Intra-patient stability of tumor mutational burden from tissue biopsies at different time points in advanced cancers

Abstract Background Tumor mutational burden (TMB) may be a predictive biomarker of immune checkpoint inhibitor (ICI) responsiveness. Genomic landscape heterogeneity is a well-established cancer feature. Molecular characteristics may differ even within the same tumor specimen and undoubtedly evolve with time. However, the degree to which TMB differs between tumor biopsies within the same patient has not been established. Methods We curated data on 202 patients enrolled in the PREDICT study (NCT02478931), seen at the University of California San Diego (UCSD), who had 404 tissue biopsies for TMB (two per patient, mean of 722 days between biopsies) to assess difference in TMB before and after treatment in a pan-cancer cohort. We also performed an orthogonal analysis of 2872 paired pan-solid tumor biopsies in the Foundation Medicine database to examine difference in TMB between first and last biopsies. Results The mean (95% CI) TMB difference between samples was 0.583 [− 0.900–2.064] (p = 0.15). Pearson correlation showed a flat line for time elapsed between biopsies versus TMB change indicating no correlation (R2 = 0.0001; p = 0.8778). However, in 55 patients who received ICIs, there was an increase in TMB (before versus after mean mutations/megabase [range] 12.50 [range, 0.00–98.31] versus 14.14 [range, 0.00–100.0], p = 0.025). Analysis of 2872 paired pan-solid tumor biopsies in the Foundation Medicine database also indicated largely stable TMB patterns; TMB increases were only observed in specific tumors (e.g., breast, colorectal, glioma) within certain time intervals. Conclusions Overall, our results suggest that tissue TMB remains stable with time, though specific therapies such as immunotherapy may correlate with an increase in TMB. Trial registration NCT02478931, registered June 23, 2015.

Download Full-text

NCMP-17. MISMATCH REPAIR MUTATIONS AND THE CENTRAL NERVOUS SYSTEM: A CASE SERIES OF GERMLINE MUTATIONS AND CNS MALIGNANCY

Neuro-Oncology ◽

10.1093/neuonc/noaa215.528 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii126-ii126

Author(s):

Amber Ruiz ◽

Jerome Graber

Keyword(s):

Treatment Response ◽

Mismatch Repair ◽

Case Series ◽

Germline Mutations ◽

Molecular Characteristics ◽

Loss Of Function ◽

Dna Mismatch ◽

Mutational Burden ◽

Increased Risk ◽

Tumor Mutational Burden

Abstract Our understanding of genetic predispositions for malignancy is continually evolving. One family of germline mutations well described in the literature is that of the DNA mismatch repair mechanism (MMR). Lynch syndrome (LS) is due to a loss of function mutation of several MMR genes- MSH2, MLH1, MSH6, and PMS2. Germline MMR mutations lead to microsatellite instability and loss of genomic integrity resulting in an increased risk for various cancers (colorectal, genitourinary, etc). LS may be as common as 1 in 400 people and some MMR mutations have been associated with gliomas. There is a paucity of information regarding frequency of glioma subtypes as well as tumor genetic and molecular characteristics which have important clinical implications. We describe a case series of 6 individuals with germline MMR mutations and brain tumors. Those with MSH2 and PMS2 mutations (n=3) developed glioblastomas at a mean age at diagnosis of 48 years. These tumors expressed MGMT hyper-methylation and high tumor mutational burden. Only one had IDH-1 mutation. Those with MLH1 mutations (n=3), did not develop gliomas. This raises the question of differential glioma subtype development based on MMR gene. It also highlights the possibility of Lynch-associated gliomas having more favorable treatment response due to MGMT methylation and potential response to immunotherapy based on high tumor mutational burden. Though the sample size is small, there appears to be a preponderance of women compared to men (5:1 respectively). Larger studies are needed to verify CNS involvement in germline MMR mutations. In doing so, we hope to identify factors that may influence clinical management and lead to a better understanding of treatment response and disease prognosis.

