Search

Purpose: Patients with small-cell lung cancer (SCLC) have an exceptionally poor prognosis, calling for improved real-time noninvasive biomarkers of therapeutic response. Experimental Design: We performed targeted error-correction sequencing on 171 serial plasmas and matched white blood cell (WBC) DNA from 33 patients with metastatic SCLC who received treatment with chemotherapy (n = 16) or immunotherapy-containing (n = 17) regimens. Tumor-derived sequence alterations and plasma aneuploidy were evaluated serially and combined to assess changes in total cell-free tumor load (cfTL). Longitudinal dynamic changes in cfTL were monitored to determine circulating cell-free tumor DNA (ctDNA) molecular response during therapy. Results: Combined tiered analyses of tumor-derived sequence alterations and plasma aneuploidy allowed for the assessment of ctDNA molecular response in all patients. Patients classified as molecular responders (n = 9) displayed sustained elimination of cfTL to undetectable levels. For 14 patients, we observed initial molecular responses, followed by ctDNA recrudescence. A subset of patients (n = 10) displayed a clear pattern of molecular progression, with persistence of cfTL across all time points. Molecular responses captured the therapeutic effect and long-term clinical outcomes in a more accurate and rapid manner compared with radiographic imaging. Patients with sustained molecular responses had longer overall (log-rank P = 0.0006) and progression-free (log-rank P < 0.0001) survival, with molecular responses detected on average 4 weeks earlier than imaging. Conclusions: ctDNA analyses provide a precise approach for the assessment of early on-therapy molecular responses and have important implications for the management of patients with SCLC, including the development of improved strategies for real-time tumor burden monitoring.

Anti-PD-1/PD-L1 agents have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). To expand our understanding of the molecular features underlying response to checkpoint inhibitors in NSCLC, we describe here the first joint analysis of the Stand Up To Cancer-Mark Foundation cohort, a resource of whole exome and/or RNA sequencing from 393 patients with NSCLC treated with anti-PD-(L)1 therapy, along with matched clinical response annotation. We identify a number of associations between molecular features and outcome, including (1) favorable (for example, ATM altered) and unfavorable (for example, TERT amplified) genomic subgroups, (2) a prominent association between expression of inducible components of the immunoproteasome and response and (3) a dedifferentiated tumor-intrinsic subtype with enhanced response to checkpoint blockade. Taken together, results from this cohort demonstrate the complexity of biological determinants underlying immunotherapy outcomes and reinforce the discovery potential of integrative analysis within large, well-curated, cancer-specific cohorts.

Purpose:Neoadjuvant anti–PD-1 therapy has shown promise for resectable non–small cell lung cancer (NSCLC). We reported the first phase I/II trial of neoadjuvant nivolumab in resectable NSCLC, finding it to be safe and feasible with encouraging major pathological responses (MPR). We now present 5-year clinical outcomes from this trial, representing to our knowledge, the longest follow-up data for neoadjuvant anti–PD-1 in any cancer type.Patients and Methods:Two doses of nivolumab (3 mg/kg) were administered for 4 weeks before surgery to 21 patients with Stage I–IIIA NSCLC. 5-year recurrence-free survival (RFS), overall survival (OS), and associations with MPR and PD-L1, were evaluated.Results:With a median follow-up of 63 months, 5-year RFS and OS rates were 60% and 80%, respectively. The presence of MPR and pre-treatment tumor PD-L1 positivity (TPS ≥1%) each trended toward favorable RFS; HR, 0.61 [95% confidence interval (CI), 0.15–2.44] and HR, 0.36 (95% CI, 0.07–1.85), respectively. At 5-year follow-up, 8 of 9 (89%) patients with MPR were alive and disease-free. There were no cancer-related deaths among patients with MPR. In contrast, 6/11 patients without MPR experienced tumor relapse, and 3 died.Conclusions:Five-year clinical outcomes for neoadjuvant nivolumab in resectable NSCLC compare favorably with historical outcomes. MPR and PD-L1 positivity trended toward improved RFS, though definitive conclusions are limited by cohort size.

