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Recent single-cell studies of cancer in both mice and humans have identified the emergence of a myofibroblast population specifically marked by the highly restricted leucine-rich-repeat-containing protein 15 (LRRC15)1–3. However, the molecular signals that underlie the development of LRRC15+ cancer-associated fibroblasts (CAFs) and their direct impact on anti-tumour immunity are uncharacterized. Here in mouse models of pancreatic cancer, we provide in vivo genetic evidence that TGFβ receptor type 2 signalling in healthy dermatopontin+ universal fibroblasts is essential for the development of cancer-associated LRRC15+ myofibroblasts. This axis also predominantly drives fibroblast lineage diversity in human cancers. Using newly developed Lrrc15–diphtheria toxin receptor knock-in mice to selectively deplete LRRC15+ CAFs, we show that depletion of this population markedly reduces the total tumour fibroblast content. Moreover, the CAF composition is recalibrated towards universal fibroblasts. This relieves direct suppression of tumour-infiltrating CD8+ T cells to enhance their effector function and augments tumour regression in response to anti-PDL1 immune checkpoint blockade. Collectively, these findings demonstrate that TGFβ-dependent LRRC15+ CAFs dictate the tumour-fibroblast setpoint to promote tumour growth. These cells also directly suppress CD8+ T cell function and limit responsiveness to checkpoint blockade. Development of treatments that restore the homeostatic fibroblast setpoint by reducing the population of pro-disease LRRC15+ myofibroblasts may improve patient survival and response to immunotherapy.

Supplemental Figures

Tumor genotyping is not routinely performed in localized non–small cell lung cancer (NSCLC) due to lack of associations of mutations with outcome. Here, we analyze 232 consecutive patients with localized NSCLC and demonstrate that KEAP1 and NFE2L2 mutations are predictive of high rates of local recurrence (LR) after radiotherapy but not surgery. Half of LRs occurred in tumors with KEAP1/NFE2L2 mutations, indicating that they are major molecular drivers of clinical radioresistance. Next, we functionally evaluate KEAP1/NFE2L2 mutations in our radiotherapy cohort and demonstrate that only pathogenic mutations are associated with radioresistance. Furthermore, expression of NFE2L2 target genes does not predict LR, underscoring the utility of tumor genotyping. Finally, we show that glutaminase inhibition preferentially radiosensitizes KEAP1-mutant cells via depletion of glutathione and increased radiation-induced DNA damage. Our findings suggest that genotyping for KEAP1/NFE2L2 mutations could facilitate treatment personalization and provide a potential strategy for overcoming radioresistance conferred by these mutations.Significance:This study shows that mutations in KEAP1 and NFE2L2 predict for LR after radiotherapy but not surgery in patients with NSCLC. Approximately half of all LRs are associated with these mutations and glutaminase inhibition may allow personalized radiosensitization of KEAP1/NFE2L2-mutant tumors.This article is highlighted in the In This Issue feature, p. 1775

Supplemental Methods

Table S1 - S7

Supplementary Figures 1-9

Genomic profiling of bronchoalveolar lavage (BAL) samples may be useful for tumor profiling and diagnosis in the clinic. Here, we compared tumor-derived mutations detected in BAL samples from subjects with non–small cell lung cancer (NSCLC) to those detected in matched plasma samples. Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) was used to genotype DNA purified from BAL, plasma, and tumor samples from patients with NSCLC. The characteristics of cell-free DNA (cfDNA) isolated from BAL fluid were first characterized to optimize the technical approach. Somatic mutations identified in tumor were then compared with those identified in BAL and plasma, and the potential of BAL cfDNA analysis to distinguish lung cancer patients from risk-matched controls was explored. In total, 200 biofluid and tumor samples from 38 cases and 21 controls undergoing BAL for lung cancer evaluation were profiled. More tumor variants were identified in BAL cfDNA than plasma cfDNA in all stages (P < 0.001) and in stage I to II disease only. Four of 21 controls harbored low levels of cancer-associated driver mutations in BAL cfDNA [mean variant allele frequency (VAF) = 0.5%], suggesting the presence of somatic mutations in nonmalignant airway cells. Finally, using a Random Forest model with leave-one-out cross-validation, an exploratory BAL genomic classifier identified lung cancer with 69% sensitivity and 100% specificity in this cohort and detected more cancers than BAL cytology. Detecting tumor-derived mutations by targeted sequencing of BAL cfDNA is technically feasible and appears to be more sensitive than plasma profiling. Further studies are required to define optimal diagnostic applications and clinical utility. Significance: Hybrid-capture, targeted deep sequencing of lung cancer mutational burden in cell-free BAL fluid identifies more tumor-derived mutations with increased allele frequencies compared with plasma cell-free DNA. See related commentary by Rolfo et al., p. 2826

Supplementary Data from Genomic Profiling of Bronchoalveolar Lavage Fluid in Lung Cancer

Supplementary Figures related to Figures 1, 2, 3, and 4.

Table S2: Surveillance plasma sample ctDNA metrics. Table S3: Baseline plasma sample ctDNA metrics. Table S4: Surveillance plasma sample variants in ctDNA detected samples. Table S5: Baseline plasma variants in available samples. Table S6: Tumor variants detected by whole exome and targeted sequencing. Table S7: Canonical genes implicated in clonal hematopoiesis.

