Clinical OMICs Presents. Immunotherapy and Cancer Harnessing the Power of Diagnostic Assays

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1 Clinical OMICs Presents Immunotherapy and Cancer Harnessing the Power of Diagnostic Assays Sponsored by Produced by

2 Immunotherapy and Cancer Harnessing the Power of Diagnostic Assays 3 A MESSAGE FROM MOLECULARMD Immuno-oncology s century-old promise now bearing fruit 5 FEATURE Achieving the Full Potential of Immuno-Oncology Harnessing the innate power of the human immune system 11 THE EVOLUTION OF CANCER THERAPIES AND BIOMARKERS 12 Q&A with Cindy Spittle, Ph.D. Precise molecular tests are crucial to the clinical success of an immuno-oncology candidate. Sponsored by 15 NEWS Biomarker Identification via NGS in the Age of Immunotherapies Taking a test from research use only to clinical diagnostic 18 POSTERS Photo credits: Getty Images: cover and p. 2: (background) Seb Oliver; (insets) Science Photo Library: Moredun Animal Health Ltd., Alfred Pasieka, Steve Gschmeissner; p. 2 & 5: D3Damon; p. 2, 14,15, 16: Jezperklauzen; p. 7: jarun11; p. 8 selvanegra; p. 9: Science Photo Library/Alfred Pasieka. Adobe Stock Photos: p. 17: sveta. Deposit Photos: p. 3: ktsdesign. Sue Seif: p. 7: from Shannon L. Maude et al. Blood 215;125: A Clinical OMICs ebook

3 Wecome to the MolecularMD ebook Immunotherapy and Cancer Harnessing the Power of Diagnostic Assays Immunotherapy is far from a novel concept and its history is intriguing. It all began in 1891 when Dr. William B. Coley attempted to creatively harness the patient s immune system. Coley achieved durable complete remission in several types of malignancies, to include sarcoma, lymphoma, and testicular carcinoma by injecting mixtures of live and attenuated bacteria into patients tumors. Needless to say, we have come a long way. Standing on the shoulders of giants: American surgeon and cancer researcher Dr. William B. Coley (left), and Australian Nobel Laureate Sir Frank Macfarlane Burnet (middle), are among the researchers who have contributed to the understanding of immunotherapy. The idea of using immunotherapy in cancer emerged in 1957 when Lewis Thomas and Sir Frank Macfarlane Burnet first proposed their theory of cancer immunosurveillance. They postulated that lymphocytes acted as sentinels to identify and eliminate somatic cells that were transformed by spontaneous mutations. It was not long thereafter, in 1976, when IL2 s clinical utility was demonstrated 3 A Clinical OMICs ebook

4 MolecularMD Senior Vicepresident Fritz Eibel welcomes your comments and inquiries. him at the address below. as an immunotherapeutic agent and achieved FDA approval for its use in combatting metastatic kidney and melanoma cancers. Other advancements include the use of antibody therapies most notably rituximab which was approved by the FDA in 1997 to target non-hodgkin s lymphoma. The mode of action is the binding of CD2 on the surface of immature B cells, subsequently targeting them for elimination by natural killer cells. Today, new advances and approaches are unlocking the power of the immune system. Checkpoint Inhibitors comprise various components such as cytotoxic T lymphocyte antigen 4 (CTLA-4), programmed cell death-1 (PD-1), and its ligand (PD-L1). These are expressed on tumor-infiltrating lymphocytes and on many types of tumor cells. This has the potential to enable malignant cells to evade cytotoxic immune responses such as checkpoint inhibitors. For example, Ipilimumab is an antibody targeting CTLA-4 which inhibits this process and facilitates T-cell activation against tumor cells. It is approved by the FDA for use in patients with melanoma. In addition, antibodies which target PD-1 and PD-L1 have demonstrated a strong clinical impact and are currently being tested across several tumor types. Adoptive cell therapy (ACT) has also made significant recent advances. In this case, a patient s T cell is isolated and then provided with a chimeric antigen receptor on its surface. The receptor enables a newly reengineered T cell to identify and destroy a given cancer. Tisagenlecleucel (Kymriah) was approved in 217 by the FDA for B-cell precursor acute lymphoblastic leukemia (ALL) that is refractory and/or in second or later relapse. We have just begun to scratch the surface of the potential which adoptive cell therapy and checkpoint inhibitors can bring to healthcare. There are many newly initiated immunotherapy programs which are underway as well as clinical trials that seek to leverage multiple vulnerabilities of a given cancer. It is going to require the diligent use of sophisticated diagnostics for these promising compounds to make it to market and for the combination trials to be successful. It is of paramount importance that the mode of action be understood and that appropriate tests are developedn for patient selection. This ebook is dedicated to the discussion and conversation of how biomarkers can be Fritz Eibel effectively deployed to mitigate risks Senior VP, Marketing as well as to compress the timeline to feibel@molecularmd.com regulatory approval. 4 A Clinical OMICs ebook

5 Achieving the Full Potential of Immuno-Oncology Cancer immunotherapies harness the innate power of the human immune system by enhancing the immune system s ability to recognize, target, and eliminate cancer cells. For the past 1 years, the fight against cancer has focused on developing targeted and immune-based drugs for oncology. Targeted drugs are seen as preferable to radiation and chemotherapy because of their Patient-selection remains one of the biggest increased specificity for tumor cells, challenges as new classes of immuno-oncology drugs have been developed. often resulting in Cindy Spittle, Ph.D., Vice President of less severe side Development and Scientific Affairs, MolecularMD effects than other drugs. Some types of immunotherapy are also designed to target specific tumor cells (CAR-T, TCR). Other types target checkpoint molecules that can be expressed in tumor and/or immune cells. The specificity of these drugs to hone-in on cancer or immune cells contributes to their overall effectiveness and attractive safety profiles. Despite the promise, current immune-oncology drug development has its challenges. Patient-selection remains one of the biggest challenges as new classes of immuno-oncology drugs have been developed, says Cindy Spittle, Ph.D., Vice President of Development and Scientific Affairs, MolecularMD. Precise molecular tests which can pinpoint the biomarkers that indicate potential patient response are crucial to the clinical success of an immuno-oncology candidate. Room For Improvement To date, there have been only a few biomarkers approved for patient selection. These include: PD-L1 IHC, or immunohistochemistry testing for PD-L1. PD-L1 is a protein found on the surface of cancer cells that attaches to the PD-1 receptor on T-cells and inactivates them. The drugs that target PD-L1 or PD-1 5 A Clinical OMICs ebook

