Multi-omics Analysis Reveals Immune Features Associated with Immunotherapy Benefit in Patients with Squamous Cell Lung Cancer from Phase III Lung-MAP S1400I Trial.

PURPOSE: Identifying molecular and immune features to guide immune checkpoint inhibitor (ICI)-based regimens remains an unmet clinical need. EXPERIMENTAL DESIGN: Tissue and longitudinal blood specimens from phase III trial S1400I in patients with metastatic squamous non-small cell carcinoma (SqNSCLC) treated with nivolumab monotherapy (nivo) or nivolumab plus ipilimumab (nivo+ipi) were subjected to multi-omics analyses including multiplex immunofluorescence (mIF), nCounter PanCancer Immune Profiling Panel, whole-exome sequencing, and Olink. RESULTS: Higher immune scores from immune gene expression profiling or immune cell infiltration by mIF were associated with response to ICIs and improved survival, except regulatory T cells, which were associated with worse overall survival (OS) for patients receiving nivo+ipi. Immune cell density and closer proximity of CD8+GZB+ T cells to malignant cells were associated with superior progression-free survival and OS. The cold immune landscape of NSCLC was associated with a higher level of chromosomal copy-number variation (CNV) burden. Patients with LRP1B-mutant tumors had a shorter survival than patients with LRP1B-wild-type tumors. Olink assays revealed soluble proteins such as LAMP3 increased in responders while IL6 and CXCL13 increased in nonresponders. Upregulation of serum CXCL13, MMP12, CSF-1, and IL8 were associated with worse survival before radiologic progression. CONCLUSIONS: The frequency, distribution, and clustering of immune cells relative to malignant ones can impact ICI efficacy in patients with SqNSCLC. High CNV burden may contribute to the cold immune microenvironment. Soluble inflammation/immune-related proteins in the blood have the potential to monitor therapeutic benefit from ICI treatment in patients with SqNSCLC.

Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities.

Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system's complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.

Immune determinants of CAR-T cell expansion in solid tumor patients receiving GD2 CAR-T cell therapy.

Chimeric antigen receptor T cells (CAR-Ts) have remarkable efficacy in liquid tumors, but limited responses in solid tumors. We conducted a Phase I trial (NCT02107963) of GD2 CAR-Ts (GD2-CAR.OX40.28.z.iC9), demonstrating feasibility and safety of administration in children and young adults with osteosarcoma and neuroblastoma. Since CAR-T efficacy requires adequate CAR-T expansion, patients were grouped into good or poor expanders across dose levels. Patient samples were evaluated by multi-dimensional proteomic, transcriptomic, and epigenetic analyses. T cell assessments identified naive T cells in pre-treatment apheresis associated with good expansion, and exhausted T cells in CAR-T products with poor expansion. Myeloid cell assessment identified CXCR3+ monocytes in pre-treatment apheresis associated with good expansion. Longitudinal analysis of post-treatment samples identified increased CXCR3- classical monocytes in all groups as CAR-T numbers waned. Together, our data uncover mediators of CAR-T biology and correlates of expansion that could be utilized to advance immunotherapies for solid tumor patients.

Heterogeneity and transcriptional drivers of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a heterogeneous disease with limited treatment options. To characterize TNBC heterogeneity, we defined transcriptional, epigenetic, and metabolic subtypes and subtype-driving super-enhancers and transcription factors by combining functional and molecular profiling with computational analyses. Single-cell RNA sequencing revealed relative homogeneity of the major transcriptional subtypes (luminal, basal, and mesenchymal) within samples. We found that mesenchymal TNBCs share features with mesenchymal neuroblastoma and rhabdoid tumors and that the PRRX1 transcription factor is a key driver of these tumors. PRRX1 is sufficient for inducing mesenchymal features in basal but not in luminal TNBC cells via reprogramming super-enhancer landscapes, but it is not required for mesenchymal state maintenance or for cellular viability. Our comprehensive, large-scale, multiplatform, multiomics study of both experimental and clinical TNBC is an important resource for the scientific and clinical research communities and opens venues for future investigation.

Peripheral blood TCR clonotype diversity as an age-associated marker of breast cancer progression.

