Abstract 5137: Cancer testis antigen burden: Pan-cancer distribution and survival implications

Purpose of Study: Cancer testis antigens (CTA) are highly immunogenic genes with the ability to cause cancer-specific immune responses when expressed. Their tumor cell-specific expression makes them a key target of natural T cell response, cancer vaccines, immune checkpoint blockade (ICB), and cell-based immunotherapies in a wide range of tumor types. In this study, we assess the pan-cancer distribution and ICB survival association of CTA burden (CTAB) in real-world solid tumors.

Procedure: Three tumor sample cohorts were studied: 1) a pan-cancer discovery cohort to develop a low- and high-CTAB cutoff (n=5450, 39 tumor types), 2) a TCGA cohort (n=19923, 32 tumor types) used to validate the classifier based on CTAB distribution and serve as a non-ICB-treated population, and 3) an ICB-treated retrospective cohort to validate the classification on overall survival (OS) (n=242, 3 tumor types). The expression levels of 17 CTA were measured using targeted RNA-Seq of FFPE tumor samples and then ranked against a pan-cancer reference population. CTAB was calculated for each sample, cohort and tumor type as the sum of the 17 CTA gene expression ranks. The discovery cohort median CTAB of 171 was used to classify all three cohorts into high- and low-CTAB groups. OS analysis was performed on the TCGA and ICB-treated cohorts using a CoxPH regression model to determine the Hazard Ratio (HR).

Results: The three cohorts demonstrated overlapping single-peak, left-skewed CTAB distribution curves centered at CTAB values between 170 (discovery cohort) and 256 (retrospective cohort). When grouping by tumor types and ordering by median CTAB, the CTAB distributions for tumor types within all three cohorts were comparable. CoxPH regression analysis revealed an association between the CTAB threshold classifier and OS in both the ICB-treated retrospective and non-ICB TCGA cohorts. However, the direction of this association differed between the two cohorts, with high-CTAB samples having better survival (HR=0.936, p=0.076) in the ICB-treated retrospective cohort and worse survival (HR: 1.007, p=0.084) in the non-ICB-treated cohort.

Conclusion: Our studies show that the CTAB distribution was maintained across the discovery and TCGA cohorts and a wide range of tumor types, supporting that the CTAB classifier is valid and histology agnostic. Additionally, when evaluating the ICB and non-ICB-treated cohorts, CTAB demonstrated the ability to predict OS, pointing to the utility of ICB in supporting CTA-specific natural immune response. However, further studies are necessary to verify these mechanisms of response to ICB as well as cancer vaccines and cell-based immunotherapies. Additional validation is needed to establish the predictive utility of CTAB alone and in combination with other immune oncology biomarkers for resistance or response.

Citation Format: R.J. Seager, Erik Van Roey, Shuang Gao, Blake Burgher, Paul DePietro, Mary Nesline, Roger Klein, Shengle Zhang, Jeffrey M. Conroy, Sarabjot Pabla. Cancer testis antigen burden: Pan-cancer distribution and survival implications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5137.

A scalable high-throughput targeted next-generation sequencing assay for comprehensive genomic profiling of solid tumors

Timely and accurate identification of molecular alterations in solid tumors is essential for proper management of patients with advanced cancers. This has created a need for rapid, scalable comprehensive genomic profiling (CGP) systems that detect an increasing number of therapeutically-relevant variant types and molecular signatures. In this study, we assessed the analytical performance of the TruSight Oncology 500 High-Throughput assay for detection of somatic alterations from formalin-fixed paraffin-embedded tissue specimens. In parallel, we developed supporting software and automated sample preparation systems designed to process up to 70 clinical samples in a single NovaSeq 6000TM sequencing run with a turnaround time of <7 days from specimen receipt to report. The results demonstrate that the scalable assay accurately and reproducibly detects small variants, copy number alterations, microsatellite instability (MSI) and tumor mutational burden (TMB) from 40ng DNA, and multiple gene fusions, including known and unknown partners and splice variants from 20ng RNA. 717 tumor samples and reference materials with previously known alterations in 96 cancer-related genes were sequenced to evaluate assay performance. All variant classes were reliably detected at consistent and reportable variant allele percentages with >99% overall accuracy and precision. Our results demonstrate that the high-throughput CGP assay is a reliable method for accurate detection of molecular alterations in support of precision therapeutics in oncology. The supporting systems and scalable workflow allow for efficient interpretation and prompt reporting of hundreds of patient cancer genomes per week with excellent analytical performance.

80 Cancer testis antigen burden: A novel predictive biomarker for immunotherapy in solid tumors

Background

When expressed in cancer cells, cancer testis antigens (CTAs) are highly immunogenic and have the capacity to elicit cancer-specific immune responses in diverse malignancies. With their expression limited to tumor cells, CTAs have become a prime target of natural T cell response, immune cell-based therapy, and cancer vaccines. In this study, we investigated CTA burden in real-world clinical tumors spanning multiple histologies, revealing a novel prognostic gene expression-based biomarker.

Methods

Targeted RNA-seq was performed on 5450 FFPE tumors representing 39 histologic types, predominantly composed of lung cancer (40.4%) followed by colorectal cancer (10.6%) and breast cancer (8.6%). Using an amplicon-based NGS approach, expression levels of 17 CTA genes were ranked against a reference population. Cancer Testis Antigen Burden (CTAB) was calculated as the sum of the gene expression rank for each CTA gene. The median CTAB of ≥171 was used as cutoff for CTAB High versus Low classification. We estimated Pearson’s correlation for all CTA genes to discover co-expression patterns between CTAs and histologies. Overall survival (OS) analysis was performed using CoxPh regression model whereas response analysis was performed using logistic regression model with p-values reported.

Results

Within the tumor samples, CTAB values ranged from 0–1700, with kidney cancer demonstrating overall lowest mean CTAB (110) and melanoma the highest (550). NSCLC had an average CTAB of 283. In an immune checkpoint blockade treated retrospective cohort of 110 NSCLC patients, High CTAB showed better OS compared to Low CTA (HR: 0.55, p=0.07). Additionally, when combined with tumor inflammation and cell proliferation biomarkers, highly inflamed but poorly proliferative tumors with High CTAB had improved OS (HR: 0.27, p=0.05). No significant association with response was detected

Conclusions

Our studies show that co-expression of multiple CTA genes occurs in many tumor types and can be reliably detected using a targeted RNA-seq approach. Utilization of this co-expression pattern to calculate CTAB reveals tumor-type associated signatures, which in a small NSCLC cohort is associated with the overall survival. The findings suggest that these immunogenic antigens expose the tumor cells to natural or immunotherapy augmented cell-based immune response, and that CTAB is a potential predictive marker for therapeutic response to checkpoint inhibitors. Further studies are needed to establish the predictive value in other tumor types, as well as the role of CTAB in immune cell therapies and vaccinations.

77 Prevalence of secondary immunotherapeutic targets in the absence of established immune biomarkers in solid tumors

Background

Immune checkpoint inhibitor-based therapies have achieved impressive success in the treatment of several cancer types. Predictive immune biomarkers, including PD-L1, MSI and TMB are well established as surrogate markers for immune evasion and tumor-specific neoantigens across many tumors. Positive detection across cancer types varies, but overall ~50% of patients test negative for these primary immune markers.1 In this study, we investigated the prevalence of secondary immune biomarkers outside of PD-L1, TMB and MSI.

Methods

Comprehensive genomic and immune profiling, including PD-L1 IHC, TMB, MSI and gene expression of 395 immune related genes was performed on 6078 FFPE tumors representing 34 cancer types, predominantly composed of lung cancer (36.7%), colorectal cancer (11.9%) and breast cancer (8.5%). Expression levels by RNA-seq of 36 genes targeted by immunotherapies in solid tumor clinical trials, identified as secondary immune biomarkers, were ranked against a reference population. Genes with a rank value ≥75th percentile were considered high and values were associated with PD-L1 (positive ≥1%), MSI (MSI-H or MSS) and TMB (high ≥10 Mut/Mb) status. Additionally, secondary immune biomarker status was segmented by tumor type and cancer immune cycle roles.

Results

In total, 41.0% of cases were PD-L1+, 6.4% TMB+, and 0.1% MSI-H. 12.6% of cases were positive for >2 of these markers while 39.9% were triple negative (PD-L1-/TMB-/MSS). Of the PD-L1-/TMB-/MSS cases, 89.1% were high for at least one secondary immune biomarker, with 69.3% having ≥3 markers. PD-L1-/TMB-/MSS tumor types with ≥50% prevalence of high secondary immune biomarkers included brain, prostate, kidney, sarcoma, gallbladder, breast, colorectal, and liver cancer. High expression of cancer testis antigen secondary immune biomarkers (e.g., NY-ESO-1, LAGE-1A, MAGE-A4) was most commonly observed in bladder, ovarian, sarcoma, liver, and prostate cancer (≥15%). Tumors demonstrating T-cell priming (e.g., CD40, OX40, CD137), trafficking (e.g., TGFB1, TLR9, TNF) and/or recognition (e.g., CTLA4, LAG3, TIGIT) secondary immune biomarkers were most represented by kidney, gallbladder, and sarcoma (≥40%), with melanoma, esophageal, head & neck, cervical, stomach, and lung cancer least represented (≥15%).