Genomic profiling of bronchoalveolar lavage (BAL) samples may be useful for tumor profiling and diagnosis in the clinic. Here, we compared tumor-derived mutations detected in BAL samples from subjects with non–small cell lung cancer (NSCLC) to those detected in matched plasma samples. Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) was used to genotype DNA purified from BAL, plasma, and tumor samples from patients with NSCLC. The characteristics of cell-free DNA (cfDNA) isolated from BAL fluid were first characterized to optimize the technical approach. Somatic mutations identified in tumor were then compared with those identified in BAL and plasma, and the potential of BAL cfDNA analysis to distinguish lung cancer patients from risk-matched controls was explored. In total, 200 biofluid and tumor samples from 38 cases and 21 controls undergoing BAL for lung cancer evaluation were profiled. More tumor variants were identified in BAL cfDNA than plasma cfDNA in all stages (P < 0.001) and in stage I to II disease only. Four of 21 controls harbored low levels of cancer-associated driver mutations in BAL cfDNA [mean variant allele frequency (VAF) = 0.5%], suggesting the presence of somatic mutations in nonmalignant airway cells. Finally, using a Random Forest model with leave-one-out cross-validation, an exploratory BAL genomic classifier identified lung cancer with 69% sensitivity and 100% specificity in this cohort and detected more cancers than BAL cytology. Detecting tumor-derived mutations by targeted sequencing of BAL cfDNA is technically feasible and appears to be more sensitive than plasma profiling. Further studies are required to define optimal diagnostic applications and clinical utility.Significance:Hybrid-capture, targeted deep sequencing of lung cancer mutational burden in cell-free BAL fluid identifies more tumor-derived mutations with increased allele frequencies compared with plasma cell-free DNA.See related commentary by Rolfo et al., p. 2826

Supplementary Figure from Genomic Profiling of Bronchoalveolar Lavage Fluid in Lung Cancer

Table S1 shows summary patient demographics, pathologic features, and treatment details

Purpose:Treatment with PD-(L)1 blockade can produce remarkably durable responses in patients with non–small cell lung cancer (NSCLC). However, a significant fraction of long-term responders ultimately progress and predictors of late progression are unknown. We hypothesized that circulating tumor DNA (ctDNA) analysis of long-term responders to PD-(L)1 blockade may differentiate those who will achieve ongoing benefit from those at risk of eventual progression.Experimental Design:In patients with advanced NSCLC achieving long-term benefit from PD-(L)1 blockade (progression-free survival ≥ 12 months), plasma was collected at a surveillance timepoint late during/after treatment to interrogate ctDNA by Cancer Personalized Profiling by Deep Sequencing. Tumor tissue was available for 24 patients and was profiled by whole-exome sequencing (n = 18) or by targeted sequencing (n = 6).Results:Thirty-one patients with NSCLC with long-term benefit to PD-(L)1 blockade were identified, and ctDNA was analyzed in surveillance blood samples collected at a median of 26.7 months after initiation of therapy. Nine patients also had baseline plasma samples available, and all had detectable ctDNA prior to therapy initiation. At the surveillance timepoint, 27 patients had undetectable ctDNA and 25 (93%) have remained progression-free; in contrast, all 4 patients with detectable ctDNA eventually progressed [Fisher P < 0.0001; positive predictive value = 1, 95% confidence interval (CI), 0.51–1; negative predictive value = 0.93 (95% CI, 0.80–0.99)].Conclusions:ctDNA analysis can noninvasively identify minimal residual disease in patients with long-term responses to PD-(L)1 blockade and predict the risk of eventual progression. If validated, ctDNA surveillance may facilitate personalization of the duration of immune checkpoint blockade and enable early intervention in patients at high risk for progression.

Supplementary Figures 1-5 from Combining ATR Suppression with Oncogenic Ras Synergistically Increases Genomic Instability, Causing Synthetic Lethality or Tumorigenesis in a Dosage-Dependent Manner

Supplementary Table 1 from Combining ATR Suppression with Oncogenic Ras Synergistically Increases Genomic Instability, Causing Synthetic Lethality or Tumorigenesis in a Dosage-Dependent Manner

Natural killer (NK) cells are educated through the binding of killer immunoglobulin like receptors (KIR) to human leukocyte antigen (HLA) proteins, but it is unknown whether the presence of these highly diverse KIR/HLA interactions influence responses to immunotherapy in solid tumors. We report herein two observations that shed light on NK cell function and abundance in anti-tumor immune responses. In patients with non-small cell lung cancer treated with anti-PD-L1 therapy, we found that individuals carrying HLA-C1 and HLA-Bw4 alleles and the genes coding for their receptors KIR2DL3 and KIR3DL1 showed improved overall survival (OS). Combined with our second finding that NK cell infiltration was independently associated with improved OS, our findings have important implications for precision medicine approaches and the development of NK cell-based therapies.

Even in patients whose tumours exhibit increased T cell infiltration, resistance to immune checkpoint inhibitor (ICI) therapy is common. We investigated mechanisms of ICI resistance using engineered mouse models with increased neoantigen burden and T cell infiltration. We found that in ICI-resistant tumours, T cells upregulate an NF-κB-driven inflammatory circuit in the tumour microenvironment (TME), culminating in PMN-MDSC recruitment and tumour escape. This resistance circuit was molecularly driven by IL-1, TNFα, and G-CSF, and their genetic or pharmacologic inhibition increased ICI efficacy. Furthermore, we identified a subset of human TNF-expressing T cells that expand following ICI therapy and are associated with increased tumour NF-κB signaling. These data reveal a surprising mechanism of ICI resistance whereby T cells instruct NF-κB-mediated inflammation to paradoxically drive a resistance circuit in participating tumours, refining our understanding of ICI response and resistance with important therapeutic implications.

Profiling of circulating tumor DNA (ctDNA) in the bloodstream shows promise for noninvasive cancer detection. Chromatin fragmentation features have previously been explored to infer gene expression profiles from cell-free DNA (cfDNA), but current fragmentomic methods require high concentrations of tumor-derived DNA and provide limited resolution. Here we describe promoter fragmentation entropy as an epigenomic cfDNA feature that predicts RNA expression levels at individual genes. We developed 'epigenetic expression inference from cell-free DNA-sequencing' (EPIC-seq), a method that uses targeted sequencing of promoters of genes of interest. Profiling 329 blood samples from 201 patients with cancer and 87 healthy adults, we demonstrate classification of subtypes of lung carcinoma and diffuse large B cell lymphoma. Applying EPIC-seq to serial blood samples from patients treated with PD-(L)1 immune-checkpoint inhibitors, we show that gene expression profiles inferred by EPIC-seq are correlated with clinical response. Our results indicate that EPIC-seq could enable noninvasive, high-throughput tissue-of-origin characterization with diagnostic, prognostic and therapeutic potential.