Immune escape is a prerequisite for tumor growth. We previously described a decline in intratumor activated cytotoxic T cells and T cell receptor (TCR) clonotype diversity in invasive breast carcinomas compared to ductal carcinoma in situ (DCIS), implying a central role of decreasing T cell responses in tumor progression. To determine potential associations between peripheral immunity and breast tumor progression, here, we assessed the peripheral blood TCR clonotype of 485 breast cancer patients diagnosed with either DCIS or de novo stage IV disease at younger (<45) or older (≥45) age. TCR clonotype diversity was significantly lower in older compared to younger breast cancer patients regardless of tumor stage at diagnosis. In the younger age group, TCR-α clonotype diversity was lower in patients diagnosed with de novo stage IV breast cancer compared to those diagnosed with DCIS. In the older age group, DCIS patients with higher TCR-α clonotype diversity were more likely to have a recurrence compared to those with lower diversity. Whole blood transcriptome profiles were distinct depending on the TCR-α Chao1 diversity score. There were more CD8+ T cells and a more active immune environment in DCIS tumors of young patients with higher peripheral blood TCR-α Chao1 diversity than in those with lower diversity. These results provide insights into the role that host immunity plays in breast cancer development across different age groups.

Mathematical Modeling Identifies Optimum Palbociclib-fulvestrant Dose Administration Schedules for the Treatment of Patients with Estrogen Receptor-positive Breast Cancer.

Cyclin-dependent kinases 4/6 (CDK4/6) inhibitors such as palbociclib are approved for the treatment of metastatic estrogen receptor-positive (ER+) breast cancer in combination with endocrine therapies and significantly improve outcomes in patients with this disease. However, given the large number of possible pairwise drug combinations and administration schedules, it remains unclear which clinical strategy would lead to best survival. Here, we developed a computational, cell cycle-explicit model to characterize the pharmacodynamic response to palbociclib-fulvestrant combination therapy. This pharmacodynamic model was parameterized, in a Bayesian statistical inference approach, using in vitro data from cells with wild-type estrogen receptor (WT-ER) and cells expressing the activating missense ER mutation, Y537S, which confers resistance to fulvestrant. We then incorporated pharmacokinetic models derived from clinical data into our computational modeling platform. To systematically compare dose administration schedules, we performed in silico clinical trials based on integrating our pharmacodynamic and pharmacokinetic models as well as considering clinical toxicity constraints. We found that continuous dosing of palbociclib is more effective for lowering overall tumor burden than the standard, pulsed-dose palbociclib treatment. Importantly, our mathematical modeling and statistical analysis platform provides a rational method for comparing treatment strategies in search of optimal combination dosing strategies of other cell-cycle inhibitors in ER+ breast cancer. SIGNIFICANCE: We created a computational modeling platform to predict the effects of fulvestrant/palbocilib treatment on WT-ER and Y537S-mutant breast cancer cells, and found that continuous treatment schedules are more effective than the standard, pulsed-dose palbociclib treatment schedule.

A paracrine circuit of IL-1ß/IL-1R1 between myeloid and tumor cells drives genotype-dependent glioblastoma progression.

Monocytes and monocyte-derived macrophages (MDMs) from blood circulation infiltrate glioblastoma (GBM) and promote growth. Here, we show that PDGFB-driven GBM cells induce the expression of the potent proinflammatory cytokine IL-1ß in MDM, which engages IL-1R1 in tumor cells, activates the NF-κB pathway, and subsequently leads to induction of monocyte chemoattractant proteins (MCPs). Thus, a feedforward paracrine circuit of IL-1ß/IL-1R1 between tumors and MDM creates an interdependence driving PDGFB-driven GBM progression. Genetic loss or locally antagonizing IL-1ß/IL-1R1 leads to reduced MDM infiltration, diminished tumor growth, and reduced exhausted CD8+ T cells and thereby extends the survival of tumor-bearing mice. In contrast to IL-1ß, IL-1α exhibits antitumor effects. Genetic deletion of Il1a/b is associated with decreased recruitment of lymphoid cells and loss-of-interferon signaling in various immune populations and subsets of malignant cells and is associated with decreased survival time of PDGFB-driven tumor-bearing mice. In contrast to PDGFB-driven GBM, Nf1-silenced tumors have a constitutively active NF-κB pathway, which drives the expression of MCPs to recruit monocytes into tumors. These results indicate local antagonism of IL-1ß could be considered as an effective therapy specifically for proneural GBM.

Tutorial: integrative computational analysis of bulk RNA-sequencing data to characterize tumor immunity using RIMA.

