Journal of Clinical Oncology (JCO) Podcast - Intratumoral Immune Infiltration in Follicular Lymphoma: Novel Insights into Early Relapse and Survival

Intratumoral Immune Infiltration in Follicular Lymphoma: Novel Insights into Early Relapse and Survival

12/01/19 • 11 min

Journal of Clinical Oncology (JCO) Podcast

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This JCO Podcast provides observations and commentary on the JCO article “Progression of Disease Within 24 Months (POD24) in Follicular Lymphoma Is Associated With Reduced Intratumoral Immune-Infiltration” by Dr. Tobin and colleagues. My name is Dr. Carla Casulo, and I am Associate Professor of Medicine, Hematology and Oncology at the Wilmot Cancer Institute of the University of Rochester in Rochester, NY, USA. My oncologic specialty is Lymphoma.

Follicular lymphoma is the most frequently occurring indolent non-Hodgkin lymphoma and has a long natural history, with median overall survival nearing two decades. Patients with follicular lymphoma may experience a variable clinical course, with periods of long remission punctuated by episodes of recurrent lymphoma requiring re-treatment. Among all patients, up to one third will have early disease recurrence, defined as occurring within 24 months of diagnosis. Please note that progression of disease within 24 months will be referred to as POD24 for the remainder of this podcast. These patients have inferior survival, ranging from 25-50% at 5 years. Consequently, POD24 has become a robust and well accepted indicator of identifying high-risk patients.

The implications of POD24 were first identified through our analysis of the National Lymphocare Study, which sought to test the hypothesis that time to disease progression had an impact on subsequent patient outcomes. 588 patients treated with RCHOP were included. Patients with POD24 were defined as early progressors, and those without relapse or death within 24 months were defined as the reference group. Patients with POD24 had OS of 50% at 5 years compared to 90% in the reference group. These findings have subsequently been independently validated by numerous investigators worldwide, corroborating the adverse prognostic impact of an early disease related event in follicular lymphoma. The largest of these validation studies pooled individual patient data from 5,453 patients on 13 Clinical Trials using the Follicular Lymphoma Analysis of Surrogacy Hypothesis (FLASH) Investigation.

In the FLASH analysis, we identified that male gender, poor performance status, high follicular lymphoma international Prognostic index (FLIPI) score, and elevated baseline beta 2 microglobulin B2M as predictors of early death and progression. Moreover, it confirmed POD24 as an early clinical endpoint of poor survival in follicular lymphoma that should be utilized to identify patients for prospective clinical trials.

The current status of biomarkers in follicular lymphoma has emerged from a wealth of clinical and laboratory-based factors to classify risk, towards a biologic based, molecular approach merging clinical factors with our current understanding of the follicular lymphoma genomic landscape.

There are numerous well-established and emerging clinical prognostic indices used at the time of diagnosis in follicular lymphoma that can help discriminate general outcome. These include the FLIPI and FLIPI -2. To an extent, these prognostic markers can identify subsets of patients with an expected POD24 with a sensitivity between 60-78%, and a specificity between 56-58%. However, in an attempt to use a precision approach, investigators from the German Low-Grade Lymphoma Study Group harmonized clinical and pathologic data to create a clinico-genetic risk model aimed at more accurate risk prognostication in patients receiving front line chemoimmunotherapy. They performed deep DNA sequencing from formalin fixed pre-treatment biopsies to analyze the mutational status of genes in 151 patients with follicular lymphoma tumor samples. The resulting prognostic tool, called the m7-FLIPI, distilled down 74 genes into 7 genes with non-silent mutations occurring at a variant allele frequency of 10% or greater, and combined these with high risk FLIPI status and ECOG performance status. These included genes that increased risk of progression, including EP300, FOX01, CREBBP, CARD11, and those that decreased risk of progression, including EZH2, ARID1A, and MEF2B. The cumulative risk score was calculated by combining relative weights of these genes in a multivariate analysis predicting failure-free survival. This m7-FLIPI score was tested to identify POD24 but only captured about 50% of patients as high risk. A later model included only 3 genes, including EP300, FOX01 and EZH2, performance status and FLIPI score. Defined as the POD24-PI, this was more sensitive at identifying POD24 patients but did not outperform other metrics due to lower specificity.