Download Full-text

Tumor mutational burden assessment in non-small-cell lung cancer samples: results from the TMB2 harmonization project comparing three NGS panels

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-001904 ◽

2021 ◽

Vol 9 (5) ◽

pp. e001904

Author(s):

Javier Ramos-Paradas ◽

Susana Hernández-Prieto ◽

David Lora ◽

Elena Sanchez ◽

Aranzazu Rosado ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Predictive Value ◽

Predictive Biomarker ◽

Small Cell ◽

Small Cell Lung ◽

Mutational Burden ◽

Reproducibility Study ◽

Tumor Mutational Burden

BackgroundTumor mutational burden (TMB) is a recently proposed predictive biomarker for immunotherapy in solid tumors, including non-small cell lung cancer (NSCLC). Available assays for TMB determination differ in horizontal coverage, gene content and algorithms, leading to discrepancies in results, impacting patient selection. A harmonization study of TMB assessment with available assays in a cohort of patients with NSCLC is urgently needed.MethodsWe evaluated the TMB assessment obtained with two marketed next generation sequencing panels: TruSight Oncology 500 (TSO500) and Oncomine Tumor Mutation Load (OTML) versus a reference assay (Foundation One, FO) in 96 NSCLC samples. Additionally, we studied the level of agreement among the three methods with respect to PD-L1 expression in tumors, checked the level of different immune infiltrates versus TMB, and performed an inter-laboratory reproducibility study. Finally, adjusted cut-off values were determined.ResultsBoth panels showed strong agreement with FO, with concordance correlation coefficients (CCC) of 0.933 (95% CI 0.908 to 0.959) for TSO500 and 0.881 (95% CI 0.840 to 0.922) for OTML. The corresponding CCCs were 0.951 (TSO500-FO) and 0.919 (OTML-FO) in tumors with <1% of cells expressing PD-L1 (PD-L1<1%; N=55), and 0.861 (TSO500-FO) and 0.722 (OTML-FO) in tumors with PD-L1≥1% (N=41). Inter-laboratory reproducibility analyses showed higher reproducibility with TSO500. No significant differences were found in terms of immune infiltration versus TMB. Adjusted cut-off values corresponding to 10 muts/Mb with FO needed to be lowered to 7.847 muts/Mb (TSO500) and 8.380 muts/Mb (OTML) to ensure a sensitivity >88%. With these cut-offs, the positive predictive value was 78.57% (95% CI 67.82 to 89.32) and the negative predictive value was 87.50% (95% CI 77.25 to 97.75) for TSO500, while for OTML they were 73.33% (95% CI 62.14 to 84.52) and 86.11% (95% CI 74.81 to 97.41), respectively.ConclusionsBoth panels exhibited robust analytical performances for TMB assessment, with stronger concordances in patients with negative PD-L1 expression. TSO500 showed a higher inter-laboratory reproducibility. The cut-offs for each assay were lowered to optimal overlap with FO.

Download Full-text

From Tumor Mutational Burden to Blood T Cell Receptor: Looking for the Best Predictive Biomarker in Lung Cancer Treated with Immunotherapy

Cancers ◽

10.3390/cancers12102974 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2974

Author(s):

Andrea Sesma ◽

Julián Pardo ◽

Mara Cruellas ◽

Eva M. Gálvez ◽

Marta Gascón ◽

...

Keyword(s):

Lung Cancer ◽

T Cell ◽

T Cell Receptor ◽

Checkpoint Inhibitors ◽

Predictive Biomarker ◽

Cell Receptor ◽

Mutational Burden ◽

Tcr Repertoire ◽

Tumor Mutational Burden ◽

Tumor Immunogenicity

Despite therapeutic advances, lung cancer (LC) is one of the leading causes of cancer morbidity and mortality worldwide. Recently, the treatment of advanced LC has experienced important changes in survival benefit due to immune checkpoint inhibitors (ICIs). However, overall response rates (ORR) remain low in unselected patients and a large proportion of patients undergo disease progression in the first weeks of treatment. Therefore, there is a need of biomarkers to identify patients who will benefit from ICIs. The programmed cell death ligand 1 (PD-L1) expression has been the first biomarker developed. However, its use as a robust predictive biomarker has been limited due to the variability of techniques used, with different antibodies and thresholds. In this context, tumor mutational burden (TMB) has emerged as an additional powerful biomarker based on the observation of successful response to ICIs in solid tumors with high TMB. TMB can be defined as the total number of nonsynonymous mutations per DNA megabases being a mechanism generating neoantigens conditioning the tumor immunogenicity and response to ICIs. However, the latest data provide conflicting results regarding its role as a biomarker. Moreover, considering the results of the recent data, the use of peripheral blood T cell receptor (TCR) repertoire could be a new predictive biomarker. This review summarises recent findings describing the clinical utility of TMB and TCRβ (TCRB) and concludes that immune, neontigen, and checkpoint targeted variables are required in combination for accurately identifying patients who most likely will benefit of ICIs.