Conclusions

Our studies show comprehensive tumor profiling that includes gene expression can detect secondary immune biomarkers targeted by investigational therapies in ~90% of PD-L1-/TMB-/MSS cases. While genomic profiling could also provide therapeutic choices for a percentage of these patients, detection of secondary immune biomarkers by RNA-seq provides additional options for patients without a clear therapeutic path as determined by PD-L1 testing and genomic profiling alone.

Reference

Huang R S P, Haberberger J, Severson E, et al. A pan-cancer analysis of PD-L1 immunohistochemistry and gene amplification, tumor mutation burden and microsatellite instability in 48,782 cases. Mod Pathol 2021;34: 252–263.

70 Novel immunotherapeutic targets in cancer of unknown primary (CUP)

Background

Cancer of unknown primary (CUP) is a rare tumor type accounting for 2% of solid cancers. In the subset of CUP cases where tumor of origin is posited and treated as such, no clear clinical benefit has been demonstrated. Furthermore, CUP patients treated by empiric platinum-based regimens have low response and survival rates of approximately 20%.1 2 Support of tissue-agnostic marker-directed immunotherapy is growing because it targets the immune system rather than the tumor, with some efficacy evidence emerging for CUP.3 Identifying new targets for immunotherapeutic opportunities in this heterogeneous and difficult to treat patient group is a critical unmet need.

Methods

Comprehensive genomic and immune marker profiling by NGS4 was performed on FFPE tissue for CUP tumors (n=298) as indicated by physicians’ test orders from >100 clinical practice sites. Histology was verified by a molecular pathologist as part of pre-analytic test quality control, with cases representing tumors with adenocarcinoma (58%), carcinoma (26%), squamous (10%), and neuroendocrine (6%) histologic features. RNA-expression levels of immune genes that are current targets in non-CUP immunotherapy clinical trials (n=36) were ranked against a reference population (≥75th percentile=high), and described by histologic type, along with PD-L1 IHC (22C3) expression, tumor mutational burden (TMB) and genomic variants.

Results

90% of all CUP tumors had at least 1 highly expressed immune gene target in active immunotherapy trials, with the most frequent being TGFB1 (47%) and CCL2 (39%). 55% of CUP tumors were PD-L1 IHC 22C3 positive (>=1% TPS), and 21% had high TMB (>=10 mut/Mb) in CUP tumors with neuroendocrine (32%), carcinoma (30%), squamous cell (21%), and adenocarcinoma (17%) histologic features. Overall, 26% of CUP patient tumors, mostly adenocarcinomas (28%) and carcinomas (27%), harbored genomic variants (n=77) with FDA approved targeted therapies in other tumor types. The most frequently immunogenic CUP tumors were carcinomas, showing high RNA-seq expression of 26/36 genes in at least 20% of patients, most represented by CD20, CD27, TLR8, and PD-L1. High expression of CD40, CSF1R, TIM3, and VISTA was most common in adenocarcinomas. Squamous cell carcinomas were relatively immunogenic, with frequent high expression of 17/36 immune genes, uniquely including MAGEA4. Neuroendocrine tumors were the least immunogenic, with frequent high expression in only 4/36 genes, including ADORA2A (42%) and MAGEA1 (37%).

Conclusions

CUP tumors diversely express both standard marker and novel immunotherapeutic targets based on histology and may benefit from selective access to clinical trials for these therapies.

References

National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) Occult Primary (Cancer of Unknown Primary [CUP]), Version 2.2021. Fort Washington, Pennsylvania: National Comprehensive Cancer Network; 2021. https://www.nccn.org/professionals/physician_gls/pdf/occult.pdf.

Laprovitera N, Riefolo M, Ambrosini E, Klec C, Pichler M, Ferracin M. Cancer of unknown primary: Challenges and progress in clinical management. Cancers (Basel) 2021;13(3):1–30. doi:10.3390/cancers130304513.

Naing A, Meric-Bernstam F, Stephen B, et al. Phase 2 study of pembrolizumab in patients with advanced rare cancers. J Immunother Cancer 2020;8(1):e000347. doi:10.1136/jitc-2019-0003474.

Conroy JM, Pabla S, Glenn ST, et al. Analytical validation of a next-generation sequencing assay to monitor immune responses in solid tumors. J Mol Diagnostics 2018;20(1):95–109. doi:10.1016/j.jmoldx.2017.10.001

Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology

Circulating tumor DNA (ctDNA) sequencing is being rapidly adopted in precision oncology, but the accuracy, sensitivity and reproducibility of ctDNA assays is poorly understood. Here we report the findings of a multi-site, cross-platform evaluation of the analytical performance of five industry-leading ctDNA assays. We evaluated each stage of the ctDNA sequencing workflow with simulations, synthetic DNA spike-in experiments and proficiency testing on standardized, cell-line-derived reference samples. Above 0.5% variant allele frequency, ctDNA mutations were detected with high sensitivity, precision and reproducibility by all five assays, whereas, below this limit, detection became unreliable and varied widely between assays, especially when input material was limited. Missed mutations (false negatives) were more common than erroneous candidates (false positives), indicating that the reliable sampling of rare ctDNA fragments is the key challenge for ctDNA assays. This comprehensive evaluation of the analytical performance of ctDNA assays serves to inform best practice guidelines and provides a resource for precision oncology.

Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response

BACKGROUND

Contemporary to the rapidly evolving landscape of cancer immunotherapy is the equally changing understanding of immune tumor microenvironments (TMEs) which is crucial to the success of these therapies. Their reliance on a robust host immune response necessitates clinical grade measurements of immune TMEs at diagnosis. In this study, we describe a stable tumor immunogenic profile describing immune TMEs in multiple tumor types with ability to predict clinical benefit from immune checkpoint inhibitors (ICIs).

METHODS

A tumor immunogenic signature (TIGS) was derived from targeted RNA-sequencing (RNA-seq) and gene expression analysis of 1323 clinical solid tumor cases spanning 35 histologies using unsupervised analysis. TIGS correlation with ICI response and survival was assessed in a retrospective cohort of NSCLC, melanoma and RCC tumor blocks, alone and combined with TMB, PD-L1 IHC and cell proliferation biomarkers.

RESULTS

Unsupervised clustering of RNA-seq profiles uncovered a 161 gene signature where T cell and B cell activation, IFNg, chemokine, cytokine and interleukin pathways are over-represented. Mean expression of these genes produced three distinct TIGS score categories: strong (n = 384/1323; 29.02%), moderate (n = 354/1323; 26.76%), and weak (n = 585/1323; 44.22%). Strong TIGS tumors presented an improved ICI response rate of 37% (30/81); with highest response rate advantage occurring in NSCLC (ORR = 36.6%; 16/44; p = 0.051). Similarly, overall survival for strong TIGS tumors trended upward (median = 25 months; p = 0.19). Integrating the TIGS score categories with neoplastic influence quantified via cell proliferation showed highly proliferative and strong TIGS tumors correlate with significantly higher ICI ORR than poorly proliferative and weak TIGS tumors [14.28%; p = 0.0006]. Importantly, we noted that strong TIGS and highly [median = not achieved; p = 0.025] or moderately [median = 16.2 months; p = 0.025] proliferative tumors had significantly better survival compared to weak TIGS, highly proliferative tumors [median = 7.03 months]. Importantly, TIGS discriminates subpopulations of potential ICI responders that were considered negative for response by TMB and PD-L1.

CONCLUSIONS

TIGS is a comprehensive and informative measurement of immune TME that effectively characterizes host immune response to ICIs in multiple tumors. The results indicate that when combined with PD-L1, TMB and cell proliferation, TIGS provides greater context of both immune and neoplastic influences on the TME for implementation into clinical practice.

Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions

BACKGROUND

Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing.

RESULTS

All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden.

CONCLUSION

This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.

Immune profiling and immunotherapeutic targets in pancreatic cancer

Background

Immunotherapeutic approaches for pancreatic ductal adenocarcinoma (PDAC) are less successful as compared to many other tumor types. In this study, comprehensive immune profiling was performed in order to identify novel, potentially actionable targets for immunotherapy.

Methods

Formalin-fixed paraffin embedded (FFPE) specimens from 68 patients were evaluated for expression of 395 immune-related markers (RNA-seq), mutational burden by complete exon sequencing of 409 genes, PD-L1 expression by immunohistochemistry (IHC), pattern of tumor infiltrating lymphocytes (TILs) infiltration by CD8 IHC, and PD-L1/L2 copy number by fluorescent in situ hybridization (FISH).

Results

The seven classes of actionable genes capturing myeloid immunosuppression, metabolic immunosuppression, alternative checkpoint blockade, CTLA-4 immune checkpoint, immune infiltrate, and programmed cell death 1 (PD-1) axis immune checkpoint, discerned 5 unique clinically relevant immunosuppression expression profiles (from most to least common): (I) combined myeloid and metabolic immunosuppression [affecting 25 of 68 patients (36.8%)], (II) multiple immunosuppressive mechanisms (29.4%), (III) PD-L1 positive (20.6%), (IV) highly inflamed PD-L1 negative (10.3%); and (V) immune desert (2.9%). The Wilcoxon rank-sum test was used to compare the PDAC cohort with a comparison cohort (n=1,416 patients) for the mean expressions of the 409 genes evaluated. Multiple genes including TIM3, VISTA, CCL2, CCR2, TGFB1, CD73, and CD39 had significantly higher mean expression versus the comparison cohort, while three genes (LAG3, GITR, CD38) had significantly lower mean expression.