6 belong to the largest class of immune-oncology drugs, known as checkpoint inhibitors (see I-O Drug Classes on page 8). MSI, or microsatellite instability, is caused by mutations in mismatch repair genes. MSI is also used as a biomarker to select patients for treatment with checkpoint inhibitors. Two emerging biomarkers include: TMB, or tumor mutational burden, which measures the amount of mutations present in tumor cells. GEx signatures, or gene expression profiling, which measures the mrna expression level of various genes involved in the immune response. There have been great successes using PD-L1 IHC, but there is room for improvement in terms of finding more patients who are likely to respond, Spittle says. Despite relative improvements over traditional treatments, she adds, the response rate is still pretty low with the one-biomarker approach for immunotherapies. Thankfully, more biomarkers are being discovered to assist researchers and clinicians with patient selection. And, evidence suggests that a multi-biomarker approach will result in the maximum number of eligible patients being identified for either approved immunotherapies, or for those moving through clinical trials. We recognize the DNA was the target of the first molecular diagnostic assays, but is the furthest from determining protein regulation which defines Cellular Function. Sequence-based techniques have provided information on multiple genes in parallel, but selection of genes and data analysis requires extensive cause and effect relationships for evidence-based diagnostic use. PCR NGS rt-pcr rt-qpcr RNA Microarrays RNAseq IHC Real-time quantitative PCR methods provide single gene analysis, which have been commonly adopted for single gene, single drug diagnostic applications. Advancements have been made with new techniques which get closer to the determination of Protein Regulation and hence, Cellular Function 6 A Clinical OMICs ebook

7 need for a multi-biomarker approach and are focused on the co-development and approval of oncology drugs and diagnostic assays, Spittle says. Need for Standardization Although both TMB and GEx signatures have shown promise, the problem with the use of these emerging biomarkers, as well as MSI, is a lack of standardization in methodology for each biomarker. There have been a lot of promising reports but the studies have used different methodologies, cut-offs and/or data analysis algorithms for each biomarker assay. It s a bit like the Wild West out there, Spittle says. This hinders the ability to determine true clinical utility. Large, controlled clinical studies are what is needed to solve the problem, she says. These studies will help pharmaceutical companies determine which biomarkers, or combination of biomarkers are most relevant to their drug and in turn which tests are most appropriate for the development of a targeted clinical diagnostic. What we do is perform direct comparisons between assays and methods as a first step in supporting global clinical trials and moving toward successful regulatory approval, Spittle says. Biomarker Selection Patient selection is critical at all stages of the drug approval process, even in Phase I clinical studies. The ideal biomarker test accurately matches drugs with patients who will benefit from them. In some cases, this can be accomplished by one biomarker, including T-cell receptor therapies, CAR- T, and antibody-based therapies (See Box: Immuno-oncology Drug Classes). However, biomarkers for drugs that target the check- Second-generation CAR used in current clinical studies at Penn and CHOP. CTL, cytotoxic T lymphocyte; MHC, major histocompatibility complex. point pathways are more complicated. We still need to find biomarkers that are related to the tumor and the tumor microenvironment, Spittle says. The efficacy of the checkpoint inhibitors depends on the presence and activity tumor-infiltrating lymphocytes. Selecting the appropriate biomarkers depends on the molecular makeup of the drug, the biology of the cancer, and requires evaluation of tumor-specific and tumor microenvironment-related biomarkers. Researchers 7 A Clinical OMICs ebook

8 Box 1: Immuno-Oncology Drug Classes The recent focus on the development of immunotherapies has resulted in a long list of relatively new and emerging drugs. Among them are: ADOPTIVE CELL THERAPIES T-cell receptor. T-cells with the most cancer-fighting potency are selected, grown into the billions, and reinfused into the patient. CAR-T. Chimeric antigen receptor (CAR) T-cell therapy involves removing T-cells from a patient s blood, inserting a gene into the T-cells for a receptor that binds to the patient s cancer cells, growing the resulting CAR-T cells to large numbers, and reinfusing them into the patient. ANTIBODIES Monoclonal Antibodies (mabs). Monoclonal antibodies are designed to bind to specific antigens on cancer cells. Bispecific Monoclonal Antibodies (bimabs). Bispecific mabs are ones in which one binding site is designed to attach to a cancer cell and another is designed to attach to T-cells. CHECKPOINT INHIBITORS Approved CTLA-4 inhibitors. Cytotoxic T-lymphocyte-associated (CTLA) protein 4 (CTLA-4) is a protein on T-cells that acts as an off switch. PD-1 inhibitors. Programmed death receptor 1 (PD-1) is a checkpoint protein on T-cells. PD-L1 inhibitors. Programmed death-ligand 1 (PD-L1) is a protein on normal (and some cancer) cells that, when it binds PD-1, inactivates the T-cells. Emerging GITR inhibitors. Glucocorticoid-induced TNFR family related gene (GITR) inhibitors prevent T-cell inactivation while activating CD8+ T effector cells. LAG-3 inhibitors. Lymphocyte activation gene 3 (LAG-3) inhibitors work in a similar way to GITR inhibitors. 4-1BB stimulators. Tumor infiltrating lymphocytes have an array of co-stimulatory receptors (those involved in activating T-cell response). 4-1BB is one of them. CD4 stimulators. Cluster of differentiation 4 (CD4) is also a co-stimulatory protein. Chimeric antigen receptor (CAR) therapy: Engineered receptors on the surface of a T-lymphocyte bind specifically to CD19-antigen molecules on the surface of a leukemia cell. CYTOKINES directly stimulate immune effector cells and stromal cells at the tumor site and enhance tumor cell recognition by cytotoxic effector cells. ONCOLYTIC VIRUS THERAPIES (VACCINES). Cancer vaccines involve modifying viruses so that they reproduce efficiently in cancer cells until the cell bursts, but do not impact healthy cells. ANTIBODY-DRUG CONJUGATES are monoclonal antibodies that target a tumor-specific antigen and carry a potent drug that is released once inside the cell. 8 A Clinical OMICs ebook

9 Making Biomarker, Assay, and Platform Selection Data-Driven Translational research studies, such as the one above, are being conducted to understand the correlation between tumor mutation burden (TMB) and MSI status across cancer types. See posters in appendix for more information. are still working on answering some important questions related to biomarker selection. These include: Do you need to determine MSI, TMB and a GEx signature for all tumor types? Are the same assay cut-offs relevant for all tumor types? Will different methodologies provide the same answer? Biomarker selection for immunotherapies is as unique as the immunotherapies and cancers they treat and require customized approaches for their identification. Current research is starting to answer these questions, as exemplified by a team of researchers at the University of Tennessee, which compared TMB, PD-L1, and MSI for 26 cancer types in 11,348 cases. 1 The team found overlap among the biomarkers in most cancers, but not others. For example, there were no MSI-high melanoma patients, but many TMB-high ones. Once a biomarker for a checkpoint inhibitor is selected, an appropriate testing method and platform must also be selected based on a number of factors, including practical utility of that platform in a clinical laboratory setting. For example, NGS is a widely used approach to biomarker analysis. However, the sample requirements, library preparation methods, sequencing chemistries and data output can vary widely in NGS. The NGS methods used to assess TMB include WES and targeted gene panels, a variety of data analysis pipelines and varying high/low cut-off values. For MSI determinations, some labs will use IHC as a testing method and others will use a PCR/capillary electrophoresis-based method. In terms of potential real-world adoption, a method or platform will be impractical and unlikely to be implemented if it requires a long turn-around time, uses an entire tissue block, or costs several times more than a comparable method or platform. T lymphocyte cell attached to a cancer cell. T lymphocytes are a type of white blood cell that recognise a specific site (antigen) on the surface of cancer cells or pathogens. 9 A Clinical OMICs ebook