RNA-sequencing (RNA-seq) has become an increasingly cost-effective technique for molecular profiling and immune characterization of tumors. In the past decade, many computational tools have been developed to characterize tumor immunity from gene expression data. However, the analysis of large-scale RNA-seq data requires bioinformatics proficiency, large computational resources and cancer genomics and immunology knowledge. In this tutorial, we provide an overview of computational analysis of bulk RNA-seq data for immune characterization of tumors and introduce commonly used computational tools with relevance to cancer immunology and immunotherapy. These tools have diverse functions such as evaluation of expression signatures, estimation of immune infiltration, inference of the immune repertoire, prediction of immunotherapy response, neoantigen detection and microbiome quantification. We describe the RNA-seq IMmune Analysis (RIMA) pipeline integrating many of these tools to streamline RNA-seq analysis. We also developed a comprehensive and user-friendly guide in the form of a GitBook with text and video demos to assist users in analyzing bulk RNA-seq data for immune characterization at both individual sample and cohort levels by using RIMA.

Single-cell heterogeneity of EGFR and CDK4 co-amplification is linked to immune infiltration in glioblastoma.

Glioblastoma (GBM) is the most aggressive brain tumor, with a median survival of ∼15 months. Targeted approaches have not been successful in this tumor type due to the large extent of intratumor heterogeneity. Mosaic amplification of oncogenes suggests that multiple genetically distinct clones are present in each tumor. To uncover the relationships between genetically diverse subpopulations of GBM cells and their native tumor microenvironment, we employ highly multiplexed spatial protein profiling coupled with single-cell spatial mapping of fluorescence in situ hybridization (FISH) for EGFR, CDK4, and PDGFRA. Single-cell FISH analysis of a total of 35,843 single nuclei reveals that tumors in which amplifications of EGFR and CDK4 more frequently co-occur in the same cell exhibit higher infiltration of CD163+ immunosuppressive macrophages. Our results suggest that high-throughput assessment of genomic alterations at the single-cell level could provide a measure for predicting the immune state of GBM.

Brief Report: Combination of Osimertinib and Dacomitinib to Mitigate Primary and Acquired Resistance in EGFR-Mutant Lung Adenocarcinomas.

PURPOSE: Primary and acquired resistance to osimertinib remain significant challenges for patients with EGFR-mutant lung cancers. Acquired EGFR alterations such as EGFR T790M or C797S mediate resistance to EGFR tyrosine kinase inhibitors (TKI) and combination therapy with dual EGFR TKIs may prevent or reverse on-target resistance. PATIENTS AND METHODS: We conducted two prospective, phase I/II trials assessing combination osimertinib and dacomitinib to address on-target resistance in the primary and acquired resistance settings. In the initial therapy study, patients received dacomitinib and osimertinib in combination as initial therapy. In the acquired resistance trial, dacomitinib with or without osimertinib was administered to patients with EGFR-mutant lung cancers with disease progression on osimertinib alone and evidence of an acquired EGFR second-site mutation. RESULTS: Cutaneous toxicities occurred in 93% (any grade) of patients and diarrhea in 72% (any grade) with the combination. As initial therapy, the overall response to the combination was 73% [95% confidence interval (CI), 50%-88%]. No acquired secondary alterations in EGFR were observed in any patients at progression. In the acquired resistance setting, the overall response was 14% (95% CI, 1%-58%). CONCLUSIONS: We observed no acquired secondary EGFR alterations with dual inhibition of EGFR as up-front treatment, but this regimen was associated with greater toxicity. The combination was not effective in reversing acquired resistance after development of a second-site acquired EGFR alteration. Our study highlights the need to develop better strategies to address on-target resistance in patients with EGFR-mutant lung cancers.

Mechanisms of response and resistance to combined decitabine and ipilimumab for advanced myeloid disease.

The challenge of eradicating leukemia in patients with acute myelogenous leukemia (AML) after initial cytoreduction has motivated modern efforts to combine synergistic active modalities including immunotherapy. Recently, the ETCTN/CTEP 10026 study tested the combination of the DNA methyltransferase inhibitor decitabine together with the immune checkpoint inhibitor ipilimumab for AML/myelodysplastic syndrome (MDS) either after allogeneic hematopoietic stem cell transplantation (HSCT) or in the HSCT-naïve setting. Integrative transcriptome-based analysis of 304 961 individual marrow-infiltrating cells for 18 of 48 subjects treated on study revealed the strong association of response with a high baseline ratio of T to AML cells. Clinical responses were predominantly driven by decitabine-induced cytoreduction. Evidence of immune activation was only apparent after ipilimumab exposure, which altered CD4+ T-cell gene expression, in line with ongoing T-cell differentiation and increased frequency of marrow-infiltrating regulatory T cells. For post-HSCT samples, relapse could be attributed to insufficient clearing of malignant clones in progenitor cell populations. In contrast to AML/MDS bone marrow, the transcriptomes of leukemia cutis samples from patients with durable remission after ipilimumab monotherapy showed evidence of increased infiltration with antigen-experienced resident memory T cells and higher expression of CTLA-4 and FOXP3. Altogether, activity of combined decitabine and ipilimumab is impacted by cellular expression states within the microenvironmental niche of leukemic cells. The inadequate elimination of leukemic progenitors mandates urgent development of novel approaches for targeting these cell populations to generate long-lasting responses. This trial was registered at www.clinicaltrials.gov as #NCT02890329.

