Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab.

The mechanisms by which immune checkpoint blockade modulates tumor evolution during therapy are unclear. We assessed genomic changes in tumors from 68 patients with advanced melanoma, who progressed on ipilimumab or were ipilimumab-naive, before and after nivolumab initiation (CA209-038 study). Tumors were analyzed by whole-exome, transcriptome, and/or T cell receptor (TCR) sequencing. In responding patients, mutation and neoantigen load were reduced from baseline, and analysis of intratumoral heterogeneity during therapy demonstrated differential clonal evolution within tumors and putative selection against neoantigenic mutations on-therapy. Transcriptome analyses before and during nivolumab therapy revealed increases in distinct immune cell subsets, activation of specific transcriptional networks, and upregulation of immune checkpoint genes that were more pronounced in patients with response. Temporal changes in intratumoral TCR repertoire revealed expansion of T cell clones in the setting of neoantigen loss. Comprehensive genomic profiling data in this study provide insight into nivolumab's mechanism of action.

Comparing anti-tumor and anti-self immunity in a patient with melanoma receiving immune checkpoint blockade.

BACKGROUND: Tumor regression following immune checkpoint blockade (ICB) is often associated with immune-related adverse events (irAEs), marked by inflammation in non-cancerous tissues. This study was undertaken to investigate the functional relationship between anti-tumor and anti-self immunity, to facilitate irAE management while promoting anti-tumor immunity. METHODS: Multiple biopsies from tumor and inflamed tissues were collected from a patient with melanoma experiencing both tumor regression and irAEs on ICB, who underwent rapid autopsy. Immune cells infiltrating melanoma lesions and inflamed normal tissues were subjected to gene expression profiling with multiplex qRT-PCR for 122 candidate genes. Subsequently, immunohistochemistry was conducted to assess the expression of 14 candidate markers of immune cell subsets and checkpoints. TCR-beta sequencing was used to explore T cell clonal repertoires across specimens. RESULTS: While genes involved in MHC I/II antigen presentation, IFN signaling, innate immunity and immunosuppression were abundantly expressed across specimens, irAE tissues over-expressed certain genes associated with immunosuppression (CSF1R, IL10RA, IL27/EBI3, FOXP3, KLRG1, SOCS1, TGFB1), including those in the COX-2/PGE2 pathway (IL1B, PTGER1/EP1 and PTGER4/EP4). Immunohistochemistry revealed similar proportions of immunosuppressive cell subsets and checkpoint molecules across samples. TCRseq did not indicate common TCR repertoires across tumor and inflammation sites, arguing against shared antigen recognition between anti-tumor and anti-self immunity in this patient. CONCLUSIONS: This comprehensive study of a single patient with melanoma experiencing both tumor regression and irAEs on ICB explores the immune landscape across these tissues, revealing similarities between anti-tumor and anti-self immunity. Further, it highlights expression of the COX-2/PGE2 pathway, which is known to be immunosuppressive and potentially mediates ICB resistance. Ongoing clinical trials of COX-2/PGE2 pathway inhibitors targeting the major COX-2 inducer IL-1B, COX-2 itself, or the PGE2 receptors EP2 and EP4 present new opportunities to promote anti-tumor activity, but may also have the potential to enhance the severity of ICB-induced irAEs.

High-dimensional Cytometry (ExCYT) and Mass Spectrometry of Myeloid Infiltrate in Clinically Localized Clear Cell Renal Cell Carcinoma Identifies Novel Potential Myeloid Targets for Immunotherapy.

Renal Cell Carcinoma (RCC) is one of the most commonly diagnosed cancers worldwide with research efforts dramatically improving understanding of the biology of the disease. To investigate the role of the immune system in treatment-naïve clear cell Renal Cell Carcinoma (ccRCC), we interrogated the immune infiltrate in patient-matched ccRCC tumor samples, benign normal adjacent tissue (NAT) and peripheral blood mononuclear cells (PBMCs isolated from whole blood, focusing our attention on the myeloid cell infiltrate. Using flow cytometric, MS, and ExCYT analysis, we discovered unique myeloid populations in PBMCs across patient samples. Furthermore, normal adjacent tissues and ccRCC tissues contained numerous myeloid populations with a unique signature for both tissues. Enrichment of the immune cell (CD45+) fraction and subsequent gene expression analysis revealed a number of myeloid-related genes that were differentially expressed. These data provide evidence, for the first time, of an immunosuppressive and pro-tumorigenic role of myeloid cells in early, clinically localized ccRCC. The identification of a number of immune proteins for therapeutic targeting provides a rationale for investigation into the potential efficacy of earlier intervention with single-agent or combination immunotherapy for ccRCC.