Conclusions

This study demonstrates that a clinically relevant unique profile of immune markers can be identified in PDAC and be used as a roadmap for personalized immunotherapeutic decision-making strategies.

Proliferative potential and response to nivolumab in clear cell renal cell carcinoma patients

Background

Biomarkers predicting immunotherapy response in metastatic renal cell cancer (mRCC) are lacking. PD-L1 immunohistochemistry is a complementary diagnostic for immune checkpoint inhibitors (ICIs) in mRCC, but has shown minimal clinical utility and is not used in routine clinical practice.

Methods

Tumor specimens from 56 patients with mRCC who received nivolumab were evaluated for PD-L1, cell proliferation (targeted RNA-seq), and outcome.

Results

For 56 patients treated with nivolumab as a standard of care, there were 2 complete responses and 8 partial responses for a response rate of 17.9%. Dividing cell proliferation into tertiles, derived from the mean expression of 10 proliferation-associated genes in a reference set of tumors, poorly proliferative tumors (62.5%) were more common than moderately (30.4%) or highly proliferative (8.9%) counterparts. Moderately proliferative tumors were enriched for PD-L1 positive (41.2%), compared to poorly proliferative counterparts (11.4%). Objective response for moderately proliferative (29.4%) tumors was higher than that of poorly (11.4%) proliferative counterparts, but not statistically significant (p = .11). When cell proliferation and negative PD-L1 tumor proportion scores were combined statistically significant results were achieved (p = .048), showing that patients with poorly proliferative and PD-L1 negative tumors have a very low response rate (6.5%) compared to moderately proliferative PD-L1 negative tumors (30%).

Conclusions

Cell proliferation has value in predicting response to nivolumab in clear cell mRCC patients, especially when combined with PD-L1 expression. Further studies which include the addition of progression-free survival (PFS) along with sufficiently powered subgroups are required to further support these findings.

Tumor inflammatory signature as a biomarker of response to immunotherapy in lung cancer

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Background: Tumor Inflammation signatures (TIS) comprising multiple immune genes have been shown to enrich for response to ICI. To study this immune phenotype in a large cohort of clinically evaluated patients, we studied gene expression data for a stable pan-cancer tumor inflammation profile and clinical response to ICI. Methods: 1323 FFPE tumors from 35 histologies were tested by RNA-seq, PD-L1 IHC and DNA-seq for TMB. Unsupervised analysis of the RNA-seq data revealed a cluster of 160 genes which separated inflamed from non-inflamed tumor microenvironments (TME). A TIS, algorithmically defined as the mean mRNA expression of the 160 genes was developed with each tumor assigned into a weak, moderate or strong inflammation group. PD-L1 IHC was performed using DAKO 22C3 antibody and considered positive if TPS ≥1%. TMB > 10 mut/Mb was considered high. The TIS, PD-L1 and TMB were independently applied to 110 NSCLC cases for association with ORR to ICIs by RECIST criterion. Results: Unsupervised clustering identified 3 inflammation clusters in the 1323 samples; inflamed (n = 439; 33.2%), borderline (n = 467; 35.3%) and non-inflamed (n = 417; 31.5%). 160 genes are over-represented by T & B-cell activation, IFNg, chemokine, cytokine and interleukin pathways. The TIS algorithm results in an inflammatory score that leads to 3 distinct groups of strong (n = 384; 29.0%), moderate (n = 354; 26.8%) and weak (n = 585; 44.2%) inflammation. Strongly inflamed tumors are over-represented by PD-L1+ tumors (240/384) whereas weakly inflamed tumors are significantly under-represented by PD-L1+ tumors (369/585; p = 1.02e-14). Strongly inflamed tumors presented with improved ORR to ICI in NSCLC (36.6%; 16/44; p = 0.051). Similar results were observed for overall survival for strongly inflamed tumors (median = 16 months; p = 0.0012) vs. weakly inflamed tumors (median = 8 months). ORR for PD-L1+ 33.96% (p = 0.026) and TMB high 21.43% (p = 0.83) were observed. Conclusions: Concurrent measurement of multiple markers led to a comprehensive, stable TIS that predicts host immune response. A strongly inflamed TIS was associated with higher ORR versus single biomarker PD-L1 and TMB in NSCLC.

Inflammation and cell proliferation signatures: Implications for response to immune checkpoint inhibition therapies

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Background: Progress in unraveling the complex tumor immune microenvironment (TIME) has aided our understanding of the host immune response to ICI therapies. To gain further insight, we associate a 160-gene tumor inflammation signature (TIS) with cell proliferation status and response to ICI. Methods: 242 FFPE tumor samples from lung, RCC and melanoma were evaluated by RNA-seq to measure expression levels of 394 immune related genes. TIS (weak, moderate, strong) and cell proliferation status (poor, moderate, high) were determined by algorithmic analysis of selected gene pathways. All cases were evaluated for association with ORR to ICIs using RECIST criterion. Furthermore, 13 pre-post ICI treated biopsy pairs were studied to understand the dynamics of these signatures following treatment. Results: In both weakly and moderately inflamed tumors, ORR was highest in moderately proliferative tumors 32.6% (15/46) and 37.8% (14/37), respectively (Table). Surprisingly, in strongly inflamed tumors, both highly and moderately proliferative tumors had a high response rate of 55% (11/20) and 43.2% (16/37), whereas poorly proliferative tumors have a significantly lower response rate of 12.5% (3/24; p= 0.03). 6 of 13 pre-post ICI treated cases demonstrated increased inflammation post treatment, with 83% (5/6) demonstrating concurrent decrease in cell proliferation. Conclusions: Together, TIS and cell proliferation predict response to ICI in NSCLC, RCC and melanoma. The data suggests that in strongly inflamed and highly proliferative tumors, the cell proliferation signal could be attributed to antigen stimulated T-cell proliferation, whereas in other categories of inflammation, moderately proliferative tumors contain signal from both immune cells and tumor cells. Further studies are required to determine the relationship between these signatures in hot and cold tumors. [Table: see text]

Development and analytical validation of a next-generation sequencing based microsatellite instability (MSI) assay

Background

We have developed and analytically validated a next-generation sequencing (NGS) assay to classify microsatellite instability (MSI) in formalin-fixed paraffin-embedded (FFPE) tumor specimens.

Methodology

The assay relies on DNA-seq evaluation of insertion/deletion (indel) variability at 29 highly informative genomic loci to estimate MSI status without the requirement for matched-normal tissue. The assay has a clinically relevant five-day turnaround time and can be conducted on as little as 20 ng genomic DNA with a batch size of up to forty samples in a single run.

Results

The MSI detection method was developed on a training set (n = 94) consisting of 22 MSI-H, 24 MSS, and 47 matched normal samples and tested on an independent test set of 24 MSI-H and 24 MSS specimens. Assay performance with respect to accuracy, reproducibility, precision as well as control sample performance was estimated across a wide range of FFPE samples of multiple histologies to address pre-analytical variability (percent tumor nuclei), and analytical variability (batch size, run, day, operator). Analytical precision studies demonstrated that the assay is highly reproducible and accurate as compared to established gold standard PCR methodology and has been validated through NYS CLEP.

Significance

This assay provides clinicians with robust and reproducible NGS-based MSI testing without the need of matched normal tissue to inform clinical decision making for patients with solid tumors.

Tumor mutational burden (TMB): Assessment of inter- and intra-tumor heterogeneity

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Background: As an emerging biomarker, a high TMB is associated with response to immune checkpoint inhibitors. The tissue selected for molecular analysis could be critical for accurate TMB assessment and therapeutic selection. We evaluated TMB and the tumor specimen tested to determine the extent tissue selection has on TMB analysis. Methods: TMB profiling was performed on 73 cases representing 8 tumor types. 8 patients with multiple specimens from the same surgical event were evaluated for intra-tumor heterogeneity, and 20 patients with specimens from multiple surgical events spanning up to seven years were tested for inter-tumor heterogeneity. Two to six specimens, including primary, recurrent and metastatic cases, were evaluated per patient. For each patient, TMB values within and between tumors were assessed for concordance using a clinically relevant TMB-high cutoff of ≥ 10 mutations/Mb. Results: TMB values ranged from 0 – 58, with 7 cases scored as TMB-high. One patient with a primary skin melanoma and a lung metastasis biopsied after 34 months was concordant for TMB-high. For 19 patients (76%), all samples were concordant for TMB-low. For intratumor testing, 2 of 8 (25%) patients had discordant specimens (crossed the TMB-cutoff). The first patient had 4 intra-tumor CRC samples with TMB values of 10.6, 10.7, 9.7 and 9.7. The second patient had 5 intra-tumor pancreatic samples with TMB values of 10.7, 4.4, 6.2, 8.8, 7.9. Of the patients with 20 inter-tumor samples, 2 patients (each with melanoma) had discordant TMB-high calls. One had a lymph node metastasis TMB of 12.3 and a liver metastasis collected five months later with a reduced TMB of 0.9. In contrast, the second discordant melanoma patient had a peritoneum metastasis with an elevated TMB of 11.4 collected 14 months after a mesentery metastasis with a TMB of 8.7. Conclusions: TMB variation within tumors and between tumors from different surgical events occurs equally and in 17% of patients. This clinical tumor heterogeneity on the inter- or intra-tumor level may limit the utility and the application of TMB at a cutoff of 10. A careful analysis of tissues to enrich for neoplastic cells may improve assay sensitivity and increase analytical accuracy at this precise cut point.