10 MolecularMD has been exclusively focused on addressing the needs of oncology drug development programs for more than a decade. That s all we do and all we have done, Spittle says. The company has seen up-close the success and failures in the drug development process. We ve evolved in our capabilities in being able to keep up with the regulatory changes that have come about in drug and diagnostic co-development. For example, the company has experience with FDA-required study risk determination submissions that determine whether an IDE will be required for the clinical trial. We ve always had the infrastructure to support global clinical trials. Comprehensive support includes project management, data management, regulatory expertise, and CLIA-certified, CAP accredited laboratories. We have the capability to receive samples from all over the world and deliver results for those samples. Years worth of experience has resulted in a unique, individualized approach to drug and diagnostic co-development. We don t offer a pre-set list of tests to choose from or offer just one test. The company instead has a variety of different platforms to choose from and offers commercially available assays as well as custom assay development. We can do platform comparisons early on to help select the best path forward using independent data that we are able to generate. In our experience, one size does not fit all. Citation 1. Vanderwalde A, Spetzler D, Xiao N, Gatalica Z, and Marshall J. Microsatellite instability status determined by next-generation sequencing and compared with PD-L1 and tumor mutational burden in 11,348 patients. Cancer Med. 218 Mar;7(3): A Clinical OMICs ebook

11 INFOGRAPHIC PAST PRESENT FUTURE View Full Screen The EVOLUTION OF CANCER THERAPIES AND BIOMARKERS COLD TUMOR Download PDF View Full Screen Download PDF HOT TUMOR All Cells Receptor-Specific Cells Tumor-Specific Immune Cells Empirically Prescribed Therapies C H E M O T HE The R A P Y RADIATION EVOLUTION of CANCER THERAPIES and BIOMARKERS ALK Simple LUNG CANCER EGFR KRAS BRAF ERBB2 IHC &/or PCR BREAST CANCER Precision and Personalized Therapies Targeted Drugs: Usually One Drug, One Dx Tissue Specific Targeted Therapies TUMOR CELL Targeted Therapy Dx Testing Today for Targeted Therapies FROM ROS1 MELANOMA COLORECTAL CANCER KRAS Targeted Therapy A Smart Bomb COLD TUMOR Invisible to the immune system with few to no tumor antigens or T-Cells. 1% of Patients BRAF PIK3CA A biomarker identifies a tumor s vulnerability and the right drug is prescribed to exploit its weakness. Immunomodulators turn cold tumors to hot: Cancer Vaccines Oncolytic Viruses T-Cell Engagers Innate ImmuneActivators Myeloid Cell Immune Modulators CANCER ANTIGEN PRESENTATION Highly visible to the immune system with high numbers of antigens and T-Cells. RELEASE OF CANCER CELL ANTIGENS Immunotherapies The Dx Testing Future for Immunotherapies TIS TMB MSI IHC I-O Drugs are likely to be administered in combination with a Targeted Therapy or other I-O Drugs Tumor Agnostic Therapy PRIMING AND ACTIVATION TO TRAFFICKING OF T-CELLS TO TUMORS KILLING OF CANCER CELLS INFILTRATION OF T-CELLS INTO TUMORS RECOGNITION OF CANCER BY T-CELLS Immunotherapies Redirecting the Host Immune System Relies on dynamic interactions between tumors and immunomodulators in the tumor microenvironment. Killer T-cells destroy infected cells. To distinguish between healthy and infected cells they identify antigens. T-CELL HOT TUMOR Complex LUNG CANCER BREAST CANCER MELANOMA COLORECTAL CANCER OTHER CANCERS 1% of Patients TUMOR CELL APCs APCs (antigen-presenting cells) fight tumors via stimulation of B and cytotoxic T-cells that produce antibodies against tumor-related antigens. 11 A Clinical OMICs ebook

12 Q&A: Cindy Spittle Mitigating Risk Biomarker Validation to the Commercial Deployment of a CDx CINDY SPITTLE, Ph.D., is the vice president of development and scientific affairs responsible for guiding cancer biomarker assay design, development and validation and new technology evaluation at MolecularMD. Dr. Spittle has held leadership positions at Pfizer, Fox Chase Cancer Center, and SmithKline Beecham Clinical Laboratories. She has demonstrated expertise in clinical trial assay designs and commercial assay development. CO: What is the current state of drug development and diagnostics? Spittle: We are seeing a shift from targeted therapies which block the growth of cancer cells to immunotherapies which bolster the patient s immune system. For targeted therapies, many of the cell signaling pathways have not been linear and are potentially redundant. The inflection point for immunotherapies has been predicated upon the success and accelerated approval of Pembrolizumab, which has ushered in a new frontier for clinical drug development. Hundreds of new immunotherapeutic agents are in development. 12 A Clinical OMICs ebook

13 To realize the potential of immunotherapies, we need to know how and why certain individuals respond to treatment. This goes beyond the approach of profiling tumor mutations as is done with targeted therapies. We need to identify the level of tumor immunogenicity, the level of immune infiltration and to characterize how effective the patient s immune system is at recognizing and engaging the tumor. CO: Why are current biomarkers, such as PD-L1 expression insufficient? CS: This issue can be viewed at two levels the first is a technical one. Analysis of PD-L1 expression is highly subjective, and requires not only standardization across laboratories, but within a laboratory. Current methods utilize different cutoff values and rely upon pathologists training and level of expertise. PD-L1 IHC staining has been adopted by clinical laboratories to assess response to immunotherapies, yet is an imperfect biomarker and requires further standardization and optimization. The second issue is that PD-L1 expression is likely to be one dimensional. Looking at one biomarker for one drug, is not dissimilar to only using EGFR testing for non-small cell lung cancer patients. Our understanding of patient response to treatment is evolving, so it becomes difficult to select one biomarker, or to establish a specific cut-off threshold for immunotherapeutic Hundreds of new agents are in patient selection. This is particularly true when different PD-L1 development. antibodies are used and different tumor types are being evaluated. Various methods are being use to gain multidimensional information about the tumor and its microenvironment, including tumor infiltration, tumor mutation burden, gene expression, and microsatellite instability. CO: What is the role of diagnostics in therapy today, and why is it critical? CS: Diagnostics need to be clinically meaningful and deployable in medical practice. Unfortunately, a wide variety of biomarker assays and methodologies are being used in clinical trials. The implications of not having standardization will become problematic as different methodologies are likely to generate conflicting results. 13 A Clinical OMICs ebook