Download Full-text

Tumor mutational burden as a predictive biomarker for checkpoint inhibitor immunotherapy

Human Vaccines & Immunotherapeutics ◽

10.1080/21645515.2019.1631136 ◽

2019 ◽

Vol 16 (1) ◽

pp. 112-115 ◽

Cited By ~ 5

Author(s):

Mark Lee ◽

Robert M. Samstein ◽

Cristina Valero ◽

Timothy A. Chan ◽

Luc G.T. Morris

Keyword(s):

Checkpoint Inhibitor ◽

Predictive Biomarker ◽

Mutational Burden ◽

Tumor Mutational Burden

Download Full-text

Abstract 3404: Landscape and genomic correlates of ctDNA-based tumor mutational burden across six solid tumor types

10.1158/1538-7445.am2019-3404 ◽

2019 ◽

Author(s):

Katie Quinn ◽

Elena Helman ◽

Tracy Nance ◽

Jennifer Yen ◽

John Latham ◽

...

Keyword(s):

Solid Tumor ◽

Mutational Burden ◽

Tumor Mutational Burden ◽

Tumor Types

Download Full-text

Defining tissue requirements for a reliable detection of tumor mutational burden (TMB) in clinical FFPE samples.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e14265 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e14265-e14265

Author(s):

Hans-Ulrich Schildhaus ◽

Thomas Herold ◽

Karl Worm ◽

Oliver Stoss

Keyword(s):

Predictive Biomarker ◽

Endoscopic Biopsy ◽

Cell Content ◽

Mutational Burden ◽

Wide Range ◽

Ffpe Samples ◽

Tumor Mutational Burden ◽

Minimum Requirements ◽

Tissue Size ◽

Definition Of

e14265 Background: In the context of immuno-oncology related cancer treatment, tumor mutational burden (TMB) is currently explored as a predictive biomarker for several human malignancies. Several sequencing assays are increasingly commercially available. Established methodologies require rather large amounts of DNA input (in the range of 100 ng) which, however, are frequently not available from small biopsies. We aim to investigate how tissue size and DNA input influence TMB scores. Methods: DNA from 20 specimens (12 biopsies of non-small cell lung cancer (NSCLC); 8 surgical resection specimens from NSCLC, colorectal cancer and endometrial carcinomas) was manually extracted by using the Qiagen GeneRead DNA FFPE kit. Cases were selected to provide a wide range of relative tumor cell content (from < 10% to > 50%) and to include microsatellite-stable and –instable (MSI) cases. Samples were analyzed in triplicates from predefined numbers of unstained sections of a standardized tissue size. DNA quantification was done fluorometrically and by qPCR. Up to 40 ng of DNA were analyzed with the QIASeq TMB Panel (incl. MSI primer boosters; Qiagen). Sequencing was done on an Illumina NextSeq platform, TMB scores and MSI status were determined by using the CLC workbench 5.0.1 (Qiagen). Results: Biopsy samples generated less numbers of DNA molecules (as detected by unique molecular identifiers, UMIs) and less overall reads compared to resection samples. UMI coverage was > 500x in all samples with > 15 ng DNA input. TMB scores were highly reproducible among all samples with > 15 ng DNA but differed significantly among samples with limited DNA input. Interestingly, TMB scores were inversely correlated with DNA input among those samples with < 15ng. Thus, valid TMB scores could also be obtained from only one slice of 1 cm2 tissue from tumor resections or 3 slices of an endoscopic biopsy (each of 5µm thickness). All pre-characterized MSI carcinomas could be detected correctly. Conclusions: We provide first evidence that TMB can be reliably determined also in small biopsies yielding limited DNA content. However, false high TMB scores can occur in samples with < 15ng DNA input. Our results contribute to the definition of minimum requirements (tissue size and DNA input) for valid TMB measurement on clinical FFPE samples.