Radiologic screening of high-risk adults reduces lung-cancer-related mortality1,2; however, a small minority of eligible individuals undergo such screening in the United States3,4. The availability of blood-based tests could increase screening uptake. Here we introduce improvements to cancer personalized profiling by deep sequencing (CAPP-Seq)5, a method for the analysis of circulating tumour DNA (ctDNA), to better facilitate screening applications. We show that, although levels are very low in early-stage lung cancers, ctDNA is present prior to treatment in most patients and its presence is strongly prognostic. We also find that the majority of somatic mutations in the cell-free DNA (cfDNA) of patients with lung cancer and of risk-matched controls reflect clonal haematopoiesis and are non-recurrent. Compared with tumour-derived mutations, clonal haematopoiesis mutations occur on longer cfDNA fragments and lack mutational signatures that are associated with tobacco smoking. Integrating these findings with other molecular features, we develop and prospectively validate a machine-learning method termed 'lung cancer likelihood in plasma' (Lung-CLiP), which can robustly discriminate early-stage lung cancer patients from risk-matched controls. This approach achieves performance similar to that of tumour-informed ctDNA detection and enables tuning of assay specificity in order to facilitate distinct clinical applications. Our findings establish the potential of cfDNA for lung cancer screening and highlight the importance of risk-matching cases and controls in cfDNA-based screening studies.

Circulating tumor DNA (ctDNA) molecular residual disease (MRD) following curative-intent treatment strongly predicts recurrence in multiple tumor types, but whether further treatment can improve outcomes in patients with MRD remains unclear. We applied CAPP-Seq ctDNA analysis to 218 samples from 65 patients receiving chemoradiation therapy (CRT) for locally advanced NSCLC, including 28 patients receiving consolidation immune checkpoint inhibition (CICI). Patients with undetectable ctDNA after CRT had excellent outcomes whether or not they received CICI. Among such patients, one died from CICI-related pneumonitis, highlighting the potential utility of only treating patients with MRD. In contrast, patients with MRD after CRT who received CICI had significantly better outcomes than patients who did not receive CICI. Furthermore, the ctDNA response pattern early during CICI identified patients responding to consolidation therapy. Our results suggest that CICI improves outcomes for NSCLC patients with MRD and that ctDNA analysis may facilitate personalization of consolidation therapy.

Improved identification of anti-tumor T cells is needed to advance cancer immunotherapies. CD39 expression is a promising surrogate of tumor-reactive CD8

Recent single-cell studies of cancer in both mice and humans have identified the emergence of a myofibroblast population specifically marked by the highly restricted leucine-rich-repeat-containing protein 15 (LRRC15)

PURPOSE Response to programmed cell death protein 1 (PD-1) blockade is often conceptualized as resulting from reinvigoration of tumor-infiltrating lymphocytes. However, recruited antitumor immunity from the periphery may also be an important contributor to response. A detailed assessment of the response dynamics of individual metastasis could provide insight to the systemic and local features that mediate response and resistance to immunotherapy. MATERIALS AND METHODS Patients with metastatic non–small-cell lung cancer (NSCLC) or mismatch repair deficiency (MMRD) carcinoma treated with PD-1 monotherapy were evaluated independently. Absolute and percent change of each target lesion were quantified at each computed tomography scan using RECIST. Patterns of progression were predefined as systemic or mixed and were correlated with clinical outcomes. RESULTS A total of 761 individual lesions from 214 patients with NSCLC and 290 lesions from 78 patients with MMRD carcinoma were examined. Individual target lesion responses aligned with best overall response of each patient (85% NSCLC and 93% MMRD lesions responded in patients with partial response/complete response). In responding patients, timing of response was uniform (73% NSCLC and 76% MMRD lesions responded synchronously), and deeper responses were associated with prolonged progression-free survival and overall survival. By contrast, at progression, mixed progression was common (45% of NSCLC and 53% of MMRD) and associated with improved survival compared with those who experienced systemic progression (NSCLC hazard ratio [HR], 0.58; P = .001; MMRD HR, 0.40; P = .07). Organ sites had differential responses, with lymph node and liver metastasis among the most and least responsive, respectively. CONCLUSION Temporal-spatial patterns of response across individual metastases tend to be uniform, favoring the role of peripheral, clonally directed antitumor immunity as a key mediator of response to PD-1 blockade. In contrast, progression is more heterogeneous, potentially revealing the clinical importance of local features and intertumoral heterogeneity.

Current regimens for the detection and surveillance of bladder cancer are invasive and have suboptimal sensitivity. Here, we present a novel high-throughput sequencing (HTS) method for detection of urine tumor DNA (utDNA) called utDNA CAPP-Seq (uCAPP-Seq) and apply it to 67 healthy adults and 118 patients with early-stage bladder cancer who had urine collected either prior to treatment or during surveillance. Using this targeted sequencing approach, we detected a median of 6 mutations per patient with bladder cancer and observed surprisingly frequent mutations of the PLEKHS1 promoter (46%), suggesting these mutations represent a useful biomarker for detection of bladder cancer. We detected utDNA pretreatment in 93% of cases using a tumor mutation–informed approach and in 84% when blinded to tumor mutation status, with 96% to 100% specificity. In the surveillance setting, we detected utDNA in 91% of patients who ultimately recurred, with utDNA detection preceding clinical progression in 92% of cases. uCAPP-Seq outperformed a commonly used ancillary test (UroVysion, P = 0.02) and cytology and cystoscopy combined (P ≤ 0.006), detecting 100% of bladder cancer cases detected by cytology and 82% that cytology missed. Our results indicate that uCAPP-Seq is a promising approach for early detection and surveillance of bladder cancer. Significance: This study shows that utDNA can be detected using HTS with high sensitivity and specificity in patients with early-stage bladder cancer and during post-treatment surveillance, significantly outperforming standard diagnostic modalities and facilitating noninvasive detection, genotyping, and monitoring. This article is highlighted in the In This Issue feature, p. 453

Growing evidence demonstrates that circulating tumor DNA (ctDNA) minimal residual disease (MRD) following treatment for solid tumors predicts relapse. These results suggest that ctDNA MRD could identify candidates for adjuvant therapy and measure response to such treatment. Importantly, factors such as assay type, amount of ctDNA release, and technical and biological background can affect ctDNA MRD results. Furthermore, the clinical utility of ctDNA MRD for treatment personalization remains to be fully established. Here, we review the evidence supporting the value of ctDNA MRD in solid cancers and highlight key considerations in the application of this potentially transformative biomarker. Significance: ctDNA analysis enables detection of MRD and predicts relapse after definitive treatment for solid cancers, thereby promising to revolutionize personalization of adjuvant and consolidation therapies.