JAK-STAT Signaling in Inflammatory Breast Cancer Enables Chemotherapy-Resistant Cell States.

Inflammatory breast cancer (IBC) is a difficult-to-treat disease with poor clinical outcomes due to high risk of metastasis and resistance to treatment. In breast cancer, CD44+CD24- cells possess stem cell-like features and contribute to disease progression, and we previously described a CD44+CD24-pSTAT3+ breast cancer cell subpopulation that is dependent on JAK2/STAT3 signaling. Here we report that CD44+CD24- cells are the most frequent cell type in IBC and are commonly pSTAT3+. Combination of JAK2/STAT3 inhibition with paclitaxel decreased IBC xenograft growth more than either agent alone. IBC cell lines resistant to paclitaxel and doxorubicin were developed and characterized to mimic therapeutic resistance in patients. Multi-omic profiling of parental and resistant cells revealed enrichment of genes associated with lineage identity and inflammation in chemotherapy-resistant derivatives. Integrated pSTAT3 chromatin immunoprecipitation sequencing and RNA sequencing (RNA-seq) analyses showed pSTAT3 regulates genes related to inflammation and epithelial-to-mesenchymal transition (EMT) in resistant cells, as well as PDE4A, a cAMP-specific phosphodiesterase. Metabolomic characterization identified elevated cAMP signaling and CREB as a candidate therapeutic target in IBC. Investigation of cellular dynamics and heterogeneity at the single cell level during chemotherapy and acquired resistance by CyTOF and single cell RNA-seq identified mechanisms of resistance including a shift from luminal to basal/mesenchymal cell states through selection for rare preexisting subpopulations or an acquired change. Finally, combination treatment with paclitaxel and JAK2/STAT3 inhibition prevented the emergence of the mesenchymal chemo-resistant subpopulation. These results provide mechanistic rational for combination of chemotherapy with inhibition of JAK2/STAT3 signaling as a more effective therapeutic strategy in IBC. SIGNIFICANCE: Chemotherapy resistance in inflammatory breast cancer is driven by the JAK2/STAT3 pathway, in part via cAMP/PKA signaling and a cell state switch, which can be overcome using paclitaxel combined with JAK2 inhibitors.

Phase I study of a novel glioblastoma radiation therapy schedule exploiting cell-state plasticity.

BACKGROUND: Glioblastomas comprise heterogeneous cell populations with dynamic, bidirectional plasticity between treatment-resistant stem-like and treatment-sensitive differentiated states, with treatment influencing this process. However, current treatment protocols do not account for this plasticity. Previously, we generated a mathematical model based on preclinical experiments to describe this process and optimize a radiation therapy fractionation schedule that substantially increased survival relative to standard fractionation in a murine glioblastoma model. METHODS: We developed statistical models to predict the survival benefit of interventions to glioblastoma patients based on the corresponding survival benefit in the mouse model used in our preclinical study. We applied our mathematical model of glioblastoma radiation response to optimize a radiation therapy fractionation schedule for patients undergoing re-irradiation for glioblastoma and developed a first-in-human trial (NCT03557372) to assess the feasibility and safety of administering our schedule. RESULTS: Our statistical modeling predicted that the hazard ratio when comparing our novel radiation schedule with a standard schedule would be 0.74. Our mathematical modeling suggested that a practical, near-optimal schedule for re-irradiation of recurrent glioblastoma patients was 3.96 Gy × 7 (1 fraction/day) followed by 1.0 Gy × 9 (3 fractions/day). Our optimized schedule was successfully administered to 14/14 (100%) patients. CONCLUSIONS: A novel radiation therapy schedule based on mathematical modeling of cell-state plasticity is feasible and safe to administer to glioblastoma patients.

Breast cancer prevention by short-term inhibition of TGFß signaling.