Tumor mutation burden is an imperfect proxy of tumor foreignness and has therefore failed to consistently demonstrate clinical utility in predicting responses in the context of immunotherapy. We evaluated mutations in regions of the genome that are unlikely to undergo loss in a pan-cancer analysis across 31 tumor types (n = 9,242) and eight immunotherapy-treated cohorts of patients with non-small-cell lung cancer, melanoma, mesothelioma, and head and neck cancer (n = 524). We discovered that mutations in single-copy regions and those present in multiple copies per cell constitute a persistent tumor mutation burden (pTMB) which is linked with therapeutic response to immune checkpoint blockade. Persistent mutations were retained in the context of tumor evolution under selective pressure of immunotherapy and tumors with a high pTMB content were characterized by a more inflamed tumor microenvironment. pTMB imposes an evolutionary bottleneck that cancer cells cannot overcome and may thus drive sustained immunologic tumor control in the context of immunotherapy.

The RAS family of small GTPases represents the most commonly activated oncogenes in human cancers. To better understand the prevalence of somatic RAS mutations and the compendium of genes that are coaltered in RAS-mutant tumors, we analyzed targeted next-generation sequencing data of 607,863 mutations from 66,372 tumors in 51 cancer types in the AACR Project GENIE Registry. Bayesian hierarchical models were implemented to estimate the cancer-specific prevalence of RAS and non-RAS somatic mutations, to evaluate co-occurrence and mutual exclusivity, and to model the effects of tumor mutation burden and mutational signatures on comutation patterns. These analyses revealed differential RAS prevalence and comutations with non-RAS genes in a cancer lineage-dependent and context-dependent manner, with differences across age, sex, and ethnic groups. Allele-specific RAS co-mutational patterns included an enrichment in NTRK3 and chromatin-regulating gene mutations in KRAS G12C-mutant non–small cell lung cancer. Integrated multiomic analyses of 10,217 tumors from The Cancer Genome Atlas (TCGA) revealed distinct genotype-driven gene expression programs pointing to differential recruitment of cancer hallmarks as well as phenotypic differences and immune surveillance states in the tumor microenvironment of RAS-mutant tumors. The distinct genomic tracks discovered in RAS-mutant tumors reflected differential clinical outcomes in TCGA cohort and in an independent cohort of patients with KRAS G12C-mutant non–small cell lung cancer that received immunotherapy-containing regimens. The RAS genetic architecture points to cancer lineage–specific therapeutic vulnerabilities that can be leveraged for rationally combining RAS-mutant allele-directed therapies with targeted therapies and immunotherapy. Significance: The complex genomic landscape of RAS-mutant tumors is reflective of selection processes in a cancer lineage–specific and context-dependent manner, highlighting differential therapeutic vulnerabilities that can be clinically translated.

Introduction Anti-PD-(L)1 immune checkpoint inhibitors (ICI) improve survival in patients with advanced non-small cell lung cancer (aNSCLC). The clinical features, survival, and burden of toxicities of patients with aNSCLC alive >1 year from ICI initiation are poorly understood. Materials and Methods We defined ICI survivors as patients alive >1 year after ICI start and retrospectively reviewed demographics, treatment, and immune-related adverse events (irAEs). Long-term irAEs were defined as ongoing irAEs lasting >1 year; burden of toxicity measures were based on percentage of days a patient experienced toxicity. Using linear and logistic regression, we evaluated association between demographics and disease characteristics with burden of toxicity. Results We identified 114 ICI survivors from 317 patients with aNSCLC. Half (52%) experienced an irAE of any grade, and 23.7% developed long-term irAEs. More ICI survivors with irAES in the first year had never smoked (P = .018) or received ICIs as frontline therapy (P = .015). The burden of toxicity in the first year significantly correlated with the burden of toxicity afterward (ρ = 0.72; P < .001). No patients with progressive disease had a high burden of toxicity, and they experienced 30.6% fewer days with toxicity than those with stable disease. Increased duration of therapy was associated with higher odds of experiencing toxicity. Half of ICI survivors with irAEs were still receiving treatment for unresolved irAEs at time of death or last follow-up. Conclusion Significant proportions of ICI survivors have unresolved long-term toxicities. These data support a growing need to understand long-term toxicity to optimize management of those treated with ICIs.