PURPOSE Patients with Diffuse Large B-cell Lymphoma (DLBCL) in need of immediate therapy are largely under-represented in clinical trials. The diagnosis-to-treatment interval (DTI) has recently been described as a metric to quantify such patient selection bias, with short DTI being associated with adverse risk factors and inferior outcomes. Here, we characterized the relationships between DTI, circulating tumor DNA (ctDNA), conventional risk factors, and clinical outcomes, with the goal of defining objective disease metrics contributing to selection bias. PATIENTS AND METHODS We evaluated pretreatment ctDNA levels in 267 patients with DLBCL treated across multiple centers in Europe and the United States using Cancer Personalized Profiling by Deep Sequencing. Pretreatment ctDNA levels were correlated with DTI, total metabolic tumor volumes (TMTVs), the International Prognostic Index (IPI), and outcome. RESULTS Short DTI was associated with advanced-stage disease ( P < .001) and higher IPI ( P < .001). We also found an inverse correlation between DTI and TMTV ( RS = −0.37; P < .001). Similarly, pretreatment ctDNA levels were significantly associated with stage, IPI, and TMTV (all P < .001), demonstrating that both DTI and ctDNA reflect disease burden. Notably, patients with shorter DTI had higher pretreatment ctDNA levels ( P < .001). Pretreatment ctDNA levels predicted short DTI independent of the IPI ( P < .001). Although each risk factor was significantly associated with event-free survival in univariable analysis, ctDNA level was prognostic of event-free survival independent of DTI and IPI in multivariable Cox regression (ctDNA: hazard ratio, 1.5; 95% CI [1.2 to 2.0]; IPI: 1.1 [0.9 to 1.3]; −DTI: 1.1 [1.0 to 1.2]). CONCLUSION Short DTI largely reflects baseline tumor burden, which can be objectively measured using pretreatment ctDNA levels. Pretreatment ctDNA levels therefore have utility for quantifying and guarding against selection biases in prospective DLBCL clinical trials.

Despite molecular and clinical heterogeneity, small cell lung cancer (SCLC) is treated as a single entity with predictably poor results. Using tumor expression data and non-negative matrix factorization, we identify four SCLC subtypes defined largely by differential expression of transcription factors ASCL1, NEUROD1, and POU2F3 or low expression of all three transcription factor signatures accompanied by an Inflamed gene signature (SCLC-A, N, P, and I, respectively). SCLC-I experiences the greatest benefit from the addition of immunotherapy to chemotherapy, while the other subtypes each have distinct vulnerabilities, including to inhibitors of PARP, Aurora kinases, or BCL-2. Cisplatin treatment of SCLC-A patient-derived xenografts induces intratumoral shifts toward SCLC-I, supporting subtype switching as a mechanism of acquired platinum resistance. We propose that matching baseline tumor subtype to therapy, as well as manipulating subtype switching on therapy, may enhance depth and duration of response for SCLC patients. Gay et al. provide a classification for four subtypes of small cell lung cancer, each with unique molecular features and therapeutic vulnerabilities. An inflamed, mesenchymal subtype predicts benefit with the addition of immunotherapy to chemotherapy. Intratumoral switching between chemosensitive and chemoresistant subtypes accompanies therapeutic resistance.

Tumor genotyping is not routinely performed in localized non–small cell lung cancer (NSCLC) due to lack of associations of mutations with outcome. Here, we analyze 232 consecutive patients with localized NSCLC and demonstrate that KEAP1 and NFE2L2 mutations are predictive of high rates of local recurrence (LR) after radiotherapy but not surgery. Half of LRs occurred in tumors with KEAP1/NFE2L2 mutations, indicating that they are major molecular drivers of clinical radioresistance. Next, we functionally evaluate KEAP1/NFE2L2 mutations in our radiotherapy cohort and demonstrate that only pathogenic mutations are associated with radioresistance. Furthermore, expression of NFE2L2 target genes does not predict LR, underscoring the utility of tumor genotyping. Finally, we show that glutaminase inhibition preferentially radiosensitizes KEAP1-mutant cells via depletion of glutathione and increased radiation-induced DNA damage. Our findings suggest that genotyping for KEAP1/NFE2L2 mutations could facilitate treatment personalization and provide a potential strategy for overcoming radioresistance conferred by these mutations. Significance: This study shows that mutations in KEAP1 and NFE2L2 predict for LR after radiotherapy but not surgery in patients with NSCLC. Approximately half of all LRs are associated with these mutations and glutaminase inhibition may allow personalized radiosensitization of KEAP1/NFE2L2-mutant tumors. This article is highlighted in the In This Issue feature, p. 1775

Immunotherapy fails to cure most cancer patients. Preclinical studies indicate that radiotherapy synergizes with immunotherapy, promoting radiation-induced antitumor immunity. Most preclinical immunotherapy studies utilize transplant tumor models, which overestimate patient responses. Here, we show that transplant sarcomas are cured by PD-1 blockade and radiotherapy, but identical treatment fails in autochthonous sarcomas, which demonstrate immunoediting, decreased neoantigen expression, and tumor-specific immune tolerance. We characterize tumor-infiltrating immune cells from transplant and primary tumors, revealing striking differences in their immune landscapes. Although radiotherapy remodels myeloid cells in both models, only transplant tumors are enriched for activated CD8+ T cells. The immune microenvironment of primary murine sarcomas resembles most human sarcomas, while transplant sarcomas resemble the most inflamed human sarcomas. These results identify distinct microenvironments in murine sarcomas that coevolve with the immune system and suggest that patients with a sarcoma immune phenotype similar to transplant tumors may benefit most from PD-1 blockade and radiotherapy., Promising results of cancer therapies in transplant tumor models often fail to predict efficacy in clinical trials. Here the authors show that, while transplant tumors are cured by radiotherapy and PD-1 blockade, autochthonous sarcomas are resistant to the identical treatment, recapitulating the immune landscape and resistance to checkpoint blockade observed in most sarcoma patients.