Neoadjuvant PD-1 Blockade in Resectable Lung Cancer.

BACKGROUND: Antibodies that block programmed death 1 (PD-1) protein improve survival in patients with advanced non-small-cell lung cancer (NSCLC) but have not been tested in resectable NSCLC, a condition in which little progress has been made during the past decade. METHODS: In this pilot study, we administered two preoperative doses of PD-1 inhibitor nivolumab in adults with untreated, surgically resectable early (stage I, II, or IIIA) NSCLC. Nivolumab (at a dose of 3 mg per kilogram of body weight) was administered intravenously every 2 weeks, with surgery planned approximately 4 weeks after the first dose. The primary end points of the study were safety and feasibility. We also evaluated the tumor pathological response, expression of programmed death ligand 1 (PD-L1), mutational burden, and mutation-associated, neoantigen-specific T-cell responses. RESULTS: Neoadjuvant nivolumab had an acceptable side-effect profile and was not associated with delays in surgery. Of the 21 tumors that were removed, 20 were completely resected. A major pathological response occurred in 9 of 20 resected tumors (45%). Responses occurred in both PD-L1-positive and PD-L1-negative tumors. There was a significant correlation between the pathological response and the pretreatment tumor mutational burden. The number of T-cell clones that were found in both the tumor and peripheral blood increased systemically after PD-1 blockade in eight of nine patients who were evaluated. Mutation-associated, neoantigen-specific T-cell clones from a primary tumor with a complete response on pathological assessment rapidly expanded in peripheral blood at 2 to 4 weeks after treatment; some of these clones were not detected before the administration of nivolumab. CONCLUSIONS: Neoadjuvant nivolumab was associated with few side effects, did not delay surgery, and induced a major pathological response in 45% of resected tumors. The tumor mutational burden was predictive of the pathological response to PD-1 blockade. Treatment induced expansion of mutation-associated, neoantigen-specific T-cell clones in peripheral blood. (Funded by Cancer Research Institute-Stand Up 2 Cancer and others; ClinicalTrials.gov number, NCT02259621 .).

Multiple genetic programs contribute to CD4 T cell memory differentiation and longevity by maintaining T cell quiescence.

While memory T-cells represent a hallmark of adaptive immunity, little is known about the genetic mechanisms regulating the longevity of memory CD4 T cells. Here, we studied the dynamics of gene expression in antigen specific CD4 T cells during infection, memory differentiation, and long-term survival up to nearly a year in mice. We observed that differentiation into long lived memory cells is associated with increased expression of genes inhibiting cell proliferation and apoptosis as well as genes promoting DNA repair response, lipid metabolism, and insulin resistance. We identified several transmembrane proteins in long-lived murine memory CD4 T cells, which co-localized exclusively within the responding antigen-specific memory CD4 T cells in human. The unique gene signatures of long-lived memory CD4 T cells, along with the new markers that we have defined, will enable a deeper understanding of memory CD4 T cell biology and allow for designing novel vaccines and therapeutics.

Multiple-instance learning of somatic mutations for the classification of tumour type and the prediction of microsatellite status.

Large-scale genomic data are well suited to analysis by deep learning algorithms. However, for many genomic datasets, labels are at the level of the sample rather than for individual genomic measures. Machine learning models leveraging these datasets generate predictions by using statically encoded measures that are then aggregated at the sample level. Here we show that a single weakly supervised end-to-end multiple-instance-learning model with multi-headed attention can be trained to encode and aggregate the local sequence context or genomic position of somatic mutations, hence allowing for the modelling of the importance of individual measures for sample-level classification and thus providing enhanced explainability. The model solves synthetic tasks that conventional models fail at, and achieves best-in-class performance for the classification of tumour type and for predicting microsatellite status. By improving the performance of tasks that require aggregate information from genomic datasets, multiple-instance deep learning may generate biological insight.