14 Thoughtfully planned biomarker assay development and validation using standardized methodologies will improve our understanding of the clinical utility of a biomarker assay for patient enrollment. A clinical trial assay (CTA) should be the prototype for the development of a complementary or companion diagnostic assay that will accelerate drug approval. This decreases development costs, improves time-to-market, and return on investment. CO: What are some of the challenges as a biomarker assay makes the journey from concept to an in vitro diagnostic? CS: In both CTA and IVD assay development, the sample type, sample quality and sample availability need to be considered. WGS and WES typically utilize DNA and RNA extracted from fresh or frozen tumor tissue. While this may be suitable in research studies, it is not practical in clinical trials or clinical practice since these sample types are not readily available. Likewise, an assay that requires a large amount of tissue or blood is not pragmatic. The amount of tissue, blood or plasma that is needed for a diagnostic assay has to align with what is routinely available. CO: How is MolecularMD addressing these challenges? CS: MolecularMD has over 1 years of experience in developing and validating CTA and IVD assays and supporting global clinical trials. We have obtained IDE approval for PCR, NGS and IHC-based assays. We have built an infrastructure which is agile and capable of bringing value and insight to drug development programs from early Phase 1 through drug and diagnostic approval. We offer the scientific, regulatory and operational expertise that provides a continuum of support that can carry an assay from RUO to clinical trial/ide to IVD. 14 A Clinical OMICs ebook

15 Biomarker Identification via NGS in the Age of Immunotherapies Next-generation sequencing (NGS) has shown broad applicability in helping clinicians to tailor treatments for disease, pharmaceutical companies to identify drug targets, and diagnostic developers to create companion and complementary diagnostic tools. Today, as immuno-oncology (I-O) therapies for cancer play an increasing role in the treatment armament, it is ever more important to better identify the relevant biomarkers that can help target a specific therapy to a specific patient. Areas of interest for I-O biomarkers that leverage NGS are tumor mutation burden (TMB), gene expression profiling of the tumor microenvironment, neoantigen profiling, identification of checkpoint inhibitor resistance mechanisms, and even detailed profiling of the microbiome. One of the most studied areas in I-O is TMB, which has shown great promise as an emerging biomarker that can indicate response to checkpoint inhibitors for non-small cell lung cancer, and melanoma. Unfortunately, TMB has also shown that at least currently it is an imperfect biomarker. TMB is dominated by a few commercial labs and institutions using their own proprietary, LDT NGS panels and analysis algorithms, says Jin Li, executive scientific director of advanced diagnostics at MolecularMD. This makes it difficult to compare high TMB results from different TMB is dominated by a few commercial labs and institutions using their own proprietary, LDT NGS panels and analysis algorithms, Jin Li, Executive Scientific Director of Advanced Diagnostics, MolecularMD labs and limits the availability of TMB assays to broad patient populations. 15 A Clinical OMICs ebook

16 Ion Torrent Ion GeneStudio S5 XL for next generation sequencing workflow at MolecularMD. The solution to this problem is the development of a standardized commercial panel that can be used at a broad array of labs and will return consistent results for both high tumor burden and low tumor burden. This is an area of expertise for MolecularMD, which has built a reputation of taking research-only assays and optimizing them for use in the clinic. There is a very big difference between the world of a research laboratory and that of a clinical laboratory, notes Cindy Spittle, MolecularMD s vice president of development and scientific affairs. That is where MolecularMD is unique: we help bridge between what is used as a research assay and what can be used in a clinical setting in making patient treatment decisions. That started with our very first test for BCR-ABL and taking that from the research setting all the way to the FDA. We can apply those same lessons learned to the world of immunotherapy biomarker assays. One example of this expertise was presented earlier this year at the AACR Annual Meeting in Chicago, where the company showed how it improved ThermoFisher s TML panel for TMB analysis using clinical FFPE samples. According to Li, his lab first looked to correlate the results from the targeted panel with exome sequencing, and showed excellent correlation of the 16 FFPE samples tested. But when it came to FFPE samples with variable quality, the results were mixed. Good quality samples provide accurate TMB measurements, but the test overestimated TMB in poorer quality samples due to deamination. MolecularMD saw an opportunity to increase the value of the Thermo Fisher assay so that it could be used for FFPE samples of varying quality. It did so by first using a duplicated TML assay which significantly reduced errors from FFPE samples by 98%. Realizing that running duplicate analysis came with a high cost, Li and team conducted in silico analysis of running duplicated library preps at half coverage and proved it was as accurate and could keep costs in line. This shows how we bridge from the Research Use Only (RUO) to the clinical assay, Li notes. If you strictly follow Thermo s protocol for everything, you might get some wrong results. We improved the workflow, and proved it works with our bioinformatics, to ensure the accuracy of our results. 16 A Clinical OMICs ebook

17 Another example of MolecularMD s ability to ensure accurate test results was demonstrated in its development of a QC assay that can help eliminate poor-quality FFPE samples. This is especially important for gene expression profiling of the tumor microenvironment, which has been shown to be a complementary method to PD-L1 IHC and mutation burden for predicting checkpoint inhibitor response. Using FFPE samples of poor quality in these studies has limited the use of transcriptome sequencing for clinical studies due to its high costs and low quality samples generating unusable data. To ensure that only FFPE samples are used that will generate usable data, the company created a simple 5-plex RT-qPCR assay to measure the expression of 5 housekeeping genes. This QC assay helps us to eliminate poor FFPE samples and ensure the accuracy of the assay and lower the assay cost for our sponsors, Li says. According to Spittle the use of multiple technologies and platforms for its work, and its focus on optimizing and improving existing assays, is something that sets MolecularMD apart from other diagnostic developers. There are a lot of assays out there, but they are manufactured and meant for research use only, Spittle concludes. We look at whether we can take one of the those tests, optimize it, change it, and modify it to make it a better assay to be used in a clinical setting. Or if there aren t any commercial assays out there for the intended use, we can also develop our own custom assays using the best platform for the biomarker of interest. 17 A Clinical OMICs ebook