Aberrant alternative splicing can generate neoantigens, which can themselves stimulate immune responses and surveillance. Previous methods for quantifying splicing-derived neoantigens are limited by independent references and potential batch effects. Here, we introduce SpliceMutr, a bioinformatics approach and pipeline for identifying splicing derived neoantigens from tumor and normal data. SpliceMutr facilitates the identification of tumor-specific antigenic splice variants, predicts MHC-binding affinity, and estimates splicing antigenicity scores per gene. By applying this tool to genomic data from The Cancer Genome Atlas (TCGA), we generate splicing-derived neoantigens and neoantigenicity scores per sample and across all cancer types and find numerous correlations between splicing antigenicity and well-established biomarkers of anti-tumor immunity. Notably, carriers of mutations within splicing machinery genes have higher splicing antigenicity, which provides support for our approach. Further analysis of splicing antigenicity in cohorts of melanoma patients treated with mono-or combined immune checkpoint inhibition suggest that the abundance of splicing antigens is reduced post-treatment from baseline in patients who progress, likely because of an immunoediting process. We also observe increased splicing antigenicity in responders to immunotherapy, which may relate to an increased capacity to mount an immune response to splicing-derived antigens. We find the splicing antigenicity to be higher in tumor samples when compared to normal, that mutations in the splicing machinery result in increased splicing antigenicity in some cancers, and higher splicing antigenicity is associated with positive response to immune checkpoint inhibitor therapies. Further, this new computational pipeline provides novel analytical capabilities for splicing antigenicity and is openly available for further immuno-oncologic analysis.

Supplementary Tables S1-S9

Longitudinal changes in cfTL were re-analyzed for each patient, considering all plasma variants independent of origin. Survival analyses revealed that CH-unfiltered ctDNA responses were not associated with either (A) overall survival-OS or (B) progression-free survival-PFS. Comparison between CH-unfiltered ctDNA responses and radiographic assessments (PR/CR vs SD/PD/MR) in these patients revealed that CH-unfiltered responses we not predictive of (C) OS at 12 and (D) 36 months, and PFS at (E) 3 and (F) 12 months, compared to best overall radiographic responses.

Purpose:Patients with small-cell lung cancer (SCLC) have an exceptionally poor prognosis, calling for improved real-time noninvasive biomarkers of therapeutic response.Experimental Design:We performed targeted error-correction sequencing on 171 serial plasmas and matched white blood cell (WBC) DNA from 33 patients with metastatic SCLC who received treatment with chemotherapy (n = 16) or immunotherapy-containing (n = 17) regimens. Tumor-derived sequence alterations and plasma aneuploidy were evaluated serially and combined to assess changes in total cell-free tumor load (cfTL). Longitudinal dynamic changes in cfTL were monitored to determine circulating cell-free tumor DNA (ctDNA) molecular response during therapy.Results:Combined tiered analyses of tumor-derived sequence alterations and plasma aneuploidy allowed for the assessment of ctDNA molecular response in all patients. Patients classified as molecular responders (n = 9) displayed sustained elimination of cfTL to undetectable levels. For 14 patients, we observed initial molecular responses, followed by ctDNA recrudescence. A subset of patients (n = 10) displayed a clear pattern of molecular progression, with persistence of cfTL across all time points. Molecular responses captured the therapeutic effect and long-term clinical outcomes in a more accurate and rapid manner compared with radiographic imaging. Patients with sustained molecular responses had longer overall (log-rank P = 0.0006) and progression-free (log-rank P < 0.0001) survival, with molecular responses detected on average 4 weeks earlier than imaging.Conclusions:ctDNA analyses provide a precise approach for the assessment of early on-therapy molecular responses and have important implications for the management of patients with SCLC, including the development of improved strategies for real-time tumor burden monitoring.