Immune checkpoint therapy responders display early clonal expansion of tumor infiltrating lymphocytes.

Immune checkpoint therapy (ICT) causes durable tumour responses in a subgroup of patients, but it is not well known how T cell receptor beta (TCRß) repertoire dynamics contribute to the therapeutic response. Using murine models that exclude variation in host genetics, environmental factors and tumour mutation burden, limiting variation between animals to naturally diverse TCRß repertoires, we applied TCRseq, single cell RNAseq and flow cytometry to study TCRß repertoire dynamics in ICT responders and non-responders. Increased oligoclonal expansion of TCRß clonotypes was observed in responding tumours. Machine learning identified TCRß CDR3 signatures unique to each tumour model, and signatures associated with ICT response at various timepoints before or during ICT. Clonally expanded CD8+ T cells in responding tumours post ICT displayed effector T cell gene signatures and phenotype. An early burst of clonal expansion during ICT is associated with response, and we report unique dynamics in TCRß signatures associated with ICT response.

Probabilistic Mixture Models Improve Calibration of Panel-derived Tumor Mutational Burden in the Context of both Tumor-normal and Tumor-only Sequencing.

Background: Tumor mutational burden (TMB) has been investigated as a biomarker for immune checkpoint blockade (ICB) therapy. Increasingly, TMB is being estimated with gene panel-based assays (as opposed to full exome sequencing) and different gene panels cover overlapping but distinct genomic coordinates, making comparisons across panels difficult. Previous studies have suggested that standardization and calibration to exome-derived TMB be done for each panel to ensure comparability. With TMB cutoffs being developed from panel-based assays, there is a need to understand how to properly estimate exomic TMB values from different panel-based assays. Design: Our approach to calibration of panel-derived TMB to exomic TMB proposes the use of probabilistic mixture models that allow for nonlinear relationships along with heteroscedastic error. We examined various inputs including nonsynonymous, synonymous, and hotspot counts along with genetic ancestry. Using The Cancer Genome Atlas cohort, we generated a tumor-only version of the panel-restricted data by reintroducing private germline variants. Results: We were able to model more accurately the distribution of both tumor-normal and tumor-only data using the proposed probabilistic mixture models as compared with linear regression. Applying a model trained on tumor-normal data to tumor-only input results in biased TMB predictions. Including synonymous mutations resulted in better regression metrics across both data types, but ultimately a model able to dynamically weight the various input mutation types exhibited optimal performance. Including genetic ancestry improved model performance only in the context of tumor-only data, wherein private germline variants are observed. Significance: A probabilistic mixture model better models the nonlinearity and heteroscedasticity of the data as compared with linear regression. Tumor-only panel data are needed to properly calibrate tumor-only panels to exomic TMB. Leveraging the uncertainty of point estimates from these models better informs cohort stratification in terms of TMB.

Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy.

T cell receptor (TCR) sequencing has been used to characterize the immune response to cancer. However, most analyses have been restricted to quantitative measures such as clonality that do not leverage the complementarity-determining region 3 (CDR3) sequence. We use DeepTCR, a framework of deep learning algorithms, to reveal sequence concepts that are predictive of response to immunotherapy. We demonstrate that DeepTCR can predict response and use the model to infer the antigenic specificities of the predictive signature and their unique dynamics during therapy. The predictive signature of nonresponse is associated with high frequencies of TCRs predicted to recognize tumor-specific antigens, and these tumor-specific TCRs undergo a higher degree of dynamic changes on therapy in nonresponders versus responders. These results are consistent with a biological model where the hallmark of nonresponders is an accumulation of tumor-specific T cells that undergo turnover on therapy, possibly because of the dysfunctional state of these T cells in nonresponders.

Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires.

