Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer

Pancreatic ductal adenocarcinoma is a lethal cancer with fewer than 7% of patients surviving past 5 years. T-cell immunity has been linked to the exceptional outcome of the few long-term survivors1,2, yet the relevant antigens remain unknown. Here we use genetic, immunohistochemical and transcriptional immunoprofiling, computational biophysics, and functional assays to identify T-cell antigens in long-term survivors of pancreatic cancer. Using whole-exome sequencing and in silico neoantigen prediction, we found that tumours with both the highest neoantigen number and the most abundant CD8+ T-cell infiltrates, but neither alone, stratified patients with the longest survival. Investigating the specific neoantigen qualities promoting T-cell activation in long-term survivors, we discovered that these individuals were enriched in neoantigen qualities defined by a fitness model, and neoantigens in the tumour antigen MUC16 (also known as CA125). A neoantigen quality fitness model conferring greater immunogenicity to neoantigens with differential presentation and homology to infectious disease-derived peptides identified long-term survivors in two independent datasets, whereas a neoantigen quantity model ascribing greater immunogenicity to increasing neoantigen number alone did not. We detected intratumoural and lasting circulating T-cell reactivity to both high-quality and MUC16 neoantigens in long-term survivors of pancreatic cancer, including clones with specificity to both high-quality neoantigens and predicted cross-reactive microbial epitopes, consistent with neoantigen molecular mimicry. Notably, we observed selective loss of high-quality and MUC16 neoantigenic clones on metastatic progression, suggesting neoantigen immunoediting. Our results identify neoantigens with unique qualities as T-cell targets in pancreatic ductal adenocarcinoma. More broadly, we identify neoantigen quality as a biomarker for immunogenic tumours that may guide the application of immunotherapies

Clinical and molecular features of acquired resistance to immunotherapy in non-small cell lung cancer

Although cancer immunotherapy with PD-(L)1 blockade is now routine treatment for patients with lung cancer, remarkably little is known about acquired resistance. We examined 1,201 patients with NSCLC treated with PD-(L)1 blockade to clinically characterize acquired resistance, finding it to be common (occurring in more than 60% of initial responders), with persistent but diminishing risk over time, and with distinct metastatic and survival patterns compared to primary resistance. To examine the molecular phenotype and potential mechanisms of acquired resistance, we performed whole transcriptome and exome tumor profiling in a subset of NSCLC patients (n=29) with acquired resistance. Systematic immunogenomic analysis revealed that tumors with acquired resistance generally had enriched signals of inflammation (including IFNγ signaling and inferred CD8+ T cells) and could be separated into IFNγ upregulated and stable subsets. IFNγ upregulated tumors had putative routes of resistance with signatures of dysfunctional interferon signaling and mutations in antigen presentation genes. Transcriptomic profiling of cancer cells from a murine model of acquired resistance to PD-(L)1 blockade also showed evidence of dysfunctional interferon signaling and acquired insensitivity to in vitro interferon gamma treatment. In summary, we characterized clinical and molecular features of acquired resistance to PD-(L)1 blockade in NSCLC and found evidence of ongoing but dysfunctional IFN response. The persistently inflamed, rather than excluded or deserted, tumor microenvironment of acquired resistance informs therapeutic strategies to effectively reprogram and reverse acquired resistance

Mutational Signatures of De-Differentiation in Functional Non-Coding Regions of Melanoma Genomes

<div><p>Much emphasis has been placed on the identification, functional characterization, and therapeutic potential of somatic variants in tumor genomes. However, the majority of somatic variants lie outside coding regions and their role in cancer progression remains to be determined. In order to establish a system to test the functional importance of non-coding somatic variants in cancer, we created a low-passage cell culture of a metastatic melanoma tumor sample. As a foundation for interpreting functional assays, we performed whole-genome sequencing and analysis of this cell culture, the metastatic tumor from which it was derived, and the patient-matched normal genomes. When comparing somatic mutations identified in the cell culture and tissue genomes, we observe concordance at the majority of single nucleotide variants, whereas copy number changes are more variable. To understand the functional impact of non-coding somatic variation, we leveraged functional data generated by the ENCODE Project Consortium. We analyzed regulatory regions derived from multiple different cell types and found that melanocyte-specific regions are among the most depleted for somatic mutation accumulation. Significant depletion in other cell types suggests the metastatic melanoma cells de-differentiated to a more basal regulatory state. Experimental identification of genome-wide regulatory sites in two different melanoma samples supports this observation. Together, these results show that mutation accumulation in metastatic melanoma is nonrandom across the genome and that a de-differentiated regulatory architecture is common among different samples. Our findings enable identification of the underlying genetic components of melanoma and define the differences between a tissue-derived tumor sample and the cell culture created from it. Such information helps establish a broader mechanistic understanding of the linkage between non-coding genomic variations and the cellular evolution of cancer.</p> </div

Signatures of plasticity, metastasis, and immunosuppression in an atlas of human small cell lung cancer