Purpose: Treatment with PD-(L)1 blockade can produce remarkably durable responses in patients with non–small cell lung cancer (NSCLC). However, a significant fraction of long-term responders ultimately progress and predictors of late progression are unknown. We hypothesized that circulating tumor DNA (ctDNA) analysis of long-term responders to PD-(L)1 blockade may differentiate those who will achieve ongoing benefit from those at risk of eventual progression. Experimental Design: In patients with advanced NSCLC achieving long-term benefit from PD-(L)1 blockade (progression-free survival ≥ 12 months), plasma was collected at a surveillance timepoint late during/after treatment to interrogate ctDNA by Cancer Personalized Profiling by Deep Sequencing. Tumor tissue was available for 24 patients and was profiled by whole-exome sequencing (n = 18) or by targeted sequencing (n = 6). Results: Thirty-one patients with NSCLC with long-term benefit to PD-(L)1 blockade were identified, and ctDNA was analyzed in surveillance blood samples collected at a median of 26.7 months after initiation of therapy. Nine patients also had baseline plasma samples available, and all had detectable ctDNA prior to therapy initiation. At the surveillance timepoint, 27 patients had undetectable ctDNA and 25 (93%) have remained progression-free; in contrast, all 4 patients with detectable ctDNA eventually progressed [Fisher P < 0.0001; positive predictive value = 1, 95% confidence interval (CI), 0.51–1; negative predictive value = 0.93 (95% CI, 0.80–0.99)]. Conclusions: ctDNA analysis can noninvasively identify minimal residual disease in patients with long-term responses to PD-(L)1 blockade and predict the risk of eventual progression. If validated, ctDNA surveillance may facilitate personalization of the duration of immune checkpoint blockade and enable early intervention in patients at high risk for progression.

Inhibitors of the programmed cell death-1 (PD-1/PD-L1) signaling axis are approved to treat non-small cell lung cancer (NSCLC) patients, based on their significant overall survival (OS) benefit. Using transcriptomic analysis of 891 NSCLC tumors from patients treated with either the PD-L1 inhibitor atezolizumab or chemotherapy from two large randomized clinical trials, we find a significant B cell association with extended OS with PD-L1 blockade, independent of CD8

Immunotherapy is a mainstay of non-small cell lung cancer (NSCLC) management. While tumor mutational burden (TMB) correlates with response to immunotherapy, little is known about the relationship between the baseline immune response and tumor genotype. Using single-cell RNA sequencing, we profiled 361,929 cells from 35 early-stage NSCLC lesions. We identified a cellular module consisting of PDCD1(+)CXCL13(+) activated T cells, IgG(+) plasma cells, and SPP1(+) macrophages, referred to as the lung cancer activation module (LCAM(hi)). We confirmed LCAM(hi) enrichment in multiple NSCLC cohorts, and paired CITE-seq established an antibody panel to identify LCAM(hi) lesions. LCAM presence was found to be independent of overall immune cell content and correlated with TMB, cancer testis antigens, and TP53 mutations. High baseline LCAM scores correlated with enhanced NSCLC response to immunotherapy even in patients with above median TMB, suggesting that immune cell composition, while correlated with TMB, may be a nonredundant biomarker of response to immunotherapy.

Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly driven by cell-of-origin subtypes and associated genomic lesions, but also by diverse cell types and cell states in the tumor microenvironment (TME). However, dissecting these cell states and their clinical relevance at scale remains challenging. Here, we implemented EcoTyper, a machine learning framework integrating transcriptome deconvolution and single-cell RNA sequencing, to characterize clinically relevant DLBCL cell states and ecosystems. Using this approach, we identified five cell states of malignant B cells that vary in prognostic associations and differentiation status. We also identified striking variation in cell states for 12 other lineages comprising the TME and forming cell-state interactions in stereotyped ecosystems. While cell-of-origin subtypes have distinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existing subtypes and extend beyond cell-of-origin and genotypic classes. These results resolve the DLBCL microenvironment at systems-level resolution and identify opportunities for therapeutic targeting (https://ecotyper.stanford.edu/lymphoma).