SARS-CoV-2 infection is characterized by a highly variable clinical course with patients experiencing asymptomatic infection all the way to requiring critical care support. This variation in clinical course has led physicians and scientists to study factors that may predispose certain individuals to more severe clinical presentations in hopes of either identifying these individuals early in their illness or improving their medical management. We sought to understand immunogenomic differences that may result in varied clinical outcomes through analysis of T-cell receptor sequencing (TCR-Seq) data in the open access ImmuneCODE database. We identified two cohorts within the database that had clinical outcomes data reflecting severity of illness and utilized DeepTCR, a multiple-instance deep learning repertoire classifier, to predict patients with severe SARS-CoV-2 infection from their repertoire sequencing. We demonstrate that patients with severe infection have repertoires with higher T-cell responses associated with SARS-CoV-2 epitopes and identify the epitopes that result in these responses. Our results provide evidence that the highly variable clinical course seen in SARS-CoV-2 infection is associated to certain antigen-specific responses.

DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires.

Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system and allows for modeling its sequence determinants of antigenicity. We present DeepTCR, a suite of unsupervised and supervised deep learning methods able to model highly complex TCR sequencing data by learning a joint representation of a TCR by its CDR3 sequences and V/D/J gene usage. We demonstrate the utility of deep learning to provide an improved 'featurization' of the TCR across multiple human and murine datasets, including improved classification of antigen-specific TCRs and extraction of antigen-specific TCRs from noisy single-cell RNA-Seq and T-cell culture-based assays. Our results highlight the flexibility and capacity for deep neural networks to extract meaningful information from complex immunogenomic data for both descriptive and predictive purposes.

Anti-PD-1 elicits regression of undifferentiated pleomorphic sarcomas with UV-mutation signatures.

Undifferentiated pleomorphic sarcoma (UPS), an aggressive soft-tissue sarcoma of adults, has been characterized by low tumor mutational burden (TMB) and high copy number alterations. Clinical trials of programmed death-1 (PD-1) blockade in UPS have reported widely varying efficacy. We describe two patients with recurrent scalp UPS that experienced clinical benefit from PD-1 blockade. These tumors had high TMB with a UV-induced mutational pattern. Analysis of additional head and neck UPS cases identified five out of seven tumors with high TMB and an ultraviolet (UV) mutational signature. Head and neck UPS tumors also had increased programmed death-ligand 1 (PD-L1) expression and CD8+ T cell infiltration as compared with UPS tumors arising from other sites. In summary, we found that UPS tumors of the head and neck, but not elsewhere, have a PD-L1+, T-cell-inflamed tumor microenvironment and high TMB, suggesting that these tumors represent a distinct genetic subgroup of UPS for which immune checkpoint inhibitor therapy might be effective.

Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features.

Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia (AML), classified by a translocation between chromosomes 15 and 17 [t(15;17)], that is considered a true oncologic emergency though appropriate therapy is considered curative. Therapy is often initiated on clinical suspicion, informed by both clinical presentation as well as direct visualization of the peripheral smear. We hypothesized that genomic imprinting of morphologic features learned by deep learning pattern recognition would have greater discriminatory power and consistency compared to humans, thereby facilitating identification of t(15;17) positive APL. By applying both cell-level and patient-level classification linked to t(15;17) PML/RARA ground-truth, we demonstrate that deep learning is capable of distinguishing APL in both discovery and prospective independent cohort of patients. Furthermore, we extract learned information from the trained network to identify previously undescribed morphological features of APL. The deep learning method we describe herein potentially allows a rapid, explainable, and accurate physician-aid for diagnosing APL at the time of presentation in any resource-poor or -rich medical setting given the universally available peripheral smear.

Integrative Tumor and Immune Cell Multi-omic Analyses Predict Response to Immune Checkpoint Blockade in Melanoma.

In this study, we incorporate analyses of genome-wide sequence and structural alterations with pre- and on-therapy transcriptomic and T cell repertoire features in immunotherapy-naive melanoma patients treated with immune checkpoint blockade. Although tumor mutation burden is associated with improved treatment response, the mutation frequency in expressed genes is superior in predicting outcome. Increased T cell density in baseline tumors and dynamic changes in regression or expansion of the T cell repertoire during therapy distinguish responders from non-responders. Transcriptome analyses reveal an increased abundance of B cell subsets in tumors from responders and patterns of molecular response related to expressed mutation elimination or retention that reflect clinical outcome. High-dimensional genomic, transcriptomic, and immune repertoire data were integrated into a multi-modal predictor of response. These findings identify genomic and transcriptomic characteristics of tumors and immune cells that predict response to immune checkpoint blockade and highlight the importance of pre-existing T and B cell immunity in therapeutic outcomes.