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low predictive value for actual peptide presentation, inadequate support for rare MHC alleles, and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method that predicts peptide–MHC binding. MHCnuggets can predict binding for common or rare alleles of MHC class I or II with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is faster than other methods. When compared with methods that integrate binding affinity and MHC-bound peptide (HLAp) data from mass spectrometry, MHCnuggets yields a 4-fold increase in positive predictive value on independent HLAp data. We applied MHCnuggets to 26 cancer types in The Cancer Genome Atlas, processing 26.3 million allele–peptide comparisons in under 2.3 hours, yielding 101,326 unique predicted immunogenic missense mutations (IMM). Predicted IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration (P < 2 × 10−16), including CD8+ T cells. Only 0.16% of predicted IMMs were observed in more than 2 patients, with 61.7% of these derived from driver mutations. Thus, we describe a method for neoantigen prediction and its performance characteristics and demonstrate its utility in data sets representing multiple human cancers.

Supplemental Legend for Figure S1

Supplementary Table 1-9

Figure S1

Supplementary Figure S1. Computed Tomographic (CT) findings in patient CGLU116. Supplementary Figure S2. CT findings in patient CGLU127. Supplementary Figure S3. CT findings in patient CGLU161. Supplementary Figure S4. Tumor burden kinetics. Supplementary Figure S5. Neoantigen-specific TCR expansion in stimulated T cell cultures for patient CGLU127. Supplementary Figure S6. Neoantigen-specific TCR expansion in stimulated T cell cultures for patient CGLU161. Supplementary Figure S7. Loss of heterozygosity analyses for patient CGLU116. Supplementary Figure S8. Loss of heterozygosity analyses for patient CGLU117. Supplementary Figure S9. Loss of heterozygosity analyses for patient CGLU127. Supplementary Figure S10. Loss of heterozygosity analyses for patient CGLU161. Supplementary Figure S11. TCR clonality analyses for patients CGLU127 and CGLU161. Supplementary Figure S12. TCR clonality analyses for a responder and a non-responder to PD-1 blockade. Supplementary Figure S13. PD-L1 expression in responsive and resistant tumors. Supplementary Figure S14. Eliminated mutations for case CGHN2. Supplementary Figure S15. Comparison of five methods for estimation of tumor purity. Supplementary Figure S16. CD8+ T cell density in resistant tumors.

This contains the figure and legend showing clonotypic expansions after 10 and 20 days

Mutation-associated neoantigens (MANA) are a target of antitumor T-cell immunity. Sensitive, simple, and standardized assays are needed to assess the repertoire of functional MANA-specific T cells in oncology. Assays analyzing in vitro cytokine production such as ELISpot and intracellular cytokine staining have been useful but have limited sensitivity in assessing tumor-specific T-cell responses and do not analyze antigen-specific T-cell repertoires. The FEST (Functional Expansion of Specific T cells) assay described herein integrates T-cell receptor sequencing of short-term, peptide-stimulated cultures with a bioinformatic platform to identify antigen-specific clonotypic amplifications. This assay can be adapted for all types of antigens, including MANAs via tumor exome-guided prediction of MANAs. Following in vitro identification by the MANAFEST assay, the MANA-specific CDR3 sequence can be used as a molecular barcode to detect and monitor the dynamics of these clonotypes in blood, tumor, and normal tissue of patients receiving immunotherapy. MANAFEST is compatible with high-throughput routine clinical and lab practices. Cancer Immunol Res; 6(8); 888–99. ©2018 AACR.

