Single-cell RNA-seq (scRNA-seq) has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of scRNA-seq data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation, or viable cells.

Tumor infiltrating leukocytes (TILs) are an integral component of the tumor microenvironment and have been found to correlate with prognosis and response to therapy. Methods to enumerate immune subsets such as immunohistochemistry or flow cytometry suffer from limitations in phenotypic markers and can be challenging to practically implement and standardize. An alternative approach is to acquire aggregative high dimensional data from cellular mixtures and to subsequently infer the cellular components computationally. We recently described CIBERSORT, a versatile computational method for quantifying cell fractions from bulk tissue gene expression profiles (GEPs). Combining support vector regression with prior knowledge of expression profiles from purified leukocyte subsets, CIBERSORT can accurately estimate the immune composition of a tumor biopsy. In this chapter, we provide a primer on the CIBERSORT method and illustrate its use for characterizing TILs in tumor samples profiled by microarray or RNA-Seq.

Identifying molecular residual disease (MRD) after treatment of localized lung cancer could facilitate early intervention and personalization of adjuvant therapies. Here, we apply cancer personalized profi ling by deep sequencing (CAPP-seq) circulating tumor DNA (ctDNA) analysis to 255 samples from 40 patients treated with curative intent for stage I–III lung cancer and 54 healthy adults. In 94% of evaluable patients experiencing recurrence, ctDNA was detectable in the fi rst posttreatment blood sample, indicating reliable identifi cation of MRD. Posttreatment ctDNA detection preceded radiographic progression in 72% of patients by a median of 5.2 months, and 53% of patients harbored ctDNA mutation profi les associated with favorable responses to tyrosine kinase inhibitors or immune checkpoint blockade. Collectively, these results indicate that ctDNA MRD in patients with lung cancer can be accurately detected using CAPP-seq and may allow personalized adjuvant treatment while disease burden is lowest.

Pretreatment ctDNA levels and molecular responses are independently prognostic of outcomes in aggressive lymphomas. These risk factors could potentially guide future personalized risk-directed approaches.

Cancer somatic mutations can generate neoantigens that distinguish malignant from normal cells 1–7. However, the personalized identification and validation of neoantigens remains a major challenge. Here we discover neoantigens in human mantle cell lymphomas using an integrated genomic and proteomic strategy that interrogates tumor antigen peptides presented by major histocompatibility complex (MHC) class I and class II molecules. We applied this approach to systematically characterize MHC ligands from 17 patients. Remarkably, all discovered neoantigenic peptides were exclusively derived from the lymphoma immunoglobulin (Ig) heavy or light chain variable regions. Although we identified MHC presentation of private polymorphic germline alleles, no mutated peptides were recovered from non-Ig somatically mutated genes. Somatic mutations within the immunoglobulin variable region were almost exclusively presented by MHC-II. We isolated circulating CD4 T-cells specific for immunoglobulin-derived neoantigens and found these cells could mediate killing of autologous lymphoma cells. These results demonstrate that an integrative approach combining MHC isolation, peptide identification and exome sequencing is an effective platform to uncover tumor neoantigens. Application of this strategy to human lymphoma implicates immunoglobulin neoantigens as targets for lymphoma immunotherapy.

Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules would be valuable for vaccine development and cancer immunotherapies. Current computational methods trained on in vitro binding data are limited by insufficient training data and algorithmic constraints. Here we describe MARIA (major histocompatibility complex analysis with recurrent integrated architecture; https://maria.stanford.edu/), a multimodal recurrent neural network for predicting the likelihood of antigen presentation from a gene of interest in the context of specific HLA class II alleles. In addition to in vitro binding measurements, MARIA is trained on peptide HLA ligand sequences identified by mass spectrometry, expression levels of antigen genes and protease cleavage signatures. Because it leverages these diverse training data and our improved machine learning framework, MARIA (area under the curve = 0.89-0.92) outperformed existing methods in validation datasets. Across independent cancer neoantigen studies, peptides with high MARIA scores are more likely to elicit strong CD4 + T cell responses. MARIA allows identification of immunogenic epitopes in diverse cancers and autoimmune disease.

PURPOSE To assess the efficacy of pembrolizumab in patients with advanced relapsed or refractory mycosis fungoides (MF) or Sézary syndrome (SS). PATIENTS AND METHODS CITN-10 is a single-arm, multicenter phase II trial of 24 patients with advanced MF or SS. Patients were treated with pembrolizumab 2 mg/kg every 3 weeks for up to 24 months. The primary end point was overall response rate by consensus global response criteria. RESULTS Patients had advanced-stage disease (23 of 24 with stage IIB to IV MF/SS) and were heavily pretreated with a median of four prior systemic therapies. The overall response rate was 38% with two complete responses and seven partial responses. Of the nine responding patients, six had 90% or more improvement in skin disease by modified Severity Weighted Assessment Tool, and eight had ongoing responses at last follow-up. The median duration of response was not reached, with a median response follow-up time of 58 weeks. Immune-related adverse events led to treatment discontinuation in four patients. A transient worsening of erythroderma and pruritus occurred in 53% of patients with SS. This cutaneous flare reaction did not result in treatment discontinuation for any patient. The flare reaction correlated with high PD-1 expression on Sézary cells but did not associate with subsequent clinical responses or lack of response. Treatment responses did not correlate with expression of PD-L1, total mutation burden, or an interferon-γ gene expression signature. CONCLUSION Pembrolizumab demonstrated significant antitumor activity with durable responses and a favorable safety profile in patients with advanced MF/SS.

Gain of chromosome 6p is a consistent feature of advanced melanomas. However, the identity of putative oncogene(s) associated with this amplification has remained elusive. The chromatin remodeling factor DEK is an attractive candidate as it maps to 6p (within common melanoma-amplified loci). Moreover, DEK expression is increased in metastatic melanomas, although the functional relevance of this induction remains unclear. Importantly, in other tumor types, DEK can display various tumorigenic effects in part through its ability to promote proliferation and inhibit p53-dependent apoptosis. Here, we report a generalized up-regulation of DEK protein in aggressive melanoma cells and tumors. In addition, we provide genetic and mechanistic evidence to support a key role of DEK in the maintenance of malignant phenotypes of melanoma cells. Specifically, we show that long-term DEK down-regulation by independent short hairpin RNAs resulted in premature senescence of a variety of melanoma cell lines. Short-term abrogation of DEK expression was also functionally relevant, as it attenuated the traditional resistance of melanomas to DNA-damaging agents. Unexpectedly, DEK short hairpin RNA had no effect on p53 levels or p53-dependent apoptosis. Instead, we identified a new role for DEK in the transcriptional activation of the antiapoptotic MCL-1. Other MCL-1-related factors such as BCL-2 or BCL-x L were unaffected by changes in the endogenous levels of DEK, indicating a selective effect of this gene on the apoptotic machinery of melanoma cells. These results provide support for DEK as a long sought-after oncogene mapping at chromosome 6, with novel functions in melanoma proliferation and chemoresistance. [Cancer Res 2009;69(16):6405-13]