Compartmental Analysis of T-cell Clonal Dynamics as a Function of Pathologic Response to Neoadjuvant PD-1 Blockade in Resectable Non-Small Cell Lung Cancer.

PURPOSE: Neoadjuvant PD-1 blockade is a promising treatment for resectable non-small cell lung cancer (NSCLC), yet immunologic mechanisms contributing to tumor regression and biomarkers of response are unknown. Using paired tumor/blood samples from a phase II clinical trial (NCT02259621), we explored whether the peripheral T-cell clonotypic dynamics can serve as a biomarker for response to neoadjuvant PD-1 blockade. EXPERIMENTAL DESIGN: T-cell receptor (TCR) sequencing was performed on serial peripheral blood, tumor, and normal lung samples from resectable NSCLC patients treated with neoadjuvant PD-1 blockade. We explored the temporal dynamics of the T-cell repertoire in the peripheral and tumoral compartments in response to neoadjuvant PD-1 blockade by using the TCR as a molecular barcode. RESULTS: Higher intratumoral TCR clonality was associated with reduced percent residual tumor at the time of surgery, and the TCR repertoire of tumors with major pathologic response (MPR; <10% residual tumor after neoadjuvant therapy) had a higher clonality and greater sharing of tumor-infiltrating clonotypes with the peripheral blood relative to tumors without MPR. Additionally, the posttreatment tumor bed of patients with MPR was enriched with T-cell clones that had peripherally expanded between weeks 2 and 4 after anti-PD-1 initiation and the intratumoral space occupied by these clonotypes was inversely correlated with percent residual tumor. CONCLUSIONS: Our study suggests that exchange of T-cell clones between tumor and blood represents a key correlate of pathologic response to neoadjuvant immunotherapy and shows that the periphery may be a previously underappreciated originating compartment for effective antitumor immunity.See related commentary by Henick, p. 1205.

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data.

With the advent of flow cytometers capable of measuring an increasing number of parameters, scientists continue to develop larger panels to phenotypically explore characteristics of their cellular samples. However, these technological advancements yield high-dimensional data sets that have become increasingly difficult to analyze objectively within traditional manual-based gating programs. In order to better analyze and present data, scientists partner with bioinformaticians with expertise in analyzing high-dimensional data to parse their flow cytometry data. While these methods have been shown to be highly valuable in studying flow cytometry, they have yet to be incorporated in a straightforward and easy-to-use package for scientists who lack computational or programming expertise. To address this need, we have developed ExCYT, a MATLAB-based Graphical User Interface (GUI) that streamlines the analysis of high-dimensional flow cytometry data by implementing commonly employed analytical techniques for high-dimensional data including dimensionality reduction by t-SNE, a variety of automated and manual clustering methods, heatmaps, and novel high-dimensional flow plots. Additionally, ExCYT provides traditional gating options of select populations of interest for further t-SNE and clustering analysis as well as the ability to apply gates directly on t-SNE plots. The software provides the additional advantage of working with either compensated or uncompensated FCS files. In the event that post-acquisition compensation is required, the user can choose to provide the program a directory of single stains and an unstained sample. The program detects positive events in all channels and uses this select data to more objectively calculate the compensation matrix. In summary, ExCYT provides a comprehensive analysis pipeline to take flow cytometry data in the form of FCS files and allow any individual, regardless of computational training, to use the latest algorithmic approaches in understanding their data.

A CD40 Agonist and PD-1 Antagonist Antibody Reprogram the Microenvironment of Nonimmunogenic Tumors to Allow T-cell-Mediated Anticancer Activity.