Background Small cell lung cancer (SCLC) is an aggressive neuroendocrine malignancy with dismal survival outcomes and no established predictive biomarkers. The landmark randomized, phase III IMpower133 trial established the new frontline standard of care for extensive-stage SCLC (ES-SCLC) as etoposide/platinum (EP) plus immune checkpoint blockade (ICB) [anti-PD-L1; atezolizumab (atezo)] based on an overall survival (OS) benefit compared to EP plus placebo. However, this survival benefit is limited in unselected populations, emphasizing the need for predictive biomarkers. Preclinically, there is emerging evidence of transcriptional heterogeneity among SCLC tumors, but the impact on therapeutic benefit remains undefined. Using non-negative matrix factorization (NMF) analysis of gene expression data from 81 SCLC tumors samples, we previously identified four subtypes, including three defined largely by differential expression of the transcription factors ASCL1 (SCLC-A), NEUROD1 (SCLC-N), and POU2F3 (SCLC-P), and a novel, fourth subtype with low expression of all three transcription factor signatures. Method and Results Using transcriptional and proteomic data from patient tumors and tumor-derived models, we molecularly characterized each of the four identified subtypes. The previously undescribed fourth subtype, dubbed SCLC-Inflamed (SCLC-I) showed high expression of non-neuroendocrine transcription factors (e.g. REST) and markers of EMT. Most distinctly, relative to the “cold” immune microenvironment typical of SCLC tumors, SCLC-I tumors possess markedly higher expression of interferon-γ signatures and immune checkpoints, including CD274 (PD-L1). Furthermore, cell type deconvolution using CIBERSORTx identified significantly higher infiltration into SCLC-I tumors by multiple immune cell types including T-cells, NK cells, macrophages, and dendritic cells. We predicted SCLC-I might derive disproportionate benefit from ICB due to its inflamed features. To test this, we applied our NMF-derived gene signature to 276 treatment-naïve, ES-SCLC patient tumors from the IMpower133 trial to assign patient subtype. The distribution of subtypes was as follows: SCLC-A 51%, SCLC-N 23%, SCLC-I 18% and SCLC-P 7%. While there was a trend toward OS benefit with the addition of atezo in each subtype, the benefit was numerically greater in SCLC-I. Specifically, median OS (atezo vs placebo arm) in months (mo) was 18.2 mo vs 10.4 mo for SCLC-I tumors, while median OS for the other three subtypes ranged from 9.6-10.9 mo (atezo arm) and 6.0-10.6 mo (placebo arm). Conclusion Unbiased transcriptional analyses identify four subtypes with distinct tumor and immune features. While all subtypes experienced improved OS with addition of anti-PD-L1 to frontline EP, SCLC-I patients appear to experience the most durable benefit. Citation Format: Carl M. Gay, C. Allison Stewart, Lixia Diao, Barzin Y. Nabet, Junya Fujimoto, Luisa M. Solis, Wei Lu, Yuanxin Xi, Robert J. Cardnell, Natalie I. Vokes, Kavya Ramkumar, Stephen G. Swisher, Jack A. Roth, Bonnie S. Glisson, David S. Shames, Ignacio I. Wistuba, Jing Wang, John Minna, John V. Heymach, Lauren A. Byers. A novel, inflamed small cell lung cancer transcriptional subtype, SCLC-I, defines a subset of patients with distinct immunotherapy vulnerability [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 22.

Exosomes are small extracellular vesicles that contain genetic material, proteins, and lipids. They function as potent signaling molecules between cancer cells and the surrounding cells that comprise the tumor microenvironment (TME). Exosomes derived from both tumor and stromal cells have been implicated in all stages of cancer progression and play an important role in therapy resistance. Moreover, due to their nature as mediators of cell-cell communication, they are integral to TME-dependent therapy resistance. In this review, we discuss current exosome isolation and profiling techniques and their role in TME interactions and therapy resistance. We also explore emerging clinical applications of both exosomes as biomarkers, direct therapeutic targets, and engineered nanocarriers. In order to fully understand the TME, careful interrogation of exosomes and their cargo is critical. This understanding is a promising avenue for the development of effective clinical applications.

8533 Background: Lung cancer is the leading cause of cancer death world-wide, and chemotherapy and radiation remain backbones of therapy for patients with locoregionally advanced and metastatic disease. However, the genetic mechanisms that mediate resistance to chemotherapy and radiation are largely unclear due to a lack of available tissue at the time of relapse. We hypothesized that circulating tumor DNA (ctDNA) analysis could identify emergent mutations after chemotherapy and radiation that may lead to treatment resistance. Methods: To identify emergent mutations at the time of progression following an initial response to chemotherapy and/or radiation therapy for lung cancer, we utilized CAncer Personalized Profiling by deep Sequencing (CAPP-Seq) to analyze plasma samples and matched leukocytes collected pre-treatment and at the time of relapse. We analyzed a targeted panel enriched for lung cancer drivers and recurrently mutated genes for 27 patients treated with chemoradiation therapy for locoregionally advanced lung cancer. In addition, we performed ultra-deep whole exome sequencing ( > 2000X deduped depth) of pre-treatment and relapse cell-free DNA for 5 patients treated with combination chemotherapy for metastatic lung cancer. Functional enrichment analysis was performed on emergent mutation gene lists to identify significantly enriched pathways. Results: We identified emergent variants in 6 out of 27 patients using targeted sequencing after chemoradiation therapy. Emergent mutations after chemoradiation were enriched for plasma membrane adhesion molecules such as PCDH17, PCDH10, and FAT3 (adjusted P = 0.03). Using ultra-deep whole exome sequencing, we observed emergent mutations in 3 out of 5 patients treated with combination chemotherapy. After combination chemotherapy, there was a trend towards enrichment in mutations in ATP-binding cassette transporters, including ABCA13 and ABCB4 (adjusted P = 0.057). Notably, there were no recurrent emergent mutations within our cohort. Conclusions: Our results suggest that ultra-deep whole exome sequencing can non-invasively identify emergent mutations at the time of progression. Resistance to cytotoxic therapy is likely multi-factorial and analysis in expanded cohorts will be helpful to identify recurrently mutated pathways that may contribute to disease progression after an initial response to therapy.

Background: We previously reported that approximately half of local recurrences (LR) after radiotherapy for localized NSCLC harbor mutations in KEAP1 or NFE2L2. Here we sought to explore factors associated with LR after radiotherapy in KEAP1/NFE2L2 wildtype NSCLC. Methods: We identified consecutive patients with stage IA1-IIIC NSCLC treated at our institution with chemoradiotherapy (CRT) or stereotactic ablative radiotherapy (SABR) from 2009-2018 and who had genotyping performed on tumor tissue with full coverage of common recurrent lung cancer driver genes including TP53, KRAS, KEAP1, and NFE2L2. We defined LR as tumor regrowth within the prescription dose radiotherapy volume. Our primary objective was to identify factors associated with LR in KEAP1/NFE2L2WT tumors. We performed RNA-seq on a subset of cases with available tissue using the SMARTer Stranded total RNA-seq Kit v2 (Takara Bio USA, Inc.). Statistical analysis was performed using R (version 3.6) with differential gene expression performed using ‘DESeq2’. All P-values were two-sided and considered significant at P Citation Format: Michael S. Binkley, Young-Jun Jeon, Monica Nesselbush, Everett J. Moding, Barzin Nabet, Diego Almanza, Christopher Yoo, David M. Kurtz, Susie Grant Owen, Leah M. Backhus, Mark F. Berry, Joseph B. Shrager, Kavitha J. Ramchandran, Sukhmani K. Padda, Millie Das, Joel W. Neal, Heather A. Wakelee, Ash A. Alizadeh, Billy W. Loo, Maximilian Diehn. Investigating gene expression profiles associated with clinical radiation resistance in KEAP1/NFE2L2 wildtype lung cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Radiation Science and Medicine; 2021 Mar 2-3. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(8_Suppl):Abstract nr PO-059.