Cell proliferation as a biomarker for response to immune checkpoint inhibitors in highly inflamed renal cell carcinoma

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Background: Cell proliferation is an important marker of survival in many tumors and we hypothesized that this attribute could be related to response to immune checkpoint inhibitors in RCC. Previously we reported (SITC 2018) moderately proliferative lung cancer has a much higher response rate than either poorly or highly proliferative tumors. Methods: 69 FFPE tumor samples of RCC were evaluated by RNA-seq to measure transcript levels of 394 immune related genes, including 10 related to cell proliferation (BUB1, CCNB2, CDK1, CDKN3, FOXM1, KIAA0101, MAD2L1, MELK, MKI67, TOP2A). Cell proliferation, defined as the mean mRNA expression of these 10 genes was evaluated for association with ORR to ICIs by RECIST v1.1 criteria for both PD-L1 IHC positive and negative cases. Cell proliferation for each case was split into 3 tertiles of poorly ( < 33), moderately (33-66) and highly ( > 66) proliferative compared to a reference population. Poorly and highly proliferative were grouped for comparison to moderately proliferative tumors. Tumors were inflamed or non-inflamed based upon RNA‐seq analysis of CD8 compared to a reference population of more than 500 cases of multiple tumors. Non-inflamed, or immune desert tumors, defined as the lower 25th percentile of rank for CD8+ T-cells, and greater than 75th percentile of rank as inflamed. Results: In our cohort of 69 patient the overall ORR was 18.8%. 15.9% of tumors were non-inflamed with an ORR of 9.1%. For 36.2% inflamed tumor the ORR was 32%. For cell proliferation 62.2% were poorly proliferative, 8.7% were highly proliferative, and 29% were moderately. ORR in moderately proliferative tumors was 30% versus 14.2% in poorly/highly proliferative tumors. In inflamed tumors, ORR in moderately proliferative tumors was 37.5% as opposed to 17.6% in poorly/highly proliferative tumors. In 11 non-inflamed tumors, there was only one responder, which was a poorly/highly proliferative tumor. Conclusions: Cell proliferation may play a crucial role in distinguishing RCC patients who may have a clinical benefit to ICI, including the important subgroup of inflamed tumors. Moderately proliferative tumors have a higher ORR than their poorly/highly counterparts.

Cell proliferation as a biomarker for response to immune checkpoint inhibitors in PD-L1 negative renal cell carcinoma

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Background: Cell proliferation is an important marker of survival in many tumors, and we hypothesized that this attribute could be related to response to immune checkpoint inhibitors (ICIs) in RCC. Previously we reported (SITC 2018) that moderately proliferative lung cancer have a much higher response rate than either poorly or highly proliferative tumors. Methods: 69 FFPE RCC tumor samples were evaluated by RNA-seq to measure transcript levels of 394 immune related genes. Cell proliferation was defined as the mean mRNA expression of 10 genes (BUB1, CCNB2, CDK1, CDKN3, FOXM1, KIAA0101, MAD2L1, MELK, MKI67, TOP2A) which was evaluated for association with ORR to ICIs by RECIST v1.1 criteria for both PD-L1 IHC positive and negative cases. Cell proliferation for each case was split into 3 tertiles of poorly ( < 33), moderately (33-66) and highly ( > 66) proliferative versus a reference population. Poorly and highly proliferative were grouped for comparison to moderately proliferative tumors. PD-L1 IHC was performed using DAKO 22C3 antibody and scored by FDA guidelines and considered positive if TPS ≥1% and negative if < 1%. Results: In our cohort of 69 patients, the overall ORR was 18.8. The majority, 91% of tumors were PD-L1 negative, with ORR of 14.8%, as opposed to ORR of 50% in PD-L1 positive cases. 62.2% of tumors were poorly proliferative, 8.7% were highly proliferative, and 29% were moderately proliferative. ORR in moderately proliferative tumors was 30% and 14.2% in poorly/highly proliferative tumors. In PD-L1 negative tumors the ORR in moderately proliferative tumors was 20% and 13% in poorly/highly proliferative tumors. For the 5 moderately proliferative, PD-L1 positive tumors, there were 2 PR and 1 CR, and in the 3 poorly/highly proliferative tumors there was 1 PR. Conclusions: Cell proliferation may play a crucial role in distinguishing RCC patients who may have a clinical benefit to ICI, including the important subgroup of PD-L1 negative tumors. Moderately proliferative tumors have a higher ORR than their poorly/highly counterparts.

Proliferative potential and resistance to immune checkpoint blockade in lung cancer patients

Background

Resistance to immune checkpoint inhibitors (ICIs) has been linked to local immunosuppression independent of major ICI targets (e.g., PD-1). Clinical experience with response prediction based on PD-L1 expression suggests that other factors influence sensitivity to ICIs in non-small cell lung cancer (NSCLC) patients.

Methods

Tumor specimens from 120 NSCLC patients from 10 institutions were evaluated for PD-L1 expression by immunohistochemistry, and global proliferative profile by targeted RNA-seq.

Results

Cell proliferation, derived from the mean expression of 10 proliferation-associated genes (namely BUB1, CCNB2, CDK1, CDKN3, FOXM1, KIAA0101, MAD2L1, MELK, MKI67, and TOP2A), was identified as a marker of response to ICIs in NSCLC. Poorly, moderately, and highly proliferative tumors were somewhat equally represented in NSCLC, with tumors with the highest PD-L1 expression being more frequently moderately proliferative as compared to lesser levels of PD-L1 expression. Proliferation status had an impact on survival in patients with both PD-L1 positive and negative tumors. There was a significant survival advantage for moderately proliferative tumors compared to their combined highly/poorly counterparts (p = 0.021). Moderately proliferative PD-L1 positive tumors had a median survival of 14.6 months that was almost twice that of PD-L1 negative highly/poorly proliferative at 7.6 months (p = 0.028). Median survival in moderately proliferative PD-L1 negative tumors at 12.6 months was comparable to that of highly/poorly proliferative PD-L1 positive tumors at 11.5 months, but in both instances less than that of moderately proliferative PD-L1 positive tumors. Similar to survival, proliferation status has impact on disease control (DC) in patients with both PD-L1 positive and negative tumors. Patients with moderately versus those with poorly or highly proliferative tumors have a superior DC rate when combined with any classification schema used to score PD-L1 as a positive result (i.e., TPS ≥ 50% or ≥ 1%), and best displayed by a DC rate for moderately proliferative tumors of no less than 40% for any classification of PD-L1 as a negative result. While there is an over representation of moderately proliferative tumors as PD-L1 expression increases this does not account for the improved survival or higher disease control rates seen in PD-L1 negative tumors.

Conclusions

Cell proliferation is potentially a new biomarker of response to ICIs in NSCLC and is applicable to PD-L1 negative tumors.

Electronic supplementary material

The online version of this article (10.1186/s40425-019-0506-3) contains supplementary material, which is available to authorized users.

Next generation sequencing of PD-L1 for predicting response to immune checkpoint inhibitors

BACKGROUND

PD-L1 immunohistochemistry (IHC) has been traditionally used for predicting clinical responses to immune checkpoint inhibitors (ICIs). However, there are at least 4 different assays and antibodies used for PD-L1 IHC, each developed with a different ICI. We set to test if next generation RNA sequencing (RNA-seq) is a robust method to determine PD-L1 mRNA expression levels and furthermore, efficacy of predicting response to ICIs as compared to routinely used, standardized IHC procedures.

METHODS

A total of 209 cancer patients treated on-label by FDA-approved ICIs, with evaluable responses were assessed for PD-L1 expression by RNA-seq and IHC, based on tumor proportion score (TPS) and immune cell staining (ICS). A subset of serially diluted cases was evaluated for RNA-seq assay performance across a broad range of PD-L1 expression levels.

RESULTS

Assessment of PD-L1 mRNA levels by RNA-seq demonstrated robust linearity across high and low expression ranges. PD-L1 mRNA levels assessed by RNA-seq and IHC (TPS and ICS) were highly correlated (p < 2e-16). Sub-analyses showed sustained correlation when IHC results were classified as high or low by clinically accepted cut-offs (p < 0.01), and results did not differ by tumor type or anti-PD-L1 antibody used. Overall, a combined positive PD-L1 result (≥1% IHC TPS and high PD-L1 expression by RNA-Seq) was associated with a 2-to-5-fold higher overall response rate (ORR) compared to a double negative result. Standard assessments of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) showed that a PD-L1 positive assessment for melanoma samples by RNA-seq had the lowest sensitivity (25%) but the highest PPV (72.7%). Among the three tumor types analyzed in this study, the only non-overlapping confidence interval for predicting response was for "RNA-seq low vs high" in melanoma.