18 Checkpoint inhibitors have been approved for treatment of solid tumors and hematological malignancies. PD-L1 immunohistochemistry (IHC) assays are currently being used as companion or complementary diagnostics to select patients for treatment. However, patient responses are variable and there is a need to identify additional predictive biomarkers beyond PD- L1 levels as measured by IHC. The assessment of tumor infiltrating lymphocytes (TILs) in addition to PD-L1 staining using IHC has also been proposed for patient stratification (Ref.1). However, IHC scoring and classification can be complicated by the definition of positivity and intratumoral heterogeneity. A more comprehensive assessment of the tumor and its microenvironment using WES for tumor mutation burden analysis (TMB) and/or WTS for gene expression profiling (GExP) has also been shown to be informative for predicting patient responses. However, comprehensive analysis using these methods can be challenging with poor quality and limited amounts of FFPE DNA and RNA from clinical samples. Here we demonstrate the feasibility of performing TMB analysis and GExP in clinical FFPE samples using targeted NGS panels. The results from these studies highlight the value of using IHC in combination with targeted NGS methods to comprehensively assess the tumor and its microenvironment. Immunohistochemistry 5-micron sections were stained using mabs, α-cd3 (clone LN1, Leica), α-cd163 (clone 1D6, Leica), α-cd8 (clone 4B11, Leica) and α-pd-l1 (clone E1L3N, Cell Signaling) on the Leica BOND III platform. Detection of the target protein was visualized using the Bond Polymer Refine Detection (Leica). IHC images were captured via an Aperio AT2 Slide Scanner (Leica). Targeted RNA Seq 128 FFPE specimens were analyzed, including renal cell carcinoma (RCC), colorectal cancer (CRC), non-small cell lung cancer (NSCLC), urothelial cancer (UC) and other solid tumor types. The quality of the RNA samples was assessed using a custom, multiplexed RT-qPCR assay. The expression level (Ct) of 5 housekeeping genes in each FFPE sample was compared to the levels in an RNA control (the Human Lung Total RNA, Thermo Fisher). The Oncomine Immune Response Research Assay (OIRRA) from Thermo Fisher was used for GExP of 391 genes involved in tumor-immune cell interactions. 1 ng RNA input per sample was used for library preparation and 32 barcoded libraries were sequenced on a 54 chip using the Ion Chef-S5 XL system. Data analysis was performed using Torrent Suite 5.4, EXCEL, dchip and pheatmap in R package. Targeted DNA Seq 7 RCC, 4 CRC and 4 NSCLC were analyzed with FFPE processed GM12878 as the negative control. The Ion AmpliSeq Comprehensive Cancer Panel (CCP49) from Thermo Fisher was used for TMB analysis. CCP49 provides full coding exon coverage of 49 cancer related genes using 4 multiplexed primer pools. The Oncomine Comprehensive Assay v3 (OCAv3) from Thermo Fisher was used to detect clinically relevant SBS, INDEL, CNV, and fusions. 4 ng DNA input per sample was used for CCP49; 2 ng DNA and 2 ng RNA per sample was used for OCAv3. 8 barcoded libraries were sequenced on a 54 chip using Ion Chef-S5 XL system. Oncomine variant analysis: alignment was performed by the Ion Torrent 5.4 software, variant calling and annotation by the Ion Reporter Software (IR 5.4). The driver gain-of-function/loss-of-function variants were identified by the Oncomine Variant Annotator plugin (v2.), with the Oncomine Knowledgebase. TMB analysis: each sample was analyzed in duplicates. Synonymous and non-synonymous SBS and small INDEL in coding regions were included in the total count. Variants were then removed from the total count based on the following filters: 1) those not reproducibly detected in the sample replicates 2) those detected in FFPE GM ) those found in dbsnp (germline mutations) and the COSMIC database 4) those with mutant allele coverage below 2 read depth and 5) those that failed in IGV inspection. Removal of variants that were not found in sample replicates reduced artifacts caused by FFPE deamination. Removal of the COSMIC variants reduced the bias toward the cancer-related genes by the CCP49 panel (Refs 2 and 3). TMB was calculated as the rate of the true variants per million bases of CCP49 ROI. The ROI is calculated at 1.25 million bases with an R-script to eliminate the overlapped amplicon design among four (4) primer pools. Immunohistochemistry Expression of PD-L1 and presence of total T cells (CD3), M2 macrophages (CD163), and cytotoxic T cells (CD8) in tumor and its microenvironment varied dramatically among the FFPE tumor samples examined (Figure 1). RCC-99 and CRC-61 showed prominent PD-L1 staining on tumor cells, whereas cases RCC-17 and CRC-124 showed only immune cell staining. All 4 cases showed a range of tumor heterogeneity, with hot spots of immune infiltrate and regions of necrosis. Checkpoint inhibitors have been approved for the treatment of solid tumor and hematological malignancies. PD-L1 immunohistochemistry (IHC) assays are currently being used as companion or complementary diagnostics to select patients for treatment. While significant responses have been observed in a subset of patients, outcomes are variable and there is a need to identify additional predictive biomarkers beyond PD-L1 levels as measured by IHC. Tumor mutation burden (TMB) has been shown to correlate with response to checkpoint inhibitors and is emerging as a potentially important predictive biomarker. So far, the methods used to assess tumor mutation burden have included exome sequencing and multiple laboratory-developed targeted NGS panels (Refs.1 and 2). Increased TMB in cancer cells could be caused by abnormal activity in several cellular pathways, including DNA damage repair and DNA replication. Microsatellite instability (MSI) is a molecular marker of a deficient mismatch repair (MMR) pathway. The positive correlation between MSI and TMB has been observed in certain cancer types. In order to fully determine the value of TMB as a predictive biomarker for immunotherapy, a standardized panel, workflow and data analysis pipeline for TMB assessment are needed. In this study we evaluated the performance of a commercially available targeted NGS panel and workflow for TMB analysis. The correlation between the MSI status and the TMB level in each sample was also evaluated. Samples A set of 24 FFPE tumor samples from cancers that occur in the gastrointestinal tract (esophagus, stomach, large intestine), the reproductive system (cervix and uterus) and lung were analyzed (Table 1). DNA was extracted using either the RecoverAll Total Nucleic Acid Isolation Kit, or Promega Maxwell CSC DNA FFPE kit on the Maxwell CSC instrument. DNA was quantified using NanoDrop