Supplementary Table S1. Summary of Patient and Sample Characteristics. Supplementary Table S2. Summary of Next-Generation Sequencing Analyses. Supplementary Table S3. Somatic Sequence Alterations*. Supplementary Table S4. Somatic Copy Number Alterations. Supplementary Table S5. Neoantigen Predictions. Supplementary Table S6. Characteristics of a Subset of Eliminated Candidate Neoantigens in the NSCLC patients*. Supplementary Table S7. Summary of Functionally Validated Eliminated, Gained, and Retained cMANAs. Supplementary Table S8. Eliminated MANA-specific T Cell Clonotypes in CGLU116, CGLU127 and CGLU161. Supplementary Table S9. Retained and Gained MANA-specific T Cell Clonotypes in CGLU116. Supplementary Table S10. Allelic Imbalance Analysis and Cellularity Estimates for CGLU116. Supplementary Table S11. Allelic Imbalance Analysis and Cellularity Estimates for CGLU117. Supplementary Table S12. Allelic Imbalance Analysis and Cellularity Estimates for CGLU127. Supplementary Table S13. Allelic Imbalance Analysis and Cellularity Estimates for CGLU161. Supplementary Table S14. Summary of Eliminated Neoantigens in NSCLC Cases. Supplementary Table S15. TCR-beta Sequencing Analysis. Supplementary Table S16. PD-L1 and CD8 Immunohistochemistry. Supplementary Table S17. Regions of Allelic Imbalance. Supplementary Table S18. Tumor Purity and Ploidy Estimates.

This is the FEST analysis output file for patient JH124

This is the FEST analysis output file for donor JH014

Supplementary Figure Legends

Supplementary Tables containing information pertaining to the viral epitopes evaluated, expanded clonotypes, clonotypes detected in pentamer-sorted populations, and the MANAs evaluated in patient JH124

Purpose:Neoadjuvant anti–PD-1 therapy has shown promise for resectable non–small cell lung cancer (NSCLC). We reported the first phase I/II trial of neoadjuvant nivolumab in resectable NSCLC, finding it to be safe and feasible with encouraging major pathological responses (MPR). We now present 5-year clinical outcomes from this trial, representing to our knowledge, the longest follow-up data for neoadjuvant anti–PD-1 in any cancer type.Patients and Methods:Two doses of nivolumab (3 mg/kg) were administered for 4 weeks before surgery to 21 patients with Stage I–IIIA NSCLC. 5-year recurrence-free survival (RFS), overall survival (OS), and associations with MPR and PD-L1, were evaluated.Results:With a median follow-up of 63 months, 5-year RFS and OS rates were 60% and 80%, respectively. The presence of MPR and pre-treatment tumor PD-L1 positivity (TPS ≥1%) each trended toward favorable RFS; HR, 0.61 [95% confidence interval (CI), 0.15–2.44] and HR, 0.36 (95% CI, 0.07–1.85), respectively. At 5-year follow-up, 8 of 9 (89%) patients with MPR were alive and disease-free. There were no cancer-related deaths among patients with MPR. In contrast, 6/11 patients without MPR experienced tumor relapse, and 3 died.Conclusions:Five-year clinical outcomes for neoadjuvant nivolumab in resectable NSCLC compare favorably with historical outcomes. MPR and PD-L1 positivity trended toward improved RFS, though definitive conclusions are limited by cohort size.