CONCLUSIONS

Measurement of PD-L1 mRNA expression by RNA-seq is comparable to PD-L1 expression by IHC both analytically and clinically in predicting ICI response. RNA-seq has the added advantages of being amenable to standardization and avoidance of interpretation bias. PD-L1 by RNA-seq needs to be validated in future prospective ICI clinical studies across multiple histologies.

Treatment recommendations to cancer patients in the context of FDA guidance for next generation sequencing

Background

Regulatory approval of next generation sequencing (NGS) by the FDA is advancing the use of genomic-based precision medicine for the therapeutic management of cancer as standard care. Recent FDA guidance for the classification of genomic variants based on clinical evidence to aid clinicians in understanding the actionability of identified variants provided by comprehensive NGS panels has also been set forth. In this retrospective analysis, we interpreted and applied the FDA variant classification guidance to comprehensive NGS testing performed for advanced cancer patients and assessed oncologist agreement with NGS test treatment recommendations.

Methods

NGS comprehensive genomic profiling was performed in a CLIA certified lab (657 completed tests for 646 patients treated at Roswell Park Comprehensive Cancer Center) between June 2016 and June 2017. Physician treatment recommendations made within 120 days post-test were gathered from tested patients’ medical records and classified as targeted therapy, precision medicine clinical trial, immunotherapy, hormonal therapy, chemotherapy/radiation, surgery, transplant, or non-therapeutic (hospice, surveillance, or palliative care). Agreement between NGS test report targeted therapy recommendations based on the FDA variant classification and physician targeted therapy treatment recommendations were evaluated.

Results

Excluding variants contraindicating targeted therapy (i.e., KRAS or NRAS mutations), at least one variant with FDA level 1 companion diagnostic supporting evidence as the most actionable was identified in 14% of tests, with physicians most frequently recommending targeted therapy (48%) for patients with these results. This stands in contrast to physicians recommending targeted therapy based on test results with FDA level 2 (practice guideline) or FDA level 3 (clinical trial or off label) evidence as the most actionable result (11 and 4%, respectively).

Conclusions

We found an appropriate “dose-response” relationship between the strength of clinical evidence supporting biomarker-directed targeted therapy based on application of FDA guidance for NGS test variant classification, and subsequent treatment recommendations made by treating physicians. In view of recent changes at FDA, it is paramount to define regulatory grounds and medical policy coverage for NGS testing based on this guidance.

Abstract 4526: Predicting response: PD-L1 biomarker testing by IHC and RNA-seq

Background: Currently, four FDA-approved biomarker assays are available to screen for PD-L1 to enrich for patient response to checkpoint inhibitors (CPI). Each are immunohistochemical (IHC) assays that approximate the percentage of immune or tumor cells expressing PD-L1 using various antibodies, staining and scoring systems. Given this inherent variability, there are concerns whether any single PD-L1 IHC assay, or IHC in general, can be used as a companion or complimentary diagnostic. This is highlighted by the number of individuals, regardless of histology or antibody used, who score below the IHC scoring threshold but respond to PD-L1 inhibitors.

Methods: In this study, we compared PD-L1 protein expression (IHC) to PD-L1 gene expression (CD274) in 436 tumors. PD-L1 IHC was assessed in melanoma using the 28-8 antibody, with kidney, HNSCC, and lung cancer assessed with 22C3. All were scored as per published guidelines. CD274 gene expression was determined by targeted RNA-seq, with each sample's expression level compared and ranked to a reference population.

Results: ANOVA demonstrated a significant relationship between RNA-seq and IHC PD-L1 measurements (p.value &lt; 2e-16). Tukey's HSD comparisons of mean TPS at &lt;1%, 1-4%, and &gt;5% demonstrate significant differences between the three groups that are consistent with gene expression rankings (p adj &lt;0.002). Additionally, for metastatic melanomas with CPI response data, a strong association of objective response rate (ORR) to high RNA-seq expression exists, regardless of IHC result (Table 1). Conclusion: In 400+ tumors, PD-L1 demonstrates correlated mean expression values when assessing protein by IHC and gene expression by RNA-seq. For the CPI treated melanomas with outcomes, PD-L1 IHC ≥1% had a 56% ORR, which improved to &gt;71% ORR when combined with high PD-L1 gene expression. With the need to better predict CPI response, this data suggests that combination PD-L1 testing is an improvement over the FDA approved IHC assays.

Table 1: ORR for 37 melanoma patients based on combination PD-L1 expression resultsPD-L1 Method CombinationsCRPRSDPDORR95% CIRNAseq High + IHC ≥1%141171.4%29.04%-96.33%RNAseq High + IHC &lt;1%120175.0%19.41%-99.37%RNAseq Low + IHC ≥1%101050.0%1.26%-98.74%RNAseq Low + IHC &lt;1%0541520.8%7.13%-42.15%

Citation Format: Jeffrey Conroy, Sarabjot Pabla, Mary Nesline, Sean Glenn, Blake Burgher, Maochun Qin, Jonathan Andreas, Vincent Giamo, Felicia L. Lenzo, Angela Omilian, Wiam Bshara, Antonios Papanicolau-Sengos, Yirong Wang, Marc Ernstoff, Mark Gardner, Carl Morrison. Predicting response: PD-L1 biomarker testing by IHC and RNA-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4526.

Abstract 5070: Chemokine expression signatures in infiltrated vs non-infiltrated tumors

Background: Previous studies in animal models have shown that expression of specific chemokines determines immune cell infiltration in the tumor microenvironment. More specifically, low expression of CXCL9/10/11, CXCR3, and CCL5, coupled with high expression of CCL2, CCR2, CCR4, CCR5, CCL22, CXCL12, and CXCR4, leads to an exclusion of effector T-cells in the tumor microenvironment while allowing the entry of Treg and MDSC(s).

Methods: 300 formalin-fixed, paraffin-embedded (FFPE) metastatic cutaneous melanoma samples were evaluated by the RNA-seq component of a comprehensive immune profile panel to measure transcript levels of chemokine genes. Resultant data was QC filtered, normalized and ranked based on an assorted reference population of various tumor types. CD8 expression was used to categorize tumors as inflamed (CD8 Rank ≥ 75), borderline ≥ 25 CD8 Rank &lt; 75) and immune deserts (CD8 Rank &lt; 25). T-cell infiltration is defined by CD8 immunohistochemistry with following definitions: Noninfiltrated - Sparse number of CD8+ T-cells that infiltrate nests of neoplastic cells and represent less than 5% of the tumor. Infiltrated - Frequent CD8+ Tcells that infiltrate nests of neoplastic cells in an overlapping fashion at least focally and represent more than 50% of the tumor cells.

Results: CD8 infiltration by immunohistochemistry showed high correlation with CD8 gene expression by RNAseq with infiltrated tumors showing significantly higher expression of CD8 than non-infiltrated tumors (v.test:9.47, p= 2.79E-21; Wilcoxon rank sum test p&lt;0.05). Moreover, 73% of inflamed tumors were categorized as “infiltrated” by IHC while 93% of Immune desert tumors were categorized as “non-infiltrated” by IHC. Chemokine expression significantly correlated with infiltration status by IHC, wherein, CCL5, CCR5, CCL4, CXCR3, CXCL9, CCL2, CCL22, CCL3, and CXCL10 were significantly under expressed in tumors lacking infiltrating effector T-cells (Wilcoxon rank sum test p&lt;0.05, Tukey HSD adjusted p &lt;0.001). Overall ANOVA results showed significant relationship between gene expression of these chemokines and Infiltration status by CD8 IHC (p &lt;0.05).

Conclusion: In 300 metastatic cutaneous melanoma cases, we demonstrated that tumor inflammation status by CD8 expression by RNAseq correlated with CD8 infiltration pattern by IHC. Moreover, expression of Infiltrated and non-infiltrated tumors shows distinct chemokine signatures where higher CD8 T-cell infiltration correlates with higher expression of studied chemokines in the tumor microenvironment. It requires further investigation to better understand the interplay of chemokines and cytokines in the tumor microenvironments that drive the immune cycle in melanoma.

Citation Format: Sarabjot Pabla, Jeffrey Conroy, Mary Nesline, Sean Glenn, Blake Burgher, Maochun Qin, Jonathan Andreas, Vincent Giamo, Felicia L. Lenzo, Angela Omilian, Wiam Bshara, Antonios Papanicolau-Sengos, Yirong Wang, Marc Ernstoff, Mark Gardner, Carl Morrison. Chemokine expression signatures in infiltrated vs non-infiltrated tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5070.

Abstract 3679: PD-L1 driven excluded phenotype in melanoma

Background: The excluded phenotype has been previously described by the presence of abundant immune cells that do not penetrate the parenchyma of these tumors but instead are retained in the stroma that surrounds nests of tumor cells. These features support a pre-existing anti-tumor immune-related response, but the details of this mechanism are not well elucidated.