and Qubit. Targeted NGS Analysis A preliminary assessment of the performance of a targeted panel compared to Whole Exome Sequencing (WES) for TMB assessment was conducted using the Ion Comprehensive Cancer Panel. This panel targets 49 genes and is similar in content to the Oncomine Tumor Mutation Load Research Assay (see below). When performing TMB analysis using the CCP49 Panel synonymous and non-synonymous single nucleotide variants (SNVs) in coding regions were included in the total count. Variants were then removed from the total count based on the following filters: 1) variant frequency below 1% 2) those found in dbsnp (germline mutations) and the COSMIC database 3) those with mutant allele coverage below 2 read depth and 4) those that failed in inspection using the Integrative Genomics Viewer (IGV). The Thermo Fisher Oncomine Tumor Mutation Load Assay (TML) was used to assess the TMB level in the tumor samples selected (Table1). The TML Assay evaluates TMB (mutations/mb) by interrogating 49 cancer-related genes, spanning ~1.7 megabases of the genome. 2ng dsdna, measured by Qubit, was used for library preparation as per the manufacturer s instruction. Subsequently, the TMB in each sample was assessed with two workflows, namely, the manufacturer s (THF) workflow and MolecularMD s (MMD) workflow (Figure 1). In both workflows, alignment was performed by the Ion Torrent 5.6 software, and variant calling and annotation by the Ion Reporter Software (IR 5.6). In the THF workflow, a single library was prepared for each sample. Eight (8) barcoded libraries from eight (8) unique samples were sequenced on one (1) 54 chip using the Ion Chef-S5 XL system. TMB was measured by counting the somatic single-base substitutions per Mb at 5% allele frequency. In the MMD workflow, 2 libraries were independently prepared for each sample. Sixteen barcoded libraries representing duplicates of eight (8) unique tumor samples were sequenced on one (1) 54 chip using the Ion Chef-S5 XL system (TS5.6 and IR5.6). The TMB was determined by counting the somatic SNVs per Mb that 1) were reproducibly detected in each duplicate sample; 2) were exonic; 3) had a mutant allele frequency above 1% in at least one duplicate; and 4) were not found in dbsnp (germline mutations) or the COSMIC databases. Figure 1: TMB analysis workflows 8 Samples/Chip Counts of variants: Intronic Synonymous Nonsense Exonic Missense 8 Duplicated Samples/Chip Counts of REPRODUCED variants: Exonic Non-dbSNP Missense Synonymous Non-COSMIC Nonsense Figure 1. Expression of PD-L1 and infiltrating immune cells. RNA QC Analysis FFPE RNA samples with a delta-ct (Ct Sample- Ct Control) <9.7 were analyzed by the OIRRA. RNA samples with a lower delta-ct value generated better sequencing QC metrics, i.e., more valid reads (Figure 2A) and longer read length (Figure 2B), demonstrating the utility of our custom in-house RNA QC assay. Figure 2. Correlation of RNA QC Assay with RNA-Seq performance A B Delta-Ct Delta-Ct Immune response signatures Three different IFNG immune response signatures were assessed using OIRRA data. The signatures ranged in content from 4 to 25 genes (Table 1). Higher expression of each of these gene signatures was predictive of better response to the relevant immunotherapy (Refs.4-6). Mean expression (log2(rpm+1)) of the component genes in respective IFNG signatures was used to score the IFNG signatures. 34 NSCLC and 48 UC samples were analyzed. The mean expression scores for the three IFNG signatures correlated to each other in urothelial cancer (Figure 3, comparing the trend of green, red and blue lines) and NSCLC (data not shown). Constant expression of housekeeping genes (Figure 3, purple line) suggested an accurate measurement of IFNG signatures. UC samples can be further clustered into subgroups based on low to high expression of the 25 gene IFNG signature (Figure 4). Similar results were also observed in NSCLC (data not shown). Table1. Gene content of 3 published immune response signatures Signatures 25-gene 4-gene 1-gene *The genes corresponding to the pink-highlighted cells are included in the respective immune response signatures. Figure 3. Comparison of immune response signature scores gene 4 4-gene 2 1-gene housekeeping genes Urothelial Cancer Samples (N=48) MSI Analysis The MSI status in each sample was determined using the Promega MSI Analysis System v1.2 on ABI 35DX Genetic Analyzer. The system is a fluorescent PCRbased assay to detect the presence of five (5) mononucleotide repeat markers (BAT-25, BAT-26, NR-21, NR-24 and MONO-27) and two pentanucleotide repeat markers (Penta C and Penta D). Two (2) ng DNA from each sample, measured by Qubit, was used as input into the assay. The MSI status was determined based on comparing allelic profiles generated from tumor vs normal DNA. The presence of alleles with altered length in the tumor sample is interpreted as exhibiting microsatellite instability. Tumor samples with no altered markers are classified as microsatellite stable (MSS), with 2 out of 5 altered marker as high MSI (MSI-H), or with 1 altered marker as low MSI (MSI-L) (Table 1). Table 1. FFPE clinical samples evaluated and their MSI status Sample ID Tissue Tumor Content (%) MSI-Status Altered Alleles MMD Stomach 6 MSS MMD Large Intestine 75 MSS MMD Large Intestine 8 MSS MMD Large Intestine 53 MSS MMD Large Intestine 68 MSS MMD Large Intestine 8 MSS MMD Large Intestine 48 MSS MMD Esophagus 5 MSI-L 1 MMD Stomach 5 MSI-H 5 MMD Large Intestine 5 MSI-H 5 MMD Large Intestine NA MSI-H 5 MMD Large Intestine 8 MSI-H 5 MMD Large Intestine 8 MSI-H 5 MMD-262-D2 Large Intestine N/A MSI-H 5 MMD-262-D3 Large Intestine N/A MSI-H 4 MMD-262-D7 Large Intestine N/A MSI-H 5 MMD-262-D8 Large Intestine N/A MSI-H 5 MMD Cervix 45 MSS MMD Uterus 1 MSS MMD Uterus 25 MSS MMD Uterus 4 MSI-H 3 MMD Uterus 1 MSI-H 4 MMD NSCLC 7 ND NA MMD NSCLC 7 ND NA WES vs Targeted NGS for TMB Analysis In a separate pilot study, a set of sixteen FFPE samples was evaluated and TMB assigned using WES and the CCP49 targeted gene panel. The CCP49 panel data was analyzed using a MMD filtering method (See Materials and Methods). A good correlation was observed (Figure 2), demonstrating the feasibility of using a targeted gene panel for TMB analysis. Use of a targeted gene panel provides advantages over WES when performing clinical sample analysis including lower DNA input requirements, lower cost and faster TAT. Figure 2. Correlation of the TMB determined by MMD workflow and the WES R² = CCP49-MMD TMB Analysis - THF workflow When assessed with the THF workflow (see Figure 1), eight (8) libraries were sequenced on one (1) 54 chip. The percentage reads on target ranged from 82% to 99%, uniformity from 87% to 97%, and mean depth from 