Download Full-text

Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors

Cancer Discovery ◽

10.1158/2159-8290.cd-20-0522 ◽

2020 ◽

Vol 10 (12) ◽

pp. 1808-1825

Author(s):

Dan Sha ◽

Zhaohui Jin ◽

Jan Budczies ◽

Klaus Kluck ◽

Albrecht Stenzinger ◽

...

Keyword(s):

Solid Tumors ◽

Predictive Biomarker ◽

Mutational Burden ◽

Tumor Mutational Burden

Download Full-text

Establishing guidelines to harmonize tumor mutational burden (TMB): in silico assessment of variation in TMB quantification across diagnostic platforms: phase I of the Friends of Cancer Research TMB Harmonization Project

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2019-000147 ◽

2020 ◽

Vol 8 (1) ◽

pp. e000147 ◽

Cited By ~ 24

Author(s):

Diana M Merino ◽

Lisa M McShane ◽

David Fabrizio ◽

Vincent Funari ◽

Shu-Jen Chen ◽

...

Keyword(s):

Linear Regression ◽

Predictive Biomarker ◽

Regression Analyses ◽

Reference Standard ◽

Bioinformatics Pipeline ◽

Mutational Burden ◽

Study Results ◽

Tumor Mutational Burden ◽

Cancer Types ◽

Prediction Limits

BackgroundTumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, demonstrates predictive biomarker potential for the identification of patients with cancer most likely to respond to immune checkpoint inhibitors. TMB is optimally calculated by whole exome sequencing (WES), but next-generation sequencing targeted panels provide TMB estimates in a time-effective and cost-effective manner. However, differences in panel size and gene coverage, in addition to the underlying bioinformatics pipelines, are known drivers of variability in TMB estimates across laboratories. By directly comparing panel-based TMB estimates from participating laboratories, this study aims to characterize the theoretical variability of panel-based TMB estimates, and provides guidelines on TMB reporting, analytic validation requirements and reference standard alignment in order to maintain consistency of TMB estimation across platforms.MethodsEleven laboratories used WES data from The Cancer Genome Atlas Multi-Center Mutation calling in Multiple Cancers (MC3) samples and calculated TMB from the subset of the exome restricted to the genes covered by their targeted panel using their own bioinformatics pipeline (panel TMB). A reference TMB value was calculated from the entire exome using a uniform bioinformatics pipeline all members agreed on (WES TMB). Linear regression analyses were performed to investigate the relationship between WES and panel TMB for all 32 cancer types combined and separately. Variability in panel TMB values at various WES TMB values was also quantified using 95% prediction limits.ResultsStudy results demonstrated that variability within and between panel TMB values increases as the WES TMB values increase. For each panel, prediction limits based on linear regression analyses that modeled panel TMB as a function of WES TMB were calculated and found to approximately capture the intended 95% of observed panel TMB values. Certain cancer types, such as uterine, bladder and colon cancers exhibited greater variability in panel TMB values, compared with lung and head and neck cancers.ConclusionsIncreasing uptake of TMB as a predictive biomarker in the clinic creates an urgent need to bring stakeholders together to agree on the harmonization of key aspects of panel-based TMB estimation, such as the standardization of TMB reporting, standardization of analytical validation studies and the alignment of panel-based TMB values with a reference standard. These harmonization efforts should improve consistency and reliability of panel TMB estimates and aid in clinical decision-making.

Download Full-text

Blood tumor mutational burden (bTMB) and tumor PD-L1 as predictive biomarkers of survival in MYSTIC: First-line durvalumab (D) ± tremelimumab (T) versus chemotherapy (CT) in metastatic (m) NSCLC.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.9016 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 9016-9016 ◽

Cited By ~ 4

Author(s):

Naiyer A. Rizvi ◽

Byoung Chul Cho ◽

Niels Reinmuth ◽

Ki Hyeong Lee ◽

Alexander Luft ◽

...

