coverage in RNA and 5% had very low to zero coverage in DNA, indicating that both sequencing assays add new genomic territory for mutation detection. Limiting to regions with both DNA and RNA coverage, 56% of mutations detected from DNA were also predicted by RNA, providing an independent validation of these mutations. To evaluate if mutation detection using DNA+RNA is superior to detection using DNA-only, cancer specimen DNA and RNA reads were randomly split into subsamples. UNCeqR was executed on each of the subsamples and mutation agreement was compared among pairs of subsamples within regions of DNA and RNA coverage. Compared with the DNA-only method, DNA+RNA mutation detection exhibited a 42% relative increase in percent agreement across subsamples and a 230% relative increase in the number of mutations detected. Therefore, RNA sequencing adds positive value to somatic mutation detection via UNCeqR. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3975. doi:1538-7445.AM2012-3975