Biologic classification of POD24 patients remains an ongoing international research priority to seek actionable targets that might change the natural history of follicular lymphoma and improve survival of patients more likely to have morbidity and death from their disease. Several years ...

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MYC Rearrangements in Diffuse Large B-Cell Lymphoma: Impact and Implications for Diagnostic Testing

This podcast summarizes the findings of the Lunenburg Lymphoma Biomarker Consortium systematic evaluation of MYC rearrangements in DLBCL, and discusses the prognostic impact of MYC, BCL2 and BCL6 rearrangements, and implications for FISH testing in newly diagnosed DLBCL.

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This JCO Podcast provides observations and commentary on the JCO article “Prognostic Significance of MYC Rearrangement and Translocation Partner in Diffuse Large B-Cell Lymphoma - A Study by the Lunenburg Lymphoma Biomarker Consortium” by Rosenwald et al. My name is Jeremy Abramson, and I am an attending physician at the Massachusetts General Hospital and an Associate Professor of Medicine at Harvard Medical School. My oncologic specialty is lymphoma.

MYC rearrangements occur in approximately 10% of diffuse large B-cell lymphomas and have been associated with a worse prognosis. When the MYC translocation occurs in concert with translocations of BCL2, BCL6, or both, initial series have suggested particularly poor outcomes with few patients achieving long term survival with conventional therapies. This entity has been classified in the current WHO classification as high-grade B-cell lymphoma with rearrangements of MYC and BCL2 and/or BCL6 and is more conventionally known as double (or triple) hit lymphoma. Though initial series painted a grim prognosis for these patients, more recent larger series have suggested that approximately 40% of double hit lymphoma patients may be cured, and that outcomes may be improved with more intensive regimens such as dose-adjusted EPOCH-R. Most of these published series to date, however, continue to be limited by significant selection bias whereby cases with the most aggressive clinical or histopathologic features are likelier to be tested for MYC translocations, thus potentially excluding more prognostically favorable patients. The small size of series to date has also made it difficult to assess more granular questions in MYC translocated diffuse large B cell lymphoma, hereafter referred to as DLBCL, such as the prognostic impact of MYC translocations when occurring without other translocations, the impact of translocation partner with MYC (Ig or non-Ig gene locus) in double hit lymphoma, and the significance of MYC/BCL6 double hit lymphomas which are far less common than MYC and BCL2. Another practical question is who should we be testing for MYC translocations in the first place? Do we need to test every new case of DLBCL? Or should we routinely test only an enriched population, such as those with MYC protein expression by immunohistochemistry, or GCB-like DLBCLs, which include the majority of double hit lymphomas?

In the article that accompanies this podcast, the Lunenberg Lymphoma Biomarker Consortium presents the single largest and most systematic analysis to date of MYC translocations in DLBCL, and sheds significant light on many of the most outstanding questions in this population. The Consortium studied over 2000 cases of DLBCL with available tissue and clinical data, drawn from large prospective cooperative group studies as well as population-based registries in Canada, the United Kingdom and the United States. The median age of the cohort was 66 years, and the median follow up was mature at greater than 6 years. All patients were treated with R-CHOP or R-CHOP-like therapy. MYC rearrangements were found in 11% of patients. One third of these occurred as a sole rearrangement, 39% in concert with a BCL2 rearrangement, 15% with a BCL6 rearrangement, and 12% with both (i.e. triple hit lymphoma).

Patients with MYC rearrangements had higher risk features by the IPI score than the entire DLBCL population, and patients with double/triple hit lymphoma had higher clinical risk factors than patients with MYC rearrangements alone. Patients with double or triple hit lymphoma had a significantly inferior outcome in terms of both progression free and overall survival compared to the overall DLBCL cohort, while no negative prognostic impact was conferred by a MYC translocation alone. Notably, the prognostic impact was only observed in the first 2 years from diagnosis. They also compared the 31 MYC/BCL6 double hit patients with 82 MYC/BCL2 double hits, and found similar outcomes in the groups, validating that MYC/BCL6 double hit lymphomas should be considered high risk along with their BCL2 translocated counterparts. Prognosis for triple hit lymphoma was no worse than double hit lymphoma. Perhaps most striking, however, was how well double and triple hit lymphoma patients did when compared to previously published retrospective studies. In this comprehensive evaluation by the Consortium, approximately 60% of double and triple hit lymphoma patients remained progression free beyond 5 years, and two thirds remained alive. These remarkable data suggest that the majority of double and triple hit lymphomas identified by broad screening of the DLBCL pop...