Cancer prevention has a profound impact on cancer-associated mortality and morbidity. We previously identified TGFß signaling as a candidate regulator of mammary epithelial cells associated with breast cancer risk. Here, we show that short-term TGFBR inhibitor (TGFBRi) treatment of peripubertal ACI inbred and Sprague Dawley outbred rats induces lasting changes and prevents estrogen- and carcinogen-induced mammary tumors, respectively. We identify TGFBRi-responsive cell populations by single cell RNA-sequencing, including a unique epithelial subpopulation designated secretory basal cells (SBCs) with progenitor features. We detect SBCs in normal human breast tissues and find them to be associated with breast cancer risk. Interactome analysis identifies SBCs as the most interactive cell population and the main source of insulin-IGF signaling. Accordingly, inhibition of TGFBR and IGF1R decrease proliferation of organoid cultures. Our results reveal a critical role for TGFß in regulating mammary epithelial cells relevant to breast cancer and serve as a proof-of-principle cancer prevention strategy.

Spatial intra-tumor heterogeneity is associated with survival of lung adenocarcinoma patients.

Intra-tumor heterogeneity (ITH) of human tumors is important for tumor progression, treatment response, and drug resistance. However, the spatial distribution of ITH remains incompletely understood. Here, we present spatial analysis of ITH in lung adenocarcinomas from 147 patients using multi-region mass spectrometry of >5,000 regions, single-cell copy number sequencing of ~2,000 single cells, and cyclic immunofluorescence of >10 million cells. We identified two distinct spatial patterns among tumors, termed clustered and random geographic diversification (GD). These patterns were observed in the same samples using both proteomic and genomic data. The random proteomic GD pattern, which is characterized by decreased cell adhesion and lower levels of tumor-interacting endothelial cells, was significantly associated with increased risk of recurrence or death in two independent patient cohorts. Our study presents comprehensive spatial mapping of ITH in lung adenocarcinoma and provides insights into the mechanisms and clinical consequences of GD.

Genomic analysis of early-stage lung cancer reveals a role for TP53 mutations in distant metastasis.

Patients with non-small cell lung cancer (NSCLC) who have distant metastases have a poor prognosis. To determine which genomic factors of the primary tumor are associated with metastasis, we analyzed data from 759 patients originally diagnosed with stage I-III NSCLC as part of the AACR Project GENIE Biopharma Collaborative consortium. We found that TP53 mutations were significantly associated with the development of new distant metastases. TP53 mutations were also more prevalent in patients with a history of smoking, suggesting that these patients may be at increased risk for distant metastasis. Our results suggest that additional investigation of the optimal management of patients with early-stage NSCLC harboring TP53 mutations at diagnosis is warranted in light of their higher likelihood of developing new distant metastases.

In vivo genome-wide CRISPR screening in murine acute myeloid leukemia uncovers microenvironmental dependencies.

Genome-wide CRISPR screens have been extremely useful in identifying therapeutic targets in diverse cancers by defining genes that are essential for malignant growth. However, most CRISPR screens were performed in vitro and thus cannot identify genes that are essential for interactions with the microenvironment in vivo. Here, we report genome-wide CRISPR screens in 2 in vivo murine models of acute myeloid leukemia (AML) driven by the KMT2A/MLLT3 fusion or by the constitutive coexpression of Hoxa9 and Meis1. Secondary validation using a focused library identified 72 genes specifically essential for leukemic growth in vivo, including components of the major histocompatibility complex class I complex, Cd47, complement receptor Cr1l, and the ß-4-galactosylation pathway. Importantly, several of these in vivo-specific hits have a prognostic effect or are inferred to be master regulators of protein activity in human AML cases. For instance, we identified Fermt3, a master regulator of integrin signaling, as having in vivo-specific dependency with high prognostic relevance. Overall, we show an experimental and computational pipeline for genome-wide functional screens in vivo in AML and provide a genome-wide resource of essential drivers of leukemic growth in vivo.

Designing optimal allocations for cancer screening using queuing network models.

Cancer is one of the leading causes of death, but mortality can be reduced by detecting tumors earlier so that treatment is initiated at a less aggressive stage. The tradeoff between costs associated with screening and its benefit makes the decision of whom to screen and when a challenge. To enable comparisons across screening strategies for any cancer type, we demonstrate a mathematical modeling platform based on the theory of queuing networks designed for quantifying the benefits of screening strategies. Our methodology can be used to design optimal screening protocols and to estimate their benefits for specific patient populations. Our method is amenable to exact analysis, thus circumventing the need for simulations, and is capable of exactly quantifying outcomes given variability in the age of diagnosis, rate of progression, and screening sensitivity and intervention outcomes. We demonstrate the power of this methodology by applying it to data from the Surveillance, Epidemiology and End Results (SEER) program. Our approach estimates the benefits that various novel screening programs would confer to different patient populations, thus enabling us to formulate an optimal screening allocation and quantify its potential effects for any cancer type and intervention.