Recurrence per PDL1 and MPR

Despite the initial successes of immunotherapy, there is an urgent clinical need for molecular assays that identify patients more likely to respond. Here, we report that ultrasensitive measures of circulating tumor DNA (ctDNA) and T-cell expansion can be used to assess responses to immune checkpoint blockade in metastatic lung cancer patients (N = 24). Patients with clinical response to therapy had a complete reduction in ctDNA levels after initiation of therapy, whereas nonresponders had no significant changes or an increase in ctDNA levels. Patients with initial response followed by acquired resistance to therapy had an initial drop followed by recrudescence in ctDNA levels. Patients without a molecular response had shorter progression-free and overall survival compared with molecular responders [5.2 vs. 14.5 and 8.4 vs. 18.7 months; HR 5.36; 95% confidence interval (CI), 1.57–18.35; P = 0.007 and HR 6.91; 95% CI, 1.37–34.97; P = 0.02, respectively], which was detected on average 8.7 weeks earlier and was more predictive of clinical benefit than CT imaging. Expansion of T cells, measured through increases of T-cell receptor productive frequencies, mirrored ctDNA reduction in response to therapy. We validated this approach in an independent cohort of patients with early-stage non–small cell lung cancer (N = 14), where the therapeutic effect was measured by pathologic assessment of residual tumor after anti-PD1 therapy. Consistent with our initial findings, early ctDNA dynamics predicted pathologic response to immune checkpoint blockade. These analyses provide an approach for rapid determination of therapeutic outcomes for patients treated with immune checkpoint inhibitors and have important implications for the development of personalized immune targeted strategies.Significance: Rapid and sensitive detection of circulating tumor DNA dynamic changes and T-cell expansion can be used to guide immune targeted therapy for patients with lung cancer.See related commentary by Zou and Meyerson, p. 1038

Correlative assays performed on each sample

Purpose:Given the pace of predictive biomarker and targeted therapy development, it is unknown whether repeat annotation of the same next-generation sequencing data can identify additional clinically actionable targets that could be therapeutically leveraged. In this study, we sought to determine the predictive yield of serial reanalysis of clinical tumor sequencing data.Experimental Design:Using artificial intelligence (AI)-assisted variant annotation, we retrospectively reanalyzed sequencing data from 2,219 patients with cancer from a single academic medical center at 3-month intervals totaling 9 months in 2020. The yield of serial reanalysis was assessed by the proportion of patients with improved strength of therapeutic recommendations.Results:A total of 1,775 patients (80%) had ≥1 potentially clinically actionable mutation at baseline, including 243 (11%) patients who had an alteration targeted by an FDA-approved drug for their cancer type. By month 9, the latter increased to 458 (21%) patients mainly due to a single pan-cancer agent directed against tumors with high tumor mutation burden. Within this timeframe, 67 new therapies became available and 45 were no longer available. Variant pathogenicity classifications also changed leading to changes in treatment recommendations for 124 patients (6%).Conclusions:Serial reannotation of tumor sequencing data improved the strength of treatment recommendations (based on level of evidence) in a mixed cancer cohort and showed substantial changes in available therapies and variant classifications. These results suggest a role for repeat analysis of tumor sequencing data in clinical practice, which can be streamlined with AI support.

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; 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

Figures S1-S8

Supplementary Table S1. Summary of patient characteristics Supplementary Table S2. Summary of plasma samples analyzed Supplementary Table S3. Summary of peripheral blood lymphocytes and tumor infiltrating lymphocytes samples analyzed Supplementary Table S4. Genes analyzed by TEC-Seq Supplementary Table S5. Summary of TEC-Seq Characteristics Supplementary Table S6. Somatic sequence alterations detected in cfDNA Supplementary Table S7. Mutation Cellularity Analysis for tumor-specific cfDNA variants Supplementary Table S8. TCR-beta sequencing analysis Supplementary Table S9. Differentially abundant clones for CGLU111 Supplementary Table S10. Differentially abundant clones for CGLU117 Supplementary Table S11. Differentially abundant clones for CGLU127 Supplementary Table S12. Differentially abundant clones for CGLU212 Supplementary Table S13. Differentially abundant clones for CGLU135 Supplementary Table S14. Differentially abundant clones for CGLU161 Supplementary Table S15. Differentially abundant clones for CGLU159 Supplementary Table S16. Differentially abundant clones for CGLU162 Supplementary Table S17. Differentially abundant clones for CGLU203 Supplementary Table S18. Differentially abundant clones for CGLU243