Methods: 300 formalin-fixed, paraffin-embedded (FFPE) metastatic cutaneous melanoma samples were evaluated by the RNA-seq component of a comprehensive immune profile panel to measure transcript levels of immune-related genes. Resultant data was QC filtered, normalized and ranked based on an assorted reference population of various tumor types. The expression of PD-L1 on the surface of tumor cells was assessed in tumor samples by means of an automated IHC assay (28-8, Dako). A tumor was considered PD-L1 positive if ≥1% of viable tumor cells exhibited complete circumferential or partial linear plasma membrane staining at any intensity. Excluded pattern of T-cell infiltration was defined by CD8 immunohistochemistry by the following definition: Restriction of more than 95% of all CD8+ T-cells present in a tumor to the periphery or interstitial stromal areas and not actively invading nest or groups of neoplastic cells.

Results: The excluded phenotype was identified in 34 of 300 (11%). PD-L1 by IHC was positive (≥1%) in the neoplastic cases in 16 of 34 (47%) cases. Membranous staining in immune cells was identified in only 3 of 34 (9%) cases, but was present in &gt;90% of all cases in a non-membranous pattern. In more than one-half of all cases the non-membranous pattern of PD-L1 IHC staining was restricted to the excluded TILs. Higher expression of PD-L1 by RNA-seq was associated with this excluded PD-L1 pattern of staining.

Conclusion: In 300 metastatic cutaneous melanoma cases we demonstrated that the excluded phenotype is frequent and represents ~10% of all cases. Moreover, in a significant number of excluded cases expression of PD-L1 by IHC was distinctly limited to the excluded TILs. This finding would support a unique mechanism of PD-L1 anti-tumor immune-related response that needs further investigation.

Citation Format: Carl Morrison, Jeffrey Conroy, Mary Nesline, Sean Glenn, Blake Burgher, Maochun Qin, Jonathan Andreas, Vincent Giamo, Felicia L. Lenzo, Angela Omilian, Wiam Bshara, Antonios Papanicolau-Sengos, Yirong Wang, Marc Ernstoff, Mark Gardner, Sarabjot Pabla. PD-L1 driven excluded phenotype in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3679.

PD-L2 amplification and durable disease stabilization in patient with urothelial carcinoma receiving pembrolizumab

We report the immunological profile of a patient with upper-tract urothelial carcinoma experiencing stable disease on pembrolizumab for 20 months. The tumor exhibited extensive infiltration by CD8⁺ cytotoxic T lymphocytes, low-to-moderate mutational burden, no PD-L1 staining by commercially available immunohistochemical assays, but amplification of CD274 (coding for PD-L1) and/or PDCD1LG2 (encoding PD-L2) by fluorescence in situ hybridization. RNA-seq revealed multiple biomarkers of an ongoing immune response and compensatory immune evasion, including moderate PD-L1 levels coupled with robust PD-L2 expression. Pending validation in additional patients, these findings suggest that PD-L2 expression levels may constitute a biomarker of response to immune checkpoint blockade in urothelial carcinoma.

Predicting response to checkpoint inhibitors in melanoma beyond PD-L1 and mutational burden

BACKGROUND

Immune checkpoint inhibitors (ICIs) have changed the clinical management of melanoma. However, not all patients respond, and current biomarkers including PD-L1 and mutational burden show incomplete predictive performance. The clinical validity and utility of complex biomarkers have not been studied in melanoma.

METHODS

Cutaneous metastatic melanoma patients at eight institutions were evaluated for PD-L1 expression, CD8+ T-cell infiltration pattern, mutational burden, and 394 immune transcript expression. PD-L1 IHC and mutational burden were assessed for association with overall survival (OS) in 94 patients treated prior to ICI approval by the FDA (historical-controls), and in 137 patients treated with ICIs. Unsupervised analysis revealed distinct immune-clusters with separate response rates. This comprehensive immune profiling data were then integrated to generate a continuous Response Score (RS) based upon response criteria (RECIST v.1.1). RS was developed using a single institution training cohort (n = 48) and subsequently tested in a separate eight institution validation cohort (n = 29) to mimic a real-world clinical scenario.

RESULTS

PD-L1 positivity ≥1% correlated with response and OS in ICI-treated patients, but demonstrated limited predictive performance. High mutational burden was associated with response in ICI-treated patients, but not with OS. Comprehensive immune profiling using RS demonstrated higher sensitivity (72.2%) compared to PD-L1 IHC (34.25%) and tumor mutational burden (32.5%), but with similar specificity.

CONCLUSIONS

In this study, the response score derived from comprehensive immune profiling in a limited melanoma cohort showed improved predictive performance as compared to PD-L1 IHC and tumor mutational burden.

Immune deserts: Correlation of low CD8 gene expression with non-response to checkpoint inhibition

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Background: Associations between presence and density of effector T-cells (CD8+) and response to checkpoint inhibitors (CPIs) has been established for protein biomarkers using immunohistochemistry but less work has been done to characterize this relationship through gene expression. Methods: We collected clinical response data (RECIST v1.1) for 184 patients who had been treated with CPIs across tumor types with FDA indications. We tested formalin-fixed paraffin embedded (FFPE) samples by NGS with a comprehensive immune response panel which interrogates the gene expression profile of 54 validated immune-related genes. Ranked gene expression was compared to a reference population. 85/184 (46%) cases were considered “responders” after six months, and 66/184 cases (36%) were considered responders after twelve months on treatment. We hypothesized that patients with in the bottom quartile of CD8 expression, as measured by the rank of the sum of normalized CD8A and CD8B transcripts compared to a reference population, would be unlikely to respond to checkpoint therapy (immune deserts), and that “hot” tumor environments (TME) with the highest quartile of expression would be most likely to respond. Results: After six months of therapy, 13/40 (32.5%) patients with immune desert tumors responded to therapy, whereas 39/82 patients (47.6%) in the middle 50% of expression responded, and 33/62 (53.2%) of patients with “hot” TME responded to treatment. The two-tailed p-value of the null hypothesis, that TME status does not correlate with response, was 0.018 and could be rejected. After twelve months of observation, 9/40 (22.5%) immune desert patients were considered “responders”. 28/82 (34.2%) of the middle 50% of expression were classified as responders, and 29/62 (46.8%) of top quartile samples came from responders. The two-tailed p-value of the null hypothesis was 0.005 and could be rejected. Conclusions: Gene expression of CD8 as measured by RNA-seq from FFPE samples can be used to compare these samples versus a reference population to predict an increase or decrease in likelihood of response to CPIs. Patients in the lowest quartile of CD8 expression were the least likely to respond to these promising therapies.

Effect of CTLA-4 overexpression on response to ipilimumab in melanoma

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Background: CD8 positive tumor infiltrating lymphocytes (TILS) are highly associated with immune response and prognosis, and are also under investigation as a marker of response to checkpoint inhibitors. Given lack of predictive biomarkers for ipilimumab, growing number of trials for new indications for combination ipilimumab + nivolumab, and evidence to support therapeutic target overexpression as markers of response, we examined the role of CTLA-4 expression and TILS in response to ipilimumab and combination ipilimumab + nivolumab in melanoma. Methods: Formalin-fixed paraffin embedded melanoma samples taken prior to treatment by ipilimumab (n = 36) or combination ipilimumab + nivolumab (n = 10) were evaluated by a comprehensive immune gene expression profile to establish the relationship between CTLA-4 and CD8 and therapeutic response (RECIST v1.1). Results: Increased CTLA-4 expression was moderately associated with increased TILS (r2= .41, p = .004). This was observed in the monotherapy group (r2= .38, p = .02), and was higher in the smaller combination therapy group, though not statistically significant (r2= .59, p = .06). Higher levels of TILS were observed in responders who achieved clinical benefit from either regimen within 6 months (n = 20). No significant difference was observed between responders (M = 57.1, SD = 30.2) and nonresponders (M = 48.6, SD = 32.9); t(44) = -.895, p = .376. Lower levels of CTLA-4 were observed in responders who achieved clinical benefit from either regimen within 6 months. No significant difference was observed between responders (M = 54, SD = 35) and nonresponders (M = 38.7, SD = 26.8); t(44) = 1.70, p = .09. The ratio of TILS to CTLA-4 expression was higher in responders who achieved clinical benefit within 6 months (n = 20).No significant difference was observed between responders (M = 5.2, SD = 14.0) and nonresponders (M = 1.4, SD = 2.7); t(41) = -1.2, p = .212. Conclusions: While not statistically significant, CTLA-4 expression in melanoma patients treated with either ipilimumab or combination ipilimumab + nivolumab was lower in responders compared to nonresponders. This analysis does not support the concept that over-expression of CTLA-4 is a biomarker of response to anti-CTLA-4 therapy.