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Resuming Life After Cancer Therapy: Treatment-Free Survival

Treatment-free survival is a novel endpoint in immunotherapy.

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This JCO Podcast provides observations and commentary on the JCO article “Treatment-Free Survival: A Novel Outcome Measure of the Effects of Immune Checkpoint Inhibition—A Pooled Analysis of Patients With Advanced Melanoma” by Regan et al. My name is Adil Daud, and I am Professor of Medicine and Dermatology and Director of the Melanoma Program at the University of California, San Francisco. My oncologic specialty is medical oncology.

Cancer therapy has achieved great success in the last 40-50 years. Where treatment with chemotherapy required inpatient hospitalization and gut-wrenching toxicity, therapy today can often be achieved with lower grade side effects and limited time in the hospital or outpatient infusion center. While these changes have brought enormous benefit to patients, many patients feel that the tug of ongoing therapy for metastatic or advanced cancer and long for a time where therapy is not continuing, and the word “cure” is not completely out of mind.

Allied to these concerns is the rise and expansion of immunotherapy for cancer. The growth and spread of neoplasm often triggers the immune system, which mobilizes in response. While cancers can use a variety of adaptive mechanisms to evade the immune system, blocking these evasive mechanisms can produce lasting responses and, in some cases, durable tumor-free intervals. Cytokine therapy as exemplified by IL-2 offered this benefit since its approval in the 1990’s for renal cell cancer and for melanoma for approximately 10-20% of patients treated. The CTLA4 antibody, ipilimumab has had a similar long-term disease control rate with a better toxicity profile. However, it is the anti-PD-1 monoclonal antibodies, used by themselves or with ipilimumab that have made treatment-free survival a tangible and achievable goal for many patients with cancer especially those with cutaneous melanoma.

Measuring the effect of treatment is traditionally done with measures of disease control. These include tumor shrinkage as measured by an agreed upon method such as RECIST or irRECIST or by lengthening of time-either lifespan or time without progressive disease (progression-free survival). However, it is possible that a similar lifespan could be achieved in one of 2 different ways-either continuous treatment with a drug or a shorter-term treatment that stops within a few months and then the patient has no further treatment. Comparing these treatments that are profoundly different for a patient but similar in terms of standard time or disease control measures demands new measures that can help describe the benefit of one or the other treatment.

In the JCO article that accompanies this podcast, Regan et al, in an analysis of 1077 patients across 2 different randomized trials, attempt to provide such a measure. They define treatment-free survival as interval between time to ICI cessation to the time to subsequent therapy or death. With this measure, the shorter the therapy and the more delayed the need for subsequent therapy (or death) the longer this interval is. Combination immunotherapy is notoriously toxic and while treatment duration can be short, the side effect duration can be prolonged. Could TFS be contaminated with toxicity ? to answer this question, Regan et al introduce another endpoint, TFS with and without toxicity and partition this with persistent and late onset grade ≥3 toxicity.

These endpoints are illustrated in Figure 1. Below the familiar Kaplan-Meier overall survival curve is a slice showing survival after subsequent therapy initiation (so factoring in progression). Below this is the TFS without toxicity. Below this slice is the TFS with toxicity and below this is the time on IO therapy. With this division, the familiar KM curve gives a lot more information and illustrates the difference between nivo-ipi combination therapy and nivolumab monotherapy. While the overall survival is not statistically different between these 2 treatments (as shown in previous publications) the TFS lets us see what is going on with patients. In Figure 4 we can see that in the combination nivo-ipi arm the TFS (mean) is 11.1 months vs 4.6 months for nivo alone and 8.7 months for ipi alone. These numbers indicate that TFS alone does not convey the full picture as ipi beats nivo in this measure due to the short duration of ipi treatment while extensive previous data including from keynote 006 show us that pd-1 therapy exceeds ctla4 therapy in virtually every other measure of health.

If we look at TFS subtracting toxicity, again ipi nivo beats nivo handily with 10 months vs 4 months but again ipi with 8.5 months slots in the middle. Figures 5A, 5 B and 5C show us the TFS partitioned by grade ≥3 treatment-related adverse events, grade ≥2 TRAEs or by the use of immune suppressants.

To summarize this inter...