312X to 897X (Figure 3). TMB results ranged from variants/mb. The majority of samples were found to harbor <125 variants/mb. Two samples were notable outliers (>4 variants/mb). The TMB determined for each sample using the THF workflow showed a certain degree of positive correlation with the MSI status measured in these samples (Figure 4), especially within related cancer types (e.g., gastrointestinal tract or the reproductive system (comparing group All, vs group GI or group Cervix_uterus, in Figure 4). The analysis of 4 samples was repeated using the THF workflow, including the 2 samples with TMB >4 and 2 samples with TMB <1 variants/mb. The variants reproducibly detected between the two runs ranged from 2% to 68% out of those initially detected ( Reproduced/THF-mean in Table 2). Previous data generated with these samples suggested that poor DNA quality/deamination could lead to the detection of false positive SNVs due to FFPE deamination errors. Figure 3. QC Metrics of the Sequencing Runs using THF workflow 12% 1% 8% 6% 4% 2% % Figure 4. Heatmap of 25-gene IFNG signature in urothelial cancer samples. Tumor mutation burden The majority of mutations (88%) detected were at >1% frequency (Figure 5B), suggesting that a 1% cut-off may be sufficient for TMB analysis. Duplicate analysis of each sample allowed for the elimination of deamination errors in FFPE (C>T), especially in two poor-quality samples (NSCLC-66 and NSCLC-77). It also allowed for reliable detection of low-level mutations (3-1%) (Figure 5 B and C). The TMB range was lowest in RCC samples ( /Mb). TMB in the NSCLC and CRC samples ranged from to 4-28/Mb, respectively (Table 2). These results are consistent with what has been previously reported for these tumor types (Ref.7). 3 RCC samples harbored mutations in genes involved in DNA repair (Table 2). Samples with somatic SETD2 mutations had higher TMB scores than the sample with a BRCA2 germline mutation. NSCLC-7 has a high TMB (Table 2). The high proportion of C>A change is consistent with a mutation signature caused by tobacco (Figure 4A) (Ref.7). NSCLC-65 has a low TMB while harboring a MSH6 frameshift mutation (Table 2). This germline mutation was predicted to be nonpathogenic which is consistent with the low TMB score (Ref.8). The high TMB sample CRC-61 contains three mutations in NF1, BRAF and PIK3CB, which have been shown to be associated with high mutation burden (Ref.2). Moreover, loss-of-function NF1 mutations and high TMB has been observed in melanoma (Ref.9). In CRC samples, there was a positive correlation between the 25-gene IFNG GEx signature score, GEx signatures for T cell subsets (Th1 and Treg), PD-L1 expression and TMB (Table 2, bottom four samples and Figure 5A, right four samples). Figure 5. Spectrum of tumor mutation burden and its association with expression of immune response signature genes. A B 4 INDEL TBX T>G Treg-mean 6 2 T>C CD4 4 1 T>A CD8A 2 C>T 1 CD274 2 C>G IFNG 25 genes 3 C>A 4 Mutation Frequency C 4 >=5% 3 2-5% 1-2% 2 5-1% 3-5% 1 Read on target Uniformity mean depth Sample ID Figure 4. Correlation of the TMB and MSI status using the THF workflow All-MSS All-MSI GI-MSS GI-MSI Cervix_Uterus-MSS Cervix_Uterus-MSI (n=1) (n=12) (n=7) (n=1) (n=3) (n=2) Cancer type and MS status Table 2. Reproducibility of TMB results using the THF workflow Reproduced Sample ID THF-1 THF-2 Reproduced MMD-pipeline* /THF-mean MMD % MMD % MMD % MMD % *: the variant filtering method in the MMD workflow (see Materials and Methods). We hypothesized that duplicate analysis could eliminate false positive results due to poor DNA quality. Therefore the MMD workflow was designed to perform duplicate library preparations while minimizing the additional cost. To demonstrate the feasibility of this approach, an in-silico analysis was first performed where 5% of the aligned reads in the bam files generated using the THF workflow were randomly removed to mimic the 5% drop in mean depth when using the MMD workflow. These half bam files were then re-analyzed in IR5.6 using the same setting in the THF Oncomine Mutation Load workflow, except that the min SNP cov for calling a SNV was reduced from 6 to 3. The results indicate that TMB result from full bam files and half bam files is comparable (Table 3). Table 3. In-silico comparison of the THF workflow and the MMD workflow Sample ID Mean depth-thf Mean depth-mmd TMB-THF TMB-MMD S S S S S S S S S S S S S S S S When assessed with the MMD workflow (Figure 1), 16 libraries generated from 8 unique samples were sequenced on one 54 chip. The percentage reads on target and uniformity from the MMD workflow were comparable to those from the THF workflow, but as expected, the mean depth dropped about 5% (compare Figures 3 and 5). Analysis of duplicate libraries allows removal of the false-positive SNV calls (predominantly G>A or C>T errors caused by deamination). These results suggest that 35% to 95% of the initial SNV calls can be due to errors (see numbers listed in Column Reproduced/mean-MMD in Table 4). These results demonstrate the advantage of using the duplicate library analysis approach, especially when sample quality is poor. Note that the two (2) potentially poor-quality DNA samples had only 4 to 5% reproduced SNVs between duplicate libraries (highlighted in yellow in Table 4) Table2. Evaluation of tumor mutation burden and mutational spectrum using CCP49 and OCAv3. OCAv3 CCP49 Tumor Sample Cancer Type Content TMB Total (%) Oncomine Variants (No.Var No. SBS Missense Synonymous Indel /Mbp) Var RCC-12 RCC 95 None RCC-17 RCC 9 BRCA2 K3326X 65% RCC11 RCC 9 None RCC-13 RCC 8 None RCC-99 RCC 7 None RCC-14 RCC 9 SETD2 G988X 29% RCC-1 RCC 7 SETD2 S23fs 18% NSCLC NSCLC-65 3 MSH6 K1358fs 43%; EGFR L858R 6% (Papillary adenocarcinoma) NSCLC KRAS G12F 19%; ATM G1672X 29%; NSCLC (Adenocarcinoma) NOTCH2 Q58X 4% NSCLC NSCLC-66 7 NOTCH1 S2467X 14% (Squamous) NSCLC-7 NSCLC (Large cell) 8 TP53 R158P 7%; STK11 K81X 7% CRC-124 CRC 7 PIK3CA K111E 53% CDK12 R1356X 12%; TP53 R36X 1%; CRC-58 CRC 7 TP53 R248Q 12%; SMAD4 R361H 11%; PIK3CA R88Q 12%; BRAF V6E 9% CRC-113 CRC 8 PIK3CA E542K 22%; BRAF V6E 22%; BRAF V6E 23%; NF1 Q186X 21%, CRC CRC 8 H1494fs 26%; EGFR G465R 4%; PIK3CB E151K 9% This study demonstrated the feasibility of employing targeted RNA-Seq and targeted DNA-Seq in the analysis of immune response signatures and tumor mutation burden in clinical FFPE tumor samples. The custom multiplexed RNA-QC assay is valuable in predicting the quality of RNA samples to be analyzed by RNA-Seq assay. Gene expression profiling with the OIRRA allows for flexibility in classification of tumor samples using different immune response and immune cell subset signatures. TMB generated from targeted NGS panels such as CCP49 and OCAv3 are comparable