Correlation of lung cancer mutational profile with immune profile

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Background: The association between neoplasm mutational and immune profiles has not been well-characterized. Methods: We collected 26 lung cancer formalin-fixed paraffin embedded (FFPE) samples which had been tested with a comprehensive mutation profile to detect clinically actionable mutations, and a comprehensive immune gene expression profile, which interrogates PD-L1 immunohistochemistry (IHC), PD-L1/2 copy number, CD3/CD8 IHC, microsatellite instability status, mutational burden, and the expression profile of 54 immune-related genes. The ranking of gene expression and 7 immune phenotypes was compared to a reference population. Six cases were positive for an activating KRAS mutation and 2 cases were positive for an activating EGFR mutation. Principal component analysis was performed to determine the association of EGFR/KRAS mutations with the measured immune landscape. Results: The proinflammatory immune phenotype was significantly correlated with KRAS mutation positive samples (first principal component, R squared = 0.53, p < 0.05). Similarly, CD38 expression was correlated with KRAS mutation (R squared = 0.47, p < 0.05). CD137, KLRD1, and DDX58 expression was significantly correlated with EGFR positive samples (second principal component, R squared = 0.47, 0.37, 0.35 respectively, p < 0.05 in all cases). Unsupervised hierarchical clustering of the samples resulted in three distinct clusters, EGFR positive, KRAS positive, and EGFR negative/KRAS negative. In the KRAS positive cluster, high proinflammatory immune phenotype, VISTA moderate expression, presence of CD3/CD8 tumor infiltrating lymphocytes (TILs), and low KLRD1 were overrepresented (p < 0.05), while low VISTA and proinflammatory moderate immune phenotype were significantly underrepresented (p < 0.05). In the EGFR positive cluster DDX58 high, very low TILs, and very low CD8 were significantly (p < 0.05) overrepresented. Conclusions: KRAS mutation positivity is significantly associated with a proinflammatrory immune phenotype and CD3/CD8 infiltration. KRAS positive, EGFR positive, and KRAS/EGFR negative clusters are immunophenotypically distinct. A higher number of specimens is necessary to verify and expand these findings.

Comprehensive immune and mutational profile of melanoma

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Background: The association between tumor mutational profiles and immune signatures has not been well-characterized. Methods: 306 melanoma samples were tested by NGS using a comprehensive cancer panel for mutational status and an immune response panel which interrogates the expression profile of 54 validated immune-related genes. The ranking of gene expression, mutational burden and 7 immune phenotypes was compared to a reference population. 38% cases were positive for activating BRAF mutations, 12% for RAS, and 6% for NF1. The remaining 44% were considered triple wild type. Principal component analysis (PCA) followed by hierarchical clustering was performed to determine association of BRAF/RAS/NF1 mutations and triple wild type with immune phenotypes, mutational burden and gene expression as measured by the NGS panels. Results: PCA showed that the first and second dimension explain 86% of the variation in the mutation profiles of the 306 melanomas. The first principal component highly correlated with BRAF positive status (pval < 0.001), the second highly correlated with RAS positive status (pval < 0.001), and the third principal component, although not informative, highly correlated with NF1 status (pval < 0.001) and Mutation Burden (pval < 0.001). Hierarchical clustering of the samples resulted in 4 distinct clusters: RAS positive, BRAF Positive, NF1 positive and triple wild type. The RAS positive cluster demonstrated significantly lower expression of ICOSLG, ICOS, CD4, C10orf54, CD40 and CD244 genes. Similarly, the BRAF positive cluster under-expresses immune escape and proinflammatory immune phenotypes, but over-expressed OX40L. The NF1 positive cluster had significantly higher mutational burden than other clusters. On the contrary, the triple wild type cluster over-expressed 6 out of 7 immune phenotypes. Conclusions: BRAF/RAS/NF1 mutation status are immunophenotypically distinct and do not associate with a typical immune phenotype in the tumor microenvironment. Triple wild type samples present with an overall activated immune phenotype, representative of an inflamed tumor. Additional studies are necessary to include additional activating or loss of function mutations to expand these findings.

Abstract 1625: NGS reveals specimen characteristics have minimal impact on immune gene expression signature

Background: Therapeutic antibodies targeting immune checkpoint molecules have been approved by the FDA for the treatment of several types of cancer. Currently, evaluation of the tumor checkpoint blockade is limited to FDA-approved IHC assays measuring PD-L1 ligand status which is subjective and not analytically robust. As the number of antibodies targeting immune checkpoints expands, assays that can evaluate additional biomarkers in tumor specimens are needed to accurately predict patient response to these drugs. To address these issues, a custom immune response NGS assay was developed to measure the transcript level of 54 genes involving T-cell receptor signaling (TCRS) and tumor infiltrating lymphocytes (TILs) in solid tumors of various characteristics including heterogeneity, disease, biopsy type and age. As part of the study, we evaluated the impact these variable have on the immune gene expression signature and their role as possible assay interferents.

Methods: Studies were designed to evaluate the analytical performance of a targeted RNA-seq assay for FFPE samples from NSCLC, melanoma, RCC, HNSCC, kidney and bladder cancer. To assess degree of assay tolerance to the wide range of specimen characteristics that are inherent in tumors, samples and mixed samples of various histopathologic characteristics were included. PCA and unsupervised clustering was performed on samples with checkpoint inhibition, TCRS and TILs genes to reveal sample groups with three distinct immune signatures (low, indeterminate and high). Further correlation and over-representation analysis was performed to determine impact of specimen characteristics on these three immune signatures.

Results: Immune signatures were maintained for the majority of characteristics studied within a specified range. As expected, only TIL status was significantly associated with the high expression group. Other factors including architecture, neoplastic content, percent necrosis, stroma quality/quantity, T-Path, PMR, specimen type, tissue amount and specimen age were not over-represented in any immune signature.

Conclusion: Tumor samples harbor a mixture of potential assay interferents including variable benign, neoplastic and immune cells populations with both naïve and reactive stroma contributing to a complex tumor microenvironment that is difficult to catalogue prior to testing. Our study demonstrates that the immune signature present in the tumor microenvironment is sufficiently strong to withstand a wide range of tumor heterogeneity, thereby reducing the need of extensive tissue macrodissection and the exclusion of samples previously thought to be non-evaluable.

Citation Format: Jeffrey Conroy, Sean Glenn, Blake Burgher, Sarabjot Pabla, Maochun Qin, Jon Andreas, Vincent Giamo, Marc Ernstoff, Mary Nesline, Ji He, Mark Gardner, Carl Morrison. NGS reveals specimen characteristics have minimal impact on immune gene expression signature [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1625. doi:10.1158/1538-7445.AM2017-1625

Abstract 1626: Technical variability in NGS immune gene expression and mutation profiling has a nominal effect on tumor classification

Background: A custom NGS cancer immune gene expression assay was developed which measures the transcript level of &gt;350 genes involved in T-cell receptor signaling (TCRS), tumor infiltrating lymphocyte (TILs) complement as well as other key targets expected to predict the likelihood of patient response to checkpoint inhibitors (CPI). In parallel to the gene expression assay, mutational profiling was carried out using the 409 gene Comprehensive Cancer Panel (ThermoFisher). As variability between runs is common when performing NGS assays a detailed comparison of specific technical variations were assessed for their ability to effect gene expression and mutation profiles of clinical FFPE samples.

Methods: Studies were designed to characterize the analytical performance of the immune response NGS assay using RNA and DNA from a subset of 300 FFPE tissues representing NSCLC, melanoma, renal cell carcinoma and bladder cancer. As part of the study, we tested the impact of variability in RNA and DNA input quantity at the library preparation step, sample batch size which affects mapped reads/sample and depth of coverage, and linearity of expression and sensitivity of mutation profiling through serial dilutions of pico-molar (pM) input of normalized library. PCA and unsupervised clustering was performed on samples with checkpoint inhibition, TCRS and TILs genes as well as mutational profiling to reveal sample groups with three distinct immune signatures (low, indeterminate and high). Further correlation and over-representation analysis was performed to determine impact of technical characteristics on these three immune signatures.

Results: Immune signatures including mutation profiles and gene expression levels were maintained throughout variable RNA/DNA input amounts at the library generation level as well as with diminution of pM levels of library pooled at the sequencing step. Increase in the number of mapped reads and sequencing depth through decreasing the number of batched samples per sequencing run also did not affect the gene expression and mutation profile signatures of the FFPE derived samples.

Conclusion: The gene expression and mutation profiles responsible for classifying FFPE samples using NGS are not affected by variation normally introduced in the technical workflow commonly associated with these platforms. The analytical assessment of input at the nucleic acid, library, and sample size level has shown the plasticity available when using amplicon based NGS technologies for classifying the immune gene expression signature as well as mutational profiles of FFPE derived clinical tumor samples. This flexibility increases the strength and utility of NGS-base gene expression profiling and mutational analysis of tumor samples for both basic research and clinical applications.