Summary To identify disease-relevant T cell receptors (TCRs) with shared antigen specificity, we analyzed 778,938 TCRβ chain sequences from 178 non-small cell lung cancer patients using the GLIPH2 (grouping of lymphocyte interactions with paratope hotspots 2) algorithm. We identified over 66,000 shared specificity groups, of which 435 were clonally expanded and enriched in tumors compared to adjacent lung. The antigenic epitopes of one such tumor-enriched specificity group were identified using a yeast peptide-HLA A∗02:01 display library. These included a peptide from the epithelial protein TMEM161A, which is overexpressed in tumors and cross-reactive epitopes from Epstein-Barr virus and E. coli. Our findings suggest that this cross-reactivity may underlie the presence of virus-specific T cells in tumor infiltrates and that pathogen cross-reactivity may be a feature of multiple cancers. The approach and analytical pipelines generated in this work, as well as the specificity groups defined here, present a resource for understanding the T cell response in cancer., Graphical abstract, Highlights • The algorithm GLIPH2 enables analysis of shared TCR specificity and HLA prediction • Tumor-infiltrating T cells cross-react to EBV antigens and shared tumor antigens • EBV-specific T cells expanded in patients responding to immune checkpoint blockade • Cross-reactive CD8 T cells express GZMK, Chiou, Tseng, et al. analyze TCRβ chain sequences from 178 non-small cell lung cancer patients and identify shared specificity groups, which in turn enable antigen identification. One such antigenic epitope—a peptide from an epithelial protein—is cross-reactive to epitopes from Epstein-Barr virus and E. coli, suggesting that cross-reactivity may underlie the presence of pathogen-specific T cells in tumor infiltrates.

Summary Although treatment of non-small cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICIs) can produce remarkably durable responses, most patients develop early disease progression. Furthermore, initial response assessment by conventional imaging is often unable to identify which patients will achieve durable clinical benefit (DCB). Here, we demonstrate that pre-treatment circulating tumor DNA (ctDNA) and peripheral CD8 T cell levels are independently associated with DCB. We further show that ctDNA dynamics after a single infusion can aid in identification of patients who will achieve DCB. Integrating these determinants, we developed and validated an entirely noninvasive multiparameter assay (DIREct-On, Durable Immunotherapy Response Estimation by immune profiling and ctDNA-On-treatment) that robustly predicts which patients will achieve DCB with higher accuracy than any individual feature. Taken together, these results demonstrate that integrated ctDNA and circulating immune cell profiling can provide accurate, noninvasive, and early forecasting of ultimate outcomes for NSCLC patients receiving ICIs.

Although treatment of non-small cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICI) can produce remarkably durable responses, most patients develop early disease progression. Furthermore, initial response assessment by conventional imaging is often unable to identify which patients will achieve durable clinical benefit (DCB). Here, we analyze 211 samples from 99 patients and demonstrate that pre-treatment circulating tumor DNA (ctDNA) and circulating immune profiles are independently associated with DCB. We further show that ctDNA dynamics after a single ICI infusion can identify the majority of patients who will achieve DCB. Integrating these determinants, we describe an entirely noninvasive multi-analyte assay (DIREct-On, Durable Immunotherapy Response Estimation by immune profiling and ctDNA- On-treatment) that robustly predicted DCB, and that was validated in two independent cohorts (AUC = 0.89-0.93, PPV = 92-100%, HR = 0.04-0.11). Taken together, these results demonstrate that integrated ctDNA and circulating immune cell profiling can provide accurate, noninvasive, and early forecasting of ultimate outcomes for NSCLC patients receiving ICI. Citation Format: Barzin Y. Nabet, Mohammad S. Esfahani, Emily G. Hamilton, Jacob J. Chabon, Everett J. Moding, Hira Rizvi, Chloe B. Steen, Aadel A. Chaudhuri, Chih Long Liu, Angela B. Hui, Henning Stehr, Linda Goljenola, Michael C. Jin, Young-Jun Jeon, Diane Tseng, Taha Merghoub, Joel W. Neal, Heather A. Wakelee, Sukhmani K. Padda, Kavitha J. Ramchandran, Millie Das, Rene F. Bonilla, Christopher Yoo, Emily L. Chen, Ryan B. Ko, Aaron M. Newman, Matthew D. Hellmann, Ash A. Alizadeh, Maximilian Diehn. A noninvasive approach for early prediction of therapeutic benefit from immune checkpoint inhibition for lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5666.