to those obtained via whole exome sequencing. The criteria by which TMB is considered as high or low may vary in different tumor types. Assessment of the mutation profile in addition to TMB can identify specific variants that may aid in the design of an individualized therapeutic strategy. The study also suggested that tumor mutation burden in clinical FFPE tumor samples could be analyzed without the inclusion of the matched normal. The use of GExP, TMB analysis and PD-L1/TIL IHC allows for a more comprehensive assessment of the tumor and its microenvironment. A multiassay approach may improve patient selection and stratification criteria and lead to the identification of more cancer patients who may benefit from immunotherapy. Comparison of GExP, TMB, mutation profiles and MSI status. Optimization of TMB assay workflow and tumor-specific cut-offs. Evaluation of SETD2 mutations, TMB and therapy response in a RCC patient cohort. Evaluation of NF1 mutations, TMB and therapy response in a CRC patient cohort. 1. Teng MW, et al., (215) Classifying Cancers Based on T-cell Infiltration and PD-L1. Cancer Res., 75(11): Chalmers ZR, et al., (217) Analysis of 1, human cancer genomes reveals the landscape of tumor mutational burden. Genome Med., 9(1):34 3. Khagi Y, et al., (217) Hypermutated Circulating Tumor DNA: Correlation with Response to Checkpoint Inhibitor-Based Immunotherapy. Clin. Cancer Res., 23(19): Sharma P,et al., (217) Nivolumab in metastatic urothelial carcinoma after platinum therapy (CheckMate 275):a multicentre, single-arm, phase 2 trial. Lancet Oncol., 18(3): Streicher K,et al., (217) Gene expression analysis of tumor biopsies from a trial of durvalumab to identify subsets of NSCLC with shared immune pathways. 217 ASCO Annual meeting,chicago, IL 6. Ayers M, et al., IFN-gamma gene signature biomarkers of tumor response to pd-1 antagonists. WIPO Patent WO A1 7. Ludmil B, et al., (213)Signatures of mutational processes in human cancer.nature, 5: Hirotsu Y,et al., (215)Multigene panel analysis identified germline mutations of DNA repair genes in breast and ovarian cancer. Mol Genet Genomic Med.,3(5): The Cancer Genome Atlas Network (215) Genomic Classification of Cutaneous Melanoma. Cell, 161:1681 Please contact BD@molecularmd.com or visit The MMD workflow TMB results correlated well with the duplicate THF workflow TMB results (Figure 6A, R 2 >.92 ). The correlation between the standard (singlet) THF workflow TMB results and the MMD workflow TMB results was low (Figure 6B, R 2 <.6). In addition, a poor correlation was also observed between the duplicate THF workflow TMB (i.e., no MMD-filtering) and standard (singlet) THF workflow analysis (Figure 6C, R 2 <.8). The TMB determined in each sample using the MMD workflow correlated well with the MSI status for those samples (Figure 7). Figure 5. QC Metrics of the Sequencing Runs using MMD workflow 12% 6 1% 5 8% 4 6% 3 4% Read on target 2 Uniformity 2% mean depth 1 % Sample ID Table 4. TMB determined by the MMD workflow Figure 6. Correlation of TMB results using different workflows Sample ID MMD % MMD % A MMD % 5 MMD % R² = MMD % 3 MMD % MMD % 2 MMD % 1 MMD-262-D % MMD-262-D % MMD-262-D % TMB-MMD MMD-262-D % B MMD % 5 MMD % 4 MMD % 3 MMD % 2 MMD % MMD % 1 R² =.58 MMD % MMD % TMB-MMD C 5 Figure 7. Correlation of the TMB and MSI status using the MMD workflow R² = All-MSS (n=1) All-MSI (n=12) GI-MSS (n=7) GI-MSI (n=1) Cancer type and MS status Cervix_Uterus-MSS (n=3) Cervix_Uterus-MSI (n=2) Commercially available targeted NGS panels such as the Oncomine TML Assay can provide advantages over WES for TMB analysis such as lower FFPE DNA input requirements, lower cost and faster TAT. Poor quality FFPE DNA samples can generate false positive SNV calls and falsely elevated TMB scores. The MolecularMD workflow, including duplicate analysis and variant filtering, reduces the false positive variant calls in the TMB calculation allowing for higher accuracy in the TMB determination. Additional studies are on-going to determine the clinical utility of TMB analysis for predicting patient response to checkpoint inhibitors. 1. Chalmers ZR, et al., (217) Analysis of 1, human cancer genomes reveals the landscape of tumor mutational burden. Genome Med., 9(1): Rizvi H, et al (218) Molecular Determinants of Response to Anti-Programmed Cell Death (PD)-1 and Anti-Programmed Death-Ligand 1 (PD-L1) Blockade in Patients WithNon-Small-CellLungCancer Profiled WithTargeted Next-Generation Sequencing. J Clin Oncol., 36(7): Please contact BD@molecularmd.com or visit TMB-THF Immunotherapy and Cancer Harnessing the Power of Diagnostic Assays POSTERS Click for Full Screen Immunotherapy biomarker assessment in FFPE samples from solid tumors using IHC, gene expression profiling and mutation burden assessment Introduction Materials and Methods Results Immunotherapy biomarker assessment in FFPE sample from solid tumors using IHC, gene expression profiling and mutation burden assessment Peng Fang, Zhenyu Yan, Xiaodong Wang, Wes Chang, Chad Galderisi, Cindy Spittle and Jin Li, MolecularMD Corp., Portland OR and Cambridge MA TBX21 PTPRC PDCD1 IL2RG IL2RB HLA-DRA GZMB CXCR6 CXCL13 CD8A CD74 CD4 CD3D CD274 CD27 CD2 CCL5 STAT1 PRF1 LAG3 IDO1 GZMA CXCL11 CXCL1 CCR5 IFNG CXCL9 Mean expression level (Log2(RPM+1)) CRC-61 CRC-113 CRC-58 CRC-124 NSCLC-7 NSCLC-66 NSCLC-77 NSCLC-65 RCC-1 RCC-14 RCC-99 RCC-13 RCC-11 RCC-17 RCC-12 Number of Variants RCC-12 RCC-17 RCC-11 RCC-13 RCC-99 No Variants RCC-14 RCC-1 NSCLC-65 NSCLC-77 NSCLC-66 NSCLC-7 CRC-124 CRC-58 CRC-113 CRC-61 Conclusions Future Directions References For Further Information Evaluation of a commercial targeted NGS panel for tumor mutation burden assessment in FFPE tissue Introduction Materials and Methods THF WORKFLOW MMD WORKFLOW Evaluation of a commercial targeted NGS panel for tumor mutation burden assessment in FFPE tissue 3 5x 25 3x Peng Fang, Zhenyu Yan, Quyen Vu, Dave Smith, Chad Galderisi, Cindy Spittle and Jin Li, MolecularMD Corp., Cambridge, MA and Portland, OR Materials and Methods continued Results continued Results continued TMB Analysis - MMD workflow Results WES TMB by the THF workflow Read on Target/Uniformity (%) TMB (No.Var/Mb) MMD MMD MMD MMD MMD MMD MMD MMD MMD-262-D2 MMD-262-D3 MMD-262-D7 MMD-262-D8 MMD MMD MMD MMD MMD MMD MMD MMD MMD MMD MMD MMD In silico TMB analysis - MMD workflow TMB Analysis - MMD workflow Mena Depth (X) TMB (No.Var/Mb) Average Read on Target /Uniformity (%) Conclusions References For Further Information MMD MMD MMD MMD MMD MMD MMD MMD MMD-262-D2 MMD-262-D3 MMD-262-D7 MMD-262-D8 MMD MMD MMD MMD MMD MMD MMD MMD MMD-1 MMD-2 Reproduced MMD-pipeline Reproduced/ MMD-mean TMB-Reproduced TMB-THF TMB-Reproduced Average Mena Depth (X) 18 A Clinical OMICs ebook