Small cell lung cancer (SCLC) is an aggressive malignancy that includes subtypes defined by differential expression of ASCL1, NEUROD1, and POU2F3 (SCLC-A, -N, and -P, respectively). To define the heterogeneity of tumors and their associated microenvironments across subtypes, we sequenced 155,098 transcriptomes from 21 human biospecimens, including 54,523 SCLC transcriptomes. We observe greater tumor diversity in SCLC than lung adenocarcinoma, driven by canonical, intermediate, and admixed subtypes. We discover a PLCG2-high SCLC phenotype with stem-like, pro-metastatic features that recurs across subtypes and predicts worse overall survival. SCLC exhibits greater immune sequestration and less immune infiltration than lung adenocarcinoma, and SCLC-N shows less immune infiltrate and greater T cell dysfunction than SCLC-A. We identify a profibrotic, immunosuppressive monocyte/macrophage population in SCLC tumors that is particularly associated with the recurrent, PLCG2-high subpopulation

Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer

Pancreatic ductal adenocarcinoma is a lethal cancer with fewer than 7% of patients surviving past 5 years. T-cell immunity has been linked to the exceptional outcome of the few long-term survivors, yet the relevant antigens remain unknown. Here we use genetic, immunohistochemical and transcriptional immunoprofiling, computational biophysics, and functional assays to identify T-cell antigens in long-term survivors of pancreatic cancer. Using whole-exome sequencing and in silico neoantigen prediction, we found that tumours with both the highest neoantigen number and the most abundant CD8 + T-cell infiltrates, but neither alone, stratified patients with the longest survival. Investigating the specific neoantigen qualities promoting T-cell activation in long-term survivors, we discovered that these individuals were enriched in neoantigen qualities defined by a fitness model, and neoantigens in the tumour antigen MUC16 (also known as CA125). A neoantigen quality fitness model conferring greater immunogenicity to neoantigens with differential presentation and homology to infectious disease-derived peptides identified long-term survivors in two independent datasets, whereas a neoantigen quantity model ascribing greater immunogenicity to increasing neoantigen number alone did not. We detected intratumoural and lasting circulating T-cell reactivity to both high-quality and MUC16 neoantigens in long-term survivors of pancreatic cancer, including clones with specificity to both high-quality neoantigens and predicted cross-reactive microbial epitopes, consistent with neoantigen molecular mimicry. Notably, we observed selective loss of high-quality and MUC16 neoantigenic clones on metastatic progression, suggesting neoantigen immunoediting. Our results identify neoantigens with unique qualities as T-cell targets in pancreatic ductal adenocarcinoma. More broadly, we identify neoantigen quality as a biomarker for immunogenic tumours that may guide the application of immunotherapies

The regulatory signature of metastatic melanoma.

<p>Genome-wide DNase-Seq identifies (DHS) regulatory elements in the cell culture sample from our study and the colo-829 cell line. (A) Hierarchical clustering of all DHSs shows that the regulatory architecture of metastatic melanoma cells (red) adopts that of a more derived melanocyte (blue). (B) Focusing on exon-overlapping DHSs to identify the open chromatin landscape in gene regions shows that the metastatic melanoma cells are de-differentiated relative to melanocytes. Of the DHSs that occur in exonic regions and are specific to the metastatic melanoma samples (and not present in any others), the important melanoma genes MITF, NEDD9, and DCC are identified. (C) Melanoma transcription at melanoma-specific (dark blue) TSS-distal DHSs is significantly more frequent (P<2.2e−16; Fisher's Exact Test) than at melanocyte-specific (light blue) TSS-distal DHSs. (D) Mutational bias in melanoma DHSs is asymmetric with respect to orientation relative to the transcribed strand. The 12 possible mutations are collapsed into 6 such that the key mutation (A>C, for example; blue) and its complement (T>G; yellow) version are represented with different colors. An asterisk (*) represents P<0.05 for a Binomial test, using a 50% expectation, on the counts for a pair of key and complement mutations.</p

Non-coding Melanocyte DHSs are dis-enriched for accumulating melanoma somatic mutations.

<p>(A) Genic partitioning of melanocyte DHSs such that every DHS occurs in a single category shows that most categories are depleted for mutation accumulation (TSS P = Transcription Start Site Proximal [within 5 Kb]; TSS D = Transcription Start Site Distal [greater than 5 Kb]). Common SNPs are based on 1000 Genomes calls that have at least 5% minor allele frequency (MAF). (B) Intergenic TSS-distal cell-type-specific and ubiquitous DHSs show different levels of enrichment or depletion. (C) Enrichment or depletion at cell-type combinations of intergenic TSS-distal melanocyte and non-melanocyte DHSs. For these analyses, the set of regions representing any data point must have overlapped at least 10 somatic variants to be considered. The horizontal black line at zero represents no enrichment. The GSC method was used to measure enrichment. Error bars represent one standard deviation from the mean of the null distribution. (D) A hierarchical tree based on DHS Euclidean distance among 29 different cell states. Note the positioning of melanocytes “Melano” relative to aortic smooth muscle cells “AosmcSerumfree” and human embryonic stem cells “H1hesc”, which are among the most depleted for somatic mutation accumulation (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002871#pgen-1002871-g003" target="_blank">Figure 3B</a>).</p

Melanoma tissue and cell culture similarities.

<p>(A) The experimental design of our study. Concordance between the somatic calls in the tissue (blue) and cell culture (yellow) for SSNVs (B), CNV amplifications (C), and CNV deletions (D). The two samples are highly concordant at the SNV level, but more different at the CNV level.</p