Citation Format: Sean Glenn, Jeffrey Conroy, Blake Burgher, Sarabjot Pabla, Maochun Qin, Jon Andreas, Vincent Giamo, Marc Ernstoff, Mary Nesline, Ji He, Mark Gardner, Carl Morrison. Technical variability in NGS immune gene expression and mutation profiling has a nominal effect on tumor classification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1626. doi:10.1158/1538-7445.AM2017-1626

Algorithmic prediction of response to checkpoint inhibitors: Hyperprogressors versus responders

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Background: Predicting response to checkpoint inhibitors (CPIs) using biological knowledge-based decision processes with machine learning (ML) has a great potential to predict rapid progression in patients treated with checkpoint inhibitors (CPIs) (hyperprogressive disease (HPD)) as well as responders. ML models risk overfitting data and do not always evaluate the underlying biology, thus performing well in the initial training cohort but lack generalizability when extended to other cohorts. Biology-based decision may not perform as well initially due to limited understanding and a simplified rule set, but often perform equally well when extended to larger similar cohorts of patients. Methods: A custom NGS cancer immune gene expression assay compared 87 patients treated with CPIs classified as CR, PR, or SD versus 12 HPD. A ML-based polynomial regression model based on 54 immune-related genes combined with mutational burden was optimized for prediction of response. A biological 4-gene decision tree model was constructed independently based on ML. A second biological decision tree incorporated the weighted average relative rank of the expression of multiple genes in 4 different immune functions including immune cell infiltration, regulation, activation, and cytokine signaling. Bayesian model average (BMA) incorporated all three models’ results into the final prediction. Results: For87 patients classified as CR, PR, or SD the PPV >96% for responders and a NPV >90% for non-responders was achieved with the regression model, however with response indeterminate for 24% of the population. While the two biological decision tree models’ PPV were in the 70% range, they accurately revealed the critical genes’ roles in immune response with strong literature support. BMA process integrated these three models resulted in a PPV >96% and a NPV >90% and eliminated the indeterminate group. For HPD a unique biology related to priming of short term memory T-cells was identified. Conclusion: Prediction of response to CPIs is best attained by combining ML with biological knowledge. Decision tree models using a large panel of immune related genes in the context of archival samples from patients treated with CPIs can be used to better understand the biology of responders versus non-responders and provides new insights into HPD.

The inflamed phenotype in PD-L1 negative non-small cell lung cancer (NSCLC) and response to checkpoint inhibitors

Background

NSCLC with a PD-L1 tumor proportion score (TPS) by immunohistochemistry (IHC) of greater than 50% has a positive predictive value (PPV) of 42% for response to pembrolizumab. Across all tumor types and different checkpoint inhibitors (CPIs), the evidence supports the inflamed phenotype associated with responders. Currently very little is known about predicting response to CPIs in PD-L1 negative NSCLC. Additionally, the association of the PD-L1 negative NSCLC with the inflamed phenotype has not been well described.

Methods

PD-L1 (22C3) IHC and a custom NGS cancer immune gene expression assay were used to interrogate 50 NSCLC samples of which 23 were treated with one or more CPIs. PD-L1 TPS &gt;50% was considered positive for IHC. Select over expression of 43 genes related to T-cell activation and 11 genes associated with tumor infiltrating lymphocytes were considered positive for the inflamed phenotype. RECIST v1.1 was used to assess patient response.

Results

9 of the 50 samples were PD-L1 IHC positive and showed an inflamed phenotype. An additional 7 samples exhibited an inflamed phenotype that were PD-L1 IHC negative. Of the 23 patients that could be evaluated for response to CPIs, 9 showed an inflamed phenotype while 14 were non-inflamed.

Conclusion

A subset of PD-L1 IHC negative NSCLC patients with an inflamed phenotype show a favorable response rate to CPIs. No complete or partial responses were identified in patients that were PD-L1 IHC negative with a non-inflamed phenotype.

The inflamed phenotype in PD-L1 negative melanoma and response to checkpoint inhibitors

Background

PD-L1 positive melanoma patients, with a MEL score of 4 or 5 by immunohistochemistry (IHC), exhibited a response rate of &gt;50% to pembrolizumab in the KEYNOTE-001 trial. Across multiple tumor types, evidence supports that response to checkpoint inhibitors (CPIs) is associated with an inflamed phenotype. Currently there is very little known about predicting response to CPIs in PD-L1 negative inflamed and non-inflamed melanomas.

Methods

86 melanomas were tested using PD-L1 IHC and a custom NGS immune gene expression assay. 72 of these patients were treated with one or more CPIs. A PD-L1 MEL score of 4 or 5 was considered positive for IHC, and over expression of a majority of 43 genes related to T-cell activation and 11 genes associated with tumor infiltrating lymphocytes were considered positive for the inflamed phenotype. RECIST v1.1 was used to assess patient response

Results

8 of the 86 samples were PD-L1 IHC positive, 10 of the 86 were PD-L1 IHC negative, and all 18 samples in this group showed an inflamed phenotype. Of the 72 patients that could be evaluated for response to CPIs, 8 demonstrated an inflamed phenotype while 64 were non-inflamed.

Conclusion

A positive PD-L1 IHC MEL score of 4 or 5 fails to identify most melanoma patients with response to CPIs. The inflamed phenotype also fails. Most responders demonstrate a PD-L1 IHC negative/non-inflamed phenotype. Additional studies of the immune phenotype in melanoma are required to evaluate prediction of response to CPIs.

Analytical validation of an immune response assay for classifying solid tumors

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Background: An advanced diagnostic laboratory test (Immune Advance) was developed that analyzes multiple DNA and RNA biomarkers to predict the likelihood of response to checkpoint inhibitors in patients with solid tumors. Using RNA-seq and DNA-seq, the NGS test measures gene expression of immune response genes and overall mutational burden. The studies described here were designed to validate the analytical performance of the test on the Ion Torrent System in our CLIA lab. Methods: Studies were designed to characterize the analytical performance of an immune response NGS assay using total nucleic acids from >100 FFPE tissues representing NSCLC, melanoma, renal cell carcinoma, and bladder cancer. Performance variables with respect to gene-specific amplicon specificity, linearity, and limits of detection were estimated with various sample dilutions and input nucleic acids. The effects of the tumor micro-environment (adjacent benign tissue, necrosis) was evaluated by including these potential interferents in the assay. Analytical precision including intra-assay, inter-assay, and inter-operator reproducibility was measured by testing replicate FFPE tissue sections. Accuracy was determined by comparing select transcript and DNA level variants with those from established IHC, RT-PCR, and NGS assays. Transcript stability in FFPE specimens was evaluated in serial sections from blocks with routine storage and compared to originating matched fresh frozen specimens. Results: RNA stability was demonstrated by high degree of correlation between matched frozen and FFPE samples. Analytic precision was demonstrated by high correlation between RNA-Seq and TaqMan results for genes evaluated. As compared to IHC the results for RNA-Seq were continuous rather than bimodal and allowed for a much more detailed analysis of the immune response. Immune signatures were maintained with variable RNA/DNA input amounts, altered tumor micro-environments, and potential interferents demonstrating tolerance to typical sample types tested. Reproducibility results show little variation between runs and operators. Conclusions: The analytical performance of the Immune Advance assay has been validated for clinical use using FFPE tumor specimens.

Whole-genome sequencing of a malignant granular cell tumor with metabolic response to pazopanib

Granular cell tumors are an uncommon soft tissue neoplasm. Malignant granular cell tumors comprise <2% of all granular cell tumors, are associated with aggressive behavior and poor clinical outcome, and are poorly understood in terms of tumor etiology and systematic treatment. Because of its rarity, the genetic basis of malignant granular cell tumor remains unknown. We performed whole-genome sequencing of one malignant granular cell tumor with metabolic response to pazopanib. This tumor exhibited a very low mutation rate and an overall stable genome with local complex rearrangements. The mutation signature was dominated by C>T transitions, particularly when immediately preceded by a 5' G. A loss-of-function mutation was detected in a newly recognized tumor suppressor candidate, BRD7. No mutations were found in known targets of pazopanib. However, we identified a receptor tyrosine kinase pathway mutation in GFRA2 that warrants further evaluation. To the best of our knowledge, this is only the second reported case of a malignant granular cell tumor exhibiting a response to pazopanib, and the first whole-genome sequencing of this uncommon tumor type. The findings provide insight into the genetic basis of malignant granular cell tumors and identify potential targets for further investigation.

Perception and knowledge of clinical trials and factors affecting participation of regional and rural cancer patients of North Queensland

e17558

Background: The best treatment option for most cancers is participation in clinical trials. Participation in trials is generally low and among rural patients it is likely to be even lower. The aim of this study was to assess knowledge about and attitudes towards clinical trials among rural and regional cancer patients of North Queensland. Methods: A questionnaire-based survey was conducted in outpatient clinics at the Townsville Cancer Centre on all types of cancer patients. Results: The mean age of the 178 participants was 56 years and 45.4% lived in rural or remote areas. Median distance to the trial centre (Townsville) for rural participants was 180 km (range 80 - 1300 km). Being asked whether they would take part in a RCT, 13.2% of participants said no, 56.3% said yes, and 30.5% were unsure. There were no significant relationships between willingness to participate and rurality (p = 0.896) or education level (p = 0.943). For the majority of patients, the number of clinic visits and blood tests required did not matter. Cost of travel (41.1% rural/remote; 23.5% regional; p < 0.001) and the need for family or friends to accompany (38.9% rural/remote; 24.1% regional; p = 0.021) were more important for rural/remote than regional patients as factors affecting participation. Only 16.4% of participants were aware of early studies. After education, percentage of patients willing to participate in phase I and II studies were 57% and 84%, respectively. Rural patients were less willing to participate in phase I studies than regional patients (33.9% vs 52.6%, p = 0.029). Conclusions: Rural patients are as interested in participating in clinical trials as urban patients except for phase 1 trials and should not be excluded because of rurality. Knowledge of trials is poor and there is a need for education early. Cost of travel seems more important for rural patients and as such budgets should include cost of travel to encourage participation of rural patients.

No significant financial relationships to disclose.