The tumor microenvironment (TME) plays critical roles in cancer development, tumor progression, and susceptibility to therapy. However, the phenotypic states and interaction patterns of its underlying cell types remain poorly understood. To address this challenge, we developed a new computational framework, EcoTyper, for large-scale dissection of cell states and cellular communities (i.e., ecosystems) from tumor genomic profiles. EcoTyper integrates single-cell RNA sequencing (scRNA-seq) with CIBERSORTx, an algorithm for bulk RNA-seq deconvolution (Newman et al., Nat Biotechnol, 2019), to identify and validate cellular states and ecosystems that reflect fundamental distinctions in TME biology. Here we applied EcoTyper to diffuse large B cell lymphoma (DLBCL), an aggressive B cell malignancy with clinically distinct molecular subtypes and several immunologically-active therapies. We sought to determine whether EcoTyper could reveal novel biological variation in the DLBCL TME linked to tumor subtypes and genotypes, therapeutic responses, and clinical outcomes. We applied our approach to define transcriptional states from 13 cell types, including malignant, immune, and stromal cells, in over 1,500 DLBCL tumors. Remarkably, nearly all cell states identified by EcoTyper were validated in independent scRNA-seq and bulk RNA-seq datasets. Moreover, many cells states reflected novel phenotypic groupings, and the majority showed strong associations with overall survival, specific mutational profiles, and tumor molecular subtypes. Additionally, by identifying DLBCL tumors with similar communities of cellular states, we defined novel cellular ecosystems, or “ecotypes”, with distinct biological characteristics and clinical outcomes. Several ecotypes showed significant enrichments in canonical or novel tumor genotypes, suggesting an evolutionary interplay between the tumor and host TME. In summary, we developed a novel computational framework to dissect the TME at scale and present the most comprehensive atlas to date of cell states and cellular communities in DLBCL. Our approach is extensible to nearly any cancer type and may lead to the development of novel diagnostics and individualized immunotherapies. Citation Format: Chloe B. Steen, Bogdan Luca, Mohammad S. Esfahani, Barzin Y. Nabet, Brian Sworder, Farshad Farshidfar, David Kurtz, Chih Long Liu, Ranjana H. Advani, Yasodha Natkunam, June H. Myklebust, Maximilian Diehn, Andrew Gentles, Ash Alizadeh, Aaron M. Newman. Landscape of tumor cell states and cellular ecosystems in diffuse large B cell lymphoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1557.

9046 Background: Circulating tumor DNA (ctDNA) molecular residual disease after curative intent therapy predicts disease progression in localized lung cancer. We hypothesized that integrating pre-CRT features and ctDNA levels during chemoradiation therapy (CRT) can predict patient outcomes earlier to enable response-adapted therapy. Methods: We identified pre-CRT features prognostic of disease progression after CRT for Stage II-III non-small cell lung cancer (NSCLC) in a historical “pre-CRT” training cohort of 109 patients. In addition, we applied CAPP-Seq ctDNA analysis pre-CRT and a median of 21 days into CRT (mid-CRT) to a “ctDNA” training cohort of 42 patients treated at MD Anderson and an independent validation cohort of 21 patients treated at Stanford. Prognostic pre-CRT features and mid-CRT ctDNA concentration were integrated using a Bayesian proportional hazards approach to generate a Continuous Individualized Risk Index (Kurtz et al. Cell 2019) for NSCLC (CIRI-NSCLC) to predict freedom from progression (FFP). Results: Adenocarcinoma histology (HR 2.6, P = 0.0005) and KEAP1 mutation (HR 2.7, P = 0.002) but not stage (P = 0.16), age (P = 0.60), or gender (P = 0.98) were significantly associated with FFP in the pre-CRT training cohort. Mid-CRT ctDNA concentration as a continuous variable was significantly associated with FFP in the ctDNA training cohort (HR 1.6, P = 0.04), and applying an optimal threshold identified in the training cohort (3.2 hGE/ml) significantly stratified FFP in the independent ctDNA validation cohort (HR 4.8, P = 0.02). CIRI-NSCLC enabled individualized real-time updating of the probability of FFP as model features became available over the course of CRT. CIRI-NSCLC outperformed individual model features in the independent validation cohort when compared by C-statistic (CIRI-NSCLC: 0.85; mid-CRT ctDNA: 0.76; histology: 0.66; KEAP1: 0.60). Across the whole cohort, patients with a greater than 66% risk of progression predicted by CIRI-NSCLC (n = 10) had an FFP of 10.0% at 12 months while patients with a less than 33% risk of progression predicted by CIRI-NSCLC (n = 22) had an FFP of 79.7% at 12 months (HR 15.0, P < 0.001). Conclusions: Our results suggest that CIRI-NSCLC can identify patients at very high and low risk of progression. Prospective evaluation will be necessary to test the potential utility of adapting treatment based on CIRI-NSCLC.

3087 Background: While there has been much attention on mutation-associated neoantigens in tumors, there is less known about non-mutated tumor antigens that are shared across individuals. Understanding tumor-infiltrating T cell recognition of shared tumor antigens is important for understanding cancer immune recognition and escape, and may reveal novel targets for therapy. Methods: We have established a novel approach for discovering shared tumor antigens in human lung cancer. This approach involves identifying candidate T cell receptor (TCR) alpha/beta pairs that are predicted to exhibit specificity for shared tumor antigens in the context of a given human leukocyte antigen (HLA). We then screen the T cell receptor for binding to yeast display libraries of peptide-HLA. The Mark Davis lab at Stanford has previously developed an algorithm that groups T cell receptors into antigen specificity groups based on shared motifs within the TCR complementarity-determining region 3 (CDR3) sequences. Leveraging a dataset of over 700K CDR3 sequences from 178 HLA-typed non-small cell lung cancer (NSCLC) patients, we have found up to 4,300 antigen specificity groups after applying stringent cutoffs. We sequenced TCR alpha/beta pairs from 15 patients with lung adenocarcinoma (n = 4,705). Results: We identified an antigen specificity group enriched in tumor compared to adjacent uninvolved lungs. Antigen screening of the T cell receptor belonging to this specificity group using an A02 yeast display libraries led to the identification of a dominant peptide after four rounds of enrichment. We functionally validated that the peptide derived from the protein TMEM161A stimulated Jurkat cells expressing the TCR alpha/beta receptor of interest. We show that full-length TMEM161A protein is processed and presented into a peptide that stimulates primary T cells expressing the TCR alpha/beta receptor. We observe that a peptide from Epstein-Barr virus (EBV) protein LMP2 also stimulated the same TCR alpha/beta receptor. We have show that TMEM161A RNA and protein are overexpressed in human lung cancer compared to adjacent uninvolved lungs. Conclusions: We have demonstrated a novel approach toward antigen discovery and identified a shared tumor antigen TMEM161A in human lung cancer.