Keyword(s):

Clinical Trial ◽

Predictive Biomarkers ◽

Predictive Biomarker ◽

Small Sample ◽

First Line ◽

Open Label ◽

Mutational Burden ◽

Tumor Mutational Burden ◽

Small Sample Sizes ◽

Clinical Trial Information

9016 Background: MYSTIC, an open-label, Ph3 trial of first-line D (anti-PD-L1) ± T (anti-CTLA-4) vs platinum-based CT, showed an improvement in OS with D vs CT in pts with tumor cell PD-L1 expression ≥25% (PD-L1 TC ≥25%; HR 0.76 [97.54% CI 0.56–1.02], p = 0.036). Exploratory analyses showed bTMB was a predictive biomarker for OS with D±T vs CT. We report further exploratory analyses of OS according to PD-L1 and bTMB. Methods: Immunotherapy/CT-naïve pts with mNSCLC were randomized (1:1:1) to D, D+T or CT. bTMB levels (mut/Mb) were evaluated with the GuardantOMNI platform (Guardant Health), and PD-L1 TC expression with the VENTANA PD-L1 (SP263) IHC assay. Results: D improved OS vs CT in pts with PD-L1 TC ≥25% across bTMB levels (PD-L1 TC ≥25%/bTMB≥20 HR 0.79 [95% CI 0.45, 1.39]; PD-L1 TC ≥25%/bTMB < 20 HR 0.64 [95% CI 0.45, 0.90]). In contrast, D+T improved OS vs CT in pts with bTMB≥20 across different PD-L1 TC expression levels (Table; PD-L1 TC ≥25%/bTMB≥20 HR 0.44 [95% CI 0.23, 0.84]; PD-L1 TC < 1%/bTMB≥20 HR 0.42 [95% CI 0.17, 0.97]). Additional cutoffs and outcomes in subgroups defined by both biomarkers will be presented. Conclusions: These exploratory analyses from MYSTIC support PD-L1 TC expression as an appropriate predictive biomarker for OS with D vs CT, while suggesting bTMB as a predictive biomarker for OS with D+T in mNSCLC. These biomarkers appear to be independent and both may be important for mNSCLC treatment decisions. Interpretation of these data may be limited by small sample sizes; further investigations are warranted. Clinical trial information: NCT02453282. [Table: see text]

Download Full-text

Primary versus metastatic intrahepatic cholangiocarcinoma: A comparative comprehensive genomic profiling (CGP) study.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.4_suppl.578 ◽

2020 ◽

Vol 38 (4_suppl) ◽

pp. 578-578

Author(s):

Jeffrey S. Ross ◽

Ethan Sokol ◽

Jo-Anne Vergilio ◽

Keith Killian ◽

Douglas I. Lin ◽

...

Keyword(s):

Drug Response ◽

Mutation Frequency ◽

Soft Tissues ◽

Genomic Profiling ◽

Upper Gi ◽

Metastatic Lesions ◽

Mutational Burden ◽

Comprehensive Genomic Profiling ◽

Tumor Mutational Burden ◽

Tumor Biopsies

578 Background: Genomic alterations (GA) characteristic of IHCC are well known. We queried whether the GA of IHCC from primary tumor biopsies (p-bx) would differ from IHCC metastasis biopsies (m-bx). Methods: CGP was performed on 1,268 cases of advanced stage IHCC using p-bx in 1,048 cases and m-bx from 220 cases. Tumor mutational burden (TMB) was determined on 0.8-1.1 Mbp of sequenced DNA and microsatellite instability (MSI) was determined on 114 loci. PD-L1 expression in tumor cells (Dako 22C3) was measured by IHC. Results: M-bx sites included: lymph nodes (63), soft tissues (47), peritoneum (34), lung/pleura (27), omentum (15), bone (10), GYN tract (5), brain (2), Upper GI (2), colon (2), bladder (1), abdomen (1) and adrenal (1). The GA per sample were similar as were biomarkers of immuno-oncology (IO) drug response. The KRAS mutation frequency was doubled in the m-bx compared to the p-bx (p < 0.001), and enrichment of the potentially targetable KRAS G12C was also observed. Frequencies of untargetable GA were similar overall. IDH1 (p < 0.001) and FGFR2 GA known to be enriched in IHCC were less frequent in the m-bx cohort. GA in STK11 were more frequently identified in m-bx. Conclusions: GA found in p-bx vs m-bx in IHCC are significantly different; the m-bx cohort featuring greater KRAS and lower IDH1 and FGFR2 GA. This suggests that the m-bx group may contain a significant number of non-IHCC cases whose metastatic lesions were actually derived from other primary sites incorrectly assigned the diagnosis of IHCC. [Table: see text]

Download Full-text