In cancers with tumor-infiltrating lymphocytes (TILs), monoclonal antibodies (mAbs) that block immune checkpoints such as CTLA-4 and PD-1/PD-L1 promote antitumor T-cell immunity. Unfortunately, most cancers fail to respond to single-agent immunotherapies. T regulatory cells, myeloid derived suppressor cells (MDSCs), and extensive stromal networks within the tumor microenvironment (TME) dampen antitumor immune responses by preventing T-cell infiltration and/or activation. Few studies have explored combinations of immune-checkpoint antibodies that target multiple suppressive cell populations within the TME, and fewer have studied the combinations of both agonist and antagonist mAbs on changes within the TME. Here, we test the hypothesis that combining a T-cell-inducing vaccine with both a PD-1 antagonist and CD40 agonist mAbs (triple therapy) will induce T-cell priming and TIL activation in mouse models of nonimmunogenic solid malignancies. In an orthotopic breast cancer model and both subcutaneous and metastatic pancreatic cancer mouse models, only triple therapy was able to eradicate most tumors. The survival benefit was accompanied by significant tumor infiltration of IFNγ-, Granzyme B-, and TNFα-secreting effector T cells. Further characterization of immune populations was carried out by high-dimensional flow-cytometric clustering analysis and visualized by t-distributed stochastic neighbor embedding (t-SNE). Triple therapy also resulted in increased infiltration of dendritic cells, maturation of antigen-presenting cells, and a significant decrease in granulocytic MDSCs. These studies reveal that combination CD40 agonist and PD-1 antagonist mAbs reprogram immune resistant tumors in favor of antitumor immunity.

Correction to: persistent mutant oncogene specific T cells in two patients benefitting from anti-PD-1.

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Persistent mutant oncogene specific T cells in two patients benefitting from anti-PD-1.

BACKGROUND: Several predictive biomarkers are currently approved or are under investigation for the selection of patients for checkpoint blockade. Tumor PD-L1 expression is used for stratification of non-small cell lung (NSCLC) patients, with tumor mutational burden (TMB) also being explored with promising results, and mismatch-repair deficiency is approved for tumor site-agnostic disease. While tumors with high PD-L1 expression, high TMB, or mismatch repair deficiency respond well to checkpoint blockade, tumors with lower PD-L1 expression, lower mutational burdens, or mismatch repair proficiency respond much less frequently. CASE PRESENTATION: We studied two patients with unexpected responses to checkpoint blockade monotherapy: a patient with PD-L1-negative and low mutational burden NSCLC and one with mismatch repair proficient colorectal cancer (CRC), both of whom lack the biomarkers associated with response to checkpoint blockade, yet achieved durable clinical benefit. Both maintained T-cell responses in peripheral blood to oncogenic driver mutations - BRAF-N581I in the NSCLC and AKT1-E17K in the CRC - years after treatment initiation. Mutation-specific T cells were also found in the primary tumor and underwent dynamic perturbations in the periphery upon treatment. CONCLUSIONS: These findings suggest that T cell responses to oncogenic driver mutations may be more prevalent than previously appreciated and could be harnessed in immunotherapeutic treatment, particularly for patients who lack the traditional biomarkers associated with response. Comprehensive studies are warranted to further delineate additional predictive biomarkers and populations of patients who may benefit from checkpoint blockade.

ImmunoMap: A Bioinformatics Tool for T-cell Repertoire Analysis.

Despite a dramatic increase in T-cell receptor (TCR) sequencing, few approaches biologically parse the data in a fashion that both helps yield new information about immune responses and may guide immunotherapeutic interventions. To address this issue, we developed a method, ImmunoMap, that utilizes a sequence analysis approach inspired by phylogenetics to examine TCR repertoire relatedness. ImmunoMap analysis of the CD8 T-cell response to self-antigen (Kb-TRP2) or to a model foreign antigen (Kb-SIY) in naïve and tumor-bearing B6 mice showed differences in the T-cell repertoire of self- versus foreign antigen-specific responses, potentially reflecting immune pressure by the tumor, and also detected lymphoid organ-specific differences in TCR repertoires. When ImmunoMap was used to analyze clinical trial data of tumor-infiltrating lymphocytes from patients being treated with anti-PD-1, ImmunoMap, but not standard TCR sequence analyses, revealed a clinically predicative signature in pre- and posttherapy samples. Cancer Immunol Res; 6(2); 151-62. ©2017 AACR.